In this episode, we discuss how a PhD student from the Technical University of Eindhoven might’ve cracked the code for high performance computer vision without compromising efficiency.

Podcast: Super Fast Computer Vision: Final Piece of The Autonomous Vehicle Puzzle

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/e49f0ede-9619-4925-a77f-f739c3df998e?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr><section id="isPasted">In order to deploy mobile robots, self-driving cars, or drones into the real world, they need to be able to observe and understand the world around them, just like humans do. PhD candidate at TU/e Daan de Geus developed algorithms for automatic image recognition that are faster and more accurate than existing models. Last Wednesday, he obtained his PhD (with honors) at the Department of Electrical Engineering.</section><section tabindex="-1">De Geus thumbs through his dissertation and shows us a photo of a street, featuring different objects such as people, vehicles, traffic posts, and traffic lights (see image below). “Mobile robots and self-driving cars need to know what’s happening around them,” he says.Among other things, they need to be able to recognize and localize objects, so they can take these into account. “This allows them to move around or toward a certain object, and maybe pick it up to carry out a task.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3NjYxNzYyLWNzbV9mcmFua2Z1cnRfMDAwMDAxXzAzMDY2OV9pbWFnZXNfMGE5MmMxZTcyNS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Traffic in Frankfurt. Image: Daan de GeusTo make robots aware of their environment, different computer vision techniques are used. It concerns an area of research that revolves around automatic extraction of relevant information from camera footage. “Simply put, we try to make models that can distill as much information from a photo as possible,” the PhD candidate explains.“The goal of computer vision is to make a system that mimics our own visual system, allowing computers to see in the same way humans do and, as a result, interact properly with the world around them.”His research focuses on improving image recognition techniques in the area of scene understanding. This is a small, but crucial part of computer vision. “Its goal is to recognize different objects and regions in an image and to give them a semantic label, such as ‘streetlight’, ‘road’, ‘car’, or ‘person’,” he explains.In other words, the objects are given a significance that is clear to humans.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3Njk0NDUyLWNzbV9zY2VuZV91bmRlcnN0YW5kaW5nX2V4YW1wbGVfMTg5ZTdjYzU4NS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">An example of scene understanding. Objects (person, traffic light, car) and image regions (the road) are automatically identified, color-coded, and semantically labeled. Image: Daan de Geus<h2 id="isPasted">Accuracy and efficiency</h2>For automatic image analyses, neural networks are used – systems that learn to carry out a certain task by receiving training involving a large amount of data. By training these neural networks in a focused manner, you end up with different models that specialize in specific tasks, such as identifying all cars in an image.In the first part of his dissertation, De Geus looked at how you can improve the accuracy and efficiency of those models. “Improving one aspect is often at the expense of another. To get more accurate results, existing methods tend to lose some of their efficiency, and vice versa,” he explains.“Which makes sense, because greater accuracy often demands more computing power, which directly impacts efficiency.” The big question, therefore, is: how can you improve these aspects without having to compromise?He came up with several solutions to this conundrum. “Efficiency is important for two reasons. You want the algorithm to use as little energy as possible and to make a prediction as quickly as possible,” he says. Speed is of the essence in self-driving vehicles, as cars must be able to respond to situations that arise in a timely manner.“If the calculation takes a second, it may already be too late to take action.”Using a process known as model unification, he combined two models to create a more efficient one. “Some tasks focus on the objects in the foreground, such as cars and people; others on ‘background regions’ such as vegetation and the sky. These tasks are performed by two different neural network modules, as each module specializes in a different task,” he explains.“Using two network modules isn’t very efficient, because you have to run them parallel to each other.” He found out that you can supply extra information to the network module processing the background information, allowing it to also identify foreground objects.“As a result, the module for foreground objects is no longer necessary, which greatly enhances efficiency. This makes this model twice as fast as its precursors, while its accuracy is comparable.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3NzI3MDQ5LWNzbV9pbWFnZV93aXRoX3BhdGNoZXNfMGNlMmIzOGZmNy5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Image with image regions. Image: Daan de GeusAnother way of improving efficiency is based on the observation that many regions in an image are very similar. For instance, the sky generally takes up a large part of any picture you take outside. “In spite of the fact that a lot of information is similar, neural networks process each image region separately, which is very inefficient,” says De Geus.The entire image is divided up into patches consisting of pixels (see image below). Normally, these would be labeled and assessed individually, but the PhD candidate developed a method for clustering patches with comparable information, reducing the total number of patches and, by extension, requiring less computing power.“This allows us to improve speed by as much as 110%, without taking away from the accuracy.”Incidentally, this method for grouping similar image regions is widely applicable to many different models and a great many applications other than self-driving cars and mobile robots. For example, you could use these algorithms to segment medical images. “Pretty much in any situation where you need to automatically analyze images.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3NzUyNjQxLWNzbV90b2tlbl9zaGFyaW5nX2RpYWdyYW1fYzk3N2FmMzAxMi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">To improve efficiency, semantically comparable patches can be clustered. Image: Daan de Geus<h3 id="isPasted">Multiple abstraction levels</h3>In the second part of his dissertation, he focused on what he calls multiple abstraction levels. “Existing algorithms either focus on entire objects, such as cars, or on their components, such as car tires or the license plate,” he explains.His goals was to develop an algorithm that can simultaneously understand an image at multiple abstraction levels. “This will allow a mobile robot to observe both the car and its components, as well as other objects and the background, all at the same time, which gives it a comprehensive picture of the surroundings,” says De Geus.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3Nzc4Mzc3LWNzbV9mcmFua2Z1cnRfMDAwMDAxXzAzMDY2OV9wYW5vcHRpY181YWJiNGJhNDM4LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Automatic recognition of objects and image regions. Image: Daan de GeusYou can have both algorithms carry out their work separately and merge the results afterwards, but this is cumbersome. And it can also lead to conflicts between the two calculations. Instead, he developed a new algorithm that can identify objects and components all at once.“That’s not only more accurate, but also more efficient.” What’s more, this improved model is widely applicable and has many advantages. “It means, for instance, that a robot isn’t just able to recognize a door, but also the door handle, and to understand that the latter is part of the door. Which, in turn, allows it to open the door,” he says to illustrate.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NjM3ODAxNTYzLWNzbV9mcmFua2Z1cnRfMDAwMDAxXzAzMDY2OV9wcHNfM2IzMmE1ZDQ3ZC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">A more detailed segmentation, where components of objects are also identified. Image: Daan de GeusTo be able to use self-driving cares or mobile robots in practice, there’s a lot left to do still. “Image recognition is just a tiny part of the puzzle,” he admits.&nbsp;And there are plenty of remaining challenges in the area. For one thing, models could be developed that can recognize more objects at once and that operate even faster and more accurately.The generalization of the model could also be improved, so it also produces good results when the data are different from the data sets with which the network was trained, allowing it to perform better in real-world scenarios.But at least his dissertation constitutes a step forward in making the system more capable, accurate, and efficient.<hr>Source: <a href="https://www.cursor.tue.nl/en/news/2024/april/week-3/home-stretch-teaching-robots-to-see-better/" target="_blank" rel="noreferrer">Cursor/Martina Silbrníková</a>Daan de Geusdefended his thesis“<a href="https://research.tue.nl/en/publications/characterization-of-abdominal-aortic-aneurysms-using-time-resolve" target="_blank" rel="noreferrer">Advances in Scene Understanding - Towards Efficient Image Segmentation at Multiple Abstraction Levels</a>” at the Department of Electrical Engineering on April 17th, 2024.Supervisors: Gijs Dubbelman en Peter de With</section>

In his doctoral research, Daan de Geus worked on advanced image processing methods that allow robots and cars to better recognize what they see around them.

Teaching robots to see better

The automotive industry is currently in the midst of a revolutionary transformation. Fueled by the relentless pace of digital integration into the vehicle, this evolution is fundamentally redefining what vehicles are and how they operate. At the heart of this transformation is the shift towards the concept of software-defined vehicles (SDVs). SDVs pivot away from the traditional, rigid frameworks that have characterized automotive design for decades, instead embracing a future where software, more than hardware, determines a vehicle's capabilities and performance. As vehicles become increasingly connected and autonomous, the transition to software-centric architectures is set to redefine mobility itself.<h1>The Factors Behind Software-Defined Vehicles</h1>The shift towards software-defined vehicles is propelled by a confluence of technological, consumer-driven, competitive, and regulatory forces [1]. Central to this transition is the advancement in hardware architectures, notably zonal architectures, which organize vehicle electronics into fewer, more centralized units. This arrangement reduces complexity and weight while enhancing the vehicle's adaptability to software updates and integrations. Connectivity further bolsters this shift, enabling real-time data exchange between vehicles and their environments, thereby unlocking the potential for improved safety, efficiency, and user experiences.Modern consumer expectations also play a pivotal role in the ascendancy of SDVs. Today, consumer expectation extends beyond the concept of a vehicle as just a means of transportation. Instead, drivers demand continuous improvement, personalization, and seamless connectivity with their digital lives. This desire for vehicles that can adapt and evolve over time aligns perfectly with the ethos of SDVs, where software updates can introduce new features, refine performance, and personalize the driving experience long after the vehicle has left the showroom floor.Competition among automakers and tech companies further accelerates the drive toward SDVs. As these entities compete for market dominance, SDVs offer an opportunity to differentiate themselves through advancements in safety features, performance enhancements, and unique user experiences. Finally, regulations and the increasing focus on sustainability are crucial drivers. Governments worldwide are tightening emissions standards and pushing for more efficient, cleaner transportation solutions. SDVs, with their ability to optimize performance and efficiency through software, offer a promising pathway to meet these environmental goals while also enhancing road safety through advanced driver-assistance systems (ADAS) and autonomous driving technologies. This regulatory environment, coupled with societal demands for sustainability, propels the automotive industry further towards the adoption of SDVs, marking a significant shift in how vehicles are designed, built, and experienced. [2]<h1>The Anatomy of a Software-Defined Vehicle</h1>At the core of a software-defined vehicle lies the zonal architecture, which is a large juxtaposition to the traditional, distributed architecture that has dominated automotive design for many years. In a distributed architecture, each component or system within the vehicle—such as lighting, infotainment, or power windows—has its own control unit and wiring running directly to it. This setup results in a complex network of wiring and numerous electronic control units (ECUs) scattered throughout the vehicle, each dedicated to specific functions.The concept of the zonal architecture is to segment the vehicle into zones, each responsible for specific functions. This architectural paradigm significantly simplifies wiring layouts, reduces the overall weight of the vehicle, and facilitates the integration of more advanced features and sensors. By consolidating functions and control units, zonal architecture streamlines manufacturing and maintenance and creates a modular platform that can support a wide array of technologies. [3]Within these technologies, connectivity – encompassing vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), and cloud communications – is an integral aspect of modern SDVs. V2V connectivity allows for real-time data exchange between vehicles, enhancing safety through collaborative awareness and action. V2X connectivity extends this communication to infrastructure, pedestrians, and other elements in the environment, enabling a more comprehensive understanding and interaction with the surrounding world. Cloud connectivity, on the other hand, facilitates dynamic software updates, feature enhancements, and personalization, ensuring that the vehicle remains at the cutting edge of technology throughout its lifecycle. [4]Underpinning these features are the operating systems and application platforms that serve as the backbone of SDVs. The role of open-source software is particularly noteworthy, offering a foundation for innovation, collaboration, and rapid development. Open-source platforms not only reduce development costs but also encourage standardization and interoperability among different manufacturers and systems.However, a shift towards SDVs is not without its concerns. Given their reliance on software and connectivity, cybersecurity is a growing focus for SDV designers. Automakers and technology providers are investing heavily in robust security measures, including encryption, secure boot mechanisms, and continuous monitoring and updates, to safeguard against potential threats. Such a comprehensive approach to cybersecurity will be integral to maintaining trust in the technology and ensuring the long-term viability of software-defined vehicles. [5]<h1>Transforming the Driving Experience</h1>One of the unique value propositions of SDVs is the capability of leveraging over-the-air (OTA) updates to continuously enhance functionality and performance long after the vehicle has left the showroom. This capability means that new features, performance improvements, and even critical software fixes can be delivered directly to the vehicle through wireless networks, negating the need for physical modifications or dealership visits. Whether it’s upgrading the infotainment system, enhancing the navigation software, or improving autonomous driving algorithms, OTA updates ensure that the vehicle evolves with the latest advancements in technology.As part of this, the safety landscape of driving is profoundly transformed by the software capabilities of SDVs. ADAS such as automatic emergency braking, lane-keeping assistance, and adaptive cruise control are continually refined through software updates, reducing the likelihood of accidents and enhancing road safety. Predictive maintenance algorithms analyze vehicle data in real time, alerting drivers to potential issues before they become serious problems, while real-time hazard alerts can warn drivers of dangerous conditions ahead, further enhancing safety. [6]Software also plays a crucial role in optimizing vehicle performance, including fuel efficiency and battery life in electric vehicles. Through sophisticated algorithms, the vehicle’s software can adjust driving modes, manage battery usage, and optimize energy consumption based on driving patterns, road conditions, and battery health, thereby extending the vehicle's range and improving its efficiency.Infotainment and user interface advancements offer a customizable and dynamic user experience, with software allowing for the personalization of displays, settings, and features to match driver preferences. Accessibility features can be enhanced and tailored to support drivers with specific needs, ensuring a more inclusive driving experience. These dynamic updates and personalizations, enabled by software, mark a new era in how drivers interact with and enjoy their vehicles, making every journey safer, more efficient, and uniquely tailored to individual preferences.<h1>Conclusion</h1>Software-defined vehicles represent a transformative shift in the automotive landscape, enhancing safety, elevating performance, and reimagining the driving experience. However, the journey towards fully realizing the vision of software-defined mobility necessitates a sustained commitment to innovation, a robust regulatory framework, and an open ecosystem that encourages cross-sector collaboration. The road ahead for automakers, software developers, and regulators will require collaborative innovation and stringent oversight. Together, these stakeholders face the challenge of harnessing the full potential of SDVs while addressing concerns such as cybersecurity and ensuring the interoperability of emerging technologies.  The journey towards this future is as exciting as it is challenging, demanding the best of technology, creativity, and vision to fully realize the transformative potential of software-defined vehicles.For more information on software-defined vehicles and zonal architecture, see the latest installment of Mouser Electronics' Empowering Innovation Together series: <a href="https://www.mouser.com/empowering-innovation/zonal-architecture" target="_blank">https://www.mouser.com/empowering-innovation/zonal-architecture  </a>References<ol><li>https://www2.deloitte.com/cn/en/pages/consumer-business/articles/software-defined-cars-industrial-revolution-on-the-arrow.html</li><li>https://www.sustainability.gov/federalsustainabilityplan/</li><li><a href="https://community.cadence.com/cadence_blogs_8/b/breakfast-bytes/posts/zonal" target="_blank">https://community.cadence.com/cadence_blogs_8/b/breakfast-bytes/posts/zonal</a></li><li><a href="https://www.ntsb.gov/Advocacy/safety-topics/Pages/v2x.aspx" target="_blank">https://www.ntsb.gov/Advocacy/safety-topics/Pages/v2x.aspx</a></li><li><a href="https://www.wardsauto.com/industry-news/cybersecurity-threat-likely-grow-exponentially-sdvs-emerge" target="_blank">https://www.wardsauto.com/industry-news/cybersecurity-threat-likely-grow-exponentially-sdvs-emerge</a></li><li>https://blogs.blackberry.com/en/2024/01/motortrend-panel-la-autoshow-sdvs-shaping-our-automotive-future</li></ol>

Software-defined vehicles represent a revolutionary shift in the automotive landscape

Embracing the Future with Software-Defined Vehicles

<blockquote>“For a new product to be successful, it is not enough for it to work—almost everything around us works quite well. The design matters.”</blockquote>This is what Iván Arakistain, a technologist and researcher at Tecnalia said. After winning the Connect for Good challenge, we interviewed Arakistain to discuss his award-winning project on sustainable logistics infrastructure and his insights into the logistics industry, particularly that of perishable goods.Logistics is a dynamic field fraught with challenges, and sustainability is a growing concern, particularly in transporting perishable goods. Iván Arakistain, representing Tecnalia, won first place in the Connect for Good challenge with an innovative concept that leverages a low-power IoT solution to monitor and enable eco-friendly transport of perishable goods.The&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-low-power-wireless-sustainability-challenge" target="_blank">Connect for Good Challenge</a> is a joint initiative by Nordic Semiconductor, Mouser Electronics, Soracom, and Crowd Supply aimed at fostering innovation for social and environmental good. It brought together a worldwide network of engineers, entrepreneurs, and developers around one mission: “to devise low-power wireless solutions that contribute towards achieving the United Nations Sustainable Development Goals.”&nbsp;Arakistain's winning project is a game-changer in sustainable logistics. Through advanced monitoring and anomaly detection, this innovative solution optimizes the transport of perishable goods while eliminating the need for energy-intensive refrigerated cargo trailers. Thus, it significantly reduces carbon emissions and operational costs while ensuring the integrity of the cold chain. Through the integration of IoT technology, including low-energy sensors and GPS and coupled with mini solar panels for autonomy, the project sets a new standard for eco-friendly transport in the logistics industry, with broad implications for global sustainability efforts. The key component of this project is the integration of the&nbsp;<a href="https://www.wevolver.com/specs/nrf9160-development-kit" target="_blank">nRF9160 Development Kit</a>, chosen specifically for its low energy consumption and reliable cellular connectivity.In this exclusive interview, Arakistain sheds light on his journey, the inspiration behind his project, and the impact he envisions on the logistics industry's future.<h2 dir="ltr">Interview with Iván Arakistain of Tecnalia</h2><h3 dir="ltr">Can you briefly describe your winning project and the main problem it addresses in the logistics industry?</h3>One of the main challenges of the logistics industry is the sustainable transport transition. This focuses on decarbonizing the transport industry, especially because today, at least in Spain, it’s heavily based on trucks; most of the logistics happen on the road, and trucks consume a lot of fuel. So, we have to shift from that to a cleaner way of transport.Some years ago, we had a customer who sold precooked food.&nbsp;They mentioned something to us that sparked this idea process: If they could deliver the food in one day from their factory to the end customer, they would be able to avoid refrigerated transportation, especially since it’s relatively expensive, requires a lot of fuel, and is very complex.So, we started exploring that. At that time, the technology was not quite ready for the application, mainly for areas like the south of Spain, where it's very hot in the summer. The logistics were not as fast as today, but as things have evolved, we are now able to transport food—or perishable goods—in proper condition to the end customer in a short a time as needed so it arrives safely.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160590293-0_Ivan+Arakistain.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr">What inspired you to focus on developing a more sustainable method for transporting perishable goods?</h3>Well, the main goal is to deliver goods fast and with as low carbon emissions as possible. For that, we must change from only using transport by trucks to combining truck and train, which is obviously much cleaner environmentally. By doing so we can ensure more availability, lower cost per kilometer, and a smaller cargo footprint.&nbsp;Let’s look at how your project aligns with the Sustainability Development Goals. Could you tell us how your solution targets these goals?Our solution’s practical implications impact goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 13 (Climate Action). We are very focused on energy efficiency and sustainability goals for cities and districts. Our application can help reduce the cargo footprint and accelerate the transition from the current fuel-based transport to a more electrified way of transport.&nbsp;Of course, we cannot transition directly from 0 to 100%, so we must ensure the transition is smooth. In our area of focus, the technology background is in artificial intelligence for microcontrollers and small devices. We embed this intelligence into the embedded device so that we can know via our electronic sensors if the transported food is in good condition. We can also sense whether any volatile compounds can impact the condition of the food.By making those changes, we estimate that up to 10% of carbon reduction is possible. And that also applies to energy consumption, even though I’m not sure if it’s a linear relationship. However, the estimation stands.<h3 dir="ltr">Let's move onto the technical side. How does your solution work? Is embedded AI the differentiator from traditional energy-intensive refrigeration?</h3>As I said, we are focused on AI for microcontrollers. So, first, we develop the AI models in servers and powerful computers. Then, we reduce those models so that they can run in a small controller with a few kilobytes or a small RAM. For this application, we are focused on two types of inference.The first type of inference is air quality to determine the state of the product. The second inference type is AI for vibration analysis, which is something we have implemented here and in other projects. In this case, we can analyze if the vibration of products in the truck matches the orientation to make sure everything is good. In other cases, we also do this AI analysis of vibration for structural health and many other applications.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160621737-1_Manufactured+PCB+Top.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr">How accurate and precise are the AI model results in a real-world application?&nbsp;</h3>There are two processes that we do. First, we collect all the data, and then we perform model training and model testing on our computers. We split the data set into two parts: about 80% of the data is used for training the model, and the remaining 20% or so is used for testing the model.In our computer simulation, the results were very good; you can get up to 96% accuracy. But we both know that’s not the reality. Getting a very high score virtually means the model is quite good. However, when you go into the real world and flash the firmware into the microcontroller you’re using in the actual sensor, the accuracy decreases a bit. Still, we can get good enough accuracy with this model.Were there any particular challenges you faced in the development process, and how did you overcome them?As I said earlier, the original idea came some years ago with this customer we were working with. They produced precooked food, and we did some real testing with them. We sent the food with a trucker and other things to actual customers. At that moment, we realized that the technology was not quite ready, not because of the electronics but because of the isolation and insulation challenges. For that, you’d need excellent isolating materials so that temperatures are kept in the correct range.&nbsp;This was about eight to nine years ago. At that point, we did develop a complete solution, and we knew the idea was good, but it was not quite ready because of the isolation challenge. Since then, we have kept thinking and working on it, and now we know that it is a feasible application, especially since there are much more efficient containers for food transport than years ago. There are better isolation materials, which helped this improve.<h3 dir="ltr">Looking into the next decade, what are the biggest hurdles in scaling and implementing your solution across different regions and logistics networks?</h3>The main difficulty is the limited time for delivery: It has to be done within the first 24 to 48 hours and the products need a low temperature range.Of course, I think it's scalable. Still, one of the biggest challenges is the container. We need to use special containers with good insulation for this. Basically, once we are able to prove to customers that they are going to save so much on each transportation, then it makes sense.So far, we have tested with first-class precooked food, which is already prepared and ready for eating upon reheating in a restaurant or somewhere else. We are not so focused on uncooked food.<h3 dir="ltr">Let’s dive a bit into the economic side of things. What are the economic benefits of your solution for logistics companies and end users?</h3>The economic benefits mainly rely on the use of nonrefrigerated transportation. As a result, you can save about 10% of the transportation cost. It consumes less fuel and energy. It's simply more efficient. We are also very interested in combining ways of transportation: using not only trucks but also trains. Here in Spain, we have quite a robust train infrastructure, but nowadays, it is more focused on passengers than goods. So, I think we must leverage the train infrastructure and combine it with trucks in the future.Also, for international shipments, combining the truck with the train is important so that the driver does not need to drive so far. The truck driver would simply mount the truck with the goods from a train carriage, move onto another carriage to collect goods from there, and then deliver them locally.This is a standard logistics process. We are simply using the existing infrastructure. The only additional requirement would be the special containers for the food that I mentioned earlier.<h3 dir="ltr">You mentioned one customer already.&nbsp;Have you discussed this with other members of your target market? How have your target customers responded to your solution so far?</h3>Yes. 15 days ago, I was at a logistics and transportation fair in Bilbao. I gave a speech there and talked to potential customers. Clearly, there is some potential, but of course, we need to keep the focus on the end customer.<h3 dir="ltr">By focusing on the end customer, what strategies are you employing to encourage the adoption of your technology within the market?&nbsp;</h3>In Tecnalia, we work with different approaches to ensure the technology adoption. We identify and manage deep-tech business opportunities, through a venture building process to accelerate their industrial commercialization. We create new startups or commercialize licenses, accelerating the incubation of technological assets until they are turned into investment opportunities capable of generating economic and social value.&nbsp;<h3 dir="ltr">How do you see your solution evolving in the next five years?</h3>I think that it has quite a lot of potential. We are still in a transition stage, where not everything is well-defined. The path is clear: we have to shift to the electrification of transport. But the speed at which this is going to happen is yet to be clear. Yet, I think that it has significant potential for the future because logistics is a fast-growing sector.<h3 dir="ltr">Is there close competition to your technology from bigger companies already established in the logistics market?</h3>Yes, there is. For example, some years ago, big companies like Amazon tried to sell perishable food to end customers in a very short time. However, they were not very successful because it’s not easy to deliver that fast in a cost-efficient way.Our case is a little different. It focuses on precooked food for industrial partners. The customer I mentioned earlier is not selling to end customers but rather to restaurants and other businesses. So yes, there are big players, but they are not entirely in the same market niche as ours.<h3 dir="ltr">Are there other applications or industries where your technology could be applied beyond the niche cold chain logistics?</h3>Yes, we use embedded AI for structural health monitoring in various cases. Here, you would need to know the condition of a structure. In Tecnalia, for example, we have some dedicated lab equipment, such as sensitive accelerometers. But the cost of such devices is typically quite high. They are not devices you can leave on a structure but rather devices you use within a specific timeframe.So, we are developing small, affordable devices that can use MEMS accelerometers, inexpensive microcontrollers, and embedded AI to provide early warnings if there is an anomaly, failure, or any other event on which the model is trained.The new devices we are developing would cost as much as an order of magnitude less than the typical measuring and monitoring equipment. They would only provide an early warning, after which the experts could go and perform more extensive measurement and testing.Another example of our embedded AI would be audio detection. Consider a large dataset of street sounds that can take over six gigabytes – that is a lot of information. For this, it's possible to train an AI model that takes no more than kilobytes – which is significantly less – and embed it into a microcontroller to classify the sounds. We tested this, and the model was able to accurately identify and classify the sounds of a car horn, for example, a dog barking or children playing. This is a solution to tackle problems with lots of data using a compressed model that can accurately classify the data without requiring so much power.<h3 dir="ltr">Going back to the project experience, what have you learned from working on this project that you think is crucial for innovation in sustainable technologies?</h3>At Tecnalia, we are experts in many different fields. The most interesting thing about working in an applied research and technological development centre like Tecnalia is that I can collaborate with experts in multiple fields, such as electronics, logistics, structures, and energy efficiency. For such projects to work, I have to work with lots of people to solve the problem.&nbsp;Also, experts in AI are not all the same. Some of them are more focused on big processors like NVidia devices, and we need to collaborate with them to understand the models and be able to quantize and reduce the models down to the microcontroller level.What advice would you give to other entrepreneurs or innovators looking to make a difference in the green technology space?&nbsp;My advice would be to prototype as much as they can. In electronics, we are quite used to prototyping fast. That's a good thing for our sector. But in other areas, it might be more difficult to prototype. I would also encourage people to collaborate with other experts and learn from them.<h2 dir="ltr">Conclusion</h2>Iván Arakistain's winning project exemplifies the potential for low-power IoT solutions to revolutionize the transportation of perishable goods. By leveraging advanced monitoring and anomaly detection, Arakistain's project offers a promising avenue for reducing carbon emissions and operational costs while maintaining the integrity of the cold chain.&nbsp;As we look to the future, the scalability and adoption of such solutions present ongoing challenges. Yet the momentum generated by initiatives like the Connect for Good Challenge signals a promising trajectory towards more innovation and sustainable solutions and systems. For more information about the challenge and its winners, check out our dedicated article:&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-challenge-winners-logistics-infrastructure-monitoring-smart-bees-and-bioreactors" target="_blank">Connect for Good Challenge Winners: Logistics Infrastructure Monitoring, Smart Bees and Bioreactors</a>.

Introducing the Winner of the Connect for Good Challenge 2023


Towards Sustainable Logistics Infrastructure: An Interview with Tecnalia

<h3 id="isPasted">Who are we?&nbsp;</h3>Team Polar is an Eindhoven-based student team filled with students from different disciplines. Our mission is to facilitate and improve the research going on in Antarctica. We aim to achieve this by building an autonomous, affordable, and electric rover. The reason we go with Antarctica is that the current research going on is led by human-driven vehicles using fossil fuels. Building an autonomous vehicle means it is safer and less expensive since there is no&nbsp;driver to pay.&nbsp;&nbsp;<h3>The Ice Cube&nbsp;</h3>After working for more than a year, Team Polar came up with the&nbsp;Ice Cube. Ice Cube was just the first step towards our vision of building a sustainable and affordable research environment in Antarctica. Ice Cube has 2 square meters of&nbsp;solar panels&nbsp;which helps Ice Cube to go up to 65 kilometers (about half the distance from Washington, D.C. to New York City) per day. With its 28' wheels, the Ice Cube weighs around 350 kilograms (about 771.62 lb).&nbsp;&nbsp;<h3>Progress on Ice Cube&nbsp;</h3>Team Polar is&nbsp;in&nbsp;the second prototype's design process, and the Ice Cube is only used for testing.&nbsp;Since the Ice Cube was built in&nbsp;a short time, some features were missing, such as autonomy.&nbsp;Our software team is using&nbsp;the Ice Cube&nbsp;to test their software and the new sensors&nbsp;that&nbsp;we are aiming to implement on our second prototype.&nbsp;While these tests are&nbsp;being made, new structural components are also being tested. Such as gas springs, hinges, handles, etc.&nbsp;Meanwhile, we are developing a force balance that helps&nbsp;determine&nbsp;possible ranges&nbsp;of mass of the next vehicle. We&nbsp;are also looking into the location of research, which will help&nbsp;determine&nbsp;the&nbsp;driving range and strategy for the energy balance (which will&nbsp;probably be&nbsp;made next year).&nbsp;<h3>Processing REMA Image&nbsp;</h3>Under the “Autonomous Department”, our engineers successfully developed and implemented code to process &nbsp; Reference Elevation Model of Antarctica (REMA) images.&nbsp;REMA provides the first, high-resolution (2-meter) terrain map of&nbsp;nearly the&nbsp;entire continent. Since each REMA grid point has a timestamp, any past or future point observation of elevation provides a measurement of elevation change.&nbsp;REMA may provide corrections for a wide range of remote sensing processing activities, such as image orthorectification, and interferometry, and provide constraints for geodynamic and ice flow modeling, mapping of grounding lines, and surface processes. REMA also provides a powerful new resource for field logistics planning.&nbsp;This is an important step for the autonomy of the Ice Cube. The goal is to calculate the altitudes in Antarctica to find out the clearest and most optimized path for the rover.&nbsp;&nbsp;The next step for these images is to transform the processed REMA images into a format that is compatible with our A* algorithm.&nbsp;&nbsp;<h3>A* Algorithm&nbsp;</h3>The A* search algorithm is one of the best techniques used in path-finding and graph traversals. The path we derived will be the shortest of all the other options or the most efficient by all parameters considered. &nbsp;A* works by making a lowest-cost path&nbsp;<a href="https://brilliant.org/wiki/trees-basic/" target="_blank" rel="noreferrer noopener">tree</a>&nbsp;from the start node to the target node. What makes A* different and better for many searches is that for each node, A* uses a function f(n) that gives an estimate of the total cost of a path using that node.The environment of Antarctica and the conditions are the hardest part of our project. We need to come up with innovative and technical solutions. Understanding the altitudes and general geography of Antarctica is crucial.&nbsp;&nbsp;Furthermore, the design of the rover plays a huge role in our path to success. Our Dynamics Sub team is trying to come up with new ideas for the newest version of the Ice Cube.&nbsp;&nbsp;<h3>Suspension&nbsp;</h3>Unlike the Ice Cube, the new vehicle will use a suspension system. The test in Norway in 2022 showed that; the main purpose of having a suspension is important for keeping all wheels on the ground, for improved traction. Also, it will dampen vibrations to keep the vehicles' most important components as stable as possible.&nbsp;&nbsp;The suspension system the new vehicle will be using is a so-called double wishbone suspension. For this suspension, every wheel is connected to two-wishbone-shaped arms. Every wheel is independently controlled.&nbsp;&nbsp;The main advantage of the double-wishbone configuration is its good ratio of kinematic versatility vs. complexity, it being slightly more complex than other systems like the McPherson strut and slightly simpler than others like multi-link suspensions.&nbsp;&nbsp;<h3>Ground pressure&nbsp;</h3>Getting the right ground pressure is extremely crucial for the environment the vehicle will operate. A ground pressure that is too high will cause the vehicle to get stuck in the snow or even fall into crevasses. To prevent this, the ground pressure that will be aimed for in the new vehicle is 0.1 bar. This value is also used by Artic Trucks or other snow vehicles such as the tracked vehicles made by Mattracks. In comparison, the average human exerts a pressure of 0.17 bar on the ground.&nbsp;The low ground pressure is achieved by maximizing the contact area using large and not fully inflated&nbsp;tires.&nbsp;Next Steps?&nbsp;

Facilitating the human-led and autonomous research in the Antarctic continent.

Pioneering Autonomous Antarctic Research

At recent 2023 Consumer Electronics Show (CES), Sony and Honda revealed their first electric vehicle prototype, which is equipped with 45 cameras and sensors, as well as Electric Rearview Mirrors. Electronic rearview mirrors have already made an appearance on roads in many countries. A question has been raised: will traditional rearview mirrors eventually become obsolete in smart cars? In this article, we will delve into this inquiry.<h2 dir="ltr">Transform from Light to Electronics</h2>The rearview mirror was invented in 1911 by Elmer Berger, providing drivers with an easier way to view what was behind them while driving. Modifications have been made over the years, including the “day/night” switchable mirror and the “automatic dimming” mirror, making driving safer and more comfortable.Electronic rearview mirrors have emerged in recent years in both commercial and passenger vehicles. However, it should be noted that they have not yet been widely adopted in automobile, let alone replacing traditional rearview mirrors.<h2 dir="ltr">No Wonder It’s Getting Popular</h2>Electronic Rearview Mirror is a new type of “indirect vision device”, also known as the Camera-Monitor System (CMS).The electronic rearview mirror consists of two parts: the “electronic internal rearview mirror” (also known as “streaming media rearview mirror”) is a display screen installed inside the car, and the “electronic external rearview mirror” corresponds to the traditional external rearview mirror on cars. Compared to traditional rearview mirrors, electronic rearview mirrors have three advantages.1. &nbsp; &nbsp; &nbsp;Smaller structure and lower air resistanceIn August 2022, Elon Musk stated on social media that electronic side mirrors on electric cars can reduce energy consumption by about 5%. Since an electronic side mirror is just a camera, its size can be reduced to one-third of a traditional side mirror, or even smaller. From a professional aerodynamics perspective, a smaller structure means a smaller frontal area of the vehicle, which reduces air resistance, reduces wind noise, and to some extent, decreases fuel consumption for combustion engine vehicles and increases the driving range for electric vehicles.2. &nbsp; &nbsp; &nbsp;Less affected by natural factorsWith advancements in camera display and artificial intelligence technology, the solution of “external camera + internal display” can significantly improve the adverse effects of natural conditions, such as rainwater covering side mirrors, fogging, insufficient night lighting, and other problems that plague drivers. In front of electronic side mirrors, these are “child’s play”.3. &nbsp; &nbsp; &nbsp;Smaller blind spotThis is the most intuitive advantage of electronic side mirrors. The curvature, shape, and size of traditional optical reflective mirrors are fixed, resulting in a limited field of view. Electronic side mirrors can use cameras at different angles, providing drivers with a wider field of view. Especially for large commercial vehicles, blind spots are often a fatal area.Electronic rearview mirrors also provide assistance for promotion of car intelligence. Firstly, electronic side mirrors enrich the content of intelligent cockpits. Secondly, it serves as sensors for future autonomous driving systems.<h2 dir="ltr">Not Easy to Get On Board</h2>Although electronic rearview mirrors have many advantages, they also have some limitations. The high cost is a significant drawback, and the stability requirement is high for driving safety. Furthermore, drivers may have difficulty adjusting to the new display screen after using traditional rearview mirrors for over 100 years.But the industry is optimistic that with the decrease in cost and improvement in stability of electronic rearview mirrors, more car companies will embrace electronic rearview mirrors to enhance the differentiation of their models in the process of intelligentization. Though, consumers’ attitudes remain to be seen.<h2 dir="ltr">Order the Parts that Drive Smart Cars Go Faster</h2>“Mirror, mirror on the car, who's the most reliable of them all - traditional or electronic rearview mirrors?” Although there is no definite answer to this question yet, the ongoing process of intelligentization traditional gas-powered vehicles and electric cars is transforming the automotive industry.At RPWORLD, we provide manufacturing services that include CNC machining, injection molding, sheet metal fabrication, and finishing options to help you accelerate your smart drive, faster and more cost-effectively. We have extensive experience in manufacturing a wide range of auto parts for various automotive clients, including:·&nbsp; &nbsp;&nbsp;Exterior parts: head lamp, bumper, radiator, grilles·&nbsp; &nbsp;&nbsp;Interiors parts: EPB, door panels, instrument panels· &nbsp; &nbsp;Electronic parts: battery packs, charging stations, voltage motor

At recent 2023 Consumer Electronics Show (CES), Sony and Honda revealed their first electric vehicle prototype, which is equipped with 45 cameras and sensors, as well as Electric Rearview Mirrors.

Mirror, Mirror on the Car, Who's the Most Reliable of Them All?

Humans, you might have noticed, aren’t perfect. We crash cars. We injure ourselves performing hazardous labor like mining and construction. And we fare poorly in extreme environments we’d like to explore – like the deep ocean or the vacuum of space.But <a href="https://profiles.stanford.edu/marco-pavone" data-extlink="" target="_blank">Marco Pavone</a>, associate professor of aeronautics and astronautics, and his students in the <a href="https://stanfordasl.github.io/" data-extlink="" target="_blank">Autonomous Systems Lab (ASL)</a> hope that emerging technologies like self-driving cars and space robots could one day help us get around these shortcomings.“It takes a lot of effort and equipment to keep people alive in space,” said <a href="https://stanfordasl.github.io/people/stephanie-newdick/" data-extlink="" target="_blank">Stephanie Newdick</a>, a PhD student in Pavone’s lab. And we’re not exactly indestructible on Earth, either. Sometimes, she said, “It just makes more sense to send a robot.”An ideal self-driving car, for instance, should make fewer mistakes than a human driver, allowing passengers to reach their destinations more efficiently and lowering the risk of an accident. And autonomous space robots could repair satellites or explore Martian landscapes – saving astronauts from life-threatening tasks while also pushing the boundaries of space exploration.<h2>An endless stream of decisions</h2>Achieving that level of competence for a space robot or self-driving car, however, is easier said than done. Autonomous systems like these must be able to make decisions and then translate them into physical actions in unpredictable – or completely alien – environments.The ASL tackles both issues, starting with designing decision-making algorithms that treat any scenario as a cost-benefit optimization problem. A self-driving car, for instance, must calculate which series of actions allow it to cross an intersection fastest while avoiding pedestrians and not jostling passengers – all in a few milliseconds. In addition to following fundamentals of good driving, such as staying within lines and abiding by traffic signals, this also involves anticipating the reactions of people in its environment.“If the robot does something, how might a nearby human respond?” Pavone said. “And based on that, how does the robot make its next decision?”Decision-making models also have to navigate the mishmash of cues a car might encounter – recognizing, for instance, that a stop sign printed on a billboard does not mean “stop.” Pavone’s lab is using large language models to design systems that can account for context in scenarios like this. Ideally, Pavone said, those systems will be able to learn from experience.“You see a lot of ‘firsts’ in your life, but you appeal to past experience and you don’t freak out,” Pavone said. “It’s pretty hard to do that with a robot.”<h2>New tools for new worlds</h2>All that decision-making does no good, however, unless a robot can translate it into effective physical actions in unfamiliar environments – for instance, zero gravity.That’s why Pavone’s lab has an air hockey table (or something close to one). Robot prototypes on its ultra-smooth granite surface shoot jets of air downwards and sideways, gliding over it like a puck. This way, Pavone’s lab can test how well the robots’ algorithms translate into lateral (though not vertical) motion in a zero-gravity-like environment.To ensure robots can do their jobs once they get where they’re going, the ASL also works on gripping tools – such as microspines for hooking into irregular surfaces and a <a href="https://news.stanford.edu/2021/05/20/gecko-gripper-tested-aboard-iss/" data-extlink="" target="_blank">gecko-inspired adhesive</a> co-developed with the <a href="http://bdml.stanford.edu/Profiles/MarkCutkosky" data-extlink="" target="_blank">Cutkosky lab</a>. Technologies like these could enable robots like Astrobee, a free-floating assistant aboard the ISS, to move cargo, fetch tools, make repairs, and clear debris.“An astronaut’s time is very valuable, but a significant amount of it is spent on housekeeping. Ideally, you would use a robot for that,” Pavone said, chuckling. “It’s fascinating work, but at the end of the day, we’re building robots to remove junk.”But the ASL’s designs could also help with space exploration. Take <a href="https://news.stanford.edu/2021/05/03/bringing-sci-fi-concepts-real-space-exploration/" data-extlink="" target="_blank">ReachBot</a>, one of Newdick’s main projects. Its eight arms extend like measuring tape with grippers at the ends. Paired with an autonomous navigation system, this could let it clamber around caves and tunnels on Mars without risking a human life to explore in person – but probably not anytime soon.“If I’m really lucky, in 20 years, my work will inspire another robot that does something different, but related,” Newdick said. “But if some part of what you do can contribute to something bigger, then that’s the goal.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1703984910214-20231129_ReachBot__95A0075.png" style="width: 300px;" class="fr-fic fr-dib">Schematic of the newer generation of ReachBot, showing some of the technology and features it could have to help with exploration of other planets and celestial bodies. | Image courtesy of Autonomous Systems Lab<h2 id="isPasted">Always more to explore</h2>Deploying autonomous systems has its downsides – especially on Earth, where robots could potentially take some jobs currently held by humans.“That’s always in the back of my mind,” Pavone said. “We develop systems that hopefully will create opportunities in the long run, but in the short term might cause significant pain for some segments of society.”Pavone and his students collaborate with other schools across Stanford to understand those consequences and design different types of strategies to mitigate them – such as <a href="https://hai.stanford.edu/news/equitable-approach-reducing-traffic-through-congestion-pricing" data-extlink="" target="_blank">using AI to redistribute traffic toll money</a> from drivers who can afford tolls to those who can’t.But with space robots, not even the sky’s the limit. Pavone gives the example of Mars: 20 years ago, the goal was just to land a robot there. Now, we’ve sent helicopters to take aerial views of the surface, and with ReachBot, we’re hoping to look underground.“You always want to get into more complicated environments,” Pavone said. Newdick agreed: Given the resources, she said, she’d love to design a robot to crawl down the vents in the icy crust of Enceladus, a moon of Saturn, to search for signs of life – another exploration likely impossible without autonomous robots.“Learning where we came from, where the planets and stars came from – that’s always been a curiosity,” Newdick said. “We can learn a lot doing science on Earth, but we can learn more if we also look elsewhere.”

Associate Professor Marco Pavone and PhD student Stephanie Newdick holding their ReachBot prototype. | Photo courtesy of Andrew Brodhead

From space robots to self-driving cars, Stanford’s Autonomous Systems Lab looks to push the boundaries of exploration and boost the safety and efficiency of everyday tasks.

The limitations of being human got you down? There's a robot for that

Example output of DeepScenario’s AI software that detects and tracks all moving objects in a scene in a fully automated way. (DeepScenario) 

There are many divergent-but-related ideas about how best to leverage simulation to ‘teach’ and test automated vehicles. Global AV simulation experts continue to attack the technology’s persistent obstacles.

AV simulation testing faces a long and winding road

In this episode, we discuss how active noise canceling technology works, what makes it so complex yet so useful, and why it is the unsung and underappreciated hero in your car.

Podcast: How To Make a Honda As Quiet As a Rolls Royce

The switch to e-mobility is also taking shape in Austrian cab fleets. This year, ten cab ranks and 66 e-vehicles will be retrofitted with "Matrix Charging" in Vienna and Graz as part of the "eTaxi Austria" project. The automated conductive charging technology was developed by the high-tech company Easelink. For the globally unique pilot project, FACTUREE is supplying various turned and milled parts made of aluminum. They are installed in the integrated "Matrix Charging Connector". During the charging process of an eTaxi, the connector is automatically lowered from the vehicle underbody and connected to a charging pad at the parking lot for charging.&nbsp;According to the national energy and climate plan, only zero-emission cabs and rental cars will be allowed in Austria from 2025. This measure paves the way for e-mobility. The stated goal is to reduce emissions and improve the quality of life in cities. However, previous eTaxi initiatives have shown that trips to charging stations as well as on-site charging times lead to uneconomic waiting times. The eTaxi Austria project aims to remedy this situation and relies on automated charging directly at charging stations. This avoids interruptions to get to the next charging station and makes the economic operation of electric cabs possible.&nbsp;<h2>Automated charging, a precursor to autonomous driving</h2>The eTaxi Austria project supports the switch of Vienna and Graz cab fleets to e-mobility primarily by providing charging options at the cab stand. Among others, the high-tech company Easelink, headquartered in Graz, is involved in the ambitious project. Over 60 eTaxis in the Vienna and Graz area will be equipped with the automated charging technology "Matrix Charging" developed by Easelink. Users of an e-car are relieved of having to plug in and unplug the vehicle thanks to the special technology. Easelink's goal is to standardize automated charging and thus prepare the e-fueling process for autonomous driving as well.&nbsp;The Matrix Charging System consists of two main components. Its compact design allows the Matrix Charging Connector to be easily integrated into the underbody of an electric vehicle. The vehicle unit is joined by an infrastructure unit directly at the parking space, the Matrix Charging Pad. As soon as the e-vehicle parks above the Charging Pad at the parking lot, the Connector is automatically lowered by the e-vehicle and physically connected to the Charging Pad at the cab stand. The charging process is automated without the driver having to leave the e-vehicle.&nbsp;<h2>FACTUREE supplies important components for the core element of the charging system </h2>FACTUREE supplies important components for the core element of the charging system For one of the two core components of the conductive charging system, the Matrix Charging Connector, the online manufacturer FACTUREE supplies various small turned and milled parts made of aluminum. Easelink benefits from great flexibility and plannability through the cooperation. This is because FACTUREE draws on a wide range of network partners, manufacturing techniques and surface treatments. As a result, the online manufacturer can constantly offer a very wide range of materials and has virtually unlimited production capacities - this is a major competitive advantage in times when supply problems are more frequent for classic contract manufacturers due to global crises.The Matrix Charging System can already be retrofitted to the most common electric vehicles on the market. The eTaxi Austria project is preparing the rollout of the automated charging infrastructure, thus setting the course for the mobility turnaround.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1702283550100-Matrix-Charging-System-with-Text_%C2%A9-Easelink_1200x800-768x512.jpg" style="width: 300px;" class="fr-fic fr-dib"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1702283555195-Matrix-System-Wireframe-Vehicle_%C2%A9Easelink_1200x675-768x432.jpg" style="width: 300px;" class="fr-fic fr-dib">

The switch to e-mobility is also taking shape in Austrian cab fleets. This year, ten cab ranks and 66 e-vehicles will be retrofitted with "Matrix Charging" in Vienna and Graz as part of the "eTaxi Austria" project. 

Climate-friendly mobility is gaining momentum: FACTUREE is on board in Austrian eTaxis

<h2 dir="ltr" id="isPasted">FREE REPORT:&nbsp;Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems</h2>Thousands die or are injured each year in automobile crashes. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs).Learn more about ADAS and ADS implementation with the goal of reaching zero deaths and serious injuries by downloading this FREE report from SAE International (valued at $50).&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyMzIxNDMwMTE3LXNhZSBmcmVlIHJlcG9ydC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><iframe src="https://c8056756.sibforms.com/serve/MUIFAIm7QnvQQ9ZyzkX--H5jNS-TSb9GWr5SQBXi_SUjRPpt67CBveq_yevZYMSeiJ-O1dwYw05f2kl-Q7sfNDRem1Y-03GXYZV4qkTbJeR_K_VpJzgMk_8Ry8fjyfgJ7fAMrCmcRHc1KXsSTuhIU5p5pBa3F1-TGDgOdQgcF-pU8cSTrl8JF2j2b9gl6-5e66Ro-rltcIrGxsRG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 740px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe><hr><h2>Provizio promises its 5D Perception stack can safely compete with expensive lidar sensors at a fraction of the cost.</h2>"Safety first” is more than a catchphrase. For sensing company Provizio, it’s the only way the transportation industry should introduce autonomous vehicles. In Provizio’s view, using AV building blocks – technology such as automatic emergency braking and lane-keep assist – can be valuable in ADAS systems, but they should not be used to drive vehicles until the perception problem has been solved.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAyMjgyNDM3MTM3LXByb3ZpemlvLXJhZGFyLXN1cGVyLXJlc29sdXRpb24tZ2FsbGVyeS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">An example image using data taken from Provizio’s “super-resolution” radar sensor.<blockquote>“It’s not that we’re skeptical about autonomous driving, it’s just that we strongly believe that the industry has taken this wrong path,” Dane Mitrev, machine learning engineer at Provizio, told SAE at September 2023’s AutoSens Brussels conference.</blockquote>“The industry has looked at things the other way around. They tried to solve autonomy first, without looking at accident prevention and simpler ADAS systems. We are building a perception technology which will first eliminate road fatalities, then deliver next-generation ADAS – and then solve autonomy. We believe that’s the right order.”Obviously, the industry is in favor of AVs. A group of 11 mobility and automotive companies released a 157-page paper in 2019 called “Safety First for Automated Driving.” Those companies — Aptiv, Audi, Baidu, BMW, Continental, Daimler, FCA US LLC, HERE, Infineon, Intel and Volkswagen — used the paper to propose a non-binding organized framework focused on “the development, testing and validation of safe automated passenger vehicles,” but did not attempt to establish any actual limits on what companies could do as they developed AVs. In the years since, AV technology has continued to advance, but not with the mission to solve sensing and important safety issues before teaching the vehicles how to maneuver.Provizio’s solution is its 5D Perception driving platform, named for the way it uses five different inputs to sense the environment. There’s the 3D radar information of an object in space, plus velocity information, plus the fifth dimension: perception, which means AI embedded in the hardware. Mitrev said the final vision of this technology will be powered by radar and cameras, not light detection and ranging (lidar). Radar’s benefits are well known: the sensors are smaller (and thus simpler to integrate) and less expensive than lidar and can see in all weather conditions. For now, though, Provizio is working with lidar, especially around its hometown.<h3>Irish sensors are smiling</h3>Provizio is headquartered in Limerick, Ireland, and has offices in North Ireland and Pittsburgh, Pennsylvania. In late 2022, Provizio signed a deal with micromobility company Voi to test 5D perception on Voi’s scooters. Alongside potential OEM and Tier 1 partnerships he couldn’t discuss, Mitrev said Provizio also has a partnership with Jaguar Land Rover, supporting tests at a future mobility campus in Ireland.These real-world tests involve collecting data from lidar to train the new radar AI. Provizio synchronizes the lidar and radar sensors, then extracts value from the lidar data at training time, embedding it into the neural net and teaching the neural networks based on these data streams. Imagine lidar as an old professor teaching a young radar sensor a few things, but sooner or later, the student – Provizio’s radar – won’t need the teacher anymore.<blockquote>“We lean fully towards a lidar-less path,” he said. “Lidars should not be involved, even in [SAE Level 5] vehicles. They’re not necessary.”</blockquote>The same is not true for cameras. To understand semantic information such as traffic signs, Provizio’s system uses cameras to augment the radar data.“Provizio is a perception company, first of all,” Mitrev said. “We are not a radar company. We’re not a car manufacturer. We’re a perception company, we have this radar and we have a perception platform that fuses radars and cameras and provides this low-cost solution.”Provizio was founded by Barry Lunn following the acquisition of his company Arralis, which focused on millimeter-wave communication technology for satellite communications and aerospace markets. Provizio’s radar technology grew out of radar work Lunn was familiar with in the aerospace industry. Instead of a commercial, off-the-shelf radar unit, Provizio is “redefining radars,” Mitrev said, by adding AI neural networks to the radar GPUs so all critical decisions can be made on the radar itself with less than five milliseconds latency to make those decisions. He said Provizio’s goal for its 5D radar is to see up to 600 m (1,969 feet) in 360 degrees in all weather conditions.<blockquote>“Once we have this 5D perception, there is literally no reason to build any other system that will do ADAS or autonomy,” he said, adding that accident prevention is “something tangible that we can solve in the next one, three-to-five, years.”</blockquote>At CES 2023, Provizio said it planned to deliver the first of its 5D Perception platforms by 2025. Provizio’s commercial strategy relies on licensing its technology to Tier 1 suppliers and OEMs. Once better sensors are in more vehicles that accurately understand the world around them, that’s when we can start letting vehicles drive themselves, Mitrev said.“The end goal is mass adoption, to have our 5D perception on all vehicles or a lot of vehicles worldwide, then we bring down the road deaths,” he said. “We believe that this is the right path. The industry has taken this the other way around. [Starting by building a self-driving car] is just too complex, let alone with only cameras, as some companies claim.”

Provizio offered demo drives of its technology on the road in Las Vegas during CES in January 2023. These images show some of what the sensors saw during the drive, along with some of Provizio’s identifying data. (Provizio)

"Safety first” is more than a catchphrase. For sensing company Provizio, it’s the only way the transportation industry should introduce autonomous vehicles.

Autonomus safety with radar, not LiDAR

Thousands die or are injured each year in automobile crashes. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs).The Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems report demonstrates in depth that ADAS can work. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxOTYxMzUxNTQ5LVNBRSBHVUlERS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:300px" class="fr-fic fr-dib" /><iframe src="https://c8056756.sibforms.com/serve/MUIFAJl8SmluAjOAycAr2OpXmABvTik4MZpIg4yCUp4J-ghnIci4zby0uUWVXzDYpEnY1OYUMIwPYv1BAJ_S1MaiBkQiN83WvKNWN5H2Odi0GDYnP3N9VRi1nXnoJqbbBRqBDp5GVg9xARQNnanIv3Yx6xoEPoRS2WEC7nBFGz_W6L6Dbc2OqqdFngN4PiyqugMaXnxAM6CbRegs" frameborder="0" scrolling="auto" style="display:block;margin-left:auto;margin-right:auto;max-width:100%;width:540px;height:450px" class="fr-draggable"></iframe> Get your copy of this report now. Free for Wevolver readers in December (usually $50). The path towards zero road deaths requires combining ADAS and ADS implementation with infrastructure engineering, law enforcement, education, emergency response, and public policy.<h2>Unveiling the Potential of ADAS in Modern Transportation</h2>Together with SAE International we are offering our community free access to this report, a part of SAE International’s EDGE Research Research portfolio. The report delves into the heart of ADAS technologies, exploring their role in reducing human driver errors, which have been a longstanding challenge in the realm of road safety. With a focus on cutting-edge research and real-world applications, the report explores  the critical ways in which ADAS is transforming everyday driving experiences, making roads safer for drivers, passengers, and pedestrians alike.Compiled by leading experts in the field, the report offers a comprehensive overview of the latest advancements and future prospects of ADAS. From autonomous braking systems to adaptive cruise control and lane-keeping assist, it covers a wide range of technologies that are integral to the modern driving landscape. This report is an essential resource for industry professionals and technology enthusiasts who are keen to stay ahead in the rapidly evolving world of automotive technology.<h2>A Step Towards a Zero-Fatality Future</h2>SAE’s vision aligns with the global goal of achieving zero deaths and serious injuries on the road. By harnessing the power of ADAS, we are not only looking at a future where driving is more comfortable and convenient but also significantly safer. This report serves as a guide to understanding how close we are to realizing this vision and the steps needed to get there.Download Your Copy Today for Free!<iframe src="https://c8056756.sibforms.com/serve/MUIFAJl8SmluAjOAycAr2OpXmABvTik4MZpIg4yCUp4J-ghnIci4zby0uUWVXzDYpEnY1OYUMIwPYv1BAJ_S1MaiBkQiN83WvKNWN5H2Odi0GDYnP3N9VRi1nXnoJqbbBRqBDp5GVg9xARQNnanIv3Yx6xoEPoRS2WEC7nBFGz_W6L6Dbc2OqqdFngN4PiyqugMaXnxAM6CbRegs" frameborder="0" scrolling="auto" style="display:block;margin-left:auto;margin-right:auto;max-width:100%;width:540px;height:450px" class="fr-draggable"></iframe> Ready to explore the world of ADAS and its impact on road safety? Download your copy of "Reducing Human Driver Errors with ADAS" today. Simply fill out the form on this page, and you'll gain immediate access to this essential resource. Join us in driving towards a safer, more efficient future.Read more content from SAE International on Wevolver <a href="https://www.wevolver.com/profile/sae.international" target="_blank">here. </a>

Download the limited edition free report now. 

New Report: The Technology Behind ADAS and a Crash-Free Future

Testing of a new automated shuttle is underway at the University of Michigan’s Mcity Test Facility to ensure the technology is ready to safely serve older adults and people with disabilities in the city of Detroit.Earlier this month, U-M researchers kicked off several months of testing with an automated vehicle provided by <a href="https://maymobility.com/" target="_blank" rel="noreferrer noopener">May Mobility</a>, a leader in the development and deployment of AV technology.&nbsp;The vehicle under test is identical to those destined for Detroit. The evaluation is taking place at the <a href="https://mcity.umich.edu/" target="_blank" rel="noreferrer noopener">Mcity Test Facility</a>—the world’s first purpose-built environment for testing connected and automated vehicles and technologies under controlled, realistic conditions. The shuttle will undergo a two-part test developed by Mcity, called the Mcity Safety Assessment Program.The protocol includes:<ul><li>A “Driver’s License Test” that serves a purpose similar to a human driver’s test, measuring basic competency in ordinary scenarios.&nbsp;</li><li>A “Driving Intelligence Test” that challenges AV software with a diverse set of dangerous driving scenarios—representing those that most often result in crashes, injuries and fatalities.</li></ul>The augmented reality program combines physical vehicles, infrastructure and obstacles with computer-simulated people, vehicles and other objects.“Automated vehicles have the potential to make transportation safer and cleaner while ensuring equitable access to mobility options—but only if consumers can trust the vehicles are safe,” said Mcity Director Henry Liu, who is also the Bruce D. Greenshields Collegiate Professor of Engineering and director of the Center for Connected and Automated Transportation.“We believe the Mcity Safety Assessment Program could serve as the blueprint for a publicly visible safety framework, helping to bring automated vehicle technology to market in a manner that truly benefits society.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701141819329-AVTesting-content2.jpg" style="width: 766px;" class="fr-fic fr-dib">The Driverless May Mobility vehicle stops automatically to avoid colliding with a child-size test dummy in the road. Photo: Marcin Szczepanski/Michigan EngineeringTesting of the shuttle for Detroit comes as recent traffic crashes related to driverless vehicles have raised broad concerns about safety.&nbsp;To prioritize safety, the city of Detroit’s Automated Driving Systems shuttle pilot will undergo a thorough evaluation process, beginning with the testing at Mcity, and the deployment of the shuttles will feature a skilled safety operator on board in each May Mobility vehicle. These operators will be available to answer questions and assist wheelchair users when boarding and exiting the shuttle.“From design to launch and beyond, we have embedded strong safety practices into all of our processes,” said Satvir Singh, director of product safety at May Mobility.&nbsp;“Participating in Mcity’s Safety Assessment Program is one crucial step to ensuring that each AV we deploy will bring our riders to their destinations safely and comfortably. With the help of our partners at Mcity, we feel confident that our vehicles will exceed expectations and set a new standard for AV technology safety in Detroit and across the world.”Testing of the shuttle at Mcity will likely run through February. From there, it will be sent to the <a href="https://acmwillowrun.org/" target="_blank">American Center for Mobility</a> in Ypsilanti, for extended high-speed testing.&nbsp;Designed with equity in mind, the pilot aims to provide Detroit residents aged 65 and older or those living with disabilities with free transportation. It is intended to facilitate seamless travel to and from essential destinations such as stores, medical appointments, workplaces and more.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701141849330-AVTesting-content1.jpg" style="width: 766px;" class="fr-fic fr-dib">U-M researchers Xintao Yan (center) and Linxuan He (left) adjust the safety testing technology while Tinghan Wang looks on from the challenge vehicle at Mcity. Photo: Marcin Szczepanski/Michigan EngineeringThe Detroit City Council unanimously voted to approve a $2.4 million contract between the Office of Mobility Innovation and May Mobility in July, and the city plans to deploy the shuttle in June 2024.&nbsp;“We are excited that the rigorous self-driving shuttle testing is underway with Mcity,” said Tony Geara, deputy chief of the city of Detroit’s Office of Mobility Innovation. “This thorough evaluation process underscores the city of Detroit’s commitment that self-driving technology will be safe and effective before it is on our streets.&nbsp;“We are starting this ambitious pilot project with the Mcity Safety Assessment Program as a critical step toward learning how self-driving vehicles can effectively serve real community needs in Detroit.”May Mobility is a U-M startup that was launched in 2017 with the help of Innovation Partnerships.

The University of Michigan, May Mobility and the City of Detroit look to boost public trust in self-driving vehicle technology.

Automated shuttle planned for Detroit starts safety testing at Mcity

ETH Zurich researchers deployed an autonomous excavator, called HEAP, to build a six metre-high and sixty-five-metre-long dry-stone wall. The wall is embedded in a digitally planned and autonomously excavated landscape and park.<iframe width="640" height="360" src="https://www.youtube.com/embed/P7wmotyKgXc?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Video: ETH Zurich / Girts ApskalnsThe team of researchers included: Gramazio Kohler Research, the Robotics Systems Lab, Vision for Robotics Lab, and the Chair of Landscape Architecture. They developed this innovative design application as part of the National Centre of Competence in Research for Digital Fabrication (NCCR dfab).Using sensors, the excavator can autonomously draw a 3D map of the construction site and localise existing building blocks and stones for the wall’s construction. Specifically designed tools and machine vision approaches enable the excavator to scan and grab large stones in its immediate environment. It can also register their approximate weight as well as their centre of gravity. An algorithm determines the best position for each stone, and the excavator then conducts the task itself by placing the stones in the desired location. The autonomous machine can place 20 to 30 stones in a single consignment – about as many as one delivery could supply.<hr><h2 id="isPasted">Reference &nbsp; &nbsp; &nbsp; &nbsp;</h2>Johns RL, Wermelinger M, Mascaro R, Jud D, Hurkxkens I, Vasey L, Chli M, Gramazio F, Kohler M, Hutter M: A framework for robotic excavation and dry stone construction using on-site materials, Science Robotics, 22 November 2023, DOI: <a href="https://doi.org/10.1126/scirobotics.abp9758" target="_blank">external page10.1126/scirobotics.abp9758</a>

The Excavator picks and scans each boulder to be placed in the correct position. Circularity Park in Oberglatt, Eberhard AG. (Photograph: ETH Zurich / Marc Schneider)

ETH Zurich researchers taught an autonomous excavator to construct dry stone walls itself using boulders weighing several tonnes and demolition debris.

Autonomous excavator constructs a six-metre-high dry-stone wall

Ian Dickson spoke to Christian Thiele, Director, Global Vehicle Standards at SAE International about their missionto connect and educate mobility professionals to enable safe, clean, and accessible mobility solutions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxMTE2MDcyMzkwLUFWIGRvd25sb2FkIGJhbm5lci5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><iframe width="540" height="450" src="https://c8056756.sibforms.com/serve/MUIFAMBWtqlAziu5EwFUIjLCWmRdzCiwOJRx3R3LyYkfG1dfYIvOKLn1dfBY8M58jYUZY8sQm7DnIOmrBjZ93ycsAv4ICzMbRD95I3S4FIWFM6EU4i3C7yzFNYn-Kfm0vJN96NgNO2_w4BQcrnOEwEVShkElbpBLjOT3ARKFBFADGHd-PDjMwJZI5avI3TUfr-xTC_fVunqO5MKo" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>&nbsp;<hr><h3 dir="ltr" id="isPasted">What is SAE?</h3>Christian Thiele: SAE International has been around for about 118 years, our primary focus is dealing with the development of standardization and selling and getting those standards out to the industry. Recognizing that our mission is to advance mobility, knowledge, and solutions for the benefit of humanity, with the vision to connect and educate mobility professionals to enable a safe, clean, and accessible mobility solution at the end of the day, we do this through mutual forums. And by that we enable these mutual forms to develop consensus-based global standards for the global mobility industry.&nbsp;We are the largest organization that drives mobility standards. But in order to deal with that, we also have a foundation and we also do STEM research, to help develop and grow through learning, through professional development, and pre-professional development, driving people to engage through engineering technology. Basically, fill those coffers for future committee members and future engineers who help these consensus-based standards being developed and produced.<h3 dir="ltr">Thanks for the overview. Then when it comes to autonomous cars, you've probably answered this question in part already but how is SAE involved in autonomous cars?&nbsp;</h3>Christian Thiele: Let's look at it this way. Right now, we're not at the autonomous phase. Let's look at automated driving, and recognizing and understanding the automated space. We have specifically through our J3016 identified the levels of automation and clearly, level five and beyond is when you get to a level where you could call it autonomous.&nbsp;But at the end of the day, what our focus is, is helping drive those standards and develop those standards. To allow interoperability and harmonisation, especially the communication side of how automated vehicles are managed.&nbsp;Automated driving, ultimately from a vehicle and driver perspective, is to make certain that active safety standards that are built into a vehicle, i.e. the automatic braking, lane change sensing, holding steering wheel position of the car in the lane so it doesn't leave the lane, etc. These are technologies and spaces that we have helped develop in the industry, and helped through our standard organization bodies, building those standards that define what that needs to be.<h3 dir="ltr">What are some of the key challenges your members are facing particularly with AV?</h3>The key, especially from a technology point of view, is getting things that are working, to scale and interoperability. This is what we tried to do through our harmonization and through defining those standards for those communication protocols that need to happen, which allow everybody to communicate at one frequency, at one time. It's not English to Chinese; it's everyone speaking English, so to speak, to put it in layman's terms.<h3 dir="ltr">Okay, perfect. And then talking about the organization in a more general capacity. What are some of the benefits to your members of being in SAE and the adherence to these standards?</h3>Let's look at what standardization does for a company and organization itself. First, you reduce costs and design development, because you are convening a large group of industry experts that come together to help better define, narrow, and develop what standards we need to follow and to make design and development easier on the individual corporations and companies. This makes engineering easier and it reduces the cost. It also increases performance as it permits a common interface. Increased productivity and processes enable companies to become more effective and efficient at what they do as a corporation.&nbsp;So ultimately, what does this do at the end of the day? It helps to enhance safety, create a common language out there for everybody to communicate to, and facilitates throughput through regulations. It harmonizes the global marketplace. Standards ultimately give compatibility, consistent quality product, regulatory compliance, clear expectations, and consistent product performance. Everybody understands what needs to be when, where and how. It also allows for efficient procurement, which is vitally critical for organizations and companies to be able to buy and procure products. &nbsp;These reasons show why the SAE and the standards bodies organizations are vitally critical. &nbsp;<h3 dir="ltr">Then how do you actually create global standards when regulations requirements vary so much by country?</h3>Yeah, so, what we do is we work on a regional, national, and international space. Standards by themselves are not the finding. The right standards give you a direction of what is perhaps seen as the best technical information out there.We support harmonization through the UN. For example, we sit as a consultant on a consultative body. Where we contribute expertise on dealing with communication protocol and connected vehicle protocols. We helped derive some of those standards that ultimately will be implemented at a local government level.&nbsp;<h3 dir="ltr">How different are the SAE standards for autonomous vehicles versus the current generation of vehicles?</h3>Let's understand two things. First, before, the car was a self-contained environment. At the end of the day, it never communicated with the outside environment. The person or thing that was communicating was you, the individual driving the vehicle who was seeing and hearing etc. And you would make that vehicle go where it needed to go. Now you have systems that are going to define where, when, and how to best engage that vehicle under certain circumstances. &nbsp;And that's where the challenge is. &nbsp;How much confidence do we have from a security point of view to ensure that the systems are secure, without being hacked or compromised, through other means? Now, even EV vehicles recognize they're plugging in and charging. Anytime you plug in the vehicle, something happens as it relates to communication between the vehicle and the outside world. And there are threats and risks there.&nbsp;<h3 dir="ltr">So in your opinion, where do you think we are with autonomous vehicles? And what's your vision of an autonomous future?&nbsp;</h3>I definitely see an opportunity. I don't think we're there yet with a fully automated vehicle. I think level one or two are pretty sound and robust. Level three and beyond that, that's where it becomes challenging and I think in the next five to 10 years, you will see major technology advancements allowing for reliability and capability to execute to a certain level that is acceptable. You don't want a braking system working 60% of the time, you'd like it to be in the 99 percentile, recognizing and understanding that these things will help and benefit the occupants in the vehicles and the drivers themselves.&nbsp;We had over 43,000 deaths last year in the US related to automotive mobility related deaths. That is a frighteningly large number and we need to do whatever we can do from a technology point of view, to advance these vehicles to better utilize technology to better enable automated driving down the road.&nbsp;<h3 dir="ltr">Thank you. How do you think AVs can contribute to society? I think you've really touched on it there in terms of reducing the number of deaths and serious injuries.</h3>We need to acknowledge the ongoing revisions to the J3016, which is the standard for defining levels of automation in vehicles. It's crucial to consider human factors in this context. From the perspective of distracted driving, it's important to recognize the potential benefits of automated and autonomous vehicles. These technologies can significantly aid in addressing distracted driving issues.Currently, we are exploring technologies that are already available. For instance, in Europe, every new vehicle will be equipped with Driver Management Systems (DMS). These systems, which include vision systems and driver monitoring capabilities, focus on tracking the driver's head and eye positions. This is a key step in enhancing safety through DMS.It's essential to always be aware of the driver's actions, particularly the position of their hands. Implementing these technologies immediately can help drivers stay focused on driving rather than being distracted by other activities.<hr><h3 dir="ltr">Click through to read each of the report's chapters.</h3><main><h3><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report" target="_blank" rel="noopener noreferrer">Introduction</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=1" target="_blank" rel="noopener noreferrer">I: Sensing Technologies</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=2" target="_blank" rel="noopener noreferrer">II: Thinking and Learning</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=3" target="_blank" rel="noopener noreferrer">III: EDGE and RTOS</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=4" target="_blank" rel="noopener noreferrer">IV: Communication and Connectivity</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=5" target="_blank" rel="noopener noreferrer">V: Security</a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report?chapter=6" target="_blank" rel="noopener noreferrer">VI: Tech Stack</a> </h3></main><h2>Leadership Interviews</h2><h3 dir="ltr"><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-accelerating-av-development-through-customer-collaboration" target="_blank" rel="noopener noreferrer">Interview with Mouser </a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-building-trust-in-autonomous-driving-navigating-future-reliability-and-milestone-achievements" target="_blank" rel="noopener noreferrer">Interview with Murata&nbsp;</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-the-role-of-pcb-in-shaping-autonomous-vehicle-development/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-the-role-of-pcb-in-shaping-autonomous-vehicle-development" target="_blank" rel="noopener noreferrer">Interview with MacroFab</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-exploring-the-future-of-high-performance-computing-in-autonomous-vehicles/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-exploring-the-future-of-high-performance-computing-in-autonomous-vehicles" target="_blank" rel="noopener noreferrer">Interview with Nexperia</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-driving-global-standards/draft" target="_blank"></a> Interview with SAE International<a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-advancing-open-source-development-in-the-automotive-industry/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-advancing-open-source-development-in-the-automotive-industry" target="_blank" rel="noopener noreferrer">Interview with Autoware Foundation</a><a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-insights-from-nvidia-about-artifical-intelligence-in-avs/draft" target="_blank"></a> <a href="https://www.wevolver.com/article/2023-autonomous-vehicle-report-interview-insights-from-nvidia-about-artifical-intelligence-in-avs" target="_blank" rel="noopener noreferrer">Interview with NVIDIA</a></h3>

In this interview Christian Thiele, Director, Global Vehicle Standards at SAE discusses advanced mobility, knowledge sharing and automotive solutions for the benefit of humanity.

Autonomous Vehicles