As part of TechBlick series on additive and printed electronics, Michael LeFebvre draws upon his four decades of industry experience to highlight 3 recent technical and application innovations impacting transparent 5G antennas, ADAS, and wearable therapy all based on printed & additive electronics

Printed Electronics: The Future is Thin, Flat, and Flexible

The automotive industry undergoes a profound transformation through the IoT's power. Vehicle connectivity and autonomous driving redefine transportation, offering innovative opportunities. Embracing IoT's potential, our daily lives are poised for a significant shift in the automotive sector.

Driving Innovation: Harnessing IoT Potential in the Automotive Sector

In this episode, we delve into a game-changing approach developed by the University of Michigan, where &#39;simulated terrible drivers&#39; in a mixed reality environment are used to drastically cut the time and cost of Autonomous Vehicle (AV) safety testing. Discover how this innovative method enhances testing by focusing on rare but critical driving events, potentially accelerating the deployment and public acceptance of self-driving cars.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/4e11e15a-7cd7-4ab1-a302-e4db87801132?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><hr>This podcast is sponsored by&nbsp;<a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1685583021357-Mouser-sponsor-1-a.jpg" style="width: 766px;" class="fr-fic fr-dib"><hr>(3:49) - <a href="https://www.wevolver.com/article/simulated-terrible-drivers-cut-the-time-and-cost-of-av-testing-by-a-factor-of-one-thousand" style="background-color: transparent;" target="_blank">Simulated terrible drivers cut the time and cost of AV testing by a factor of one thousand</a>This episode was brought to you by Mouser, our favorite place to get electronics parts for any project, whether it be a hobby at home or a prototype for work. Click <a href="https://resources.mouser.com/automotive/autonomous-vehicles-the-way-forward" target="_blank">HERE</a> to learn about how electronics suppliers are planning to meet the technical and regulatory requirements to make autonomous vehicles possible!<hr>As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">&nbsp;shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" target="_blank">&nbsp;Apple</a> podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" target="_blank">&nbsp;Spotify</a>, and most of your favorite podcast platforms!

Xintao Yan and Haojie Zhu, both civil engineering PhD students, prepare to take one of the autonomous vehicles for a test run at Mcity. Photo: Brenda Ahearn/Michigan Engineering

In this episode, we delve into a game-changing approach developed by the University of Michigan, where 'simulated terrible drivers' in a mixed reality environment are used to drastically cut the time and cost of Autonomous Vehicle (AV) safety testing.

Podcast: Augmented Bad Drivers Turbocharge Autonomous Vehicle Development

This article was first published on&nbsp;<a href="https://news.mit.edu/2023/exploring-methods-increasing-safety-reliability-autonomous-vehicles-0523" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>When we think of getting on the road in our cars, our first thoughts may not be that fellow drivers are particularly safe or careful &mdash; but human drivers are more reliable than one may expect. For each fatal car crash in the United States, motor vehicles log a whopping hundred million miles on the road.Human reliability also plays a role in how autonomous vehicles are integrated in the traffic system, especially around safety considerations. Human drivers continue to surpass autonomous vehicles in their ability to make quick decisions and perceive complex environments: Autonomous vehicles are known to struggle with seemingly common tasks, such as taking on- or off-ramps, or turning left in the face of oncoming traffic. Despite these enormous challenges, embracing autonomous vehicles in the future could yield great benefits, like clearing congested highways; enhancing freedom and mobility for non-drivers; and boosting driving efficiency, an important piece in fighting climate change.MIT engineer Cathy Wu envisions ways that autonomous vehicles could be deployed with their current shortcomings, without experiencing a dip in safety. &ldquo;I started thinking more about the bottlenecks. It&rsquo;s very clear that the main barrier to deployment of autonomous vehicles is safety and reliability,&rdquo; Wu says.One path forward may be to introduce a hybrid system, in which autonomous vehicles handle easier scenarios on their own, like cruising on the highway, while transferring more complicated maneuvers to remote human operators. Wu, who is a member of the Laboratory for Information and Decision Systems (LIDS), a Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering (CEE) and a member of the MIT Institute for Data, Systems, and Society (IDSS), likens this approach to air traffic controllers on the ground directing commercial aircraft.In a&nbsp;<a href="https://ieeexplore.ieee.org/document/10101675" target="_blank">paper</a> published April 12 in&nbsp;IEEE Transactions on Robotics, Wu and co-authors Cameron Hickert and Sirui Li (both graduate students at LIDS) introduced a framework for how remote human supervision could be scaled to make a hybrid system efficient without compromising passenger safety. They noted that if autonomous vehicles were able to coordinate with each other on the road, they could reduce the number of moments in which humans needed to intervene.<h2>Humans and cars: finding a balance that&rsquo;s just right</h2>For the project, Wu, Hickert, and Li sought to tackle a maneuver that autonomous vehicles often struggle to complete. They decided to focus on merging, specifically when vehicles use an on-ramp to enter a highway. In real life, merging cars must accelerate or slow down in order to avoid crashing into cars already on the road. In this scenario, if an autonomous vehicle was about to merge into traffic, remote human supervisors could momentarily take control of the vehicle to ensure a safe merge. In order to evaluate the efficiency of such a system, particularly while guaranteeing safety, the team specified the maximum amount of time each human supervisor would be expected to spend on a single merge. They were interested in understanding whether a small number of remote human supervisors could successfully manage a larger group of autonomous vehicles, and the extent to which this human-to-car ratio could be improved while still safely covering every merge.With more autonomous vehicles in use, one might assume a need for more remote supervisors. But in scenarios where autonomous vehicles coordinated with each other, the team found that cars could significantly reduce the number of times humans needed to step in. For example, a coordinating autonomous vehicle already on a highway could adjust its speed to make room for a merging car, eliminating a risky merging situation altogether.The team substantiated the potential to safely scale remote supervision in two theorems. First, using a mathematical framework known as queuing theory, the researchers formulated an expression to capture the probability of a given number of supervisors failing to handle all merges pooled together from multiple cars. This way, the researchers were able to assess how many remote supervisors would be needed in order to cover every potential merge conflict, depending on the number of autonomous vehicles in use. The researchers derived a second theorem to quantify the influence of cooperative autonomous vehicles on surrounding traffic for boosting reliability, to assist cars attempting to merge.When the team modeled a scenario in which 30 percent of cars on the road were cooperative autonomous vehicles, they estimated that a ratio of one human supervisor to every 47 autonomous vehicles could cover 99.9999 percent of merging cases. But this level of coverage drops below 99 percent, an unacceptable range, in scenarios where autonomous vehicles did not cooperate with each other.&ldquo;If vehicles were to coordinate and basically prevent the need for supervision, that&rsquo;s actually the best way to improve reliability,&rdquo; Wu says.<h2>Cruising toward the future</h2>The team decided to focus on merging not only because it&rsquo;s a challenge for autonomous vehicles, but also because it&rsquo;s a well-defined task associated with a less-daunting scenario: driving on the highway. About half of the total miles traveled in the United States occur on interstates and other freeways. Since highways allow higher speeds than city roads, Wu says, &ldquo;If you can fully automate highway driving &hellip; you give people back about a third of their driving time.&rdquo;If it became feasible for autonomous vehicles to cruise unsupervised for most highway driving, the challenge of safely navigating complex or unexpected moments would remain. For instance, &ldquo;you [would] need to be able to handle the start and end of the highway driving,&rdquo; Wu says. You would also need to be able to manage times when passengers zone out or fall asleep, making them unable to quickly take over controls should it be needed. But if remote human supervisors could guide autonomous vehicles at key moments, passengers may never have to touch the wheel. Besides merging, other challenging situations on the highway include changing lanes and overtaking slower cars on the road.Although remote supervision and coordinated autonomous vehicles are hypotheticals for high-speed operations, and not currently in use, Wu hopes that thinking about these topics can encourage growth in the field.&ldquo;This gives us some more confidence that the autonomous driving experience can happen,&rdquo; Wu says. &ldquo;I think we need to be more creative about what we mean by &lsquo;autonomous vehicles.&rsquo; We want to give people back their time &mdash; safely. We want the benefits, we don&rsquo;t strictly want something that drives autonomously.&rdquo;<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/exploring-methods-increasing-safety-reliability-autonomous-vehicles-0523" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot; &nbsp;

Autonomous vehicles are known to struggle with seemingly common tasks, such as taking on- or off-ramps, or turning left in the face of oncoming traffic. Image: Shutterstock

A new study finds human supervisors have the potential to reduce barriers to deploying autonomous vehicles.

Exploring new methods for increasing safety and reliability of autonomous vehicles

Completely new vehicle concepts are needed to solve the pressing problems of car traffic, such as high CO2-emissions, traffic jams and overcrowding in inner cities. Converting or technically upgrading current vehicles is not enough. That&#39;s why researchers have developed vehicles from scratch in&nbsp;<a href="https://www.unicaragil.de/en/" target="_blank">a Germany-wide project&nbsp;</a>with the participation of Karlsruhe Institute of Technology (KIT). The result is revolutionary electric and driverless prototypes for individual and public transportation as well as freight transport.<h2>Rethinking the car thanks to automation</h2>&quot;In current vehicles, everything is in a certain place because that has been the optimal choice at a certain point in their development history. But with today&#39;s automation possibilities, we were able to rethink the car completely,&quot; says Martin Lauer from the KIT Institute of Measurement and Control Systems (MRT). The newly developed vehicles have four individually steerable wheels, which enables completely new motion sequences when maneuvering, such as parking by driving sideways. &quot;Since the vehicles don&#39;t have a steering wheel, we didn&#39;t have to consider a person steering. Therefore vehicles are able to drive in both directions without any problems,&quot; Lauer explains. This is made possible by novel sensor modules developed at the MRT, which ensure all-round visibility even in difficult situations.<h2>Automated package delivery</h2>Equipped with a robotic arm, a variant of the vehicle developed at the Institute of Materials Handling and Logistics (IFL) can deliver packages autonomously. &quot;The system drives up to parcel boxes set up at the side of the road and picks up or hands over parcels with its robotic arm,&quot; explains Kai Markert of the IFL. &quot;Through a sophisticated system of cameras, the parcels are measured, identified and safely grabbed.&quot;The UNICARagil project involved 17 universities and companies. It was funded by the German Federal Ministry of Education and Research with about 32 million euros.

To solve the pressing problems of automobile traffic, researchers developed a completely new vehicle concept. (Photo: Amadeus Bramsiepe, KIT)

Cars are far too low on flexibility in their current form, revolutionary concepts for passenger and freight traffic provide a solution

Prototypes: New vehicles for future mobility

This article was first published on&nbsp;<a href="https://news.mit.edu/2023/using-reflections-shiny-objects-camera-0510" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>As a car travels along a narrow city street, reflections off the glossy paint or side mirrors of parked vehicles can help the driver glimpse things that would otherwise be hidden from view, like a child playing on the sidewalk behind the parked cars.Drawing on this idea, researchers from MIT and Rice University have created a computer vision technique that leverages reflections to image the world. Their method uses reflections to turn glossy objects into &ldquo;cameras,&rdquo; enabling a user to see the world as if they were looking through the &ldquo;lenses&rdquo; of everyday objects like a ceramic coffee mug or a metallic paper weight. &nbsp;&nbsp;Using images of an object taken from different angles, the technique converts the surface of that object into a virtual sensor which captures reflections. The AI system maps these reflections in a way that enables it to estimate depth in the scene and capture novel views that would only be visible from the object&rsquo;s perspective. One could use this technique to see around corners or beyond objects that block the observer&rsquo;s view.This method could be especially useful in autonomous vehicles. For instance, it could enable a self-driving car to use reflections from objects it passes, like lamp posts or buildings, to see around a parked truck.&ldquo;We have shown that any surface can be converted into a sensor with this formulation that converts objects into virtual pixels and virtual sensors. This can be applied in many different areas,&rdquo; says Kushagra Tiwary, a graduate student in the Camera Culture Group at the Media Lab and co-lead author of a <a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="https://arxiv.org/pdf/2212.04531.pdf" target="_blank">paper</a> on this research.Tiwary is joined on the paper by co-lead author Akshat Dave, a graduate student at Rice University; Nikhil Behari, an MIT research support associate; Tzofi Klinghoffer, an MIT graduate student; Ashok Veeraraghavan, professor of electrical and computer engineering at Rice University; and senior author Ramesh Raskar, associate professor of media arts and sciences and leader of the Camera Culture Group at MIT. The research will be presented at the Conference on Computer Vision and Pattern Recognition.<h2>Reflecting on reflections</h2>The heroes in crime television shows often &ldquo;zoom and enhance&rdquo; surveillance footage to capture reflections &mdash; perhaps those caught in a suspect&rsquo;s sunglasses &mdash; that help them solve a crime.&nbsp;&ldquo;In real life, exploiting these reflections is not as easy as just pushing an enhance button. Getting useful information out of these reflections is pretty hard because reflections give us a distorted view of the world,&rdquo; says Dave.This distortion depends on the shape of the object and the world that object is reflecting, both of which researchers may have incomplete information about. In addition, the glossy object may have its own color and texture that mixes with reflections. Plus, reflections are two-dimensional projections of a three-dimensional world, which makes it hard to judge depth in reflected scenes.The researchers found a way to overcome these challenges. Their technique, known as ORCa (which stands for Objects as Radiance-Field Cameras), works in three steps. First, they take pictures of an object from many vantage points, capturing multiple reflections on the glossy object.Then, for each image from the real camera, ORCa uses machine learning to convert the surface of the object into a virtual sensor that captures light and reflections that strike each virtual pixel on the object&rsquo;s surface. Finally, the system uses virtual pixels on the object&rsquo;s surface to model the 3D environment from the point of view of the object.<h2>Catching rays</h2>Imaging the object from many angles enables ORCa to capture multiview reflections, which the system uses to estimate depth between the glossy object and other objects in the scene, in addition to estimating the shape of the glossy object. ORCa models the scene as a 5D radiance field, which captures additional information about the intensity and direction of light rays that emanate from and strike each point in the scene.The additional information contained in this 5D radiance field also helps ORCa accurately estimate depth. And because the scene is represented as a 5D radiance field, rather than a 2D image, the user can see hidden features that would otherwise be blocked by corners or obstructions.In fact, once ORCa has captured this 5D radiance field, the user can put a virtual camera anywhere in the scene and synthesize what that camera would see, Dave explains. The user could also insert virtual objects into the environment or change the appearance of an object, such as from ceramic to metallic.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1683771128106-ezgif-1-b97d11cf83.gif" style="width: 766px;" class="fr-fic fr-dib">The additional information that is captured in the 5D radiance field that ORCa learns enables a user to change the appearance of objects in the scene, in this case, by rendering the glossy sphere and mug as metallic objects instead. Credit: Courtesy of the researchers&ldquo;It was especially challenging to go from a 2D image to a 5D environment. You have to make sure that mapping works and is physically accurate, so it is based on how light travels in space and how light interacts with the environment. We spent a lot of time thinking about how we can model a surface,&rdquo; Tiwary says.<h2>Accurate estimations</h2>The researchers evaluated their technique by comparing it with other methods that model reflections, which is a slightly different task than ORCa performs. Their method performed well at separating out the true color of an object from the reflections, and it outperformed the baselines by extracting more accurate object geometry and textures.They compared the system&rsquo;s depth estimations with simulated ground truth data on the actual distance between objects in the scene and found ORCa&rsquo;s predictions to be reliable.&nbsp; &nbsp;&ldquo;Consistently, with ORCa, it not only estimates the environment accurately as a 5D image, but to achieve that, in the intermediate steps, it also does a good job estimating the shape of the object and separating the reflections from the object texture,&rdquo; Dave says.Building off of this proof-of-concept, the researchers want to apply this technique to drone imaging. ORCa could use faint reflections from objects a drone flies over to reconstruct a scene from the ground. They also want to enhance ORCa so it can utilize other cues, such as shadows, to reconstruct hidden information, or combine reflections from two objects to image new parts of a scene.&ldquo;Estimating specular reflections is really important for seeing around corners, and this is the next natural step to see around corners using faint reflections in the scene,&rdquo; says Raskar.&ldquo;Ordinarily, shiny objects are difficult for vision systems to handle. This paper is very creative because it turns the longstanding weakness of object shininess into an advantage. By exploiting environment reflections off a shiny object, the paper is not only able to see hidden parts of the scene, but also understand how the scene is lit. This enables applications in 3D perception that include, but are not limited to, an ability to composite virtual objects into real scenes in ways that appear seamless, even in challenging lighting conditions,&rdquo; says Achuta Kadambi, assistant professor of electrical engineering and computer science at the University of California at Los Angeles, who was not involved with this work. &ldquo;One reason that others have not been able to use shiny objects in this fashion is that most prior works require surfaces with known geometry or texture. The authors have derived an intriguing, new formulation that does not require such knowledge.&rdquo;<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/using-reflections-shiny-objects-camera-0510" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot; &nbsp;

Researchers from MIT and Rice University have created a computer vision technique that leverages reflections to image the world by using them to turn glossy objects into “cameras,” enabling a user to see the world as if they were looking through the “lenses” of everyday objects like a ceramic coffee mug or a metallic paper weight. Credit: Courtesy of the researchers

A new computer vision system turns any shiny object into a camera of sorts, enabling an observer to see around corners or beyond obstructions.

Using reflections to see the world from new points of view

The highly automated and connected vehicles of the future will require powerful computer systems that perform sophisticated calculations and process huge amounts of data.&nbsp;30 partners from industry and research are developing suitable processors, interfaces and system architectures in the CeCaS project.&nbsp;The Karlsruhe Institute of Technology (KIT) is involved with two institutes in the project coordinated by Infineon.&nbsp;The Federal Ministry of Research supports CeCaS within the MANNHEIM initiative.&nbsp;The next generations of vehicles will be increasingly automated and networked in order to move more and more autonomously on the road and gradually relieve drivers.&nbsp;This requires enormous computing power, which only the most powerful computer systems can provide - whether in the vehicles themselves, along the roads or in the higher-level data centers.&nbsp;In addition to internal communication systems connected to the outside world, the vehicles require a central computer.&nbsp;This, in turn, consists of sub-components that perform sophisticated calculations, process huge amounts of data and have to achieve maximum reliability.<h2>KIT and TUM have assumed scientific coordination</h2>&nbsp;In the CeCaS (stands for: CentralCarServer) research project, 30 partners from industry and research are working on the architectures, the software engineering principles and the forms of implementation for future high-performance computers in cars.&nbsp;Infineon Technologies AG is responsible for coordinating the overall project.&nbsp;The KIT with Professor J&uuml;rgen Becker, head of the Institute for Information Processing Technology (ITIV), and Professor J&ouml;rg Henkel, head of the research area Embedded Electronic Systems at the Institute for Technical Informatics (ITEC-CES), as well as the Technical University of Munich ( TUM) with Professor Alois Knoll, Head of the Chair for Robotics, Artificial Intelligence and Real-Time Systems (AIR).&nbsp;&quot;The development of energy- and cost-efficient high-performance computers with full automotive qualifications, which meet the enormous demands on computing power and complexity in a scalable manner, makes a decisive contribution to the future viability and technological sovereignty of the German automotive industry,&quot; says Becker.&nbsp;In CeCaS, automotive supercomputing is created that meets the highest standards of safety and reliability.&nbsp;The project consortium designs processors, interfaces and system architectures.&nbsp;A flexible software environment is tailored to the requirements of the latest algorithms in automobiles - especially, but not only, for the use of artificial intelligence (AI).<h2>Hardware accelerators enable highly effective image processing</h2>In CeCaS, the KIT has taken over the design of novel multi-purpose hardware accelerators for highly effective image processing including the integration of reliable AI in the automobile.&nbsp;The innovative accelerators are connected via high-speed interfaces and integrated into the high-performance processors.&nbsp;The researchers are particularly focusing on the AI components between the sensor nodes and the central computer.&nbsp;In addition, the KIT is working within CeCaS on new development tools for the analysis and compliance with real-time criteria as well as on comprehensive benchmarking software for evaluating the hardware accelerator.&nbsp;&quot;Progress in automotive technology is directly dependent on progress in computer technology and information technology - but above all on the ability of the automotive industry to use modern chip technologies for itself,&quot; explains Becker.&nbsp;&quot;CeCaS supports the German automotive industry in playing a leading role in global competition, even in the digital age.&quot;&nbsp;<h2>About CeCaS</h2>The Federal Ministry of Education and Research (BMBF) is funding CeCaS in its MANNHEIM initiative, named after the birthplace of the automobile, with around 46 million euros.&nbsp;The total project volume is almost 90 million euros.&nbsp;CeCaS is scheduled for three years.The 30 cooperation partners of the MANNHEIM-CeCaS project are: Bosch, Continental Automotive, ZF Friedrichshafen, Hella, AVL Software &amp; Functions, Ambrosys, Infineon Technologies AG (coordination; with Infineon Technologies Dresden GmbH &amp; Co. KG and Infineon Technologies Semiconductor GmbH), core concept , Berlin Nanotest and Design, Missing Link Electronics, Inchron, Gl&uuml;ck Engineering, STTech, Steinbeis ZFW, Swissbit Germany, Karlsruhe Institute of Technology (KIT) with the institutes ITIV and ITEC-CES, FZI Research Center for Information Technology, Technical University of Munich with the chairs AIR , LIS and SEC, Munich University of Applied Sciences, L&uuml;beck University, Chemnitz University of Technology, Fraunhofer ENAS, IMWS, IPMS and IZM.&nbsp;

Computer systems in future highly automated and networked cars must perform sophisticated calculations, process huge amounts of data and achieve maximum reliability. (Icon graphic: © kaptn – stock.adobe.com, KIT)

The CeCaS research project creates processors, interfaces and system architectures for highly automated networked vehicles - KIT develops hardware accelerators and benchmarking software

Supercomputers to Control Cars of the Future

The first statistically realistic roadway simulation has been developed by researchers at the University of Michigan. While it currently represents a particularly perilous roundabout, future work will expand it to include other driving situations for testing autonomous vehicle software.The simulation is a machine-learning model that trained on data collected at a roundabout on the south side of Ann Arbor, recognized as one of the most <a href="https://www.michiganautolaw.com/legal-resources/most-dangerous-intersections/" rel="noreferrer noopener" target="_blank">crash-prone intersections</a> in the state of Michigan and conveniently just a few miles from the offices of the research team.Known as the Neural Naturalistic Driving Environment or NeuralNDE, it turned that data into a simulation of what drivers experience everyday. Virtual roadways like this are needed to ensure the safety of autonomous vehicle software before other cars, cyclists and pedestrians ever cross its path.&nbsp;&ldquo;The NeuralNDE reproduces the driving environment and, more importantly, realistically simulates these safety-critical situations so we can evaluate the safety performance of autonomous vehicles,&rdquo; said Henry Liu, U-M professor of civil engineering and director of Mcity, a U-M-led public-private mobility research partnership.&nbsp;Liu is also director of Center for Connected and Automated Transportation and corresponding author of the study in Nature Communications. The publication has been featured as an Editor&rsquo;s Highlight.Safety critical events, which require a driver to make split-second decisions and take action, don&rsquo;t happen that often. Drivers can go many hours between events that force them to slam on the brakes or swerve to avoid a collision, and each event has its own unique circumstances.&nbsp;Together, these represent two bottlenecks in the effort to simulate our roadways, known as the &ldquo;curse of rarity&rdquo; and the &ldquo;curse of dimensionality&rdquo; respectively. The curse of dimensionality is caused by the complexity of the driving environment, which includes factors like pavement quality, the current weather conditions, and the different types of road users including pedestrians and bicyclists.&nbsp;To model it all, the team tried to see it all. They installed sensor systems on light poles which continuously collect data at the State Street/Ellsworth Road roundabout.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1683325837438-Roundabout-content3.jpg" style="width: 766px;" class="fr-fic fr-dib">A closer look at the sensor system attached to the State Street/Ellsworth Road roundabout.&nbsp;&ldquo;The reason that we chose that location is that roundabouts are a very challenging, urban driving scenario for autonomous vehicles. In a roundabout, drivers are required to spontaneously negotiate and cooperate with other drivers moving through the intersection. In addition, this particular roundabout experiences high traffic volume and is two lanes, which adds to its complexity,&rdquo; said Xintao Yan, a Ph.D. student in civil and environmental engineering and first author of the study, who is advised by Liu.&nbsp;The NeuralNDE serves as a key component of the CCAT Safe AI Framework for Trustworthy Edge Scenario Tests, or SAFE TEST, a system developed by Liu&rsquo;s team that uses artificial intelligence to <a href="https://news.engin.umich.edu/2023/03/simulated-terrible-drivers-cut-the-time-and-cost-of-av-testing-by-a-factor-of-one-thousand/" rel="noreferrer noopener" target="_blank">reduce the testing miles required</a> to ensure the safety of autonomous vehicles by 99.99%. It essentially breaks the &ldquo;curse of rarity,&rdquo; introducing safety-critical incidents a thousand times more frequently than they occur in real driving. The NeuralNDE is also critical to a project designed to enable the Mcity Test Facility to be used for remote testing of AV software.But unlike a fully virtual environment, these tests take place in mixed reality on closed test tracks such as the Mcity Test Facility and the American Center for Mobility in Ypsilanti, Michigan. In addition to the real conditions of the track, the autonomous vehicles also experience virtual drivers, cyclists and pedestrians behaving in both safe and dangerous ways. By testing these scenarios in a controlled environment, AV developers can fine-tune their systems to better handle all driving situations.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1683325858906-Roundabout-content1.jpg" style="width: 766px;" class="fr-fic fr-dib">An intersection inside the Mcity Test Facilty&nbsp;The NeuralNDE is not only beneficial for AV developers but also for researchers studying human driver behavior. The simulation can interpret data on how drivers respond to different scenarios, which can help develop more functional road infrastructure.&nbsp;In 2021, the U-M Transportation Research Institute was awarded&nbsp;<a href="https://news.umich.edu/9-95m-for-smart-intersections-across-ann-arbor/" target="_blank">$9.95 million in funding</a> by the U.S. Department of Transportation to expand the number of intersections equipped with these sensors to 21. This implementation will expand the capabilities of the NeuralNDE and provide real-time alerts to drivers with connected vehicles.&nbsp; The research was funded by Mcity, CCAT and the U-M Transportation Research Institute. Founded in 1965, UMTRI is a global leader in multidisciplinary research and a partner of choice for industry leaders, foundations and government agencies to advance safe, equitable and efficient transportation and mobility. CCAT is a regional university transportation research center that was recently awarded a&nbsp;<a href="https://news.engin.umich.edu/2023/02/15-million-for-connected-and-automated-transportation-renewing-u-m-led-midwest-hub/" target="_blank">$15 million, five-year renewal</a> by the USDOT.

Real-time data collected at the two-lane roundabout will be used to efficiently test the safety of autonomous vehicles.



First realistic simulated driving environment based on 'crash-prone' Michigan intersection

In this article, we will dive deep into the world of simultaneous localization and mapping using Lidar technology. Lidar SLAM has been gaining popularity in recent years, thanks to its versatility and applications across various domains, including autonomous vehicles, mobile robotics, and indoor mapping.

automotives