PROGRAM
Details
Integrating Infrastructure-Assisted V2X Communication for Next-Generation Cooperative Autonomous Driving
Speakers: Manabu Tsukada, University of Tokyo
Abstract: This talk explores how combining smart road technology and vehicle communication (V2X) can make self-driving cars (CAD) safer and smarter. It will discuss how roadside sensors and V2X can help cars understand their surroundings better and make safer decisions. The talk will also introduce projects like AutowareV2X and AutoMCM, showing how cars working together can make driving smoother and safer. Additionally, it will cover how roadside LiDAR can help cars know exactly where they are, even in tough situations. Overall, the presentation will show how these advanced technologies work together to make future driving safer, more connected, and efficient.
Abstract: This talk explores how combining smart road technology and vehicle communication (V2X) can make self-driving cars (CAD) safer and smarter. It will discuss how roadside sensors and V2X can help cars understand their surroundings better and make safer decisions. The talk will also introduce projects like AutowareV2X and AutoMCM, showing how cars working together can make driving smoother and safer. Additionally, it will cover how roadside LiDAR can help cars know exactly where they are, even in tough situations. Overall, the presentation will show how these advanced technologies work together to make future driving safer, more connected, and efficient.
Mitigating Vulnerable Road Users Occlusion Risk Via Collective Perception: An Empirical Analysis
Speakers: Vincent Albert Wolff, Edmir Xhoxhi
Abstract: Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk — a scenario where VRUs are hidden from drivers’ view by obstacles like parked vehicles — being a critical contributing factor. To address this, we present a novel algorithm that quantifies occlusion risk based on the dynamics of both vehicles and VRUs. This algorithm has undergone testing and evaluation using a real-world dataset from German intersections. Additionally, we introduce the concept of Maximum Tracking Loss (MTL), which measures the longest consecutive duration a VRU remains untracked by any vehicle in a given scenario. Our study extends to examining the role of the Collective Perception Service (CPS) in VRU safety. CPS enhances safety by enabling vehicles to share sensor information, thereby potentially reducing occlusion risks. Our analysis reveals that a 25% market penetration of CPS-equipped vehicles can substantially diminish occlusion risks and significantly curtail MTL. These findings demonstrate how various scenarios pose different levels of risk to VRUs and how the deployment of Collective Perception can markedly improve.
Abstract: Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk — a scenario where VRUs are hidden from drivers’ view by obstacles like parked vehicles — being a critical contributing factor. To address this, we present a novel algorithm that quantifies occlusion risk based on the dynamics of both vehicles and VRUs. This algorithm has undergone testing and evaluation using a real-world dataset from German intersections. Additionally, we introduce the concept of Maximum Tracking Loss (MTL), which measures the longest consecutive duration a VRU remains untracked by any vehicle in a given scenario. Our study extends to examining the role of the Collective Perception Service (CPS) in VRU safety. CPS enhances safety by enabling vehicles to share sensor information, thereby potentially reducing occlusion risks. Our analysis reveals that a 25% market penetration of CPS-equipped vehicles can substantially diminish occlusion risks and significantly curtail MTL. These findings demonstrate how various scenarios pose different levels of risk to VRUs and how the deployment of Collective Perception can markedly improve.
The Good, the Sparse, and the Ugly: Investigating the Impact of Corrupted HD-Map Features on Ego-Vehicle Localization
Speakers: Lukas Beer, Thorsten Luettel, Mirko Maehlisch
Abstract: This paper investigates the impact of a corrupted map on the quality of GNSS-free localization. Using a High Definition (HD) map, we gradually remove and shift features in space. From originally 200 features per kilometer, we randomly discard 90% of our map and introduce perturbations of up to +/-0.9m in each direction to our HD map features. Our evaluation is based on a GNSS-free LiDAR-SLAM, which utilizes panoptic segmentation to observe geometric primitives. In the back-end, a graph optimization is performed to estimate the vehicle’s and the landmarks’ position. Further, we also assess the impact of conducting pure localization instead of Simultaneous Localization and Mapping (SLAM). The effects are evaluated using Mean Absolute Error for accuracy evaluation. For stability assessment, we calculate two percentiles: one for deviations of 0.3m or less, and the second for deviations of 2m or less. We conduct real-life experiments with our testing vehicle which is equipped with a reference system based on RTK-GNSS. We use a commercial, standardized HD map of our suburban campus. Our findings aim to provide insights into the trade-offs between mapping effort, map maintenance, and accurate positioning. We demonstrate that mapping the environment enhances the stability of localization.
Abstract: This paper investigates the impact of a corrupted map on the quality of GNSS-free localization. Using a High Definition (HD) map, we gradually remove and shift features in space. From originally 200 features per kilometer, we randomly discard 90% of our map and introduce perturbations of up to +/-0.9m in each direction to our HD map features. Our evaluation is based on a GNSS-free LiDAR-SLAM, which utilizes panoptic segmentation to observe geometric primitives. In the back-end, a graph optimization is performed to estimate the vehicle’s and the landmarks’ position. Further, we also assess the impact of conducting pure localization instead of Simultaneous Localization and Mapping (SLAM). The effects are evaluated using Mean Absolute Error for accuracy evaluation. For stability assessment, we calculate two percentiles: one for deviations of 0.3m or less, and the second for deviations of 2m or less. We conduct real-life experiments with our testing vehicle which is equipped with a reference system based on RTK-GNSS. We use a commercial, standardized HD map of our suburban campus. Our findings aim to provide insights into the trade-offs between mapping effort, map maintenance, and accurate positioning. We demonstrate that mapping the environment enhances the stability of localization.
INVITED SPEAKERS & PANELISTS BIO
We are planning for three keynote speakers from Industry, Academia, and Government. Stay tuned for more details.
Dr. Manabu Tsukada is currently an associate professor at the Graduate School of Information Science and Technology, the University of Tokyo, Japan. He is also a designated associate professor at the Center for Embedded Computing Systems, Nagoya University, Japan. He received his B.S. and M.S degrees from Keio University, Japan, in 2005 and 2007, respectively. He worked in IMARA Team, Inria, France, during his Ph.D. course and obtained his Ph.D. degree from Centre de Robotique, Mines ParisTech, France, in 2011. During his pre and postdoc research stages, he has participated in a multitude of international projects in the networked ITS area, such as GeoNet, ITSSv6, SCORE@F, CVIS, Nautilus6, or ANEMONE. He served as a board member of the WIDE Project 2014-2022. His research interests are mobility support for the next-generation Internet (IPv6), Internet audio-visual media, and communications for intelligent vehicles.
Reginald Viray has over 15 years of experience in the research, development, implementation, operation, maintenance, management, and commercialization of Intelligent Transportation System (ITS) solutions in both the public and private sectors. He has been involved in over 30 ITS projects in roles ranging from individual technical contributor to principal investigator across a variety of topics such as advanced driver assistance systems, automated vehicles, connected vehicles, connected infrastructure, cyber security, human factors, and smart cities. Reg obtained a B.S. in electrical engineering in 2008 and an M.S. in industrial and systems engineering in 2014, both from Virginia Tech.
Rahul builds safe autonomous systems at the intersection of formal methods, machine learning and controls. He applies his work to safety-critical autonomous vehicles, urban air mobility, life-critical medical devices, and AI Co-designers for complex systems. He is the Penn Director for the Department of Transportation's $20MM Safety21 National UTC [2023-2028] which focuses on technologies for safe and efficient movement of people and goods. Rahul is the Director of the Autoware Center of Excellence for Autonomous Driving, a consortium of 70+ companies and universities focused on open-source AV software for open-standards EV platforms. Rahul received the 2016 US Presidential Early Career Award (PECASE) from President Obama for his work on Life-Critical Systems. He also received the 2016 Department of Energy’s CleanTech Prize (Regional), the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the National Academy of Engineering for the 2012 and 2017 US Frontiers of Engineering. He has won several ACM and IEEE best paper awards in Cyber-Physical Systems, controls, machine learning, and education.
