With the progress of automated driving technologies, self-driving cars are driving safely on highways and freeways in most circumstances. However, the lack of safe and smooth interactions with pedestrians becomes one of the significant obstacles preventing fully autonomous vehicles in city streets. Protections of vulnerable road users like pedestrians are of the highest priority in traffic safety, and crashes with pedestrians will significantly impact the trust and public attitudes towards the new mobility technology. Disruptive interactions with pedestrians may also lower both the riding experiences and driving efficiency in the pedestrian-rich road environments. It is vital to understand pedestrians and make motion planning based on the predictions of their behaviors.
Besides pedestrian detection and tracking, many research efforts have been put into recognizing pedestrians' behaviors and predicting their trajectories in the past few years. The achievements can predict potential crashes and make motion-planning decisions accordingly in a longer duration. This Workshop will focus on the detection, recognition, and prediction of pedestrian behaviors for automated driving cars to interact with them smoothly. The goal is to build a platform for sharing state-of-the-art models, algorithms, and datasets in the field and identifying research needs and directions.
The workshop will have two sections. The first section is invited talks, each of which will be 30 minutes including about 20 minutes of presentation and 10 minutes of Q/A. The second section will be discussions for everyone to participate about the research needs, open questions, existing resources, and future directions of the research area about pedestrian behavior predictions.
The workshop will be hybrid. Both participants and speakers can join in-person or remotely. Zoom link for the workshop will be provided to speakers and participants who cannot join in person.
Short bio: Mohan Trivedi received his PhD in Electrical Engineering from Utah State University in 1979, after completing undergraduate work in India. He has published extensively and has edited over a dozen volumes including books, special issues, video presentations, and conference proceedings. Trivedi is a recipient of the Pioneer Award and the Meritorious Service Award from the IEEE ComputerSociety; and the Distinguished Alumnus Award from Utah State University. He isa Fellow of the International Society for Optical Engineering (SPIE). He is a founding member of the Executive Committee of the UC System-wide Digital MediaInnovation Program (DiMI). Trivedi is also Editor-in-Chief of Machine Vision& Applications (http://link.springer-ny.com/link/service/journals/00138/index.htm).
Short bio: Josh Domeyer is a senior researcher in the Toyota Collaborative Safety Research Center. He received a Ph.D. in Industrial and Systems Engineering at the University of Wisconsin-Madison in 2021 and B.S. and M.S. degrees in Experimental Psychology from Central Michigan University in 2009 and 2011. He has been a human factors researcher at Toyota since 2011 where his research has focused on driver-vehicle interface, distraction, and human-automation interaction. He joined the Collaborative Safety Research Center in 2017 to explore the communication between drivers and pedestrians with the goal of informing the development of vehicle automation. He is the Chair of SAE’s Safety and Human Factors Steering Committee and a U.S. expert for the ISO TC22/SC39/WG8 standards group.
Short bio: Amir Rasouli is a senior research scientist leading the behavior prediction team atNoah’s Ark Laboratory, Huawei, Canada. He received B.Eng. degree in computer systems engineering and B.A. in Business Management from the Royal MelbourneInstitute of Technology. He obtained M.A.Sc. degree in computer engineering under the supervision of Prof. John Tsotsos at York University studying the role of attention in active visual object search for mobile robots. He completed his Ph.D. degree in the same lab on the topic of pedestrian behavior understanding and prediction in the context of autonomous driving. His current work focuses on various aspects of road user behavior prediction including analysis of naturalistic driving data, understanding theoretical foundations of interactions between heterogeneous traffic participants, modeling vehicle and pedestrian behavior, simulation, and development of predictive models for intelligent driving systems.
Short bio: Dr.Ding is a Tenure-Track Assistant Professor at the Department of ComputerScience, Tulane University. He is an active researcher in the fields of face recognition, transfer learning, multi-view learning, and has published over 50 articles in the flagship conferences/journals and books. He serves as Associate Editor of IET ImageProcessing, and Journal of Electronic Imaging. He also serves many leading conferences/workshops in the vision and data mining fields, e.g., the Publicity Chair of AMFG2017, program co-chair of IEEE Big Data Workshop 2017, and workshop Chairs ofAMFG 2021.He organizes four tutorials on multi-view learning in the FG 2017, CVPR 2018,IEEE BigData 2018, AAAI 2019 and IJCAI 2020. He also serves as senior program committee for AAAI 2019/2020, IJCAI 2021, and program committee for CVPR2018-2021, FG 2017-2019, and reviewers of many prestigious journals includingIEEE TPAMI, TNNLS, TKDE, TIP, TMM.
Short bio: Dr. Renran Tian is an assistant professor in the Department of Computer Information & Graphics Technology and the Transportation & Autonomous Systems Institute (TASI) at Indiana University-Purdue University Indianapolis (IUPUI). He received his Ph.D. degree from the School of Industrial Engineering at Purdue University –West Lafayette in 2013, and B.S. and M.S. in Mechanical
Engineering from Tsinghua University – Beijing, China in 2002 and 2005. His research interests include human-centered computing, cognitive ergonomic, computational behavior analysis, human-AI interaction, and autonomous driving. He serves as the co-chair for Technical Activities Committee on Human Factors in Intelligent Transportation Systems (HFITS), IEEE Intelligent Transportation Systems Society.