Plenary Speakers

J. Christian Gerdes, Stanford University

Title: Racing towards the future of automated vehicles

Abstract: For over a century, automobile manufacturers have used the challenge of racing to better understand and improve upon vehicle design. Can the development of autonomous race cars advance the development of driver assistance systems and automated vehicles in a similar way? This talk explores our work with automated race cars at Stanford’s Dynamic Design Lab, identifying the basic challenges of racing and how control systems can handle these challenges. While automated vehicles hold significant advantages in computation and response time, head-to-head comparison with expert drivers shows humans can still teach the machine a few tricks. How, then, should we close this gap? Should we rely on our knowledge of physics to harness increasingly detailed models of the vehicle dynamics? Should we instead turn to AI to learn models directly from data and potentially eliminate the need to estimate physical parameters like friction? Or is there a path forward that can leverage the benefits of these two very different approaches? The talk at concludes with a look at some of our latest experiments, the current state of the art and open questions on the road to the future.

Biography: Chris Gerdes is Professor Emeritus of Mechanical Engineering at Stanford University. His laboratory studies how cars move, how humans drive cars and how to design cars that work cooperatively with the driver or drive themselves. Vehicles in the lab include X1, an entirely student-built test vehicle; Niki, an automated Volkswagen GTI that can lap a racetrack as quickly as an expert driver; and MARTY, an electrified DeLorean capable of controlled drifts. Chris and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.
From February 2016 to January 2017, Chris served as the first Chief Innovation Officer at the United States Department of Transportation. He was part of the team that developed the Federal Automated Vehicles Policy and represented the Department on the National Science and Technology Committee Subcommittee on Machine Learning and Artificial Intelligence. Chris was a co-founder of truck platooning company Peloton Technology and served as Peloton’s Principal Scientist before joining U.S. DOT.

Hong Chen, Tongji University

Title: MPC for Intelligent Driving: The old and the new

Abstract: Model predictive control (MPC) solves repeatedly a constrained optimization problem updated by the actual state. This basis fits how a human driver to drive a car and provides a good framework for intelligent driving. This talk discusses the old and the new of the development.

Biography: Hong Chen is an IEEE Fellow, CAA-China Fellow and SAE-China Fellow. She received the B.S. and M.S. degrees in process control from Zhejiang University, China, in 1983 and 1986, respectively, and the Ph.D. degree in system dynamics and control engineering from the University of Stuttgart, Germany, in 1997. In 1986, she joined Jilin University of Technology, China. From 1993 to 1997, she was a Wissenschaftlicher Mitarbeiter with the Institut fuer Systemdynamik und Regelungstechnik, University of Stuttgart. Since 1999, she has been a professor in Jilin University and hereafter a Tang Aoqing professor. Since 2019, she has worked at Tongji University as a distinguished professor and currently serves as Dean of the College of Electronic and Information Engineering. Her current research interests include model predictive control, automotive control and automated driving.

Alexandre Alahi, EPFL

Title: Representation Learning for Autonomous Mobility: 7 Foundational Principles

Abstract: Professor A. Alahi's keynote presentation explores the burgeoning field of representation learning in AI, particularly focusing on its implications for Autonomous Mobility. Despite the transformative impacts of representation learning in areas like Computer Vision and Natural Language Processing, challenges abound when applying these methods to the autonomous mobility domain, notably due to data scarcity in diverse and adverse conditions. Prof. Alahi emphasizes the limitations of traditional data-heavy approaches, such as their inadequacy in handling novel or rare events crucial for robust perception in traffic scenes. His work critically evaluates the prevalent assumption that massive datasets are sufficient for accurate forecasting, revealing key deficiencies in current deep learning methods when faced with diverse real-world interactions. Additionally, he discusses the shortcomings of existing planning algorithms in dynamic and uncertain environments. To address these gaps, Prof. Alahi introduces seven foundational principles aimed at enhancing the robustness of representation learning for the unique demands of Autonomous Mobility, advocating for a paradigm shift towards more adaptable and resilient AI systems.

Biography: Alexandre Alahi is a professor at EPFL leading the Visual Intelligence for Transportation laboratory (VITA). Before joining EPFL in 2017, he was a Post-doc and Research Scientist at Stanford University. His research lies at the intersection of Computer Vision, Machine Learning, and Robotics applied to transportation & mobility. He works on the theoretical challenges and practical applications of socially-aware Artificial Intelligence, i.e., systems equipped with perception and social intelligence. His research enables systems to detect, track, and predict human motion dynamics at all scales.
In 2022&2023, Alexandre was recognized as the top 100 Most Influential Scholar in Computer Vision over the past 10 years. His work on human motion prediction received the editor’s choice award from the journal Image and vision computing (2021). His work on human pose estimation received an honourable mention at an ICCV workshop (2019). Finally, his research was transferred to a startup detecting and tracking more than 100 million pedestrians in public spaces (including train stations).