Ching-Yao Chan

Ching-Yao Chan picture

C0-Director, Berkeley DeepDrive


Bldg. 452, Richmond Field Station, MC 3580
1357 South 46th Street, Richmond, CA 94804, USA
Phone: 510-665-3621
Email Address:


Dr. Ching-Yao Chan is Co-Director of Berkeley DeepDrive (BDD). Dr. Chan leads research projects on intelligent autonomy of dynamic systems. His recent projects involve deep reinforcement learning, meta-learning, pedestrian trajectory projection, multi-person action forecasting based on machine learning, and driver drowsiness.

Dr. Chan has three decades of research experience in a broad range of automotive and transportation systems. His research ranges from automated systems, sensing and wireless communication technologies, data analytics and safety assessment, to applications of artificial intelligence on autonomy.

Dr. Chan is a Research Faculty at California PATH (Partners for Advanced Transportation Technology). At PATH, Dr. Chan leads research projects in automation, advanced technologies, human factors, and transportation systems. He is the recipient of the Team Leadership Award from Berkeley Institute of Transportation Studies in 2020.

Dr. Chan serves as a member of the board of directors for the School of Computing, National Cheng-Kung University, Taiwan. Dr. Chan was a Visiting Professor at National Taiwan University in 2022 and a Visiting Professor at University of Tokyo in 2006-2007.


His current research is focused in the following topic areas.


  • Berkeley DeepDrive: Chan is PI for a variety of BDD projects, with research on machine learning for automated driving, pedestrian-vehicle interaction, and driver drowsiness.
  • USDOT-CCTA ADS Demo: Chan is PI for this USDOT-sponsored project. California PATH is a partner, along with Verizon, Nissan, and Amazon Web Service, with Contra Costa Transportation Authority, with a focus on the evaluation of safety performance of automated driving systems.
  • California SB-1 Project, Drivers’ Responses to Eco-driving Applications: Effects on Fuel Consumption and Driving Safety: Chan led this project to evaluate the effects of driver behaviors on the effectiveness of eco-driving and related policy complications.
  • Survey of Autonomous Vehicle Industry: Chan is PI for a project sponsored by California DOT, which involves discussions with the AV industry to investigate the AV operational requirements and needs of infrastructure collaboration.
  • Meta Learning for Autonomous Driving: Chan leads this collaborative research with Guangdong Automotive Research Silicon Valley, to explore the implementation of model agnostic meta learning for driving.
  • Drive for All Foundation: Chan led Berkeley participation in this international consortium, headed by MINES ParisTech of France, with international partners working on autonomous driving in urban environment.
  • Safety Effects of Yellow Alert: Chan leads this human-factor study sponsored by California Department of Transportation, to evaluate the impact of highway changeable messages on driver’s behaviors.
  • Design of Interactive Display Boards and Their Impacts on Driving: Chan leads this human-factor study sponsored by California Department of Transportation, to evaluate interactive display boards on I-80.
  • NSF Cyberphysics System Project on Advanced Traffic Systems: Chan collaborates jointly with Professors Varaiya, Horowitz, Moura of UCB on an NSF project that develops vehicle technologies for traffic operation efficiency.
  • California SB-1 Project, User Acceptance of Vehicle Automation and Public Policy:Chan led this project to develop an acceptance model of automation that helps assess user acceptance and to help define public policy.
  • Powertrain Technology and Fuel Efficiency Evaluation in a Traffic Network: 2016-2017: Chan led this Hyundai-sponsored eco-driving project, focused on traffic simulation platform and advanced power-train technology.
  • Assisting California DMV in Developing Regulations for Automated Vehicles; 2013-2016: Chan was a major contributor to support California DMV in drafting regulations for Autonomous Technology.
  • Connected Vehicles Test Bed for Nissan Motor; 2015-2016: Chan was PI for a project with Nissan Motor to establish a DSRC communication-enabled test bed in Sunnyvale, for research on connected vehicles.
  • Cooperative Adaptive Cruise Control (CACC) with DENSO; 2015-2016: Chan led this DENSO project to evaluate safety and mobility benefits as well as human-machine interface for CACC in freeway driving scenarios.
  • Eco-Driving Applications for Freeway and Arterial Driving: 2013-2014: Chan led this Hyundai Motor-sponsored eco-driving project, incorporating all relevant data to achieve a realistic and implementable solution.
  • DSRC and High-Precision Positioning – Industrial Technology Research Institute; 2013: Chan managed the collaboration with ITRI on vehicle on-board implementation of DSRC related applications.


  • Advisory Board member, School of Computing, National Cheng-Kung University, Taiwan, September 2020 –.
  • Visiting Professor, National Taiwan University, 2022.
  • Visiting Professor, University of Tokyo, JAPAN, May 2006 – Jan. 2007.
  • Visiting Scholar, INRETS (now IFSTTAR, French National Transportation Research Institute), 2004
  • Professional Engineer, California, since 1994


Click here to see a complete list of publications


  • I-H Kao, C-Y Chan, “Analysis of Pedestrians Intention Prediction Based on Gradient-weighted Class Activation Mapping,” IEEE ICCE-TW, July 2023.
  • Yang, B., et al. “A Novel Graph-Based Predictor with Pseudo Oracle,” IEEE Transactions on Neural Network and Learning Systems, Vol. 33, Issue 12, pp. 7064-7078, December 2022.
  • C-Y Lin, L-J Kau, C-Y Chan, “Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction,” Sensors, The Intelligent Sensing Technology of Transportation System, Sensors 2022, 22(21), 8231;, Oct. 2022.
  • Du, Y., Zhu, Y., Zhao, C., Liao, F., & Chan, C. Y. (Accepted/In press). “A Novel Direct Trajectory Planning Approach based on Generative Adversarial Networks and Rapidly Exploring Random Tree,” IEEE Transactions on Intelligent Transportation Systems, Vol. 23, Issue 10, October 2022.
  • I-H Kao, C-Y Chan, “Comparison of Eye and Face Features on Drowsiness Analysis,” Sensors, The Intelligent Sensing Technology of Transportation System, Sensors 2022, 22(17), 6529;, Aug. 2022.
  • Y. Li, et al., “Energy-based Learning for Multi-Agent Activity Forecasting,” AAAI 2022, February 2022.
  • Arian Ranjbar, et al., “Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation,” IEEE Transactions on Intelligent Vehicles, Print ISSN: 2379-8858, Online ISSN: 2379-8904, Digital Object Identifier: 10.1109/TIV.2022.3152084, Feb. 2022.
  • X. Zhou, et al, ”Prediction of Pedestrian Crossing Behavior Based on Surveillance Video, “ Sensors, 22(4), , February 2022.
  • Y. Ma, et al, “Multi-vehicle Interactive Lane-changing Velocity Change Model Based on Potential Energy Field,” 2022 Transportation Research Records.
  • Pei Wang, et al, “Automated Vehicles Industry Survey of Transportation Infrastructure Needs,” 2022 Transportation Research Records.
  • X. Zhang et al., An Efficient Framework of Developing Video-Based Driving Simulation for Traffic Sign Evaluation,” Journal of Safety Research, 81(1), Feb. 2022, 10.1016/j.jsr.2022.02.001.
  • I.H. Kao, C-Y Chan “Impact of Posture and Social Features on Pedestrian Road-Crossing Trajectory Prediction,” IEEE Transactions on Instrumentation and Measurement, accepted January 2022, Print ISSN: 0018-9456, Online ISSN: 1557-9662, 10.1109/TIM.2021.3139691.
  • I.H. Kao, C-Y Chan “Estimation of vehicle dynamics by fusion image and radar based on subtraction convolutional neural network,” 9th International Conference on Mechanical, Automotive and Materials Engineering (CMAME2021), Dec. 2021.
  • Y. Du et al, “Comfortable and Energy-Efficient Speed Control of Autonomous Vehicles on Rough Pavements using Deep Reinforcement Learning,” Transportation Research Part C, accepted November 2021.
  • Y. Li, et al., “Imitative Learning for Multi-Person Action Forecasting,” ACM Multimedia, October 2021.
  • I-Hsi Kao, et al, “A Posture Features Based Pedestrian Trajectory Prediction with LSTM,” IEEE-International Conference on Consumers Electronics, Best Paper Award, September 2021.
  • Fei Ye, et al., “Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles, “IEEE Intelligent Vehicles, July 2021.
  • Y. Lee, P. Wang, C-Y Chan, “RESTEP into the Future: Relational Spatio-Temporal Learning for Multi-Person Action Forecasting,” IEEE Transactions on Multimedia, Print ISSN: 1520-9210, Online ISSN: 1941-0077, June 2021.
  • B. Yang et al, "A Novel Graph-based Trajectory Predictor with Pseudo Oracle," IEEE Transactions on Neural Networks and Learning Systems,” Print ISSN: 2162-237X, Online ISSN: 2162-2388, June 2021.
  • I-Hsi Kao, et al, “A Posture Features Based Pedestrian Trajectory Prediction with LSTM,” IEEE-International Conference on Consumers Electronics, June 2021.
  • P. Wang et al, "Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning", IEEE International Conference on Robotics and Automation, May 2021.
  • P. Wang, H. Li, C-Y Chan, "Learning Adaptable Policy via Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks", IEEE International Conference on Robotics and Automation, May 2021.
  • B. Yang et al, " Crossing or not? Context-based recognition of pedestrian crossing intention in the urban environment", T-ITS-19-03-0334, IEEE Transactions on ITS, Print ISSN: 1524-9050, Online ISSN: 1558-0016, February 2021.
  • Sanaz Motamedi, Pei Wang, Ching-Yao Chan, "Exploring Public Perception of Level-2 Automation and Full Automation: Interview Based Study", Paper 21-01824, Transportation Research Board Meeting, January 2021.
  • X. Zhou, T. Zhang, P. Wang, C-Y Chan, "Visualization of Driving Scenes for Realistic Simulator Experimentation - An Efficient Framework", Paper 21-03634, Transportation Research Board Meeting, January 2021.
  • P. Wang, et al, “Pedestrian Interaction with Automated Vehicles at Uncontrolled Intersections, “Transportation Research Part F: Psychology and Behaviour, Dec. 2020.
  • L. Bai, et al, “Capacity Estimation of Midblock Bike Lanes with Mixed Two-Wheeled Traffic, “Transportation A: Transport Science, Dec. 2020.
  • Yanli Ma, et al., “Impact of Lane Changing on Adjacent Vehicles considering Multi-Vehicle Interaction in Mixed Traffic Flow: A Velocity Estimating Model, “Physics A: Statistical Mechanics and Its Applications, November 2020.
  • Fei Ye, et al., “Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning, “IEEE Intelligent Vehicles, October 2020.
  • J. M. Salt Ducaju, et al., “Application Specific System Identification for Model-Based Control in Self-Driving Cars, “IEEE Intelligent Vehicles, October 2020.
  • Wenshuo Wang, et al., “Learning Representations for Multi-Vehicle Spatio-temporal Interactions with Semi-Stochastic Potential Fields, “IEEE IV, October 2020.
  • Arian Ranjbar, et al., “Scene Novelty Prediction from Unsupervised Discriminative Feature Learning, “IEEE ITSC, September 2020.
  • I-Ming Chen, C-Y Chan, "Deep Reinforcement Learning Based Path Tracking Controller for Autonomous Vehicle" accepted for publication in Proc. IMechE, Part D: Journal of Automobile Engineering, August 2020.
  • Yanli Ma, et al., “Drivers’ Visual Attention Characteristics under Different Cognitive Workload: An On-road Driving Behavior study,” International Journal of Environmental Research and Public Health, July 2020.
  • Yanli Ma, et al., “Support vector machines for the identification of real-time driving distraction using in-vehicle information systems,” Journal of Transportation Safety and Security, DOI: 10.1080/19439962.2020.1774019, June 2020.
  • Yi He, et al., “Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain,” IEEE Transactions on Intelligent Transportation Systems, Print ISSN: 1524-9050, Online ISSN: 1558-0016, May 2020.
  • Tianyi Li, et al., “Lane-level localization system using surround view cameras adaptive to different driving conditions,” International Journal of Advanced Robotic Systems, March 2020.
  • Tingting Li, et al., “A Cooperative Lane Change Model for Connected and Automated Vehicles,” IEEE Access, page(s): 1-12, ISSN: 2169-3536, March 2020.
  • S. Motamedi, P. Wang, C-Y Chan, “Acceptance of Full Driving Automation: Personally Owned and Shared-Use Concepts,” Human Factors: The Journal of the Human Factors and Ergonomics Society, March 2020.
  • Zhaoting Li, Wei Zhan*, Liting Sun, Ching-Yao Chan, Masayoshi Tomizuka, “Adaptive sampling-based motion planning with a non-conservatively defensive strategy for autonomous driving,” IFAC 2020.
  • Huanjie Wang, et al., “Tactical driving decisions of unmanned ground vehicles in complex highway environments: A deep reinforcement learning approach,” Proceedings of the Institution of Mechanical Engineers, Part D, February 2020.
  • Yanli Ma, et al., “Psychological and Environmental Factors Affecting Driver’s Frequent Lane-changing Driving Behaviors: A National Sample of Drivers in China,” IET Intelligent Transport Systems, January 2020.
  • Pin Wang, Hanhan Li, Ching-Yao Chan, "Structured Quadratic Q-network for Learning Continuous Vehicle Control", Paper 20-02002, Transportation Research Board Meeting, presentation only, January 2020.
  • Sanaz Motamedi, et al., "External Interface of Automated Driving Systems: Communication and Interaction with Pedestrians", Paper 20-03984, TRB 2020.
  • Pei Wang, et al., "Freeway Traffic Sign Design for Interstate 80 Smart Corridor in California: A Driving Simulator Study", Paper 20-03907, TRB 2020.
  • Yanli Ma, et al., “An On-Road Driving Test of Cognitive Distraction: Characteristics of Drivers’ Visual Behavior,” Paper 20-02501, TRB 2020.
  • Hongyu Hu, et al., “Driver Identification Using 1-D Convolutional Neural Networks with Vehicular CAN Signals,” Paper 20-01857, TRB 2020.


  • B.S., Mechanical Engineering, National Taiwan University, 1981
  • M.S., Mechanical Engineering, University of California at Berkeley, 1985
  • Ph.D., Mechanical Engineering, University of California at Berkeley, 1988


After receiving his Ph.D. degree in Mechanical Engineering from UC Berkeley in 1988, he worked in the private sector before returning to Berkeley in 1994. Prior to joining PATH, Dr. Chan worked in the field of vehicular passive safety systems. While being involved in the research and development of crash sensing technologies, he also gained first-hand knowledge on general passive restraint systems, as he worked with automotive tier-one suppliers and automotive OEMs. During 1990-1994, he worked in litigation support on accident reconstruction and participated in numerous cases of vehicle crashes, through which he gained insights on the interaction of drivers, vehicle characteristics, roadway environment, and their impacts on driving risks.

Due to his nationally recognized expertise in crash sensing and vehicular safety, Dr. Chan was invited by Society of Automotive Engineers (SAE) to provide tutorials to more than 500 automotive professionals in an SAE seminar series. He has given lectures to various organizations. He collaborated with SAE to publish a book and a video tutorial, and he was the recipient of the 1998 SAE Forest R. MacFarland Award for his outstanding contributions to engineering education. 

Dr. Chan was also significantly involved in the research and development of vehicle automation technologies. During the years of the National Automated Highway Systems Consortium in the 1990s, he represented PATH in the national working group of technology development and evaluation. Subsequently, he also worked on projects that involved the use of various technologies for vehicular automation systems. In 2003, he led a team of researchers and engineers in the Demonstration of Bus Automation Technology in San Diego. The project subsequently won the prestigious award of the Best of ITS Research Award from the ITS America in 2004.

Dr. Chan also collaborated with industrial and academic partners in developing and implementing communication-enabled cooperative systems in multiple projects. Applications include vehicle-to-vehicle and vehicle-to-infrastructure, vehicle-to-pedestrian, and road equipment-to-network operation scenarios. These projects were supported by and jointly conducted with federal and state governments, automaker consortium, and private-sector partners.

Dr. Chan served as a visiting Professor at the University of Tokyo, Institute of Industrial Science from May 2006 to January 2007 and a visiting scholar at Institute of French National Transport Research (INRETS, which is now IFSTTAR) in the summer of 2004.