The Workshop
Autonomous driving depends on smooth collaboration between humans and vehicles so that shared control remains safe, comfortable, and trustworthy. In future road networks, automated vehicles will need to understand how humans drive, anticipate their intentions in mixed traffic, and respond in a way that feels natural to surrounding road users. At the same time, these vehicles must correctly interpret human instructions, including spoken words, gestures, and traffic rules, and then adapt their driving behavior accordingly.
This workshop focuses on next-generation human behavior modeling for autonomous driving. We are especially interested in methods that can generate realistic and diverse human behaviors, quantify uncertainty and risk in these behaviors, and support careful analysis of safety-critical and rare situations. The workshop will bring together researchers and practitioners to discuss models of drivers and vulnerable road users, behavior-based simulation environments and digital twin of traffic systems, evaluation of behavior realism, and interaction between humans and automated vehicles in real traffic. Our goal is to identify open challenges, useful data sources, and promising research directions that can lead to human-centered behavior models and more reliable autonomous driving in complex real-world environments.
Keywords: Human Behavior Modeling, Human–Machine Interaction, Generative AI
Program
| Time | Session | Presenter / Authors |
|---|---|---|
| 8:30–8:40 | Welcome and Introduction | Kehua ChenUniversity of Washington |
| 8:40–9:30 |
Keynote Talk Title TBD |
Xiaopeng LiKeynote SpeakerUniversity of Wisconsin–Madison |
| 9:30–9:40 | Coffee Break | |
| 9:40–10:05 | Pedestrian Crossing Intent Prediction Via Psychological Features and Transformer Fusion | Sima Ashayer; Hoang H. Nguyen; Yu Liang; Mina SartipiThe University of Tennessee at Chattanooga |
| 10:05–10:30 | Diffusion2: Dual Diffusion Model with Uncertainty-Aware Adaptive Noise for Momentary Trajectory Prediction | Yuhao LuoUniversity of Wisconsin–Madison |
| 10:30–10:55 | Human-Aligned LLM Agents for Behavior Modeling in Transportation | Tianming LiuUniversity of Michigan |
| 10:55–11:05 | Coffee Break | |
| 11:05–11:30 | Car-Following Models and Congestion Control with Followerstopper on a Ring-Road under Known Delay — Examining Limit Cycle | Trevor McClain; Rahul BhadaniThe University of Alabama in Huntsville |
| 11:30–11:55 | Camera-Less In-Seat Posture Classification & Template Based Pose Reconstruction Using Pressure Sensor Fusion | Siddhant Deore, Hrushikesh Sawant, Madhav RaoInternational Institute of Information Technology Bangalore |
| 11:55–12:00 | Close | Kehua ChenUniversity of Washington |
ITSS Technical Committee
Relevant TCs: Emerging Transportation Technology Testing; Automated Mobility in Mixed Traffic; Human Factors in ITS; Human-Centered AI in Transportation.