Next-Generation Human Behavior Modeling for Autonomous Driving Half-day Workshop

Autonomous driving depends on smooth collaboration between humans and vehicles so that shared control remains safe, comfortable, and trustworthy. Future automated vehicles must understand how humans drive, anticipate intentions in mixed traffic, and respond naturally—while interpreting human instructions (spoken words, gestures, and traffic rules) and adapting driving behavior accordingly.

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

Call For Papers

The topics of interest of the workshop include, but are not limited to:

  • Modeling & prediction of human behavior: driver/VRU intent, interaction-aware forecasting, uncertainty & risk.
  • Human–AV interaction: communication (gesture/language), interpretability, trust calibration, user comfort.
  • Generative modeling for behavior: diffusion/LLM-based scenario and trajectory generation, rare-event synthesis.
  • Simulation & digital twins: behavior-based simulation, mixed autonomy, evaluation of behavior realism.
  • Safety-critical scenarios: long-tail events, corner cases, robustness in complex real-world environments.

Program

TBD

ITSS Technical Committee

Relevant TCs: Emerging Transportation Technology Testing; Automated Mobility in Mixed Traffic; Human Factors in ITS; Human-Centered AI in Transportation.

Organizers

The workshop is organized by:

KC
Dr. Kehua Chen

University of Washington
zeonchen@uw.edu

BW
Mr. Bingzhang Wang

University of Washington
bzwang@uw.edu

YW
Dr. Yinhai Wang

University of Washington
yinhai@uw.edu

XL
Dr. Xiaopeng Li

University of Wisconsin–Madison
xli2485@wisc.edu

MK
Dr. Michael Knodler

UMass Amherst
mknodler@umass.edu

SD
Dr. Xuan Sharon Di

Columbia University
sharon.di@columbia.edu

JS
Dr. Jian Sun

Tongji University
sunjian@tongji.edu.cn

MZ
Dr. Meixin Zhu

Southeast University
meixin@seu.edu.cn

JS
Dr. Jie Sun

Tongji University
jie sun@tongji.edu.cn

XS
Dr. Xiaowei Shi

UW–Milwaukee
tomshi@uwm.edu