Guided Conditional Diffusion for Controllable Traffic Simulation

Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make vehicles follow specific trajectories and traffic rules. On the other hand, data-driven approaches generate realistic and human-like behaviors, improving transfer from simulated to real-world traffic. However, to the best of our knowledge, no traffic model offers both controllability and realism. In this work, we develop a conditional diffusion model for controllable traffic generation (CTG) that allows users to control desired properties of trajectories at test time (e. g. , reach a goal or follow a speed limit) while maintaining realism and physical feasibility through enforced dynamics. The key technical idea is to leverage recent advances from diffusion modeling and differentiable logic to guide generated trajectories to meet rules defined using signal temporal logic (STL). We further extend guidance to multi-agent settings and enable interaction-based rules like collision avoidance. CTG is extensively evaluated on the nuScenes dataset for diverse and composite rules, demonstrating improvement over strong baselines in terms of the controllability-realism tradeoff. Demo videos can be found at https://aiasd.github.io/ctg.github.io

Authors

• Ziyuan Zhong
• Davis Rempe
• Danfei Xu
• Yuxiao Chen 0008
• Sushant Veer
• Tong Che
• Baishakhi Ray
• Marco Pavone 0001

Keywords

• Pedestrians
• Traffic control
• Controllability
• Trajectory
• Behavioral sciences
• Vehicle dynamics
• Task analysis
• Traffic Simulation
• Collision
• Diffusion Model
• Autonomous Vehicles
• Speed Limit
• Traffic Model
• Strong Baseline
• Loss Function
• Learning Models
• Diffusion Process
• Pedestrian
• Latent Space
• Inference Time
• Reward Function
• State Trajectories
• Future Trajectories
• Rule Violations
• Vehicle Trajectory
• Actual Trajectory
• Trajectory Generation
• Target Speed
• Traffic Behavior
• Real Trajectory
• Ground Truth Trajectory
• Rule Compliance
• Trajectory Dataset
• Stop Sign
• Pedestrian Trajectory