Realistic World Model for Autonomous Driving: Integrating Physical Constraints and Multi-agent Interactions

Authors: Jeonghwan Kim, Jiongdao Jin, Jay Heo, Jaehong Yoon, Sung Ju Hwang
Primary area: learning on time series and dynamical systems
Keywords: Autonomous Driving, World Model, Trajectory Forecasting, Motion Planning

Abstract:
Ensuring safety in autonomous driving, particularly in complex and dynamic environments, remains a significant challenge. To address this issue, we propose a novel traffic world model. While existing trajectory forecasting methods typically focus on predicting individual agents and may neglect critical factors such as vehicle dimensions, orientation, and physical constraints, our model incorporates these elements comprehensively. Unlike previous methods that often result in unrealistic scenarios such as collisions or off-road driving, our model integrates physical constraints and introduces innovative loss functions—including safe distance loss and road departure loss—to ensure that the generated trajectories are both realistic and feasible. By simultaneously predicting the trajectories of all agents and explicitly modeling interactions across various scenarios, our approach significantly enhances realism and safety. Our world model functions as a generator, simulator, and trajectory forecasting tool, demonstrating substantial improvements over traditional methods and achieving competitive performance in reducing collision and off-road rates.

Decision: reject

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