Multimodal Vehicular Trajectory Prediction With Inverse Reinforcement Learning and Risk Aversion at Urban Unsignalized Intersections

Understanding human drivers' intentions and predicting their future motions are significant to connected and autonomous vehicles and traffic safety and surveillance systems. Predicting multimodal vehicular trajectories at urban unsignalized intersections remains challenging due to dynamic traffic flow and uncertainty of human drivers' maneuvers. In this paper, we propose a comprehensive trajectory prediction framework that combines a multimodal trajectory generation network with inverse reinforcement learning (IRL) and risk aversion (RA) modules. Specifically, we first construct a multimodal spatial-temporal Transformer network (mmSTTN) to generate multiple trajectory candidates, using trajectory coordinates as inputs. Accounting for spatio-temporal features, we formulate the IRL reward function for evaluating all candidate trajectories. The optimal trajectory is then selected based on the computed rewards, a process that mimics human drivers' decision-making. We further develop the RA module based on the driving risk field for optimal risk-averse trajectory prediction. We conduct experiments and ablation studies using the inD dataset at an urban unsignalized intersection, demonstrating impressive human trajectory alignment, prediction accuracy, and the ability to generate risk-averse trajectories. Our proposed framework reduces prediction errors and driving risks by 25% and 30% compared to baseline methods. Results validate vehicles' human-like risk-averse diverging-and-concentrating behavior as they traverse the intersection. The proposed framework presents a novel approach for forecasting multimodal vehicular trajectories by imitating human drivers and incorporating physics-based risk information derived from the driving field. This research offers a promising direction for enhancing the safety and efficiency of connected and autonomous vehicles navigating urban environments.