A Probabilistic Model for Long-Term Trajectory Prediction in Multi-Agent Systems

The accurate prediction of vehicle trajectories in complex traffic environments is essential for ensuring the safety and effectiveness of autonomous driving systems. In this paper, we propose a novel probabilistic model for long-term trajectory prediction that consists of two components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent probabilistic model is based on a dynamic Bayesian network, which considers the driver's maneuvering decisions and integrates surrounding lane information. In the multi-agent risk-averse sampling algorithm, feasible future positions are sampled simultaneously for all agents based on the probabilistic model, and a risk potential field model is then applied to reject the high-collision-risk samples. Eventually, a probability distribution of the combinations of long-term trajectories is predicted. After conducting experiments on the nuScenes dataset, our method achieved competitive performance in trajectory prediction compared with other state-of-the-art methods.