A Model-Based Spatio-Temporal Behavior Decider for Autonomous Driving

Spatio-temporal planning has emerged as a robust methodology for solving trajectory planning challenges in complex autonomous driving scenarios. By integrating both spatio and temporal variables, this approach facilitates the generation of highly accurate, human-like, and interpretable trajectory decisions. This paper presents a novel model-based spatio-temporal behavior decider, engineered to produce optimal and explainable driving trajectories with enhanced efficiency and passenger comfort. The proposed decider systematically evaluates the action space of the ego vehicle, selecting the trajectory that optimizes overall driving performance. This method is particularly significant for autonomous driving systems, as it ensures the generation of human-like trajectories while maintaining high driving efficiency. The efficacy of the proposed framework has been comprehensively validated through rigorous simulations and real-world experimental trials on a commercial passenger vehicle platform, demonstrating its practical utility and performance advantages.