Knowledge Distillation-Based Edge-Decision Hierarchies for Interactive Behavior-Aware Planning in Autonomous Driving System

Interactive behavior-aware planning benefits from the hierarchical learning process when adapting to dense traffic. However, the difficulty in the Intelligent Transportation System (ITS) is that the autonomous vehicle fails to execute real-time response due to hardly perceiving dynamic objects beyond the visual range. This problem can be tackled by vehicle-road-cloud cooperation that synchronously collects global perception information and makes strategic policy for deployment. Here we propose a hierarchical edge-decision framework, which addresses real-time motion skill that distills from analogical reasoning of spatial-temporal events. The first step is establishing the goal-conditioned motion library from the centralized edge-cloud perception, to compose the belief-based best response with collision avoidance. In addition, a novel perspective of latent space is presented to promote motion rehearsal in the cloud, which could generate prior credible trajectories based on the policy distillation procedure of extracting informative action from thoroughly exploring changing events. Moreover, the two-stage hierarchy decision is developed to boost the efficiency of advanced policy modification, through evaluating the hierarchical judgment matrices considering conditional criteria, thereby constituting an optimum auto-driving motion with vehicle-road-cloud collaborative system. Extensive validation on challenging autonomous driving scenarios outperforms, demonstrating that our edge-decision method significantly promotes adaption to the complex time-varying environment in ITS system in a smooth and sustainable manner.