Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization

arXiv:2403.02882v1 Announce Type: cross
Abstract: Most of the current studies on autonomous vehicle decision-making and control tasks based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under rule-based microscopic traffic flow, with little consideration of migrating them to real or near-real environments to test their performance. It may lead to a degradation in performance when the trained model is tested in more realistic traffic scenes. In this study, we propose …

abstract arxiv autonomous autonomous vehicle control cs.lg cs.ro cs.sy current decision eess.sy environments flow making randomization reinforcement reinforcement learning studies tasks testing them through traffic training type