Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios

arXiv:2404.19087v1 Announce Type: cross
Abstract: Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead, often overlooking potential risks from following vehicles. This oversight can lead to ineffective handling of high risk situations, such as high speed, closely spaced, multi vehicle scenarios where emergency braking by one vehicle might trigger a pile up collision. To overcome these limitations, this study introduces a novel deep reinforcement learning based algorithm for longitudinal control and collision avoidance. This proposed algorithm effectively …

abstract advanced advanced driver assistance arxiv collision control cs.ai cs.lg cs.ro cs.sy driver driving eess.sy emergency focus oversight reinforcement reinforcement learning risk risks speed systems type vehicles