EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning

arXiv:2311.12261v2 Announce Type: replace-cross
Abstract: Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks, and reduced road capacity utilization. While previous research demonstrates that Robot Vehicles (RVs) can be leveraged to mitigate these issues, most such studies rely on simulations with simplistic models of human car-following behaviors. In this work, we analyze real-world driving trajectories and extract a wide range of acceleration profiles. We then incorporates these profiles …

abstract amplify arxiv capacity collision congestion consumption contributor cs.lg cs.ro efficiency human major mixed reinforcement reinforcement learning research risks robot safety stability traffic type vehicles via world