Deep Adaptive Control: Deep Reinforcement Learning-Based Adaptive Vehicle Trajectory Control Algorithms for Different Risk Levels

Abstract

In this study, we explore the problem of adaptive vehicle trajectory control for different risk levels. Firstly, we introduce a sliding window-based car-following scenario extraction method, propose a new alternative traffic conflict assessment metric, and build a comprehensive traffic scenario library. Secondly, based on deep reinforcement learning (RL), we design an adaptive car-following trajectory control algorithm, which is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Adaptive Control</i> , to cope with different traffic risk levels. Thirdly, we design five metrics in terms of safety, comfort, and energy consumption, and experimentally compare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Adaptive Control</i> with human drivers and RL benchmarks. The experimental results show the superiority of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Adaptive Control</i> compared to human drivers and existing RL methods, which can follow the preceding vehicle closely in low-risk situations to improve traffic efficiency, keep distance from the preceding vehicle in high-risk situations to improve safety and be optimal in terms of comfort and fuel consumption metrics.