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

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
Deep Adaptive Control
, to cope with different traffic risk levels. Thirdly, we design five metrics in terms of safety, comfort, and energy consumption, and experimentally compare
Deep Adaptive Control
with human drivers and RL benchmarks. The experimental results show the superiority of
Deep Adaptive Control
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.