SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
This addresses the challenge of improving autonomous vehicle performance in complex traffic environments, though it appears incremental as it builds on existing ACC and RL methods.
The paper tackled the problem of adaptive cruise control for autonomous vehicles by developing a deep reinforcement learning system that optimizes traffic efficiency, safety, and comfort through dynamic inter-vehicle gap adaptation. Results from over 12,000 simulation runs showed significant enhancements in traffic flow, safety, and comfort compared to a state-of-the-art approach.
We present a novel adaptive cruise control (ACC) system namely SAINT-ACC: {S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based on deep reinforcement learning (RL). A novel dual RL agent-based approach is developed to seek and adapt the optimal balance between traffic efficiency and driving safety/comfort by effectively controlling the driving safety model parameters and inter-vehicle gap based on macroscopic and microscopic traffic information collected from dynamically changing and complex traffic environments. Results obtained through over 12,000 simulation runs with varying traffic scenarios and penetration rates demonstrate that SAINT-ACC significantly enhances traffic flow, driving safety and comfort compared with a state-of-the-art approach.