Risk quantification based Adaptive Cruise control and its application in approaching behavior at signalized intersections
提出一种风险量化自适应巡航控制（RACC），通过将接近信号交叉口的约束设计为预期风险，使网联自动驾驶车辆平稳通过停止线，显著降低延误和油耗，仿真显示延误最多减少87.1%，油耗最多减少54.8%。
Traffic signals, while reducing conflicts within intersections, often lead to stop-and-go behaviors in approaching vehicles, negatively impacting traffic flow in terms of safety, efficiency, and fuel consumption. Aimed at minimizing the traffic oscillations caused by traffic signals through Connected and Autonomous Vehicles (CAVs) and meeting real-time operational needs, this paper proposes a Risk-Based Adaptive Cruise Control (RACC). RACC designs the constraints of approaching a signalized intersection as expected risks, enabling compliance with all constraints while being adaptable to basic road scenarios. Theoretical analysis indicates that RACC, under specific parameter conditions, achieves string stability and overdamped characteristics while maintaining high throughput efficiency. Simulations confirm RACC's sensitivity to risks, allowing it to timely adjust to return to a stable state, thus ensuring platoon safety under high throughput conditions. At signalized intersections, RACC enables CAVs to cross stop lines with smooth trajectories, significantly reducing risk, delays, and fuel consumption for all downstream vehicles. Further simulations demonstrate that RACC significantly reduces average travel time delay and fuel consumption across various traffic volumes and Market Penetration Rates (MPRs), with reductions of up to 87.1% in delays and 54.8% in fuel consumption, showcasing substantial computational efficiency improvements over benchmarks. Furthermore, extending this study to scenarios with higher traffic conflicts, such as multi-lane roads or intersections, while considering the impact of lane-changing behavior, is a promising direction for future research.