A Dynamic Traffic Augmented Safety Interval Reserve Approach for Decision-Making and Planning of Connected Autonomous Vehicles

Connected Autonomous Vehicle (CAV) technology can significantly reduce the number of accidents and improve traffic efficiency. However, the current research on CAV decision-making and planning is relatively simple or focuses on idealized scenarios. This paper proposes a decision-making and planning approach: a dynamic traffic augmented Safety Interval Reserve (SIR) method (D-SIR) for CAV under mixed traffic flow. A dynamic traffic congestion rate (DTCR) is first derived based on SIR. Then, an interpretable and computationally efficient decision-making and planning approach is constructed by combining DTCR with a basic SIR network. This approach is then applied to mixed traffic with different penetration rates. Finally, the superior performance of D-SIR in terms of traffic flow efficiency, safety, and stability under different traffic volume conditions is verified through a simulation in Simulation of Urban Mobility. In addition, the impact mechanism of CAV penetration rates on mixed traffic flow was examined. The results show the following: (1) As the CAV penetration rate increases, the efficiency of mixed flow improves, and the CAV-mixed flow can effectively avoid the efficiency decline phenomenon. (2) The driving style of CAVs affects the efficiency of mixed flow differently, and the affected penetration rate range is approximately 55% 75%. This study provides theoretical support and practical guidance for the promotion and application of CAV technology in mixed traffic environments.