Safe Passage Strategy With Swarm Intelligence for CAVs in Urban Road Heterogeneous Traffic Flow Using Standard Alliance Game

Abstract

This study introduces an innovative approach to distributed cooperative gaming for CAVs in urban road traffic scenarios, aimed at ensuring safe passage. This method treats every connected vehicle in the heterogeneous traffic flow as a player in the game. The individual payoffs for these players are clearly defined by quantifying factors such as travel safety risk, fairness and efficiency. Furthermore, three protocols are developed from the perspectives of enhancing individual payoff and improving alliance stability. These protocols enable CAVs to achieve logical control under conflicting interference from CHVs. By utilising alliance cooperative gaming, CAVs can collectively determine their strategies, avoiding the pitfalls of individual decision-making that could result in mutually detrimental outcomes. The proposed alliance solution method addresses the multi-vehicle simultaneous conflict problem by employing a structured, step-by-step approach that involves conflict decoupling and classification. The following important findings are derived from simulation analysis: the CAV achieves swarm intelligence robust control in a heterogeneous traffic environment through a standard alliance game, which not only effectively ensures safe passage, but also increases the passage efficiency of heterogeneous traffic flow by at the very least 10%, and the suggested approach works better in situations with low densities and high CAV penetration rates.