Intelligent Platoon Operating Slot Optimization Method based on Drivers’ Overtaking Behavior

Vehicles were operating as platoon is a fundamental approach to solve the problem of road traffic efficiency. When a vehicle travels in the two-lane highway, it could overtake only by using the opposing lane. The platoon makes the ego vehicle face the problem of overtaking, which not only interfering with the traffic efficiency improving but also adding the risk of collision. In order to solve this problem, a step-by-step VOPDS (vehicle overtakes the platoon based on driving style) algorithm was proposed, in which an optimal speed matching scheme by researching the relationship between the speed of vehicles before the slot, the speed of vehicles behind the slot, the speed of vehicle joining a platoon and the required safety slot was designed. Also, a Hierarchical Constrained Multi-objective Optimization Method Based on Improved Particle Swarm Optimization is proposed, which provides the optimized speed guidance strategy for the vehicle overtaking a platoon model. In order to make the speed guidance information be accepted by drivers, this paper got driver behavior characteristics statistics by using open-source driving-simulation tool CARLA, through experiments. The algorithm-simulation results imply that the improved particle swarm optimization algorithm has stronger robustness and faster convergence than the traditional algorithms, optimized results which be more in line with drivers' driving style showed an improved both overall operating efficiency and comfortable for single vehicle overtaking a platoon scenario.