自动驾驶车辆混行集聚MAS控制模型

随着车路协同技术和自动驾驶技术的不断发展,越来越多的网联自动驾驶车辆(Connected and Autonomous Vehicle,CAV)涌入道路交通,与传统人工驾驶车辆(Human Pilot Vehicle,HPV)形成混合交通流(Mixed Traffic Stream,MTS).为在提高MTS交通流量的同...

Agglomeration Control Model Based on Multi-agents for Autonomous Vehicles in Mixed Traffic Environment

With the continuous development of cooperative vehicle infrastructure systems and automatic driving technology, an increasing number of connected and autonomous vehicles (CAVs) flow into road traffic, with traditional human-pilot vehicles(HPVs) forming mixed traffic streams (MTSs). To improve the traffic flow of the MTS and ensure traffic safety, considering that CAVs require less headway and have fewer speed fluctuations when driving in a platoon, an agglomeration control model of connected and autonomous vehicle based on multi-agent system(ACMOCAV-MAS) was designed. Based on the controllability of CAVs and the randomness of HPV, the model aimed to promote the scattered driving CAVs to agglomerate into a platoon with better driving conditions. The underlying vehicle agents (CAV agent and HPV agent) and the upper management agent were designed as an agent. This paper proposes platoon-level agglomeration (PLA) and lane-level agglomeration (LLA), which differ from no aggregation (NOA) as strategies, to match homogeneous elements and risk aversion among heterogeneous elements. In addition, algorithms related to the agglomeration of CAV agents are also proposed. Simulation experiments, based on the ACMCAV-MAS and cellular automata models, were conducted at different traffic flow densities and different CAV agent penetration rates, with the results showing that the agglomeration strategy achieves the best benefit at a CAV agent penetration rate of 60%. Concomitantly, at a density of 60 veh·km^-1, PLA can increase the traffic flow by 38.14% on average, which is 9.73% higher than that of LLA. Platoon-level agglomeration can also effectively alleviate traffic congestion in the density range of 40-50 veh·km^-1 at a 50%-70% CAV-agent penetration rate. Through a longitudinal risk analysis of medium-and high-density traffic flows, no significant difference was found between the two agglomeration strategies at low CAV agent penetration rates, and the maximum risk reduction ratio reached more than 20%. However, in actual traffic situations, the agglomeration strategy, to some extent, may increase the risk of lateral collisions. In future work, methods to reduce the risk of lateral collisions will continue to be explored. Meanwhile, efforts were expended to solve the deficiency of heterogeneous modeling of artificial driving behavior in the current simulation framework, and the ACMCAV-MAS will be improved to provide a theoretical basis for the formulation of automatic driving strategies in future MTSs.