Using Particle Swarm Optimization to Learn a Lane Change Model for Autonomous Vehicle Merging

This paper presents the results of experiments applying a Particle Swarm Optimization (PSO) approach to lane changing for autonomous vehicles. The lane change model proposed is rule-based, where PSO learns the parameters of the rules. A study was conducted to compare the proposed lane change model to the existing lane change model in the microscopic simulator, SUMO. Experiments performed include simulating vehicles using the Krauss car-following model with the SUMO lane change model, with the proposed PSO lane change model, and with all lane changing decisions turned off. The latter case, where merges are replaced by vehicle reset, serves as a baseline for missed merge opportunities. The objective was to develop an adaptive approach to improve merge efficiency as an example of lane changing behavior. Varying vehicle densities and levels of congestion on the merge lane and through-lane were tested. Empirical results show the proposed lane change model is able to learn merging strategies with minimal collisions and is comparable to the SUMO lane change model in some scenarios. Further investigation is needed to improve performance and safety, but initial results show promise for the proposed PSO-based approach to autonomous lane changing.