A Game Theory-Based Model Predictive Controller Considering Intension for Mandatory Lane Change

<div class="section abstract"><div class="htmlview paragraph">In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model. The innovation of this paper lies in that we proposed a multivehicle cooperative lane-changing controller that combines game theory and model predictive control (MPC) based on vehicle-to-vehicle (V2V) communication; Moreover, we designed a multilane vehicle-ordering method and decided the optimal time and acceleration of lane change by considering the aggressiveness of the surrounding vehicles and mutual interaction between vehicles. Typical scenarios were tested to verify that a lane-changing vehicle could interact with other vehicles and change lanes without collision. We verified this approach of lane changing through CarSim and MATLAB cosimulation and compared it with the conventional rule-based lane-change decision controller. Test results show that the controller is capable of changing lanes in a smarter manner, guaranteeing the safety and efficiency of the autonomous vehicle.</div></div>