Research on lane-changing decision and control of autonomous vehicles based on game theory

Designing a secure and reliable decision-planning model for vehicle lane changing is of utmost practical significance because it is one of the most frequent driving behaviors and has a substantial impact on the safety of drivers' lives and property. First, a Gaussian mixed Hidden Markov model (GMHMM) is trained for lane change intention recognition (LCIR), and the results reveal that the model has a great performance. This will simplify the game process and provide drivers and passengers with warnings. Second, the safety, efficiency, and comfort payoffs of vehicle lane changes are taken into account when building the game model. When building the safety payoff function, temporal collision risk and spatial collision risk of vehicles are two of them that are carefully taken into account. After that, the vehicle's trajectory tracking control is decoupled into lateral LQR + feedforward control and longitudinal dual proportional integral derivative (PID) control based on the Frenet coordinate system. Finally, a vehicle lane change scenario is built for simulation analysis, and the effects of driving comfort factor and driving efficiency factor on lane change results are considered. The results show that the proposed game theory lane change model ensures lane change safety while satisfying human drivers' requirements for lane change efficiency and comfort.