Lane-change conflict resolution of intelligent connected vehicles in continuous curve area of mountain city based on quantum genetic optimization

The mountainous city's continuous curve area features complex topography, steep slopes, roads with wide-ranging curvatures, a rapidly changing dynamic traffic environment, and unpredictable vehicle trajectories. To address these challenges, a lane-change conflict resolution method for intelligent connected vehicles (ICVs) based on quantum genetic optimization is proposed for such continuous curves in mountainous urban settings. The kinematic and dynamic models of ICVs are established, with cosine-based lane-change trajectories and trapezoidal acceleration lane-change trajectories designed for different lane-change scenarios. A tracking control method based on the pose error differential equation is introduced, and dynamic rotation gates and quantum catastrophe are incorporated to optimize the parameters of the vehicle’s driveline. A conflict resolution model between the lane-changing vehicle and the target-lane vehicle is established. Vector-based analysis is applied to evaluate collision risks between the two vehicles. Experimental results demonstrate that the proposed method achieves a higher average speed for autonomous vehicles. After introducing a minor disturbance (the lead vehicle brakes for 3 s), the speed of the lead vehicle stabilizes at 13.5 m/s. The proposed control strategy allows the following vehicles to decelerate smoothly to 13.5 m/s as well, avoiding collisions or lane departures.