Intelligent vehicle lane change decision and motion planning in mixed traffic scenarios
To address the safety risks and low driving efficiency in mixed traffic scenarios with both pedestrians and vehicles,this paper proposes a novel lane-changing decision-making model for intelligent vehicles.Based on the dissatisfaction of the ego vehicle,it considers its desired speed,the speed of the preceding vehicle,acceleration,and following distance.Meanwhile,a minimal safety distance model for lane changing is built to assess the feasibility of lane changing throughout the entire process.To enhance the real-time performance of motion planning algorithms,the Frenet coordinate system is chosen,and a decoupled approach for path planning and velocity planning is employed.For path planning,a fifth-order polynomial curve is selected,incorporating three evaluation criteria:safety,comfort,and efficiency.Dynamic programming combined with quadratic programming is utilized for velocity planning to obtain a smooth speed profile.Finally,a joint simulation platform using CarSim/PreScan/Simulink is employed to validate the proposed model in a mixed traffic scenario.Our simulation results show the lane-changing decision-making model based on driving dissatisfaction effectively selects more efficient and safer driving strategies.The motion planning module ensures the safety and stability of the ego vehicle when changing lanes or shunning pedestrians.
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