Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential

• Published in: Applied sciences 2024-06, Vol.14 (11), p.4659
• Main Authors: Li, Juan, Qu, Dayi, Chen, Yicheng, Yang, Yuxiang, Cui, Shanning
• Format: Article
• Language: English
• Subjects: acceptable gap; Analysis; Driverless cars; Equilibrium; interaction potential; lane changing behavior; Molecular dynamics; networked autonomous vehicles; Roads & highways; Simulation; Traffic accidents & safety; Traffic flow; Vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14114659

Aiming at the interactive behavior of vehicles changing lanes in the intelligent network traffic environment, the relative position of vehicle, speed, acceleration, and steering angle are introduced to correct the molecular interaction potential, and the vehicle interaction potential model is established. The spatial distribution map of the vehicle interaction potential field under different motion states was drawn, and the changing law of the vehicle interaction potential field was described. By analyzing the longitudinal critical distance of the lane changing safe critical time, the expression of acceptable clearance for lane changing is defined, and the relationship between acceptable clearance and the speed and acceleration of the interactive vehicle is verified by simulation. Combined with the acceptable clearance of lane changing, a lane changing behavior model based on the vehicle interaction potential is established, and the situation of lane changing vehicles subjected to the interaction potential and potential field forces generated by the surrounding vehicles is analyzed, and the driving decision of lane changing vehicles under the action of different potential field forces is given. Compared with the molecular dynamics lane changing model and LS2015 lane changing model, the results show that the vehicle action potential lane changing model can effectively improve the running speed and stability of traffic flow. The research has theoretical significance for the lane changing behavior decisions of networked autonomous vehicles in the future.