A Guidence Method for Lane Change Detection at Signalized Intersections in Connected Vehicle Environment

About 6 percent of traffic accidents are related to lane changing. Moreover, on average 10% of traffic delay is caused by the result of improper lane change operations. In order to improve the traffic capacity at signalized intersection and reduce the negative effect caused by improper lane change at the intersection, an innovative method using lane change model is proposed. This method is aiming to guide the traffic in connected vehicles environment. Firstly, based on the real-time information collected from each vehicle (position and speed) in the connected and autonomous vehicle (CAV) environment, the lane change conditions can be analyzed using real-time data. Secondly, considering the effects of surrounding vehicles and the lateral offset, a guidance on how to perform proper lane change and its model is constructed to guide the vehicle to successfully complete the lane change behavior. Finally, simulation experiments were designed and conducted using MATLAB and the simulation experiment of vehicle lane change guidance was carried out. The results of lane change trajectories show that the proposed approach and the model can improve the safety level and stability of vehicle lane change. Also, our model can reduce negetive impacts of vehicle lane change on the traffic efficiency at signalized intersections.