Recognition of Fast Lane Changing Behavior

摘要

Aiming at the lane change behavior recognition requirements of lane change warning system, natural lane change samples were captured by using a test vehicle. Steering angle and distance between vehicle and lane mark were used as characteristic parameters of lane change behavior. Support vector machine (SVM) method was used to establish recognizing model of lane change. The sample data were filtered by Kalman filter. Variance-Bayesian filter model was used to fast lane change behavior identification. Final recognition results show that the recognition rate for the real lane change samples can reach 92.5273% and the proposed model can also meet the real time and reliability requirements of lane change warning system.