Lane-change prediction method for adaptive cruise control system with hidden Markov model

Abstract

A test platform with a millimeter-wave radar sensor, lane-line sensor, gyroscope, and controller area network was established to improve safety in using adaptive cruise control systems in vehicles. The motion-state characterization data of the host vehicle and surrounding vehicles in a real traffic environment were captured. The prediction method for the lane-changing maneuver of the vehicle ahead was developed using a hidden Markov model based on the distance between the host vehicle and the front vehicle, as well as the lateral and longitudinal velocities of the vehicle in front. The adaptive cruise control system control algorithm for assessing the target vehicle was optimized. The model was tested, and its predictions were compared with measured data. Result shows that the lane-changing and lane-keeping behaviors of the vehicle ahead can be predicted efficiently and accurately by the model. The maximum prediction accuracy rate for straight roads was 97% with the time window length of 4.5 s, whereas that for curved roads was 96% with the time window length of 3.5 s.