Preceding Vehicle Trajectory Prediction by Multi-Cue Integration

Abstract

In this paper we describe an approach to detect and predict the driving trajectory of a preceding vehicle on highway. In particular, we focus on detecting and predicting the changing lane intention and action of the preceding vehicle. Our algorithm employs SVM for driving pattern recognition by integrating two different cues: motion cue and appearance cue, which is trained on two class feature sets extracted from examples of lane changing and lane keeping video sequences. The method is evaluated on the real-world data collected in an intelligent vehicle test-bed. The method is applied to a vision-based safety driving system, which tracks the lane, the preceding vehicle, and uses the vehicle lane-change warning to serve for other intelligent vehicle controls.