Lane Change Intention Recognition for Intelligent Connected Vehicle Using Trajectory Prediction

Abstract

A key issue in high-speed automatic driving scenarios, is the recognition of lane change intention of surrounding vehicles. Conventional methods are effective in frequent lane change scenarios. However, considering the variance of individual driving characteristics and measurement uncertainty, false intention recognition remains a major problem to be resolved. We propose an approach to reduce false lane change recognition by introducing the predicted trajectories of the target vehicles. A rule-based approach infers initial lane change intention and a sampling-based approach generates a predicted trajectory according to the inference. Considering the physical inertia, a constant acceleration motion model is used to generate another reference trajectory. The preparatory lane change intention updates in real-time if the predicted trajectory deviates from the reference trajectory. Results demonstrate the approach to improve the lane change intention recognition process.