Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models

<div class="section abstract"><div class="htmlview paragraph">Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.</div></div>