Trajectory Prediction for Safety Critical Maneuvers in Automated Highway Driving

Abstract

Situation understanding and interpretation are one of the essential features for automated vehicles. To enable safe and comfortable driving, sensing the current situation is not sufficient, but accurately predicted trajectories of other traffic participants are required. The paper presents a novel trajectory prediction approach utilizing a combination of maneuver classification and probabilistic estimation of temporal properties with a model based trajectory representation. Probabilistic time-to-lane-change estimation is applied to gather information about the conditional distribution for the time of lane marking crossing. Lower tails of the distribution, which represent more critical lane change maneuvers, are utilized with a suitable prediction model to estimate appropriate trajectories. The three parts of the prediction framework are evaluated on the NGSIM data set. It shows, that based on a good performance of the maneuver prediction as well as the time-to-lane-change estimation, lane change trajectories with high accuracy can be predicted. In particular, the consideration of critical maneuver executions shows promising results and demonstrates its general applicability in real-world scenarios.