Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss

Abstract

Prior studies in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory, which is highly biased toward straight maneuvers. Such problem formulations and imbalanced distribution of datasets, however, frequently lead to a loss of diversity and biased trajectory predictions. Therefore, they are unsuitable for real-world autonomous driving, where diverse and road-dependent multimodal trajectory predictions are critical for safety. To this end, this study proposes a novel trajectory prediction model that ensures map-adaptive diversity and accommodates geometric constraints. A two-stage trajectory prediction architecture with a novel trajectory candidate proposal module, <italic>Trajectory Prediction Attention (TPA)</italic>, which is trained with <italic>Lane Loss</italic>, encourages multiple trajectories to be diversely distributed in a map-aware manner. Furthermore, the diversity of multiple trajectory predictions cannot be properly evaluated by existing metrics, and thus a novel quantitative evaluation metric, termed the minimum lane final displacement error (minLaneFDE), is also proposed to evaluate the diversity as well as the accuracy of multiple trajectory predictions. Experiments conducted on the Argoverse dataset show that the proposed method simultaneously improves the diversity and accuracy of the predicted trajectories.