Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles
Li Ding, Qichao Zhang, Shuai Lu, Yifeng Pan, Dongbin Zhao
Abstract
Predicting future trajectories of pairwise traffic agents in highly interactive scenarios, such as cut-in, yielding, and merging, is challenging for autonomous driving. The existing works either treat such a problem as a marginal prediction task or perform single-axis factorized joint prediction, where the former strategy produces individual predictions without considering future interaction, while the latter strategy conducts conditional trajectory-oriented prediction via agentwise interaction or achieves conditional rollout-oriented prediction via timewise interaction. In this article, we propose a novel double-axis factorized joint prediction pipeline, namely, conditional goal-oriented trajectory prediction (CGTP) framework, which models future interaction both along the agent and time axes to achieve goal and trajectory interactive prediction. First, a goals-of-interest network (GoINet) is designed to extract fine-grained features of goal candidates via hierarchical vectorized representation. Furthermore, we propose a conditional goal prediction network (CGPNet) to produce multimodal goal pairs in an agentwise conditional manner, along with a newly designed goal interactive loss to better learn the joint distribution of the intermediate interpretable modes. Explicitly guided by the goal-pair predictions, we propose a goal-oriented trajectory rollout network (GTRNet) to predict scene-compliant trajectory pairs via timewise interactive rollouts. Extensive experimental results confirm that the proposed CGTP outperforms the state-of-the-art (SOTA) prediction models on the Waymo open motion dataset (WOMD), Argoverse motion forecasting dataset, and In-house cut-in dataset. Code is available at https://github.com/LiDinga/CGTP/.