IP3: Integrated Path-Guided Prediction and Planning for Safe Autonomous Driving

Ding Li, Qichao Zhang, Dongfang Yang, Zhi Wang, Fan Ren, Dongbin Zhao

Abstract: Autonomous vehicles (AVs) require accurately predicting future trajectories of neighboring agents and identifying highly interactive agents from them to make safe downstream planning. However, the immense uncertainty easily results in temporal incoherence between trajectory predictions from adjacent frames, misleading the selection of interactive agents and posing safety risks for planning. We find that the temporal coherence of trajectory predictions can be improved by predetermined, map-adaptive paths. This paper proposes an Integrated Path-guided Prediction and Planning (IP3) framework under the pre-train and fine-tune learning paradigm for temporal coherence prediction and safe planning. First, we adopt a new concept of path-trajectory query pairs to pre-train a dual-query Transformer for performing modality alignment between path and trajectory predictions, estimating accurate distributions over them, and further achieving better temporal coherence trajectory prediction. Second, we fine-tune the planning policy by a collision-aware imitation loss to prevent potential collision threats from highly interactive agents. Besides, we incorporate a path-guided interactive agent selection strategy to enable accurate identification of highly interactive agents and boost safe planning with temporal coherence prediction. Experiments validate IP3 outperforms previous state-of-the-arts in both open-loop and closed-loop planning tests. Especially, IP3 surpasses strong baselines on the challenging Waymax benchmark, greatly decreasing the safety-critical metric collision rate by 68.40 $\%$.

External IDs:dblp:journals/tvt/LiZYWRZ25