SIPTraj: Map-Free End-to-End Trajectory Prediction via Physics-Guided Scene Interaction
Abstract
Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD) maps, sensor-derived bird's-eye-view (BEV) features provide no explicit lane topology or drivable-area priors, making it inherently difficult to ground each agent in its surrounding scene context. Moreover, physical feasibility remains difficult to capture through data-driven learning alone, as kinematic constraints on agent motion cannot be explicitly encoded without structured supervision. Existing map-free predictors extract scene context in an agent-agnostic manner through a single fusion step and treat physical constraints only as output-level penalties, leaving both challenges unaddressed. We propose SIPTraj, a map-free trajectory prediction framework that jointly addresses scene grounding and physical feasibility. SIPTraj introduces a Hierarchical Agent-Scene Encoder (HASE) progressively grounding each agent in agent-guided scene evidence and refining inter-agent relations within the scene-grounded space. To tackle physical infeasibility in predicted trajectories, we develop a Physics-Guided Iterative Decoder (PGID). It conditions decoding on instantaneous kinematic states, propagating physical supervision into internal representations rather than output trajectories alone. Extensive experiments on nuScenes and Argoverse 2 Sensor show that SIPTraj surpasses prior map-free predictors and strong map-based baselines without any HD map at inference. Our code will be released as open-source.
Cite
@article{arxiv.2608.00779,
title = {SIPTraj: Map-Free End-to-End Trajectory Prediction via Physics-Guided Scene Interaction},
author = {Feifei Liu and Zejun Wei and Haozhe Wang and Yazhi Ye and Yuying Zhang and Jintao Cheng and Chi Man Vong and Xieyuanli Chen and Xiaoyu Tang},
journal= {arXiv preprint arXiv:2608.00779},
year = {2026}
}