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arXiv · 2304.11295

Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving

Ding Li·Qichao Zhang·Zhongpu Xia·Kuan Zhang·Menglong Yi·Wenda Jin·Dongbin Zhao
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Abstract

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. {Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents.} Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map.

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Ding Li, Qichao Zhang, Zhongpu Xia, Kuan Zhang, Menglong Yi, Wenda Jin, Dongbin Zhao. 2023-04-22. Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving. https://arxiv.org/abs/2304.11295

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