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

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 <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>ntegrated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>ath-guided <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>rediction and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>lanning (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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ld-av/IP3/</uri>.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2025-06-06
DOI
https://doi.org/10.1109/tvt.2025.3576204
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Qichao Zhang, Dongfang Yang, Zhi Wang, Dongbin Zhao et al.
IEEE Transactions on Vehicular Technology
Autonomous Vehicle Technology and Safety
article

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

Qichao Zhang, Dongfang Yang, Zhi Wang, Dongbin Zhao, F. Ren, Li Ding
article en

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 <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>ntegrated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>ath-guided <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>rediction and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>lanning (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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ld-av/IP3/</uri>.

IEEE Transactions on Vehicular TechnologyVol. 74(11)
Chinese Academy of Sciences (CN), Institute of Automation (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 87%
Autonomous Vehicle Technology and Safety
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