ROAICVSep 17, 2025

MAP: End-to-End Autonomous Driving with Map-Assisted Planning

arXiv:2509.13926v1h-index: 4Has Code2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Originality Incremental advance
AI Analysis

This work addresses trajectory planning for autonomous driving systems by explicitly leveraging semantic map features, representing an incremental improvement in structure design for end-to-end frameworks.

The paper tackles the underutilization of online mapping in end-to-end autonomous driving by proposing MAP, a map-assisted trajectory planning framework that integrates segmentation-based map features and ego status, resulting in a 16.6% reduction in L2 displacement error, 56.2% reduction in off-road rate, and 44.5% improvement in overall score compared to a baseline.

In recent years, end-to-end autonomous driving has attracted increasing attention for its ability to jointly model perception, prediction, and planning within a unified framework. However, most existing approaches underutilize the online mapping module, leaving its potential to enhance trajectory planning largely untapped. This paper proposes MAP (Map-Assisted Planning), a novel map-assisted end-to-end trajectory planning framework. MAP explicitly integrates segmentation-based map features and the current ego status through a Plan-enhancing Online Mapping module, an Ego-status-guided Planning module, and a Weight Adapter based on current ego status. Experiments conducted on the DAIR-V2X-seq-SPD dataset demonstrate that the proposed method achieves a 16.6% reduction in L2 displacement error, a 56.2% reduction in off-road rate, and a 44.5% improvement in overall score compared to the UniV2X baseline, even without post-processing. Furthermore, it achieves top ranking in Track 2 of the End-to-End Autonomous Driving through V2X Cooperation Challenge of MEIS Workshop @CVPR2025, outperforming the second-best model by 39.5% in terms of overall score. These results highlight the effectiveness of explicitly leveraging semantic map features in planning and suggest new directions for improving structure design in end-to-end autonomous driving systems. Our code is available at https://gitee.com/kymkym/map.git

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