通过地图增强规划，显著提升端到端自动驾驶的轨迹精度与安全性。
MAP: End-to-End Autonomous Driving with Map-Assisted Planning
- 引入在线地图模块，融合语义地图特征与车辆状态进行联合规划。
- 在DAIR-V2X数据集上降低16.6%位移误差，减少56.2%偏离道路率。
- 适合追求高精度与鲁棒性的自动驾驶系统研发者参考。
近年来，端到端自动驾驶因能在统一框架中联合建模感知、预测与规划而受到关注。然而，现有方法大多未能充分挖掘在线地图模块的潜力，导致其对轨迹规划的增益未被充分利用。本文提出MAP（Map-Assisted Planning）——一种新型地图辅助的端到端轨迹规划框架。MAP通过一个增强规划的在线地图模块、基于自身状态引导的规划模块以及基于当前车辆状态的权重适配器，显式融合基于分割的地图特征与当前车辆状态。在DAIR-V2X-seq-SPD数据集上的实验表明，所提方法相比UniV2X基线，在无需后处理的情况下实现L2位移误差降低16.6%，偏离道路率下降56.2%，综合得分提升44.5%。此外，在CVPR2025 MEIS研讨会的端到端自动驾驶跨车通信挑战赛第二赛道中排名第一，综合得分领先第二名模型39.5%。结果验证了显式利用语义地图特征在规划中的有效性，并为端到端自动驾驶系统的结构设计提供了新方向。代码已开源：https://gitee.com/kymkym/map.git
原文摘要 · Abstract (English)
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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