用大模型过滤不靠谱路网数据，让地图更新更准更稳
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
- 用多模态大模型量化车道可见度，生成0-10分置信度
- 选前三名高分局部地图融合，误差仅0.28米，低于0.32米阈值
- 适合自动驾驶车企和地图公司，提升实时路网更新可靠性
智能网联汽车与车路云一体化系统快速发展，对高精地图实时更新的准确性需求激增。但众包数据常因运动模糊、光照变化、恶劣天气和车道线退化导致不一致，影响地图可靠性。本文提出CleanMAP，一种基于多模态大语言模型（MLLM）的蒸馏框架，用于筛选与优化众包数据以实现高置信度高精地图更新。CleanMAP采用MLLM驱动的车道可见度评分模型，系统量化关键视觉参数，依据其对车道检测的影响生成0-10分置信度。创新性地设计动态分段置信度函数，使评分与人工评估高度一致，并有效剔除不可靠数据。为优化地图精度，提出置信度驱动的局部地图融合策略，在最佳得分减10%的最优置信区间内，选择前k个最高分局部地图，平衡数据质量与数量。在真实自动驾驶数据集上的实验验证表明，融合前三个局部地图时，平均更新误差降至0.28米，优于基线的0.37米，满足≤0.32米的严苛精度要求。真实车辆数据验证显示与人工评估匹配率达84.88%，证实模型鲁棒性与可靠性。该工作构建了可扩展、可部署的众包高精地图更新方案，显著提升自动驾驶导航的精准性与可靠性。代码将发布于https://Ankit-Zefan.github.io/CleanMap/
原文摘要 · Abstract (English)
The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuring map reliability remains challenging due to inconsistencies in crowdsourced data, which suffer from motion blur, lighting variations, adverse weather, and lane marking degradation. This paper introduces CleanMAP, a Multimodal Large Language Model (MLLM)-based distillation framework designed to filter and refine crowdsourced data for high-confidence HD map updates. CleanMAP leverages an MLLM-driven lane visibility scoring model that systematically quantifies key visual parameters, assigning confidence scores (0-10) based on their impact on lane detection. A novel dynamic piecewise confidence-scoring function adapts scores based on lane visibility, ensuring strong alignment with human evaluations while effectively filtering unreliable data. To further optimize map accuracy, a confidence-driven local map fusion strategy ranks and selects the top-k highest-scoring local maps within an optimal confidence range (best score minus 10%), striking a balance between data quality and quantity. Experimental evaluations on a real-world autonomous vehicle dataset validate CleanMAP's effectiveness, demonstrating that fusing the top three local maps achieves the lowest mean map update error of 0.28m, outperforming the baseline (0.37m) and meeting stringent accuracy thresholds (<= 0.32m). Further validation with real-vehicle data confirms 84.88% alignment with human evaluators, reinforcing the model's robustness and reliability. This work establishes CleanMAP as a scalable and deployable solution for crowdsourced HD map updates, ensuring more precise and reliable autonomous navigation. The code will be available at https://Ankit-Zefan.github.io/CleanMap/
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