融合激光雷达、摄像头与高精地图，提升自动驾驶安全行驶空间识别能力
An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion
- 基于多源数据融合的自适应地面去除与路缘检测方法
- 在真实道路环境中实现全天候稳定运行，包括大雪天气
- 适合对安全性要求高的自动驾驶系统部署
本文提出一种精确且鲁棒的自动驾驶感知模块，用于提取安全可行驶区域。现有深度学习方法虽在基准数据集上表现良好，但在复杂多变环境中的泛化能力不足。本工作通过融合激光雷达、摄像头与高精地图数据，构建具有强泛化能力的感知模块，在各种天气条件下实现可靠的安全行驶空间估计。提出自适应地面去除与路缘检测方法，结合高精地图提升障碍物检测可靠性；设计针对降水噪声优化的自适应DBSCAN聚类算法，并开发低成本、抗标定误差的激光雷达-摄像头视锥关联策略。最终的行驶空间表示融合全部感知信息，兼容车辆尺寸与道路法规。该方法在真实数据集上验证，其可靠性已在自动驾驶接驳车WATonoBus的日常运营中得到证实，涵盖严寒下雪等恶劣天气条件。
原文摘要 · Abstract (English)
In this paper, we propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Perception is crucial in autonomous driving, where many deep learning-based methods, while accurate on benchmark datasets, fail to generalize effectively, especially in diverse and unpredictable environments. Our work introduces a robust easy-to-generalize perception module that leverages LiDAR, camera, and HD map data fusion to deliver a safe and reliable drivable space in all weather conditions. We present an adaptive ground removal and curb detection method integrated with HD map data for enhanced obstacle detection reliability. Additionally, we propose an adaptive DBSCAN clustering algorithm optimized for precipitation noise, and a cost-effective LiDAR-camera frustum association that is resilient to calibration discrepancies. Our comprehensive drivable space representation incorporates all perception data, ensuring compatibility with vehicle dimensions and road regulations. This approach not only improves generalization and efficiency, but also significantly enhances safety in autonomous vehicle operations. Our approach is tested on a real dataset and its reliability is verified during the daily (including harsh snowy weather) operation of our autonomous shuttle, WATonoBus
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