提升高速路自动驾驶定位精度与鲁棒性，应对环境变化与信号干扰。
Robust Localization for Autonomous Vehicles in Highway Scenes
- 双似然激光雷达前端分离几何与纹理特征，适应环境变化。
- 控制-卡尔曼滤波融合车辆控制指令，降低延迟并改善闭环表现。
- 百万公里实测验证，公开163公里高速挑战数据集供研究。
相较于城市道路，高速公路场景下的自动驾驶定位研究仍不充分，现有城市方法直接迁移至高速时性能下降。本文识别出高速定位的关键挑战：环境信息同质化、严重遮挡、GNSS信号劣化以及对精度和延迟的严苛要求。为此提出一种鲁棒定位系统：采用双似然激光雷达前端，解耦三维几何结构与二维道路纹理线索以应对环境变化；引入控制-卡尔曼滤波器，利用转向与加速度指令减少滞后，提升闭环行为表现；构建自动化离线地图与真值生成流水线，实现高频率地图更新以保障最优定位性能。为推动进展，发布包含城市与高速场景的公开数据集，聚焦典型高速挑战片段，总里程达163公里；采用面向产品的真实精度指标与认证真值进行基准测试。相较Apollo与Autoware，系统在城市道路表现相当，但在复杂高速场景中显著更优。系统已通过超过一百万公里的道路测试验证。
原文摘要 · Abstract (English)
Localization for autonomous vehicles on highways remains under-explored compared to urban roads, and state-of-the-art methods for urban scenes degrade when directly applied to highways. We identify key challenges including environment changes under information homogeneity, heavy occlusion, degraded GNSS signals, and stringent downstream requirements on accuracy and latency. We propose a robust localization system to address highway challenges, which uses a dual-likelihood LiDAR front end that decouples 3D geometric structures and 2D road-texture cues to handle environment changes; a Control-EKF further leverages steering and acceleration commands to reduce lag and improve closed-loop behavior. An automated offline mapping and ground-truth pipeline keep maps fresh at high cadence for optimal localization performance. To catalyze progress, we release a public dataset covering both urban roads and highways while focusing on representative challenging highway clips, totaling 163 km; benchmarking is standardized using product-oriented accuracy metrics and certified ground truth. Compared to Apollo and Autoware, our system performs similarly on urban roads but shows superior robustness on challenging highway scenarios. The system has been validated by more than one million kilometers of road testing.
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