用惯性数据融合多摄像头，实现赛车场景下300Hz高帧率3D车道线检测。
3D Lane Detection with Odometry for High-Speed Vehicle Racing
- 融合摄像头与惯性测量，通过预积分建模时空道路几何。
- 实测达到>0.9的F1分数，车辆附近误差低于0.18米。
- 适用于高速赛车场景，适合自动驾驶系统开发者参考。
车道边界检测是自动驾驶系统的关键组件，在常规驾驶场景中已有深入研究，但在高速赛车场景中仍较少探索，因车辆以更高速度行驶于更极端的道路几何结构中。为此，我们引入一个全新的赛车场景3D车道线检测数据集，包含超过25万张来自多路摄像头和惯性测量的数据，采集自一辆雷克萨斯LC 500在封闭赛道上的行驶过程。基于该数据集，我们对比了多种3D车道线检测方法，并提出改进方案，使帧处理速率接近300Hz，同时保持高预测性能。该方法支持多摄像头集成，在硬件上验证有效。我们证明，利用惯性测量进行预积分，可联合建模相机与时间维度的道路几何，显著提升关键指标。相比BevLaneDet等方法，加入里程计信息与集成预测后，F1分数提升3个百分点，近车区域平均绝对误差（MAE）降低超过30%。实际部署中实现F1分数>0.9，横向误差<0.18米。
原文摘要 · Abstract (English)
Lane boundary detection is a critical component in autonomous driving systems and has been rigorously studied in regular driving scenarios. However, it is less explored in vehicle racing, where the car moves at higher speeds across more extreme road geometries. To study this problem, we introduce a new dataset for 3D lane detection in racing, featuring >$250$k images from multiple camera feeds and inertial measurements taken with a Lexus LC 500 driving on a closed circuit. With this dataset, we compare various approaches to 3D lane detection and propose modifications that permit frames to be processed at rates of almost 300Hz while retaining high predictive performance in the racing application. This facilitates a multi-camera ensemble approach that is validated on hardware. We show that sensing modalities such as inertial measurements can be leveraged for pre-integration to regress road geometries over both cameras and time, yielding improvements in key metrics. Compared to methods such as BevLaneDet, adding odometry and ensemble predictions improves the F1 score by 3 points and reduces near-vehicle mean absolute errors (MAEs) by $>30 \%$. We show F1 scores $>$0.9 and lateral MAEs of $<$0.18m in vehicle deployments.
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