Lane detection is a fundamental task in autonomous driving. While the problem is typically formulated as the detection of continuous boundaries, we study the problem of detecting lane boundaries that are sparsely marked by 2D points with many false positives. This problem arises in the Formula Student Driverless (FSD) competition and is challenging due to its inherent ambiguity. Previous methods are inefficient and unable to find long-horizon solutions. We propose a deterministic algorithm called CLC that uses backtracking graph search with a learned likelihood function to overcome these limitations. We impose geometric constraints on the lane candidates to guarantee a geometrically sound lane. Our exhaustive search leads to finding the global optimum in 45% of instances, and the algorithm is overall robust to up to 50% false positives. Our algorithm runs in less than 15 ms on a single CPU core, meeting the low latency requirements of autonomous racing. We extensively evaluate our method on real data and realistic racetrack layouts, and show that it outperforms the state-of-the-art by detecting long lanes over 100 m with few (0.6%) critical failures. This allows our autonomous racecar to drive close to its physical limits on a previously unknown racetrack without being limited by perception. We release our dataset with realistic Formula Student racetracks to enable further research.

翻译：车道线检测是自动驾驶中的一项基础任务。尽管该问题通常被表述为连续边界的检测，但本研究关注的是检测由二维点稀疏标记且存在大量误检的车道边界。这一问题源于Formula Student Driverless（FSD）竞赛，由于其固有的模糊性而极具挑战性。现有方法效率低下且难以找到长距离解决方案。我们提出一种名为CLC的确定性算法，该算法结合回溯图搜索与学习得到的似然函数，以克服这些局限。我们对车道候选施加几何约束，以确保生成几何上合理的车道线。我们的穷举搜索能在45%的实例中找到全局最优解，且算法整体上对高达50%的误检率具有鲁棒性。该算法在单CPU核上运行时间低于15毫秒，满足自动驾驶赛车对低延迟的要求。我们在真实数据和模拟赛道布局上对本方法进行了全面评估，结果表明其性能优于现有最优方法，能够检测超过100米的长车道线，且关键故障率极低（0.6%）。这使得我们的自动驾驶赛车能够在未知赛道上接近其物理极限行驶，而不受感知能力的限制。我们发布了包含模拟Formula Student赛道的数据集，以促进进一步研究。