Lane-detection methods are still facing robustness issues when confronted with challenging road surfaces, road markings and illumination conditions. Such combined challenges occur infrequently but are crucial for driving safety. Although advanced learning-based methods (using deep learning) demonstrate an impressive performance, they rely on plenty of training images for varying scenes and their performance is limited for scenes not covered by the training data. Also, multi-lane detection is indispensable for determining the exact position of both ego-car and surrounding vehicles as well as lane changing behavior on the road. In this paper we propose a new multi-lane detection algorithm, detecting all visible lane boundaries in front of the ego-car. In contrast to the Hough transforms often used for lane boundaries detection, our approach uses moments to calculate the deflection angles and the centroids of lane segments, achieving more precise lane boundaries. We propose a novel algorithm based on moments and Kalman filtering to achieve lane tracking. State-of-the-art neural-network-based methods are compared with the proposed method concretely. Experimental results show that our method outperforms other (recently published) multi-lane detection algorithms regarding detection rate as well as accuracy.

当遇到具有挑战性的路面，道路标记和照明条件时，车道检测方法仍然面临鲁棒性问题。这样的综合挑战很少发生，但对于行车安全至关重要。尽管基于高级学习的方法（使用深度学习）表现出令人印象深刻的性能，但它们依赖大量的训练图像来处理各种场景，并且对于训练数据未涵盖的场景，它们的性能受到限制。同样，多车道检测对于确定自我和周围车辆的确切位置以及在道路上的车道变换行为也是必不可少的。在本文中，我们提出了一种新的多车道检测算法，该算法可检测到自我车前方的所有可见车道边界。与通常用于车道边界检测的霍夫变换不同，我们的方法使用力矩来计算车道段的偏转角和质心，从而获得更精确的车道边界。我们提出了一种基于矩和卡尔曼滤波的新算法来实现车道跟踪。具体地将基于最新神经网络的方法与所提出的方法进行比较。实验结果表明，在检测率和准确性方面，我们的方法优于其他（最新发表的）多车道检测算法。