The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

基于视觉的道路车道检测技术在驾驶员辅助系统中起着关键作用。尽管现有的车道识别算法显示出超过90％的检测率，但验证测试通常是在有限的情况下进行的。当应用于现实自动驾驶时，仍然存在巨大差距。本文的目的是找出这些差距，并提出可以弥补这些差距的研究方向。在本研究中，以基于线性霍夫变换（HT）的直行车道检测算法为例，评估了在挑战性场景（包括各种道路类型，不同的天气条件和阴影，变化的照明条件等）下可能出现的感知问题。研究发现，基于HT的算法在简单背景下显示出可接受的检测率，例如在高速公路上行驶或在车道边界与其周围环境之间表现出明显对比的条件。但是，它无法识别变化的照明条件下的道路分隔线。失败归因于二值化过程未能在检测之前提取车道特征。此外，现有的基于HT的算法将受到类似车道的干扰，例如护栏，铁路，自行车道，电线杆，人行道，建筑物等。总体而言，所有这些发现都支持进一步改进当前道路车道检测算法的需求，以使其能够抵御干扰和光照变化。而且，