In this work, a lane departure warning system (LDWS) algorithm for embedded platforms which has restricted resources is proposed. An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. Real-time test results on embedded hardware demonstrated that the processing time does not exceed 41.67 ms with an accuracy of over 91.5%.

在这项工作中，提出了一种针对资源受限的嵌入式平台的车道偏离警告系统（LDWS）算法。LDWS由车道检测和车道跟踪两个主要子功能组成。尽管已经为这两个子功能开发了复杂的方法，但它们通常需要高处理能力甚至 GPU 处理能力。因此，它们不适用于资源有限的硬件。在这项工作中，应用了基于霍夫变换 (HT) 的车道检测算法。所提出的过滤算法消除了基于 HT 的方法对误导性图像的脆弱性。所提出的滤波算法与文献中的经典方法的主要区别在于它应用于参数空间而不是图像，并且仅用于确定车道。在车道跟踪阶段，K-means 聚类算法已被修改为在线运行。这样，在换道或超车时，可以自适应地跟踪检测到的车道的参数。嵌入式硬件的实时测试结果表明，处理时间不超过 41.67 ms，准确率超过 91.5%。