The ability to sense the surrounding environment is an important developing technology in the field of automated vehicles. Lane line detection could determine a vehicle's travelable area. An embedded road boundary detection system based on deep learning was developed in this study. The system can detect structured and unstructured roads in a variety of situations. To obtain an image with clear lane markings, a convolution auto-encoder with the characteristics of noise reduction and reconstruction was used to remove all objects in the images except lane markings. Then, the feature points of the lane line were extracted, and the lane line was fitted with a hyperbolic model. Finally, a particle filter was used for lane tracking. The road boundary detection system was implemented on the NVIDIA Jetson TX2 platform. Three different situations, day, night, and rainy day were selected to demonstrate the performance of the proposed algorithm. Additionally, to deal with structured roads, some special scenes, such as shadows, tunnels, degenerate lane markings, and blocked lane markings, were considered. According to the experimental results, the accuracy of the proposed lane detection system for structured and unstructured roads was 90.02%.

感知周围环境的能力是自动车辆领域中的重要发展技术。车道线检测可以确定车辆的可行驶区域。本研究开发了一种基于深度学习的嵌入式道路边界检测系统。该系统可以在各种情况下检测结构化和非结构化道路。为了获得具有清晰车道标记的图像，具有降噪和重构特征的卷积自动编码器用于去除图像中除车道标记之外的所有对象。然后，提取车道线的特征点，并用双曲线模型拟合车道线。最后，将粒子过滤器用于车道跟踪。道路边界检测系统是在NVIDIA Jetson TX2平台上实现的。三种不同的情况，一天，选择了晚上和雨天来演示所提出算法的性能。此外，为了处理结构化的道路，还考虑了一些特殊的场景，例如阴影，隧道，退化的车道标记和阻塞的车道标记。根据实验结果，所提出的用于结构化和非结构化道路的车道检测系统的准确性为90.02％。