Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this paper proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this paper developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This paper proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV.

中文翻译：

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基于混合跟踪器的最佳路径跟踪系统，适用于复杂的自动驾驶道路环境

路径跟踪系统在自动驾驶中起着关键技术的作用。该系统应沿着车道准确行驶，并注意不要给乘客带来任何不便。为了解决这些任务，本文提出了一种基于混合跟踪器的最优路径跟踪系统。通过应用基于深度学习的车道检测算法和指定的快速车道拟合算法，本文开发了一种车道处理算法，该算法以最小的计算成本显示了与实际车道的匹配率。此外，使用基于GPS的路径或基于视觉的路径设计了三种改进的路径跟踪算法。在驾驶系统中，正确理想路径的匹配率不一定代表驾驶稳定性。本文通过施加观察者的概念提出了混合跟踪器基于最佳路径跟踪系统，该系统选择最优的跟踪器适当地复杂的道路环境。已经在复杂的道路环境中研究了行驶稳定性，例如在具有多个三通路口，环形交叉路口，交叉路口和隧道的直路中。因此，即使在路况复杂性很高的情况下，通过将车辆精确地保持在车道内，所提出的系统在实验上也显示出具有一致驾驶舒适性的高性能。代码将在https://github.com/DGIST-ARTIV中提供。因此，即使在路况复杂性很高的情况下，通过将车辆精确地保持在车道内，所提出的系统在实验上也显示出具有一致驾驶舒适性的高性能。代码将在https://github.com/DGIST-ARTIV中提供。因此，即使在路况复杂性很高的情况下，通过将车辆精确地保持在车道内，所提出的系统在实验上也显示出具有一致驾驶舒适性的高性能。代码将在https://github.com/DGIST-ARTIV中提供。