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.
翻译:路径跟踪系统是自动驾驶的关键技术。 系统应该沿着车道精确驾驶, 谨慎地避免给乘客造成任何不便。 为了完成这些任务, 本文提出基于混合追踪器的最佳路径跟踪系统。 通过应用基于深深学习的航道检测算法和指定的快速航道安装算法, 本文开发了一条车道处理算法, 显示与实际航道的匹配率, 且计算成本最低。 此外, 三个修改过的路径跟踪算法是使用全球定位系统路径或视景路径设计的。 在驾驶系统中, 正确的理想航道的匹配率并不一定代表驾驶稳定性。 本文提出基于混合追踪器的最佳路径跟踪系统, 采用观察员在复杂的公路环境中适当选择最佳追踪器的概念。 驱动稳定性在复杂的公路环境中进行了研究, 例如有多条三路交叉路、 环路、 交叉路和隧道的直径路段。 因此, 拟议的系统实验性地展示了高速驾驶的性能, 并保持车辆的舒适性, 即使存在高度复杂的道路状况。 代码将在 https://github. com/DGISST-ARTIV 。