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Abstract
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 research 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 research 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 research 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 .
Highlights
In recent years, the requirements for the driving system of the autonomous vehicle are increasing in difficulty with respect to levels of driving automation proposed by SAE [1]
This research notes that there are several advantages of using the Hybrid tracker based optimal tracking system: (a) it is capable of driving in a complex road environment with high performance of driving stability and accuracy; (b) additional correction of the computational speed is not required even if each tracker is modified; (c) the proposed algorithm enhances high usage of vision based lane following which can be widely used in real-world environments; (d) our selection system induces the improvements of driving stability and tracking performance despite of its simple implementation
2) TEST RESULTS UNDER COMPLEX ROAD ENVIRONMENT In the previous section, it was found that three tracking algorithms and Hybrid system succeeded in driving the normal road environment
Summary
The requirements for the driving system of the autonomous vehicle are increasing in difficulty with respect to levels of driving automation proposed by SAE [1]. The deep learning based lane detection and the GPS based driving path tracking were applied to deal with complex road environments [8]. The algorithm designed to process the deep learning method has been applied the three lane fitting methods in parallel to provide the most optimized path. This research notes that there are several advantages of using the Hybrid tracker based optimal tracking system: (a) it is capable of driving in a complex road environment with high performance of driving stability and accuracy; (b) additional correction of the computational speed is not required even if each tracker is modified; (c) the proposed algorithm enhances high usage of vision based lane following which can be widely used in real-world environments; (d) our selection system induces the improvements of driving stability and tracking performance despite of its simple implementation. This research adopted soft voting that can be parallel processing guaranteed fast computational speed in lane fitting algorithm. Algorithm 1 shows a pseudo code that contains the overall fast optimal lane processing described in the section III-B
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