• Journal of Drive and Control
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• v.17 no.4
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• pp.80-86
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• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.