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- W4383560300 abstract "This paper uses artificial potential field method to solve obstacle avoidance and lane keeping of autonomous driving safety assistance systems. It focuses on the lane line, direction, car obstacle, and static obstacle, which the car must pay attention to on the road. The artificial potential field method can assign different potential functions to different types of obstacles and road structures. This method also considers safety distance, limited speed, traffic rules, and safety problems. Superimposing these conditional functions will eventually result in a system that will continue to return the safety and appropriate next point until reaching the set goal point. Experiments used PyCharm to simulate a one-way, two-lane environment. The final results showed that regardless of the vehicle's initial position, the vehicle could return to the center of the regular lane and effectively avoid static and dynamic obstacles, in addition to controlling its speed to maintain a safe distance from obstacles. The result of experiments also proved that the artificial potential field method could be effectively used in the obstacle avoidance and lane keeping of automatic driving, which means that such a simple algorithm can also be installed on more lightweight navigation devices that require real-time dynamic obstacle avoidance." @default.
- W4383560300 created "2023-07-08" @default.
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- W4383560300 date "2023-06-14" @default.
- W4383560300 modified "2023-10-16" @default.
- W4383560300 title "Artificial potential field method for obstacle avoidance and lane keeping" @default.
- W4383560300 doi "https://doi.org/10.54254/2755-2721/5/20230641" @default.
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