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Risk estimation for self-driving simulation using fuzzy logic and genetic algorithm
Author(s): Tatsuya Kurosaka; Tad Gonsalves
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Paper Abstract

Recent years, self-driving technology attracts people’s attention. On self-driving, the most important thing is safety. In order to keep driving safe, driver needs to dodge obstacles on a road safely. So computer should control the car properly. This study focus on avoidance based on “human sense”. People preferentially avoid children or elderly people. So human have some priority of obstacle to dodge. But it’s very ambiguous information. “Fuzzy logic” is mathematical logic that can deal with vague information. This logic is a useful to let computer reproduce human sense. To reproduce more faithfully, I used Genetic Algorithm on optimizing the shape of graph of Fuzzy logic’s function. Using these method, I made risk calculator. The calculator can give us the risk level (0~10) of each obstacles from two materials: “distance from drivers” and “priority of avoidance”. Then, I tried “vision-based self-driving simulation” on 3D environment using the calculator. By controlling the car based on that risk level, computer can drive a car more humanly. It turns out that Fuzzy logic and GA are good tool to simulate human-like driving.

Paper Details

Date Published: 29 October 2018
PDF: 7 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083623 (29 October 2018); doi: 10.1117/12.2514031
Show Author Affiliations
Tatsuya Kurosaka, Sophia Univ. (Japan)
Tad Gonsalves, Sophia Univ. (Japan)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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