Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment

Xue mei Chen*, Min Jin, Yi song Miao, Qiang Zhang

*此作品的通讯作者

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19 引用 (Scopus)
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摘要

The decision-making under complex urban environment become one of the key issues that restricts the rapid development of the autonomous vehicles. The difficulty in making timely and accurate decisions like human beings under highly dynamic traffic environment is a major challenge for autonomous driving. Car-following has been regarded as the simplest but essential driving behavior among driving tasks and has received extensive attention from researchers around the world. This work addresses this problem and proposes a novel method RSAN (rough-set artificial neural network) to learn the decisions from excellent human drivers. A virtual urban traffic environment was built by PreScan and driving simulation was conducted to obtain a broad set of relevant data such as experienced drivers’ behavior data and surrounding vehicles’ motion data. Then, rough set was used to preprocess these data to extract the key influential factors on decision and reduce the impact of uncertain data and noise data. And the car-following decision was learned by neural network in which key factor was the input and acceleration was the output. The result shows the better convergence speed and the better decision accuracy of RSAN than ANN. Findings of this work contributes to the empirical understanding of driver’s decision-making process and it provides a theoretical basis for the study of car-following decision-making under complex and dynamic environment.

源语言英语
页(从-至)1476-1482
页数7
期刊Journal of Central South University
24
6
DOI
出版状态已出版 - 1 6月 2017

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引用此

Chen, X. M., Jin, M., Miao, Y. S., & Zhang, Q. (2017). Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment. Journal of Central South University, 24(6), 1476-1482. https://doi.org/10.1007/s11771-017-3551-4