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中南大学学报(英文版)

Journal of Central South University

Vol. 24    No. 6    June 2017

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Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment
CHEN Xue-mei(陈雪梅), JIN Min(金敏), MIAO Yi-song(苗一松), ZHANG Qiang(张强)

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Abstract: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.

 

Key words: autonomous vehicle; car-following; decision-making; rough set (RS); artificial neural network (ANN); PreScan

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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