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Citation |
Xie S, Chen S, Zheng J, Tomizuka M, Zheng N, Wang J. IEEE Trans. Intel. Transp. Syst. 2022; 23(7): 6189-6205. |
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Copyright |
(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers)) |
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DOI |
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PMID |
unavailable |
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Abstract |
Humanlike automated driving (AD) strategies which are inspired by drivers' cognition ways may show advantages in dealing with complicated scenarios. However, many humanlike AD strategies just mimic drivers' behaviors or some specific characteristic. Learning algorithms are powerful technics to realize these strategies, but the architectures in learning-based strategies are too simple or with no detailed foundations. Therefore, we mean to summarize drivers' cognition characteristics and design a comprehensive and well-founded architecture for humanlike AD solutions. We review the massive studies about drivers with human driving or AD and summarize the characteristics from three perspectives, cognition foundation, cognition process, and cognition strategies. As for cognition foundation, we propose a simple analogy to show the working mechanisms of biological neural networks; as for cognition foundation, the important role of previous experience is highlighted; as for cognition strategies, we discuss drivers' cognition compensation strategies under the influences of environment, vehicle automation, and personal states systematically. After the above review of drivers' characteristics, we classify the methods to model drivers. We find that models based on cognition processes can maintain more cognition details, and thus we design a driving-dedicated cognitive architecture. This architecture works by the cooperation of several modules including long-term memory, management module, and so on. It has solid theoretical and factual foundations and can reflect drivers' cognition characteristics comprehensively. Finally, we discuss what needs to be done in the near future for us to improve humanlike AD solutions gradually. Language: en |
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Keywords |
Automation; Vehicles; Cognition; Computer architecture; Driver behavior; Task analysis; human factors; Adaptation models; Biological neural networks; cognitive architecture; humanlike automated driving |