Driver Decision Space Inversion Method Based on DC-GAN

Shuanfeng Zhao; Leping Li; Yang Li; Mengwei Wang; Shuaijun Wang; Mingyue Li

doi:doi:10.11648/j.ijtet.20230901.13

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Driver Decision Space Inversion Method Based on DC-GAN

Shuanfeng Zhao, Leping Li, Yang Li, Mengwei Wang, Shuaijun Wang, Mingyue Li

Published in International Journal of Transportation Engineering and Technology (Volume 9, Issue 1)

Received: 27 February 2023 Accepted: 11 April 2023 Published: 15 April 2023

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Abstract

In the driving process, drivers need to constantly perceive the surrounding environment to make decisions and perform operations. This cognitive space formed in the driver's mind is the driver's decision space. For the actual driving environment, factors such as complex road sign settings, unreasonable road planning, long-time fatigue driving, and reduced reaction ability of elderly drivers may interfere with the normal perception of the driver's decision space, resulting in reduced driving safety. In this paper, a driver decision space inversion method based on deep convolutional neural networks and generative adversarial networks is proposed to study driver perception in near-domain traffic scenarios. The steps of the method include: near-domain target element extraction, driving data collection, sample data generation, adversarial generative network model learning, data enhancement and decision space inversion. The experimental results show that the method in this paper can accurately identify the driver's decision space in both real and simulated driving scenarios by implementing real-time monitoring of driver perception. The method is important for studying the driver's decision space, which can promote the development of intelligent driving technology and the synergistic development of human-vehicle-road-loop. In the context of today's sustainable transportation and smart mobility, driver decision space research is not only an important basic research, but also an inevitable requirement to promote innovation and upgrading in the transportation field.

Published in	International Journal of Transportation Engineering and Technology (Volume 9, Issue 1)
DOI	10.11648/j.ijtet.20230901.13
Page(s)	19-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Deep Convolutional Neural Networks, Generative Adversarial Networks, Driver Perception, Decision Space Inversion

References

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[3]	Cheng Peiyao, Meng Fangang, Yao Jie, Wang Yiran. Driving With Agents: Investigating the Influences of Anthropomorphism Level and Physicality of Agents on Drivers' Perceived Control, Trust, and Driving Performance [J]. Frontiers in Psychology, 2022, 13.
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[7]	Ding Hongwei, Sun Yu, Huang Nana, Shen Zhidong, Wang Zhenyu, Iftekhar Adnan, Cui Xiaohui. RVGAN-TL: A generative adversarial networks and transfer learning-based hybrid approach for imbalanced data classification [J]. Information Sciences, 2023, 629.
[8]	Wang Shihong, Guo Jiayi, Zhang Yueting, Wu Yirong. Multi-baseline SAR 3D reconstruction of vehicle from very sparse aspects: A generative adversarial network based approach [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 197.
[9]	Ma Liling, Guo Jian, Li Jiehao, Wang Junzheng. A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot [J]. Unmanned Systems, 2023, 11 (02).
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[12]	Zhao N, Wang B, Xiong Y, Su R. Safety Prompt Advanced Driver-Assistance System with Lane-Change Prediction and Free Space Detection. 2022 IEEE 17th International Conference on Control & Automation (ICCA), Control & Automation (ICCA), 2022 IEEE 17th International Conference on. June 2022: 734-739.
[13]	Kong H, Fang Y. The Car-Following Model Based on the Drivers’ Psychological Characteristics. International Journal of Intelligent Transportation Systems Research. June 2022: 1-9.
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APA Style

Shuanfeng Zhao, Leping Li, Yang Li, Mengwei Wang, Shuaijun Wang, et al. (2023). Driver Decision Space Inversion Method Based on DC-GAN. International Journal of Transportation Engineering and Technology, 9(1), 19-26. https://doi.org/10.11648/j.ijtet.20230901.13

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ACS Style

Shuanfeng Zhao; Leping Li; Yang Li; Mengwei Wang; Shuaijun Wang, et al. Driver Decision Space Inversion Method Based on DC-GAN. Int. J. Transp. Eng. Technol. 2023, 9(1), 19-26. doi: 10.11648/j.ijtet.20230901.13

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AMA Style

Shuanfeng Zhao, Leping Li, Yang Li, Mengwei Wang, Shuaijun Wang, et al. Driver Decision Space Inversion Method Based on DC-GAN. Int J Transp Eng Technol. 2023;9(1):19-26. doi: 10.11648/j.ijtet.20230901.13

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@article{10.11648/j.ijtet.20230901.13,
  author = {Shuanfeng Zhao and Leping Li and Yang Li and Mengwei Wang and Shuaijun Wang and Mingyue Li},
  title = {Driver Decision Space Inversion Method Based on DC-GAN},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {9},
  number = {1},
  pages = {19-26},
  doi = {10.11648/j.ijtet.20230901.13},
  url = {https://doi.org/10.11648/j.ijtet.20230901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20230901.13},
  abstract = {In the driving process, drivers need to constantly perceive the surrounding environment to make decisions and perform operations. This cognitive space formed in the driver's mind is the driver's decision space. For the actual driving environment, factors such as complex road sign settings, unreasonable road planning, long-time fatigue driving, and reduced reaction ability of elderly drivers may interfere with the normal perception of the driver's decision space, resulting in reduced driving safety. In this paper, a driver decision space inversion method based on deep convolutional neural networks and generative adversarial networks is proposed to study driver perception in near-domain traffic scenarios. The steps of the method include: near-domain target element extraction, driving data collection, sample data generation, adversarial generative network model learning, data enhancement and decision space inversion. The experimental results show that the method in this paper can accurately identify the driver's decision space in both real and simulated driving scenarios by implementing real-time monitoring of driver perception. The method is important for studying the driver's decision space, which can promote the development of intelligent driving technology and the synergistic development of human-vehicle-road-loop. In the context of today's sustainable transportation and smart mobility, driver decision space research is not only an important basic research, but also an inevitable requirement to promote innovation and upgrading in the transportation field.},
 year = {2023}
}

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TY  - JOUR
T1  - Driver Decision Space Inversion Method Based on DC-GAN
AU  - Shuanfeng Zhao
AU  - Leping Li
AU  - Yang Li
AU  - Mengwei Wang
AU  - Shuaijun Wang
AU  - Mingyue Li
Y1  - 2023/04/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ijtet.20230901.13
DO  - 10.11648/j.ijtet.20230901.13
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 19
EP  - 26
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20230901.13
AB  - In the driving process, drivers need to constantly perceive the surrounding environment to make decisions and perform operations. This cognitive space formed in the driver's mind is the driver's decision space. For the actual driving environment, factors such as complex road sign settings, unreasonable road planning, long-time fatigue driving, and reduced reaction ability of elderly drivers may interfere with the normal perception of the driver's decision space, resulting in reduced driving safety. In this paper, a driver decision space inversion method based on deep convolutional neural networks and generative adversarial networks is proposed to study driver perception in near-domain traffic scenarios. The steps of the method include: near-domain target element extraction, driving data collection, sample data generation, adversarial generative network model learning, data enhancement and decision space inversion. The experimental results show that the method in this paper can accurately identify the driver's decision space in both real and simulated driving scenarios by implementing real-time monitoring of driver perception. The method is important for studying the driver's decision space, which can promote the development of intelligent driving technology and the synergistic development of human-vehicle-road-loop. In the context of today's sustainable transportation and smart mobility, driver decision space research is not only an important basic research, but also an inevitable requirement to promote innovation and upgrading in the transportation field.
VL  - 9
IS  - 1
ER  -

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Author Information

Shuanfeng Zhao

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
Leping Li

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
Yang Li

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
Mengwei Wang

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
Shuaijun Wang

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
Mingyue Li

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China

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Plain Text BibTeX RIS

APA Style

Shuanfeng Zhao, Leping Li, Yang Li, Mengwei Wang, Shuaijun Wang, et al. (2023). Driver Decision Space Inversion Method Based on DC-GAN. International Journal of Transportation Engineering and Technology, 9(1), 19-26. https://doi.org/10.11648/j.ijtet.20230901.13

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ACS Style

Shuanfeng Zhao; Leping Li; Yang Li; Mengwei Wang; Shuaijun Wang, et al. Driver Decision Space Inversion Method Based on DC-GAN. Int. J. Transp. Eng. Technol. 2023, 9(1), 19-26. doi: 10.11648/j.ijtet.20230901.13

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AMA Style

Shuanfeng Zhao, Leping Li, Yang Li, Mengwei Wang, Shuaijun Wang, et al. Driver Decision Space Inversion Method Based on DC-GAN. Int J Transp Eng Technol. 2023;9(1):19-26. doi: 10.11648/j.ijtet.20230901.13

Copy | Download

@article{10.11648/j.ijtet.20230901.13,
  author = {Shuanfeng Zhao and Leping Li and Yang Li and Mengwei Wang and Shuaijun Wang and Mingyue Li},
  title = {Driver Decision Space Inversion Method Based on DC-GAN},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {9},
  number = {1},
  pages = {19-26},
  doi = {10.11648/j.ijtet.20230901.13},
  url = {https://doi.org/10.11648/j.ijtet.20230901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20230901.13},
  abstract = {In the driving process, drivers need to constantly perceive the surrounding environment to make decisions and perform operations. This cognitive space formed in the driver's mind is the driver's decision space. For the actual driving environment, factors such as complex road sign settings, unreasonable road planning, long-time fatigue driving, and reduced reaction ability of elderly drivers may interfere with the normal perception of the driver's decision space, resulting in reduced driving safety. In this paper, a driver decision space inversion method based on deep convolutional neural networks and generative adversarial networks is proposed to study driver perception in near-domain traffic scenarios. The steps of the method include: near-domain target element extraction, driving data collection, sample data generation, adversarial generative network model learning, data enhancement and decision space inversion. The experimental results show that the method in this paper can accurately identify the driver's decision space in both real and simulated driving scenarios by implementing real-time monitoring of driver perception. The method is important for studying the driver's decision space, which can promote the development of intelligent driving technology and the synergistic development of human-vehicle-road-loop. In the context of today's sustainable transportation and smart mobility, driver decision space research is not only an important basic research, but also an inevitable requirement to promote innovation and upgrading in the transportation field.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Driver Decision Space Inversion Method Based on DC-GAN
AU  - Shuanfeng Zhao
AU  - Leping Li
AU  - Yang Li
AU  - Mengwei Wang
AU  - Shuaijun Wang
AU  - Mingyue Li
Y1  - 2023/04/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ijtet.20230901.13
DO  - 10.11648/j.ijtet.20230901.13
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 19
EP  - 26
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20230901.13
AB  - In the driving process, drivers need to constantly perceive the surrounding environment to make decisions and perform operations. This cognitive space formed in the driver's mind is the driver's decision space. For the actual driving environment, factors such as complex road sign settings, unreasonable road planning, long-time fatigue driving, and reduced reaction ability of elderly drivers may interfere with the normal perception of the driver's decision space, resulting in reduced driving safety. In this paper, a driver decision space inversion method based on deep convolutional neural networks and generative adversarial networks is proposed to study driver perception in near-domain traffic scenarios. The steps of the method include: near-domain target element extraction, driving data collection, sample data generation, adversarial generative network model learning, data enhancement and decision space inversion. The experimental results show that the method in this paper can accurately identify the driver's decision space in both real and simulated driving scenarios by implementing real-time monitoring of driver perception. The method is important for studying the driver's decision space, which can promote the development of intelligent driving technology and the synergistic development of human-vehicle-road-loop. In the context of today's sustainable transportation and smart mobility, driver decision space research is not only an important basic research, but also an inevitable requirement to promote innovation and upgrading in the transportation field.
VL  - 9
IS  - 1
ER  -

Copy | Download