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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Zhang Zhi, Guo Yingshi, Yuan Wei, Wang Chang. The Impact of Cognitive Distraction on Driver Perception Response Time Under Different Levels of Situational Urgency [J]. IEEE Access, 2019, 7.
[2]	Zang Jing, Jeon Myounghoon. The Effects of Transparency and Reliability of In-Vehicle Intelligent Agents on Driver Perception, Takeover Performance, Workload and Situation Awareness in Conditionally Automated Vehicles [J]. Multimodal Technologies and Interaction, 2022, 6 (9).
[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.
[4]	Riexinger Luke E, Fortenbaugh David M. A methodology for assessing driver perception-response time during unanticipated cross-centerline events. [J]. Traffic injury prevention, 2021, 22 (S1).
[5]	Quanzhen Guan, Hong Bao, Zuxing Xuan. The research of prediction model on intelligent vehicle based on driver’s perception [J]. Cluster Computing, 2017, 20 (4).
[6]	Zhanji Zheng, Zhigang Du, Qiaojun Xiang, Guojun Chen. Influence of multiscale visual information on driver’s perceived speed in highway tunnels [J]. Advances in Mechanical Engineering, 2018, 10 (12).
[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).
[10]	Zhang Yucun, Li Tao, Li Qun, Fu Xianbin, Kong Tao. Image motion deblurring via attention generative adversarial network [J]. Computers & Graphics, 2023, 111.
[11]	Walker G, Mendes LMN, Lenne M, et al. Modelling driver decision-making at railway level crossings using the abstraction decomposition space. Cognition, Technology & Work. 2021; 23 (2).
[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.
[14]	Khaliq A, der Waerden P van, Janssens D, Wets G. A Conceptual Framework for Forecasting Car Driver’s On-Street Parking Decisions. Transportation Research Procedia. 2019; 37: 131-138.
[15]	Bennajeh A, Bechikh S, Said LB, Aknine S. Bi-level Decision-making Modeling for an Autonomous Driver Agent: Application in the Car-following Driving Behavior. Applied artificial intelligence. 2019; 33 (13): 1157-1178.
[16]	Liwei Hu, Zheng Chen, Ting Zhang, et al. Driver’s Dynamic Decision Car-following Model and Speed Limit in Plateau Extra-long Tunnel. Journal of Transportation Systems Engineering and Information Technology. 2018; 18 (5): 211-240.

Cite This Article

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

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

Copy | Download

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

Copy | Download

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