Cognitive Driving Data Visualization and Driving Style Transfer

Hiraishi, Hironori

doi:10.1109/iccicc53683.2021.9811337

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…With the rapid development of intelligent vehicles, the research on driving behavior analysis and intelligent assistance system design has gradually deepened. Existing research methods on driving skills and styles are mainly about neural networks [2], deep learning [10], data visualization [11], graph analysis [12], and so on. For driving behavior, the existing research mainly focuses on fuzzy control-based driving behavior modeling, neural network-based driving behavior modeling, and driving style-based behavior modeling.…”

Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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“…In conclusion, drivers expect automated driving systems to operate as they do [9] [26] [27], although they tend to evaluate their own driving styles as being aggressive [26]. As individual drivers have different driving styles [28,29], drivers with an aggressive driving style may prefer a relatively more aggressive automated driving system. Even for the same driver, a more aggressive driving style is preferred when he/she is in a hurry.…”

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confidence: 99%

Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

Wang

Guan

Lan

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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I. INTRODUCTIONUTOMATED driving has attracted significant attention because it has substantial potential for reducing driver workload and improving driving safety. There is an even more urgent need for automated truck driving owing to the labor shortage and aging problem of truck drivers [1], [2]. Lane change is a highly demanding driving task that induces a tremendous number of traffic accidents [3]. Therefore, the development of automated lane change systems has generated wide interest [4], [5].According to SAE "Levels of Driving Automation" Standard [6], before fully autonomous driving is available in This work was supported mainly by Hino Motors, Ltd. and partially supported by a Grant-in-Aid for Early-Career Scientists (no. 21K17781) from the Japan Society for the Promotion of Science. However, this article solely reflects its authors' opinions and conclusions and not Hino Motors.

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Reproduction of mental states in driving using a visual filter

Hiraishi

2022

2022 IEEE 21st International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Cognitive Driving Data Visualization and Driving Style Transfer

Cited by 3 publications

References 7 publications

Driving Violation Prediction Based on an Emotional Style Transfer Network

Driving Violation Prediction Based on an Emotional Style Transfer Network

Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

Reproduction of mental states in driving using a visual filter

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