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Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status

Author

Listed:
  • Huiqin Chen

    (College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Hao Liu

    (College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Hailong Chen

    (College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jing Huang

    (College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China)

Abstract

Precise driving status recognition is a prerequisite for human–vehicle collaborative driving systems towards sustainable road safety. In this study, a simulated driving platform was built to capture multimodal information simultaneously, including vision-modal data representing driver behaviour and sensor-modal data representing vehicle motion. Multisource data are used to quantify the risk of distracted driving status from four levels, safe driving, slight risk, moderate risk, and severe risk, rather than detecting action categories. A multimodal fusion method called vision-sensor fusion transformer (V-SFT) was proposed to incorporate the vision-modal of driver behaviour and sensor-modal data of vehicle motion. Feature concatenation was employed to aggregate representations of different modalities. Then, successive internal interactions were performed to consider the spatiotemporal dependency. Finally, the representations were clipped and mapped into four risk level label spaces. The proposed approach was evaluated under different modality inputs on the collected datasets and compared with some baseline methods. The results showed that V-SFT achieved the best performance with an recognition accuracy of 92.0%. It also indicates that fusing multimodal information effectively improves driving status understanding, and V-SFT extensibility is conducive to integrating more modal data.

Suggested Citation

  • Huiqin Chen & Hao Liu & Hailong Chen & Jing Huang, 2023. "Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9661-:d:1172653
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    References listed on IDEAS

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    1. Huacai Xian & Yujia Hou & Yu Wang & Shunzhong Dong & Junying Kou & Zewen Li, 2022. "Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    2. Neska Haouij & Jean-Michel Poggi & Raja Ghozi & Sylvie Sevestre-Ghalila & Mériem Jaïdane, 2019. "Random forest-based approach for physiological functional variable selection for driver’s stress level classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 157-185, March.
    3. Muhammad Muhitur Rahman & Md Kamrul Islam & Ammar Al-Shayeb & Md Arifuzzaman, 2022. "Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
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