Cognitive Ergonomics in Intelligent Systems: Screen Analysis and Design Proposal for Reducing Mental Load in the Design of User Interfaces of Autonomous Vehicles

Öz

This study aims to reduce the mental load of drivers and increase driving safety by designing user interfaces in autonomous vehicles according to cognitive ergonomics principles. Today, autonomous vehicles offer a usage scenario where the driver is only expected to intervene in critical situations and is in the role of observer or guest. In the design of user interfaces in these vehicles, cognitive ergonomics principles are of great importance and play a critical role to reduce the mental load of the driver and increase driving safety. In existing AR-based user interfaces, it is proposed to add new features to improve driving safety. In particular, detecting driver fatigue and displaying this information in the user interface will enable the driver to monitor the fatigue level and take necessary precautions. In this study, a design proposal for displaying driver fatigue level in an AR-based user interface is presented. In addition to improving driving safety, this proposal will contribute to a comfortable driving experience, personal health and well-being, analysis of driving habits and legal compliance.

Anahtar Kelimeler

• autonomous vehicles
• cognitive ergonomics
• intelligent systems
• interface design

Kaynakça

1. Fu, S., Yang, Z., Ma, Y., Li, Z., Xu, L., Zhou, H. (2024). Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review. Applied Sciences, 14(7), 3016. https://doi.org/10.3390/app14073016
2. Guo, J. M., Markoni, H. (2019). Driver drowsiness detection using hybrid convolutional neural network and long short-term memory. Multimedia tools and applications, 78, 29059-29087.
3. Peng, K., Fei, J., Yang, K., Roitberg, A., Zhang, J., Bieder, F., Stiefelhagen, R. (2022). MASS: Multi-attentional semantic segmentation of LiDAR data for dense top-view understanding. IEEE Transactions on Intelligent Transportation Systems, 23(9), 15824-15840..
4. Kettle, L., Lee, Y.-C. (2022). Augmented Reality for Vehicle-Driver Communication: A Systematic Review. Safety, 8(4), 84. https://doi.org/10.3390/safety8040084
5. Li, G., Chung, W.-Y. (2013). Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier. Sensors, 13(12), 16494-16511. https://doi.org/10.3390/s131216494
6. Mandal, B., Li, L., Wang, G. S., Lin, J. (2016). Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State. IEEE Transactions on Intelligent Transportation Systems, 18(3), 545-557.
7. Shah, S., Dey, D., Lovett, C., Kapoor, A. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and Service Robotics: Results of the 11th International Conference (pp. 621-635). Springer International Publishing.
8. Vu, T. H., Dang, A., Wang, J. C. (2019). A deep neural network for real-time driver drowsiness detection. IEICE TRANSACTIONS on Information and Systems, 102(12), 2637-2641.

9. Xie, Y., Chen, K., Murphey, Y. L. (2018). Real-time and robust driver yawning detection with deep neural networks. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 532-538). IEEE.
10. Youtube, Video Link: https://www.youtube.com/watch?v=DCgy3askMcM Accessed: 10.08.2024