@inproceedings{29126e9667fe497a956d67c0589a85ea,
title = "Driver-TRN: An Approach to Driver Behavior Detection Enhanced SOTIF in Automated Vehicles",
abstract = "Detecting and understanding driver behaviors in automated vehicles are critical for reducing the risk of misuse and ensuring the Safety Of The Intended Functionality (SOTIF). Using a Temporal Recurrent Network (TRN), the paper proposes the driver-TRN model to detect and predict driver behaviors based on real-time vehicular data, such as in-car video, road camera, and sensor data, which addresses intended risks resulting from the interaction between drivers and automated systems that may be overlooked. This paper improves the performance of the driver-TRN with multiple data sources and multi-task loss fine-tuning. The experiments based on the Brain4Cars dataset show that the driver-TRN can enhance the safety and reliability of automated vehicles by providing a more comprehensive detection of driver behaviors.",
keywords = "SOTIF, Temporal Recurrent Network, behavior detection, intelligent vehicle",
author = "Zhonglin Hou and Yongle Fu and Shouwei Wang and Dong Liu and Hong Liu and Yanzhao Yang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 98th IEEE Vehicular Technology Conference, VTC 2023-Fall ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/VTC2023-Fall60731.2023.10333638",
language = "英语",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings",
address = "美国",
}