Abstract
This study proposes a novel methodology for modeling driver takeover behavior in conditionally automated vehicles (AVs) when exiting a freeway using deep learning (DL) network architectures. While previous research has focused on modeling takeover time in emergency scenarios, which require quick responses, these models may not be applicable to scheduled, non-time-critical takeovers. In such situations, drivers may employ varying strategies and take longer to resume control of the vehicle when there is no time pressure. To address this problem, a deep learning architecture based on a convolutional neural network (CNN) was implemented to predict drivers’ takeover behaviors in scheduled takeovers. The model was trained on drivers’ driving data and eye gaze with varying time windows, facilitating an analysis of drivers’ takeover decisions to various takeover request designs. The model achieved good performance metrics, with an F1 Score of 0.993, a recall of 0.996, and a precision of 0.991. The application of these models holds substantial potential for refining the design of the human-machine interface, specifically in calibrating the takeover request (ToR) lead time, thereby promoting safe freeway exiting takeovers in conditionally AVs.
Original language | English (US) |
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Pages (from-to) | 657-663 |
Number of pages | 7 |
Journal | Proceedings of the Human Factors and Ergonomics Society |
Volume | 67 |
Issue number | 1 |
DOIs | |
State | Published - 2023 |
Event | 67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023 - Columbia, United States Duration: Oct 23 2023 → Oct 27 2023 |
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics