Driver Takeover Performance Prediction Based on LSTM-BiLSTM-ATTENTION Model
Ensuring the driver’s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. In this regard,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/1/46 |
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| Summary: | Ensuring the driver’s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. In this regard, this study proposes a hybrid LSTM-BiLSTM-ATTENTION algorithm for driver takeover performance prediction. By building a takeover scenario and conducting experiments in the driving simulation experimental platform under the human–machine co-driving environment, the relevant state indicators in the 15 s per second before the takeover request is sent are extracted from three perspectives, namely, driver state, traffic environment, and personal attributes, as model inputs, and the level of takeover performance was labeled; the hybrid LSTM-BiLSTM-ATTENTION algorithm is used to construct a driver takeover performance prediction model and compare it with other five algorithms. The results show that the algorithm proposed in this study performs optimally, with an accuracy of 93.11%, a precision of 93.02%, a recall of 93.28%, and an F1 score of 93.12%. This study provides new ideas and methods for realizing the accurate prediction of driver takeover performance, and it can provide a decision basis for the safe design of self-driving vehicles. |
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| ISSN: | 2079-8954 |