Abstract
The accurate and prompt recognition of a driver’s cognitive distraction state is of great significance to intelligent driving systems (IDSs) and human-autonomous collaboration systems (HACSs). Once the driver’s distraction status has been accurately identified, the IDS or HACS can actively intervene or take control of the vehicle, thereby avoiding the safety hazards caused by distracted driving. However, few studies have considered the time–frequency characteristics of the driving behavior and vehicle status during distracted driving for the establishment of a recognition model. This study seeks to exploit a recognition model of cognitive distraction driving according to the time–frequency analysis of the characteristic parameters. Therefore, an on-road experiment was implemented to measure the relative parameters under both normal and distracted driving via a test vehicle equipped with multiple sensors. Wavelet packet analysis was used to extract the time–frequency characteristics, and 21 pivotal features were determined as the input of the training model. Finally, a bidirectional long short-term memory network (Bi-LSTM) combined with an attention mechanism (Atten-BiLSTM) was proposed and trained. The results indicate that, compared with the support vector machine (SVM) model and the long short-term memory network (LSTM) model, the proposed model achieved the highest recognition accuracy (90.64%) for cognitive distraction under the time window setting of 5 s. The determination of time–frequency characteristic parameters and the more accurate recognition of cognitive distraction driving achieved in this work provide a foundation for human-centered intelligent vehicles.
Highlights
Distracted driving has developed as one of the dominating inducements of crashes [1], and happens when a driver consciously or unconsciously transfers their attention from the main driving operation to other tasks unrelated to driving; this attention shift impairs the driver’s scenario perception, decision-making, and manipulative effects [2]
The accuracy of the recognition model will be reduced if the time window is set to beistoo consecutive process
The accuracy of the recognition model will be reduced if the time window is short; in contrast, the accuracy may increase as the time window lengthens, but the intelligent driving systems (IDSs) will be set to betotoo short; indistracted contrast, driving
Summary
Distracted driving has developed as one of the dominating inducements of crashes [1], and happens when a driver consciously or unconsciously transfers their attention from the main driving operation to other tasks unrelated to driving; this attention shift impairs the driver’s scenario perception, decision-making, and manipulative effects [2]. With the widespread use of information media such as in-vehicle information systems and cell phones, more and more distracted driving has appeared and seriously threatens traffic safety [3]. For intelligent driving systems (IDSs), determining how to effectively detect and recognize driver distraction is the key to, and prerequisite for, taking intervention measures [4]. Operational distraction refers to the transfer of the driver’s senses or locomotive organs from the vehicle handling structure required by the main driving.
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