Preventing distracted driving: A program from initiation through to evaluation

Ehrlich, Peter F.; Costello, Beth; Randall, Amy

doi:10.1016/j.amjsurg.2019.07.043

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…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]. It is evident that distraction severely impacts driving safety.…”

Section: Introductionmentioning

confidence: 99%

Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments

Sun

Wang

Guo

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%