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Citation |
Collet C, Musicant O. Front. Hum. Neurosci. 2019; 13: e131. |
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Affiliation |
Department of Industrial Engineering and Management, Ariel University, Ariel, Israel. |
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Copyright |
(Copyright © 2019, Frontiers Research Foundation) |
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DOI |
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PMID |
31114489 |
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PMCID |
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Abstract |
In the near future, vehicles will gradually gain more autonomous functionalities. Drivers' activity will be less about driving than about monitoring intelligent systems to which driving action will be delegated. Road safety, therefore, remains dependent on the human factor and we should identify the limits beyond which driver's functional state (DFS) may no longer be able to ensure safety. Depending on the level of automation, estimating the DFS may have different targets, e.g., assessing driver's situation awareness in lower levels of automation and his ability to respond to emerging hazard or assessing driver's ability to monitor the vehicle performing operational tasks in higher levels of automation. Unfitted DFS (e.g., drowsiness) may impact the driver ability respond to taking over abilities. This paper reviews the most appropriate psychophysiological indices in naturalistic driving while considering the DFS through exogenous sensors, providing the more efficient trade-off between reliability and intrusiveness. The DFS also originates from kinematic data of the vehicle, thus providing information that indirectly relates to drivers behavior. The whole data should be synchronously processed, providing a diagnosis on the DFS, and bringing it to the attention of the decision maker in real time. Next, making the information available can be permanent or intermittent (or even undelivered), and may also depend on the automation level. Such interface can include recommendations for decision support or simply give neutral instruction. Mapping of relevant psychophysiological and behavioral indicators for DFS will enable practitioners and researchers provide reliable estimates, fitted to the level of automation. Language: en |
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Keywords |
activation level; automated vehicles; driver functional state; drowsiness; level of automation; monitoring; road safety; vigilance |