Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future - PubMed (original) (raw)

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Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future

Christian Collet et al. Front Hum Neurosci. 2019.

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.

Keywords: activation level; automated vehicles; driver functional state; drowsiness; level of automation; monitoring; road safety; vigilance.

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Figures

FIGURE 1

FIGURE 1

Illustration of required capacities (black) and available capacities (blue) by level of automation (subplot). Level 5 is not present in the figure since driver involvement is not required in complete automation.

FIGURE 2

FIGURE 2

Steps from recording the driver’s functional state through body sensors until informing the emergency services, the driver himself and the environment. Adapted from Reyes-Muñoz et al. (2016), with permission of the editorial board of the journal “Sensors.” ECG, electrocardiography; EDA, electrodermal activity; EEG, electroencephalography; EMG, electromyography; EOG, electro-oculography.

FIGURE 3

FIGURE 3

Real assistance capacity of an automated system based on the knowledge of the user.

FIGURE 4

FIGURE 4

(A) A limited-capacity model of driver information processing (adapted from Shinar, 1978) with added paths for DFS. Reproduction with permission of the author. (B) A hybrid limited-capacity model of driver information processing paths for DFS (adapted from Shinar, 1978 and Sanders et al., 1990). Here, we integrate all the factors that are supposed to make the DFS varying. The operations of information processing (perception, attention, memory access, decision making, and motor adjustments) require the mobilization of mental resources (attentional effort) and, thus change the DFS so that the driver is able to drive efficiently and safely. The DFS can be evaluated by a set of physiological and behavioral indices, and adjust as a function of the level of vehicle automation.

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