State of the art on measuring driver state and technology-based risk prevention and mitigation Findings from the i-DREAMS project

Advanced vehicle automation and the incorporation of more digital technologies in the task of driving, bring about new challenges in terms of the operator/vehicle/environment framework, where human factors play a crucial role. This paper attempts to consolidate the state-of-the-art in driver state measuring, as well as the corresponding technologies for risk assessment and mitigation, as part of the i-DREAMS project. Initially, the critical indicators for driver profiling with regards to safety risk are identified and the most prominent task complexity indicators are established. This is followed by linking the aforementioned indicators with efficient technologies for real-time measuring and risk assessment and finally a brief overview of interventions modules is outlined in order to prevent and mitigate collision risk. The results of this review will provide an overall multimodal set of factors and technologies for driver monitoring and risk mitigation, essential for road safety researchers and practitioners worldwide<br

i-DREAMS: an intelligent driver and road environment assessment and monitoring system

The objective of the Horizon2020 project i-DREAMS is to setup a framework for the definition, development and validation of a context-aware ‘safety tolerance zone’. Taking into account, on the one hand, driver-related background factors and real-time risk-related physiological indicators, and on the other hand, driving task-related complexity indicators a real-time assessment will be made to determine if a driver is within acceptable boundaries of safe operation. Additionally, interventions will be developed to prevent drivers from getting too close to the boundaries of unsafe operation. These will be composed of in-vehicle interventions, and interventions aimed at enhancing the knowledge, attitudes and behavioural reaction of drivers. A holistic approach will be taken suitable for use in multiple transport modes. Initial testing will take place in a driving simulator after which promising interventions will be tested and validated under real-world conditions in a testbed of 600 drivers across 5 EU countries

Planned contrasts (P) and post hoc comparisons involving the 5 selected types of smiles.

<p><i>Notes</i>:*p<.05.</p><p>**p<.01.</p><p>***p<.001.</p

Results of the rmANOVA on Authenticity ratings with factors AU12, AU25&26, and AU6.

<p><i>Note</i>.***p<.001;</p><p>**p<.01;</p><p>*p<.05.</p

Activation of involved Action Units (AUs), Smile-Stimulus scores and average authenticity rating, for the 19 stimulus types.

<p>Activation of involved Action Units (AUs), Smile-Stimulus scores and average authenticity rating, for the 19 stimulus types.</p

Intensity of smiling in the stimulus significantly predicted judgments of authenticity.

<p>Data on the x-axis was jittered to improve display.</p

Representation of the apex of the 18 different types of smiles, which were created by varying the activation intensity of 3 muscle region factors: Zygomaticus (AU12), Orbicularis Oculi (AU6), Mouth and lips opening (AU25&26).

<p>An additional 19^th Mixed smile (lower right of the figure) was created by adding activation of the Corrugator (AU1&4) to weak smiles.</p

Average (and standard error) EMG for the four sampled muscles, across five stimulus sub-types, averaged over the entire epoch after SO.

<p>A Stimulus X Muscle X Time ANOVA resulted in a significant Stimulus X Muscle interaction (see main text for details, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099194#pone-0099194-t003" target="_blank">Table 3</a> for post-hoc tests). CS = Corrugator Supercilii, OC = Orbicularis Oculi, ZM = Zygomaticus Major, MA = Masseter.</p

Diagrams of 1) expected effects of smiling intensity in the stimulus on perceived smile authenticity, and 2) mediation of this effect by participants’ facial mimicry.

<p>Smiling in the stimulus significantly predicted both smiling in the participant (a) and perceived authenticity (c). Moreover, authenticity was predicted by smiling in the participant (b). However, no clear signs of mediation (path c’</p

Ratings of authenticity, which resulted in a triple interaction.

<p>The leading factor in making a smile appear authentic was the intensity of AU12, as evident in the two vertically stacked clusters of lines on the graph. Importantly, although the degree of AU6 activation increased perceived authenticity, it did so in interaction with AU12 and AU25&26.</p