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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Personalized Driver State Profiles: A Naturalistic Data-Driven Study
verfasst von : Arash Tavakoli, Mehdi Boukhechba, Arsalan Heydarian
Erschienen in: Advances in Human Aspects of Transportation
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Abstract
Previous studies suggest that variation in driver’s states, such as being under stress, can degrade drivers’ performance. Moreover, different drivers may have varying behaviors and reactions in different road conditions and environments (contexts). Thus, personalized driver models given different contextual settings can assist in better predicting the drivers’ states (behavioral and psychological); this can then allow vehicles to adjust the driving experience around the driver and passengers’ preferences and comfort levels. This paper aims at developing personalized hierarchical driver’s state models by considering driver’s heart rate variability (HRV) in relation to the changes in various contextual settings of road, weather, and presence of a passenger. Results from 12 participants over 150 h of driving data suggest that drivers are on average less stressed in highways compared to cities, when being with a passenger compared to alone, and when driving in non-rainy conditions compared to rainy weather.