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Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of ‘unease’. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced ‘unease’, and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as ‘uneasy’. These driving patterns were matched with the data in a NDS database and the method was validated using video data. Two data mining approaches were applied.

NHTSA recently proposed performance guidelines for visual-manual interaction with non-driving related in-vehicle systems. While a commendable effort to reduce distraction related crashes, in part they seem overly strict. In particular, NHTSA proposes that for each driver performing a secondary task, no more than15 % of the off-road eye glances can be longer than 2.0 s, and 21 in 24 drivers must meet this criterion. The applicability of this criterion was assessed in a study using data from two eye-tracker based studies, involving 35 subjects performing a range of secondary tasks on normal roads. Results showed that over tasks, the average off-road glance duration lengths were quite robust within drivers but varied widely between drivers. Off-road glance duration length thus seems more to reflect individual driver attention allocation strategy than in-vehicle task complexity. Also, several drivers failed to meet the suggested criterion.