Analysis of vehicle-based lateral performance measures during distracted driving due to phone use

• Published in: Transportation research. Part F, Traffic psychology and behaviour Traffic psychology and behaviour, 2017-01, Vol.44, p.120-133
• Main Authors: Choudhary, Pushpa, Velaga, Nagendra R.
• Format: Article
• Language: English
• Subjects: Accidents; Age; Analysis of variance; ANOVA; Automobile driving; Cell phones; Cellular telephones; Conversation; Demographics; Device driver programs; Distracted driving; Driver behavior; Drivers; Driving; Lanes; Lateral performance; Short message service; Standard deviation; Steering; Steering reversal rates; Task complexity; Texting; Traffic accidents & safety
• ISSN: 1369-8478; 1873-5517
• DOI: 10.1016/j.trf.2016.11.002

•Phone conversation and texting distraction effects are considered.•Vehicle-based parameters to detect distracted driving conditions are analysed.•Seven lateral performance measures are investigated and analysed.•Steering reversal rates is identified as the major lateral measure. Distracted driving due to mobile phone use has been identified as a major contributor to accidents; therefore, it is required to develop ways for detecting driver distraction due to phone use. Though prior literature has documented various visual behavioural and physiological techniques to identify driver distraction, comparatively little is known about vehicle based performance features which can identify driver’s distracted state during phone conversation and texting while driving. Therefore, this study examined the effects of simple conversation, complex conversation, simple texting and complex texting tasks on vehicle based performance parameters such as standard deviation of lane positioning, number of lane excursions, mean and standard deviation of lateral acceleration, mean and standard deviation of steering wheel angle and steering reversal rates (for 1°, 5° and 10° angle differences). All these performance measures were collected for 100 licensed drivers, belonging to three age groups (young, mid-age and old age), with the help of a driving simulator. Effects of all the phone use conditions and driver demographics (age, gender and phone use habits) on the measures were analysed by repeated measures ANOVA tests. Results showed that 1°, 5° SRRs are able to identify all the distracted conditions except for simple conversation; while, 10° SSR can detect all the distracted conditions (including simple conversation). The results suggest that 10° SRR can be included in intelligent in-vehicle devices in order to detect distraction and alert drivers of their distracted state. This can prevent mobile phone use during driving and therefore can help in reducing the road accidents due to mobile phone distractions.