Prevalence and distribution of young driver distraction errors in naturalistic driving

2014 ◽  
Author(s):  
Cher Carney ◽  
Daniel V. McGehee ◽  
Michelle L. Reyes
2014 ◽  
Vol 72 ◽  
pp. 177-183 ◽  
Author(s):  
Jonny Kuo ◽  
Sjaan Koppel ◽  
Judith L. Charlton ◽  
Christina M. Rudin-Brown

2019 ◽  
Vol 30 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Kristie Young ◽  
Rachel Osborne ◽  
Sjaan Koppel ◽  
Judith Charlton ◽  
Raphael Grzebieta ◽  
...  

Using data from the Australian Naturalistic Driving Study (ANDS), this study examined patterns of secondary task engagement (e.g., mobile phone use, manipulating centre stack controls) during everyday driving trips to determine the type and duration of secondary task engaged in. Safety-related incidents associated with secondary task engagement were also examined. Results revealed that driver engagement in secondary tasks was frequent, with drivers engaging in one or more secondary tasks every 96 seconds, on average. However, drivers were more likely to initiate engagement in secondary tasks when the vehicle was stationary, suggesting that drivers do self-regulate the timing of task engagement to a certain degree. There was also evidence that drivers modified their engagement in a way suggestive of limiting their exposure to risk by engaging in some secondary tasks for shorter periods when the vehicle was moving compared to when it was stationary. Despite this, almost six percent of secondary tasks events were associated with a safety-related incident. The findings will be useful in targeting distraction countermeasures and policies and determining the effectiveness of these in managing driver distraction.


2014 ◽  
Vol 54 (5) ◽  
pp. S16-S21 ◽  
Author(s):  
Lisa Buckley ◽  
Rebekah L. Chapman ◽  
Mary Sheehan

Author(s):  
Yulan Liang ◽  
John D. Lee ◽  
Lora Yekhshatyan

Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.


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