Identifying effects of driving and secondary task demands, passenger presence, and driver characteristics on driving errors and traffic violations – Using naturalistic driving data segments preceding both safety critical events and matched baselines

2017 ◽  
Vol 51 ◽  
pp. 103-144 ◽  
Author(s):  
Lisa Precht ◽  
Andreas Keinath ◽  
Josef F. Krems
Keyword(s):  
Secondary Task ◽  
Task Demands ◽  
Critical Events ◽  
Safety Critical ◽  
Traffic Violations ◽  
Naturalistic Driving

scholarly journals Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy

2021 ◽  
Vol 13 (8) ◽  
pp. 4426
Author(s):  
Zihao Wen ◽  
Hui Zhang ◽  
Ronghui Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Traffic Safety ◽  
Information Entropy ◽  
Traffic Accidents ◽  
Support Vector ◽  
Identification Performance ◽  
Critical Events ◽  
Safety Critical ◽  
Naturalistic Driving

Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently.

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment

2021 ◽  
Vol 156 ◽  
pp. 106086
Author(s):  
Zulqarnain H. Khattak ◽  
Michael D. Fontaine ◽  
Wan Li ◽  
Asad J. Khattak ◽  
Thomas Karnowski
Keyword(s):  
Critical Events ◽  
Safety Critical ◽  
Naturalistic Driving

scholarly journals Mining crowdsourced data on bicycle safety critical events

2021 ◽  
Vol 10 ◽  
pp. 100360
Author(s):  
Subasish Das ◽  
Zihang Wei ◽  
Xiaoqiang “Jack” Kong ◽  
Xiao Xiao
Keyword(s):  
Critical Events ◽  
Safety Critical ◽  
Bicycle Safety ◽  
Crowdsourced Data

Interval Production as a Secondary Task Workload Measure: Consideration of Primary Task Demands for Interval Selection

2012 ◽  
Vol 56 (1) ◽  
pp. 1664-1668 ◽  
Author(s):  
Will Seidelman ◽  
C. Melody Carswell ◽  
Russell C. Grant ◽  
Michelle Sublette ◽  
Cindy H. Lio ◽  
...  
Keyword(s):  
Secondary Task ◽  
Task Demands ◽  
Primary Task ◽  
Interval Selection ◽  
Interval Production

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

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