Towards Mitigating Teenagers’ Distracted Driving Behaviors

2016 ◽  
Vol 60 (1) ◽  
pp. 1879-1883
Author(s):  
Maryam Merrikhpour ◽  
Birsen Donmez
Keyword(s):  
Standard Deviation ◽  
Real Time ◽  
Average Duration ◽  
Study Data ◽  
Driving Performance ◽  
Driver Distraction ◽  
Distracted Driving ◽  
Driving Behaviors ◽  
Simulator Study ◽  
Lane Position

Distraction contributes significantly to teens’ crash risks. Previous studies show that feedback can help mitigate distraction among young and adult drivers; however, the type of feedback that is effective for teenagers remains unexamined. This paper investigates whether real-time and post-drive feedback can mitigate teens’ driver distraction and reports preliminary findings from an ongoing simulator study. Data reported was collected in a between-subjects experiment with three conditions: real-time (n= 8), post-drive (n= 8), and no feedback (n= 9). Real-time feedback was provided as auditory warnings when teens had long offroad glances (>2 sec). Post-drive feedback was an end-of-trip report on teens’ off-road glances and driving performance provided on an in-vehicle display. Compared to no feedback, real-time feedback resulted in significantly smaller number of long off-road glances (>2 sec), smaller average duration of off-road glances, and smaller standard deviation of lane position. The effects observed for post-drive feedback were relatively minor.

scholarly journals Study of the Effects of Alcohol on Drivers and Driving Performance on Straight Road

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaohua Zhao ◽  
Xingjian Zhang ◽  
Jian Rong
Keyword(s):  
Standard Deviation ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Linear Trend ◽  
Blood Alcohol Concentration ◽  
Driving Performance ◽  
Alcohol Concentration ◽  
Accident Rate ◽  
Significant Linear Trend ◽  
Lane Position

Drinking driving is responsible for a high proportion of traffic accidents. To study the effects of alcohol on drivers and driving performance, 25 drivers’ subjective feelings and driving performance data in different blood-alcohol concentration (BAC) levels were collected with simulated driving experiment. The investigation results revealed that alcohol affected drivers in many aspects, including attitude, judgment, vigilance, perception, reaction, and controlling. The analysis of accident rate showed that higher BAC level would lead to higher accident rate. The statistical analysis results of driving performance indicated that average speed, speed standard deviation, and lane position standard deviation were significantly higher under the influence of alcohol. They also had a statistically significant linear trend as the function of BAC level. The discrimination of drinking driving based on driving performance was performed with Fisher discrimination method. The results showed that drinking driving with higher BAC level was easier to discriminate from normal driving. Also, the results indicated that the three significant indicators on straight roadway could be used in the discrimination of drinking driving state. The conclusions can provide references for the study of drinking driving and the identification of driving state and then contribute to traffic safety.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer

2021 ◽  
pp. 001872082199500
Author(s):  
Ziyang Xie ◽  
Li Li ◽  
Xu Xu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Driver Distraction ◽  
Distracted Driving ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Objective We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). Background Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. Methods Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver’s right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. Results The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. Conclusion The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Effect of Distraction Complexity on Driving Performance and Emergency Event Response

2002 ◽  
Vol 46 (22) ◽  
pp. 1834-1838
Author(s):  
Michael P. Manser ◽  
Dana M. Even
Keyword(s):  
Standard Deviation ◽  
Driving Performance ◽  
Driver Distraction ◽  
Automobile Crashes ◽  
Driver Performance ◽  
Emergency Event ◽  
Event Response

Driver distraction is widely accepted as one factor that contributes to automobile crashes. Driver distracters include those objects or events both inside and outside the vehicle that serve to redirect the driver's attention away from the task of driving. Previous research has indicated that various degrees of distraction complexity may influence driver performance differentially. However, these results are mixed and require further examination. The present investigation examines the influence of varying levels of complexity of an in-vehicle distracter on driving performance and on the driver's reaction to an emergency event. Results indicated males exhibited greater standard deviation of lane deviation than females, and both low and high levels of distraction complexity resulted in greater lane deviation than no distraction. The theoretical and practical ramifications of the present research are discussed.

Taxi-Hailing Apps

2016 ◽  
Vol 60 (1) ◽  
pp. 1957-1961 ◽  
Author(s):  
Yuexin Xiong ◽  
Guozhen Zhao
Keyword(s):  
Cell Phone ◽  
Negative Impact ◽  
Driving Performance ◽  
Driver Distraction ◽  
Baseline Condition ◽  
Performance Decrement ◽  
Taxi Drivers ◽  
Device Development ◽  
Driving Behaviors ◽  
Time Period

The use of taxi-hailing apps is quite popular in recent years. Although a number of epidemiological, on- road and simulator-based studies reported the negative impact of distraction on a driver’s behavior, the effect of using taxi-hailing apps on a professional driver’s performance has not been fully studied. This study aimed to identify the influence of using taxi-hailing apps on driving performance in a simulated driving environment. Forty male taxi drivers were divided into two groups, using one taxi-hailing app or two taxi hailing apps. They were asked to complete two driving tasks with one or two apps (i.e., distracted condition) and two tasks without any app used (i.e., baseline condition). The current study found that participants using two taxi-hailing apps at the same time drove slower and spent less time speeding, but exhibited shorter time to avoid a collision, made more wrong decisions, and spent a longer time period of cell phone interface glance duration and glanced at the interface more frequently than the baseline condition. Taxi drivers with single app exhibited similar driving behaviors but performance decrement was less severe than those using dual hailing apps. These findings increase our understanding of driver distraction and have potential implications for public safety and device development.

Evaluation of a Motion Seat System for Reduction of a Driver’s Passive Task-Related (TR) Fatigue

2018 ◽  
Vol 62 (1) ◽  
pp. 1843-1847 ◽  
Author(s):  
Seunghoon Lee ◽  
Minjae Kim ◽  
Sunwoo Choi ◽  
Heecheon You
Keyword(s):  
Standard Deviation ◽  
Driving Performance ◽  
Driving Safety ◽  
Performance Standard ◽  
Significant Difference ◽  
Driving Condition ◽  
Reduction Effect ◽  
Lane Position ◽  
And Performance ◽  
Eye Closure

A passive task-related (TR) fatigue that occurs monotonous driving environment can degrade driver's alertness and performance, thereby impairing driving safety. This study evaluated the driver's passive TR fatigue reduction effect of the motion seat system in terms of driving performance, physiological response, and subjective fatigue. 17 Korean drivers (6 females and 11 males) measured the driving performance (standard deviation of lane position, SDLP; break reaction time, BRT), percentage of eye closure (PERCLOS), and standard deviation of NN interval (SDNN) of the ECG during simulated driving for 90 minutes on a monotonous highway. The evaluation of the driving consisted of the first half (45 min) and the second half (45 min), while static seat condition in the first half and seat motion (bow, wave motion profile) condition in the second half. During static seat condition driving, SDLP, BRT, and PERCLOS were significantly higher (α = .05) in the second half compared with first half by 6.0 cm, 92.8 msec and 1.3%, respectively. However, there was no significant difference between first half and second half under motion seat conditions. In addition, subjective passive mental fatigue was observed to be 1.2 times lower during motion seat conditions than static seat condition ( p < 0.01). The results of this study indicated that motion seat system have some effect on the driver’s passive TR fatigue reduction. Our findings may not extend to on road driving condition because we tested only simulation driving condition. Therefore, effect of motion seat system on driver’s passive TR fatigue need to be evaluated in future studies under real road condition.

The Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction

2003 ◽  
Author(s):  
Thomas A. Ranney ◽  
Joanne L. Harbluk ◽  
Larry Smith ◽  
Kristen Huener ◽  
Ed Parmer ◽  
...  
Keyword(s):  
Driving Performance ◽  
Driver Distraction ◽  
Test Track ◽  
Voice Technology
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