Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics

Atiquzzaman, -; Qi, Yan; Fries, Ryan

doi:10.1016/j.trf.2018.06.027

Cited by 41 publications

(17 citation statements)

References 21 publications

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“…Using a simulator and applying a support vector machine, Liang et al (48) detected driver distraction with an accuracy of 81.1%. RF techniques have been used for driver distraction tasks and promising results have been obtained (49,50). Osman et al (49) achieved an accuracy of 82.2% to detect distraction (defined as handheld cellphone calling, cellphone texting, and interaction with a passenger); however, the study was limited to a single road type.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

Ahangari

Jeihani

Ardeshiri

et al. 2021

Transportation Research Record

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Distracted driving is known to be one of the main causes of crashes in the United States, accounting for about 40% of all crashes. Drivers’ situational awareness, decision-making, and driving performance are impaired as a result of temporarily diverting their attention from the primary task of driving to other unrelated tasks. Detecting driver distraction would help in adapting the most effective countermeasures. To tackle this problem, we employed a random forest (RF) classifier, one of the best classifiers that has attained promising results for a wide range of problems. Here, we trained RF using the data collected from a driving simulator, in which 92 participants drove under six different distraction scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performance measures such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Using the RF method, we achieved 76.5% prediction accuracy on the independent test set, which is over 8.2% better than results reported in previous studies. We also obtained a 76.6% true positive rate, which is 14% better than those reported in previous studies. Such results demonstrate the preference of RF over other machine learning methods to identify driving distractions.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

Ahangari

Jeihani

Ardeshiri

et al. 2021

Transportation Research Record

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“…Lee et al, 2018), whereas few have focused solely on vehicle-based measures (e.g. Atiquzzaman, Qi, & Fries, 2018;Tango & Botta, 2013). Further, some of these studies used simulator (e.g.…”

Section: Driver Distraction and Environmental Demand Detectionmentioning

confidence: 99%

Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

Kanaan

Ayas

Donmez

et al. 2019

International Journal of Mobile Human Computer Interaction

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This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.

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“…Thus, IMU sensors have significant potential for use in the traffic safety domain. This makes them attractive for research about task-based distractions while driving, which is an understudied area [33].…”

Section: Introductionmentioning

confidence: 99%

Driver Distraction Recognition Using Wearable IMU Sensor Data

Sun

Guo

et al. 2021

Sustainability

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Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.

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Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics

Cited by 41 publications

References 21 publications

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

Driver Distraction Recognition Using Wearable IMU Sensor Data

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