Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Lobo, António; Ferreira, Sara; Couto, António

doi:10.3390/s20143836

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…Xing et al [ 12 ] and Luo et al [ 19 ] both found that driver fatigue and speeding significantly impact the HAZMAT crash injury severity. One of the most prevalent causes of traffic crashes related to behavioral error is driver inattention [ 43 ]. It is associated with impaired driving performance and significant deficiencies in cognitive performance, both of which could have a negative impact on road safety.…”

Section: Resultsmentioning

confidence: 99%

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Sun

Zhou

Jiao

et al. 2022

IJERPH

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Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

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Section: Resultsmentioning

confidence: 99%

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Sun

Zhou

Jiao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…These sections also often have speed limits. The study showed that higher average speeds increased risk of inattention [43]. To shorten the travel time, the driver must take the risk of being too close and having a higher speed to complete the lane change and overtaking.…”

Section: Lane Change Across Solid Linementioning

confidence: 99%

Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System

Zhu

2022

Sustainability

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Due to inertia, heavy trucks are often involved in serious losses in accidents. To prevent such accidents, since 2020, the transportation department has promoted the free installation of intelligent video surveillance systems on key vehicles of “two passengers, one danger, and one cargo”. The system can provide real-time warnings to drivers for various risky driving behaviors. The data collected by the system are often managed by third-party platforms, and such platforms do not have authority beyond the information that the authority system can collect. Therefore, it is necessary to use the trajectory data and warning behavior records that the system can collect for behavior analysis and accident prevention. To analyze the correlation between different warning factors, 88,841 warning records and 1033 trip records of heavy trucks for construction in the second half of 2021 were collected from a third-party supervision platform. The research associated the warning records with the vehicle operation records according to the warning time and the license plate and established a multiple linear regression equation associated with operational attributes and warning factors. The factor selection results showed that only two warning factors, “too close distance” and “lane change across solid line”, can be used as dependent variables to construct a regression model. The results showed that many distracted behaviors had a significant impact on aggressive driving behavior. Companies need to focus on behaviors that are prone to other warning behaviors. This paper provides a theoretical basis for the optimization of the warning function of the electrified supervision system and the continuing education of drivers by exploring the internal correlation between different warning factors.

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“…An approach for the effective monitoring of drivers current state of action was proposed in [27]. In this paper, driver distraction was predicted by designing driver monitoring systems.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

et al. 2021

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The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.

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Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Cited by 9 publications

References 34 publications

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

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