On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Masello, Leandro; Sheehan, Barry; Castignani, German; Shannon, Darren; Murphy, Finbarr

doi:10.1016/j.aap.2023.106969

Cited by 15 publications

(3 citation statements)

References 57 publications

(95 reference statements)

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“…Furthermore, it is recommended that such an in-vehicle system should offer visual warnings exclusively for forward collision, whereas it should offer both visual and auditory warnings for lateral and rear-end collisions. Similarly, the effects of warning-based ADAS on driving distraction was investigated using naturalistic driving data from light commercial vehicles [ 96 ]. The results demonstrated that active monitoring of warning-based ADAS (1) helped reduce warnings of driver inattention, forward collisions, and lane departures, (2) did not reduce smoking, fatigue driving, and driver yawning, and (3) reduced aggressive driving behaviors tied to harsh acceleration and harsh braking.…”

Section: Analyzing Safety Critical Eventsmentioning

confidence: 99%

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Yang,

Ridgeway,

Miller

et al. 2024

Sensors

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Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

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Section: Analyzing Safety Critical Eventsmentioning

confidence: 99%

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Yang,

Ridgeway,

Miller

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Although the timing and type of DSM feedback has varied across studies, current trends suggest that real-time, driver-facing, camera-based monitoring systems with active, immediate feedback offer advantages over alternative approaches [19,20] , represent the current best practice for monitoring [21][22][23] , and are likely to see wider deployment in the near future [24][25][26] . Recent research on camera-based monitoring showed considerable safety improvements after several months in a fleet of commercial vehicles [27] .…”

Section: An Exploratory Study Of Acceptance Of Driver Monitoring Syst...mentioning

confidence: 99%

“…A recent survey of the literature [28] concluded that research to date has focused "almost exclusively" on using technology to determine driver states-very few studies have considered human factors concerns with DSM such as acceptability, interface design, etc. Much of the evidence of successful deployment of DSM comes from data from commercial fleet drivers (e.g., [14,27] ). The reactions of non-commercial drivers to ubiquitous DSM may be different from those of people who drive for employment.…”

Section: An Exploratory Study Of Acceptance Of Driver Monitoring Syst...mentioning

confidence: 99%

Acceptability of Driver State Monitoring

Nees,

Tian

2024

Preprint

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Though widespread monitoring of driver states and behaviors appears to be imminent, research on perceived acceptability of driver state monitoring has lagged behind research on acceptance of automated driving per se. The current study used an original 29-item driver state monitoring acceptability scale to examine potential theoretical subdimensions of acceptability of driver state monitoring (DSM) systems with an exploratory factor analysis approach. The study used an online, U.S.-based sample. Three subdimensions of DSM acceptability emerged: (1) general acceptability of DSM; (2) concerns over DSM; and (3) perceived ease of use. Correlational analyses were conducted to investigate relationships of subdimensions of DSM acceptability with driving styles, accident concerns, perceived driving skill, tendency to speed, self-reported accident and violations history, and demographic variables. Results showed significant but weak relationships between subdimensions of DSM acceptability with a number of variables, including positive relationships with cautious driving styles, familiarity with advanced driver assistance technology (ADAS), and accidents and violations history. Negative relationships were found between acceptability and anxious driving styles, driving experience, and worries of being involved in car accidents. Female participants reported a lower level of acceptability than male participants, whereas current/previous DSM users reported a higher level of acceptability than non-users. Participants also were randomly assigned to receive descriptions indicating that DSM would occur during all driving periods or only during periods when automated systems were engaged. There were no significant differences in acceptability ratings for monitoring in the presence versus absence of automated driving.

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Behavioral models of drivers in developing countries with an agent-based perspective: a literature review

Gracian,

Galland,

Lombard

et al. 2024

Auton. Intell. Syst.

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The traffic in developing countries presents its own specificity, notably due to the heterogeneous traffic and a weak-lane discipline. This leads to differences in driver behavior between these countries and developed countries. Knowing that the analysis of the drivers from developed countries leads the design of the majority of driver models, it is not surprising that the simulations performed using these models do not match the field data of the developing countries. This article presents a systematic review of the literature on modeling driving behaviors in the context of developing countries. The study focuses on the microsimulation approaches, and specifically on the multiagent paradigm, that are considered suitable for reproducing driving behaviors with accuracy. The major contributions from the recent literature are analyzed. Three major scientific challenges and related minor research directions are described.

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On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Cited by 15 publications

References 57 publications

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Acceptability of Driver State Monitoring

Behavioral models of drivers in developing countries with an agent-based perspective: a literature review

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