The development of a naturalistic data collection system to perform critical incident analysis: An investigation of safety and fatigue issues in long-haul trucking

Dingus, Thomas A.; Neale, Vicki L.; Klauer, Sheila G.; Petersen, A; Carroll, Robert

doi:10.1016/j.aap.2006.05.001

Cited by 73 publications

(32 citation statements)

References 7 publications

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“…More importantly, about 28.2% reported that they were clearly fatigued, with scores ranging from (4) "Somewhat foggy" to (8) "Asleep" while they were driving. These results suggest that a fairly large portion of truck drivers often continued to drive even when they felt tired and sleepy, a result that is consistent with several previous studies (Williamson et al 1996;Arnold et al 1997;Bass 2000;Häkkänen and Summala 2001;Hanowski et al 2003;Dingus et al 2001Dingus et al , 2006.…”

Section: As Shown Insupporting

confidence: 81%

“…Although the factors contributing to commercial truck crashes are complex, driving while fatigued is widely accepted as one of the main contributing factors (Dingus et al 2006, Gander et al 2006Hanowski et al 2003;Williamson et al 2001;Häkkänen and Summala 2001;Baas et al 2000;Arnold et al 1997;Feyer et al 1997;Williamson et al 1996). The factors influencing driver fatigue in the general driving population, and among commercial trucking in particular, have been a topic of research interest in road safety.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

Tay¹,

Tan²,

Cheng³

2010

J Transp Res Forum

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Section: As Shown Insupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

Tay¹,

Tan²,

Cheng³

2010

J Transp Res Forum

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“…The selection and specification of the sensors employed in this study were the outcome of many tradeoffs and tests and were inspired by previous collection of naturalistic data from cars [19], [20], trucks [21], [22], and motorcycles [23]. We prioritized the sensors based on cost (including installation) and potential use for safety analysis.…”

Section: A Data Collectionmentioning

confidence: 99%

“…The bicycle logger presented in this paper is small, light, waterproof, tolerant of shocks and vibrations, runs on its own battery, turns on and off automatically, and uses very little energy. Whereas these characteristics are important for successful application to bicycles, for cars and trucks such requirements are of minor or no importance [19].…”

Section: A Data Collectionmentioning

confidence: 99%

Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012)

Dozza

Fernandez

2014

IEEE Trans. Intell. Transport. Syst.

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As technology advances, motorized vehicles employ newer, more intelligent systems to improve drivers' safety and mobility. The evolution of these systems is supported by increasingly accurate models for driver behavior and vehicle dynamics. Despite the significant role of nonmotorized vehicles such as bicycles in traffic accidents, the evolution of in-vehicle intelligent systems has no counterpart for bicycles. Part of the reason is that, to date, models for bicyclist behavior are absent and models for bicycle dynamics are limited. This paper presents a platform for collecting field data from bicycles and shows how such data can support the development of intelligent systems by offering novel insights into bicycle dynamics and bicyclist behavior.

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“…These tasks, namely secondary tasks, which may lead to distraction [13], include eating, drinking, the act of taking something or tuning the radio or the use of cell phones and other technologies. The secondary tasks that take drivers’ eyes off the forward roadway [14,15] reduce visual scan [16] and increase cognitive load may be particularly dangerous [13]. For example, the use of cell phones while driving, according to naturalistic studies [17], causes thousands of fatalities in the United States every year [18,19].…”

Section: Introductionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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The development of a naturalistic data collection system to perform critical incident analysis: An investigation of safety and fatigue issues in long-haul trucking

Cited by 73 publications

References 7 publications

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012)

Driver Distraction Using Visual-Based Sensors and Algorithms

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