Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

Albert, Gila; Musicant, Oren; Lotan, Tsippy; Toledo, Tomar; Grimberg, Einat

doi:10.17077/drivingassessment.1417

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…Young drivers accumulated around 41,000 safety-related maneuvers, whereas adults accumulated 30,000. Albert et al extended this study analyzing the behavior of 32 of these drivers after 3 and 4 years using the same DAS (in this study, the DAS was called GreenRoad IVDR) ( 85 ). In total, the study collected approximately 6,500 trips in 8 months.…”

Section: Review Of Datasets and Use Casesmentioning

confidence: 99%

From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

Masello

Sheehan

Murphy

et al. 2021

Transportation Research Record: Journal of the Transportation R

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The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.

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Section: Review Of Datasets and Use Casesmentioning

confidence: 99%

From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

Masello

Sheehan

Murphy

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…These initial experiments have revealed an encouraging degree of results for using CAN-Bus transmissions for classifying behavior of drivers and task distractions. A thorough analysis and comparison of drivers in different age groups and experience years has been presented in [5]. The baseline hypothesis is to present lousiness of drivers in younger age group that is observed to increase after 3-4 experience years compared with careful behavior in first year of acquiring the license.…”

Section: Literature Surveymentioning

confidence: 99%

“…Different techniques have been proposed for driver categorization 1–5 that categorize driver profiles by exploiting the “unsupervised mode” of learning for classification. Some techniques take into account only driver's behavior for categorizing the drivers as given in the works of Angkititrakul and Hansen 1 and Barón and Green 2 .…”

Section: Introductionmentioning

confidence: 99%

“…Some techniques evaluate the drivers over a specific temporal scale that does not fully depict the category of driver in normal circumstances. Moreover, few techniques consider certain age groups 5 for their studies. The driving patterns of vehicle are used to classify drivers based on their reactiveness to uncertain situations 6 .…”

Section: Introductionmentioning

confidence: 99%

“…Once the picture of the driver's ability is clear by rightly categorizing the drivers, he can be delegated to certain vehicle, region and route. Different techniques have been proposed for driver categorization [1,2,3,4,5] that categorize driver profiles by exploiting the "unsupervised mode" of learning for classification. Some techniques take into account only driver's behavior for categorizing the drivers as given in [1,2].…”

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confidence: 99%

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Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Sarwar

Zia

Qayyum

et al. 2019

Trans Emerging Tel Tech

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In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. So before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one-time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents and age group etc. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning based dynamic and adaptive technique named D-CHAIT (Driver Categorization based on Hybrid Artificial Intelligence Techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise and inclinations. A comparative analysis of D-CHAIT with three other machine learning techniques (Fuzzy Logic, Case Based Reasoning, and Artificial Neural Networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, f-measure performance and associated costs. These empirical quantifications assert D-CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data and adaptivity.

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Smartphones vs. in-vehicle data acquisition systems as tools for naturalistic driving studies: A comparative review

2020

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Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

Cited by 5 publications

References 8 publications

From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Smartphones vs. in-vehicle data acquisition systems as tools for naturalistic driving studies: A comparative review

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