A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions.
ADAS
Driver behaviour
Intelligent transportation systems
Naturalistic driving
Sensors
Traffic safety
Journal
PeerJ. Computer science
ISSN: 2376-5992
Titre abrégé: PeerJ Comput Sci
Pays: United States
ID NLM: 101660598
Informations de publication
Date de publication:
2021
2021
Historique:
received:
22
04
2021
accepted:
18
06
2021
entrez:
20
9
2021
pubmed:
21
9
2021
medline:
21
9
2021
Statut:
epublish
Résumé
In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author's predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future.
Identifiants
pubmed: 34541305
doi: 10.7717/peerj-cs.632
pii: cs-632
pmc: PMC8409336
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e632Informations de copyright
© 2021 Ahmed Al-Hussein et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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