ArticlePeerJ. Computer science2021
A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Correlates of unsafe driving in non-professional drivers: insights from a data-driven modeling approach.The Journal of the Egyptian Public Health Association · 2026Article
- Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.PeerJ. Computer science · 2024Article
- Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia.International journal of environmental research and public health · 2022Article
- GPS Digital Nudge to Limit Road Crashes in Non-Expert Drivers.Behavioral sciences (Basel, Switzerland) · 2022Article
- Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines.International journal of environmental research and public health · 2022Article
- Improving Our Understanding of Driving Changes in Preclinical and Early Symptomatic Alzheimer's Disease: The Role of Naturalistic Driving Studies.Journal of Alzheimer's disease reports · 2022Review
- Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.