Identification of the driving style for the adaptation of assistance systems

Büyükyildiz, Görkem; Pion, Olivier; Hildebrandt, Christoph; Sedlmayr, Martin; Henze, Roman; Küçükay, Ferit

doi:10.1504/ijvas.2017.10004258

Cited by 1 publication

(3 citation statements)

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“…The first subgroup, driver identification, is related to studies that analyzed driving data to identify an individual's driving pattern. The first three articles (Cai et al, 2018;Li et al, 2018;Büyükyildiz et al, 2017) propose methods to identify drivers based on their driving data, i.e. personal 'fingerprint'; the first two studies, Cai et al (2018) and Li et al (2018) use machine learning methods, while the third study (Büyükyildiz et al, 2017) use statistical methods to identify, and draw conclusions on, various driving patterns.…”

Section: Overall Driving Patternmentioning

confidence: 99%

“…The first three articles (Cai et al, 2018;Li et al, 2018;Büyükyildiz et al, 2017) propose methods to identify drivers based on their driving data, i.e. personal 'fingerprint'; the first two studies, Cai et al (2018) and Li et al (2018) use machine learning methods, while the third study (Büyükyildiz et al, 2017) use statistical methods to identify, and draw conclusions on, various driving patterns. Similarly, Liu et al (2017) proposed a visualization method, called deep sparse autoencoder (DSAE), to recognize of distinctive driving behavioural patterns in continuous driving data.…”

Section: Overall Driving Patternmentioning

confidence: 99%

“…Include a wider range of drivers to ensure the sample is diverse in age and gender representation (Büyükyildiz et al, 2017).…”

Section: Recommendations To Researchersmentioning

confidence: 99%

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A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

Al-Hussein

Kiah

Por

et al. 2021

PeerJ Computer Science

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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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