UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 10 Issue 3
March-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2303795


Registration ID:
510267

Page Number

h752-h763

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Title

DRIVING PERFORMANCE ANALYSIS AND RISK DETECTION

Abstract

Several studies have demonstrated that operating an unfamiliar vehicle can potentially present additional risks, particularly for inexperienced drivers. The evaluation, however, may be subjective and constrained because these studies have often employed statistical approaches to analyze collision and near-crash data from various driver groups. We proposed that it would be valuable to take into account the vehicle dynamic signals from the CAN-Bus for a more impartial viewpoint. In this study, 20 drivers took part in an experiment where individual driver behaviour was modeled using a Gaussian model and driving performance was assessed using a dissimilarity score, which is calculated as the square of the Euclidean distance in the space of dynamic features of the vehicle. The findings demonstrate that the difference in driving performance brought on by driver experience and vehicle familiarity (i.e., experienced driver vs. inexperienced driver; familiar with car vs. unfamiliar with vehicle) was plainly seen. Furthermore, we discovered that the brake signal among the signals looked at better captures this variance, which might be employed for advanced car technology to lower collisions and increase road safety. Although it can occasionally be stressful for drivers, parallel parking is thought to be a solid indication of a driver's driving abilities. The time utilised to calculate each driver's completion time for the parallel parking challenge comes from their third attempt, indicating that they are already comfortable with the car and the road. We can observe that experienced drivers, who often do better behind the wheel, take less time overall to complete this activity. One of the most crucial driving abilities is the ability to perceive possible accidents and take the appropriate precautions. An accurate risk perception model and evaluation can efficiently pinpoint driver perception weaknesses and act as a crucial human aspect in the development of advanced driver assistance systems. It is challenging to quantify the assessment of perception abilities using standard methods since they typically rely on macroscopic statistics results and lack useful mathematical models.

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"DRIVING PERFORMANCE ANALYSIS AND RISK DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.h752-h763, March-2023, Available :http://www.jetir.org/papers/JETIR2303795.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"DRIVING PERFORMANCE ANALYSIS AND RISK DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. pph752-h763, March-2023, Available at : http://www.jetir.org/papers/JETIR2303795.pdf

Publication Details

Published Paper ID: JETIR2303795
Registration ID: 510267
Published In: Volume 10 | Issue 3 | Year March-2023
DOI (Digital Object Identifier):
Page No: h752-h763
Country: KARUR, TAMIL NADU, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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