A Practical Deep Learning Approach to Detect Aggressive Driving Behaviour

Accidents while driving might result in minor injuries. Alternatively, it might result in a loss of life, which is highly detrimental to society. The loss of an expert due to fatalities can have a tremendous influence on humanity's scientific growth. Three factors can lead to accidents on the road: 1) The human, 2) the road, and 3) the vehicle. We look at the first element in our analysis, accounting for 93 percent of all accident causes. We will not look at the psychological aspects of driving behaviour in this study. The first step is to classify the vehicle; Self-driving vehicles and regular automobiles, both of which may be used to evaluate driving, are the two types of vehicles that can be checked. Aggressive driving behaviours have been identified as one of the most critical subcategories of human factors that contribute to accidents. To prevent road accidents, constant monitoring of drivers' driving behaviour can modify the driver's driving behaviour or notify the driver of a potential hazard. As a result, it is vital to devise a method of detecting aggressive driving behaviour. Aggressive driving is every day among American drivers. According to AAA Foundation for Traffic Safety data from 2019, approximately 80% of drivers displayed severe anger, hostility, or road rage while driving at least once in the preceding 30 days. Aggressive driving has been a significant source of concern for many road users. There are numerous methods for detecting aggressive driving behaviour, including changes in vehicle speed, lane shifts, eye and hand movement analyses, and others. We conducted this study using deep machine learning approaches rather than classic time series analysis methods. We analyzed roughly sixty similar publications to learn the procedures employed in the prior studies. The CNN was utilized in most publications to determine how to drive. We used RNN algorithms to execute this experiment since the vehicle GPS data is a time series. We employed an external test technique during the experiment that was not used in earlier studies that dealt with the same data set. The provided model produced satisfactory results incorporated in the dissertation's conclusion.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Engineering – Electrical & Computer

Grantor dc:publisher.institution

Schulich School of Engineering

Year dc:date.issued

2022

Author dc:creator

• Talebloo, Farid

Advisors dc:contributor.advisor

• Far, Behrouz
• Mohammed, Emad

Committee members dc:contributor.committeemember

• Sanati Nezhad, Amir
• Moshirpour, Mohammad

• Aggressive Driving Behaviour Detection
• Driving Behaviour Analysis

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/39546

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/114334