Year: 2024
Type: article
Abstract: The rising number of car owners has increased the frequency of drunk driving‐related traffic accidents, which is a significant danger to traffic safety. Many drawbacks of traditional drunk driving detection techniques include missed detection, interference with regular drivers, inadequate real‐time monitoring, and excessive labour costs. In this work, the intent is to increase the accuracy, real‐time performance, and coverage of drunk driving detection by proposing a method for differentiating abnormal driving conditions while intoxicated by utilizing unmanned aerial vehicle technology. The approach uses an unmanned aerial vehicle to identify the driver's facial expression to determine whether there is an evidence of drunk driving behaviour is drunk driving behaviour. It then uses these models to score vehicle trajectory anomalies, including vehicle sway, vehicle sudden speed change, and signalized intersection waiting time. According to the trial data, the system can successfully identify drunk drivers, and its accuracy has increased by 10.8% compared to the high accuracy and real‐time performance of traditional drunk driving detection methods.
DOI: 10.1049/itr2.12608
Authors: Guiliang Zhou, Kaiwen Xu, Jian Chen, Lina Mao
Institution: Huai'an University
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,660
Funder: Government of Jiangsu Province
OpenAlex ID: W4405604596