Advances in Computer and Communication

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Article http://dx.doi.org/10.26855/acc.2024.02.012

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

Jiren Gu1,2,*, Joan P. Lazaro3

1Graduate School, University of East, Sampaloc, Manila, Philippines.

2Jiangxi New Energy Technology Institute, Xinyu, Jiangxi, China.

3School of Information Technology, University of East, Sampaloc, Manila, Philippines.

*Corresponding author: Jiren Gu

Published: April 10,2024

Abstract

In modern intelligent transportation systems, vehicle safety has always been a major concern. Rear-end collisions, as one of the most common types of traffic accidents, pose a serious threat to the safety of vehicles and occupants. Rear-end collisions can alter the car's initial running state, making it challenging to control the braking distance during emergency collision avoidance. In order to address this issue, we propose an intelligent vehicle longitudinal rear-end collision avoidance method based on machine vision. The core of this method involves utilizing machine vision technology and the CXZK-SM binocular camera as a specific visual device to perceive the surrounding environment of the vehicle in real-time. This enables accurate measurement of the distance between the vehicle and obstacles ahead. The binocular camera can capture rich three-dimensional spatial information, which enhances the accuracy and reliability of distance measurements. In addition, we combined the current driving state of the vehicle with the impact of the rear-end vehicle on its state to formulate a set of targeted collision avoidance execution strategies. The research on emergency collision avoidance methods for longitudinal rear-end collisions of intelligent vehicles based on machine vision holds significant practical importance and application value. Through continuous optimization and improvement of this method, we expect to provide safer and more efficient safeguards for future intelligent transportation systems and promote the sustainable development of the automotive industry.

References

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[2] Ying Zhao, Haijun Li, Yan Huang, & Junyu Hang. (2023). Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles. Sensors, (1). 

[3] Fei Lai & Xiaoyu Wang. (2023). Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance. Applied Sciences, (24). 

[4] Liu Qingling & Xu Xiaowen. (2023). Eco-speed optimization model for active rear-end collision avoidance of connected and automated vehicles on freeways. Sustainable Energy Technologies and Assessments.

[5] Wang Lichao, Yang Min, Li Ye, Wang Boqing, & Zhang Jiyang. (2023). Resolution strategies for cooperative vehicle fleets for reducing rear-end collision risks near recurrent freeway bottlenecks. Journal of Intelligent Transportation Systems, (5), 587-605.

How to cite this paper

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

How to cite this paper: Jiren Gu, Joan P. Lazaro. (2024) A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles. Advances in Computer and Communication5(1), 72-77.

DOI: http://dx.doi.org/10.26855/acc.2024.02.012