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

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.