ACTIVE COLLISION AVOIDANCE CONTROL BASED ON CONVOLUTIONAL NEURAL NETWORK FOR BLIND ZONE PERCEPTION OF AUTOMOTIVE SENSORS

Nenghui Jiang and Cheng Li

Keywords

Convolutional neural network, active collision avoidance system, sensors, safe distance, simulation

Abstract

With the growth of the economy and the progress of people’s life quality, cars have become the primary choice for most people to travel. However, as travel becomes more convenient, traffic accidents are increasingly frequent. This study focuses on the active collision avoidance system (ACAS) of intelligent vehicles and the related strategies that have been analysed. First, utilising the principles of automotive safety control theory and an overall structural dynamics model of the vehicle, an active collision avoidance control system for the vehicle is developed. Next, convolutional neural networks (CNNs) are implemented to estimate the vehicle’s safety. After collecting information from car sensors and radar, the system evaluates the car’s driving status and actively takes measures to avoid collisions. The final safety distance serves as a determining factor for labelling a car as dangerous. Simulation experiments have verified the accuracy of the model. When the vehicle is stationary and at a speed of 60 km/h, and 60 m distant from the vehicle in front, the system can anticipate and control the distance between them within a safe range. Even in motion, this system can perform collision avoidance tasks and exhibit outstanding capabilities. The results indicate that the ACAS designed in this experiment based on CNNs has excellent performance in various complex road conditions.

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