Exploration of the intelligent control system of autonomous vehicles based on edge computing

• Published in: PloS one 2023-02, Vol.18 (2), p.e0281294-e0281294
• Main Author: Ming, Guo
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - prevention & control; Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Automation; Automobile Driving; Autonomous Vehicles; Brakes; Braking; Collision avoidance; Computer and Information Sciences; Control algorithms; Control systems; Control theory; Controllers; Data transmission; Deceleration; Deep learning; Deviation; Driverless cars; Driving ability; Edge computing; Effectiveness; Emergency vehicles; Engineering and Technology; Fuzzy control; Innovations; Intelligence; Low speed; Machine learning; Management; Motion control; Neural networks; Physical Sciences; Planning; Reproducibility of Results; Research and Analysis Methods; Science and technology; Steering; Technology; Test vehicles; Tracking control; Traffic accidents & safety; Trajectory control; Vehicle safety; Wiring
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281294

The development of science and technology continues to promote the progress of society. The current intelligence and automation technology has become widely used in society. To this end, this study proposes a vehicle intelligent control system based on edge computing and deep learning to promote the far-reaching development of intelligent technology and automation technology. First, control algorithms are used to design a switch control strategy combining accelerator and brake. Second, a fuzzy control algorithm based on vehicle tracking and trajectory deviation is designed to enhance the vehicle's stability during steering. A Convolutional Neural Network (CNN) is used to recognize the car's surroundings as it drives. In addition, accelerator and brake controllers and vehicle tracking and trajectory deviation controllers are connected to the vehicle's wiring. Then, the data transmission function based on edge computing is applied to the vehicle's intelligent control system. Finally, trajectory tracking and emergency braking experiments are carried out on the control system to verify the practicability and reliability of the method and the effectiveness of CNN. The simulation experiments are carried out on two states of medium speed and high speed to verify the effectiveness of the longitudinal anti-collision system of the test vehicle when the target vehicle suddenly decelerates. The results demonstrate that the driving speed of the experimental vehicle is set to 50km/h, the distance between the experimental vehicle and the target vehicle is 40m, and the target vehicle in front drives at a constant speed of 50km/h. The target vehicle in front of the car suddenly decelerates in 5 seconds, and the speed drops to 0 after 5 seconds. The actual distance between the experimental vehicle and the target vehicle is very close to the expected safe space, and the experimental vehicle is in a safe state during this process. When the experimental vehicle starts to decelerate, the experimental vehicle adopts emergency deceleration to ensure a safe distance between the two vehicles. At this time, the car enters the second-level early warning state, but driving safety can still be guaranteed. It is advisable to maintain low-speed emergency braking in this state. This study provides creative research ideas for the follow-up research on the intelligent control system of uncrewed vehicles and contributes to the development of intelligence and automation technology.