Accelerating the Response of Self-Driving Control by Using Rapid Object Detection and Steering Angle Prediction

A vision-based autonomous driving system can usually fuse information about object detection and steering angle prediction for safe self-driving through real-time recognition of the environment around the car. If an autonomous driving system cannot respond fast to driving control appropriately, it w...

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Bibliographic Details
Published in:Electronics (Basel) 2023-05, Vol.12 (10), p.2161
Main Authors: Chang, Bao Rong, Tsai, Hsiu-Fen, Hsieh, Chia-Wei
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Cameras
Control systems
Driverless cars
Frames per second
Image processing
Neural networks
Object recognition
Object recognition (Computers)
Pattern recognition
Roads & highways
Skills
Steering
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Summary:A vision-based autonomous driving system can usually fuse information about object detection and steering angle prediction for safe self-driving through real-time recognition of the environment around the car. If an autonomous driving system cannot respond fast to driving control appropriately, it will cause high-risk problems with regard to severe car accidents from self-driving. Therefore, this study introduced GhostConv to the YOLOv4-tiny model for rapid object detection, denoted LW-YOLOv4-tiny, and the ResNet18 model for rapid steering angle prediction LW-ResNet18. As per the results, LW-YOLOv4-tiny can achieve the highest execution speed by frames per second, 56.1, and LW-ResNet18 can obtain the lowest prediction loss by mean-square error, 0.0683. Compared with other integrations, the proposed approach can achieve the best performance indicator, 2.4658, showing the fastest response to driving control in self-driving.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12102161