Accelerating the Response of Self-Driving Control by Using Rapid Object Detection and Steering Angle Prediction
Bao Rong Chang, Hsiu-Fen Tsai, Chia‐Wei Hsieh
Abstract
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.