Implementing High-Speed Object Detection and Steering Angle Prediction for Self-Driving Control

In the previous work, we proposed LWGSE-YOLOv4-tiny and LWDSG-ResNet18, leveraging depthwise separable and Ghost Convolutions for fast self-driving control while achieving a detection speed of 24.9 FPS. However, the system fell short of Level 4 autonomous driving safety requirements. That is, the control response speed of object detection integrated with steering angle prediction must exceed 39.2 FPS. This study enhances YOLOv11n with dual convolution and RepGhost bottleneck, forming DuCRG-YOLOv11n, significantly improving the object detection speed while maintaining accuracy. Similarly, DuC-ResNet18 improves steering angle prediction speed and accuracy. Our approach achieves 50.7 FPS, meeting Level 4 safety standards. Compared to previous work, DuCRG-YOLOv11n boosts feature extraction speed by 912.97%, while DuC-ResNet18 enhances prediction speed by 45.37% and accuracy by 12.26%.