The swift and accurate detection of traffic signs in traffic scenes is a pivotal aspect of environmental perception technology in autonomous driving systems. Traffic signs provide essential road information and regulatory instructions, which are critical to ensuring road safety. This paper presents the HCLT-YOLO model to address the challenges of false alarms and missed detections in complex traffic environments. Specifically, we propose a novel hybrid CNN-transformer network architecture that efficiently integrates both local and global features, thereby improving traffic sign feature representation. To further enhance the model's sensitivity to small traffic signs, we optimize the structure by introducing a dedicated small-object detection layer through upsampling and by leveraging SIoU to improve detection accuracy and computational efficiency. However, the addition of the small object detection layer and the Transformer module increases the overall computational complexity and parameter count, potentially affecting real-time performance. To address this issue, we introduce the DG-C2f module, which employs linear transformations for feature mapping, streamlining the convolution process and enhancing real-time feasibility. Experimental evaluations on the GTSDB and TT100K datasets demonstrate that the proposed model improves detection accuracy by 2.5% and 6.8%, respectively, compared to YOLOv8s models. Notably, the detection accuracy for small traffic signs improved significantly, by 6.9% and 11.7%, respectively. Additionally, processor-in-the-loop experiments on the NVIDIA Jetson AGX Orin show that the model achieves an inference speed of 46 FPS, meeting the real-time requirements for in-vehicle applications.

To suppress the severe output torque fluctuations caused by clutch engagement when a hybrid electric vehicle equipped with a dedicated hybrid transmission (DHT) switches from pure electric (E) drive mode to hybrid (H) drive mode, a coordinated control method for power source switching is proposed. First, an adaptive fuzzy proportional-integral (PI) controller regulates the engine speed based on the speed difference between the engine and the P2 motor. Second, an active disturbance rejection control (ADRC) controller is employed for trajectory tracking to eliminate the speed difference across the synchronizer’s friction surfaces. This compensates for clutch torque variations during engine startup and ensures rapid synchronizer engagement. Finally, the torque interruption caused by the decoupling of the engine and P2 motor from the driveline is compensated via feedforward control from the P3 motor. The proposed strategy was validated through MATLAB Simulink simulations and CANape calibration tests. The results indicate that applying the proposed method to E-H mode switching slightly extended the total duration by 0.02 s. However, compared with uncoordinated control, the maximum longitudinal jerk was reduced by 73.8%, and the clutch sliding work decreased by 38.6%. This significantly enhances switching smoothness and prolongs the clutch’s service life.