Xiaogang Wei
YOLOv4 algorithm, multi-source information fusion, vehicle A-pillar, blind spots, early warning detection
With the continuous increase in the number of cars, the A-pillar blind spot has also become a safety hazard on the road. To better address this issue, a vehicle A-pillar blind spot warning and detection model based on object detection algorithm and multi-source information fusion is proposed. Firstly, vehicle detection is achieved through object detection algorithms to extract vehicle location information. Then, the multi-source information fusion technology is adopted, including cameras, sensors, etc., to obtain information about the surrounding environment of the vehicle. Finally, the performance verification is conducted through simulation experiments. The results show that the proposed model performs better than comparison methods in terms of object detection rate, error rate, recognition accuracy, and recall rate for different objects in A-pillar blind spot in the construction dataset. This verifies the reliability and stability of the proposed model in detecting A-pillar blind spot. The study provides an effective solution to the safety issues caused by blind spots in car A-pillars.
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