Research on Adaptive Driving Beam System Control Strategy Based on Multimodal Perception and Data Fusion

The correct use of high beams is essential when driving at night, and improper use can lead to glare or even temporary blindness for oncoming drivers, increasing the risk of accidents. Although adaptive driving beam system can mitigate this issue, shortcomings remain in environmental perception and beam control accuracy. To address these deficiencies, this paper proposes the following improvements. First, we propose an improved Yolov5s-CBAM-Pruning visual detection model and a two-level fusion method based on camera and millimeter-wave radar for “target-decision” fusion. By adding the CBAM attention module into the Yolov5s backbone network and applying channel pruning, the model’s detection performance is enhanced. The Global Nearest Neighbor algorithm enables target-level fusion between the camera and radar, followed by decision-level fusion for unmatched targets. Second, we proposes a light attenuation model based on the “distance-angle” framework, achieving precise beam control by turning off light sources that exceed the glare threshold. The experiments show that the improved Yolov5s-CBAM-Pruning model achieves a 3.2% increase in mAP and a 34.81% reduction in complexity compared to Yolov5s. The two-level fusion detection accuracy improves by 10.4% compared to single-vision methods. An adaptive LED shutdown control was implemented with Arduino UNO, validating the strategy’s effectiveness.