A lightweight traffic police gesture recognition method for autonomous driving was proposed to address the challenge of maintaining high recognition accuracy while achieving lightweight deployment, particularly given the subtle variations in gestures and complex application scenarios. An efficient adaptive weight downsampling module was designed on the basis of the YOLOv8n algorithm. This module replaced the standard convolutions in the backbone network to capture spatial differences, reducing both parameter count and computational complexity. Additionally, a C2f-G module was introduced to better leverage local and contextual features, improving the recognition accuracy in complex backgrounds. A triplet attention mechanism was incorporated into the neck network to comprehensively capture fine-grained feature details. Experimental validation on the Chinese traffic police gesture dataset showed that compared to the baseline model, the proposed method reduced the parameter count by 44.0%, increased the mean average precision by 3 percentage points, and achieved a detection speed of 294 frames/s. The proposed method effectively addresses the challenge of dynamic traffic police gesture recognition in complex environments, achieves fast and high-precision recognition, realizes a lightweight architecture, and significantly reduces the deployment complexity.