With the rapid advancement of artificial intelligence technologies, autonomous vehicles are increasingly being deployed in complex open road scenarios. However, existing autonomous driving systems generally lack effective recognition and response capabilities when confronted with onsite traffic police gesture commands, which constitutes a significant safety hazard for the deployment of autonomous driving technology. Focusing on the core pipeline of "visual perception-instruction parsing-vehicle control," this paper proposes a real-time traffic police gesture recognition and response system based on lightweight deep learning models and edge computing architecture. The scheme adopts a dual algorithm parallel technical route combining Media Pipe Hands and a lightweight YOLO, and employs a master–slave hardware architecture consisting of a Raspberry Pi and an STM32 microcontroller. A gesture control set covering core instructions such as "stop," "go straight," "turn left," and "accelerate" is designed, incorporating state machine logic and multilevel latency optimization strategies. The proposed system balances recognition accuracy and real-time performance, featuring low cost, transferability, and ease of deployment, and may serve as a reference for human–machine collaborative interaction in autonomous driving within complex traffic scenarios.
gesture recognition; autonomous driving; Media Pipe; YOLO; edge computing; human–computer interaction.
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