Flagman traffic gesture recognition
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Solution Overview
Problem
Autonomous vehicles face challenges in interpreting real-time gestures from flagmen for safe navigation, as existing methods rely on pre-programmed data or maps, which are inadequate for immediate and variable environmental conditions.
Innovation Solution
A system utilizing a camera and neural networks to generate encoded hand vectors, skeletons, and representation vectors from images of flagmen, combined with recurrent neural networks to predict gestures and operate the vehicle accordingly, allowing for immediate and accurate interpretation of flagmen commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed methods or map databases are used to determine traffic control commands, then the system has simple processing logic, but it cannot respond to variable environmental conditions and flagmen gestures
Solution Approach 1:
The patent replaces traditional mechanical rule-based traffic control systems with a neural network-based vision system. The neural network processes images of flagmen and gestures to determine traffic control commands, enabling the autonomous vehicle to adapt to variable environmental conditions and understand human gestures without complex pre-programming of every possible scenario
Solution Approach 2:
The system uses image copying and processing to capture and analyze visual information about flagmen and their gestures. By converting real-world visual scenes into digital images and processing them through neural networks, the system can recognize gestures and determine appropriate responses without physical interaction or complex mechanical sensors
2Measurement precision
If complex neural network processing is used to recognize gestures accurately, then measurement precision of gestures improves, but processing time and latency increase
Solution Approach 1:
The neural network processing is segmented into multiple specialized networks: a first neural network extracts gesture features from images, a second neural network determines gesture meaning from those features, and a third neural network translates gestures into traffic control commands. This segmentation allows each network to be optimized for its specific task, improving overall accuracy while reducing total processing time compared to a single monolithic network
Solution Approach 2:
The system performs preliminary action by continuously capturing and pre-processing images of the environment, even before a specific gesture recognition task is initiated. The neural networks are pre-trained with large datasets of gestures and scenarios, so when a gesture needs to be recognized, the system can quickly process the input without extensive computation, reducing latency while maintaining high accuracy
Data Source
AI summary
A vehicle and a system and method of operating the vehicle based on a gesture made by a traffic director. The system includes a camera and at least one neural network. The camera obtains an image of a flag operator. The at least one neural network is to generates an encoded hand vector based on a configuration of a hand of the traffic director from the image, combines a skeleton of the traffic director generated from the image and the encoded hand vector to generate a representation vector, and predicts a gesture of the traffic director from the representation vector.