Intersection Navigation Using Traffic Police Gesture Recognition
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Solution Overview
Problem
Current autonomous vehicle navigation systems fail to handle extreme and unexpected events, such as non-functional traffic signals at intersections, and lack adaptive decision-making capabilities in complex scenarios with multiple traffic police or signboards, leading to potential accidents and congestion.
Innovation Solution
An intersection management system that receives sensor data from autonomous vehicles, detects traffic police and auxiliary objects, generates a correlation matrix to determine gestures, and uses Convolutional Neural Networks to infer dynamic hand gestures, enabling adaptive navigation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined gesture detection methods are used for traffic police, then the system can identify basic gestures, but it fails to detect runtime variations and complex gestures in real-time
Solution Approach 1:
The system transitions from static predefined gesture detection to dynamic real-time gesture recognition using deep learning models. The CNN-based gesture recognition module continuously processes video frames to detect and interpret traffic police gestures, adapting to runtime variations and complex scenarios that predefined methods cannot handle.
Solution Approach 2:
The patent replaces traditional mechanical rule-based gesture detection systems with intelligent vision-based systems using convolutional neural networks. This substitution enables the system to automatically learn and recognize diverse gesture patterns without explicit programming, significantly improving both accuracy and adaptability.
2Reliability
If sophisticated navigation techniques are implemented, then the system can handle standard traffic scenarios, but it fails to handle extreme and unexpected events like non-functional traffic signals
Solution Approach 1:
The intersection management system is designed with multi-functional capabilities to handle both standard traffic scenarios and extreme unexpected events. By integrating object detection, gesture recognition, correlation matrix generation, and multiple decision-making modules, the system can adaptively respond to various situations including non-functional traffic signals, multiple traffic police, and complex intersection environments.
Solution Approach 2:
The system employs feedback mechanisms where the correlation matrix continuously updates based on detected objects and gestures, and the navigation decisions are adjusted in real-time based on the interpreted traffic police gestures and environmental conditions, enabling adaptive response to unexpected events.
3Adaptability or versatility
If the system detects multiple traffic police and auxiliary objects, then it can handle complex scenarios, but it lacks adaptive decision-making capability leading to traffic congestions
Solution Approach 1:
The correlation matrix serves as an intermediary data structure that integrates information from multiple detected objects and gestures. This matrix enables the system to synthesize complex scenarios involving multiple traffic police and auxiliary objects, translating raw detection data into actionable navigation decisions that maintain traffic flow efficiency.
4Measurement precision
If the system uses deep learning models for gesture recognition, then it can interpret dynamic gestures accurately, but it increases computational complexity and processing time
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames and detecting objects of interest before applying computationally intensive gesture recognition. The object detection module identifies traffic police and auxiliary objects first, allowing the gesture recognition module to focus computational resources only on relevant regions and frames, thereby reducing overall processing complexity.
Data Source
AI summary
Disclosed subject matter relates to a field of vehicle navigation system that performs a method for navigating an autonomous vehicle at an intersection of roads. An intersection management system may receive sensor data including at least one of depth of an object, images and a video of environment surrounding the autonomous vehicle. Further, traffic police and auxiliary objects associated with each traffic police are detected from plurality of objects of interest present in the images, when the autonomous vehicle is within a predefined distance from an intersection of roads. Thereafter, a correlation matrix comprising inferred data related to each traffic police and the auxiliary objects may be generated. Based on the correlation matrix, the video and the images, a gesture of the traffic police may be determined accurately. Finally, navigation information may be determined based on the correlation matrix and determined gesture, for navigating the autonomous vehicle.