Vehicle Classification via RNN GPS Trajectory Analysis
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Solution Overview
Problem
Current vehicle classification methods, such as physical sensors and image processing, face challenges in accuracy and cost due to high installation and maintenance costs, as well as limitations in classifying vehicles into multiple classes, especially under varying conditions like traffic and environmental factors.
Innovation Solution
A vehicle classification system utilizing GPS data and a recurrent neural network (RNN) to classify vehicles into multiple classes by processing GPS data through feed-forward and recurrent layers, reducing the need for physical devices and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensors and image processing are used for vehicle classification, then installation and maintenance costs are high, but classification accuracy and capability to handle multiple vehicle classes improve
Solution Approach 1:
The patent replaces physical sensing systems (inductive loop detectors, piezoelectric sensors, weigh-in-motion systems) with a computational system based on GPS data and recurrent neural networks. This substitution eliminates the need for installing and maintaining complex physical infrastructure while achieving accurate vehicle classification through software-based processing of trajectory, speed, and temporal pattern data.
Solution Approach 2:
The patent uses GPS trajectory data as a digital copy of vehicle movement patterns to classify vehicles. Instead of directly measuring physical vehicle properties with sensors, the system processes copied position and movement information from GPS receivers, enabling classification without physical contact or specialized sensing infrastructure.
2Adaptability or versatility
If physical sensors are deployed for vehicle classification, then classification capability improves, but resource consumption and installation requirements increase
Solution Approach 1:
The patent creates a universal classification system that handles multiple vehicle types (cars, trucks, buses, motorcycles, bicycles) using a single RNN-based platform. The system processes GPS data from any standard GPS receiver, eliminating the need for different specialized sensors for different vehicle classes and enabling versatile classification through software configuration rather than hardware diversity.
Solution Approach 2:
The patent replaces multiple types of physical sensing equipment with a single computational system that processes GPS data. This substitution dramatically reduces the quantity of physical devices needed while maintaining or improving classification capability across diverse vehicle types.
3Ease of manufacture
If image processing is used for vehicle classification, then visual identification capability improves, but accuracy under varying environmental conditions deteriorates
Solution Approach 1:
The patent uses a recurrent neural network that dynamically processes sequential GPS data to capture temporal patterns in vehicle behavior. This dynamic approach adapts to varying conditions by learning from historical trajectory patterns, enabling accurate classification regardless of environmental factors that may affect static image processing.
Solution Approach 2:
The patent replaces optical image processing systems with GPS-based trajectory analysis. This substitution eliminates the sensitivity to lighting, weather, and camera resolution issues that plague image processing, providing more reliable classification under varying environmental conditions through computational analysis of movement patterns.
Data Source
AI summary
A device can receive GPS data or values for a set of metrics at a set of GPS points that form a GPS track of a vehicle. The device can determine additional values for additional metrics using the GPS data or the values for the set of metrics. The device can determine a set of vectors for the set of GPS points using the GPS data, the values, or the additional values. The set of vectors can be used in a recurrent neural network (RNN) to classify the vehicle. The device can process the set of vectors via one or more sets of RNN layers of the RNN. The device can determine a classification of the vehicle using a result of processing the set of vectors. The result can be output by the output layer. The device can perform an action based on the classification of the vehicle.