Vehicle Identification via Machine Learning and Onboard Sensors
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Solution Overview
Problem
Vehicle of interest alerts often result in false positive sightings and have low reliability due to difficulties in communication and driver distraction during searches.
Innovation Solution
A system and method using onboard vehicle sensors, such as cameras, and machine-learned models to identify vehicles of interest by analyzing images of nearby vehicles and transmitting identified matches to law enforcement agencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If vehicle of interest alerts are communicated to a large portion of the public, then the search coverage is improved, but the reliability decreases due to false positive sightings
Solution Approach 1:
The patent replaces the manual mechanical process of public visual identification with an automated optical-mechanical system. Sensors (cameras) mounted on vehicles automatically capture images of other vehicles, and machine-learned models process these images to identify vehicles of interest. This substitution eliminates human error and distraction while maintaining wide search coverage, thereby improving reliability without sacrificing area coverage.
Solution Approach 2:
The patent introduces machine-learned models as an intermediary between the sensor data and the identification decision. These models act as a mediator that processes raw image data from sensors, compares vehicle characteristics against the vehicle of interest description, and determines matches with high accuracy. This intermediary layer filters out false positives that would occur in direct human observation, resolving the contradiction between wide coverage and high reliability.
2Productivity
If drivers manually search for vehicles of interest, then the search effort is increased, but driver distraction occurs reducing safety
Solution Approach 1:
The patent implements a self-service system where the vehicle's own sensors and onboard computing resources perform the search function automatically. The system uses the vehicle's existing infrastructure (sensors, processors) to conduct independent vehicle identification without requiring driver intervention. This eliminates driver distraction while maintaining or enhancing search productivity, as the system operates autonomously during normal driving.
Solution Approach 2:
The patent replaces the human driver's manual visual search process with an automated sensor-based system. Instead of drivers actively looking for and identifying vehicles of interest, sensors continuously capture images and machine-learned models automatically process them. This substitution removes the harmful distraction effect while preserving or improving search productivity, as the automated system can process data continuously without attention demands.
3Ease of operation
If conventional alert systems are used, then the communication simplicity is maintained, but the false positive rate increases
Solution Approach 1:
The patent segments the vehicle identification process into distinct functional components: sensor image capture, machine-learned model processing, characteristic extraction (make, model, color, license plate), and match determination. This segmentation allows each component to specialize in its function, with machine-learned models providing high-precision analysis while the overall system maintains simple alert communication. The segmentation enables complex processing without complicating the user interface or alert transmission.
Solution Approach 2:
The patent uses machine-learned models as intermediaries that handle the complex analysis work, allowing the alert communication system to remain simple. The models process detailed vehicle characteristics and generate structured identification data, which can then be transmitted through existing simple alert channels. This intermediary approach maintains measurement precision through sophisticated processing while preserving the ease of operation in alert communication.
Data Source
AI summary
A method for identifying a vehicle of interest includes: accessing data corresponding to a vehicle of interest alert; accessing data from a sensor corresponding to images of one or more other vehicles; computing, with a machine-learned model, a vehicle of interest match estimate for the one or more other vehicles based at least in part on the data from the sensor; and computing a vehicle of interest identification.