Vehicle Tracking via External Sensor Data Fusion
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Solution Overview
Problem
Existing vehicle tracking systems are limited in their ability to determine the behavior, location, and characteristics of vehicles that lack suitable sensors, as they rely solely on the vehicle's own sensors for information, which can result in incomplete or unavailable data.
Innovation Solution
A vehicle monitoring system that includes a database for vehicle information, sensors to capture data from observing vehicles, and processors to request, receive, and store information about target vehicles, even if they lack suitable sensors, allowing for comprehensive tracking and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle tracking relies solely on the vehicle's own sensors, then the system is simpler to implement, but the tracking capability is insufficient for vehicles without suitable sensors
Solution Approach 1:
The patent introduces a monitoring system as an intermediary that collects sensor data from multiple vehicles and uses machine learning to infer characteristics of target vehicles. This mediator enables tracking of vehicles without suitable sensors by leveraging data from observing vehicles, resolving the contradiction between tracking reliability and system complexity.
Solution Approach 2:
The monitoring system performs multiple functions: it collects sensor data from observing vehicles, processes this data through machine learning algorithms, and generates tracking information for various target vehicles. This multi-functional approach enables the system to track different types of vehicles using a single unified platform, improving reliability without proportionally increasing complexity.
2Productivity
If the system uses sensors from additional vehicles to track target vehicles, then the tracking range and efficiency improve, but the system complexity increases
Solution Approach 1:
The monitoring system automatically processes sensor data from observing vehicles using machine learning algorithms without requiring manual intervention. The system self-manages data collection, processing, and tracking generation, which improves productivity by eliminating manual tracking operations while the automated nature keeps complexity manageable.
Solution Approach 2:
The system transforms raw sensor data parameters into meaningful tracking information through machine learning parameter transformations. By changing the parameters from raw sensor readings to inferred vehicle characteristics and tracking data, the system achieves high tracking efficiency while managing complexity through automated parameter transformation.
3Measurement precision
If the system collects and processes data from multiple observing vehicles, then the information accuracy improves, but the data processing complexity increases
Solution Approach 1:
The machine learning model uses feedback from multiple sensor data sources to continuously improve tracking accuracy. The system processes feedback from observing vehicles, adjusts its predictions accordingly, and generates more accurate tracking information for target vehicles, resolving the contradiction between precision and processing complexity through iterative learning.
Solution Approach 2:
The system combines multiple types of sensor data from different observing vehicles into a composite information set. By integrating data from various sources (cameras, sensors, etc.) into a unified tracking model, the system achieves high measurement precision while managing complexity through data fusion and composite analysis.
Data Source
AI summary
A vehicle monitoring system, includes a vehicle database, which includes information associated with a plurality of vehicles. The vehicle monitoring system also includes a sensor database, which includes information captured by a sensor of an observing vehicle, and one or more processors. The one or more processors may generate a request to determine information associated with a target vehicle and to transmit the request to the sensor in response to determining that the information captured by the sensor does not include the information associated with the target vehicle. The sensor captures the information associated with the target vehicle based at least in part on the request. The one or more processors may also receive and store the information associated with the target vehicle from the sensor. Further, the one or more processors may output the information associated with the target vehicle to a computing device.