Iterative Sensor Fusion Data Association for Real-Time V2X Tracking

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Solution Overview

Problem

Existing sensor fusion methods in V2X environments require high computational capacity due to the large volume of data from multiple sources, making real-time data association challenging.

Innovation Solution

A data processing method that reduces computational needs by assigning unique identifiers to sensor data based on their source type, allowing for efficient data association and reuse of previous associations, thereby minimizing the number of necessary matches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If probabilistic data association methods are used to process sensor data from multiple sources, then measurement accuracy and reliability are improved, but computational complexity and processing time increase significantly

Engineering Contradiction:

Improvedata association accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments sensor data into two distinct categories: data from sources that exclusively determine entities (first type) and data from sources that track entities (second type). This segmentation allows the system to apply different processing strategies to each type, reducing overall computational complexity while maintaining association accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary data association in iterative cycles, where previous association results are stored and reused in subsequent cycles. This preliminary action reduces the number of necessary matches in real-time processing, as the system only needs to process new or changed data rather than re-evaluating all associations from scratch.

Inventive Principle:

Principle #10Preliminary action

2Loss of time

If all sensor data from multiple sources are processed in real-time, then data freshness and relevance are improved, but computational load and energy consumption increase

Engineering Contradiction:

Improvedata processing timeVSAvoidcomputational energy consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by selectively processing only the necessary subset of sensor data in each iterative cycle. By identifying and processing only new data or data that has changed since the previous cycle, the system maintains data freshness without the energy cost of processing every data point repeatedly.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent implements continuous iterative processing where association results from previous cycles are carried forward and refined. This continuity allows the system to maintain up-to-date associations with minimal additional computational effort, as the useful action of data association continues from where it left off rather than restarting.

Inventive Principle:

Principle #20Continuity of useful action

3Reliability

If the number of data sources is increased to improve coverage and detection capability, then system reliability and detection accuracy are improved, but the volume of data to be processed increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoiddata volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent segments the growing volume of sensor data based on the characteristics of its sources. By categorizing data into two types based on source capabilities, the system can efficiently manage and process data from an increasing number of sources without proportionally increasing computational load, as each segment can be processed according to its specific requirements.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250342176A1Data processing method and system for iterative sensor fusion, computer program product and computer readable medium for implementing the method

Publication Date: 2025.11.06 COMMSIGNIA KFT

AI summary

The invention relates to a data processing method for iterative sensor fusion. The method comprises receiving sensor data from a first data source type and a second data source type. The first data source type is adapted for providing sensor data for exclusively determining an entity and the second data source type is adapted for providing sensor data of a tracked entity. The method further comprises generating a data source identifier for each of the sensor data. The data source identifier comprises a data source type identifier corresponding to a data source type from which the sensor data is received. A first data source type identifier corresponds to the first data source type and a second data source type identifier corresponds to the second data source type. The method further comprises compiling a dataset that comprises newest sensor data received, and iteratively performing a data association step on the dataset.