History-Based Incompatible Track Identification
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Solution Overview
Problem
The challenge in automotive systems is to accurately associate or disassociate tracks from different sensors while managing the high memory and computational complexity of historical data, which is crucial for safety and reliability, especially in cluttered environments.
Innovation Solution
The method involves maintaining a feasibility matrix and an incompatibility matrix to assess the probability that tracks from different sensors identify the same object, adjusting the feasibility matrix based on historical data to minimize memory consumption and computational complexity, and outputting information to avoid collisions by identifying separate objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data is kept to accurately determine track associations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the historical data processing into two distinct matrices: a feasibility matrix that stores probability values for track associations, and an incompatibility matrix that tracks historical incompatibility relationships. This segmentation allows the system to process historical data in a structured, manageable way that improves association accuracy without overwhelming computational complexity.
Solution Approach 2:
The patent transforms historical trajectory data into simplified probability parameters stored in the feasibility matrix and binary incompatibility indicators in the incompatibility matrix. By converting complex historical trajectories into these standardized parameter formats, the system achieves accurate track association while maintaining manageable computational requirements.
2Measurement precision
If historical data is kept to accurately determine track associations, then measurement precision is improved, but loss of substance increases
Solution Approach 1:
The patent extracts only the essential features from historical trajectory data - specifically, the feasibility probabilities and incompatibility relationships - and stores them in compact matrix structures. This extraction process eliminates the need to store complete historical trajectory information, significantly reducing memory consumption while preserving the accuracy needed for track association.
Solution Approach 2:
Instead of storing complete historical trajectory data, the patent creates simplified copies in the form of probability values in the feasibility matrix and binary indicators in the incompatibility matrix. These copied representations capture the essential association information without requiring the full historical data, thereby reducing memory usage.
Data Source
AI summary
This document describes methods and systems directed at history-based identification of incompatible tracks. The historical trajectory of tracks can be advantageous to accurately determine whether tracks originating from different sensors identify the same object or different objects. However, recording historical data of several tracks may consume vast amounts of memory or computing resources, and related computations may become complex. The methods and systems described herein enable a sensor fusion system of an automobile or other vehicle to consider historical data when associating and pairing tracks, without requiring large amounts of memory and without tying up other computing resources.