Multi-Sensor Track Pair Clustering for Lower-Complexity Association
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Solution Overview
Problem
The computational complexity of associating low-level tracks from multiple sensors in a cluttered environment increases exponentially, making it challenging for automotive systems to efficiently match tracks and ensure safety and reliability, particularly in autonomous or semi-autonomous vehicles.
Innovation Solution
The system clusters track pairs by identifying and pruning the weakest connections between tracks from different sensors, reducing the pattern-matching problem into smaller subproblems, thereby limiting the number of candidate track pairs and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If track association algorithms use pattern-matching processes to match tracks from multiple sensors, then the accuracy of object identification is improved, but the computational complexity increases exponentially
Solution Approach 1:
The patent divides the set of candidate track pairs into multiple clusters based on spatial proximity and association strength. Each cluster contains track pairs that are more likely to represent the same object, allowing the algorithm to process smaller subsets independently rather than evaluating all possible pairs, thus reducing computational complexity while maintaining association accuracy
Solution Approach 2:
The patent applies different processing strategies to different clusters of track pairs. High-strength associations are processed with more rigorous validation, while lower-strength associations use simplified matching criteria. This localized differentiation optimizes computational resources by applying appropriate levels of analysis to each group, reducing overall complexity without sacrificing critical association accuracy
2Reliability
If the quantity of low-level tracks increases in a cluttered environment, then the completeness of object detection is improved, but the processing speed decreases due to exponential increase in computational complexity
Solution Approach 1:
The patent performs preliminary clustering of track pairs based on spatial proximity and initial association strength before conducting full pattern-matching analysis. This preliminary organization groups likely matches together and separates unlikely matches, allowing the system to quickly eliminate non-candidates and focus computational resources on promising associations, thereby maintaining processing speed even as the number of tracks increases
Solution Approach 2:
The patent extracts and removes weak or unlikely track pair associations from the candidate set through filtering criteria based on spatial distance, velocity consistency, and association strength thresholds. By eliminating these low-probability pairs beforehand, the system reduces the search space for pattern-matching algorithms, maintaining processing speed while preserving all potentially valid associations for thorough evaluation
Data Source
AI summary
This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.