Merged Sensor Data Clusters for Accurate Object Detection
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Solution Overview
Problem
Autonomous vehicles face inaccuracies and inefficiencies in computer vision operations, particularly in object detection, which can compromise safe and efficient navigation.
Innovation Solution
A method for determining whether sensor data point clusters are associated with the same object by considering azimuth range, height difference, and aspect ratio conditions, using techniques such as clustering algorithms and machine learning models to improve object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision operations are used for object detection, then object detection can be performed, but accuracy and efficiency are insufficient
Solution Approach 1:
The patent segments sensor data into multiple clusters representing different objects or object parts. Each cluster is processed independently through geometric characteristic analysis, allowing parallel computation that improves efficiency while maintaining high detection accuracy through focused analysis of each segment.
Solution Approach 2:
The patent extends traditional 2D image-based object detection into 3D space by incorporating depth information and spatial coordinates. By analyzing clusters in three-dimensional space with multiple geometric characteristics, the system achieves more accurate detection while efficiently processing spatial relationships.
2Measurement precision
If traditional object detection methods are used, then processing is simpler, but detection accuracy is lower
Solution Approach 1:
The detection method is segmented into distinct modular steps: cluster identification, geometric characteristic determination, and association decision-making. Each module performs a specific function with well-defined inputs and outputs, making the complex overall process more manageable and implementable while achieving high accuracy.
Solution Approach 2:
The patent introduces multiple geometric parameters (azimuth range, height difference, aspect ratio) to characterize object clusters. By changing from simple 2D pixel-based detection to multi-parameter 3D geometric analysis, the system achieves superior accuracy while the structured parameter-based approach keeps implementation complexity manageable.
Data Source
AI summary
Techniques for object detection based on sensor data are discussed herein. In some cases, the techniques described herein include determining that a first sensor data cluster and a second sensor data cluster are associated with the same object if the two clusters satisfy a set of conditions. For example, a condition may be defined based on at least one of: (i) whether the second cluster is within an azimuth range associated with the first cluster, (ii) whether a height difference associated with the two clusters falls below a threshold, (iii) whether an aspect ratio associated with a combination of the two clusters is acceptable (e.g., satisfies at least one of one or more predefined aspect ratio conditions, falls within one of one or more predefined aspect ratio ranges, and/or the like), and/or (iv) whether cluster tracks associated with the two clusters jointly move across time.