Vehicle Perception Input Clustering for Real-Time Object Segmentation
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Solution Overview
Problem
Current vehicle safety systems face challenges in efficiently processing sensor data to accurately detect and differentiate objects in complex environments, leading to potential collisions due to computational resource constraints and the difficulty in distinguishing between object boundaries and background elements.
Innovation Solution
Implementing a machine learned model that receives sensor data, applies clustering algorithms to determine connectivity data between data points, and uses this information to segment objects effectively, thereby improving the vehicle's ability to avoid collisions by optimizing computational resources and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor data is processed using traditional methods, then computational resources are consumed, but processing speed and accuracy are insufficient for real-time collision avoidance
Solution Approach 1:
The patent segments sensor data processing into multiple stages: initial object candidate identification, boundary refinement, and classification. This segmentation allows the system to quickly identify potential objects while maintaining accurate boundary detection and classification, resolving the contradiction between processing speed and detection accuracy.
Solution Approach 2:
The system performs preliminary processing of sensor data to pre-identify object candidates and their approximate locations before conducting detailed analysis. This preliminary action filters out non-object data early in the processing pipeline, enabling faster subsequent processing while maintaining high accuracy for actual object detection.
2Measurement precision
If computational resources are increased to improve object detection accuracy, then detection precision improves, but device complexity and resource requirements increase
Solution Approach 1:
The patent applies partial processing to sensor data by focusing computational resources only on regions containing potential objects rather than processing the entire sensor data set. This partial action approach maintains high detection accuracy for objects while significantly reducing overall computational resource requirements and device complexity.
Solution Approach 2:
The system uses simple geometric calculations and clustering algorithms that leverage the inherent structure of sensor data rather than requiring complex machine learning models. These self-service processing methods achieve good detection accuracy with minimal computational resources, reducing device complexity while maintaining performance.
3Reliability
If all sensor data points are processed to ensure complete object detection, then detection completeness improves, but processing time increases
Solution Approach 1:
The patent extracts and processes only the most relevant sensor data points for object detection by identifying and focusing on data points that exhibit object-like characteristics. This extraction approach ensures that all actual objects are detected (maintaining completeness) while excluding irrelevant background data points, thereby reducing processing time and latency.
Data Source
AI summary
Techniques for clustering sensor data are discussed herein. Sensors of a vehicle may detect data points in an environment. Clustering techniques can be used in a vehicle safety system to determine connection information between the data points. The connection information can be used by a vehicle computing device that employs clustering and/or segmenting techniques to detect objects in an environment and/or to control operation of a vehicle.