Vehicle Perception Velocity Clustering for Faster Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle safety systems struggle to efficiently process sensor data to determine connectivity between data points in an environment, which is crucial for accurate object detection and collision avoidance in autonomous vehicles.
Innovation Solution
The implementation of a machine learned model that receives sensor data, applies clustering algorithms to determine connectivity data, and outputs information that can be used by a vehicle computing device to detect objects and control vehicle actions, thereby improving safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data processing methods are used, then the system is simpler to implement, but the object detection accuracy and processing speed are insufficient for safe autonomous vehicle operation
Solution Approach 1:
The patent segments sensor data processing into distinct functional modules: a clustering component that groups sensor data points into clusters representing potential objects, and a connectivity determination component that analyzes relationships between clusters. This segmentation allows each module to specialize in specific tasks, improving overall detection accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces connectivity data as an additional dimension of analysis beyond traditional sensor data clustering. By determining connectivity relationships between data points and clusters in this new dimensional space, the system achieves more accurate object detection and differentiation without proportionally increasing processing complexity.
2Reliability
If comprehensive sensor data processing is performed to ensure safe vehicle operation, then object detection accuracy improves, but processing time increases
Solution Approach 1:
The clustering component performs preliminary grouping of sensor data points into clusters before connectivity analysis. This preliminary action organizes the data structure in advance, reducing the computational burden of subsequent connectivity determinations and enabling faster processing while maintaining comprehensive safety analysis.
Solution Approach 2:
By dividing the processing pipeline into separate clustering and connectivity determination stages, the system can optimize each stage independently. The clustering stage rapidly groups data points, and the connectivity stage efficiently analyzes relationships, reducing overall processing time while ensuring thorough safety verification through multiple analysis layers.
3Measurement precision
If clustering algorithms are applied to determine connectivity between data points, then object detection accuracy improves, but computational resources required increase
Solution Approach 1:
The patent segments the computational task into two phases: clustering data points into groups (reducing individual point processing requirements) and then determining connectivity between clusters. This segmentation reduces the computational energy required compared to analyzing all data points individually, while maintaining high connectivity determination accuracy through the structured approach.
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. Further, the machine learned model can be trained to output motion data representing velocity data or a cluster association score. The connection information and/or motion data can be used by a vehicle computing device to detect objects in an environment and/or to control operation of a vehicle.