Sensor Object Consolidation Using Velocity and Elevation Cues
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Solution Overview
Problem
Existing object detection systems using light-based sensors often incorrectly identify large objects, such as trucks, as multiple separate objects instead of a single entity, particularly in stop-and-go or slow-moving traffic scenarios, leading to erroneous outputs.
Innovation Solution
The system employs a processor configured to obtain sensor information indicating multiple objects, determine their velocity and elevation, and consolidate objects with similar velocity or elevation into a common object, using a machine learning model to enhance object detection by grouping radar data and camera inputs for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If light-based sensors detect multiple objects in the environment, then object detection capability is improved, but large objects may be incorrectly identified as multiple separate objects
Solution Approach 1:
The patent merges multiple sensor detections (radar and camera) into a unified object identification. When multiple objects are detected in proximity, the system combines their sensor data and consolidates them into a single coherent object identity, preventing the misclassification of large objects as multiple separate entities.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives sensor information from both radar and camera systems. This intermediary layer analyzes the combined data, determines spatial relationships, and resolves ambiguities before final object classification, thereby improving reliability without sacrificing detection accuracy.
2Measurement precision
If the system consolidates objects based on velocity and elevation, then object detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent utilizes changes in physical parameters (velocity and elevation) to simplify the consolidation process. By monitoring these specific parameters across multiple sensor detections, the system can automatically determine when objects should be merged without requiring complex analysis of all sensor data dimensions.
Solution Approach 2:
The patent segments the complex object consolidation task into distinct analytical steps: first analyzing velocity parameters, then elevation parameters, and finally combining results. This segmentation reduces overall system complexity by breaking down the decision-making process into manageable, independent evaluation stages.
Data Source
AI summary
The present disclosure generally relates to an object detection system. For example, aspects of the present disclosure relate to systems and techniques for performing object detection using sensor information, such as elevation and/or velocity information from one or more light-based sensors. One example apparatus generally includes one or more processors operably configured to: obtain sensor information indicating at least two objects in an environment; determine at least one of a velocity or an elevation associated with each object of the at least two objects; consolidate the at least two objects into a common object based on the at least one of the velocity or the elevation; and output an indication of the common object.