Vehicle Surroundings Labeling Using Past and Future Observations
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Solution Overview
Problem
Manual methods for labeling objects in a mobile device's environment, such as robots or vehicles, are time-consuming and costly, and existing automated methods often lack precision and reliability by only considering observations prior to and at the time of detection without accounting for past or future data.
Innovation Solution
A method that generates label objects by selecting observations from three distinct points in time (present, past, and future) using a labeling module to ascertain attributes, employing pattern detection and multi-target tracking algorithms for holistic processing, which improves accuracy and reliability by considering the entire temporal context of object dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to label objects in a mobile device's environment, then labeling precision can be maintained through human judgment, but time consumption and costs increase significantly
Solution Approach 1:
The system enables automated labeling by having the mobile device itself perform the labeling task through its sensors and processing units, eliminating the need for external human annotators while maintaining acceptable precision through algorithmic object detection and attribute extraction
Solution Approach 2:
The patent replaces the mechanical human labeling process with an automated computational system that uses sensor data, pattern recognition algorithms, and machine learning models to automatically generate labels for objects in the environment, thereby reducing time consumption while maintaining labeling capability
2Productivity
If existing automated labeling methods are used that only consider observations prior to and at the time of detection, then processing speed is maintained, but labeling reliability and precision decrease
Solution Approach 1:
The system performs preliminary labeling actions by considering past observations and predictions before finalizing labels at the current detection moment, allowing the mobile device to incorporate temporal context from previous frames to improve labeling reliability without sacrificing processing speed
Solution Approach 2:
The patent implements feedback mechanisms where labeling results from past observations are fed back into the current labeling process, allowing the system to refine labels by comparing historical data with current observations, thereby improving reliability while maintaining efficient processing through iterative refinement
3Device complexity
If existing automated labeling methods are used that only consider observations at the time of detection, then device complexity is kept low, but measurement precision and reliability of labels deteriorate
Solution Approach 1:
The system dynamically adjusts the temporal scope of observations used for labeling, allowing it to incorporate variable amounts of historical and predictive data based on scene complexity and processing requirements, thereby improving precision without requiring permanently complex system architecture
Solution Approach 2:
The patent adds a temporal dimension to the labeling process by incorporating observations from multiple time points (past, present, and predicted future states) rather than relying solely on spatial information from a single moment, thereby improving precision through time-dependent analysis without significantly increasing spatial system complexity
Data Source
AI summary
A method and a labeling system for generating a label object for the symbolic description of an object of an environment of a mobile device, e.g., a robot or a vehicle. The label object includes at least one attribute of an object at a first point in time, from observations of this object. The method includes selecting, from the observations, a first observation recorded at a first point in time, a second observation recorded at a second point in time, the second point in time being a point in time before the first point in time, as well as a third observation recorded at a third point in time, the third point in time being a point in time after the first point in time; and ascertaining, by using the selected observations, the at least one attribute of the object.