Context-Aware Navigation Control for Multi-Object Collision Prediction
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Solution Overview
Problem
Existing electronic devices lack the ability to accurately analyze and predict future situations by considering the relationships between multiple objects, leading to potential collisions and inaccuracies in movement prediction.
Innovation Solution
An electronic apparatus equipped with a camera, memory, and processor that identifies objects based on attribute and environment information, determines their relationships, and controls its traveling state accordingly, including predicting potential movements and adjusting modes such as protection, low-noise, monitoring, or private modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the electronic device considers relationships between multiple objects and their attributes, then the accuracy of situation analysis and collision prediction is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the environment into multiple objects with distinct attributes (static/dynamic, movable/non-movable). Each object is independently identified and classified, allowing the processor to handle complex scenes by breaking them down into manageable individual object analyses rather than processing the entire scene as one unit.
Solution Approach 2:
The system performs preliminary classification of objects into categories (static/dynamic, movable/non-movable) before conducting detailed situation analysis. This preliminary action organizes data structures and attribute information in advance, reducing the computational burden during real-time collision prediction and navigation decisions.
2Reliability
If the electronic device predicts future movements of objects based on their relationships, then the ability to avoid collisions is improved, but the complexity of prediction algorithms increases
Solution Approach 1:
The system dynamically adjusts its prediction behavior based on object attributes. For dynamic objects with movable attributes, the system performs movement prediction; for static or non-movable objects, it assumes fixed positions. This dynamic approach optimizes computational resources by applying prediction algorithms only where necessary rather than uniformly to all objects.
Solution Approach 2:
The system changes prediction parameters based on object attributes. When a dynamic object is identified within threshold distance of a static object, the system activates movement prediction with specific parameters (moving direction, moving distance). This parameter-based control allows the system to scale prediction complexity according to the actual risk level in the environment.
3Adaptability or versatility
If the electronic device identifies multiple contexts and predicted contexts for objects, then the adaptability of navigation control is improved, but the processing time and computational load increase
Solution Approach 1:
The system autonomously identifies multiple contexts (current context and predicted context) for each object based on its attributes and relationships with other objects. This self-service capability allows the electronic device to independently adapt its navigation control without external intervention, selecting appropriate modes (protection mode, low-noise mode, monitoring mode, private mode) based on contextual analysis.
Solution Approach 2:
The system performs contextual analysis to a sufficient degree rather than attempting complete exhaustive analysis. It identifies key contexts needed for safe navigation (current state and predicted state) without over-analyzing every possible attribute combination. This partial action approach provides adequate adaptability while controlling processing time by focusing on the most relevant contextual information for navigation decisions.
Data Source
AI summary
Disclosed is an electronic apparatus. The electronic apparatus includes: a camera; a memory configured to store attribute information and environment information; and a processor configured to identify a plurality of objects based on an image obtained by the camera, identify a first context of a first object, from among the plurality of objects, based on a relationship between attribute information of the plurality of objects and the environment information, and control a traveling state of the electronic apparatus based on the first context.