3D Spatial Sensing for Autonomous Object Tracking
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Solution Overview
Problem
Current technologies face challenges in effectively collecting and utilizing spatial information for autonomous vehicles, drones, and robots to ensure stable and accurate operation.
Innovation Solution
A vehicle, sensing device, and server system that utilize sensors to acquire spatial information over time, apply neural network-based object classification models to identify and track objects, and control vehicle operations based on this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors are used to collect spatial information for autonomous vehicles, then the ability to operate autonomously is improved, but the complexity of the system increases
Solution Approach 1:
The system segments spatial information processing into multiple specialized modules: sensor units for data collection, neural network-based object classification models for identification, and tracking systems for monitoring. This segmentation allows each component to handle specific tasks efficiently, improving autonomous operation while managing overall system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing spatial information through neural network-based object classification models before full autonomous decision-making. Objects are identified and tracked in advance, allowing the autonomous vehicle system to operate with reduced real-time computational burden, thus enhancing automation capability while controlling system complexity.
2Measurement precision
If neural network based object classification models are applied to identify objects, then object identification accuracy is improved, but processing time increases
Solution Approach 1:
The neural network-based object classification model performs preliminary classification of spatial information before detailed tracking and decision-making processes. By pre-identifying and categorizing objects in advance, the system achieves high identification accuracy while reducing the computational time required during critical real-time operations.
Solution Approach 2:
The system maintains continuous object tracking after initial identification by the neural network model. Once objects are classified, their trajectories are continuously monitored using tracking algorithms, allowing the system to reuse identification results over time rather than re-processing spatial information repeatedly, thus maintaining accuracy while minimizing processing time.
3Extent of automation
If spatial information is tracked over time, then autonomous navigation capability is improved, but computational load increases
Solution Approach 1:
The system implements continuous tracking of identified objects over time, maintaining their positional and movement information throughout the autonomous navigation process. This continuous tracking allows the vehicle to navigate autonomously by referencing previously identified objects, reducing the need for repeated full-scale spatial analysis and thereby managing computational load while enhancing navigation capability.
Solution Approach 2:
The system creates and maintains copies of spatial information and object trajectories in memory during tracking. These copied data structures allow rapid access to historical spatial information without requiring re-processing of原始 sensor data, reducing computational load while supporting continuous autonomous navigation decisions based on tracked object positions and movements.
Data Source
AI summary
A method of sensing a three-dimensional (3D) space using at least one sensor is proposed. The method can include acquiring spatial information over time for the sensed 3D space, applying a neural network based object classification model to the acquired spatial information over time to identify at least one object in the sensed 3D space. The method can also include tracking the sensed 3D space including the identified at least one object, and using information related to the tracked 3D space.