Multi-Sensor 3D Spatial Tracking for Autonomous Vehicle Navigation
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Solution Overview
Problem
Current systems for autonomous vehicles, drones, and robots face challenges in reliably collecting and utilizing spatial information for navigation and control, as existing sensor technologies have limitations in sensing range and data integration.
Innovation Solution
A vehicle and sensing device equipped with sensors like LiDAR, radar, and cameras, utilizing neural network-based object classification models to acquire and track 3D spatial information, with a server reconstructing data from multiple sources to provide comprehensive spatial awareness for enhanced navigation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensors are used to expand sensing range, then spatial information coverage is improved, but device complexity increases
Solution Approach 1:
The system segments the sensing function across multiple independent sensors (LiDAR, radar, cameras) rather than using a single complex sensor. Each sensor type targets specific spatial information needs, allowing the system to achieve comprehensive coverage while maintaining manageable complexity through modular sensor design and independent operation of each sensing component.
Solution Approach 2:
The sensor unit is designed with multi-functionality, where a single integrated sensor system performs multiple sensing tasks simultaneously (detecting objects, measuring distances, identifying spatial relationships). This allows the system to expand sensing range without proportionally increasing complexity, as each sensor component serves multiple purposes in the overall spatial awareness system.
2Measurement precision
If spatial information from multiple sources is integrated, then navigation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system merges spatial information from multiple sensors and sources (vehicle-mounted sensors, road-side sensing devices, server data) into a unified spatial model. The processor integrates data from LiDAR, radar, cameras, and external sources to create a comprehensive and accurate representation of the environment, achieving high measurement precision through combined information while managing complexity through systematic data fusion procedures.
Solution Approach 2:
The server acts as an intermediary that receives, processes, and reconstructs spatial information from multiple vehicles and road-side sensing devices before distributing it back to vehicles. This intermediary layer simplifies the data integration complexity for individual vehicles by providing pre-processed, consolidated spatial information that has already been synthesized from multiple sources.
3Measurement precision
If neural network based object classification is applied, then object identification accuracy is improved, but computational load increases
Solution Approach 1:
The server performs preliminary object classification and spatial information processing using neural networks before distributing data to vehicles. By pre-processing spatial information and identifying objects of interest, the system reduces the computational load on vehicle processors, allowing high-accuracy object identification while minimizing real-time energy consumption during vehicle operation.
4Reliability
If real-time tracking of 3D space is implemented, then autonomous navigation reliability is improved, but processing time requirements increase
Solution Approach 1:
The system implements continuous tracking of the 3D space and spatial information updates without interruption. The sensor unit continuously acquires spatial data, and the processor continuously updates the spatial model and object tracking information. This continuous operation ensures high reliability for autonomous navigation by maintaining up-to-date spatial awareness, while the distributed architecture between vehicle and server processors manages processing time requirements through parallel computation.
Data Source
AI summary
Provided is a method of sensing a three-dimensional (3D) space using at least one sensor. 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.