AV Fleet Phase Array for Underground Mapping
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Solution Overview
Problem
Current systems for autonomous vehicles (AVs) face challenges in effectively collecting and processing large quantities of data from various sensors to generate accurate mappings of underground, landscape, and atmospheric features, as well as identifying aircraft locations, which is crucial for navigation and resource detection.
Innovation Solution
A system that collects data from a fleet of AVs using sensors like LiDAR, radar, and vibration sensors, synchronizes location and time data, and uses this information to generate detailed mappings by associating sensor data with discrete locations and times, enabling the identification of underground features, atmospheric conditions, and aircraft locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from multiple distributed autonomous vehicles, then measurement precision and mapping accuracy improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system divides the large-scale data collection task into segments performed by multiple autonomous vehicles, each collecting local sensor data independently. This segmentation allows the fleet to cover larger areas and achieve better measurement precision without requiring a single complex centralized system
Solution Approach 2:
Data from multiple autonomous vehicles is merged and combined to create comprehensive mappings. By integrating sensor data from distributed vehicles, the system achieves improved mapping accuracy and resource detection capabilities while distributing the processing load across the fleet
2Loss of information
If sensor data is collected from distributed autonomous vehicles, then information about underground features and atmospheric conditions is obtained, but loss of time for data synchronization and processing increases
Solution Approach 1:
Each autonomous vehicle performs preliminary data collection and local processing of sensor information before contributing to the overall mapping. This preliminary action ensures that when data is aggregated, the synchronization and processing time is reduced, as individual vehicles have already prepared their data in advance
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of feature mapping, facilitates the detection of resources like oil and minerals, and provides three-dimensional atmospheric data, improving navigation and resource detection capabilities.
Implementation Method 1
radar sensors
Implementation Method 2
Light Detection and Ranging (LiDAR) sensors
Implementation Method 3
accelerometer (vibration) sensors... mappings of underground features generated based on an evaluations of vibration data
Data Source
AI summary
The present disclosure is directed to collecting and processing data from computing devices of a plurality of autonomous vehicles (AVs). The data received from each of these AV computing devices may include raw sensor data or data that has been generated using data received by one or more sensors at respective AVs. Once this data is collected and associated with discrete locations and times, the data may be evaluated and used to generate mappings of various sorts. These mappings may include mappings of underground features generated based on an evaluations of vibration data. Alternatively, or additionally, these mapping may include mappings of landscape features, atmospheric features, or the locations of aircraft from data associated with certain types of sensing apparatus, for example radar apparatus or light detecting and ranging (LiDAR) apparatus.