Return-to-Home Route Mapping With Coordinate Deduplication
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Solution Overview
Problem
Existing systems for computing efficient return-to-home routes for autonomous or pilot-assisted vehicles face challenges in minimizing data storage and computational overhead while optimizing route points of interest and deduplicating redundant coordinate data.
Innovation Solution
The system employs non-volatile memory, wireless communication, and advanced vector and yaw analysis to discard redundant coordinate pairs, optimize route points, and generate efficient trajectories, utilizing a processor-readable media with executable instructions to manage coordinate samples and yaw conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all coordinate samples are stored to ensure complete route information, then route accuracy is maintained, but data storage requirements and computational overhead increase
Solution Approach 1:
The system extracts only the essential coordinate pairs that define meaningful changes in route direction or position, discarding redundant intermediate points. This is achieved by comparing consecutive coordinate pairs and identifying those that represent significant navigational decisions, thereby maintaining route accuracy while reducing data storage requirements.
Solution Approach 2:
The system changes the parameter of coordinate representation by filtering and selecting only critical coordinate pairs based on spatial and temporal thresholds. Instead of storing all raw coordinate samples, the system transforms the data set to include only those points that meet specific criteria for route definition, reducing data quantity while preserving essential route information.
2Manufacturing precision
If all coordinate samples are processed to ensure complete route optimization, then route quality is improved, but computational overhead increases
Solution Approach 1:
The system extracts only the critical coordinate pairs that need to be processed for route optimization, eliminating redundant calculations on intermediate points. By identifying and processing only the essential coordinates that define route changes, the system maintains high route quality while reducing computational overhead.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on the critical coordinate pairs that significantly impact route quality, rather than processing all coordinate samples equally. This selective approach ensures that the most important route decisions are optimized while avoiding unnecessary computational expenditure on redundant data.
3Quantity of substance
If redundant coordinate pairs are discarded to reduce data storage, then data efficiency improves, but route information completeness may be compromised
Solution Approach 1:
The system changes the parameter of data representation by transforming the complete set of coordinate samples into a filtered set of critical coordinate pairs. This parameter change involves applying spatial and temporal threshold criteria to identify which coordinates are essential for route completeness, thereby improving data efficiency without sacrificing necessary route information.
Solution Approach 2:
The system uses feedback mechanisms to verify that discarded coordinate pairs do not contain essential route information. By comparing the original coordinate set with the filtered set and evaluating route reconstruction accuracy, the system ensures that information completeness is maintained while achieving data efficiency through redundant data removal.
4Measurement precision
If comprehensive coordinate data is retained for accurate route reconstruction, then route fidelity is maintained, but memory usage increases
Solution Approach 1:
The system extracts only the essential coordinate pairs needed for accurate route reconstruction, removing redundant data that consumes memory. By identifying coordinates that represent significant route changes and retaining only those, the system maintains high route fidelity while reducing memory usage requirements.
Solution Approach 2:
The system changes the parameter of data retention by applying filtering criteria that transform the complete coordinate data set into a compressed representation. This parameter change involves retaining only coordinates that meet specific spatial and temporal thresholds, thereby maintaining route fidelity while optimizing memory usage.
Data Source
AI summary
Embodiments of the present disclosure may include a system for lossy optimization of a return-to-home route, the system including a non-volatile memory. Embodiments may also include a wireless transceiver. Embodiments may also include a processor in communication with a non-volatile memory including a processor-readable media having thereon a set of executable instructions, configured, when executed, to cause the processor to receive via the wireless transceiver of coordinate samples (kn) over a time interval. In some embodiments, each coordinate sample may include at least two-dimensional pairs (x,y) and a vehicle yaw, the two-dimensional pairs (x,y) indicative of a pilot-assisted vehicle path over the time interval. Embodiments may also include identify a first coordinate pair of interest (x0,y0) and yaw0, a subsequent second coordinate pair (x1,y1), and a third subsequent coordinate pair (x2,y2) and yaw2.