Autonomous Vehicle Lattice Traversal Using Tolerance-Filtered Paths
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Solution Overview
Problem
Existing autonomous vehicle systems lack the ability to generate specific traversal plans that accurately reflect actual operator traversal data within a predetermined tolerance range, leading to inefficiencies and deviations from optimal paths.
Innovation Solution
A method for formulating a specific traversal plan based on a general plan and actual operator traversal data, involving the determination of a tolerance range and subsets of data within and outside this range, to optimize the path for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a general plan is used for autonomous vehicle traversal, then the system is simple to implement, but the traversal efficiency and accuracy deteriorate due to deviations from optimal paths
Solution Approach 1:
The patent segments the general plan into multiple specific plans by dividing operator traversal data into subsets based on tolerance ranges. Each specific plan represents a segmented portion of the overall traversal task, allowing the system to maintain simplicity while improving efficiency through targeted optimizations for different spatial and temporal segments of the traversal data.
Solution Approach 2:
The patent changes parameters by filtering operator traversal data through tolerance range criteria to generate specific plans. By adjusting the tolerance parameters and selecting data subsets that fall within these ranges, the system transforms general traversal instructions into optimized specific plans that improve traversal efficiency while maintaining implementation simplicity.
2Measurement precision
If actual operator traversal data is used directly, then the traversal accuracy improves, but the system complexity increases due to data processing requirements
Solution Approach 1:
The patent extracts only the necessary portions of operator traversal data by filtering through tolerance range criteria. Instead of processing all raw operator data, the system takes out and processes only those data points that fall within acceptable tolerance ranges, thereby improving traversal accuracy while minimizing system complexity through selective data extraction.
Solution Approach 2:
The patent applies partial action by processing only a subset of operator traversal data rather than the complete dataset. By focusing computational resources on data within tolerance ranges and excluding excessive or irrelevant data points, the system achieves high traversal accuracy without requiring complex processing of all available data.
3Manufacturing precision
If tolerance range filtering is applied to generate specific plans, then traversal precision improves, but the data processing time increases
Solution Approach 1:
The patent applies partial action by filtering and processing only data within tolerance ranges rather than analyzing the complete dataset. This selective approach to data processing improves plan precision by focusing on relevant data while reducing overall processing time by excluding excessive or irrelevant data points from the analysis.
Data Source
AI summary
A specific plan for traversing a lattice of a geographic region is generated and provided to an autonomous vehicle. A general plan that includes the lattice and a planned sequence of operator traversal data of the lattice is received. Actual operator traversal data used to traverse the geographical region is determined. A tolerance range that defines an allowable deviation from the general plan is determined. A first subset of the actual operator traversal data that is within the tolerance range and a second subset of the actual operator traversal data that is not within the tolerance range is determined. A specific plan for traversing the geographical region is formulated based on the first subset of the actual operator traversal data that is within the tolerance range. The specific plan is provided to the autonomous vehicle for controlling traversal of the geographical region by the autonomous vehicle.