Drive Segment Alignment Using Human Consensus Priors
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Solution Overview
Problem
Current location-based services, particularly in autonomous driving, face challenges in achieving centimeter-level accuracy in digital map data due to misalignment issues caused by local errors in data captured from various sources, leading to scalability limitations and lack of context in fully automated alignment techniques.
Innovation Solution
A method that aligns drive segments by retrieving manual alignment data from human users, determining a set of common drive alignment locations, and using machine learning to automate the alignment process, learning from human inputs to establish context-based priors for subsequent drive segment alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automated alignment techniques are used, then alignment speed and scalability are improved, but alignment accuracy and contextual understanding deteriorate
Solution Approach 1:
The system performs preliminary manual alignment by human users to establish initial correspondence between drive segments. These manual alignments serve as training data that teaches the automated system how to accurately align segments, combining the speed of automation with the accuracy of human judgment.
Solution Approach 2:
The system uses feedback from human users' manual alignment inputs to train and improve the automated alignment model. By continuously learning from human feedback, the automated system refines its accuracy while maintaining high processing speed, resolving the contradiction between speed and precision.
2Measurement precision
If manual alignment by human users is used, then alignment accuracy is improved, but processing time and scalability deteriorate
Solution Approach 1:
The system creates a computational model that copies and learns from human users' alignment patterns. Instead of requiring continuous manual input, the model replicates human expertise through machine learning, achieving both high accuracy and automated processing speed.
Solution Approach 2:
The automated alignment system serves itself by using the manual alignment data to train its own algorithms. Once trained, the system can autonomously perform alignments without ongoing human intervention, maintaining accuracy while dramatically improving processing speed and scalability.
3Productivity
If automated alignment is used without context, then processing efficiency is improved, but alignment reliability deteriorates due to local errors
Solution Approach 1:
The system applies local quality by considering the specific contextual characteristics of each drive segment and its surrounding environment. The alignment model learns to identify and prioritize locally significant features and patterns, ensuring reliable alignment that adapts to local conditions rather than applying uniform automated processing.
Solution Approach 2:
Manual alignment data is collected and processed in advance to create a contextual model of reliable alignment patterns. This preliminary learning phase enables the automated system to make reliable alignment decisions by referencing established contextual patterns, improving reliability while maintaining efficiency.
Data Source
AI summary
An approach is provided for aligning one or more drive segments based on a consensus set of user defined inputs. The approach involves, for example, retrieving manual drive alignment data collected from a plurality of human users, wherein the manual drive alignment data indicates one or more regions of at least two drive data segments selected by the human users to align the at least two drive segments. The approach also involves processing the manual drive alignment data to determine a set of common drive alignment locations of the one or more regions. The approach further involves processing a plurality of subsequent drive segments to automatically align the plurality of subsequent drive segments based on the set of common drive alignment locations.