Surface Bias Estimation for Road Cross-Slope Measurement Correction
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Solution Overview
Problem
Existing visual measurement systems for vehicles assume a flat ground plane, leading to erroneous object measurements due to road cross-sloping, which affects the accuracy of keypoint location estimation.
Innovation Solution
A surface bias estimation system using factor graphs to process probe trace data, correcting keypoint locations by estimating and accounting for road cross-sloping through a factor graph optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a flat ground plane assumption is used in visual measurement systems, then the system complexity is reduced and ease of operation is improved, but measurement precision deteriorates due to road cross-sloping
Solution Approach 1:
The system changes the parameter representation by introducing a bias term that accounts for road cross-sloping effects. Instead of assuming a flat ground plane, the system modifies the measurement model to include slope compensation parameters, allowing accurate measurements on sloped surfaces while maintaining system simplicity
Solution Approach 2:
The patent introduces an intermediary bias estimation mechanism that mediates between the flat ground plane assumption and the actual sloped road surface. This bias term acts as a mediator that corrects the discrepancy between the simplified model and reality, improving measurement precision without complicating the overall system architecture
2Measurement precision
If road cross-sloping is accounted for in visual measurement systems, then measurement precision is improved, but device complexity increases due to the need for bias estimation and correction mechanisms
Solution Approach 1:
The system incorporates slope compensation by changing the mathematical parameters of the measurement model. A bias term is added to account for cross-sloping effects, allowing the system to maintain simplicity while improving precision through parameter modification rather than structural complexity
Solution Approach 2:
The problem is segmented by separating the bias estimation from the main visual measurement process. The bias is estimated independently using probe trace data and then applied as a correction to the keypoint locations, dividing the complex task into manageable segments that can be processed separately
3Measurement precision
If factor graph optimization is used to estimate surface bias, then measurement precision is improved, but loss of time increases due to the computational processing required
Solution Approach 1:
The system performs preliminary bias estimation using factor graph optimization on collected probe trace data before actual measurement operations. By pre-computing the bias characteristics of the road surface, the system avoids real-time computational delays during critical measurement phases, reducing time loss while maintaining precision
Solution Approach 2:
The system applies partial optimization by focusing the factor graph computation only on the bias parameters rather than optimizing all measurement parameters. This selective approach reduces computational burden and time loss while still achieving the necessary measurement precision through targeted bias correction
Data Source
AI summary
System, methods, and other embodiments described herein relate to implementing surface bias estimation strategies. In one embodiment, a method includes processing probe trace data with a factor graph having nodes and factors that describe an estimate of surface bias; and correcting the probe trace data based on the estimate of surface bias.