Vehicle Sensor Calibration Using Flat Road Grade Detection
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Solution Overview
Problem
Existing vehicle sensors require calibration to account for sensor bias, which is challenging due to the lack of reliable methods for detecting a flat horizontal road surface during vehicle traversal.
Innovation Solution
A method and system that utilize GPS data, map data, and vehicle dynamics to determine a flat horizontal surface by calculating multiple measures of road grade, adjusting longitudinal force for constant velocity, and calibrating sensors when the surface is confirmed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor calibration is performed during vehicle operation, then sensor alignment and bias correction can be achieved, but reliable detection of flat horizontal road surface becomes challenging
Solution Approach 1:
The flat surface detection is divided into three independent measurement approaches: GPS-based elevation change detection, map data-based grade detection, and vehicle dynamics-based longitudinal force detection. Each method segments the overall detection task and can independently determine road grade, allowing the system to reliably identify flat horizontal surfaces for sensor calibration without requiring a single complex detection mechanism.
Solution Approach 2:
The system introduces multiple intermediary measurement systems (GPS receiver, map data processor, vehicle dynamics sensor) that indirectly measure road grade through different physical principles. These intermediaries translate complex flat surface detection into measurable quantities like elevation changes, map-based grade data, and longitudinal force requirements, making reliable calibration possible during vehicle operation.
2Measurement precision
If multiple measures of road grade are calculated using GPS data, map data, and vehicle dynamics, then accurate flat surface detection is achieved, but system complexity increases
Solution Approach 1:
The system merges three different measurement approaches (GPS-based elevation detection, map data-based grade detection, and vehicle dynamics-based force detection) into a unified flat surface detection system. By combining these independent measurement methods, the system achieves high measurement precision through mutual validation while managing complexity through integrated processing that determines calibration readiness based on consensus among all three measures.
Solution Approach 2:
The detection system performs multiple functions simultaneously: it monitors vehicle position via GPS, processes map data for grade information, measures vehicle dynamics parameters, and determines calibration readiness. This multi-functional approach consolidates what could be separate systems into a single universal detection mechanism that handles all aspects of flat surface identification, reducing overall system complexity.
Data Source
AI summary
A vehicle includes a system for calibrating a sensor of a vehicle. A first measure of road grade is determined for a road section being traversed by the vehicle based on Global Positioning Satellite (GPS) data. A second measure of road grade is determined for the road section using map data and vehicle dynamics data. An applied longitudinal force on the vehicle is controlled to maintain the vehicle at a constant velocity when the first measure and the second measure indicate that the road section is a flat horizontal surface. A third measure of road grade is determined based on the applied longitudinal force. A parameter of the sensor is adjusted when the third measure indicates that the road section is the flat horizontal surface.