LIDAR Intensity Calibration for Real-Time Road Marking Change Detection
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Solution Overview
Problem
LIDAR intensity values in autonomous vehicles are affected by factors like angle of incidence, range, surface composition, and moisture, leading to inconsistent results and requiring time-consuming offline calibration, which is challenging for large-scale operations and managing different sensor models.
Innovation Solution
A system for simultaneous online LIDAR intensity calibration and road marking change detection using a normal distribution lookup table that cross-calibrates intensities from one modality to another, eliminating the need for prior knowledge of sensor model characteristics and attributes, and allowing for real-time detection of road marking changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline calibration procedures are used to ensure consistent LIDAR intensity results, then measurement precision is improved, but loss of time increases due to the time-consuming nature of offline calibration
Solution Approach 1:
The system performs preliminary calibration actions by pre-computing lookup tables containing calibration parameters for different surface types and conditions. These pre-computed calibration data are stored and readily available for immediate use during online operations, eliminating the need for time-consuming offline calibration procedures while maintaining measurement precision.
Solution Approach 2:
The system creates simplified representations of calibration data in the form of lookup tables that capture the essential calibration information. These lookup tables serve as copies of the full calibration datasets, allowing rapid access to calibration parameters without processing the complete offline calibration data during online operations.
2Measurement precision
If offline calibration is performed for each sensor model to achieve accurate intensity values, then measurement precision is improved, but device complexity increases due to managing different sensor models
Solution Approach 1:
The system implements a universal calibration approach using lookup tables that can accommodate multiple sensor models and surface types through a unified framework. The lookup tables contain generalized calibration parameters that can be applied across different sensor models, eliminating the need for separate calibration procedures for each sensor type and reducing overall system complexity.
Solution Approach 2:
The system manages different sensor models by changing calibration parameters based on the specific sensor type and surface conditions. Instead of maintaining separate calibration systems for each sensor model, the lookup tables store multiple sets of calibration parameters that can be selected and applied according to the active sensor model and detected surface type, simplifying sensor model management.
3Difficulty of detecting and measuring
If LIDAR intensity values are used for road marking detection, then detection capability is improved, but reliability decreases due to inconsistent intensity values caused by angle of incident, range, surface composition, and moisture
Solution Approach 1:
The system applies local quality calibration by using lookup tables that contain specific calibration parameters tailored to different surface types and conditions. Instead of using a single global calibration parameter, the system selects calibration parameters that are locally optimized for the specific surface type and environmental conditions detected, improving both detection capability and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms that use detected surface type and environmental conditions to dynamically select appropriate calibration parameters from the lookup tables. This feedback loop ensures that the most appropriate calibration parameters are applied based on real-time conditions, compensating for variations in angle of incident, range, surface composition, and moisture.
Data Source
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AI summary
A system, method, and computer program for updating calibration lookup tables within an autonomous vehicle or transmitting roadway marking changes between online and offline mapping files is disclosed. A LIDAR sensor may be used for generating an online (rasterized) mapping file with online intensity values which are compared against a correlated offline (rasterized) mapping file having offline intensity values. The online intensity value may be used to acquire a lookup table having a normal distribution that is compared against the offline intensity value. The lookup table may be updated when the offline intensity value is within the normal distribution. Or the vehicle may transmit a roadway marking change when the offline intensity value is outside the normal distribution.