Autonomous Vehicle Pose Validation Using Lidar Range Images
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Solution Overview
Problem
Current autonomous vehicle systems require human validation for pose validation, which introduces errors and can lead to dangerous mislocalization, as they may contain false positives, necessitating a method to automate this process for enhanced safety and accuracy.
Innovation Solution
The proposed solution automates autonomous vehicle pose validation using a combination of real-time lidar data and high-definition maps, employing registration algorithms and machine learning techniques to align query and reference point clouds, and validate the localization point cloud solutions through statistical and machine learning methods, reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators validate autonomous vehicle pose solutions, then validation can be performed with human judgment, but human error may lead to incorrect validation of false positive solutions
Solution Approach 1:
The patent introduces an automated validation system that acts as an intermediary between pose estimation and navigation. This system uses multiple validation techniques including checking if the pose solution is consistent with vehicle dynamics, verifying against map data, and using machine learning models to detect false positives, thereby eliminating human error while maintaining validation accuracy
Solution Approach 2:
The patent implements feedback mechanisms where the validation system continuously monitors pose solutions and provides corrective feedback. When false positives are detected through multiple validation checks, the system feeds back to reject invalid solutions and request re-estimation, creating a closed-loop system that improves both reliability and measurement precision
2Reliability
If automated validation systems are implemented, then human error is reduced, but system complexity increases
Solution Approach 1:
The patent divides the validation system into multiple independent modules: vehicle dynamics consistency check, map data verification, machine learning-based false positive detection, and cross-validation algorithms. Each module handles a specific aspect of validation, making the overall complex system manageable through functional segmentation
Solution Approach 2:
The patent creates a multi-functional validation system that performs multiple validation tasks simultaneously using the same infrastructure. The system can validate pose solutions through various methods (dynamics consistency, map matching, ML classification) without requiring separate hardware systems, thereby managing complexity through universal resource utilization
3Measurement precision
If multiple validation techniques are used, then false positive detection improves, but processing time increases
Solution Approach 1:
The patent performs preliminary filtering using fast checks before applying more computationally intensive validation methods. The system first checks basic consistency with vehicle dynamics and map data, then applies machine learning models only to borderline cases, thereby reducing overall processing time while maintaining high validation accuracy
Solution Approach 2:
The patent implements a tiered validation approach where not all validation techniques are applied to every pose solution. The system uses a combination of mandatory fast checks and optional deeper validation based on confidence levels, performing partial validation for high-confidence solutions and excessive validation only when necessary, optimizing the balance between accuracy and processing time
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.