Vehicle Pose Integrity Monitoring via Semantic Map Correlation
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Solution Overview
Problem
Existing autonomous vehicle driving applications face inaccuracies in estimating vehicle pose due to conflicting sensor signals, leading to potentially hazardous events such as wrong turns, unsafe lane changes, and collisions, as they fail to accurately detect abnormal sensor functioning.
Innovation Solution
A vehicle pose integrity monitor system that utilizes a semantically-labeled digital map to correlate observed sensor data with expected features, computing localization integrity through correlation coefficients and probability distributions to ensure accurate vehicle pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor measurements are used to estimate vehicle pose, then the vehicle can navigate autonomously, but the pose estimation may be inaccurate due to conflicting sensor signals
Solution Approach 1:
The system uses semantic map data as feedback to validate and correct sensor-based pose estimates. By continuously comparing observed semantic features with expected features from the map, the system detects inconsistencies and adjusts the pose estimate accordingly, resolving conflicts between different sensor signals through iterative validation.
Solution Approach 2:
The semantic map serves as an intermediary between raw sensor measurements and the final pose estimate. Instead of directly trusting sensor outputs, the system uses the map as a mediator to verify whether the sensed environment matches the expected environment, thereby improving pose accuracy without requiring direct sensor-to-pose mapping.
2Measurement precision
If multiple sensors are used to improve measurement accuracy, then more data is available for pose estimation, but conflicting signals from different sensors increase the risk of inaccurate estimates
Solution Approach 1:
The system implements a feedback mechanism where the semantic map provides ground truth information to validate sensor measurements. When multiple sensors provide conflicting data, the map-based validation feedback helps identify which sensor readings are consistent with the actual environment, thereby maintaining reliability while utilizing multiple sensors.
Solution Approach 2:
The system performs preliminary validation of sensor data against the semantic map before final pose estimation. By pre-checking whether sensed features match expected map features, the system filters out conflicting or erroneous sensor signals before they can compromise the reliability of the pose estimate.
3Reliability
If the system validates sensor data against the digital map, then pose estimation integrity is improved, but the computational complexity increases
Solution Approach 1:
The system extracts only the relevant semantic features from the full digital map data that are necessary for validation. Instead of comparing all map information against sensor data, the system identifies and extracts specific features (such as lane markings, curbs, buildings) that are currently observable and relevant, reducing computational complexity while maintaining validation effectiveness.
Solution Approach 2:
The validation process focuses on local semantic features in the immediate vicinity of the vehicle rather than the entire map. By applying validation only to the local field of view and comparing it with corresponding local map data, the system achieves pose integrity verification with reduced computational burden compared to global map comparison.
4Measurement precision
If the system computes localization integrity using correlation coefficients, then the accuracy of pose validation is improved, but the processing time increases
Solution Approach 1:
The system computes correlation coefficients only for the most relevant and discriminative semantic features rather than all possible features. By selecting a subset of key features (such as prominent landmarks or unique road markings) that provide the most validation information, the system achieves accurate pose validation with reduced computation time compared to exhaustive feature comparison.
Solution Approach 2:
The system extracts and prioritizes the most informative semantic features for correlation computation. By identifying and extracting only the features that provide the highest validation value (such as unique or highly distinguishable features), the system reduces the number of correlation calculations needed while maintaining high validation accuracy.
Data Source
AI summary
Subject matter disclosed herein may relate to systems, devices and/or processes for monitoring and/or evaluating vehicle pose integrity for use with an autonomous driving controller. A system may include memory, the memory comprising one or more memory devices and a processor coupled to the one or more memory devices, the processor configured to access, from the one or more memory devices, sensor measurements generated by one or more sensors mounted in a vehicle. The system may additionally observe one or more semantic classes to one or more physical entities based on the generated sensor measurements and extract one or more semantically-labeled map parameters from an electronic map the system may additionally determine a correlation between the one or more observed semantic classes and one or more expected classes extracted from the semantically-labeled map parameters.