Autonomous Vehicle Perception Review for Sensor Parameter Calibration
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting objects due to variations in sensor types and parameters, leading to inconsistencies in object recognition, which can affect their ability to operate safely and efficiently.
Innovation Solution
An apparatus and method that optimize object detection parameters by comparing object labels applied by the vehicle's computing system with manually reviewed images, using an object identification server to verify and adjust sensor parameters, ensuring accurate object recognition across different sensor types and perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the autonomous vehicle uses multiple sensor types and adjustable detection parameters to improve object detection flexibility, then the adaptability of the detection system is improved, but the measurement precision and reliability of object detection deteriorate due to parameter variations and inconsistencies
Solution Approach 1:
The system dynamically adjusts detection parameters such as sensitivity thresholds, detection ranges, and confidence levels based on environmental conditions, object types, and sensor performance characteristics. This allows the system to optimize detection precision for different scenarios while maintaining adaptability across diverse operating conditions.
Solution Approach 2:
The system employs multiple sensor types (cameras, LIDAR, radar) that can function independently or in combination, with each sensor type optimized for specific detection scenarios. This multi-functional approach allows the system to maintain high detection precision across various object types and environmental conditions while preserving overall system adaptability.
2Measurement precision
If the computing system performs numerous calculations with multiple parameters to improve object detection accuracy, then the measurement precision is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The computing system divides object detection into multiple processing stages: initial object candidate identification, parameter-based filtering, detailed classification, and verification. Each stage processes only relevant data with appropriate computational complexity, reducing overall system complexity while maintaining high detection accuracy through progressive refinement.
Solution Approach 2:
The system performs preliminary processing of sensor data including calibration, noise filtering, and feature extraction before main detection algorithms are applied. Detection parameters and thresholds are pre-computed based on historical data and environmental conditions, reducing real-time computational requirements while preserving detection accuracy.
3Measurement precision
If the autonomous vehicle uses manual reviewer verification to improve object detection accuracy, then the measurement precision is improved, but the productivity and response time of the system deteriorate
Solution Approach 1:
Manual reviewer verification is applied selectively only to detection cases that fall below a confidence threshold or exhibit ambiguous characteristics. The majority of clear, high-confidence detections are processed automatically without manual review, maintaining high productivity while improving accuracy for uncertain cases through targeted human verification.
Solution Approach 2:
Manual review results are fed back into the system to continuously refine detection algorithms, adjust parameters, and update training data. This feedback loop progressively improves automated detection accuracy over time, reducing the proportion of cases requiring manual review and thereby increasing overall system productivity while maintaining high precision.
Data Source
AI summary
A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.