Perception Quality Evaluation Using Augmented Sensor Streams
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Solution Overview
Problem
Existing perception systems in robotics and autonomous vehicles struggle to detect and predict perception failures, such as misclassifications and false detections, which are often triggered by subtle changes in input data and are difficult to identify in both offline and online operational modes.
Innovation Solution
A method and system that involves data augmentation of sensor data streams to replicate potential changes, comparing the results of the original and augmented data through perception algorithms to identify perception weaknesses and failures by analyzing differences in detection lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perception systems use standard processing without data augmentation, then processing speed is maintained, but perception failures such as misclassifications and false detections occur due to inability to detect subtle changes in input data
Solution Approach 1:
The system performs data augmentation in advance to create multiple variations of input data before actual perception processing. By pre-generating augmented data samples with subtle transformations, the system prepares for potential perception failures ahead of time, allowing the main perception algorithm to operate on already-processed data without real-time computational overhead.
Solution Approach 2:
The system creates multiple copies of the original sensor data with different augmentations (rotations, translations, noise additions, etc.). These copied and augmented data samples are then processed through the perception system to identify consistent detection patterns. This copying approach allows failure detection without requiring fundamentally new processing architecture.
2Reliability
If perception systems process multiple augmented data streams, then perception failures are detected more effectively, but processing time and computational resources increase
Solution Approach 1:
The system applies data augmentation with varying degrees of transformation - some samples receive mild augmentations while others receive more extreme transformations. This partial application approach allows the system to detect perception failures across a spectrum of data variations without processing every possible augmentation, balancing detection capability with computational efficiency.
Solution Approach 2:
The perception evaluation system operates periodically by evaluating detection lists at intervals rather than continuously processing every augmented sample in real-time. The system compares detection results from augmented data streams at periodic evaluation points, reducing continuous processing overhead while maintaining effective failure detection capability.
3Measurement precision
If perception systems compare multiple detection lists from augmented data, then brittle detection patterns are identified, but system complexity and computational overhead increase
Solution Approach 1:
The system segments the detection evaluation process into distinct components: generating augmented data samples, processing each sample through the perception system, collecting detection results, and comparing detection lists for consistency. This segmentation allows each component to be optimized independently and simplifies the overall complexity by breaking down the complex comparison task into manageable stages.
Solution Approach 2:
The system introduces an intermediary evaluation layer that sits between the perception system and the final detection output. This intermediary component compares detection lists from augmented data streams and identifies discrepancies or brittle patterns. The intermediary acts as a mediator that translates complex multi-stream comparisons into simplified consistency metrics without requiring fundamental changes to the core perception system.
Data Source
AI summary
A system and method for measuring the performance of a perception system is provided that includes obtaining a pair of sensor data input streams to be evaluated by the perception system. Data augmentation is then added into at least one of the pair of sensor data input streams and the pair of sensor data input streams is then processed with the perception system to obtain a first detection list and a second detection list. The first detection list is then compared with the second detection list to identify one or more perception weaknesses.