Perception Output Validation Using Multi-Module Ground Truth
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying perception errors, which can lead to false negatives or false positives, affecting their ability to safely navigate and perform tasks, as existing systems lack effective validation mechanisms to ensure the accuracy of perception outputs.
Innovation Solution
The implementation of independent perception validation modules, which are configured and trained differently, using machine-learning models to generate perception outputs independently and compare them with the main perception module outputs to identify discrepancies, with a consensus rule determining the ground-truth perception output through voting mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If independent perception validation modules are implemented to identify perception errors, then perception output accuracy is improved, but device complexity increases
Solution Approach 1:
Independent perception validation modules are introduced as intermediary components that receive sensor data and generate perception outputs separately from the main perception module. These validation modules act as mediators to cross-check the main perception module's outputs, identifying discrepancies through comparison. The validation modules are configured and trained differently to provide diverse validation perspectives, thereby improving perception accuracy without requiring complete system redesign.
2Reliability
If multiple perception modules with different configurations are used for validation, then error identification capability is improved, but computational resources required increase
Solution Approach 1:
The system implements partial validation by comparing perception outputs from multiple modules only when necessary, rather than continuously processing all validation data at full capacity. The validation mechanism selectively activates discrepancy detection and escalation processes based on the need for verification, reducing unnecessary computational overhead while maintaining reliable error identification capability when actual perception errors occur.
3Measurement precision
If perception outputs from multiple modules are compared using voting mechanisms, then ground-truth determination accuracy is improved, but processing time increases
Solution Approach 1:
Perception outputs from multiple validation modules are generated in advance and stored before final ground-truth determination is required. The voting mechanism processes these pre-generated outputs to identify discrepancies and determine ground truth, rather than performing real-time comparison during critical decision-making moments. This preliminary generation and storage of validation outputs reduces processing time when ground-truth determination is urgently needed.
Data Source
AI summary
The disclosed technology provides solutions for validating/verifying perception outputs, e.g., using multiple perception modules. In some aspects, a process of the disclosed technology can include steps for receiving sensor data, providing the sensor data to each of a plurality of perception modules, receiving a perception output from each of the plurality of perception modules, and determining a ground-truth perception output based on the perception outputs received from each of the plurality of perception modules. Systems and machine-readable media are also provided.