Perception Performance Modeling for Autonomous Vehicle Safety Testing
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Solution Overview
Problem
Current methods for testing autonomous vehicle safety are inefficient and impractical, as they require extensive real-world testing or high-fidelity simulations that are computationally expensive and fail to accurately model perception errors, particularly those caused by weather, lighting, and sensor data quality.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to simulate realistic perception outputs by modeling probabilistic uncertainty distributions based on actual perception outputs, allowing for efficient and robust safety testing by directly computing perception ground truths and applying probabilistic uncertainty distributions to simulate various real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world testing is used to ensure safety, then safety reliability is improved, but testing time and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios by capturing actual sensor data and reconstructing them in a simulation environment. These synthetic scenarios preserve the statistical properties and edge cases of real-world data while enabling unlimited replay and variation generation, thus achieving safety testing without additional real-world driving time
Solution Approach 2:
The system performs preliminary capture and annotation of real-world scenarios during normal operation, storing them for later use. This allows the actual safety-critical testing to be performed in simulation using pre-collected data, separating the data collection phase from the intensive testing phase and enabling parallel processing
2Measurement precision
If photorealistic simulation is used to model perception accurately, then perception accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent extracts only the essential perceptual elements from photorealistic simulations by using trained perception models to generate ground truth data. Instead of rendering complete photorealistic scenes, the system directly computes ground truth perceptions from simplified scene representations, retaining accuracy while eliminating redundant rendering computations
Solution Approach 2:
The system replaces complex photorealistic rendering mechanics with statistical perception models trained on real data. Rather than simulating light transport, material properties, and sensor physics, the patent uses machine learning models that directly map scene features to perception outputs, achieving equivalent accuracy with fraction of the computational cost
3Reliability
If the number of test miles is increased to reduce errors per decision, then safety reliability is improved, but testing resources and time increase
Solution Approach 1:
The patent merges multiple instances of the same driving scenario into a single test case by capturing variations through fuzzy logic. Instead of requiring separate real-world testing for each scenario variation, the system combines them into unified synthetic test cases that represent entire classes of scenarios, multiplying testing efficiency while maintaining comprehensive safety coverage
Solution Approach 2:
The system varies scenario parameters systematically by applying fuzzy logic to captured scenarios, generating controlled variations in environmental conditions, object positions, and sensor readings. This allows efficient exploration of parameter space to stress-test the autonomous vehicle system without requiring proportional increases in real-world testing
Data Source
AI summary
Herein, a “perception statistical performance model” (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.