Perception Statistical Models for Autonomous Vehicle Runtime Testing
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Solution Overview
Problem
Current approaches to performance testing of autonomous vehicles are inefficient and impractical, particularly when aiming for a level of safety matching human drivers, due to the complexity of simulating realistic perception outputs and the computational resources required.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to simulate realistic perception outputs by modeling perception errors in terms of probabilistic uncertainty distributions, based on robust statistical analysis of actual perception outputs, allowing for more efficient and robust safety testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full photorealistic simulation is used to accurately model perception outputs, then realism and accuracy of testing improve, but computational complexity and resource requirements increase enormously
Solution Approach 1:
The patent creates simplified copies of perception outputs by generating synthetic sensor data that mimics the statistical properties of real perception outputs without requiring full photorealistic simulation. This allows accurate modeling of perception errors while avoiding the computational burden of complete scene reconstruction
Solution Approach 2:
The patent changes the approach from simulating complete visual scenes to directly modeling perception output parameters using statistical distributions. By parameterizing perception errors based on ground truth data, the system achieves accurate error modeling with significantly reduced computational requirements
2Reliability
If the number of test miles is increased to reduce errors per decision to human-level safety, then safety level improves, but testing time and resources increase prohibitively
Solution Approach 1:
The patent performs preliminary modeling of perception errors using statistical distributions derived from ground truth data before actual safety testing. This pre-characterization of perception uncertainties allows the system to evaluate safety performance through simulation without requiring extensive real-world test miles
Solution Approach 2:
The patent introduces statistical performance models as an intermediary between ground truth data and safety evaluation. These models serve as mediators that translate real-world perception characteristics into simulatable error distributions, enabling efficient safety testing at human-level precision
3Productivity
If planning-level simulation with fuzzed variables is used to test variations of real scenarios, then testing efficiency improves, but perception errors and their effects are not adequately accounted for
Solution Approach 1:
The patent replaces the mechanical approach of physically simulating perception processes with a statistical modeling approach. Instead of attempting to mechanically reproduce perception errors through complex sensor simulation, the system uses statistical distributions to model perception output uncertainties, achieving both efficiency and accuracy
Data Source
AI summary
Herein, a “perception statistical performance model” (PSPM) for modelling 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; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled. The modelled perception slice includes an online error estimator, and the computer system is configured to use the PSPM to obtain a predicted online error estimate for the perception output in response to the perception ground truth. This recognizes that online perception error estimates may, themselves, be subject to error.