Perception Statistical Models for Realistic Robotic Safety Testing
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Solution Overview
Problem
Current approaches to simulation-based safety testing for autonomous vehicles are inefficient and unrealistic, particularly in modeling perception errors and computational efficiency, making it challenging to achieve human-level safety standards.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to model perception errors in terms of probabilistic uncertainty distributions, allowing for the simulation of realistic perception outputs that are more robust and efficient than photorealistic simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If photorealistic simulation is used to model perception errors, then realism of simulation is improved, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent creates a simplified copy of the perception system's error characteristics through statistical models rather than replicating the full photorealistic simulation pipeline. PSPMs capture the essential perception errors (false positives, false negatives, detection inaccuracies) through probabilistic distributions derived from real sensor data, avoiding the need to render and process realistic images while preserving the critical error patterns that affect safety testing.
Solution Approach 2:
The patent transforms the complex visual perception problem into a statistical parameter estimation problem. Instead of simulating visual scenes and running perception algorithms on them, the system models perception errors by estimating parameters of probability distributions (mean, variance, correlation structures) from real sensor data and simulation ground truth, then uses these parameters to generate realistic perception outputs efficiently.
2Reliability
If the number of test miles is increased to achieve human-level safety standards, then safety assessment reliability is improved, but testing time and resources increase
Solution Approach 1:
The patent enables continuous safety testing by allowing simulations to be run in parallel without requiring sequential real-world testing. Multiple simulated scenarios can be executed simultaneously using the PSPM framework, generating large volumes of safety-critical test cases much faster than physical testing, while maintaining the statistical rigor needed for safety certification.
Solution Approach 2:
The patent performs preliminary modeling of perception errors using real sensor data and ground truth annotations before conducting safety testing. By pre-computing the statistical characteristics of perception errors from available data, the system prepares the PSPMs in advance, allowing subsequent safety simulations to proceed efficiently without needing to collect and process additional real-world data during the testing phase.
3Measurement precision
If perception stack changes are made to improve detection accuracy, then perception performance is improved, but the number of test miles required increases
Solution Approach 1:
The patent implements a feedback mechanism where PSPMs are trained on real perception data and then used to generate synthetic perception outputs that are fed back into the safety testing pipeline. This closed-loop approach allows the system to automatically evaluate how perception stack changes affect safety metrics, providing immediate feedback on whether improvements in detection accuracy actually translate to better safety performance without requiring extensive additional real-world testing.
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 PSPM comprises a time-dependent model such that the perception output sampled at the current time instant depends on at least one of: an earlier one of the perception outputs sampled at a previous time instant, and an earlier one of the perception ground truths computed for a previous time instant.