Autonomous Vehicle Perception Testing With Statistical Error Models
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Solution Overview
Problem
Current methods for simulating autonomous vehicle safety testing are inefficient and fail to accurately model realistic perception outputs, particularly due to the sensitivity of perception components like CNNs to simulated data quality and the difficulty in simulating sensor data like RADAR, leading to high computational costs and limited generalizability to real-world conditions.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to model probabilistic uncertainty distributions based on actual perception outputs, incorporating confounders like lighting and weather, allows for the simulation of realistic perception errors, reducing the need for high-fidelity simulations and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If photorealistic simulation is used to accurately model perception outputs, then simulation realism is improved, but computational cost and device complexity increase significantly
Solution Approach 1:
The patent creates simplified copies of perception outputs by generating synthetic perception data that mimics the statistical properties and error characteristics of real perception systems. Instead of simulating entire photorealistic scenes with complex sensor models, the invention copies the essential statistical patterns of perception errors and uncertainties, achieving realistic simulation behavior with reduced computational complexity.
Solution Approach 2:
The patent transforms the simulation approach by changing from fixed deterministic perception outputs to probabilistic parameterized distributions. By modeling perception errors as statistical distributions with learnable parameters (mean, variance, correlations) that can adapt to different environmental conditions, the system achieves realistic perception behavior without requiring computationally expensive photorealistic rendering of every scene detail.
2Reliability
If the number of test miles is increased to achieve human-level safety guarantees, then safety assurance is improved, but testing time and resources increase
Solution Approach 1:
The patent performs preliminary characterization of perception system behavior by learning statistical models from real-world perception data before actual safety testing. By pre-learning the error distributions, correlations, and environmental dependencies of the perception stack, the system prepares probabilistic models that can be efficiently sampled during simulation, eliminating the need for extensive real-world test miles while maintaining safety assurance.
Solution Approach 2:
The patent replaces the mechanical approach of accumulating physical test miles with a computational sampling approach. Instead of physically driving vehicles to collect safety data, the invention uses probabilistic models to generate and evaluate numerous hypothetical scenarios computationally, substituting physical testing with efficient statistical sampling that achieves the same safety validation goals.
3Measurement precision
If perception components like CNNs are used to improve object detection accuracy, then perception precision is improved, but sensitivity to simulated data quality increases
Solution Approach 1:
The patent uses inexpensive synthetic perception data generated from probabilistic models as disposable test inputs instead of requiring expensive, high-fidelity simulated sensor data. By modeling perception errors statistically, the system can generate unlimited low-cost synthetic test cases that capture the essential challenges of real perception without needing computationally intensive photorealistic rendering, making extensive testing feasible.
4Adaptability or versatility
If sensor data like RADAR is simulated to cover all environmental conditions, then coverage of operational design domain is improved, but simulation complexity and data quality requirements increase
Solution Approach 1:
The patent creates a universal probabilistic modeling framework that can represent multiple sensor types (cameras, LIDAR, RADAR) and environmental conditions through a unified statistical approach. Instead of developing separate complex simulation models for each sensor and condition, the invention uses a single probabilistic modeling paradigm that can be configured to represent different sensor modalities and environmental scenarios, achieving broad ODD coverage with reduced simulation complexity.
Data Source
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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.