Robotic Perception Testing With Statistical Error Models
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Solution Overview
Problem
Current methods for simulating autonomous vehicle perception systems are inefficient and fail to accurately model realistic perception errors, particularly due to the sensitivity of neural networks to simulated data quality and the difficulty in simulating sensor data like RADAR, leading to high computational costs and limited generalizability to real-world scenarios.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to model perception errors probabilistically, based on actual perception outputs, allowing for realistic perception outputs to be generated without simulating sensor data, focusing on probabilistic uncertainty distributions and set-to-set modeling of multiple objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If photorealistic simulation is used to model perception systems, then realism of simulated scenarios is improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent creates simplified statistical copies of perception system behavior through PSPMs that replicate the probabilistic output characteristics of neural networks without requiring full photorealistic simulation. These models copy the essential uncertainty distributions and error patterns of real perception systems while using significantly fewer computational resources.
Solution Approach 2:
The patent transforms the simulation approach by changing from simulating raw sensor data and full perception pipelines to directly modeling the statistical parameters of perception outputs. PSPMs use learned probability distributions (mean, covariance, higher-order moments) to represent perception uncertainty, fundamentally altering the simulation parameters from pixel-level or point-cloud-level details to statistical moment parameters.
2Measurement precision
If full photorealistic simulation with sensor data simulation is implemented, then accuracy of perception error modeling is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts only the essential statistical characteristics of perception outputs (uncertainty distributions, error patterns, moment parameters) from the complex perception pipeline, separating these critical features from the computationally intensive sensor simulation and full perception processing that are unnecessary for safety testing purposes.
Solution Approach 2:
The patent replaces expensive, computationally intensive photorealistic simulation with inexpensive statistical models that can be rapidly evaluated. PSPMs use simple probabilistic calculations based on learned parameters rather than running full neural networks on simulated sensor data, making the simulation process computationally affordable and scalable.
3Measurement precision
If neural networks are used for object detection in simulation, then perception accuracy is improved, but sensitivity to simulated data quality causes performance deterioration
Solution Approach 1:
The patent copies the behavioral characteristics and uncertainty patterns of neural networks through statistical models rather than using the neural networks themselves in simulation. PSPMs replicate the detection accuracy and error patterns of trained perception systems by learning from real perception outputs, avoiding the sensitivity issues of running actual neural networks on imperfect simulated data.
Data Source
AI summary
A computer-implemented method of modelling a perception system for perceiving objects captured in sensor data comprises: receiving a plurality of training examples, each comprising a ground truth scene for a set of sensor data and a corresponding perceived scene obtained by applying the perception system to the set of sensor data; fitting to the training examples noise model parameters, encoding a noise distribution over perceived scenes given a misdetection scene, and misdetection model parameters, encoding a misdetection distribution over misdetection scenes given a ground truth scene; computing a perception distribution over perceived scenes for a given ground truth scene by marginalizing the product of noise and misdetection distributions over multiple misdetection scenes, wherein individual objects in the ground truth scene are not associated with individual objects in the perceived scenes; fitting the noise and misdetection model parameters to match the perception distribution to the perceived scene for each training example.