Neural Network Surrogate for Autonomous Agent Sensor Verification
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Solution Overview
Problem
Current full-stack verification systems for autonomous agents face challenges in recreating high-fidelity sensor information, leading to significant processing time and bottlenecks, particularly in generating photo-realistic sensor data, which hinders efficient simulation and safety verification of autonomous systems interacting with humans.
Innovation Solution
A method and system that train a convolutional neural network to learn a noise model associated with ground truth 3D sensor image data, replacing the object detection module with the neural network and using ground truth information to simulate sensor data, thereby generating system-simulated sensor information for the planner module, reducing the need for photo-realistic rendering frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If photo-realistic sensor data generation is used for full-stack verification, then high-fidelity simulation of sensor information is achieved, but processing time increases significantly and bottlenecks occur
Solution Approach 1:
The patent creates a simplified copy of the sensor data generation process using a neural network that replicates the essential characteristics of photo-realistic sensor data without requiring computationally expensive rendering. The neural network learns to map ground truth 3D sensor data to simulated sensor information, producing sufficiently accurate data for verification while dramatically reducing processing time.
Solution Approach 2:
The patent replaces the mechanical/photo-realistic rendering system with a neural network-based system. Instead of using complex rendering frameworks to generate photo-realistic sensor data, the invention substitutes this with a learned model that directly generates simulated sensor information from ground truth data, eliminating the computational bottleneck while maintaining verification quality.
2Productivity
If ground truth information is used to simulate sensor data, then processing efficiency is improved, but the complexity of the verification system increases
Solution Approach 1:
The patent introduces a neural network as an intermediary component between ground truth 3D sensor data and the verification process. This intermediary learns the noise model and data transformation characteristics, enabling efficient simulation without requiring complex photo-realistic rendering pipelines. The neural network acts as a bridge that simplifies the overall system architecture while maintaining verification fidelity.
3Speed
If neural network is trained to learn noise model, then sensor data generation speed increases, but training time and computational resources are required
Solution Approach 1:
The patent performs the computationally intensive neural network training in advance, before the actual verification process. By pre-training the neural network to learn the noise model and data transformation characteristics, the system prepares a ready-to-use model that can rapidly generate simulated sensor data during verification without requiring real-time training. This separates the one-time training cost from the ongoing verification operations.
Data Source
AI summary
A method for full-stack verification of autonomous agents includes training a neural network to learn a noise model associated with an object detection module of an autonomous agent system of an autonomous vehicle. The method also includes replacing the object detection module of the autonomous agent system with the neural network and a sensory input of the object detection module with ground truth information to apply a surrogate function to the ground truth information. The method further includes verifying the autonomous agent system including the trained neural network to apply the surrogate function in response to the ground truth information to simulate sensor information data to at least a planner module of the autonomous agent system. The method also includes controlling a behavior of the autonomous vehicle using the verified autonomous agent system including the object detection module.