Simulation Renderer Attribution for AV Pose Divergence
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Solution Overview
Problem
Autonomous vehicle (AV) training and testing in simulated environments face challenges due to simulation divergence, where the simulated environment differs from real-world scenarios, leading to inaccuracies in object placement, sensor emulation, and AV behavior modeling, resulting in significant performance aberrations.
Innovation Solution
A system that uses machine-learning models to identify and quantify contributions of simulation renderer components to simulation divergence by analyzing divergence metrics, allowing for the attribution of errors to specific components and improving the fidelity of rendered environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation renderer components are used to generate simulated environments, then AV testing and training can be performed, but simulation divergence occurs causing inaccuracies in object placement, sensor emulation, and AV behavior modeling
Solution Approach 1:
The patent segments the simulation renderer into multiple independent components (e.g., object placement module, sensor emulation module, behavior modeling module) and individually evaluates each component's contribution to simulation divergence using attribution tools. This allows identifying which specific component causes the most pose divergence, enabling targeted improvements rather than blanket optimization.
2Adaptability or versatility
If multiple simulation renderer components are used to create comprehensive simulated environments, then more realistic AV testing is possible, but it becomes difficult to identify which specific component causes simulation divergence
Solution Approach 1:
The patent implements a feedback mechanism where the attribution tool analyzes the output of multiple simulation renderer components and provides information about which component contributes most to simulation divergence. This feedback loop enables developers to identify and correct issues in specific components while maintaining the comprehensiveness of the overall simulation system.
Data Source
AI summary
The disclosed technology provides solutions for improving simulation generation, and in particular for diagnosing problems with a simulation renderer configured to generate simulated (or synthetic) environments for use in autonomous vehicle (AV) testing and training. In some aspects, a process of the disclosed technology includes steps for receiving a set of divergence metrics, wherein the divergence metrics comprise performance statistics for one or more components of a simulation renderer, providing the divergence metrics to a machine-learning model, and identifying, using an attribution tool, one or more components of the simulation renderer that contributed to the AV pose divergence based on one or more weights of the machine-learning model. Systems and machine-readable media are also provided.