AV Simulation Validation via ML Performance Correspondence
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Solution Overview
Problem
Autonomous vehicles face challenges in acquiring sufficient training data to optimize machine-learning models for perception, prediction, and control layers, with simulated environments often deviating from real-world scenarios, making it difficult to identify and address these divergences.
Innovation Solution
A machine-learning model is trained to predict the correspondence between autonomous vehicle performance in simulated and real-world environments using real-world sensor data, allowing for the identification of performance gaps and enabling the creation of high-fidelity simulated environments without relying on physical sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simulated environments are used for AV testing and training, then productivity and data acquisition efficiency are improved, but the fidelity and accuracy of performance evaluation deteriorate due to simulation divergence from real-world scenarios
Solution Approach 1:
The patent implements a feedback mechanism by training a machine learning model to predict the correspondence between simulated and real-world AV performance. The model receives performance metrics from both environments and generates predictions that feedback into the simulation validation process, enabling continuous improvement of simulation fidelity through iterative comparison and model retraining.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between simulated and real-world performance evaluations. This intermediary translates and compares performance metrics from different environments, enabling quantitative assessment of simulation divergence without requiring direct physical testing for every scenario.
2Measurement precision
If high-fidelity simulated environments are created using real-world sensor data, then measurement precision and simulation fidelity are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent creates simplified copies of real-world sensor data to generate simulated environments. Instead of processing and storing all raw sensor data, the system creates representative simulations that capture essential characteristics of real-world scenarios, reducing data processing complexity while maintaining adequate fidelity for performance evaluation.
Solution Approach 2:
The patent transforms real-world sensor data into simulated environment parameters through controlled parameter changes. By adjusting and selecting specific parameters that most influence AV performance, the system generates high-fidelity simulations without requiring complete replication of all real-world data dimensions, thereby managing complexity.
3Measurement precision
If machine-learning models are trained to predict performance correspondence, then the ability to identify simulation divergence is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the performance evaluation process into distinct components: real-world performance measurement, simulated performance measurement, and machine learning model prediction. This segmentation allows each component to be optimized independently and simplifies the overall system by breaking down the complex task of divergence identification into manageable parts.
Solution Approach 2:
The machine learning model serves multiple functions: it predicts performance correspondence, identifies simulation divergence, and validates simulation fidelity. This multi-functionality reduces overall system complexity by consolidating multiple evaluation tasks into a single versatile model rather than requiring separate specialized systems for each function.
Data Source
AI summary
The subject technology provides solutions for evaluating AV performance in simulated test environments. In some aspects, the disclosed technology includes a process for receiving road data comprising autonomous vehicle (AV) sensor data, generating simulation (SIM) data based on the road data, and measuring one or more first performance metrics, wherein the one or more first performance metrics correspond with a performance of an AV in the real-world environment. The process can further include steps for measuring one or more second performance metrics, wherein the one or more second performance metrics correspond with a performance of the AV in the simulated environment, training a machine-learning (ML) model to predict a correspondence between the performance of the AV in the real-world environment and the performance of the AV in the simulated environment. Systems and machine-readable media are also provided.