Surrogate Road Response Model for Faster AV Simulation Evaluation
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Solution Overview
Problem
Training and testing autonomous vehicles (AVs) in physical environments is costly and time-consuming, and it is challenging to recreate all driving scenarios, especially those that are dangerous or difficult to simulate.
Innovation Solution
A surrogate model, known as the road data response prediction model, is generated to simulate real-world driving scenarios, allowing for the evaluation of AV performance in a virtual environment by mapping input features to statistical measures of sensor responses, enabling efficient testing and continuous improvement of simulation fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical testing and training of autonomous vehicles is conducted, then realistic performance evaluation is achieved, but time consumption and costs increase significantly
Solution Approach 1:
The patent creates a surrogate model that copies the input-output behavior of complex sensor simulations. Instead of running time-consuming physical tests or detailed simulations, the surrogate model provides a simplified copy that predicts sensor responses based on scene features, achieving fast performance evaluation while maintaining reasonable accuracy.
Solution Approach 2:
The surrogate model acts as an intermediary between the complex simulation environment and the performance evaluation process. It mediates by translating scene features into predicted sensor responses, eliminating the need for direct engagement with the computationally intensive simulation system while preserving the essential evaluation capabilities.
2Adaptability or versatility
If complex simulation environments are created to replicate all driving scenarios, then comprehensive testing coverage is achieved, but computational resources and time required increase
Solution Approach 1:
The patent extracts only the essential input-output behavior from the complex simulation system. The surrogate model captures the relationship between scene features and sensor responses without including the full complexity of the original simulation, thereby reducing computational requirements while maintaining the ability to evaluate performance across diverse driving scenarios.
Solution Approach 2:
The surrogate model changes the parameters of the evaluation system by using simplified scene features as inputs rather than full simulation environments. This parameter transformation allows the system to maintain versatility in handling different driving scenarios while dramatically improving computational efficiency and productivity.
3Measurement precision
If detailed sensor simulations are run to accurately predict sensor responses, then measurement accuracy is improved, but computational time and resources increase
Solution Approach 1:
The surrogate model creates a simplified copy of the sensor simulation's input-output behavior. It replicates the essential prediction capability without the computational burden of the detailed simulation, providing fast sensor response predictions that are sufficient for performance evaluation purposes.
Solution Approach 2:
The surrogate model performs partial action by capturing only the necessary input-output relationship rather than the complete simulation physics. This partial modeling approach achieves sufficient prediction accuracy for evaluation while dramatically reducing computational time and resource requirements.
Data Source
AI summary
Systems and methods for generation and application of a road data response prediction model for evaluation of vehicle simulation are provided. A computer-implemented system, including one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations including receiving road data collected from a reference driving scene; extracting, from the road data, features of the reference driving scene; obtaining, from the road data, response data associated with a vehicle in the reference driving scene and responsive to the extracted features; calculating a statistical measure of the response data; and generating a road data response prediction model to map the extracted features of the road data to the statistical measure of the response data.