End-to-End Vehicle Simulation Fidelity Through Feedback Calibration
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Solution Overview
Problem
Autonomous vehicles face challenges in training and testing due to the cost and time required for physical road tests, and the difficulty in recreating or simulating complex driving scenarios, necessitating a method to evaluate and improve simulation fidelity for end-to-end driving simulations.
Innovation Solution
A computer-implemented system that measures and quantifies the fidelity of end-to-end autonomous vehicle simulations by comparing real-world data with simulated data, using a continuous feedback loop to identify and address gaps in simulation fidelity, and employing a simulation integrity validation model to determine the integrity of the simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical road tests are used for training and testing autonomous vehicles, then the training data is more realistic and accurate, but the cost and time required increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulation environments. These simulations replicate physical road conditions, traffic patterns, and environmental factors, allowing autonomous vehicle training without physical road tests. The virtual copies maintain sufficient fidelity to provide realistic training data while eliminating the time and cost constraints of physical testing.
Solution Approach 2:
The system adjusts simulation parameters such as scene complexity, weather conditions, traffic density, and vehicle dynamics to match real-world conditions. By dynamically changing these parameters, the simulation can reproduce diverse driving scenarios efficiently, providing accurate training data without the linear time requirements of physical road tests.
2Measurement precision
If physical road tests are used for training and testing autonomous vehicles, then the training data is more realistic and accurate, but the cost increases significantly
Solution Approach 1:
Virtual simulation environments serve as cost-effective copies of expensive physical road tests. The simulation platform replicates real-world driving conditions, sensor data, and vehicle responses without requiring actual vehicles, test tracks, or safety personnel, dramatically reducing testing costs while maintaining training data quality.
Solution Approach 2:
The system uses computationally efficient simulation models that can be rapidly instantiated and discarded for each training iteration. These virtual test scenarios are inexpensive to generate and execute compared to physical road tests, allowing extensive training without proportional cost increases.
3Measurement precision
If complex driving scenarios are simulated, then the simulation fidelity improves, but the difficulty in recreating or simulating these scenarios increases
Solution Approach 1:
The simulation system breaks down complex driving scenarios into modular components such as environmental elements, traffic participants, road conditions, and vehicle dynamics. Each component can be independently configured and validated, making it easier to recreate high-fidelity scenarios without overwhelming system complexity.
Solution Approach 2:
The patent introduces intermediate representation layers that bridge real-world complexity and simulation simplicity. These intermediaries include standardized scene description formats, physics engine abstractions, and sensor model interfaces that simplify the process of recreating complex scenarios while maintaining fidelity.
Data Source
AI summary
Systems and methods for improving an end-to-end (E2E) vehicle simulation in replicating a real-world driving scene based on a continuous feedback loop are provided. For example, a method includes receiving first data collected from a real-world driving scene, the first data associated with the real-world driving scene and a performance of a real-world vehicle in the real-world driving scene; and adjusting, for a current iteration, a parameter associated with an E2E vehicle simulation that simulates operations of a simulated vehicle driving in a simulated driving scene, wherein the adjustment is based on a fidelity value of the E2E vehicle simulation in a previous iteration, and the fidelity value is based on a comparison between second data collected from the E2E simulation in the previous iteration and the first data.