Autonomous Vehicle HIL Simulation Using Latency Profiles
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Solution Overview
Problem
Current methods for predicting the behavior of autonomous vehicles are inefficient, often requiring extensive testing to identify discrepancies between simulated and real-world behaviors, which can be time-consuming and costly, especially when minor discrepancies are detected.
Innovation Solution
A computer-implemented method that generates a latency distribution profile by simulating the operating software of autonomous vehicles based on real-world scenarios and artificially generated scenarios, allowing for more accurate prediction of real-world behavior through virtual simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive real-world testing is performed to identify discrepancies between simulated and real-world behaviors, then prediction accuracy is improved, but development time and costs increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulated environments that replicate actual road conditions, traffic patterns, and sensor data. By testing autonomous vehicle software in these virtual copies rather than physical real-world testing, the system achieves high prediction accuracy while dramatically reducing development time and costs associated with extensive road testing.
Solution Approach 2:
The patent performs preliminary testing and validation of software updates in virtual simulations before deploying them to real vehicles. By conducting Hardware-in-Loop (HIL) testing and generating latency profiles in advance, the system identifies and resolves discrepancies between simulated and real-world behaviors beforehand, eliminating the need for extensive post-deployment real-world testing.
2Measurement precision
If extensive real-world testing is performed to identify discrepancies between simulated and real-world behaviors, then prediction accuracy is improved, but costs increase significantly
Solution Approach 1:
The patent replaces expensive real-world testing with virtual simulations that copy actual driving scenarios. By using simulated environments to validate software updates and predict vehicle behavior, the system maintains high prediction accuracy while avoiding the substantial costs associated with fuel, vehicle wear, tester compensation, and logistics of extensive real-world testing programs.
Solution Approach 2:
The system performs self-validation through automated Hardware-in-Loop testing and virtual scenario generation. The autonomous vehicle software tests itself in simulated environments without requiring extensive human-operated real-world testing, reducing both direct costs (tester salaries, vehicle rental) and indirect costs (time, resource allocation).
3Productivity
If virtual simulations are used to predict real-world behavior, then development time is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary Hardware-in-Loop testing and latency profile generation in virtual simulations to establish accurate baseline data before real-world deployment. By pre-characterizing system behavior and identifying discrepancies in the virtual environment, the simulations become increasingly accurate predictors of real-world performance, maintaining measurement precision while enabling rapid development iterations.
Solution Approach 2:
The system uses feedback from comparing virtual simulation results with actual real-world vehicle data to continuously improve simulation accuracy. Latency profiles and performance metrics from real vehicles are fed back into the simulation model to refine its predictions, ensuring that virtual testing maintains high measurement precision while preserving the speed advantages of virtual environments.
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices associated with testing, simulation, or operation of an autonomous device including an autonomous vehicle are provided. For example, a service entity computing system can perform operations including obtaining operating software data associated with operating software of the autonomous vehicle. Log data associated with one or more real-world scenarios can also be obtained. One or more first simulations of the operating software can be performed based on the one or more real-world scenarios. A latency distribution profile associated with the operating software can be generated based on the one or more first simulations. One or more second simulations of the operating software can be performed based on the latency distribution profile and one or more artificially generated scenarios. Furthermore, a real-world behavior of the autonomous vehicle can be predicted based on the one or more second simulations.