Autonomous Vehicle Simulation for Realistic Interaction Testing
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Solution Overview
Problem
Robust testing of autonomous vehicle algorithms in real-world driving environments is dangerous or infeasible, necessitating the development of effective simulators that can realistically model vehicle interactions in a time-efficient manner.
Innovation Solution
The use of super real-time simulation frameworks that configure primary and secondary vehicular models with algorithms, calculate trajectories, and generate updated algorithms through simulations, allowing for the integration of these updates into autonomous vehicle systems, leveraging entity-component-system architecture and multi-threaded frameworks for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulators are used to test autonomous vehicle algorithms, then testing safety is improved, but testing realism is worsened
Solution Approach 1:
The patent creates virtual copies of real vehicles, environments, and sensor data to build realistic simulation scenarios. By copying actual driving data, vehicle behaviors, and environmental conditions into the simulator, the system maintains high realism while ensuring testing safety through virtual environment isolation.
Solution Approach 2:
The system performs preliminary analysis of real-world driving data to construct accurate simulation scenarios before actual testing begins. By pre-processing and structuring real driving data into reusable simulation templates, the system prepares realistic test cases in advance that can be safely executed in the virtual environment.
2Reliability
If simulators model vehicle interactions realistically, then testing robustness is improved, but computing time is worsened
Solution Approach 1:
The simulation system divides complex vehicle interactions into separate modular components and scenarios. By segmenting the simulation into independent test cases, each focusing on specific interaction types, the system can efficiently process multiple scenarios in parallel while maintaining comprehensive robustness testing coverage.
Solution Approach 2:
The system implements selective realism by applying high-fidelity modeling only to critical vehicle interaction aspects that most impact autonomous driving decisions. Non-critical elements use simplified models, reducing overall computing time while maintaining sufficient robustness for safety-critical testing areas.
3Reliability
If more simulation scenarios are tested, then algorithm robustness is improved, but computational resources are worsened
Solution Approach 1:
The simulation system dynamically adjusts scenario complexity and detail level based on testing progress and identified algorithm weaknesses. By adaptively modifying simulation parameters, the system focuses computational resources on the most critical robustness gaps while maintaining comprehensive testing coverage through prioritized scenario selection.
Solution Approach 2:
The system uses feedback from simulation results to identify and prioritize the most critical test scenarios that expose algorithm weaknesses. By analyzing simulation outcomes and automatically selecting follow-up scenarios that target identified vulnerabilities, the system maximizes robustness improvement per unit of computational resource expended.
Data Source
AI summary
Disclosed are devices, systems and methods for the operational testing on autonomous vehicles. One exemplary method includes configuring a primary vehicular model with an algorithm, calculating one or more trajectories for each of one or more secondary vehicular models that exclude the algorithm, configuring the one or more secondary vehicular models with a corresponding trajectory of the one or more trajectories, generating an updated algorithm based on running a simulation of the primary vehicular model interacting with the one or more secondary vehicular models that conform to the corresponding trajectory in the simulation, and integrating the updated algorithm into an algorithmic unit of the autonomous vehicle.