Switchable Autonomous Driving Simulation Models for Targeted Testing
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Solution Overview
Problem
Current autonomous driving simulation systems are limited by their inability to integrate variably-sourced simulation data models, leading to unnecessary costs, processing burdens, and extended development cycles, as they often require all models to be included regardless of the specific testing requirements of different applications.
Innovation Solution
A customizable autonomous driving simulation architecture that allows for the integration of multiple simulation data models from different sources, enabling the selective use or replacement of models with lower-accuracy 'dummy' models based on application-specific needs, thereby reducing processing load and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a common simulation architecture integrates all simulation data models (vehicle simulator, sensor simulator, etc.) to meet diverse testing requirements, then the system can accommodate various application testing needs, but it incurs unnecessary costs, increases processing load, and extends testing time for applications that do not require all models
Solution Approach 1:
The simulation architecture is segmented into modular components (vehicle simulator, sensor simulator, core simulator) that can be independently selected and configured. Each simulator is a separate module that can be activated or deactivated based on specific testing requirements, allowing the system to be customized for different applications without requiring all components to be present simultaneously.
Solution Approach 2:
The system dynamically configures which simulation models are active based on the testing requirements of each application. The architecture allows for flexible activation/deactivation of simulators, enabling the system to adapt its complexity level to match the specific needs of each testing scenario, thereby optimizing resource utilization.
2Measurement precision
If highly accurate simulation models are used for all applications, then the testing precision and reliability are improved, but the processing time and computational resources increase unnecessarily for applications that do not require high accuracy
Solution Approach 1:
Different levels of simulation accuracy are applied locally based on the specific testing requirements. Each simulator can be configured with appropriate accuracy levels matched to its intended use, allowing high-accuracy models to be used only where necessary while lower-accuracy models handle applications where they are sufficient, optimizing the balance between precision and processing time.
3Adaptability or versatility
If all simulation data models are integrated into the core simulator to ensure comprehensive testing capability, then the system can test any application, but the device complexity and integration difficulty increase
Solution Approach 1:
The core simulator is designed as a universal platform that can interface with various types of simulators (vehicle, sensor, etc.) through standardized mechanisms. This multi-functional design allows the core simulator to work with different simulator combinations without requiring custom integration for each scenario, reducing overall system complexity while maintaining versatility.
Data Source
AI summary
A device and a method for providing an autonomous driving simulation architecture for testing an application. The method includes integrating a plurality of simulation data models with a first core simulator that runs an autonomous vehicle simulation for testing the application; selectively connecting the first core simulator to the application; and selectively disconnecting a first simulation data model, among the plurality of simulation data models, from the first core simulator.