Scene Description Generation for Autonomous Vehicle Simulation Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learned models generate synthetic environments with limited detail, making it difficult to assess their representation of real-world environments and hindering effective testing of robotic devices.
Innovation Solution
Implement a machine learned model to generate detailed text descriptions of simulated scenes, including scene characteristics, intersection types, and object information, which can be validated and stored for later use in simulations, enhancing scene selection and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed text descriptions are generated for each synthetic environment, then the assessment accuracy of how well synthetic environments represent real-world environments improves, but the computational resources and time required increase
Solution Approach 1:
The system generates text descriptions for synthetic environments in advance, before they are needed for assessment. This preliminary generation allows the descriptions to be stored and reused multiple times, avoiding repeated generation and reducing overall computational resource consumption while maintaining high assessment accuracy.
Solution Approach 2:
The system creates text descriptions as representations or copies of the visual synthetic environments. These textual copies capture essential scene characteristics, objects, and conditions, enabling accurate assessment without requiring continuous access to the original computationally intensive visual data.
2Loss of information
If detailed text descriptions are generated for each synthetic environment, then the understanding of when to use synthetic environments for testing improves, but the complexity of the system increases
Solution Approach 1:
The system introduces text descriptions as an intermediary layer between the visual synthetic environments and the assessment process. This intermediary captures essential scene information in a structured, interpretable format, improving understanding of when and how to use synthetic environments without requiring direct complex analysis of visual data.
Solution Approach 2:
The system transforms visual scene data into textual parameters that describe key characteristics, objects, and conditions. This parameter transformation maintains essential scene information while presenting it in a more manageable and interpretable format, reducing the effective complexity of the assessment system.
3Reliability
If comprehensive scene descriptions including objects, characteristics, and conditions are generated, then the usefulness of synthetic environments for testing improves, but the time required to generate descriptions increases
Solution Approach 1:
The system generates comprehensive text descriptions in advance, capturing all relevant scene information including objects, characteristics, and conditions. This preliminary comprehensive generation ensures high testing usefulness while allowing the descriptions to be stored and reused, thereby reducing the time cost for subsequent testing activities.
Data Source
AI summary
Techniques for determining a text description of a scene in an environment by a computing device are discussed herein. The scene can represent a simulated scene for use in a simulation between a vehicle (e.g., an autonomous vehicle) and one or more objects. The computing device can implement a machine learned model to generate a text description of the simulated scene based on receiving a title and one or more scenes as input. Text descriptions can be validated and used for testing a controller of a vehicle, or other robotic device, during simulation.