Geometry-Aware Image Simulation for Autonomous Vehicle Testing
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Solution Overview
Problem
Current methods for generating synthetic imagery for autonomous vehicle testing are costly and resource-intensive, requiring expensive data recording equipment and labor, and often result in a significant realism gap in image simulation, making them unsuitable for complex use cases.
Innovation Solution
A computer-implemented method that generates simulated imagery by obtaining environment data, determining insertion locations for simulated objects, and using occlusion data to create photorealistic images, refined through machine-learned models, leveraging real-world sensor data and Light Detection and Ranging (LiDAR) data to create geometrically consistent and physically plausible scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physics-based rendering is used to create photorealistic images, then image realism is improved, but computing resources and cost increase significantly
Solution Approach 1:
The patent uses real-world captured images and sensor data as templates to generate synthetic images. Instead of performing complex physics-based rendering from scratch, the system copies and transforms existing real-world data (images, LiDAR point clouds, depth maps) to create photorealistic synthetic imagery that maintains geometric consistency while requiring significantly fewer computing resources.
Solution Approach 2:
The patent replaces traditional physics-based rendering mechanisms with a data-driven approach using machine learning models. The system substitutes complex physical simulations with learned transformations from real-world sensor data, achieving photorealism through statistical patterns rather than physical computation.
2Manufacturing precision
If traditional data collection methods are used for autonomous vehicle testing, then data quality is improved, but cost and time consumption increase
Solution Approach 1:
The system captures real-world data once using expensive equipment and then creates numerous synthetic variations through geometric transformations, occlusion simulations, and viewpoint changes. This copying approach maintains data quality characteristics while eliminating the need for repeated expensive data collection campaigns.
Solution Approach 2:
The patent performs comprehensive data collection and processing in advance, creating a library of real-world images and sensor data that can be repeatedly used to generate synthetic test scenarios. This preliminary action eliminates the need for time-consuming on-site data collection during software testing phases.
3Speed
If real-time rendering engines are used for image generation, then processing speed is improved, but image realism deteriorates due to significant realism gap
Solution Approach 1:
The system copies geometric and appearance information directly from real-world captured data rather than generating it through real-time rendering algorithms. By transforming and compositing real captured images with simulated occlusions and viewpoint changes, the system achieves both speed (inheriting from real captures) and realism (maintained from source data).
Data Source
AI summary
The present disclosure provides systems and methods for generating photorealistic image simulation data with geometry-aware composition for testing autonomous vehicles. In particular, aspects of the present disclosure can involve the intake of data on an environment and output of augmented data on the environment with the photorealistic addition of an object. As one example, data on the driving experiences of a self-driving vehicle can be augmented to add another vehicle into the collected environment data. The augmented data may then be used to test safety features of software for a self-driving vehicle.