Augmented Scene Rendering for Autonomous Vehicle Weather Testing
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Solution Overview
Problem
Autonomous vehicles face challenges in testing under diverse weather conditions, as existing methods lack the ability to simulate realistic and dynamic environmental scenarios, limiting their ability to perform autonomous operations effectively across various weather conditions.
Innovation Solution
A system and method that augment images captured by autonomous vehicles with weather-related and traffic-related graphics, using AI and physics-based techniques to generate photorealistic visual representations of environments under predetermined weather conditions, allowing for the simulation of diverse traffic and weather scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles are tested in real diverse weather conditions, then the testing reliability improves, but the ability to simulate specific and extreme weather scenarios deteriorates
Solution Approach 1:
The patent uses image augmentation technology to create virtual copies of weather conditions by overlaying synthesized weather graphics (rain, snow, fog) onto real captured images. This allows the autonomous vehicle to experience simulated extreme weather scenarios that may be rare or impossible to encounter in real testing, while maintaining the realistic background environment structure.
Solution Approach 2:
The system introduces an intermediary processing layer that captures real images through the vehicle's camera, processes them through weather simulation software, and generates augmented images with叠加 weather effects. This intermediary process bridges the gap between real-world testing and extreme scenario simulation, allowing controlled introduction of specific weather conditions.
2Reliability
If more weather conditions are simulated, then the operational reliability improves, but the device complexity increases
Solution Approach 1:
The weather simulation system uses a universal image augmentation framework that can handle multiple weather types (rain, snow, fog, etc.) through a single processing pipeline. The same software module processes different weather conditions by applying different graphic overlays, avoiding the need for separate complex systems for each weather type.
Solution Approach 2:
Instead of creating multiple physical testing environments for different weather conditions, the system creates virtual copies of weather effects through image processing. A single camera and processing system can simulate multiple weather scenarios by overlaying different weather graphics, significantly reducing hardware complexity.
3Measurement precision
If photorealistic weather graphics are generated, then the measurement precision improves, but the computational resources required increase
Solution Approach 1:
The weather graphics are applied locally to specific regions of the image where weather effects are most visible and impactful, rather than processing the entire image at maximum detail. This allows photorealistic quality in critical areas while reducing overall computational burden.
Solution Approach 2:
The system applies weather graphics with varying intensity levels - using full photorealistic detail only when necessary for accurate testing, and reducing detail when extreme precision is not required. This partial application of maximum quality maintains accuracy where needed while conserving computational resources.
Data Source
AI summary
The embodiments provide a method of augmenting environmental scenes and an autonomous vehicle testing system. The method includes: obtaining, from an autonomous vehicle, image data representing an image, which depicts an environment where the autonomous vehicle drives; generating virtual object graphics representing virtual object(s) that, when rendered over the image, result in an object-augmented image; generating global scene graphics based on the object-augmented image that, when rendered along with the object-augmented image, result in a visual representation as the environment would appear when experiencing predetermined weather conditions; generating detailed weather effect graphics representing detailed weather effect(s); generating a composite weather-object-augmented image based on the virtual object graphics, the global scene graphics, and the detailed weather effect graphics; and causing the composite weather-object-augmented image to be inputted into an onboard vehicle controller of the autonomous vehicle.