Virtual Sensor Data Generation with Depth Annotation
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Solution Overview
Problem
The development of robust computer vision algorithms for vehicle-based applications requires extensive and diverse real-world sensor data, which is costly and time-consuming to collect, especially when considering various environmental conditions.
Innovation Solution
Generating virtual sensor data using three-dimensional modeling and animation techniques to simulate diverse scenarios and conditions, allowing for the creation of annotated depth maps that mimic real-world data without the need for physical sensor deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world sensor data is collected through physical sensor deployment and actual driving runs, then the quality and diversity of sensor data improve, but the time and resources required increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving environments, vehicles, and sensors through 3D modeling. These virtual replicas generate sensor data that mimics real-world data characteristics without requiring actual physical deployment. The virtual sensor data includes realistic noise patterns, occlusions, and environmental variations while being generated computationally rather than through time-consuming field collection.
Solution Approach 2:
The system pre-generates comprehensive virtual sensor data covering diverse driving scenarios, weather conditions, and environmental variations before actual algorithm development begins. By creating a extensive library of annotated virtual data in advance, the patent eliminates the need for repeated real-world data collection campaigns during algorithm training and testing phases.
2Adaptability or versatility
If diverse environmental conditions are included in real-world sensor data collection, then the robustness of computer vision algorithms improves, but the complexity and cost of data collection increase
Solution Approach 1:
The virtual environment system dynamically adjusts environmental parameters such as weather conditions, lighting scenarios, time of day, and road surface characteristics. These parameters can be programmatically modified to create infinitely varied driving scenarios without requiring physical reconfiguration of sensor deployment or travel to different locations, thus maintaining algorithm robustness while reducing collection complexity.
Solution Approach 2:
The virtual sensing system serves multiple functions simultaneously: it generates training data, validates algorithms, tests edge cases, and evaluates performance across diverse conditions all within a single computational platform. This multi-functionality replaces the need for separate real-world data collection campaigns for each scenario type.
3Measurement precision
If thousands of diverse images are collected from hundreds or thousands of miles of road, then the accuracy of computer vision detection algorithms improves, but the cost and resources required increase enormously
Solution Approach 1:
The patent generates synthetic images that replicate the visual characteristics, resolution, noise properties, and realistic artifacts of real camera captures. These virtual images include accurate depth information, object boundaries, and environmental features that match real-world data distributions, providing sufficient training material for high-accuracy algorithms without requiring physical collection of thousands of miles of road data.
Data Source
AI summary
Methods and systems for generating virtual sensor data for developing or testing computer vision detection algorithms are described. A system and a method may involve generating a virtual environment. The system and the method may also involve positioning a virtual sensor at a first location in the virtual environment. The system and the method may also involve recording data characterizing the virtual environment, the data corresponding to information generated by the virtual sensor sensing the virtual environment. The system and the method may further involves annotating the data with a depth map characterizing a spatial relationship between the virtual sensor and the virtual environment.