Machine-Learning Sensor Label Translation for Rapid Sensor Upgrades
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Solution Overview
Problem
Current methods for replacing or upgrading sensors in autonomous vehicles, such as LiDAR and cameras, are time-consuming and expensive due to the need for extensive machine learning to understand the properties and structures of new sensors, and existing techniques are inefficient as they rely on point cloud data and voxel representations, failing to effectively translate sensor label data.
Innovation Solution
A system and method using machine-learning models to translate sensor data and label data by applying a multi-dimensional matrix of camera sensor parameters, utilizing encoder-decoder networks and generative adversarial networks to convert sensor data between different operational characteristics, enabling efficient translation without relying on ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning techniques are used to learn the properties of new sensors during replacement or upgrade, then the autonomous vehicle system can adapt to new sensors, but the process becomes excessively time-consuming and expensive
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with synthetic sensor data and label translations before actual sensor replacement occurs. This advance preparation creates a ready-to-use translation framework that can quickly adapt to new sensors without requiring extensive on-site learning time.
Solution Approach 2:
The patent creates synthetic copies of sensor data and label translations through simulation environments. These synthetic copies serve as training data that mimics real sensor behavior, allowing the system to learn sensor properties and label translations without requiring physical sensor replacements during the learning process.
2Measurement precision
If machine learning models are trained to understand sensor properties and point cloud structures, then accurate sensor data translation is achieved, but the computational cost and complexity increase significantly
Solution Approach 1:
The patent segments the complex sensor translation task into distinct components: point cloud processing, label translation, and coordinate system transformation. Each component is handled by specialized sub-models or processing modules, reducing the complexity of any single model while maintaining overall translation accuracy.
Solution Approach 2:
The system transitions from working solely with 3D point cloud data to incorporating 2D label translation dimensions. By adding this additional dimension of label space transformation, the model handles both spatial and semantic aspects of sensor translation simultaneously, improving accuracy without proportionally increasing complexity.
3Manufacturing precision
If existing up-sampling techniques are used to upgrade sensors from low-resolution to high-resolution, then sensor resolution is improved, but the techniques are inefficient as they rely on point cloud data and voxel representations
Solution Approach 1:
The patent replaces traditional geometric up-sampling methods with machine learning-based synthesis approaches. Instead of using deterministic geometric algorithms to interpolate point cloud data, the system employs trained neural networks that learn the underlying patterns and structures, producing higher-quality results more efficiently.
Solution Approach 2:
The system changes the fundamental parameters of the up-sampling process by transitioning from fixed geometric transformation rules to adaptive learned parameters. The machine learning models adjust their transformation parameters based on the specific sensor characteristics and data patterns, enabling more efficient and accurate resolution enhancement.
Data Source
AI summary
Technologies and techniques for converting sensor data, used in a vehicle or other device. A machine-learning model is applied to first sensor data, including a first operational characteristic capability and first sensor label data, wherein the machine-learning model is trained to second sensor data including a second operational characteristic capability. New sensor data is generated that corresponds to the applied machine-learning model, wherein the new sensor data includes translated first sensor label data. A loss function may be applied to the new sensor data to determine the accuracy of the new sensor data and translated first sensor label data. In some examples, a multi-dimensional matrix of camera sensor parameters may be applied to the first sensor data labels to transform the first sensor data labels to second sensor data labels.