Sensor Virtualization for Scalable 3D Vehicle Perception
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Solution Overview
Problem
Existing methods for training neural networks for 360-degree environment modeling in vehicles are not scalable due to the need for separate models for each sensor setup, which is time-consuming and expensive.
Innovation Solution
A method involving sensor virtualization, where physical sensor data from multiple modalities is mapped to virtual sensors with predefined characteristics, allowing for a unified training process in a 3D model space, using an encoder-decoder architecture and sensor-agnostic perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are trained for each sensor setup, then sensor-specific performance is optimized, but training time and computational cost increase significantly
Solution Approach 1:
The patent creates a universal neural network architecture that can process multiple sensor modalities (camera, lidar, radar) through a single unified model. The network uses sensor-agnostic feature extraction layers that can handle different input types, eliminating the need for separate specialized models for each sensor while maintaining high detection accuracy across all modalities.
Solution Approach 2:
The patent merges multiple sensor input streams into a single neural network framework. Different sensor modalities are integrated through a common feature processing architecture that combines camera images, lidar point clouds, and radar data in a unified representation space, enabling simultaneous training on heterogeneous sensor data without requiring separate model instances.
2Measurement precision
If separate models are trained for each sensor setup, then sensor-specific optimization is achieved, but computational expenses increase
Solution Approach 1:
The neural network employs universal feature extraction layers that can process multiple sensor types through the same computational graph. This eliminates redundant model instances and shared computational resources, significantly reducing the total GPU hours and computational energy required for training while maintaining high detection performance across all sensor modalities.
Solution Approach 2:
By combining multiple sensor processing paths into a single unified neural network architecture, the patent reduces the total computational burden. The merged architecture processes all sensor modalities through shared convolutional and processing layers, decreasing the aggregate FLOPs and energy consumption compared to running separate specialized models for each sensor type.
3Adaptability or versatility
If physical sensor data from heterogeneous sensors is used directly, then real-world sensor diversity is preserved, but training uniformity and model scalability are compromised
Solution Approach 1:
The patent introduces virtual sensor representations as intermediaries between physical sensors and the neural network. Each physical sensor is mapped to a corresponding virtual sensor with standardized characteristics, creating a mediation layer that translates heterogeneous sensor data into a uniform representation format. This intermediary layer enables diverse physical sensors to be trained together while maintaining training simplicity and model scalability.
Solution Approach 2:
The patent applies parameter transformation to normalize sensor data from different physical sensors to match a target virtual sensor configuration. By adjusting parameters such as field-of-view, resolution, and sensor positioning through coordinate transformations and resampling operations, the system converts heterogeneous sensor inputs into a standardized format that simplifies the training process while preserving the diversity of real-world sensor setups.
Data Source
AI summary
The present invention provides a method for training a neural network to predict objects in a surrounding of a vehicle, the method comprising:obtaining physical sensor data from physical sensors having one or more modalities,performing a first mapping from the physical sensor data to virtual sensors to obtain virtual sensor data,performing a second mapping from the virtual sensor data to a 3D model space, andtraining the neural network based on the virtual sensor data and one or more annotations in the 3D model space.