Sensor Viewpoint Transformation for Cross-Vehicle Perception Models
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Solution Overview
Problem
Existing autonomous vehicle perception systems face challenges in maintaining accuracy due to variations in camera characteristics, requiring separate training and deployment for each vehicle model, which is resource-intensive and costly.
Innovation Solution
Applying viewpoint transformations to sensor data to normalize camera perspectives, allowing a single machine learning model to be trained and deployed across different vehicles, independent of specific camera configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate neural networks are trained for each vehicle model with consistent camera characteristics, then perception accuracy is maintained, but training data collection cost and time increase significantly
Solution Approach 1:
The patent applies parameter changes by transforming camera characteristics (intrinsic parameters like focal length, optical center, and extrinsic parameters like position and orientation) to create a standardized reference configuration. This allows training data collected from one vehicle model to be adapted for other models through parameter transformation, eliminating the need to collect separate training data for each vehicle type while maintaining perception accuracy.
Solution Approach 2:
The patent creates a universal neural network model that can be deployed across multiple vehicle models by establishing a reference camera configuration. Instead of creating separate specialized models for each vehicle, a single universal model is trained on transformed data that accounts for different camera characteristics, making the system multi-functional across diverse vehicle platforms.
2Measurement precision
If multiple separate neural networks are maintained and updated for different vehicle models, then each model's specific camera characteristics are optimized, but storage requirements and bandwidth consumption increase
Solution Approach 1:
The patent reduces storage requirements by maintaining a single universal neural network model instead of multiple separate models for different vehicle models. The universal model stores camera transformation parameters and a standardized set of weights that can be applied across all vehicle types, significantly reducing the quantity of data that needs to be stored and updated.
Solution Approach 2:
The patent uses parameter transformation to encode different camera characteristics into a compact reference configuration. Instead of storing complete separate models for each vehicle, the system stores transformation parameters that map various camera setups to a reference configuration, reducing storage requirements while preserving camera-specific optimization.
3Device complexity
If a single neural network is used across different vehicle models without transformation, then system complexity is reduced, but perception accuracy degrades due to camera characteristic variations
Solution Approach 1:
The patent applies preliminary action by pre-processing training data through camera characteristic transformations before neural network training. The training data is transformed to match a reference camera configuration in advance, so that when the single neural network processes data from different vehicle models, the accuracy degradation is minimized. This preliminary transformation prepares the data to be compatible with a unified model.
Solution Approach 2:
The patent introduces an intermediary transformation layer that mediates between diverse camera characteristics and the single neural network model. This intermediary process transforms input images from various camera configurations into a standardized reference format that the neural network can process accurately, bridging the gap between system simplicity and perception accuracy.
Data Source
AI summary
In various examples, sensor data used to train an MLM and/or used by the MLM during deployment, may be captured by sensors having different perspectives (e.g., fields of view). The sensor data may be transformed—to generate transformed sensor data—such as by altering or removing lens distortions, shifting, and/or rotating images corresponding to the sensor data to a field of view of a different physical or virtual sensor. As such, the MLM may be trained and/or deployed using sensor data captured from a same or similar field of view. As a result, the MLM may be trained and/or deployed—across any number of different vehicles with cameras and/or other sensors having different perspectives—using sensor data that is of the same perspective as the reference or ideal sensor.