Sensor Data Adaptation via Deep Learning Loop for New Sensing Conditions
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Solution Overview
Problem
Training and testing autonomous vehicles in the physical world is costly and time-consuming, and building realistic simulation environments for new cities or environmental conditions is impractical due to the need for extensive data collection and model training, which can take months or years.
Innovation Solution
A closed-loop deep learning system using a discriminator model and a generator model to adapt sensor data, where the generator modifies sensor data to assist the discriminator in correct object classification, enabling efficient training and deployment of autonomous vehicles in new environments without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation environments are built for new cities or environmental conditions through extensive data collection and model training, then the realism and accuracy of the simulation improve, but the time required increases to months or years
Solution Approach 1:
The system pre-trains the discriminator model on source domain data (e.g., city A sensor data) before it is needed for the target domain. This preliminary preparation allows the model to have baseline capabilities that can be quickly adapted to new environments through transfer learning, rather than training from scratch each time a new city is encountered.
Solution Approach 2:
The system changes the domain parameters by using domain adaptation techniques that modify the discriminator model's behavior when transitioning from source domain to target domain. The generator model transforms target domain sensor data to match the source domain distribution, effectively changing the data parameters to leverage pre-trained model knowledge and reduce retraining time.
2Reliability
If autonomous vehicles are trained and tested in the physical world to ensure safety and accuracy, then the reliability of the system improves, but the cost and time consumption increase significantly
Solution Approach 1:
The system creates a virtual copy of the physical environment through simulation that preserves the essential characteristics needed for training. The discriminator model learns from simulated sensor data that replicates real-world conditions, allowing most training to occur in the virtual environment rather than requiring extensive physical world testing, thereby improving development efficiency while maintaining reliability.
3Measurement precision
If a discriminator model is trained for a specific sensing condition, then its classification accuracy for that condition improves, but its ability to handle different sensing conditions deteriorates
Solution Approach 1:
The generator model serves as an intermediary between the target domain sensor data and the discriminator model. It transforms the target domain data into source domain-like representations that the pre-trained discriminator can process accurately. This intermediary transformation enables the discriminator to maintain high classification accuracy across different sensing conditions without requiring retraining for each condition.
Solution Approach 2:
The system separates the domain adaptation function (handled by the generator) from the classification function (handled by the discriminator). This segmentation allows each component to be optimized independently - the generator handles the variability of different sensing conditions while the discriminator focuses on accurate classification, thereby achieving both precision and adaptability.
Data Source
AI summary
Systems and methods for dynamic sensor data adaptation using a deep learning loop are provided. A method includes classifying, using a discriminator model, a first object from first sensor data associated with a first sensing condition, wherein the discriminator model is trained for a second sensing condition different from the first sensing condition; generating, using a generator model in response to the discriminator model failing to classify the first object, second sensor data representing a second object comprising at least a modified element of the first object; classifying, using the discriminator model, the second object from the second sensor data; and adapting, based at least in part on a difference between the first object and the second object in response to the discriminator model successfully classifying the second object, a machine learning model associated with object classification for the first sensing condition.