Vehicle OS Closed-Loop AI Training With Teacher-Student Edge Models
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Solution Overview
Problem
Training AI models for vehicle operating systems in autonomous vehicles is time-consuming and inefficient, and existing systems lack an effective mechanism for continuous refinement and deployment.
Innovation Solution
A vehicle operating system that utilizes a cloud platform to automatically train AI models through a teacher-student edge model framework, where real-time data is used to refine and redeploy models over-the-air, enabling continuous learning and improvement of AI models such as power consumption reduction, storage life extension, and temperature prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models are trained using traditional methods, then model accuracy can be improved, but the training time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and preparing training data in advance during vehicle operation. The VOS accumulates real-time data from sensors and operational parameters, pre-processing it for future model training. This allows the actual training to use pre-prepared data, reducing the time needed when training is actually required.
Solution Approach 2:
The patent introduces an intermediary mechanism - a dedicated training data collection and processing layer between data generation and model training. This intermediary layer pre-processes, validates, and organizes training data during idle periods, mediating between raw operational data and the training process, thereby accelerating actual training when needed.
2Adaptability or versatility
If AI models are trained continuously with real-time data, then model accuracy and adaptability improve, but system complexity and computational resources increase
Solution Approach 1:
The system implements dynamic training where the training process adapts based on operational conditions. The VOS dynamically determines when to collect data, when to train models, and what priority to assign to different training tasks based on current vehicle operation state, available resources, and data quality. This dynamic approach enables continuous improvement without requiring constant high-level system complexity.
Solution Approach 2:
The VOS performs self-service by automatically managing the entire training pipeline - data collection, preprocessing, model training, validation, and deployment - without requiring external intervention. The system autonomously allocates computational resources, selects appropriate training datasets, and manages model versions, reducing the operational complexity despite continuous training.
3Productivity
If more computational resources are allocated to AI model training, then training speed and model quality improve, but power consumption and heat generation increase
Solution Approach 1:
The system employs periodic action by scheduling training operations at specific intervals rather than continuously. The VOS performs training during designated time windows when computational resources are available and power consumption can be managed. Training is periodically triggered based on data accumulation thresholds, computational resource availability, and vehicle operation states, enabling high-speed training bursts without sustained high power consumption.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting training parameters such as batch size, learning rate, and computational precision based on available resources. The VOS modifies training configuration parameters in real-time to optimize the balance between training speed and power consumption, using higher precision and larger batches when resources are abundant and reducing parameters when power is constrained.
4Adaptability or versatility
If AI models are deployed over-the-air for continuous updates, then system adaptability improves, but deployment reliability and security risks increase
Solution Approach 1:
The system implements feedback mechanisms where deployed models continuously monitor their own performance and operational conditions. The VOS collects feedback data from model predictions, comparison results, and system performance metrics, using this feedback to trigger retraining when performance degradation is detected. This closed-loop feedback ensures reliable updates only when necessary and verified, maintaining deployment reliability while enabling adaptability.
Solution Approach 2:
The patent applies beforehand cushioning by implementing pre-deployment validation and rollback mechanisms. Before deploying updated models over-the-air, the system performs validation checks, creates backup versions, and prepares rollback procedures. This cushioning approach protects against deployment failures and security issues, ensuring that even with frequent updates, system reliability is maintained through prepared contingency measures.
Data Source
AI summary
A vehicle operating system (VOS) in an autonomous driving vehicle (ADV) can communicate with a cloud platform to automatically train AI models. The VOS collects real-time data from the ADV, and generates inference data based on the real-time data using a teacher edge model of an AI model and generates second inference data based on the real-time data using a student edge model of the AI model. The VOS then obtains one or more differences between the first inference data and the second inference data, and retrains the student edge model of the AI model based on the one or more differences. Both real-time data and the retrained student edge model are uploaded to a cloud platform for use in upgrading the student edge model and the teacher edge model on the cloud platform. The upgraded teacher edge model and the student edge model can be redeployed over-the-air (OTA) through a software define process. The above process of training AI models can be repeated in a closed-loop automatically without user intervention.