Student Generative AI for Low-Latency AV Trajectory Prediction
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Solution Overview
Problem
Existing generative AI models for autonomous vehicle trajectory generation require significant processing resources and memory, making them unsuitable for deployment in resource-constrained environments like autonomous vehicles, while also lacking in model simplicity, latency, and tuning efficiency.
Innovation Solution
Implement a GPT-based student model that is pre-trained and fine-tuned using a teacher model with a larger data set, sharing a similar input format but differing in architecture, to reduce model size and enhance performance in resource-limited environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large-scale generative AI model is used for trajectory generation, then trajectory prediction accuracy is improved, but processing resources and memory requirements increase significantly
Solution Approach 1:
The model undergoes pre-training on a large teacher model to learn general trajectory patterns beforehand, then is fine-tuned on smaller datasets for specific applications. This preliminary learning phase enables the student model to achieve good performance without requiring large-scale computational resources during actual deployment.
Solution Approach 2:
A student model is created as a simplified copy of the teacher model, inheriting its learned parameters and patterns. The student model replicates the essential functionality of the large-scale model while using fewer resources, allowing deployment in resource-constrained autonomous vehicle systems.
2Device complexity
If model architecture is simplified to reduce size, then resource requirements are reduced, but training complexity and tuning steps increase
Solution Approach 1:
The complex training process is performed in advance during the pre-training phase on the teacher model. The student model then inherits these pre-learned parameters, significantly reducing the tuning steps and complexity required for deployment while maintaining simplified architecture.
3Reliability
If pre-training on large datasets is performed, then model performance is improved, but training time and computational cost increase
Solution Approach 1:
The time-consuming pre-training on large datasets is performed once on the teacher model beforehand. The student model then leverages these pre-trained parameters, requiring minimal additional training time for fine-tuning, thus achieving high performance without repeated lengthy training processes.
Solution Approach 2:
The student model copies the pre-trained knowledge from the teacher model, transferring the performance benefits of large-scale training without requiring the student to undergo the same extensive training process, thereby reducing training time while maintaining reliability.
Data Source
AI summary
Disclosed are embodiments for facilitating generative artificial intelligence (AI) to pre-train and fine-tune models for multiple autonomous vehicle (AV) trajectories generation. In some aspects, an embodiment includes training a teacher generative AI model on a first set of training data; providing a student generative AI model with at least one distillation of the teacher generative AI model, the at least one distillation comprising transformer weights, embeddings, or predictions labels of the teacher generative AI model; training the student generative AI model that is initialized with the at least one distillation of the teacher generative AI model, wherein the student generative AI model is trained using a second set of training data that is smaller than the first set of training data; and deploying the student generative AI model to a resource-constrained environment.