One-Shot Trajectory Prediction via Distilled Teacher-Student Models
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately and efficiently predicting the behavior of objects in driving environments, particularly due to high computational latency and resource requirements of autoregressive models.
Innovation Solution
The implementation of a distillation-trained one-shot model that leverages an autoregressive model as a teacher to predict trajectories, reducing latency and resource needs while maintaining accuracy through distillation training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autoregressive models are used for trajectory prediction, then prediction accuracy is improved, but computational latency and resource requirements increase
Solution Approach 1:
The patent creates a simplified copy (student model) of the complex autoregressive model (teacher model). The student model is trained to replicate the teacher model's predictions through knowledge distillation, enabling fast inference while maintaining prediction accuracy. The student model copies the essential predictive capabilities without the computational overhead of the original autoregressive architecture.
Solution Approach 2:
The student model serves as a lightweight, computationally inexpensive alternative to the heavy teacher model. It is designed to be deployed in resource-constrained environments where fast predictions are critical, sacrificing the complex architecture of the teacher model for a simpler, faster-running structure that can be executed efficiently on autonomous vehicle systems.
2Measurement precision
If autoregressive models are used for trajectory prediction, then prediction accuracy is improved, but device complexity and resource requirements increase
Solution Approach 1:
The student model is a simplified copy of the teacher model that retains predictive accuracy while reducing architectural complexity. By copying only the essential functional elements and training them to match teacher model outputs, the system eliminates unnecessary computational components while preserving prediction quality.
Solution Approach 2:
The patent extracts the essential predictive knowledge from the complex teacher model and transfers it to a simpler student model structure. This extraction process removes unnecessary complexity from the deployed model while maintaining the core predictive capabilities needed for accurate trajectory forecasting in autonomous driving applications.
3Productivity
If distillation training is used, then computational efficiency is improved, but training complexity increases
Solution Approach 1:
The teacher model is trained first to achieve high prediction accuracy before the distillation process begins. This preliminary training phase prepares the knowledge source that will be transferred to the student model, allowing the student to learn from pre-computed accurate predictions rather than learning from raw data directly, which streamlines the overall training workflow.
Solution Approach 2:
The teacher model acts as an intermediary during the training process, mediating the transfer of knowledge from training data to the student model. Instead of training the student model directly on raw trajectory data, the teacher model serves as an intermediate layer that provides softened labels and guidance signals, making the training process more efficient and effective.
Data Source
AI summary
The described aspects and implementations enable training and deploying of accurate one-shot models capable of predicting trajectories of vehicles and other objects in driving environments. The disclosed techniques include, in one implementation, obtaining training data that includes a training input representative of a driving environment of a vehicle and one or more ground truth trajectories associated with a forecasted motion of the vehicle within the driving environment. The one or more ground truth trajectories are generated by a teacher model using the training input. The techniques further include training, using the training data, a student model to predict one or more trajectories of the vehicle and/or objects in the driving environment of the vehicle.