Offline Perception Model for Autonomous Driving Data Annotation
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Solution Overview
Problem
The annotation of training data for autonomous driving systems is expensive and labor-intensive, especially when using spatiotemporal models that require annotated sequence data.
Innovation Solution
A computer-implemented method for providing an offline perception model that uses a foundation model trained to predict a vehicle's trajectory based on sensor data sequences, with a task-specific layer added for perception tasks, and fine-tuned using annotated data to automate the annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation is used for training data, then annotation quality is high, but annotation cost and time are excessive
Solution Approach 1:
The system performs preliminary annotation using the offline perception model trained on foundation model predictions, preparing annotated training data in advance before online model training. This preliminary action reduces the need for expensive human annotation during the main training process, thereby improving annotation efficiency while maintaining quality.
Solution Approach 2:
The offline perception model acts as an intermediary between raw sensor data and the online perception model training. It generates preliminary annotations that serve as training data for the online model, reducing direct human involvement while maintaining annotation quality through the model's learned predictions.
2Reliability
If more annotated training data is collected, then model training quality improves, but annotation cost increases
Solution Approach 1:
The offline perception model enables the system to self-generate annotated training data without requiring external human annotation resources. By using foundation model predictions and sensor data sequences, the system autonomously creates high-quality training datasets, improving model training quality while eliminating the need for additional annotation resources.
3Productivity
If automated annotation is implemented, then annotation cost decreases, but annotation accuracy may be reduced
Solution Approach 1:
The system performs preliminary training of the foundation model using available annotated data to establish accurate prediction patterns. This preliminary action ensures that the automated annotation process maintains high accuracy by leveraging pre-learned temporal and spatial relationships from the foundation model before generating new annotations.
Solution Approach 2:
The system uses feedback from the offline perception model's performance and foundation model predictions to continuously improve annotation accuracy. By monitoring prediction quality and adjusting the annotation process based on model confidence and performance metrics, the system maintains high annotation accuracy while benefiting from automated efficiency.
Data Source
AI summary
A method for providing an offline perception model for subsequent annotation of training data for use in training of an online perception model is disclosed. The method includes: training a foundation model, using a first training dataset, to predict a trajectory of a vehicle based on a sensor data sequence, wherein the first training dataset includes sensor data sequences and information indicative of a driven trajectory associated with a respective sensor data sequence; forming the offline perception model by adding a task-specific layer to the trained foundation model, wherein the task-specific layer is configured to perform a perception task of the offline perception model; and fine-tuning the offline perception model, using a second training dataset, to perform the perception task, the second training dataset includes sensor data annotated for the perception task. The method further includes annotating data for use in subsequent training of an online perception model.