Autonomous Driving Video Prediction Training for Scenario Generalization
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Solution Overview
Problem
Autonomous driving models trained with single and simple data sets struggle to generalize well in complex real-world scenarios, leading to safety hazards and hindering the implementation of autonomous driving.
Innovation Solution
Construct a VQA network graph based on an autonomous driving data set and/or simulator, using it as a sample set to train driving behavior and trajectory prediction models, and evaluate them using a training and test set, while also training a video prediction model with initial video frames and control texts to enhance generalization and interactivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single and simple data set is used for training, then the training process is simple and fast, but the model's generalization ability deteriorates in complex real-world scenarios
Solution Approach 1:
The patent merges multiple data sources including autonomous driving datasets (nuScenes, Waymo), simulator data (CARLA), and synthetic data generated by video prediction models. This combination creates a diverse and comprehensive training dataset that improves model generalization while maintaining training efficiency through unified processing pipelines.
Solution Approach 2:
The patent develops a universal video prediction model that can handle multiple autonomous driving tasks including trajectory prediction, behavior prediction, and scene understanding. This multi-functional model is trained on diverse data and can adapt to various real-world scenarios, improving versatility without requiring separate models for each task.
2Adaptability or versatility
If complex and diverse data sets are used for training, then the model's generalization ability improves, but the training complexity and time increase
Solution Approach 1:
The patent segments the training process into distinct stages: first training on autonomous driving datasets, then incorporating simulator data, and finally adding synthetic data from video prediction models. This segmented approach manages training complexity by breaking down the large-scale diverse dataset into manageable portions that can be processed systematically.
Solution Approach 2:
The patent performs preliminary data processing and augmentation during the data preparation phase, including generating synthetic training samples using video prediction models before the main training begins. This preliminary action reduces the complexity during actual model training by having pre-processed and prepared diverse training data ready for use.
Data Source
AI summary
Provided method for training an autonomous driving model including a video prediction model, and the method including: determining, according to at least one of an initial video frame collected by a target vehicle or scenario description metadata of an initial video frame, a scenario context of the initial video frame; determining a vehicle movement instruction of the target vehicle according to at least one of the initial video frame or trajectory data of the target vehicle corresponding to the initial video frame; and training an initial model using the initial video frame and a control text corresponding to the initial video frame, to obtain the video prediction model, where the control text comprises the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.