World Model Transfer Learning for Sim-to-Real Autonomous Driving
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Solution Overview
Problem
Existing automatic driving models face challenges in mobility and adaptability due to modularization methods requiring manual annotation and cross-module configuration, limiting their ability to adapt to new environments and tasks, and lack a seamless transition from virtual to real-world applications.
Innovation Solution
A learning-oriented transferable automatic driving system utilizing a generative world model that constructs a simulation environment with real-world configurations, collects data from both simulated and real environments, trains the model using multi-sensor-image fusion, and performs domain adaptive transferring training to deploy in real vehicles, enabling virtuality-to-reality transitioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a modularization method is used for automatic driving models, then the system can be constructed with defined modules, but the method requires large amounts of artificial engineering and manual annotation, resulting in poor mobility and inability to adapt to new environments and tasks
Solution Approach 1:
The patent creates a virtual copy of the real-world driving environment through a world model that simulates camera images, aerial views, and traffic conditions. This virtual environment copy allows the system to train and adapt without requiring manual annotation of real-world data, thereby improving adaptability while reducing artificial engineering efforts
Solution Approach 2:
The system performs preliminary training in a simulated virtual environment before deploying to real-world applications. By pre-training the world model with synthetic data generated from virtual camera images and aerial views, the system prepares adaptive capabilities in advance, reducing the need for extensive manual annotation when deployed to new environments
2Adaptability or versatility
If existing automatic driving models are upgraded for new environments, then new tasks can be performed, but manual redesign is required for algorithm upgrade, making the method poor in mobility
Solution Approach 1:
The world model serves multiple functions: it generates virtual training data, simulates different driving environments, and adapts to various vehicle configurations (autonomous, assisted, manual modes). This universal model eliminates the need for separate manual redesigns for different tasks and environments, reducing algorithm configuration complexity while maintaining versatility
Solution Approach 2:
The system dynamically adapts to new environments through continuous learning from virtual and real-world data. The world model can be retrained and adjusted on-the-fly for different driving conditions, vehicle types, and traffic scenarios without requiring static manual redesign, thereby improving mobility and reducing configuration complexity
3Device complexity
If a single camera is used to model the environment, then the system structure is simplified, but a complete picture of the environment cannot be obtained, compromising safety
Solution Approach 1:
The patent merges data from multiple camera sources (front, rear, left, right cameras) to create a comprehensive environmental model. By combining inputs from four-directional cameras with aerial view data, the system achieves complete environmental coverage while maintaining a unified world model structure, thus improving safety without excessive complexity
Solution Approach 2:
The world model acts as an intermediary that integrates and processes data from multiple camera sources. It fuses information from front, rear, left, and right cameras along with aerial views to create a unified environmental representation, ensuring complete picture acquisition while managing sensor configuration complexity through centralized processing
4Productivity
If virtual world training is used for automatic driving, then training efficiency is improved, but the method does not consider how to train from virtual world to real world for application
Solution Approach 1:
The system implements a feedback loop where the world model is trained on virtual data, then evaluated and fine-tuned using real-world driving data. This feedback mechanism ensures that the model learns from both simulated and actual environments, enabling effective virtuality-to-reality transfer while maintaining high training efficiency through the use of synthetic training data
Data Source
AI summary
The present invention relates to a learning-oriented transferable automatic driving method and system driven by a world model. The method includes the following steps: constructing a simulation environment; collecting a large batch of data in the simulation environment, and collecting a small batch of data in the real environment; constructing a world model, and performing model training in the simulation environment by using the large batch of data, wherein the world model takes a camera image as an input to model an environment by using a generative world model; storing and transmitting historical moment information by hidden variables, and outputting an aerial view and a control instruction; and performing domain adaptive transferring training in the real environment on the basis of the small batch of data, and deploying the model in an autonomous vehicle in the real world to achieve virtuality-to-reality transferring general integrated automatic driving.