Brain-Like Driving World Model for Adaptive End-to-End Autonomy
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Solution Overview
Problem
Existing automatic driving models rely on modularization methods that require extensive artificial engineering and manual annotation, limiting their adaptability to new environments and tasks, and are not well-suited for the requirements of general artificial intelligence.
Innovation Solution
A driving world model based on a brain-like neural circuit, incorporating a perception module, environment memory module, and convolutional network module, which uses a monocular camera image to extract and process environment dynamics information, simulating a nematode neural network to achieve end-to-end automatic driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If modularization method is used for automatic driving models, then the model can be constructed with clear module divisions, but the method requires extensive manual engineering and annotation, reducing adaptability to new environments
Solution Approach 1:
The patent applies universality by designing a unified neural network architecture that handles multiple driving tasks (perception, prediction, planning, control) within a single end-to-end model. This universal framework eliminates the need for separate manual module configurations and enables automatic adaptation to new environments through continuous learning, resolving the contradiction between ease of construction and adaptability
Solution Approach 2:
The patent implements self-service through autonomous learning mechanisms where the system automatically annotates data, configures modules, and adapts to new environments without human intervention. The neural network performs self-training and self-optimization, eliminating extensive manual engineering while maintaining high adaptability to changing conditions
2Stability of the object's composition
If modularization method is used for automatic driving models, then the model structure is well-defined, but manual redesign is needed for new tasks, reducing mobility
Solution Approach 1:
The patent applies dynamics by transitioning from a static modular structure requiring manual redesign to a dynamic end-to-end neural network that automatically adapts its parameters and architecture through continuous learning. The system maintains structural integrity while enabling flexible adaptation to new tasks through autonomous retraining, resolving the contradiction between structural stability and algorithmic mobility
3Reliability
If existing automatic driving models are used, then they can handle specific tasks, but they cannot adapt to general artificial intelligence requirements
Solution Approach 1:
The patent applies universality by creating a general-purpose end-to-end neural network architecture that can perform multiple driving tasks (perception, prediction, planning, control) and adapt to various environments. This universal model replaces task-specific modular systems, maintaining reliability through comprehensive training while achieving versatility for general AI applications
Data Source
AI summary
The present application relates to the technical field of vehicle control, and in particular, to a driving world model based on a brain-like neural circuit. The driving world model includes: a perception module, an environment memory module, a brain-like neural circuit network module, and a convolutional network module; the perception module includes a two-dimensional feature encoding unit, a three-dimensional feature encoding unit, a summing pooling unit which are connected in sequence; the environment memory module is configured to acquire a current moment and memorize environment dynamics information; the brain-like neural circuit network module is configured to establish a brain-like neural circuit network. The present application uses a monocular camera image as an input image. The world model is applied to extracting and memorizing environment dynamics information, simulating a nematode nervous system to establish the brain-like neural circuit to process the environment dynamics information, completing an end-to-end automatic driving task.