Neural Vehicle Dynamics Modeling Without Component-Level Data
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Solution Overview
Problem
Conventional autonomous vehicle simulation systems are inefficient and unwieldy due to the need for detailed information about specific vehicle components and characteristics, making them difficult to adapt to various types of vehicles and requiring extensive data collection and maintenance.
Innovation Solution
A neural network-based vehicle dynamics model that predicts vehicle accelerations and torque using historical driving data, allowing for accurate simulation without requiring detailed engine or transmission component information, and can be easily adapted to different vehicle types by changing the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation systems use detailed vehicle component information, then simulation accuracy is improved, but system complexity and data maintenance difficulty increase
Solution Approach 1:
The patent replaces the mechanical system of detailed component-based simulation with a data-driven neural network model. Instead of using complex physical models of engines and transmissions, the system uses historical driving data to train neural networks that predict vehicle dynamics, substituting mechanical modeling with computational learning.
Solution Approach 2:
The patent creates simplified copies of vehicle dynamics through neural network models that replicate the behavior of complex vehicle systems without requiring detailed component information. The neural networks learn from historical data to produce accurate predictions of acceleration, torque, and other dynamic parameters.
2Reliability
If conventional simulation systems collect detailed vehicle data, then simulation fidelity is improved, but data collection and maintenance effort increase
Solution Approach 1:
The patent performs preliminary action by collecting and utilizing historical driving data that already exists from real vehicle operations. Instead of collecting new detailed component data for each simulation scenario, the system pre-processes and stores historical data that can be reused across multiple simulation contexts, eliminating repeated data collection efforts.
Solution Approach 2:
The neural network model serves itself by learning from historical data to automatically capture vehicle-specific characteristics. The model adapts to different vehicle types through data-driven learning rather than requiring manual configuration of component parameters, making the system self-configuring and reducing maintenance overhead.
3Measurement precision
If conventional systems model specific vehicle components, then vehicle-specific accuracy is improved, but adaptability to new vehicle types decreases
Solution Approach 1:
The patent creates a universal neural network framework that can handle multiple vehicle types through a single system architecture. The same neural network structure adapts to different vehicles by learning from their respective historical data, eliminating the need for separate component models for each vehicle type while maintaining vehicle-specific accuracy.
Solution Approach 2:
The patent enables adaptability through parameter changes in the neural network based on historical data characteristics. By adjusting the training data and model parameters rather than the fundamental system architecture, the system can accurately simulate different vehicle types without requiring structural modifications or detailed component specifications.
4Measurement precision
If conventional simulation systems are built for specific vehicle types, then simulation accuracy for those vehicles is improved, but model rebuilding time for new vehicles increases
Solution Approach 1:
The neural network model performs self-service by automatically adapting to new vehicle types through data-driven learning. When a new vehicle type needs to be simulated, the system simply trains the neural network on historical data for that vehicle type, and the model self-configures its parameters and characteristics without requiring manual model rebuilding or expert configuration.
Solution Approach 2:
The patent performs preliminary action by pre-training neural networks on historical driving data from various vehicle types. This preliminary learning phase enables the model to quickly adapt to new vehicles by leveraging previously learned patterns, significantly reducing the time required to create accurate simulations for new vehicle types compared to building component models from scratch.
Data Source
AI summary
A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.