Vehicle Dynamics Neural Model for Cross-Vehicle Simulation
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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.
Innovation Solution
A neural network-based vehicle dynamics model that predicts vehicle accelerations and torque using historical driving data, eliminating the need for detailed component information and allowing easy adaptation to different vehicle types by changing the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation systems use detailed information about engine and transmission components for each specific vehicle, then simulation accuracy is improved, but system complexity and difficulty of adaptation increase
Solution Approach 1:
The patent uses neural networks to create a virtual copy of vehicle dynamics behavior without replicating the actual physical components. The neural network learns from historical driving data to reproduce vehicle acceleration patterns, effectively copying the dynamic characteristics without needing detailed component specifications.
Solution Approach 2:
The patent replaces the mechanical system of detailed component modeling with a data-driven neural network model. Instead of using physical component parameters (engine, transmission details), the system substitutes these with learned patterns from historical data, achieving the same simulation goal through a different paradigm.
2Reliability
If conventional simulation systems collect detailed information about each specific vehicle type, then simulation fidelity is improved, but data collection and maintenance difficulty increase
Solution Approach 1:
The neural network model performs self-learning by automatically processing historical driving data to extract vehicle dynamics patterns. The system serves itself by learning from existing data without requiring manual collection or curation of detailed component information, reducing the burden of data preparation.
Solution Approach 2:
The patent changes the fundamental parameters used for simulation from detailed component specifications to aggregated historical driving data. This parameter transformation allows the system to achieve similar simulation fidelity using more accessible and easier-to-obtain data sources.
3Measurement precision
If conventional simulation systems are designed for specific vehicle types with detailed component information, then simulation accuracy for those vehicles is improved, but adaptability to new vehicle types decreases
Solution Approach 1:
The neural network model achieves universality by being able to simulate different vehicle types using the same underlying architecture. By training the model with historical data from various vehicle types, it learns generalizable patterns that can be applied across different vehicle platforms without requiring model redesign.
Solution Approach 2:
The patent introduces dynamics to the simulation model by using a neural network that can adapt its parameters based on learned patterns from historical data. This allows the model to dynamically adjust to different vehicle types through retraining with new data, rather than being static and vehicle-specific.
4Manufacturing precision
If conventional simulation systems maintain detailed vehicle component information, then model accuracy is improved, but model rebuilding time increases
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network with historical driving data to capture vehicle dynamics patterns. This pre-learning process creates a robust model that can be quickly adapted to new vehicle types through retraining, avoiding the time-consuming process of collecting and processing detailed component information 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.