Neural Vehicle Dynamics Modeling 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 type, then the accuracy of vehicle simulation is improved, but the system complexity and difficulty of adaptation to new vehicle types increases significantly
Solution Approach 1:
The patent replaces the conventional mechanical approach of manually configuring vehicle simulation parameters with a neural network-based system. The neural network automatically learns vehicle dynamics characteristics from driving data, substituting the need for manual detailed component information with an automated data-driven model that achieves comparable or superior simulation accuracy.
Solution Approach 2:
The patent transforms the simulation approach by changing from fixed detailed component parameters to adaptive neural network parameters. The neural network dynamically adjusts its internal parameters (weights and biases) based on training data, allowing the system to adapt to different vehicle types without requiring manual reconfiguration of detailed component specifications.
2Adaptability or versatility
If conventional simulation systems collect detailed information about vehicle components for thousands of different vehicle types, then the comprehensiveness of simulation coverage is improved, but the time and resources required to collect, maintain, and use this information increases
Solution Approach 1:
The patent uses copying by training the neural network on driving data from various vehicle types and then applying the learned model to simulate different vehicle types. Instead of collecting and maintaining detailed component information for each vehicle type, the system copies the underlying dynamics patterns learned from training data and applies them to new vehicle types through the neural network model.
Solution Approach 2:
The patent creates a universal neural network-based vehicle dynamics model that can handle multiple vehicle types with a single system. The neural network is designed to be vehicle-type-agnostic, learning generalizable dynamics patterns that apply across different vehicle types, thereby achieving multi-functionality without requiring separate detailed models for each vehicle type.
3Manufacturing precision
If conventional simulation systems require detailed component information for each vehicle type, then the precision of vehicle dynamics modeling is improved, but the ease of operation and adaptability to new vehicle types deteriorates
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically learn and adapt to new vehicle types without requiring manual intervention. The system feeds driving data into the neural network, which automatically processes the information and generates accurate dynamics models for different vehicle types, eliminating the need for operators to manually collect and configure detailed component information.
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.