Neural network based vehicle dynamics model

a neural network and vehicle technology, applied in the field of neural network based vehicle dynamics model, can solve the problems of unfavorable vehicle type adaptation, and difficult collection, maintenance and use of detailed information for a large number of vehicle types, and achieve the effect of saving model rebuilding time and being easy to adapt to work

Active Publication Date: 2019-02-14
TUSIMPLE INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present invention provides an improved way to simulate different driving situations on a car without having to know everything about its components. It uses a trained neural network that predicts how well it will behave under certain conditions. By doing this, the simulator can quickly adapt itself to new cars while still maintaining accurate performance over similar ones.

Problems solved by technology

The technical problem addressed by this patent is how to efficiently train and configure autonomous vehicle simulations with accurate vehicle motion and dynamic data from diverse sources like engines and transmissions without requiring extensive knowledge of every type of car's parts and characteristics.

Method used

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  • Neural network based vehicle dynamics model
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  • Neural network based vehicle dynamics model

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Embodiment Construction

[0013]In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments. It will be evident, however, to one of ordinary skill in the art that the various embodiments may be practiced without these specific details.

[0014]A system and method for implementing a neural network based vehicle dynamics model are disclosed herein. The vehicle dynamics model is one of the key subsystems for producing accurate vehicle simulation results in a simulation system. In various example embodiments as disclosed herein, the data-driven modeling system and method based on a neural network allows the modeling system to predict accurate vehicle accelerations based on recorded historical vehicle driving data. To generate the predicted vehicle accelerations, a control command (e.g., throttle, brake, and steering commands) and vehicle status (e.g., vehicle pitch and speed status) are provided as inputs to

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Abstract

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.

Description

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Claims

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Application Information

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Owner TUSIMPLE INC
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