TuSimple has been granted a patent for a system and method that implements a neural network-based vehicle dynamics model. The technology involves training a machine learning system with specific datasets to generate simulated vehicle dynamics data for autonomous vehicle simulation environments. The system can modify vehicle status data based on the simulation results for further iterations. GlobalData’s report on TuSimple gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on TuSimple, Autonomous freight management was a key innovation area identified from patents. TuSimple's grant share as of May 2024 was 21%. Grant share is based on the ratio of number of grants to total number of patents.
Neural network based vehicle dynamics model for autonomous vehicles
A recently granted patent (Publication Number: US12007778B2) discloses a system that includes a data processor and a memory storing a vehicle dynamics modeling module. This module is designed to receive vehicle control command data and vehicle status data, generate simulated vehicle dynamics data using a machine learning system trained on historical vehicle driving data, and provide this data to an autonomous vehicle simulation system. The system can modify vehicle status data and generate predicted simulated vehicle acceleration data for autonomous vehicles based on the input data. The patent also covers methods and non-transitory machine-useable storage mediums embodying instructions for implementing this system.
The system described in the patent utilizes machine learning to generate simulated vehicle dynamics data based on real-world vehicle driving data. By training the machine learning system on different datasets representing specific vehicle simulation environments, the system can provide accurate predictions for simulated autonomous vehicles. The system can handle various types of vehicle control command data, such as throttle, brake, and steering information, and vehicle status data like speed and pitch. Additionally, the patent covers methods for generating validation data to ensure the accuracy of the training datasets used by the machine learning system. Overall, the patented system aims to enhance autonomous vehicle simulation by leveraging historical driving data and machine learning technology to generate realistic vehicle dynamics predictions for different simulation environments.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
