# US 12,007,778 B2 · Neural network based vehicle dynamics model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,007,778 B2 - Document ID: US12007778B2 - Status: Granted - Kind: B2 - Application number: 18/094,363 - Filed: 2023-01-08 - Published: 2023-05-25 - Granted: 2024-06-11 - Earliest claimed: 2017-08-08 - Assignees: Tusimple, Inc. - Applicants: TuSimple, Inc. - Inventors: Jun Chen; Wutu Lin; Liu Liu - Web page: https://patentgrep.com/patent/US12007778B2 ## CPC - G06N 3/08: Learning methods - G05D 1/00: Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots (drive control systems specially adapted for autonomous road vehicles B60W60/00) - G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)} - G05D 1/0285: {using signals transmitted via a public communication network, e.g. GSM network} - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/247: using signals provided by artificial sources external to the vehicle, e.g. navigation beacons - G05D 1/81: Handing over between on-board automatic and on-board manual control - G06N 3/00: Computing arrangements based on biological models - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES ## 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. ## Claims ### Claim 1 A system comprising: a data processor; and a memory storing a vehicle dynamics modeling module, executable by the data processor to: receive vehicle control command data and vehicle status data; generate a first set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of a machine learning system trained on a first training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations; generate a second set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of the machine learning system trained on a second training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations, the second set of simulated vehicle dynamics data being different from the first set of simulated vehicle dynamics data, the first training dataset and the second training dataset each representing a different particular vehicle simulation environment with particular types of vehicles having a defined set of characteristics; provide the first and the second set of simulated vehicle dynamics data to an autonomous vehicle simulation system implementing one of the particular vehicle simulation environments; modify, by use of the autonomous vehicle simulation system, the vehicle status data by use of either the first or the second set of simulated vehicle dynamics data; and generate predicted simulated vehicle acceleration data for simulated autonomous vehicles based on the vehicle control command data and the modified vehicle status data. ### Claim 2 (depends on 1) The system of claim 1 wherein the vehicle control command data comprises throttle, brake, and steering control information corresponding to simulated autonomous vehicles. ### Claim 3 (depends on 1) The system of claim 1 wherein the vehicle status data comprises speed and pitch information corresponding to simulated autonomous vehicles. ### Claim 4 (depends on 1) The system of claim 1 wherein the first training dataset includes training data different from training data of the second training dataset. ### Claim 5 (depends on 1) The system of claim 1 wherein the first training dataset or the second training dataset include training data corresponding to one of the particular vehicle simulation environments. ### Claim 6 (depends on 1) The system of claim 1 wherein the first training dataset or the second training dataset include training data corresponding to the recorded historical vehicle driving data captured from real world vehicle operations or simulated vehicle movements. ### Claim 7 (depends on 1) The system of claim 1 wherein the vehicle dynamics modeling module is further configured to use the simulated vehicle dynamics data to generate validation data to validate the first training dataset or the second training dataset. ### Claim 8 (depends on 1) The system of claim 1 wherein the vehicle dynamics modeling module is further configured to generate validation data to validate an accuracy of the first training dataset or the second training dataset. ### Claim 9 (depends on 1) The system of claim 1 wherein the simulated vehicle dynamics data comprises predicted vehicle dynamics data, wherein the predicted vehicle dynamics data is generated based on the vehicle control command data and the vehicle status data, by use of the machine learning system. ### Claim 10 (depends on 1) The system of claim 1 wherein the simulated vehicle dynamics data comprises predicted vehicle dynamics data, the predicted vehicle dynamics data comprises at least one of predicted acceleration data and predicted torque data, wherein the at least one of the predicted acceleration data and the predicted torque data is generated by use of the trained machine learning system based on the vehicle control command data and the vehicle status data. ### Claim 11 A method comprising: receiving vehicle control command data and vehicle status data; generating a first set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of a machine learning system trained on a first training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations; generating a second set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of the machine learning system trained on a second training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations, the second set of simulated vehicle dynamics data being different from the first set of simulated vehicle dynamics data, the first training dataset and the second training dataset each representing a different particular vehicle simulation environment with particular types of vehicles having a defined set of characteristics; providing the first and the second set of simulated vehicle dynamics data to an autonomous vehicle simulation system implementing one of the particular vehicle simulation environments; modifying, by use of the autonomous vehicle simulation system, the vehicle status data by use of either the first or the second set of simulated vehicle dynamics data; and generating predicted simulated vehicle acceleration data for simulated autonomous vehicles based on the vehicle control command data and the modified vehicle status data. ### Claim 12 (depends on 11) The method of claim 11 wherein the machine learning system comprises artificial neural networks or connectionist systems. ### Claim 13 (depends on 11) The method of claim 11 wherein the vehicle control command data comprises throttle and brake control information corresponding to simulated autonomous vehicles. ### Claim 14 (depends on 11) The method of claim 11 wherein the vehicle status data comprises speed information corresponding to simulated autonomous vehicles. ### Claim 15 (depends on 11) The method of claim 11 wherein the first training dataset includes training data different from training data of the second training dataset. ### Claim 16 (depends on 11) The method of claim 11 wherein the simulated vehicle dynamics data comprises predicted vehicle torque data generated by use of the trained machine learning system based on the vehicle control command data and the vehicle status data, wherein the vehicle control command data does not comprise steering control data, wherein the vehicle status data does not comprise pitch information. ### Claim 17 (depends on 11) The method of claim 11 wherein the simulated vehicle dynamics data comprises predicted vehicle torque data generated by use of the trained machine learning system based on the vehicle control command data and the vehicle status data, wherein vehicle speed data is generated based on the predicted vehicle torque data. ### Claim 18 (depends on 11) The method of claim 11 including generating validation data to validate an accuracy of the first training dataset or the second training dataset. ### Claim 19 A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to: receive vehicle control command data and vehicle status data; generate a first set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of a machine learning system trained on a first training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations; generate a second set of simulated vehicle dynamics data based on the vehicle control command data and the vehicle status data by use of the machine learning system trained on a second training dataset comprising recorded historical vehicle driving data captured from real world vehicle operations, the second set of simulated vehicle dynamics data being different from the first set of simulated vehicle dynamics data, the first training dataset and the second training dataset each representing a different particular vehicle simulation environment with particular types of vehicles having a defined set of characteristics; provide the first and the second set of simulated vehicle dynamics data to an autonomous vehicle simulation system implementing one of the particular vehicle simulation environments; modify, by use of the autonomous vehicle simulation system, the vehicle status data by use of either the first or the second set of simulated vehicle dynamics data; and generate predicted simulated vehicle acceleration data for simulated autonomous vehicles based on the vehicle control command data and the modified vehicle status data. ### Claim 20 (depends on 19) The non-transitory machine-useable storage medium of claim 19 wherein the first training dataset includes training data different from training data of the second training dataset.