System for controlling remotely connected vehicle

Patent metadata

Field	Value
Publication number	US-10223842-B1
Application number	US-201715824920-A
Country	US
Kind code	B1
Filing date	Nov 28, 2017
Priority date	Oct 30, 2017
Publication date	Mar 5, 2019
Grant date	Mar 5, 2019

Abstract

Disclosed is a system for controlling a vehicle using a remote artificial intelligence (AI) server. A vehicle communicates with an artificial intelligence server for noise, vibration and harshness (NVH) issue diagnosis. The vehicle controls its fuel combustion condition for improving NVH based on an NVH diagnosis result using the AI.

First claim

What is claimed is:

1. A vehicle control total management system comprising:
a noise sensor configured to generate a noise data signal based on measured noise;
a vibration sensor configured to generate a vibration data signal based on measured noise; and
a diagnosis unit configured to analyze the noise data signal, the vibration data signal, and driving condition data by using artificial intelligence (AI)-learning, to diagnose a noise vibration harshness (NVH) problem of the vehicle and to control a combustion condition of the vehicle for addressing the NVH problem.

2. The vehicle control total management system of claim 1 , wherein:
the diagnosis unit is configured to monitor the noise data signal, the vibration data signal, and a combustion pressure measurement result after controlling the combustion condition, and to control the combustion condition based on the monitored result, and AI-learns data based on the combustion condition control and the monitored result to construct AI for determining the combustion condition.

3. The vehicle control total management system of claim 2 , wherein:
the diagnosis unit is further configured to transmit information on the noise data signal, the vibration data signal, the combustion pressure measurement result, and the combustion condition control to a central artificial intelligence server during the AI learning.

4. The vehicle control total management system of claim 1 , wherein:
the diagnosis unit is further configured to constrict an artificial intelligence neural network for the NVH diagnosis by learning the noise data signal, the vibration data signal, and the driving condition data by a deep learning scheme and is further configured to perform the NVH diagnosis by the artificial intelligence neural network.

5. The vehicle control total management system of claim 4 , wherein:
the diagnosis unit is further configured to covert the noise data signal and the vibration data signal into image data through digital signal processing and performs at least one of a time domain image analysis algorithm and a frequency domain image analysis algorithm by using a gabor filter with respect to the image data.

6. The vehicle control total management system of claim 4 , wherein:
the diagnosis unit is further configured to apply a deep neural network (DNN) learning machine technique or a convolution neural network (CNN) learning machine technique to the noise data signal and the vibration data signal.

7. The vehicle control total management system of claim 1 , further comprising:
a signal processing controller configured to control an engine, a transmission, and the like of a vehicle,
wherein the diagnosis unit is configured to transmit an instruction for the combustion condition to the signal processing controller.

8. The vehicle control total management system of claim 1 , wherein:
when the NVH diagnosis result is an abnormal state, reservation information in which a maintenance service is providable is received based on a GPS position of the vehicle, and
a navigation interlocks with the reservation information.

9. The vehicle control total management system of claim 1 , wherein:
classification categories according to the NVH diagnosis result include at least one selected from the group consisting of transmission noise, gasoline knocking, piston noise, oil pump noise, high pressure pump noise, vacuum pump noise, timing chain noise, timing belt noise, valve system noise, turbocharger noise, injector noise, crank system noise, fuel pulsation noise, alternator noise, auxiliary machinery belt noise, driveline noise, intake/exhaust system noise, water pump noise, power train related noise, and power train false noise.

10. The vehicle control total management system of claim 1 , wherein:
parameters for the NVH diagnosis include whether the parameter is an engine state which is noise and vibration data measured in the engine room based on a position where noise and vibration are measured or whether the parameter is a vehicle state which is noise and vibration data measured in a vehicle interior, whether a temperature condition in which noise and vibration are measured is a cold or hot condition, and whether the parameter is an idle condition in a stopped state, an acceleration driving mode in a driving condition which is a driving state, a deceleration driving mode, or a constant speed driving mode.

11. A central artificial intelligence server receiving NVH diagnosis results for multiple vehicles, comprising:
a central diagnosis unit configured to classify the NVH diagnosis results received from the multiple vehicles for each vehicle type; and
a database configured to store the classified NVH diagnosis results,
wherein the central diagnosis unit configured to update parameter values for artificial intelligence of the multiple vehicles by learning data stored in the database.

12. The central artificial intelligence server of claim 11 , wherein:
the central diagnosis unit is configured to determine and update parameter values for an artificial intelligence neural network by learning the data of the database by deep learning, and
wherein the server is further configured to transmit the updated parameter values to a vehicle.

13. The central artificial intelligence server of claim 11 , wherein:
an abnormality result is notified to a driver of a vehicle in which the diagnosis result is abnormal among the multiple vehicles and information on the abnormal vehicle is transmitted to a service network in order to provide a maintenance service.

14. A vehicle control total management system comprising:
a driving pattern database configured to store data on a driving pattern and a shift pattern for each road condition and data on an acceleration pedal usage pattern of the driver for each traffic situation; and
a tone control unit configured to construct artificial intelligence by performing AI learning by inputting the data of the driving pattern database, recognizing the driving pattern based on a road type and real-time traffic information received by using the artificial intelligence, and configured to set a target tone according to the recognized driving pattern.

15. The vehicle control total management system of claim 14 , wherein:
the tone control unit is c an artificial intelligence neural network by deep-learning the data of the database and recognizes the driving pattern based on the road type and the real-time traffic information by using the constructed artificial intelligence neural network.

16. The vehicle control total management system of claim 14 , wherein:
the tone control unit is configured:
to set the target tone so as to emphasize a quiet tone when the recognized driving pattern is a country road,
to set the target tone so as to emphasize a grand tone when the recognized driving pattern is a tunnel, and
to set the target tone so as to emphasize a sporty tone when the recognized driving pattern is a highway.

17. The vehicle control total management system of claim 14 , wherein:
the tone control unit is configured to determine an engine order component for the target tone and controls a speaker tone of a vehicle to match the target tone by using the extracted engine order component.

18. The vehicle control total management system of claim 17 , wherein:
the tone control unit is configured to determine a grade and a level of the engine order component generated by vibration of an engine of the vehicle, and
the engine order component is a physical phenomenon occurring in a rotating engin

Assignees

• Hyundai Motor Co Ltd
• Kia Motors Corp

Inventors

• Lee Dongchul
• Jung In Soo

Classifications

• G07C5/008 Primary

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

• G10K2210/1282

  Automobiles · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G10K15/02

  Synthesis of acoustic waves (synthesis of speech G10L13/00) · CPC title

• G07C5/0808

  Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title