GB2612316A - A method for operating a radio device of a motor vehicle as well as a radio device - Google Patents

A method for operating a radio device of a motor vehicle as well as a radio device Download PDF

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Publication number
GB2612316A
GB2612316A GB2115417.4A GB202115417A GB2612316A GB 2612316 A GB2612316 A GB 2612316A GB 202115417 A GB202115417 A GB 202115417A GB 2612316 A GB2612316 A GB 2612316A
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GB
United Kingdom
Prior art keywords
reception
polygon
motor vehicle
data
radio device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
GB2115417.4A
Other versions
GB202115417D0 (en
Inventor
Hsu Ting-Wei
Athanikar Sachin
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mercedes Benz Group AG
Original Assignee
Daimler AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Daimler AG filed Critical Daimler AG
Priority to GB2115417.4A priority Critical patent/GB2612316A/en
Publication of GB202115417D0 publication Critical patent/GB202115417D0/en
Priority to DE102022003451.4A priority patent/DE102022003451A1/en
Publication of GB2612316A publication Critical patent/GB2612316A/en
Withdrawn legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H20/00—Arrangements for broadcast or for distribution combined with broadcast
    • H04H20/53—Arrangements specially adapted for specific applications, e.g. for traffic information or for mobile receivers
    • H04H20/61—Arrangements specially adapted for specific applications, e.g. for traffic information or for mobile receivers for local area broadcast, e.g. instore broadcast
    • H04H20/62—Arrangements specially adapted for specific applications, e.g. for traffic information or for mobile receivers for local area broadcast, e.g. instore broadcast for transportation systems, e.g. in vehicles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/38—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying broadcast time or space
    • H04H60/41—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying broadcast time or space for identifying broadcast space, i.e. broadcast channels, broadcast stations or broadcast areas
    • H04H60/42—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying broadcast time or space for identifying broadcast space, i.e. broadcast channels, broadcast stations or broadcast areas for identifying broadcast areas
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/61—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54
    • H04H60/65—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 for using the result on users' side
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/29—Arrangements for monitoring broadcast services or broadcast-related services
    • H04H60/31—Arrangements for monitoring the use made of the broadcast services

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Circuits Of Receivers In General (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention relates to a method for predicting the quality of reception by a radio device (12) of a motor vehicle (10). At least one reference reception polygon (20) for a broadcast area (22) of a radio station (24) is provided, wherein the reference reception polygon (20) describes a reception strength. A further reception polygon (34) specific for the car (10) is determined, wherein boundaries of the further reception polygon (34) are determined depending on the reference reception polygon (20), the position of a specific antenna (18) of the motor vehicle (10), by using a machine learning algorithm (14). The reception polygon may be generated externally to the motor vehicle and may rely on partial historical reception data provided by another automobile. A plurality of polygons may be provided for different cars and antenna types A radio device (12, Fig.3) may provide data to a cloud-based server (28, Fig. 3). A method for processing signal reception data by a cloud-based server is also disclosed.

Description

A METHOD FOR OPERATING A RADIO DEVICE OF A MOTOR VEHICLE AS WELL
AS A RADIO DEVICE
FIELD OF THE INVENTION
[0001] The invention relates to the field of automobiles. More specifically, the invention relates to a method for operating a radio device of a motor vehicle. Furthermore, the invention relates to a corresponding radio device.
BACKGROUND INFORMATION
[0002] According to the state of the art, there are databases used to store information regarding quality of reception of radio signals for each station throughout the world. However, this information is not always available or is not specific to a car line due to different antenna positions in each car line. Therefore, there is a need to derive the missing quality of reception data and more generally to enhance the average database for a specific line of cars.
SUMMARY OF THE INVENTION
[0003] It is an object of the invention to provide a method as well as a corresponding radio device by which a radio signal may be received in a particularly advantageous way.
[0004] This object is solved by a method as well as a corresponding radio device according to the independent claims. Advantageous forms of configuration are presented in the dependent claims.
[0005] One aspect of the invention relates to a method for operating a radio device of a motor vehicle. At least one reference reception polygon for a broadcast area of a radio station is provided, wherein the reception polygon describes a reception strength. A reception polygon specific for the motor vehicle is determined, wherein boundaries of the reception polygon are determined depending on the provided reference reception polygon and positioning of an antenna in the motor vehicle by using a machine learning algorithm. Further, a method for calculating the boundaries of the reception polygon is provided to complete the reception polygon when the boundaries are not available using a machine learning algorithm.
[0006] Therefore, a particularly advantageous way for receiving the radio signal is provided. In particular, the method enhances the accuracy of reception polygon information by creating separate reception polygons for each pair of station and car line. Furthermore, the method predicts polygons for each station in every car line. This method is furthermore usable across different car lines. Furthermore, the method can predict and complete partial polygons, eliminating the need to drive every road for each car line.
[0007] Reception polygon describes the reception quality for any radio station, for example, 97.7 FM at a specific location, wherein the signal strength for 360 degrees is measured and mapped by the polygon.
[0008] Furthermore, as data increases, reception accuracy increases and ultimately, the newly collected data can replace the predicted data.
[0009] According to an embodiment, the reception polygon is generated by the machine learning algorithm of an electronic computing device, which is outside of the motor vehicle.
[0010] According to another embodiment, the reception polygon is generated depending on historical reception data of at least one further motor vehicle.
[0011] In another embodiment, a plurality of reception polygons are provided depending on a plurality of specific antennas of a plurality of motor vehicles.
[0012] Another aspect of the invention relates to a method for providing the further reception polygon for the motor vehicle with the specific antenna.
[0013] Another aspect of the invention relates to a radio device for a motor vehicle, wherein the radio device is configured for performing a method according to the preceding aspect. In particular, the method is performed by the radio device. The radio device for a motor vehicle is for example only, the method can also be used in any edge device which may get a signal from the radio station. For example, the method may also be implemented for providing a reception polygon for a mobile phone.
[0014] Further advantages, features, and details of the invention derive from the following description of preferred embodiments as well as from the drawings. The features and feature combinations previously mentioned in the description as well as the features and feature combinations mentioned in the following description of the figures and/or shown in the figures alone can be employed not only in the respectively indicated combination but also in any other combination or taken alone without leaving the scope of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The novel features and characteristic of the disclosure are set forth in the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described below, by way of example only, and with reference to the accompanying figures.
[0016] The drawings show in: [0017] Fig. 1 shows a schematic side view according to an embodiment of a motor vehicle comprising an embodiment of a radio device; [0018] Fig. 2 shows a schematic broadcast area of a radio station; and [0019] Fig. 3 shows a schematic flow chart according to the embodiment of the method.
[0020] In the figures, the same elements or elements having the same function are indicated by the same reference signs.
DETAILED DESCRIPTION
[0021] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
[0022] While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
[0023] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion so that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus preceded by "comprises" or "comprise" does not or do not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
[0024] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that forms part hereof, and in which is shown by way of illustration a specific embodiment in which the disclosure may be practiced. This embodiment is described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
[0025] Fig. 1 shows in a side view a motor vehicle 10 comprising at least a radio device 12. Furthermore, an electronic computing device 16, which is external of the motor vehicle 10, is shown, wherein the external electronic computing device comprises at least one machine learning algorithm 14. The radio device 12 and/or and motor vehicle 10 comprises a specific antenna 18.
[0026] According to an embodiment, providing at least one reference reception polygon 20 (Fig. 2) for a broadcast area 22 (Fig. 2) of a radio station 24 (Fig. 2) is performed, wherein the reference reception polygon 20 describes a reception strength. Determining a further reception polygon 34 (Fig. 2) specific for the motor vehicle 10 is performed, wherein boundaries of the further reception polygon 34 are determined depending on the reference reception polygon 20, which is adapted for a specific antenna 18 of the motor vehicle 10, by using a machine learning algorithm 14.
[0027] Fig. 2 shows a top view according to a reception polygon 38. In particular, the reception polygon 38 is generated by the machine learning algorithm 14 of the electronic computing device 16 which is outside of the motor vehicle 10. Furthermore, a plurality of further reception polygons 34 are provided depending on a plurality of specific antennas 18 of a plurality of motor vehicles 10.
[0028] In particular, Fig. 2 shows that the database of reference polygons 20 containing average reception data throughout the world is thus enhanced and made specific for each line of car depending on the specific antennas 18 by the machine learning algorithm 14 of the electronic computing device 16 which is outside of the motor vehicle 10.
[0029] In a first step, collecting data for each car line in a selected area is performed. The data is stored, for example in a private server on a head unit of a motor vehicle 10 and sent to a cloud 28 (Fig. 3). The data is processed into 36 numbers, wherein these 36 numbers describe 360 degrees for a reception area polygon per different car line. Therefore, one number are ten degrees, the signal strength and that degree of direction at a specific location is used. The data can be updated over time.
[0030] In a second step, the machine learning algorithm 14 is used to predict areas that are not collected in the reception polygon 38, which is represented by the dashed line 36 in the reception polygon 38. The collected data is represented by the solid line 34 in the reception polygon 38.
[0031] This method fills the gap where there is not enough data to provide the reception because it is impossible to collect data by driving each and every road in the world by each car line.
[0032] As an example, if there is the radio station 24 for the specific motor vehicle 10, it will revert to the average radio station signal, and if the radio station 24 does not exist, before it reaches the no signal, it may pre-buffer and load the audio content. If this is not available, it switches to another radio station 24 of similar type, in particular by recommendation.
[0033] Furthermore, for each car line, collecting the data for all the potential stations in a designated area is provided. One reception polygon 38 is created per station, per car line and other vehicle parameters. For each reception polygon 38, data may be collected multiple times to ensure the accuracy, depending on GPS positions.
[0034] Fig. 3 shows a flow chart according to an embodiment of the method. In particular, a data collection architecture is shown. In a first step Si, a cloud based private server 28 requests data from a station landscape database 30 and the station landscape database 30 sends notifications for new updates. In a second step S2, a head unit of a motor vehicle 10 collects data from a radio device 12 on the motor vehicle 10. Also in the second step 52, the collected data is stored in the private server on the head unit. In a third step S3, the motor vehicle 10 sends data to the cloud based private server 28, when the collected data reaches a certain size. In a fourth step S4, the cloud based private server 28 confirms that data was received to the head unit of the motor vehicle 10. Therefore, the head unit of the motor vehicle 10 can remove/delete old data and maintain minimum data size.
[0035] The collected data will be processed into the 36 numbers for the reception polygon 38 per different car line by the cloud based private server 28 which then delivers the data to the station landscape database 30. The data is merged and cleaned into understandable forms, for example time stamp conversions. Furthermore, data leading to location inaccuracies are filtered out, for example the data with error greater than a thousand meters to current polygon point is discarded. The data is formatted into 36 numbers per reception polygon 38 for every station 24 and every motor vehicle 10.
[0036] The head unit of a motor vehicle 10 may send it current location data to the cloud based private server 28 and fetch the current reception polygon 38at certain time intervals.
[0037] To update and process the data over time, the motor vehicle 10 keeps sending data to the cloud based private server 28 to further improve the database. The cloud based private server 28 starts collecting the new data and processes it when it reaches a certain amount. The cloud based private server 28 creates separate containers for each car line. The data is collected in one or more databases. The data may be anonymized, no personal data information is collected. The cloud based private server 28 contains the latest reception polygon database. Notifications regarding the updated reception polygons 38 are provided by the station landscape database 30.
[0038] The accuracy of the reception polygon 38 is improved by choosing a regression model that yields the best results by comparing the predicted results against collected data. Approximately 80% of the collected data is used to train the model and 20% on testing the accuracy of the selected model especially in the places that do not have enough data. The size of the regression model and the time to predict and run the model are also taken into consideration. The machine learning operations are performed off-board on the cloud based private server 28.
[0039] In order to implement the selected machine learning algorithm 14 to populate the reception polygons 38, that does not have much collected data, a confidence level for every predict is provided. A threshold may be applied to the confidence level to draw the polygon boundary. If, at any point, the confidence ratio is improved and is above the threshold due to more collected data over time, the reception polygon 38 gets updated. Furthermore, predicted data may be replaced when the actual data is collected and updated.
Reference signs motor vehicle 12 radio device 14 machine learning algorithm 16 electronic computing device 18 antenna reference reception polygon 22 broadcast area 24 radio station 26 radio signal 28 cloud station landscape database 32 line 34 further reception polygon 36 dashed line 38 reception polygon Si first step 32 second step S3 third step 34 fourth step position

Claims (10)

  1. CLAIMS1. A method for predicting the quality of reception by a radio device (12) of a motor vehicle (10), comprising the steps of: providing at least one reference reception polygon (20) for a broadcast area (22) of a radio station (24), wherein the reference reception polygon (20) describes a reception strength; and determining a further reception polygon (34) specific for the motor vehicle (10), wherein boundaries of the further reception polygon (34) are determined depending on the reference reception polygon (20), the position of a specific antenna (18) of the motor vehicle (10), by using a machine learning algorithm (14).
  2. 2. The method according to claim 1, characterized in that the reception polygon (38) is generated on an electronic computing device (16), which is external of the motor vehicle (10).
  3. 3. The method according to claim 1 or 2, characterized in that the reception polygon (38) is generated depending on at least partial historical reception data of at least one further motor vehicle.
  4. 4. The method according to any one of claims 1 to 3, characterized in that a plurality of reception polygons (38) are provided depending on a plurality of specific antennas (18) of a plurality of motor vehicles (10).
  5. 5. A radio device (12) of a motor vehicle (10), configured to: - send current location of the radio device to a cloud based server (28); - receive reception polygons (38) calculated according to any of claims 1 to 4; and - switching to a radio station with a highest quality of reception at a point in time.
  6. 6. A motor vehicle (10) comprising the radio device (12) according to claim 5.
  7. 7. A method for processing signal reception data by a cloud based server (28), comprising the steps of: - receiving reception data from a plurality of motor vehicles (10): - storing reception data until it reaches a certain size; - determining a machine learning algorithm (14) to run on the received data; - applying the machine learning algorithm (14) on the received data; and - providing the results to a station landscape database (30).
  8. 8. The method of claim 7 wherein the cloud based server (28) determines the machine learning algorithm (14) based on accuracy of predicted results, time to run the algorithm, and size of the algorithm.
  9. 9. The method of claim 7 wherein: - the station landscape database (30) send notifications to the cloud based server (28) when new reception polygon [new reference number] is available; and - the cloud based server (28) maintains a latest version of the station landscape database (30).
  10. 10. The method of claim 8 wherein a plurality of motor vehicles (10) are configured to perform the steps of: - collecting reception data from a radio device (12); - sending reception data to a cloud based server (28) for further storing or processing; and - removing the data from the motor vehicle (10) upon receiving a confirmation from the cloud based server (28).
GB2115417.4A 2021-10-27 2021-10-27 A method for operating a radio device of a motor vehicle as well as a radio device Withdrawn GB2612316A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
GB2115417.4A GB2612316A (en) 2021-10-27 2021-10-27 A method for operating a radio device of a motor vehicle as well as a radio device
DE102022003451.4A DE102022003451A1 (en) 2021-10-27 2022-09-19 Method for operating a radio device of a vehicle and a radio device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GB2115417.4A GB2612316A (en) 2021-10-27 2021-10-27 A method for operating a radio device of a motor vehicle as well as a radio device

Publications (2)

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GB202115417D0 GB202115417D0 (en) 2021-12-08
GB2612316A true GB2612316A (en) 2023-05-03

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GB2115417.4A Withdrawn GB2612316A (en) 2021-10-27 2021-10-27 A method for operating a radio device of a motor vehicle as well as a radio device

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GB (1) GB2612316A (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110159800A1 (en) * 2009-12-25 2011-06-30 Fujitsu Ten Limited Database construction system and broadcast receiving system
US20190268779A1 (en) * 2018-02-23 2019-08-29 Google Llc Detecting Radio Coverage Problems
KR101983663B1 (en) * 2017-11-28 2019-09-03 쌍용자동차 주식회사 A station selecting apparatus and method for a car audio broadcasting station according to vehicle position and receiving sensitivity

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110159800A1 (en) * 2009-12-25 2011-06-30 Fujitsu Ten Limited Database construction system and broadcast receiving system
KR101983663B1 (en) * 2017-11-28 2019-09-03 쌍용자동차 주식회사 A station selecting apparatus and method for a car audio broadcasting station according to vehicle position and receiving sensitivity
US20190268779A1 (en) * 2018-02-23 2019-08-29 Google Llc Detecting Radio Coverage Problems

Also Published As

Publication number Publication date
DE102022003451A1 (en) 2023-04-27
GB202115417D0 (en) 2021-12-08

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