• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a method for diagnosing a sensor (16, 17) of a motor vehicle (10) adapted to detect road infrastructures (101, 102, 103, 104), said motor vehicle comprising a communication means (18). ) adapted to communicate with a remote server (50) and a computer (14) connected to the sensor and the communication means. According to the invention, the diagnostic method comprises steps in which: a) the computer identifies an infrastructure and assigns an effective score, which is relative to the visibility of this infrastructure, b) the remote server acquires a reference note which is assigned to said infrastructure and which is relative to the visibility of this infrastructure, and c) the actual score and the reference note are compared to deduce a state of operation of the sensor.
    公开号:FR3044150A1
    申请号:FR1561263
    申请日:2015-11-23
    公开日:2017-05-26
    发明作者:Pedro Moreno-Lahore
    申请人:Valeo Schalter und Sensoren GmbH；
    IPC主号:
    专利说明:

    METHOD FOR DIAGNOSING A SENSOR OF A MOTOR VEHICLE
    Technical field to which the invention relates
    The present invention generally relates to aids for driving motor vehicles.
    It relates more particularly to a method for diagnosing a motor vehicle sensor.
    It applies to motor vehicles equipped with a sensor adapted to detect road infrastructure, a communication means adapted to communicate with a remote server and a computer connected to the sensor and the communication means.
    Technological background
    To facilitate and make safer the driving of a motor vehicle, it is desired to provide the driver with information (eg the maximum speed allowed on the road) and report problems (for example when the vehicle deviates from its path).
    To develop this information and these reports, it is known to use sensors adapted to detect and interpret the infrastructure of the road (panel, position of continuous and discontinuous lines, ...).
    The reliability of this information and reports largely depends on the quality of the road infrastructure detection. Unfortunately, this detection is affected by two problems.
    The first problem is that the infrastructures deteriorate over time, for example because of the weather conditions, their exposure to the sun, and the number of cars rolling on them. It is therefore necessary to monitor their condition to replace them before they are no longer readable.
    Currently, this monitoring work is carried out by natural persons, who are used to drive on roads and to complete a database in which each infrastructure is noted, according to its readability and therefore its need or not to be repaired.
    The second problem is that sometimes the sensors have malfunctions. If it is rather easy to detect a complete stop of a sensor, it is more difficult to detect a problem affecting this sensor without causing its complete stop. Such a problem, however, may affect the quality of the detection of road infrastructure. For example, a displacement of the sensor relative to its support can affect the quality of the detection of the infrastructure of the road without being easily detectable. Dust adhered to the lens of a sensor can also affect the quality of the detection.
    Object of the invention
    In order to overcome the above-mentioned drawback of the state of the art, the present invention proposes a statistical method for diagnosing the good operating state of the sensors.
    More particularly, the invention provides a method for diagnosing a motor vehicle sensor, which comprises steps in which: a) the computer identifies an infrastructure and assigns an effective score, which is relative to the degree of visibility (b) the remote server acquires a reference note that is assigned to that infrastructure and that is related to the degree of visibility of that infrastructure, and (c) the actual score and the reference grade are compared to derive a condition from that infrastructure. sensor operation.
    Thus, the invention takes advantage of the fact that the remote server has a database in which are stored notes assigned to the infrastructure, according to their visibility. The invention then proposes to compare this note with a note that the calculator itself has calculated based on the difficulties it has had to interpret the road infrastructure.
    So, if the two notes are very close, we can deduce a good operation of the sensor.
    On the contrary, if these two notes are very different (for a predetermined period of time or for a number of occurrences identified as significant), it can be deduced that the sensor has a significant dysfunction.
    Finally, if these two notes are slightly different, and this slight difference is noted for all infrastructures encountered, we can deduce that the sensor has a slight malfunction.
    We can then follow the evolution of this difference between the two notes to control the drift reliability of the sensor. Other advantageous and non-limiting characteristics of the diagnostic method according to the invention are as follows: in step b), the remote server transmits the reference note to the computer and, in step c), operating state of the sensor is determined by the computer; at the end of step a), the computer sends to the remote server a request containing an identifier of the identified infrastructure and / or the geographical coordinates of the motor vehicle and, in step b), the remote server acquires the reference note associated with said infrastructure, taking into account said identifier and / or said geographical coordinates; - It is expected, after step a), a step of transmitting the effective score to the remote server and a step of calculation by the remote server of a new reference note according to said effective score; said calculation step is implemented only if the difference between the effective score and the reference score is less than a predetermined threshold; steps b) and c) are implemented for only part of the infrastructures identified by the calculator; - steps b) and c) are implemented at regular intervals; - prior to step b), there is provided a step of determining an indicator relating to the meteorological conditions and / or dazzle of the sensor, and steps b) and c) are implemented only when said indicator is on satisfactory meteorological and / or glare conditions; - prior to step b), there is provided a step of determining the time of day, and steps b) and c) are implemented only when the time is within a specified interval; - prior to step a), there is provided a step of determining an indicator relating to the weather conditions and / or glare conditions of the sensor and / or the time of day, and the reference note acquired by the remote server is a function of the value of said indicator.
    Detailed description of an example of realization
    The following description with reference to the accompanying drawing, given by way of non-limiting example, will make it clear what the invention is and how it can be achieved.
    In the accompanying drawing, Figure 1 is a schematic perspective view of a motor vehicle driving on a road and a server remote from this road.
    As shown in Figure 1, the motor vehicle 10 is here a car with four wheels 11. Alternatively, it could be a motor vehicle comprising two or three wheels, or more wheels.
    Conventionally, this motor vehicle 10 comprises a frame which supports including a powertrain 12 (namely a motor and means for transmitting torque from the engine to the drive wheels), body elements and cabin elements.
    The motor vehicle 10 also comprises an electronic control unit (or ECU, of the English "Electronic Control Unit"), here called calculator 14.
    This calculator 14 comprises a processor and a storage unit, for example a rewritable non-volatile memory or a hard disk. The storage unit notably memorizes computer programs comprising instructions whose execution by the processor enables the computer to implement the method described below.
    For the implementation of this method, the computer 14 is connected to different equipment of the motor vehicle 10.
    Among these devices, the motor vehicle 10 comprises at least one sensor 16, 17 and communication means 18. It also comprises a geolocation means 15.
    As shown in Figure 1, the motor vehicle 10 is equipped with several sensors adapted to acquire information relating to road infrastructure.
    The motor vehicle 10 thus comprises a camera 16 which is located at the front of the vehicle and which is oriented towards the front, so that it can acquire images of the infrastructures of the road 100.
    The motor vehicle 10 also comprises two sensors LIDAR (acronym for the expression in English language "light detection and ranging", that is to say "detection and localization by the light >>), which are presented here under the These two laser remote sensors 17 are located at the front of the vehicle and are oriented in oblique directions, so that they can determine the shape of the infrastructures of the road 100, on the one hand and on the other hand. other of the motor vehicle.
    Such a laser remote sensor is well known to those skilled in the art, it will not be described in detail here. It will simply be stated that this is a remote measurement system, the operation of which is based on the emission of a beam of light by a transmitter and on the analysis of the properties of the beam of light reflected by the obstacle to its transmitter.
    Therefore, a LIDAR sensor is able to detect a foreign body on the road, such as a tire left on the road or a fallen branch on the road. It is also able to detect snow on the road.
    One could of course provide that the vehicle has more LIDAR sensors, for example located on the sides and rear of the vehicle.
    Alternatively, the sensors could be different. These could include SONAR or RADAR sensors. They could be placed differently on the vehicle, for example to acquire images of the road in rear view of the vehicle.
    The geolocation means 15 is provided for determining the position of the vehicle and / or that of the infrastructures targeted by the sensors 16, 17.
    If the road was equipped with geolocation modules distributed along its length, it could be considered that the geolocation means is formed by an antenna adapted to communicate with these geolocation modules.
    Here, it will rather be considered that the geolocation means 15 is formed by a GPS antenna, making it possible to determine the geographical coordinates of the motor vehicle 10.
    As will be explained in the remainder of this disclosure, the geolocation means may also possibly use the signals emitted by the sensors 16, 17 to more precisely determine the position of the motor vehicle 10 on the road 100 (on which taxiway it is located, at which distance of each infrastructure it is ...).
    Finally, the communication means 18 is designed to communicate with a remote server 50, via a relay antenna 51. It is more specifically here designed to connect to a mobile telephone network which includes in particular said relay antenna 51 and a connection gateway to a public network (eg the Internet).
    The remote server 50 is then also connected to the public network so that the computer 14 of the motor vehicle 10 and the remote server 50 can enter into communication and exchange data via the mobile telephone network.
    This remote server 50 stores here a database register comprising a plurality of records each associated with a road infrastructure of the automobile network.
    Each record then stores an identifier of this infrastructure, as well as the geographical coordinates of this infrastructure and at least one note relating to the state of this infrastructure.
    The form of this identifier will be detailed later in this presentation.
    The note could have been recorded in the database by an operator responsible for monitoring the state of the infrastructure. Such an operator will then be used to drive on the roads, to observe the state of the infrastructures and to assign them a note (which he then records in the corresponding record of the database register).
    However, in the embodiment that will be described here, each record will include not one but several notes relating to the state of this infrastructure. These notes will have been previously communicated to the remote server by motor vehicles automatically monitoring the state of the infrastructure. The communication protocol of these notes will be well described later in this presentation.
    In this embodiment, the remote server 50 is then able to calculate a reference note NO relating to the state of each infrastructure, for example by determining the average of the notes stored in the recording.
    The motor vehicle 10 is shown in Figure 1 as rolling on a road 100 with different infrastructure 101,102,103,104.
    Here illustratively, this road 100 has two lanes 105 separated from each other by a continuous line 101. The lateral edges of this road 100 are formed by the shoulder 104. Dotted lines 102 mark the position 104. A traffic sign 103 is also shown on the edge of the road 100. The invention then relates to a method implemented by the computer 14 of the motor vehicle 10 and by the remote server 50 to diagnose the problem. the operating state of each sensor 16, 17 of a motor vehicle 10. The invention proposes for this purpose to verify that the sensor detects the infrastructure of the road in the same way as other vehicles traveling on the road 100. Otherwise formulated, the the invention proposes for this purpose to verify that the note assigned by the computer 14 of the vehicle to each infrastructure (as it considers this infrastructure in good condition or not) corresponds substantially to the reference note N0 stored in the remote server 50.
    More precisely, according to a particularly advantageous characteristic of the invention, the diagnostic method comprises three main stages, of which: a first step a) during which the computer 14 identifies an infrastructure 101, 102, 103, 104 and affects it an effective note N1, which relates to the visibility of this infrastructure 101,102,103,104, - a second step b) during which the remote server 50 searches in its database the record corresponding to said infrastructure 101, 102, 103, 104 then determines the reference note N0 assigned to this infrastructure 101, 102, 103, 104, and a third step c) during which the effective note N1 and the reference note N0 are compared to deduce a state of operation of the sensor. 16, 17.
    More precisely, during the first step, the camera 16 acquires an image of the road 100 on which each of the infrastructures, namely the traffic sign 103, the continuous line 101 and the discontinuous lines 102, appears.
    The laser remote sensors 17 make it possible for them to determine the shape and the position of the shoulders 104.
    Alternatively, the sensors fitted to the motor vehicle 10 could acquire more information (including the presence of a pothole on the road), but for the sake of clarity of this presentation, only this information will be considered here.
    The computer 14 then uses the signals it receives from these sensors 16, 17 to determine an effective rating N1 relating to the state of each infrastructure 101, 102, 103, 104 of the road 100.
    It will be observed at this point that each note will be assigned to a particular infrastructure, as seen by a particular sensor. In other words, if several sensors detected the same infrastructure, the state of this infrastructure would be noted several times in order to determine the way in which this infrastructure is seen by each sensor considered separately.
    More precisely, the computer 14 uses the image acquired by the camera 16 and the shapes seen by the laser remote sensors 17 in the following manner.
    He locates on the acquired image, by a conventional image analysis, the continuous lines 101 and discontinuous 102 and the signaling board 103.
    It also locates, in the signals received from the laser remote sensors 17, by a conventional signal analysis, the shoulders 104.
    An identifier is assigned to each type of infrastructure, in order to facilitate its identification. This identifier will preferably be chosen according to the type of infrastructure. Thus, it would be possible to assign the identifier # 101 to all the continuous lines, the identifier # 102 to all the discontinuous lines, the identifier # 103 to all the traffic signs with a danger symbol, and the identifier # 104 at all the verges.
    The calculator 14 will then assign an effective score N1 to each of the identified infrastructures.
    This actual score N1 may be expressed as a degree of probability that the infrastructure has been identified or in any other form that can be envisaged. Here, the effective score N1 will be determined as follows.
    The calculator 14 determines the width variations of the continuous line 101. Then, if the width of this continuous line 101 varies, which means that the continuous line 101 is probably degraded, it affects the readability of the continuous line 101 a note. effective N1 reduced (for example equal to 1). Otherwise, it assigns a high effective score N1 (for example equal to 2 or 3).
    The calculator 14 then determines the variations in widths and lengths of each line of the broken lines 102. Then, if the width or length of these lines varies, which means that the corresponding discontinuous line 102 is probably degraded, it affects the readability of the broken line 102 an effective note N1 reduced (for example equal to 1). Otherwise, it assigns a high effective score N1 (for example equal to 2 or 3).
    By means of an image recognition software stored in its storage unit and which stores the various symbols that can appear on the traffic signs, the computer 14 determines the symbol displayed on the traffic sign 13. If it does not does not achieve this meaning that the symbol is partially erased or hidden by the vegetation), it affects the readability of the sign 13 a reduced effective note N1 (for example equal to 0). In the opposite case, and according to the degree of certainty of the recognition of the symbol, it assigns an effective note N1 superior (for example equal to 1, 2 or 3) -
    The calculator 14 finally determines the variations in distances between the discontinuous lines 102 and the shoulders 104 and it identifies the irregularities of these shoulders 104. Then, if these distances vary and / or if the shoulders 104 are irregular, which means that the shoulders 104 are probably degraded, it affects on the shoulders 104 an effective note N1 reduced (for example equal to 0 or 1). Otherwise, it assigns a higher effective score N1 (for example equal to 2 or 3).
    During the second step, the computer 14 sends to the remote server 50 a request for the latter to transmit the reference note N0 associated with each infrastructure 101,102, 103, 104.
    This request contains the identifier of each of the infrastructures 101, 102, 103, 104 identified and the geographical coordinates of the motor vehicle 10 taken by the geolocation means 15. It may also contain other data, including the direction of movement of the vehicle. on the road (obtained thanks to the positions of the vehicle successively recorded by the geolocation means 15) or the traffic lane 105 on which the vehicle is traveling, here the left lane (obtained thanks to the image acquired by the camera 16).
    This data enables the remote server 50 to identify the infrastructures seen by the sensors 16, 17 and thus to find in its database register the records corresponding to these infrastructures.
    Then, the remote server 50 determines the reference notes NO associated with these infrastructures 101, 102, 103, 104, here by averaging the notes stored in each record found.
    Then, it sends these reference notes NO to the computer 14 of the motor vehicle 10.
    During the third step, the computer 14 determines, for each infrastructure, the difference ΔΝ between the deference grade NO and the effective score N1 (in absolute value).
    One could predict that if the difference ΔΝ between these two notes exceeds a predetermined threshold (for example equal to 2), the computer 14 deduces a malfunction of the corresponding sensor. Indeed, in this case, this would mean that the sensor was not able to detect the infrastructure in the same way as the other vehicles (those having transmitted to the remote server data that allowed to calculate the rating of reference N0).
    However, here, before deducing such a defect, the computer 14 will rather repeat the aforementioned steps for different infrastructures.
    If, for each infrastructure identified by the sensor considered, the difference ΔΝ between the reference note N0 and the effective score N1 exceeds the predetermined threshold, the computer deduces a malfunction of the sensor. It then stores in its storage unit an error code, which will allow a technician to visualize this fault.
    In the opposite case (ie if the sensor detects the infrastructures in the same way as the other vehicles), the computer deduces that the sensor is working correctly.
    It is also possible to exploit more finely this difference ΔΝ. Thus the computer can store in its storage unit the differences ΔΝ successively calculated for a sensor, and observe the evolution of this difference ΔΝ. Then, if it finds an increasing evolution of this difference ΔΝ, it can deduce a slight malfunction of the sensor. It can also anticipate the moment at which the sensor will be considered deficient, so as to predict when it will be necessary to replace it.
    In the embodiment considered here, it is planned to transmit the effective note N1 to the remote server 50. This transmission step can be done during the second step, when the computer transmits to the remote server 50 a request. Therefore, the remote server 50 can store this effective note N1 in the record associated with the infrastructure in question, so as to complete its database. It is thus by completing its database that the remote server 50 can obtain a large amount of notes assigned to each infrastructure, which will refine the value of the reference note NO.
    It will indeed be observed that the greater the number of notes transmitted to the remote server 50 will be important, closer to reality will be the reference note NO, even if some of the sensors of the vehicles would fail and even the weather would sometimes distort the data collected by the sensors.
    In a variant, provision can be made for the remote server 50 to record this actual score N1 in its database only if the difference ΔΝ between the reference note N0 and the actual score N1 is less than the predetermined threshold. In this way, if the sensor has a defect, the actual score N1 calculated by means of this sensor will not be recorded in the database and thus will not distort the calculation of the reference note N0.
    Furthermore, it can be provided that the remote server 50 only records this effective note N1 if the weather conditions are good enough or if the sensor 16, 17 is not dazzled by the sun or by any light source or s it is still daylight.
    For this, the calculator can determine the value of a weather indicator (1 if sunny, 2 if cloudy, 3 if snow, ...) and the value of a glare indicator (1 if dazzled, 0 otherwise ), and to transmit these values to the remote server 50, so that it records the effective note N1 only if these values are satisfactory (for example if it does not rain or snows, if it does not there is no fog and if the sensor is not dazzled). It can also be provided that the effective score N1 is recorded only if it is still daylight in the place where the vehicle is located (taking into account the time and times of sunrise and sunset in the place where it is located. the vehicle).
    Alternatively, it could be expected that the remote server 50 always records the effective score N1 in its database, regardless of the weather and glare conditions and whatever the time, but that it associates this effective rating N1 to the meteorological and glare conditions encountered and on time. More precisely, the remote server 50 will be able to store the actual score in a sub-record corresponding to the meteorological conditions encountered, to the degree of glare of the sensor and to the fact that it is day or night. In this variant, the request transmitted by the computer 14 to the remote server 50 will include the aforementioned indicators. In this way, the reference note NO returned by the remote server 50 to the computer 14 will be equal to the average of the notes stored in the sub-recording corresponding to the meteorological and / or lighting conditions (day or night) and / or glare encountered by the vehicle.
    Finally, it will be noted that the second and third steps of the aforementioned diagnostic method may be implemented for each detected infrastructure or at each time step.
    Alternatively, to prevent this process consumes a large part of the computing power of the processor, the second and third steps may be performed less often.
    They may for example be performed at regular intervals (for example once a day, or after each start of the vehicle).
    They may for example also be performed only if the weather conditions and / or glare sensor 16, 17 are very satisfactory (in sunny weather, when the sensor is not dazzled).
    权利要求:
    Claims (10)
    [1" id="c-fr-0001]
    A method of diagnosing a motor vehicle sensor (16, 17) adapted to detect road infrastructures (101, 102, 103, 104), said motor vehicle (10) comprising a communication means (18). ) adapted to communicate with a remote server (50) and a computer (14) connected to the sensor (16, 17) and the communication means (18), characterized in that it comprises steps in which: a) the calculator (14) identifies an infrastructure (101, 102, 103, 104) and assigns it an effective score (N1), which is relative to the visibility of that infrastructure (101,102,103,104), b) the remote server (50) acquires a rating reference (NO) which is assigned to the infrastructure (101, 102, 103, 104) and which relates to the visibility of this infrastructure (101,102,103,104), and (c) the actual score (N1) and the reference note (NO ) are compared to deduce an operating state of the sensor (16,17).
    [2" id="c-fr-0002]
    2. Diagnostic method according to the preceding claim, wherein, in step b), the remote server (50) transmits the reference note (NO) to the computer (14) and, in step c), the operating state of the sensor (16, 17) is determined by the computer (14).
    [3" id="c-fr-0003]
    3. Diagnostic method according to the preceding claim, wherein, at the end of step a), the computer (14) sends to the remote server (50) a request containing an identifier of the infrastructure (101, 102 , 103, 104) identified and / or the geographical coordinates of the motor vehicle (10) and, in step b), the remote server (50) acquires the reference note (NO) associated with said infrastructure (101, 102, 103, 104), given said identifier and / or said geographical coordinates.
    [4" id="c-fr-0004]
    4. Diagnostic method according to one of the preceding claims, wherein there is provided, after step a), a step of transmitting the effective score (N1) to the remote server (50) and a calculation step by the remote server (50) of a new reference note (NO) according to said effective note (N1).
    [5" id="c-fr-0005]
    5. Diagnostic method according to the preceding claim, wherein said calculation step is implemented only if the difference between the effective score (N1) and the reference note (NO) is less than a predetermined threshold.
    [6" id="c-fr-0006]
    6. Diagnostic method according to one of the preceding claims, wherein steps b) and c) are implemented for only part of the infrastructure identified by the computer (14).
    [7" id="c-fr-0007]
    The diagnostic method according to claim 6, wherein steps b) and c) are performed at regular intervals.
    [8" id="c-fr-0008]
    8. Diagnostic method according to claim 6, wherein, prior to step b), there is provided a step of determining an indicator relating to the meteorological conditions and / or dazzle of the sensor (16, 17), and wherein steps b) and c) are implemented only when said indicator relates to satisfactory meteorological and / or glare conditions.
    [9" id="c-fr-0009]
    9. Diagnostic method according to claim 6, wherein prior to step b), there is provided a step of determining the time of day, and steps b) and c) are implemented only when the time is within a specified interval.
    [10" id="c-fr-0010]
    10. Diagnostic method according to one of claims 1 to 7, wherein, prior to step a), there is provided a step of determining an indicator relating to the weather conditions and / or glare conditions of the sensor (16, 17) and / or at the time of day, and wherein the reference note (NO) acquired by the remote server (50) is a function of the value of said indicator.
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    同族专利:
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    WO2017089428A1|2017-06-01|
    FR3044150B1|2017-12-29|
    US20180350233A1|2018-12-06|
    引用文献:
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    US20130033603A1|2010-03-03|2013-02-07|Panasonic Corporation|Road condition management system and road condition management method|
    US9060164B2|2012-09-12|2015-06-16|Xerox Corporation|Intelligent use of scene and test pattern analyses for traffic camera diagnostics|US10991179B2|2018-03-28|2021-04-27|Panasonic Intellectual Property Management Co., Ltd.|Abnormality notifying apparatus, vehicle, abnormality notifying method, and non- transitory recording medium|
    CN108919780A|2018-06-29|2018-11-30|深圳市元征科技股份有限公司|Remote diagnosis method for vehicle fault and relevant device|
    JP2021071908A|2019-10-30|2021-05-06|株式会社日立製作所|Abnormality determination device, vehicle support system, and server|
    法律状态:
    2016-11-30| PLFP| Fee payment|Year of fee payment: 2 |
    2017-05-26| PLSC| Publication of the preliminary search report|Effective date: 20170526 |
    2017-11-30| PLFP| Fee payment|Year of fee payment: 3 |
    2019-11-29| PLFP| Fee payment|Year of fee payment: 5 |
    2020-11-30| PLFP| Fee payment|Year of fee payment: 6 |
    2021-11-30| PLFP| Fee payment|Year of fee payment: 7 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1561263A|FR3044150B1|2015-11-23|2015-11-23|METHOD FOR DIAGNOSING A SENSOR OF A MOTOR VEHICLE|FR1561263A| FR3044150B1|2015-11-23|2015-11-23|METHOD FOR DIAGNOSING A SENSOR OF A MOTOR VEHICLE|
    PCT/EP2016/078602| WO2017089428A1|2015-11-23|2016-11-23|Method of diagnosis of a motor vehicle sensor|
    EP16801190.6A| EP3381023A1|2015-11-23|2016-11-23|Method of diagnosis of a motor vehicle sensor|
    US15/778,382| US10339801B2|2015-11-23|2016-11-23|Method for diagnosing a motor vehicle sensor|
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