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WO2017125369A1 - Procédé de détection de voies de circulation sur une chaussée au moyen de la distribution de fréquence de valeurs de distance, dispositif de commande, système d'aide à la conduite et véhicule automobile - Google Patents

Procédé de détection de voies de circulation sur une chaussée au moyen de la distribution de fréquence de valeurs de distance, dispositif de commande, système d'aide à la conduite et véhicule automobile Download PDF

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Publication number
WO2017125369A1
WO2017125369A1 PCT/EP2017/050853 EP2017050853W WO2017125369A1 WO 2017125369 A1 WO2017125369 A1 WO 2017125369A1 EP 2017050853 W EP2017050853 W EP 2017050853W WO 2017125369 A1 WO2017125369 A1 WO 2017125369A1
Authority
WO
WIPO (PCT)
Prior art keywords
motor vehicle
lane
lanes
distance values
basis
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.)
Ceased
Application number
PCT/EP2017/050853
Other languages
German (de)
English (en)
Inventor
Alexander SUHRE
Youssef-Aziz GHALY
Natascha Schoeneck
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.)
Valeo Schalter und Sensoren GmbH
Original Assignee
Valeo Schalter und Sensoren GmbH
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 Valeo Schalter und Sensoren GmbH filed Critical Valeo Schalter und Sensoren GmbH
Publication of WO2017125369A1 publication Critical patent/WO2017125369A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/867—Combination of radar systems with cameras
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/932—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles using own vehicle data, e.g. ground speed, steering wheel direction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/9323—Alternative operation using light waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G01S2013/9327—Sensor installation details
    • G01S2013/93271—Sensor installation details in the front of the vehicles

Definitions

  • the present invention relates to a method for detecting lanes on a roadway, in which sensor data is received by at least one sensor of a motor vehicle, on the basis of which sensor data a plurality of vehicles located on the lanes are detected, distance values which are a distance of each describe the detected vehicles to the motor vehicle in the vehicle transverse direction of the motor vehicle, are determined, a frequency distribution of the distance values is determined and the lanes are recognized by the frequency distribution. Furthermore, the present invention relates to a control device for a driver assistance system of a motor vehicle. Furthermore, the present invention relates to a driver assistance system for a motor vehicle. Finally, the present invention relates to a motor vehicle with such a driver assistance system.
  • the driver assistance system may include one or more sensors that
  • sensors for example, distributed to the motor vehicle are arranged. These sensors can emit a sensor signal, which is then reflected by an object in the surrounding area of the motor vehicle and returned to the sensor. Based on the transit time between the emission of the sensor signal and the reception of the sensor signal reflected by the object or the echo of the sensor signal, the distance between the motor vehicle and the object can then be determined.
  • sensors may be, for example, ultrasonic sensors, laser scanners, lidar sensors or radar sensors.
  • a relative speed between the motor vehicle and the object and / or an angle between the motor vehicle and the object can be determined on the basis of the sensor signals.
  • the interest here is directed to the detection of lanes of a roadway.
  • the sensor of the driver assistance system or of the motor vehicle is to be used to detect the individual lanes of a roadway. In this way, the number of lanes can be determined. In addition, it can be determined on which lane the motor vehicle is currently located.
  • Information can then be from other driver assistance systems of the motor vehicle be used.
  • this information can be a
  • Lane change assistants are supplied.
  • WO 2015/106914 A1 describes a method for detecting a
  • the vehicle positions are detected for each of the detected other vehicles. Furthermore, a degree of expression of one or more indicators for the presence of the emergency gas situation is determined. It is provided that the one or more indicators are indicative of at least one of: a lateral distance between other vehicles on adjacent lanes, or a lateral offset of other vehicles relative to lane mark progressions, or a lateral offset of other vehicles relative to a respective one
  • DE 101 15 551 A1 describes a method for lane assignment of successive vehicles.
  • moving objects which are located in front of the own vehicle, are detected by means of a radar sensor.
  • Cross-offset histogram of detected radar objects determined.
  • the maxima of the transverse offset histogram are identical to the position of the different lanes. From the number of maxima can thus determine the number of lanes.
  • Lane width is calculated by correlating the histogram with reference histograms where the lane width is known.
  • An inventive method is used to detect lanes on a
  • Vehicle transverse direction of the motor vehicle describe determined.
  • a frequency distribution of the distance values is determined and the lanes are recognized on the basis of the frequency distribution.
  • the distance values are assigned to the recognized lanes on the basis of the frequency distribution and a lane width of at least one of the lanes is determined on the basis of those distance values which are assigned to this lane.
  • the individual lanes of a roadway are to be recognized, on which the motor vehicle is currently located. It is in particular
  • the sensor may be, for example, a radar sensor, a laser scanner or a lidar sensor. Measuring cycles can be carried out continuously with this sensor. At each measurement cycle, the sensor can emit a sensor signal, which is then reflected by the other vehicles that are on the lane or on the lanes. Based on the transit time between the emission of the sensor signal and the reception of the sensor signal reflected by the respective vehicle, the distance between the motor vehicle and each of the vehicles can then be determined.
  • the sensor signals describe a relative speed between the motor vehicle and each of the vehicles on the lanes.
  • the sensor signals may describe an angle, in particular an azimuthal angle between the motor vehicle and each of the vehicles on the lanes. In this case, the sensor has a detection range in which with the aid of the sensor
  • Vehicles on the lanes can be detected.
  • the vehicles may be, for example, passenger cars, trucks, buses, motorcycles or the like.
  • the sensor signals that are provided with the at least one sensor can be supplied to a control device of the motor vehicle.
  • Control device may be, for example, an electronic control unit (ECU - Electronic Control Unit).
  • the control device may comprise, for example, a microprocessor or a digital signal processor.
  • the sensor signals can be evaluated.
  • the sensor signals describe the relative position of each of the detected vehicles to the motor vehicle.
  • distance values are determined in each case, the
  • Vehicle transverse direction can be specified. For each of these distance ranges, the number of distance values that lie within this range can then be determined. This results in a frequency distribution which can have, for example, several maxima. The respective maxima can be assigned to the individual lanes of the roadway. This can be used to determine how many lanes are on the road. Furthermore, it can be determined on which lane the motor vehicle itself is located.
  • Frequency distribution associated with the detected lanes For the assignment of the distance values to the respective recognized lanes, known algorithms for clustering, for example the so-called mean shift algorithm linear, can be used. Such algorithms can detect how many clusters in the record the
  • the lane width of at least one of the lanes is determined. It is preferably provided that the respective lane width of all lanes on the roadway is determined. The lane width is determined on the basis of those distance values that were also assigned to this lane. Thus, the lane width is determined based on the distance values obtained with the sensor. Thus, for example, it is not necessary to use a corresponding reference model with predetermined lane widths. This allows a safe and reliable determination of the width of the respective lanes.
  • the lane width of the at least one lane is preferably determined on the basis of a variance of the distance values assigned to this lane.
  • Frequency distribution can have different regions or different modes, which are assigned to the respective lanes.
  • the respective distance values assigned to one of the lanes or the respective modes may have a distribution that is similar to a normal distribution.
  • the respective distance values assigned to one of the lanes or the respective modes may have a distribution that is similar to a normal distribution.
  • the variance describes the expected quadratic deviation of the random variable from its expected value.
  • the variance of the distance values relative to the distance in the vehicle transverse direction can then be used as the basis for the calculation of the lane width.
  • the lane width of the at least one lane can be determined in a simple manner.
  • the lane width of the at least one lane is determined by a first distance value, which is the shortest distance to the lane
  • lane width of the at least one lane may also be determined based on a first distance value, which is arranged, for example, on the far right in the lane, and the distance value, which is located furthest to the left in the lane. Based on the lateral distance between the two outermost ones
  • Distance values can then be determined, the lane width. It can also be provided that the lateral distance between the outermost distance values is multiplied by a predetermined factor. This allows a simple and reliable determination of the lane width.
  • the lane width of the at least one lane is additionally determined on the basis of the distance values which are assigned to a lane adjacent to the at least one lane. This means that to
  • Determining the lane width not only the distance values are taken into account, which are assigned to this lane, but also distance values can be taken into account, which are assigned to an adjacent lane.
  • the lane width is smaller than the distance of the distance value that is furthest to the leftmost in the right adjacent cluster from the track right in the left adjacent cluster.
  • the outermost distance values can be taken into account for each lane and, if present, the directly adjacent distance values in the adjacent lanes can also be taken into account.
  • the outermost distance values of a track can be as minimal
  • the track width can be determined as a mean of the minimum and the maximum track width.
  • At least one object is detected which limits the roadway and the lanes are additionally recognized on the basis of the at least one object.
  • static objects that are part of the infrastructure and that limit the roadway on one or both sides.
  • Such objects may be, for example, a guardrail, a wall, a curb, a planting or the like.
  • Such an object does not move and is therefore relatively easy to recognize on the basis of the sensor data.
  • a boundary for example a guardrail
  • the lateral distance between the two objects can be determined. This lateral distance indicates the maximum width of the road or the road.
  • the number of lanes on the road can be determined. By dividing the maximum width of the roadway by the number of lanes or modes, an estimate for the lane width of the respective lanes results.
  • Receive motor vehicle which describes the road, in the image, at least one lane marking is detected and the lanes are detected on the basis of at least one lane marking.
  • a camera can be provided which, for example, continuously provides images of the roadway. With a corresponding object recognition algorithm, the lane markings can then be recognized on the road.
  • the road markings may be, for example, white or colored markings that are on the
  • the lane markings can be applied.
  • Shaping be recognized. On the basis of the images of the camera can also the road or the road surface are detected. It can also be determined where road markings are on the road surface. This information can be merged or merged with the distance values. In this case, it can be determined whether the division or the clustering of the distance values coincides with the positions of the lane markings. In this way, the
  • the lane width of the at least one lane may be determined based on the variance of the distance values. Further, the lane width may be determined based on the outermost distance values in the lane and / or the distance values of the adjacent lanes. Furthermore, lane boundaries and / or lane markings can be used.
  • the results of the individual methods for determining the lane width can also be combined with one another, for example by defining a tolerance level for each method or for each algorithm. Furthermore, the results of the individual methods can be interpreted as a condition for a cost function and entered into an optimization algorithm, for example a linear programming. Then the optimization algorithm can find the optimal solution and thus the lane width can be reliably determined.
  • Positioning system can be taken into account, where the motor vehicle is currently on the digital map. From the digital map, for example, information about how many lanes the road has. In addition, a type of lane can be taken from the digital map. For example, it can be taken into account whether it is a motorway, a federal highway, a country road or the like. This information can also be used to make the assignment of the respective distance values to the lanes plausible.
  • the sensor data which is received describe the roadway in the direction of travel in front of the motor vehicle and / or the roadway in the direction of travel behind the motor vehicle and / or the roadway next to the motor vehicle.
  • the motor vehicle or the driver assistance system can have a plurality of sensors which are arranged distributed on the motor vehicle. In this way, the vehicles can be detected in the direction of travel in front of the motor vehicle, in the direction of travel behind the motor vehicle or laterally next to the motor vehicle. Based on the detected vehicles then the respective lanes can be reliably determined.
  • a control device for a driver assistance system of
  • the control device can be formed by an electronic control unit of the motor vehicle.
  • An inventive driver assistance system for a motor vehicle comprises a control device according to the invention and at least one sensor. It is preferably provided that the driver assistance system comprises, as the at least one sensor, a radar sensor or a laser sensor, for example a lidar sensor or a laser scanner. Furthermore, it is advantageous if the
  • Driver assistance system is designed to maneuver the motor vehicle depending on the detected lanes at least semi-autonomous.
  • the driver assistance system may be a lane departure warning assistant or a lane change assistant. Based on the information, the lane departure warning and / or the
  • a motor vehicle according to the invention comprises an inventive
  • the motor vehicle is designed in particular as a passenger car.
  • FIG. 1 is a schematic representation of a motor vehicle according to a
  • Embodiment of the present invention which comprises a driver assistance system with a plurality of radar sensors and a camera;
  • Fig. 2 shows a traffic situation in which the motor vehicle on a
  • Fig. 3 is a frequency distribution of distance values, each one
  • Fig. 1 shows a motor vehicle 1 according to an embodiment of the present invention in a plan view.
  • the motor vehicle 1 is in the present case as
  • the motor vehicle 1 comprises a
  • Driver assistance system 2 which comprises at least one sensor 3.
  • This at least one sensor 3 for example, a laser scanner, a lidar sensor or a
  • the driver assistance system 2 includes four sensors 3, which are each designed as a radar sensor.
  • a sensor signal in the form of electromagnetic radiation can be emitted, which is then reflected by an object in a surrounding area 6 of the motor vehicle 1.
  • the reflected electromagnetic radiation returns as an echo back to the respective sensors 3.
  • a distance between the sensor 3 and the object can be determined.
  • the sensors 3 or the radar sensors can be arranged concealed behind a bumper of the motor vehicle 1, for example. With the respective radar sensors can in the horizontal direction an azimuthal
  • Angle range can be detected, which may be in a range between 150 ° and 180 °.
  • the driver assistance system 2 comprises a camera 4. With the aid of the camera 4, images of the surrounding area 6 can be provided.
  • the driver assistance system 2 comprises a control device 5, which may be formed for example by a computer, a digital signal processor, a microprocessor or the like.
  • the control device 5 can be formed in particular by an electronic control unit of the motor vehicle 1.
  • the control device 5 is connected to the sensors 3 and to the camera 4 for data transmission.
  • control device 5 can receive data from further sensors which describe the current speed and / or the current steering angle of the motor vehicle.
  • FIG. 2 shows a schematic illustration of a traffic situation in which the motor vehicle 1 is located on a roadway 9.
  • the roadway 9 includes in the
  • On the lanes 10, 1 1, 12 are other vehicles 13, which also passenger cars are.
  • On the left lane 10 are present four vehicles 13.
  • On the middle lane 1 1 there are two vehicles 13.
  • the vehicles 13 can be detected.
  • the vehicle 13 are the objects that are detected based on the sensor data of the sensors 3. In this case, the vehicles 13 are located in a detection range of the sensors 3.
  • the sensor signals provided with the sensors 3 are applied to the
  • Control device 5 transmitted. Based on the sensor signals can then
  • Control device 5 determine respective distance values. These distance values describe a distance between each of the vehicles 13 and the motor vehicle 1 along a vehicle transverse direction q. In other words, the respective distance values describe the lateral distance or the lateral distance between each of the vehicles 13 and the motor vehicle 1.
  • a frequency distribution 14 or a histogram is then determined. This is shown by way of example in FIG. 3.
  • Frequency distribution 14 or this histogram is assigned to the traffic scenario according to FIG. 2.
  • the frequency distribution 14 has three regions 15, 16, 17 or three modes. Each region 15, 16, 17 of the frequency distribution 14 has a maximum. Each maximum describes the mean distance in the vehicle transverse direction q to the motor vehicle 1. Based on the number of maxima of the respective areas 15, 16, 17, the number of lanes 10, 1 1, 12 can be determined. From the respective regions 15, 16, 17 it follows that the roadway has three lanes 10, 11, 12. The position of the motor vehicle 1 corresponds to the distance in the vehicle transverse direction q with the value 0.
  • the respective lane width b of the lanes 10, 1 1, 12 are determined. In the present case, all lanes 10, 1 1, 12 have the same
  • Lane width b on To determine the lane width b of the respective lanes 10, 1 1, 12, the distance values at respective lanes 10, 1 1, 12 and
  • the variance of the distance values is determined and from this the vehicle width b is derived.
  • the respective regions 15, 16, 17 of the frequency distribution 14 are essentially normally distributed.
  • the respective variance of these areas 15, 16, 17 can be determined.
  • the two extreme distance values can be used, which are assigned to this lane 1 1.
  • the respective position of these outermost distance values can be taken from the frequency distribution 14. These correspond to points 18 and 19 of the region 16 of the frequency distribution 14.
  • the lateral distance between the points 18 and 19 can be regarded as minimum lane width.
  • the distance values that are assigned to the adjacent lanes 10, 12 can be used.
  • the distance value of the left lane 10 that is closest to the middle lane 1 1 can be used. In the present case, this corresponds to the point 20 of the region 15 of the frequency distribution 14. Further, the distance values of the right lane 12, which is located closest to the middle lane, may be used. In the present case, this corresponds to the point 21 of the region 17 of the frequency distribution 14. The lateral distance between the points 20 and 21 can be taken into account as the maximum lane width. The mean value between the minimum and the maximum lane width can then be used as the lane width b. From this, the lane width b can then be determined. In the present case, it is assumed that the respective lane width b is the same for all lanes 10, 11, 12.
  • objects 22 can be determined which delimit the roadway 9.
  • the objects 22 are in the present case guardrails. Again, the lateral distance between the objects 22 can be determined. This distance can then be divided by the number of lanes 10, 1 1, 12. Also, thus can be deduced on the respective lane width b.
  • control device 5 recognizes lane markings 23 on the basis of the images provided with the camera 4.
  • a corresponding object recognition algorithm can be used.
  • it can be determined on the basis of the images that the road markings 23 are located on the roadway 9. This information can be used to check the plausibility of the distribution of the distance values.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé de détection de voies de circulation (10, 11, 12) sur une chaussée (9), consistant à recevoir des données provenant d'au moins un capteur (3) d'un véhicule automobile (1), à détecter au moyen des données du capteur une pluralité de véhicules (13) se trouvant sur les voies de circulation (10, 11, 12), à déterminer des valeurs de distance qui décrivent un éloignement de chacun des véhicules détectés (13) par rapport au véhicule automobile (1) dans la direction transversale (q) du véhicule automobile (1), à déterminer une distribution de fréquence (14) des valeurs de distance, et à détecter les voies de circulation (10, 11, 12) au moyen de la distribution de fréquence (14). Les valeurs de distance sont associées au moyen de la distribution de fréquence (14) aux voies de circulation (10, 11, 12) détectées, et une largeur (b) de voie de circulation d'au moins une des voies de circulation (10, 11, 12) est déterminée au moyen de celles des valeurs de distance qui sont associées à cette voie de circulation (10, 11, 12).
PCT/EP2017/050853 2016-01-18 2017-01-17 Procédé de détection de voies de circulation sur une chaussée au moyen de la distribution de fréquence de valeurs de distance, dispositif de commande, système d'aide à la conduite et véhicule automobile Ceased WO2017125369A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016100718.8A DE102016100718A1 (de) 2016-01-18 2016-01-18 Verfahren zum Erkennen von Fahrspuren auf einer Fahrbahn anhand einer Häufigkeitsverteilung von Abstandswerten, Steuereinrichtung, Fahrerassistenzsystem sowie Kraftfahrzeug
DE102016100718.8 2016-01-18

Publications (1)

Publication Number Publication Date
WO2017125369A1 true WO2017125369A1 (fr) 2017-07-27

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PCT/EP2017/050853 Ceased WO2017125369A1 (fr) 2016-01-18 2017-01-17 Procédé de détection de voies de circulation sur une chaussée au moyen de la distribution de fréquence de valeurs de distance, dispositif de commande, système d'aide à la conduite et véhicule automobile

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DE (1) DE102016100718A1 (fr)
WO (1) WO2017125369A1 (fr)

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DE102018207950A1 (de) * 2018-05-22 2019-11-28 Bayerische Motoren Werke Aktiengesellschaft Verfahren zur Verarbeitung von Daten im Zusammenhang mit einem Fahrzeug, Decodierverfahren, Codier- und Decodierverfahren, System, Computerprogramm und Computerprogrammprodukt
US10935977B2 (en) 2018-06-28 2021-03-02 Aptiv Technologies Limited Lane assignment system
CN114379552B (zh) * 2021-11-11 2024-03-26 重庆大学 一种基于高精度地图和车载传感器的自适应车道保持控制系统及方法
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