EP3465653A1 - Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens - Google Patents

Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens

Info

Publication number
EP3465653A1
EP3465653A1 EP17717425.7A EP17717425A EP3465653A1 EP 3465653 A1 EP3465653 A1 EP 3465653A1 EP 17717425 A EP17717425 A EP 17717425A EP 3465653 A1 EP3465653 A1 EP 3465653A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
particles
data
wrong
map
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.)
Pending
Application number
EP17717425.7A
Other languages
German (de)
English (en)
Inventor
Simon GEISLER
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.)
Robert Bosch GmbH
Original Assignee
Robert Bosch 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 Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP3465653A1 publication Critical patent/EP3465653A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/056—Detecting movement of traffic to be counted or controlled with provision for distinguishing direction of travel
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3697—Output of additional, non-guidance related information, e.g. low fuel level
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/164—Centralised systems, e.g. external to vehicles

Definitions

  • the invention is based on a device or a method according to the preamble of the independent claims.
  • the subject of the present invention is also a computer program.
  • Navigation device (street class and direction) is too late for most cases, i. the wrong-way driver is already on the wrong lane (at high speed and with a high probability of collision).
  • An example cloud-based forwarder warning can be advantageously realized with a specially adapted to the application detection with a particle filter.
  • a method for detecting wrong-way drivers comprises the following steps:
  • Reading position data via an interface, the position data representing a measured position of a vehicle; Reading in map data depicting road sections accessible by the vehicle;
  • the deviation can be used to determine whether the majority of particles match the measured position.
  • the vehicle may be a road vehicle.
  • a wrong travel can be understood to mean a journey of the vehicle on a road contrary to a prescribed direction of travel.
  • the measured position may have been measured using a sensor disposed in the vehicle.
  • the map data may map a road network drivable by the vehicle.
  • the plurality of particles may have been determined using a method used with known particulate filters or using a particulate filter.
  • the particles may have different assumed positions, which may be grouped around the measured position, for example.
  • Deviation can be used to determine or protect a current position of the vehicle.
  • the current position can be an under
  • the Particle Filter uses the Particle Filter to present the estimated position that can be used as the actual position of the vehicle.
  • the current position can be used instead of the measured position for detecting a wrong-way of the vehicle.
  • the method may include a step of determining a wrong-way signal using the current position.
  • the wrong-way signal can indicate whether a wrong-way drive of the vehicle is present or not present.
  • the wrong-way signal can be provided only if a wrong-way is assumed.
  • the method may include a step of determining the plurality of particles using a particulate filter. For example, a weighting of the particles can be changed by the particle filter.
  • the deviation may be determined in the step of determining using a distance between the plurality of particles and the measured position.
  • the map data may be parameters of the vehicle passable
  • the deviation can be determined using the parameters.
  • the information included in the map data can be used to determine the deviation.
  • the method may include a step of reading in movement data representing measured movements of the vehicle.
  • the deviation may be determined based on a match between the motion data and the parameters. As a result, the deviation can be determined even more accurately.
  • the movement data can map a lateral acceleration of the vehicle.
  • the parameters may map a curvature of a road segment mapped by the map data associated with or attributable to at least one of the plurality of particles. This makes it possible to check whether a road section matches a movement performed by the vehicle.
  • the motion data may map a direction of travel of the vehicle and the parameters map a heading specification of a road segment mapped by the map data associated with or attributable to at least one of the plurality of particles. In this way, it can be checked whether a road section fits to a direction of travel of the vehicle.
  • the method may include a step of selecting at least one plausible road segment from those represented by the map data
  • Road sections include.
  • the plausible road section may represent a road section to which at least one of the plurality of particles can be assigned, and one with the direction of travel of the vehicle
  • the deviation may be based on a
  • the position data can be read in via an interface of a computer cloud, a so-called cloud. This enables a cloud-based solution.
  • a corresponding device for identifying wrong-way drivers is set up to execute steps of said method in corresponding units.
  • a device may comprise a read-in device, which is designed to read position data via an interface, has a further read-in device, which is designed to read in map data depicting vehicle passable road sections, having a further read-in device, which is embodied to read in a plurality of particles, wherein a particle represents an assumed position of the vehicle and a weight assigned to the assumed position, and a determination device configured to detect a deviation between the plurality of filtered particles and the measured position comprised by the measurement signal Use of map data to determine.
  • the device may comprise a particle filter for producing and / or further processing the particles.
  • a corresponding system for detecting wrong-way drivers comprises at least one transmitting device which can be arranged or arranged in a vehicle and is designed to transmit position data, as well as a named one False driver recognition device, which is designed to receive the position data transmitted by the at least one transmitting device
  • Another system for false driver detection includes at least one
  • a transmission device that can be arranged or arranged in a vehicle and is configured to transmit position data, the position data representing a measured position of a vehicle, and at least one receiving device that can be arranged or arranged in the vehicle and is configured to supply data to a device received, which is designed according to the approach described here for wrong driver identification to receive the transmitted from the at least one transmitting device position data.
  • the method described may be implemented in software or hardware or in a hybrid of software and hardware, for example in a device.
  • the device can have at least one arithmetic unit for processing signals or data, at least one memory unit for storing signals or data, and / or at least one communication interface for reading in or outputting data that is included in a
  • the arithmetic unit can
  • the memory unit is a flash memory, an EPROM or a
  • the magnetic storage unit can be.
  • the communication interface can be designed to read or output data wirelessly and / or by line, wherein a communication interface that can read or output line-bound data, for example, electrically or optically read this data from a corresponding data transmission line or output to a corresponding data transmission line.
  • a device can be understood as meaning an electrical device which processes sensor signals and outputs control and / or data signals in dependence thereon.
  • the device may have an interface, which may be formed in hardware and / or software.
  • the interfaces may be part of a so-called system ASIC, which includes various functions of the device.
  • the interfaces are their own integrated circuits or at least partially consist of discrete components.
  • the interfaces may be software modules that are present, for example, on a microcontroller in addition to other software modules.
  • a computer program product or computer program with program code which can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and for carrying out, implementing and / or controlling the steps of the method according to one of the above
  • Fig. 1 shows a system for Falzablyerkennung according to a
  • FIG. 2 is a flowchart of a method for detecting wrong-way drivers according to an embodiment
  • Fig. 5 shows a system for wrong driver identification according to a
  • FIG. 6 shows a vehicle according to an embodiment
  • 7 shows a program sequence according to an embodiment
  • FIG. 8 shows a program sequence of a particle filter according to a
  • Fig. 1 shows a system for wrong driver identification according to a
  • the system includes a vehicle 100 that has a
  • Transmission device 102 which is configured to wirelessly using a at least one sensor device 104 arranged in the sensor 100 measured data, here for example position data 106 and optionally motion data 107, wirelessly to a device 110 for
  • the device 110 is designed to prepare the measurement data into prepared data and to further process the processed data using a particle filter
  • the wrong-way signal 112 indicates, according to one embodiment, that the vehicle 100 whose measurement data has been processed currently performs a wrong-way drive.
  • both the transmission device 102 of the vehicle 100 and a transmission device 102 of another vehicle 100 are configured to receive the wrong-way signal 112 and to activate a warning device of the respective vehicle 100, 114, which, for example, is one in response to a reception of the wrong-way signal 112 Driver of the respective vehicle 100, 114 before the wrong drive warns or according to a
  • the Transmission device 102 may be designed only as a transmitting device or as a transceiver device.
  • the measurement data includes the position data 106 obtained by using a position determination device of the
  • the measurement data further comprises the movement data 107, which were acquired, for example, using at least one acceleration sensor of the vehicle 100 and information about a current movement of the vehicle 100,
  • information about a direction of travel For example, information about a direction of travel, a
  • Longitudinal acceleration, a lateral acceleration or over a rotation of the vehicle about a vehicle axis include.
  • the device 110 is configured to read in map data 116 indicative of a vehicle passable by the vehicle 100
  • Map road network According to one embodiment, the
  • Map data 116 for example, information about road sections of the road network.
  • the map data 116 with respect to each road section further comprises at least one parameter that defines, for example, a driving direction specification for the respective road section or a course of the respective road section. For example, it can be defined via the parameter whether the road section runs in a straight line or describes a curve.
  • the device 110 has a memory device in which the map data 116 are stored.
  • the device 110 is configured to read a plurality of particles.
  • the particles can be read from an internal or external storage device.
  • Each particle may represent an assumed position of the vehicle and a weight assigned to the assumed position.
  • the apparatus 110 is configured to determine and directly process the plurality of particles using the position data 106 and the map data 116.
  • the device 110 is configured to detect a deviation between the plurality of particles and that through which
  • Position data 106 mapped measured position of the vehicle 100 using the map data to determine.
  • the deviation is used or taken into account in the determination of the wrong-way driver signal 112 according to one exemplary embodiment.
  • the device is 110 or
  • Function blocks of the device 110 are arranged or realized in a cloud 118.
  • the described approach can be used in addition to or instead of various methods for detecting a wrong-way driver, in which e.g. the use of a video sensor is used to detect the passage of a "forbidden entry" sign or the use of a digital map is used in conjunction with a navigation to detect a detection of a wrong direction of travel on a road section, which is only passable in one direction
  • the approach can be combined with wireless methods that detect wrong-way drivers by means of infrastructure such as beacons in the lane or at the lane.
  • the described approach offers many possibilities of responding to a wrong-way driver. Examples are the warning of the wrong driver himself via a display or acoustic information. Also, methods may be used to warn other drivers in the vicinity of a wrong-way driver, e.g. via vehicle-vehicle communication or via mobile radio. Furthermore, the warning of other road users on the roadside established variable traffic signs is possible. An intervention in the engine control or brake of the wrong-traveling vehicle 100 can also take place.
  • the approach described makes it possible to detect a wrong-way driver and to warn other road users in the vicinity in time, for which there is very little time available.
  • the described approach uses for a wrong-way driver detection (Wrong Way Driver Detection) with a client-server solution.
  • a client a device can be seen, located on or in a motor vehicle, which has a
  • the Intemetanitati and has at least access to position coordinates.
  • this may be the transmission device 102.
  • the transmission device 102 may be, for example, a
  • Sensor device 104 may be integrated.
  • wrong-driver-specific server-client communication can be implemented with a smartphone as an exemplary client.
  • the smartphone can be connected via a mobile radio network with a gateway (PDN_GW) to the Internet, in which the device 110, for example in the form of a server, can be arranged.
  • PDN_GW gateway
  • the device 110 in a nationwide use of this function plays a very important role.
  • the economy represents an important aspect. c) communication, data efficiency and power consumption
  • FIG. 2 shows a flowchart of a method for wrong-way driver recognition according to one exemplary embodiment.
  • the method may, for example, be carried out using devices of the device for false driver recognition shown with reference to FIG.
  • the method comprises a step 201, in which position data are read in via an interface.
  • the position data represent a measured position of a vehicle.
  • motion data of the vehicle can additionally be read in step 201.
  • map data is read in which can be driven by the vehicle
  • Map road sections may include parameters that specify the individual road sections in more detail, for example with regard to a roadway curvature or driving direction.
  • a plurality of particles are read.
  • the plurality of particles may, for example, have been created in a previous creation step using the position data and / or previously filtered particles.
  • one of a particle filter is used according to an embodiment.
  • Each of the particles represents an assumed position of the vehicle and a weight assigned to the assumed position.
  • the assumed positions are distributed according to one
  • Embodiment preferably around the measured position.
  • the assumed positions differ from the measured position and the actual position of the vehicle.
  • the determination of the deviation may represent a partial step of steps performed in the particulate filter.
  • using the particulate filter a current position of the vehicle based on the plurality of particulates and the deviation is determined using.
  • a wrong-way signal is generated and provided using a plurality of particles and the deviation.
  • the wrong-way signal can be provided if a plausible route section is determined from the plurality of particles and the deviation, which is assumed to be on the vehicle, and a current travel direction of the vehicle does not coincide with a direction indication associated with the route section.
  • Provision of the wrong-drive signal can be prevented if, due to a large deviation is assumed that a determined using the particulate filter current position of the vehicle does not match the actual position of the vehicle.
  • the particle filter is applicable to systems which are subject to a hidden Markov chain characteristic, ie a Markov chain with unobserved states.
  • Fig. 3 shows a Hidden Markov Chain Model 320 with state x and observation z at time k and k-1.
  • each particle has the weight and the condition
  • Embodiment For this purpose, a hidden Markov Chain Model with the state x and the observation z at time k and k-1 is shown in FIG.
  • the basis for this is to define the states x to be estimated.
  • Block 401 stands for the particle filter (xk-i, Uk, z)
  • block 409 is entered. From block 409, jump to block 411 until all values have passed.
  • Fig. 5 shows a system for wrong driver recognition according to a
  • the system comprises devices 102, for example in the form of the transmission means referred to with reference to FIG. 1 and a
  • Embodiment designed as a so-called WDW server.
  • the device 110 is designed to receive data 106 from the device 102,
  • the apparatus includes pre-processing means 530, particulate filter 532, and warning module 534.
  • the particulate filter 532 embeds as shown in FIG. With the particle filter 532, the probability distribution of the position of the car can be approximated.
  • FIG. 6 shows by means of a vehicle 100 values that can be included in the model shown with reference to FIG. 5.
  • the values may, for example, be states in the direction of the longitudinal axis x, the transverse axis y, the vertical axis z, as well as a roll p about the longitudinal axis, a pitch q about the transverse axis and a yaw r about the vertical axis.
  • the Bayesian filter can be understood by referring to FIG. 3
  • Xk stands for what the condition (not measured) is, for example, the latitude, longitude, latitude, Uk + i, how the car 100 moves, for example, in terms of speed and yaw rates
  • Zk stands for what can be observed for example, a G PS signal or a signal relating to the environment of the vehicle 100 (camera, etc.)
  • Fig. 7 shows a program flow according to an embodiment. The process starts with a block 701. In a block 530, a
  • a block 703 if present, the state is loaded from the previous point.
  • a map matching takes place with the particle filter.
  • a block 707 is a
  • FIG. 8 shows a program flow of a particle filter according to a
  • a block 801 stands for a beginning of the particle filter.
  • a displacement of the particles taking into account the sensor inaccuracy, for example, the sensor device described with reference to FIG. 1 takes place.
  • Such a parameter indicates, for example, whether a particle is on a road or what its title is.
  • a calculation of the new particle weights takes place.
  • a so-called resampling takes place in which an elimination of the irrelevant regions and / or particles takes place.
  • an interpretation of the individual particles takes place and in a block 813 a return of the possible roads.
  • the particulate filter By using the particulate filter, the following aspects are improved.
  • a sequential (real-time possible) working method is created, which primarily determines the current position on the road network. Furthermore, a robust estimate of the current position on the road network is possible. An uncertainty about the current estimate can be determined. This makes it possible to delay the decision on a potential wrong-way reliably to a reasonable extent.
  • FIG. 9 shows a representation of road sections 930, 932, 934, 936 according to one exemplary embodiment.
  • the road sections 930, 932, 934, 936 are part of one of a vehicle, for example with reference to FIG. 1
  • a plurality 940 of particles is substantially distributed among the three road sections 932, 934, 936. Each of the particles indicates an assumed position and an assumed probability or weighting. As can be seen from FIG. 9, a measured position 950 of the vehicle deviates significantly from the positions assumed for the plurality 940 of particles. The measured position 950 is also associated with a direction vector, which may represent a measured heading of the vehicle and may have been determined using motion data received from the vehicle. Each of the plurality 940 of particles is also associated with a direction vector indicating a direction of movement of the respective particle. From FIG. 9 it can be seen again that the direction vector associated with the measured position 950 does not coincide with the direction vectors associated with the plurality 940 of particles.
  • the plurality 940 of particles have been determined according to an embodiment as described with reference to the preceding figures using a particle filter.
  • a deviation between the measured Position 950 and the assumed positions of the plurality 940 of particles is determined as a partial functionality of the particulate filter according to one embodiment. The deviation can then, for example, for further
  • the position of the particles 940 deviates very far from the GPS. This is illustrated for example in FIG. 9.
  • the particles 940 represent particles from the current calculation cycle (k) with the direction vector.
  • the position 950 represents a current (k) GPS position with a direction vector.
  • Curvature of the road on which a particle 940 is located does not match
  • Heading the road on which a particle 940 is located does not match sensor data.
  • the median / mean / minimum / maximum distance between particle 940 and GPS position 950 is exceptionally large. In this case, for example, it suggests that the conditions for the topology are canceled (fallback level).
  • Weighting equation (observation model) according to an embodiment adapted as follows:
  • Driving direction are.
  • the road element 936 would be excluded as a possible location for the particles 940 because that part of the highway is opposite to the direction of travel.
  • the approach described can be used in conjunction with a cloud-based driver error warning with a specially adapted to the application with a particulate filter detection. Particularly advantageous are the conditions for the two described fallback levels and the approach when roads are found again.
  • an exemplary embodiment comprises an "and / or" link between a first feature and a second feature, then this is to be read so that the embodiment according to one embodiment, both the first feature and the second feature and according to another embodiment either only first feature or only the second feature.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)
  • Instrument Panels (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé pour détecter des véhicules circulant à contre-sens, comprenant une étape qui consiste à entrer des données de positionnement (106) par l'intermédiaire d'une interface, les données de positionnement (106) représentant une position mesurée d'un véhicule (100), une étape qui consiste à entrer des données de carte (116) qui représentent des tronçons routiers pouvant être empruntés par le véhicule (100), une étape qui consiste à entrer une pluralité de particules, une particule représentant une position occupée par le véhicule (100) et une pondération associée à la position occupée, et une étape qui consiste à déterminer l'écart entre la pluralité de particules et la position mesurée représentée par les données de positionnement (106), au moyen des données de carte (116).
EP17717425.7A 2016-06-07 2017-04-13 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens Pending EP3465653A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016210027.0A DE102016210027A1 (de) 2016-06-07 2016-06-07 Verfahren Vorrichtung und System zur Falschfahrererkennung
PCT/EP2017/058961 WO2017211489A1 (fr) 2016-06-07 2017-04-13 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens

Publications (1)

Publication Number Publication Date
EP3465653A1 true EP3465653A1 (fr) 2019-04-10

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EP17717425.7A Pending EP3465653A1 (fr) 2016-06-07 2017-04-13 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens

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Country Link
US (1) US10769942B2 (fr)
EP (1) EP3465653A1 (fr)
JP (1) JP2019519044A (fr)
CN (1) CN109313851B (fr)
DE (1) DE102016210027A1 (fr)
WO (1) WO2017211489A1 (fr)

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DE102020118621A1 (de) 2020-07-15 2022-01-20 Bayerische Motoren Werke Aktiengesellschaft Positionsbestimmung für ein Fahrzeug
CN112950960B (zh) * 2021-01-26 2022-12-30 北京智能车联产业创新中心有限公司 自动驾驶车辆逆行的判断方法
EP4307274A1 (fr) * 2022-07-11 2024-01-17 Volkswagen Ag Procédé de détection de conduite en sens de marche légal inverse

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US7831391B2 (en) * 2007-06-12 2010-11-09 Palo Alto Research Center Incorporated Using segmented cones for fast, conservative assessment of collision risk
JP2009140008A (ja) * 2007-12-03 2009-06-25 Sumitomo Electric Ind Ltd 危険走行情報提供装置、危険走行判定プログラム及び危険走行判定方法
JP5666812B2 (ja) * 2010-03-12 2015-02-12 クラリオン株式会社 車両逆走検出装置
JP5229293B2 (ja) * 2010-10-01 2013-07-03 株式会社デンソー 車両用運転支援装置
US8452535B2 (en) 2010-12-13 2013-05-28 GM Global Technology Operations LLC Systems and methods for precise sub-lane vehicle positioning
US20120290150A1 (en) 2011-05-13 2012-11-15 John Doughty Apparatus, system, and method for providing and using location information
US9140792B2 (en) 2011-06-01 2015-09-22 GM Global Technology Operations LLC System and method for sensor based environmental model construction
KR101881415B1 (ko) * 2011-12-22 2018-08-27 한국전자통신연구원 이동체의 위치 인식 장치 및 방법

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Publication number Publication date
WO2017211489A1 (fr) 2017-12-14
CN109313851B (zh) 2022-08-12
US10769942B2 (en) 2020-09-08
JP2019519044A (ja) 2019-07-04
US20190279504A1 (en) 2019-09-12
CN109313851A (zh) 2019-02-05
DE102016210027A1 (de) 2017-12-07

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