EP3465656A1 - 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
EP3465656A1
EP3465656A1 EP17719826.4A EP17719826A EP3465656A1 EP 3465656 A1 EP3465656 A1 EP 3465656A1 EP 17719826 A EP17719826 A EP 17719826A EP 3465656 A1 EP3465656 A1 EP 3465656A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
wrong
road
data
particles
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
EP17719826.4A
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 EP3465656A1 publication Critical patent/EP3465656A1/fr
Ceased 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
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • 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

Definitions

  • 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:
  • 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 map data can also map parameters of the road network that can be driven by the vehicle.
  • the measured position data of the vehicle may be a position measured using a sensor disposed in the vehicle
  • the plurality of particles may be determined using a method used with known particle filters.
  • the particles may have different assumed positions, which are grouped around the measured position, for example.
  • the particulate filter can be used to alter a weighting of preexisting particles. Under a plausible street element can a
  • Particle data is assumed that the vehicle is on it.
  • the plausible road element can be checked using other available data to ensure that the vehicle is actually on the tested road element. The tested
  • Road element can be used instead of the measured position for detecting a wrong ride of the vehicle.
  • the method may thus include a step of determining a
  • 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.
  • a machine learning classification method may be used.
  • measured motion data of the vehicle and parameters associated with the road elements may be used for the classification process.
  • the measured movement data may be movements of the vehicle measured using a sensor
  • the movement data can map a lateral acceleration and / or a direction of travel of the vehicle. It can be checked via the lateral acceleration whether a road course of a road element and via the direction of travel can be checked as to whether a driving direction specification of a road element matches the measured movement of the vehicle.
  • the parameters may include a curvature course and / or a heading specification of a map represented by the map data
  • Map road element associated with at least one of the plurality of particles or assignable.
  • assignability of the plurality of particles to road elements mapped by the map data may be used for the classification process. In this way, it can be checked whether particles lie on the plausible road element or not.
  • a distance between the plurality of particles and a measured position of the vehicle mapped by the measured position data can be used for the classification method. In this way, it is possible to check whether particles are arranged close to or away from a plausible road element.
  • the measured 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 wrong driver identification is set up to
  • such a device may comprise a read-in device which is designed to read in card data that contains a quantity of
  • Map road elements of a road network accessible by a vehicle having a determination device that is configured to determine a plurality of particles using measured position data of the vehicle, wherein a particle represents an assumed position of the vehicle and a weight assigned to the assumed position, a determination device , which is configured to determine at least one road element plausible with respect to a vehicle through the vehicle from the amount of road elements based on the plurality of particles using a particulate filter, and a
  • 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 recognition to the to receive from the at least one transmitting device emitted 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 can be part of a so-called system ASIC, for example, which contains a wide variety of 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 is stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical disk Memory may be stored and used for carrying out, implementing and / or driving the steps of the method according to one of the embodiments described above, in particular when the program product or program is executed on a computer or a device.
  • a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical disk Memory
  • FIG. 1 shows a system for identifying wrong-way drivers according to FIG. 1
  • 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
  • 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 106 wirelessly to a device 110 for
  • the device 110 is designed to prepare the measurement data 106 for prepared data and to prepare the processed data
  • the wrong-way signal 112 indicates that the vehicle 100 whose measurement data 106 has been processed currently makes a wrong-way drive. According to this embodiment, both the transmission device 102 of the
  • Vehicle 100 as well as a transmission device 102 of another vehicle 100 configured to receive the wrong-way signal 112 and in response to a receipt of the wrong-way signal 112 a
  • Warning device of the respective vehicle 100, 114 to activate the
  • False drive warns or engages according to an embodiment in an at least semi-automatic control, such as a brake system or steering system, the respective vehicle 100, 114.
  • an at least semi-automatic control such as a brake system or steering system, the respective vehicle 100, 114.
  • the transmission device 102 only as
  • Transmitter or be designed as a transceiver device.
  • the measurement data comprises
  • Information about a current movement of the vehicle 100 for example information about a direction of travel, a longitudinal acceleration, a
  • Vehicle axle include.
  • the device 110 is configured to read in map data 116 that maps a road network drivable by the vehicle 100.
  • map data 116 maps a road network drivable by the vehicle 100.
  • Map Data 116 Information about road elements of the road network.
  • the map data 116 with respect to each road element further comprise at least one parameter which defines, for example, a driving direction specification for the respective road element or a course of the respective road element. For example, over the
  • the device 110 has a memory device in which the map data 116 are stored.
  • the device is 110 or
  • Function blocks of the device 110 are arranged or realized in a cloud 118.
  • the apparatus 110 is configured to determine the wrong-way driver signal 112 using a tested road element.
  • the tested road element may be understood as a road element, also referred to as a road section, encompassed by the map data, which is assumed to be the vehicle 100 thereon.
  • the device 110 is designed to first determine at least one plausible road element from the road elements covered by the map data using a particle filter and then to check, using a classification method, whether the at least one plausible road element actually functions as a road Road element can be assumed on which the vehicle is highly likely.
  • the particles processed by the particle filter are determined by the device 110 using the position data transmitted via the measurement signal 106 and / or already existing particles. The classification procedure thus provides additional security through which
  • the device 110 may be configured to accommodate the Provide miss signal 112 using a tested road element.
  • 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 done to detect the passing of a "forbidden entry" sign or the use of a digital map in conjunction
  • Detecting the direction of travel on a road element which is only passable in one direction. Furthermore, are known. Furthermore, the approach can be with wireless
  • Beacons in the lane or at the edge of the lane detect wrong-way drivers.
  • Warning of the wrong driver himself via a display or audible instructions 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.
  • a device can be seen, located on or in a motor vehicle, which has a
  • Internet connection 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. Thus, a wrong driver specific
  • Server client communication with a smartphone as an exemplary client be implemented.
  • 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. In addition to the trip time, cost-efficiency also plays an important part.
  • 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 map data is transmitted via a
  • the Map data depicts a road network accessible by a vehicle.
  • a plurality of particles are determined using position data. Each of the particles represents an assumed position of the vehicle and a weight assigned to the assumed position. In this case, according to one exemplary embodiment, the assumed positions are distributed around the measured position.
  • a step 205 based on the plurality of particles, at least one road element that is plausible with respect to vehicle driving is determined from the set of road elements.
  • At least step 205 is performed using a particulate filter.
  • a road element may be determined as a plausible road element depending on a local distribution of the particles or the weights of the particles. For this purpose, it can be checked, for example, whether there is a predetermined quantity of particles on the road element or particles with a predetermined weighting are located on the road element.
  • a step 207 it is checked whether the at least one plausible road element is actually a road element, of which with sufficient safety
  • a road element checked by the classification method is dependent on a result of the method performed by the classification method
  • Method can be used to classify using the particulate filter as plausible road elements filtered road elements either as tested road elements or classified as implausible road elements.
  • movement data of the vehicle as well as the plausible road elements associated parameters can be incorporated. It is also possible to incorporate values based on an evaluation of the particles. In the evaluation, for example, a local arrangement and / or weighting of the particles can be taken into account. For example, such values may indicate how many of the particles can be assigned to a plausible road element. For example, if the number of particles greater than a threshold is greater than a plausible road element, the plausible road element may be classified as a tested road element.
  • False driver signal provided.
  • the wrong-of-way signal is provided when a current direction of travel of the vehicle does not coincide with a direction of travel of the tested road element.
  • the particle filter is applicable to systems which have a hidden Markov chain characteristic, ie a Markov filter.
  • Fig. 3 shows a Hidden Markov Chain Model 320 with state x and observation z at time k and k-1. That is, the state of a system can not be measured directly, but estimated based on other observations. In this case, it is important to estimate the position and thus the current road. For this, the following equation must be solved:
  • 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 particulate filter
  • 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.
  • Device 110 is configured to receive data 106 from 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.
  • 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 and a rolling p about the longitudinal axis, a pitch q about the transverse axis and a yaw r about the vertical axis.
  • the condition is, for example the geographical longitude, latitude and altitude, stand for how the car 100 moves, for example, in terms of speed and yaw rates and stand for what can be observed, for example, a GPS signal or the environment of the vehicle 100 concerning signal (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 described with reference to FIG. 1
  • card-related parameters For example, such a parameter indicates 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
  • 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 system for wrong-way driver recognition according to a
  • Embodiment The system corresponds to that with reference to FIG. 5
  • the device 933 is designed to implement a classification method, here, for example, of machine learning.
  • the particulate filter 532 is configured to process particulates 940 and to determine based on the particulates 940 at least one plausible road element 942 from which it is assumed that a data is derived from the data provided by the particulate filter 532 Vehicle is located on it.
  • the device 933 is designed to check whether the plausible road element 942 is actually a road element that can be assumed to be sufficiently secure on the vehicle. If so, the device 933 is configured to provide the verified plausible road element 942 as a tested road element 944 to the alert module 534.
  • the alert module 534 is configured to provide the warning in the form of a miss signal 112 using the tested
  • Possible parameters (features) for the classification method 933 including the temporal behavior / change of these are the following:
  • classification method 933 examples include regressions, K-nearest neighbor, decision trees, Random Forest, Support Vector Machine (SVL) or neural networks.
  • 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 a "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.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé pour détecter des véhicules circulant à contre-sens. Ce procédé comprend une étape qui consiste à entrer des données de carte (116) représentant une pluralité de tronçons routiers d'un réseau routier pouvant être emprunté par un véhicule (100), une étape qui consiste à déterminer une pluralité de particules au moyen de données de positionnement mesurées (106) du véhicule (100), une particule représentant une position occupée par le véhicule (100) et une pondération associée à la position occupée, une étape qui consiste à déterminer, parmi la pluralité de tronçons routiers, au moins un tronçon routier plausible, c'est à dire susceptible d'être parcouru par le véhicule (100), sur la base de la pluralité de particules, au moyen d'un filtre à particules, et une étape qui consiste à vérifier ledit au moins un tronçon routier plausible au moyen d'un procédé de classification afin d'obtenir au moins un tronçon routier vérifié.
EP17719826.4A 2016-06-07 2017-04-18 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens Ceased EP3465656A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016210032.7A DE102016210032A1 (de) 2016-06-07 2016-06-07 Verfahren Vorrichtung und System zur Falschfahrererkennung
PCT/EP2017/059192 WO2017211492A1 (fr) 2016-06-07 2017-04-18 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens

Publications (1)

Publication Number Publication Date
EP3465656A1 true EP3465656A1 (fr) 2019-04-10

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Application Number Title Priority Date Filing Date
EP17719826.4A Ceased EP3465656A1 (fr) 2016-06-07 2017-04-18 Procédé, dispositif et système pour détecter des véhicules circulant à contre-sens

Country Status (6)

Country Link
US (1) US10629073B2 (fr)
EP (1) EP3465656A1 (fr)
JP (1) JP6776373B2 (fr)
CN (1) CN109313850A (fr)
DE (1) DE102016210032A1 (fr)
WO (1) WO2017211492A1 (fr)

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DE102016210029A1 (de) * 2016-06-07 2017-12-07 Robert Bosch Gmbh Verfahren Vorrichtung und System zur Falschfahrererkennung
CN110081890B (zh) * 2019-05-24 2023-02-03 长安大学 一种结合深度网络的动态k最近邻地图匹配方法
US11348452B2 (en) * 2020-01-31 2022-05-31 Here Global B.V. Method, apparatus, and system for automatic closure verification using multiple possible vehicle paths
US11548521B2 (en) 2020-03-31 2023-01-10 Toyota Research Institute, Inc. Systems, methods and vehicles for determining wrong direction driving
US11348456B2 (en) 2020-10-06 2022-05-31 Ford Global Technologies, Llc Systems and methods for detecting wrong way travel of a vehicle
CN114023095B (zh) * 2021-09-22 2022-12-30 北京百度网讯科技有限公司 一种交通限制识别方法、装置及电子设备

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CN102081844A (zh) * 2011-01-25 2011-06-01 华中科技大学 一种交通视频行为分析与报警服务器
US20120290150A1 (en) * 2011-05-13 2012-11-15 John Doughty Apparatus, system, and method for providing and using location information
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Also Published As

Publication number Publication date
JP2019519850A (ja) 2019-07-11
US20190139404A1 (en) 2019-05-09
CN109313850A (zh) 2019-02-05
DE102016210032A1 (de) 2017-12-07
JP6776373B2 (ja) 2020-10-28
US10629073B2 (en) 2020-04-21
WO2017211492A1 (fr) 2017-12-14

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