EP3465652A1 - Verfahren vorrichtung und system zur falschfahrererkennung - Google Patents
Verfahren vorrichtung und system zur falschfahrererkennungInfo
- Publication number
- EP3465652A1 EP3465652A1 EP17717424.0A EP17717424A EP3465652A1 EP 3465652 A1 EP3465652 A1 EP 3465652A1 EP 17717424 A EP17717424 A EP 17717424A EP 3465652 A1 EP3465652 A1 EP 3465652A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- particles
- wrong
- vehicle
- road sections
- plausible
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000002245 particle Substances 0.000 claims abstract description 104
- 238000001914 filtration Methods 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 description 12
- 230000005540 biological transmission Effects 0.000 description 11
- 238000013459 approach Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 7
- 238000005259 measurement Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000012952 Resampling Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
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
- G08G1/0104—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
- 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
- G08G1/0125—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
- G08G1/01—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
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
Definitions
- a step 205 a plurality of previously filtered particles are read in:
- a step 207 a set of plausible road sections using the plurality of actual particles and the plurality
- the plurality of current particles are based on the set of plausible ones
- 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.
- 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.
- Device 110 is configured to receive data 106 from device 102,
- 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 stand for what the condition is (not measured), such as the geographical longitude, latitude and altitude, stand for how the car 100 moves, for example, in terms of speed and yaw rates and Zk are what can be observed, for example, a GPS signal or a signal (camera, etc.) relating to the environment of the vehicle 100
- Warn sent as described for example with reference to FIG. 5. If there is no wrong drive, the end of the program sequence takes place with a block 711.
- FIG. 8 shows a program flow of a particle filter according to a
- 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 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.
- observation model ie the calculation of the particle probability, thus also depends on the distance covered or the transition between two road elements, which could also be summarized as a transition probability.
- FIG. 9 shows a plurality of actual particles 901, that is to say particles from a current calculation cycle (k), and a plurality of previously filtered particles 903, that is to say particles from a previous calculation cycle (k-1).
- the green connection 911 shows a plausible particle movement
- the red connection 913 shows a non-plausible particle movement as the path traveled (on the road network) would be far too large for that one time step.
- connections 911, 913, 915 can thus be understood as distances which can be taken into account in the filtering of the particles 901, 903.
- FIG. 10 shows a program sequence of a method for wrong-way driver recognition according to one exemplary embodiment. The method can be carried out, for example, using the device described with reference to FIG.
- the reading takes place according to an embodiment by an envelope, in the form of a so-called bounding box to particles of the last and this
- a step 1007 all "new" road elements are added to a list of unsafe road elements when intersecting with a polygon, a so-called convex hull of the current particles A corresponding polygon is shown in Figure 11. Referring to Figure 11 will the
- step 1009 adding to plausible road elements occurs when the unsafe road elements are associated with the plausible ones.
- Fig. 11 is an illustration of consideration of a
- False driver recognition using the envelope 1101 first reads in the road sections 1111, 1113, 1115, 1117, 1119, 1121 as a set of preceding plausible road sections. Subsequently, the road sections 1117, 1119, 1121 cut by the polygon 1103 spanned by the current particles 901 are determined as a set of unsafe road sections. Now, the set of previous plausible road sections 1111, 1113, 1115, 1117, 1119, 1121 with those of the insecure road sections 1117, 1119, 1121, which have a connection to one of the preceding plausible road sections 1111, 1113, 1115, 1117, 1119, 1121, to the set of plausible road sections 1111, 1113, 1115, 1117, 1119, 1121 added.
- 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.
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102016210025.4A DE102016210025A1 (de) | 2016-06-07 | 2016-06-07 | Verfahren Vorrichtung und System zur Falschfahrererkennung |
PCT/EP2017/058957 WO2017211488A1 (de) | 2016-06-07 | 2017-04-13 | Verfahren vorrichtung und system zur falschfahrererkennung |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3465652A1 true EP3465652A1 (de) | 2019-04-10 |
Family
ID=58547535
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP17717424.0A Pending EP3465652A1 (de) | 2016-06-07 | 2017-04-13 | Verfahren vorrichtung und system zur falschfahrererkennung |
Country Status (6)
Country | Link |
---|---|
US (1) | US10916124B2 (de) |
EP (1) | EP3465652A1 (de) |
JP (1) | JP6944472B2 (de) |
CN (1) | CN109313849B (de) |
DE (1) | DE102016210025A1 (de) |
WO (1) | WO2017211488A1 (de) |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102017201924A1 (de) * | 2017-02-08 | 2018-08-09 | Audi Ag | Verfahren zum Informieren zumindest eines Empfängerfahrzeugs über ein Falschfahrerfahrzeug sowie Servervorrichtung und Kraftfahrzeuge |
US20200133308A1 (en) * | 2018-10-18 | 2020-04-30 | Cartica Ai Ltd | Vehicle to vehicle (v2v) communication less truck platooning |
US10748038B1 (en) | 2019-03-31 | 2020-08-18 | Cortica Ltd. | Efficient calculation of a robust signature of a media unit |
DE102019201423A1 (de) | 2019-02-05 | 2020-08-06 | Robert Bosch Gmbh | Verfahren und Vorrichtung zum Steuern einer Datenübertragung für ein Fahrzeug |
FR3095789A1 (fr) * | 2019-05-09 | 2020-11-13 | Psa Automobiles Sa | Procédé de sécurisation de véhicules en présence d’un véhicule circulant à contre sens |
DE102020212037A1 (de) | 2020-09-24 | 2022-03-24 | Robert Bosch Gesellschaft mit beschränkter Haftung | Verfahren und Vorrichtung zum Erzeugen einer Relevanzkarte für ein Fahrzeug und Verfahren und Vorrichtung zum Bereitstellen eines Positionssignals für eine Falschfahrerkennung |
US12049116B2 (en) | 2020-09-30 | 2024-07-30 | Autobrains Technologies Ltd | Configuring an active suspension |
US11335192B1 (en) | 2020-12-02 | 2022-05-17 | Here Global B.V. | System, method, and computer program product for detecting a driving direction |
US12110075B2 (en) | 2021-08-05 | 2024-10-08 | AutoBrains Technologies Ltd. | Providing a prediction of a radius of a motorcycle turn |
CN114910081B (zh) * | 2022-05-26 | 2023-03-10 | 阿波罗智联(北京)科技有限公司 | 车辆定位方法、装置及电子设备 |
Family Cites Families (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8209121B1 (en) * | 2007-10-10 | 2012-06-26 | Google Inc. | Registration of location data to street maps using hidden markov models, and application thereof |
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 |
US8452535B2 (en) * | 2010-12-13 | 2013-05-28 | GM Global Technology Operations LLC | Systems and methods for precise sub-lane vehicle positioning |
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 |
JP5479398B2 (ja) * | 2011-03-29 | 2014-04-23 | アイシン・エィ・ダブリュ株式会社 | 運転支援装置、運転支援方法及びコンピュータプログラム |
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 |
JP6169318B2 (ja) * | 2012-02-14 | 2017-07-26 | 本田技研工業株式会社 | ナビゲーションシステム |
JP5867176B2 (ja) * | 2012-03-06 | 2016-02-24 | 日産自動車株式会社 | 移動物体位置姿勢推定装置及び方法 |
JP2014169865A (ja) * | 2013-03-01 | 2014-09-18 | Hitachi Ltd | 目標トラッキング装置、目標トラッキングプログラム及び目標トラッキング方法 |
JP6036421B2 (ja) * | 2013-03-14 | 2016-11-30 | 富士通株式会社 | 道路管理支援方法、道路管理支援装置、及び道路管理支援プログラム |
DE102013209502A1 (de) * | 2013-05-22 | 2014-11-27 | Robert Bosch Gmbh | Verfahren zum automatischen Intervenieren in ein Ego-Fahrzeug bei einer Falschfahrt, insbesondere einer Geisterfahrt |
WO2015029565A1 (ja) * | 2013-08-28 | 2015-03-05 | アイシン・エィ・ダブリュ株式会社 | 運転支援システム、方法およびプログラム |
JP6511767B2 (ja) * | 2014-10-20 | 2019-05-15 | 株式会社デンソー | 逆走判断装置 |
CN105448094B (zh) * | 2015-12-31 | 2017-12-05 | 招商局重庆交通科研设计院有限公司 | 一种基于车路协同技术的逆行警告与风险规避方法 |
-
2016
- 2016-06-07 DE DE102016210025.4A patent/DE102016210025A1/de not_active Withdrawn
-
2017
- 2017-04-13 CN CN201780035503.4A patent/CN109313849B/zh active Active
- 2017-04-13 EP EP17717424.0A patent/EP3465652A1/de active Pending
- 2017-04-13 JP JP2018563797A patent/JP6944472B2/ja active Active
- 2017-04-13 WO PCT/EP2017/058957 patent/WO2017211488A1/de unknown
- 2017-04-13 US US16/301,094 patent/US10916124B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
US10916124B2 (en) | 2021-02-09 |
CN109313849B (zh) | 2021-10-15 |
JP6944472B2 (ja) | 2021-10-06 |
US20190189003A1 (en) | 2019-06-20 |
JP2019519041A (ja) | 2019-07-04 |
CN109313849A (zh) | 2019-02-05 |
WO2017211488A1 (de) | 2017-12-14 |
DE102016210025A1 (de) | 2017-12-07 |
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