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)

Applications Claiming Priority (2)

Publications (1)

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ID=58547535

Family Applications (1)

Country Status (6)

Families Citing this family (7)

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• 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 WO PCT/EP2017/058957 patent/WO2017211488A1/de unknown
  • 2017-04-13 JP JP2018563797A patent/JP6944472B2/ja active Active
  • 2017-04-13 EP EP17717424.0A patent/EP3465652A1/de active Pending
  • 2017-04-13 US US16/301,094 patent/US10916124B2/en active Active

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