EP2950290A1 - Verfahren zur Bereitstellung von Verkehrsinformationen - Google Patents

Verfahren zur Bereitstellung von Verkehrsinformationen Download PDF

Info

Publication number
EP2950290A1
EP2950290A1 EP14290156.0A EP14290156A EP2950290A1 EP 2950290 A1 EP2950290 A1 EP 2950290A1 EP 14290156 A EP14290156 A EP 14290156A EP 2950290 A1 EP2950290 A1 EP 2950290A1
Authority
EP
European Patent Office
Prior art keywords
sensors
sensor
information
vehicles
network
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.)
Withdrawn
Application number
EP14290156.0A
Other languages
English (en)
French (fr)
Inventor
Wolfgang Templ
Dieter Kopp
Klaus Stocker
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.)
Alcatel Lucent SAS
Original Assignee
Alcatel Lucent SAS
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 Alcatel Lucent SAS filed Critical Alcatel Lucent SAS
Priority to EP14290156.0A priority Critical patent/EP2950290A1/de
Publication of EP2950290A1 publication Critical patent/EP2950290A1/de
Withdrawn legal-status Critical Current

Links

Images

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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination

Definitions

  • Information about road conditions is commonly based on weather status and forecast reports, occasionally completed by information from few sensors in the road infrastructure.
  • Information about traffic flow is commonly based on data from street cameras, radar sensors or inductance loops which are built in the streets. All these sensors and their deployment are costly. Deployment of inductance loops usually involves roadwork, e.g., grinding and repair of street covering.
  • Traffic information comprises in particular information about road conditions such as humidity or ice on the road surface caused by the weather, information about the quality of the road surface which might be affected by holes etc., information about dangerous spots or curves of a road, and information about vehicles driving on one or more roads such as the number and/or size of the vehicles.
  • the objective of the invention is achieved by a method for providing traffic information, the method comprising gathering information by a network of sensors provided in and/or on one or more roads or road segments and processing the gathered information.
  • the objective of the invention is further achieved by a system for providing traffic information, the system comprising a network of sensors provided in and/or on one or more roads or road segments, wherein the network of sensors is adapted to gather information, wherein one or more of the sensors of the network of sensors and/or a central server are adapted to process the gathered information.
  • the objective of the invention is further achieved by a server for providing traffic information, wherein the server is adapted to process information gathered by one or more sensors of a network of sensors provided in and/or on one or more roads or road segments.
  • the server is also referred herein as a central server.
  • a computer comprises in particular a processor and a memory.
  • a computer adapted to the implementation of the respective device might be implemented in particular in the one or more sensors, in the central server, in the one or more vehicles and/or in a relay of the network of sensors.
  • the present invention might be implemented by hardware, software or combination of hardware and software.
  • central server instead of one central server, two or more central servers might be used.
  • two or more relays might be used.
  • a plurality of relays is used, distributed on a plurality of road segments, respectively.
  • the sensors used for the network of sensors are preferably low cost devices and fabricated in huge quantity.
  • a sensor is preferably equipped with a processor, a sender or transmitter, a receiver, and/or transceiver.
  • the sensors are provided in the one or more roads by mixing the sensors into the hot tarmac, when the road surface is deposited.
  • the sensors are provided on the one or more roads by mounting the sensors into the road surface, in particular by nailing or gluing the sensors into the road surface.
  • the mounting of the sensors considers the usage scenario and as well the chosen energy supply concepts of the sensors.
  • the senor are equipped with a long life time battery, this means one battery for each sensor.
  • the life time of the battery exceeds ten years.
  • the sensor energy consumption is very low and the sensor may be operable for ten years or more which is long enough to span the life time of an averagely used road coating.
  • the sensors are equipped with energy harvesting devices, in particular piezoelectric elements or photovoltaic elements.
  • the sensors are energized via microwave.
  • 5 sensors per square meter are provided in and/or on the roads.
  • one or more sensors of the network of sensors gather information on local road conditions, in particular measurements on humidity, temperature, pressure and/or chemicals.
  • one or more sensors of the network of sensors gather information about one or more vehicles passing the one or more sensors, in particular number of vehicles, weight, size, speed, acceleration and/or driven track.
  • a sensor gathers information about a passing vehicle when the vehicle passes a sensor.
  • a sensor gathers information about a passing vehicle by means of a piezoelectric element when the vehicle drives over the sensor.
  • the pressure which the vehicle causes on the piezoelectric element indicates the weight of the vehicle and therefore provides to the sensor information that a vehicle is passing the sensor and the weight of the vehicle.
  • the energy which is provided by the pressure of the vehicle driving over the sensor might also be used for generating energy for the operation of the sensor.
  • the sensor might be equipped with a battery, in particular re-chargeable battery which charges the energy provided by the piezoelectric element.
  • the gathered information is processed locally by the network of sensors.
  • the information might be processed only by the sensor which gathered the respective information.
  • the information might be processed by one or more further sensors, preferably in adjacency of the sensor which gathered the respective information, after information exchange with said sensor.
  • the information gathered by a sensor might be exchanged with a plurality or all sensors of the network of sensors for further processing.
  • the gathered information is transmitted from the one or more sensors to a central server for further processing.
  • control information is sent to one or more vehicles, in particular via a direct communication link between one or more sensors of the network of sensors and the one or more vehicles, via a relay or via a central server.
  • control information comprises one or more of the group: alarm information for approaching vehicles about, in particular local, poor road conditions, in particular wetness, ice, spilled oil, mud; information about recommended optimal tracks to avoid dangerous local spots or trails on the road surface; information about recommend optimal tracks through dangerous bends; information about learnt tracks taken by vehicles driving beforehand through an area and recommendations based on the experiences gained from earlier passages, possibly considering physical properties, e.g. weight, of the vehicles.
  • the experiences might be evaluated by the central server, the one or more vehicles or the network of sensors, wherein evaluation is in particular providing control information based on the previous experiences.
  • one or more sensors of the network of sensors are calibrated by one or more vehicles passing the one or more sensors and transmitting a local position to a memory of the one or more sensors.
  • the local position of a sensor is defined by the geo-coordinates of a sensor.
  • a vehicle determines the local position of a sensor when passing the respective sensor.
  • a vehicle when used to calibrate one or more sensors might be termed a calibration vehicle.
  • a vehicle is equipped with a large antenna to communicate with the one or more sensors.
  • the step of calibration comprises activating, by a calibration vehicle, the sensor of which the local position is to be determined when passing the sensor, causing the sensor to transmit the address of the sensor to the calibration vehicle, determining geo-coordinates of the sensor corresponding to the address of the sensor, in particular via GPS, triggering a calibration sequence in the sensor and transmitting the geo-coordinates to a memory of the sensor, which is preferably a static memory.
  • the geo-coordinates of a sensor are determined from a velocity vector indicating a speed of a calibration vehicle by amount and direction and from a time measured between passing - by the calibration vehicle - a sensor of which the geo-coordinates are already known and passing - by the calibration vehicle - a sensor of which the geo-coordinates are to be determined.
  • a calibration vehicle determines the local position of a sensor by means of a GPS receiver, when passing the respective sensor.
  • the geo-coordinates of the sensor are stored in the memory of the sensor and can be transmitted to a vehicle and/or to a central server, where the geo-coordinates of the sensor might be stored in addition, suitable for further information exchange within the system, in particular vehicles, sensors.
  • a calibration vehicle determines the local position of a sensor by means of its relative position to a first and a second further sensor of the network of sensors of which the local positions are already known.
  • the calibration vehicle determines the geo-coordinates of a sensor of which the geo-coordinates are to be determined from a first further sensor of which the geo-coordinates are already known and a second further sensor of which the geo-coordinates are already known.
  • a sensor of which the local position is already known is used as reference sensor.
  • (further) sensors of which the local positions are already known are used as (further) reference sensors.
  • one or more vehicles determine the geo-coordinates of a sensor, of which the local position is to be determined, by means of its relative position to a first further sensor and a second further sensor of which the geo-coordinates are already known, in particular by deriving the geo-coordinates of the sensor, of which the local position is to be determined, from its relative position to the first and the second further sensor of which the already known geo-coordinates are used as reference positions and from timing measurements and/or known velocity or speed limit information.
  • the calibration vehicle triggers a clock in all sensors when passing the first further sensor (reference sensor) of which the local position is already known, stops a time when passing the second further sensor of which the local position is already known, determines a velocity vector of the calibration vehicle by amount of the velocity and direction of the velocity and measures the time when passing the sensor of which the local position is to be determined and determines the local position of that sensor from the measured time and the determined velocity vector.
  • the local position of a next sensor might be determined, either from the measurements of time and velocity with reference to the first sensor or the last passed sensor of which the geo-coordinates are already known.
  • the calibration vehicle triggers a clock which is extern to the sensors.
  • the clock is included in the calibration vehicle itself.
  • the calibration vehicle comprises a clock and triggers the clock when passing the reference sensor. Then the calibration vehicle stops a time when passing the second further sensor of which the local position is already known, determines a velocity vector of the calibration vehicle by amount of the velocity and direction of the velocity and measures the time when passing the sensor of which the local position is to be determined and determines the local position of that sensor from the measured time and the determined velocity vector, preferably according to the above implementation and formula.
  • only the stop times of the passed sensors have to be determined, stored and processed.
  • the implementation of the sensors is simpler, the sensors need less energy and the cost of the sensors is less than in the embodiment where all sensors are equipped with individual sensors.
  • the amount of the velocity vector is pre-defined, i.e. already known, and therefore only the direction of the velocity vector is determined from the geo-coordinates of the first and second further sensor of which the local positions are already known.
  • the speed is determined by a speed limit and the time determined as a mean value of a plurality of measurements.
  • the distribution of the speeds driven by a plurality of vehicles is peaking at the speed limit value. Therefore, by statistically reasons, the speed of the vehicles correspond to the known speed limit value.
  • the time between passing - by a plurality of vehicles used in this embodiment as calibration vehicles - two sensors is determined by the mean value of a plurality of measurements of the time for the vehicles passing two sensors subsequently and storing the measurements in a memory.
  • the measurements or measurement results are stored in the memory of the sensor of which the local position is to be determined.
  • the measurements/ measurement results are not stored in the sensors, but in a further memory extern to the sensors, for example in the vehicle; in this example embodiment, it has to be ascertained that the measurement results can be assigned to an individual sensor or the individual sensors, respectively.
  • the distance between the two sensors is determined by multiplying the mean value of the time interval distribution, i.e.
  • the distance between the two sensors can be determined. If the geo-coordinates of one of the two sensors are already known, the geo-coordinates of the other of the two sensors can be determined.
  • the local position of the sensors is determined.
  • the local position of sensors is stored in a memory of the respective sensors and/or preferably in the memory of the central server.
  • the local position of sensors might be transmitted to one or more vehicles as soon as the vehicles need or want to know the local position of sensors.
  • the one or more vehicles might store the local position of one or more sensors in a memory included in the vehicle.
  • the network of sensors can be used to send control information to one or more vehicles driving on the roads where the network of sensors is installed.
  • the step of sending control information to the one or more vehicles comprises transmitting one or more signals to the one or more vehicles guiding the one or more vehicles on a recommended track.
  • any other control information may be sent, like e.g. commands for acceleration or deceleration (breaking), chassis adjustments, etc.
  • control information might be information of the total track recommended to take by the vehicle.
  • the control information might be transmitted to the vehicle by the central server and/or one or more sensors.
  • the control information might be stored in the vehicle and used to guide the vehicle on the recommended track.
  • the one or more signals are transmitted by the one or more sensors, usually by a plurality of sensors, guiding the vehicle on a recommend track.
  • the one or more signals are either attracting or repelling signals, wherein the signals contain directional vector information, wherein the directional vector information of the signals is added to a vector sum indicating the recommended track.
  • the added vector information is implemented as a field of vectors or vector field indicating the recommended track for a vehicle.
  • the vehicle is guided on the road such as a magnetic piece is attracted by a magnetic field attracted by the magnetic forces resulting from attracting and repelling magnetic lines.
  • the information exchange between the network of sensors, the vehicles, the central server and among the sensors of the network of sensors is implemented wirelessly, in particular based on a wireless platform exploiting low latency machine to machine, M2M, communication.
  • the information transfer within the network of sensors is based on a wireless platform exploiting low latency machine to machine, M2M, communication.
  • the information transfer between the one or more sensors and the central server is based on a wireless platform exploiting low latency machine to machine, M2M, communication.
  • the information transfer from one or more sensors and/or from the central server to the one or more vehicles and in the opposite direction is based on a wireless platform exploiting low latency machine to machine, M2M, communication.
  • the information is transferred between the one or more sensors and the one or more vehicles via a direct link or via a relay which is mounted at the side of the one or more roads.
  • the step of further processing information by the central server comprises to return information about traffic density for traffic control and traffic management and/or to give indication about passing vehicles, in particular considering their weight e.g. for toll pricing.
  • the sensors gather information on one or more vehicles when a respective vehicle passes a sensor, wherein the respective vehicle drives over the respective sensor.
  • a sensor gathers information about an identity of a vehicle, in case the vehicle transmits information about the identity of the vehicle to the sensor.
  • the identity might be defined by the number plate and/or by the number of the engine or chassis frame.
  • the network of sensors gathers information about the location and/or or direction of the taken track of the one or more vehicles driving over the roads in which the network of sensors is provided.
  • a vehicle is preferably a car, a bus or a motorbike.
  • the sensors are provided in an abundant quantity. Therefore, even in case of failure of part of the sensors, e.g. caused by wear out or erosion, the remaining sensors provide information with sufficient accuracy.
  • a partial damage or change of position of a part of the sensors is healed by an autonomous self calibration.
  • the information gathered by the sensors is implemented as a system of equation parameters.
  • the equations comprise parameters derived from the information gathered by the sensors as described throughout the description such as e.g. vehicles by number and weight, direction, velocity, i.e. speed, possibly identity of the vehicles, position, time corresponding to position, track and information about the road conditions, such as humidity etc.
  • the self calibration comprises excluding measurements of sensors based on logical considerations, in particular based on contradictive measurements, and/or executing a re-calibration of sensors.
  • the information of the network of sensors is supplemented by information of devices external to the network of sensors.
  • devices external to the network of sensors comprise radar stations and/or further monitoring devices gathering information of vehicles and/or of road conditions.
  • the devices external to the network of sensors feed their information to the network of sensors, preferably to the central server.
  • the information provided from the external devices might be used to supplement and/or verify the information provided by the network of sensors.
  • the information gathered by the network of sensors might be used to supplement and/or verify the information of devices external to the network of sensors.
  • Figure 1 shows a network of sensors 1 or a part of the network of sensors 1 which are provided in a road or on a road, or more specifically in or on the road segment 2, which is depicted illustratively in the figure 1.
  • a central server 4 is installed in adjacency to the road 2 .
  • the sensors 1 While the vehicle 3 drives over the road 2, the sensors 1 will gather information about the vehicle 3 and further will also provide information to the vehicle 3. However, before the sensors 1 can be used to gather and provide information, the sensors 1 need to be calibrated.
  • the local position of the sensors 1 is determined and stored, for example into the sensors 1 and/or in the central server 4. This means the storage of the local position of the sensors 1 can be located only within the respective sensors 1 or only in the central server 4 or both within the sensors 1 and in the central server 4.
  • a sensor 1 stores only its own local position
  • a sensor 1 stores its own local position and in addition the local position of further, in particular adjacent, sensors 1.
  • the local position of the sensors 1 is determined and stored, because the information gathered by the sensors 1 and provided by the sensors 1 is preferably valuable, in particular if the information is assigned to the location of the respective sensor 1, which gathered or provided the information. For example, if a sensor 1 determines that the road 2 in which the sensor 1 is situated is wet, so the information is preferable valuable if the information that the road 2 is wet is combined with the local position of the sensor 1 indicating likewise the exact location on the road 2 where the road 2 is wet.
  • one or more vehicles 3 pass the sensors 1 and determine and transmit the local position to a memory of the respective sensor 1 of which the one or more vehicles 3 have determined the local position.
  • the local position might be assigned to the sensor ID and both data stored in a memory located in the server or cloud.
  • a specific sensor 1 there are primarily two manners for calibrating a specific sensor 1 and similarly the other sensors 1 of network of sensors 1.
  • the local position of a sensor 1 is determined absolutely or directly, in particular via GPS.
  • the local position of a sensor 1 is determined in relation to a further sensor 1 (dubbed "reference sensor") of which the local position is already known.
  • the two manners are used alternatively, and exceptionally combined for verification purposes.
  • FIG. 2 illustrates the first manner, where the local position of a sensor 1 is determined absolutely or directly by a vehicle 3, which might be termed a calibration vehicle 3 as it is used to calibrate the sensor 1 and similarly the network of sensors 1.
  • the calibration vehicle 3 drives over the road 2.
  • the sensor 1 of which the local position is to be determined is activated, which is symbolized by waves indicating also the communication between the sensor 1 and the vehicle 3.
  • a vehicle 3 is equipped with one or more transceivers 14 (see figures 5 and 6 ), preferably comprising one or more antennas with a large range.
  • the calibration vehicle 3 causes the sensor 1 to transmit the address of the sensor 1 to the calibration vehicle 3.
  • the address of the sensor 1 might be the number of the sensor 1, in particular a MAC address or any name or number of the sensor 1 by which the sensor 1 can be, preferably unambiguously, identified.
  • the calibration vehicle 3 determines the local position of the sensor 1. More specifically, the calibration vehicle 3 determines the geo-coordinates of the sensor 1 corresponding to the address of the sensor 1, this is calibration vehicle 3 determines the geo-coordinates of the sensor 1 which is identified by the address of the sensor 1. In the embodiment which is illustrated in figure 2 , the calibration vehicle 3 determines the local position of the sensor 1 by means of a GPS (Global Position System) receiver. As soon as the calibration vehicle 3 passes over the sensor 1, and the activated sensor 1 has transmitted the address of the sensor 1 to the calibration vehicle 3, the calibration vehicle 3 determines the geo-coordinates by means of a GPS receiver which indicates the geo-coordinates of the calibration vehicle 3 situated above the sensor 1 at this moment and thus the geo-coordinates of the sensor 1.
  • GPS Global Position System
  • any system by which geo-coordinates can be determined might be used.
  • the calibration vehicle 3 preferably triggers a calibration sequence in the sensor 1 which thereby is informed that it will receive its geo-coordinates for calibration of the sensor 1, so that the sensor is now ready for receiving its geo-coordinates.
  • the vehicle transmits the geo-coordinates to the sensor 1 to a memory, preferably to a static memory, of the sensor 1 which accordingly stores its geo-coordinates in the memory.
  • the memory of the sensor now contains the geo-coordinates of the sensor 1 assigned to the address of the sensor 1.
  • the geo-coordinates might be assigned to the sensor ID and both data stored in a memory located in the server or cloud.
  • Figure 3 illustrates the second manner for calibrating the sensors 1 of the network of sensors.
  • the calibration vehicle 3 determines the local position of a sensor 1 by means of its relative position to a first further reference sensor 1 and a second further reference sensor 1 of which the local positions are already known.
  • the network of sensors 1 comprises in the road 2 one or more reference sensors of which the local positions are already known, for example because their geo-coordinates have been determined beforehand according to method 1, in particular via a GPS receiver by a calibration vehicle 3.
  • the local positions of sensors of which the geo-coordinates are not yet known are determined in relation to sensors of which the geo-coordinates are known.
  • the sensors of which the geo-coordinates are already known are depicted in figure 3 by squares, of which a first sensor of which the geo-coordinates are already known is specified by the reference sign 1 a and a second sensor of which the geo-coordinates are already known is specified by the reference sign 1 b.
  • the sensor of which the geo-coordinates are to be determined is specified by reference sign 1c.
  • the second manner consists in short in that the calibration vehicle 3 passes the first sensor 1 a and the second sensor 1 b of which the geo-coordinates are already known, determines its own velocity in terms of amount and direction and calculates the local position of the sensor 1c from the distance between the sensor 1 b and the sensor 1c.
  • the geo-coordinates of the sensor 1c are determined from a velocity vector indicating a speed of the calibration vehicle 3 by amount and direction and from a time measured between passing - by the calibration vehicle 3 - the sensor 1 b of which the geo-coordinates are already known and passing - by the calibration vehicle 3 - the sensor 1 c of which the geo-coordinates are to be determined.
  • the second method is implemented in more detail by the following steps.
  • the calibration vehicle 3 triggers a clock in all sensors 1, including sensors 1 a, 1 b, 1 c, when passing the first sensor 1 a of which the local position is already known.
  • the calibration vehicle 3 stops the time at its own clock as a semi-result, this means the calibration vehicle 3 measures the time for the passage from the first sensor 1 a to the second sensor 1 b. From the geo-coordinates of the first sensor 1 a and the second sensor 1 b and the measured time for the passage from first sensor 1 a to second sensor 1 b, the calibration vehicle 3 determines a velocity vector of the calibration vehicle by amount of the velocity and direction of the velocity.
  • the calibration vehicle 3 continues its driving with the determined speed indicated by the velocity vector by amount and direction. As soon as the calibration vehicle 3 passes a sensor of which the local position is to be determined because it is not yet know, here sensor 1 c, the calibration vehicle 3 determines the time between passing the sensor 1 b and the sensor 1c. From the time for the passage between sensor 1 b and sensor 1 c and from the velocity vector the calibration vehicle 3 determines the local position of sensor 1c.
  • the calibration vehicle determines the distance between sensor 1 b and sensor 1 c and further from the geo-coordinates of sensor 1 b which are already known and the distance between sensor 1 b and sensor 1 c and the direction of the velocity vector, this means the direction of the driving of the calibration vehicle 3, the calibration vehicle 3 determines the geo-coordinates of sensor 1c.
  • this holds under the assumption that the distance between sensors 1 a, 1 b and 1 c is sufficiently short so that the vehicle passes the sensor field at nearly constant speed. Otherwise, preferably, additional reference sensors need to be inserted and consequently are implemented.
  • the amount of the velocity vector is pre-defined, i.e. already known (e.g. because the vehicle drives with the pre-defined speed), and therefore only the direction of the velocity vector is determined from the geo-coordinates of the first 1a and second 1b further sensors of which the local positions are already known.
  • the clocks need not to be located in the sensors but a single clock may be implemented on the server and the stop times might be assigned to the individual sensors and stored in the server or in the cloud.
  • the speed is determined by a speed limit for vehicles passing the road or road section and the time is determined as a mean value of a plurality of measurements.
  • the vehicles 3 are used contemporaneously for calibration of the sensor 1c and similarly for other sensors 1 of which the geo-coordinates are to be determined.
  • the distribution of the speeds driven by a plurality of vehicles 3 is peaking near the speed limit value.
  • the mean value of the speed distribution of the vehicles 3 correspond to the known speed limit value.
  • the time between passing by a plurality of vehicles 3 two sensors 1 is determined by the mean value of a plurality of measurements of the time for the vehicles 3 passing two sensors 1 and storing the measurements in a memory, in particular in the memory of the sensor 1 of which the local position is to be determined, here 1c.
  • the distance between the two sensors, here between sensor 1 b and sensor 1 c is determined by multiplying the mean value of the time interval distribution, i.e. of the plurality of measurements of the time, with the speed corresponding to the speed limit value. Thereby, the distance between the two sensors 1 b and 1 c can be determined.
  • the geo-coordinates of sensor 1c can be determined.
  • an assumption is made that the distance between sensor 1 b and sensor 1 c is short and the vehicles drive from sensor 1b to sensor 1c in a straight direction, in particular in the direction which is determined from the passage from sensor 1a to sensor 1b.
  • FIG. 4 depicts a schematic view of some of the steps of the second manner for calibrating sensors 1 of the network of sensors 1.
  • a first step 9 it is determined if a vehicle 3 is detected by a sensor 1. If this is not true, the step is repeated, until a vehicle 3 is detected. If this is true, it is proceeded to step 10. If a vehicle 3 is detected, it is determined in step 10, if the clock in the sensor 1 is running. If this is true, the manner proceeds to step 11, the clock is stopped, the time is stored and the distance is calculated according to the algorithm presented above. If the clock in the sensor 1 is not running, the time cannot be stopped by the respective sensor 1. Therefore, the method proceeds to step 12, which is to send a clock start command to other sensors 1.
  • the time can be determined by the respective other sensor 1 and the distance can be calculated, so that in case the geo-coordinates of the sensor 1 which the vehicle 3 passed previously are known, the geo-coordinates of the other sensor 1 can be calculated.
  • the manner of calibrating the network of sensors 1 by a plurality of vehicles 3 passing over the sensors 1 of the network of sensors 1 is also based on the consideration that by the many vehicles 3 the sensors 1 of network of sensors 1 will be calibrated stepwise, i.e. after a certain time period as soon as enough vehicles have been passed over the sensors and implements the calibration steps.
  • a vehicle can not calculate the geo-coordinates of a sensor 1, because also the geo-coordinates of the sensor 1, which the vehicle passed previously are also not known.
  • the sensors 1 of which the geo-ordinates are not yet known will be calibrated departing from the sensors 1 of which the geo-ordinates are already known.
  • the further sensor 1 is thereby calibrated, this is its geo-coordinates are determined and stored. Its geo-coordinates might be stored in the memory of that sensor 1. Alternatively, the geo-coordinates may be stored together with the assigned sensor ID in a memory located in the server or cloud. Consequently, when a vehicle 3 then passes the later sensor 1 and drives then over a next sensor 1 of which the geo-ordinates are to be determined, the vehicle 3 can determine the geo-coordinates of this sensor 1, because the geo-coordinates of that sensor 1 before had been already determined as described.
  • the further sensors 1 of which the geo-coordinates are to not yet known will be determined consequently, step-by-step, so that in the end in principle all sensors 1 of the network of sensors 1 are calibrated.
  • the method of providing traffic information as described herein is nevertheless applicable, only the degree of accuracy of the information is a little bit lower as a sensor 1 which is not calibrated is usually regarded as non-existing, because preferably only information in connexion with the local position of a respective sensor 1 is valuable information.
  • the sensors 1 can be used to gather and provide traffic information.
  • the sensors 1 of the network of sensors 1 gather information on local road conditions, in particular measurements on humidity, temperature, pressure and/or chemicals.
  • a sensor 1 can indicate that exactly at the local position where the sensor 1 is situated, the road 2 is wet or dry.
  • the information about the kind of road condition e.g. wet or dry, can be provided with a high spatial accuracy.
  • the sensors 1 gather information about the vehicles 3 driving over the road and thereby over the sensors 1.
  • the sensor 1 detects the vehicle 3, because the sensor 1 is equipped with elements which detect in particular the pressure which is provided on the sensor1 by the weight of the vehicle 3 when driving over the sensor 1.
  • one or more further ways of detecting one or more vehicles 3 by the sensor 1 are implemented, in particular inductive, optical and acoustical.
  • the sensor 1 detects a vehicle 1 by means of inductive effect caused by the passing vehicle 1 in the sensor 1 which is therefore equipped with inductive sensitive elements, e.g. semi-conductive elements.
  • the sensor 1 detects a vehicle 1 by optical means, e.g.
  • the sensor 1 detects a vehicle 1 by by acoustical means, e.g. by semi-conductive elements which are sensitive for acoustical waves caused by the passing vehicle 3.
  • a vehicle is equipped by means which transmit optical, acoustical or further signals for being detected by the sensors 1.
  • the sensors 1 and similarly, the sensors 1 of the network of sensors 1 gather information about the traffic on the roads where the network of sensors 1 is provided.
  • the traffic information gathered thereby by the network of sensors 1 is in particular number, weight, size, speed, acceleration and/or driven track of the one or more vehicles 3 driving over the roads 2.
  • the sensors 1 might exchange, and preferably store, the information gathered by each of the sensors 1, and in particular in case the vehicle 3 identify themselves to the sensors 1 when passing over the sensors 1, parameters such as acceleration and/or driven track of a specific vehicle 3 can be determined.
  • the gathered information is processed locally by the network of sensors 1.
  • a sensor 1 might process only the information which it has gathered itself.
  • a sensor 1 exchanges information with other sensors 1, which might be a certain group of adjacent sensors 1 or in principle all sensors 1 of the network of sensors 1.
  • information gathered by the sensors 1 is transmitted from the sensor 1 to a central server 4 for further processing.
  • a central server 4 is already depicted in figure 1 .
  • the central server 4 might collect the information gathered and transmitted from the sensors 1.
  • the central server 4 might compare the information transmitted from a first group of sensors 1 of the network of sensors 1 with a second group of sensors 1 of the network of sensors 1.
  • the central server 4 might evaluate the information, e.g. by determining from the vehicles 3 counted and/or monitored by the one or more sensors 1 the total amount of vehicles 3 and/or the identity of the vehicles 3 driving on the road 2 and/or in a specific direction.
  • the central server 4 might be able to provide a more global view of the traffic on the roads 2 in which the network of sensors 1 is installed.
  • the sensors 1 themselves might evaluate the information in the way described in context of the central server 4, in case the processing and memory storage capacity of the sensors 1 is sufficiently large.
  • a central server 4 which evaluates the information gathered by the sensors 1.
  • the step of processing information comprises sending control information to one or more vehicles 3.
  • the control information is preferably provided by the central server 4 for more complex evaluation.
  • the sensors 1 might generate control information.
  • the control information might in particular be information to control the traffic by e.g. directing one or more vehicles 3 on a determined track or use particular roads 2 thereby avoiding traffic jam for example.
  • control information is preferably generated by the central server 4, the control information might be transmitted from the central server 4 via the sensors 1 to the vehicles 3. Further, control information might be generated by one or more sensors 1 and transmitted from the one or more sensors 1 to the one or more vehicles 3. Further, control information might be transmitted directly from the central server 4 to the one or more vehicles 3.
  • the one or more vehicles 3 are equipped with one or more transceivers 14 ( Fig. 5 ).
  • control information might be transmitted to the one or more vehicles 3 via a direct communication link between the one or more vehicles 3 with the central server 4. Alternatively or in addition, control information might be transmitted from the central server 4 via one or more sensors 1 to the one or more vehicles 3.
  • control information might be transmitted to the one or more vehicles 3 via a direct communication link between the one or more sensors 1 and the one or more vehicles 3 ( figure 5 ).
  • control information might be transmitted to the one or more vehicles 3 via a communication link between the one or more sensors 1 and the one or more vehicles 3 via the central server 4.
  • control information might be transmitted via a relay 5 ( figure 6 ).
  • the relay 5 might be mounted at the side of the road, e.g. on a limiting post, a sign post or a lamp post or on any further item, for example a brickwall or a tree.
  • the information transfer between any of: the one or more vehicles 3, the one or more sensors 1, the central server 4, this means in particular from and/or to the vehicles 3, from and/or to the one or more sensors 1, from and/or to the central server 4 and from and/or to one or more of the sensors to one or more of the sensors 1 might be implemented directly or via a relay and/or via the central server 4.
  • the control information comprises alarm information for approaching vehicles 3 about poor road conditions, in particular wetness, ice, spilled oil, mud on the road 2 where the vehicles are driving or where the vehicles are approaching.
  • the control information comprises information about recommended optimal tracks to avoid dangerous local spots or trails on the surface of the road 2.
  • the control information comprises information about recommended optimal tracks through dangerous bends of a road 2.
  • the control information comprises information about learnt tracks taken by vehicles 3 driving beforehand through an area and recommendations based on the experiences gained from earlier passages, in particular considering weight of the vehicles 3.
  • the network of sensors 1 might be implemented as a learning or self-learning network.
  • the sensors 1 store information gathered from previous passing vehicles 3, in particular dangerous spots causing accidents or at least dangerous situations which might be detected from extremely fast reduction of speed of driving vehicles 3.
  • This information is stored in the one or more sensors 1 and/or or in the central server 4, evaluated by the one or more sensors 1 and/or by the central server 4 and the evaluated information is stored in the one or more sensors 1 and/or in the central server 4.
  • the evaluated information is then used as control information for approaching vehicles 3.
  • the control information might in this case comprise a warning for vehicles 3 driving on a particular road of a dangerous curve thereby suggesting the vehicles 3 to slow down their speed to thereby avoid a dangerous situation.
  • the step of sending control information to a vehicle 3 comprises transmitting one or more signals 6a, 6b to the vehicles 3 guiding the vehicles 3 on a recommended track 7.
  • Figure 7 depicts a vehicle 3 driving on a road 2 in upward direction.
  • a plurality of sensors 1 are provided.
  • the sensors have gathered and now stored information about the road surface, in particular if the road surface is dry or wet.
  • the sensors 1 have detected that the road surface is dry with the exception of a frozen puddle 13, where the surface is wet or even iced. Therefore, the frozen puddle 13 has to be avoided, because driving there is dangerous.
  • the gathered information is preferably exchanged for further processing with the central server 4 and/or adjacent sensors 1 of the network of sensors 1.
  • the processed information is exchanged with and within the sensors 1 in and around the frozen puddle 13. Based on the processed information, the sensors 1 send signals to an approaching vehicle 3.
  • the signals sent to the vehicle 3 are either attracting signals 6a or repelling signals 6b.
  • the sensors 1 which are located within the frozen puddle 13 send repelling signals 6b. These are signals which direct the vehicle 3 away from the frozen puddle 13.
  • the sensors 1 outside the frozen puddle 13 send attracting signals 6a and the sensors 1 within the frozen puddle 13 send repelling signals 6b without exchanging the information gathered by the respective sensors 1 with the central server 4 and/or with further sensors 1.
  • Each sensor 1 sends signals independently from the further sensors 1 without the sensors 1 exchange the gathered information.
  • a sensor 1 within the frozen puddle 13 sends a repelling signal 6b, whereby preferably the direction of the signal 6b considers the local position of the sensor 1, so the repelling signal 6b guides away from the frozen puddle 13 in the direction of e.g. the middle of the road 2 and not in direction outside the road 2.
  • a sensor 1 outside the frozen puddle 13 sends an attracting signal 6a, preferably considering the local position of the sensor 1.
  • Each sensor might preferably consider, in which direction a vehicle 3 drives in principle, this means in figure 7 up or downwards for generating the signals 6a and 6b respectively.
  • the signals 6a, 6b contain directional vector information.
  • the signals 6a, 6b contain a direction for the vehicle 3 where to go.
  • the directional vector information of the signals 6a, 6b is added to a vector sum 8 indicating the recommended track 7.
  • the vehicle 3 receives the attracting signals 6a and the repelling signals 6b as sent by the sensors 1 and implements the addition of the vector information to the vector sum 8 in a processor of the vehicle 3.
  • the attracting signals 6a and the repelling signals 6b are transmitted from the sensors 1 to the central server 4, which adds the directional vector information of the attracting signals 6a and of the repelling signals 6b to a vector sum 8, thus to a resulting vector indicating the recommended track and then sends the resulting vector to the vehicle 3.
  • a relay 5 might be used.
  • Figure 7 indicates the recommended track which the vehicle 3 will take due to the resulting vector 8 corresponding to the vector sum 8.
  • the resulting vector 8 considers the direction of the driving vehicle 3 and gives a correction signal to the direction in which the vehicle 3 is driving.
  • the resulting vector 8 indicates the recommended track directly, and the vehicle 3 will process a correction of its direction to follow the recommended track 7.
  • Figure 8 is a variation to the embodiment described with reference to figure 7 .
  • a sensor which is situated outside the frozen puddle 13 sends a repelling signal 6b to the vehicle 3 or to the central server 4 for further processing.
  • the processing of the information gathered by the sensors 1 detecting the frozen puddle 13 has been exchanged with the further sensors 1 outside the frozen puddle, possibly after further processing by the central server 4. From the processing of the information, it has been determined that it is appropriate that also a sensor 1 outside the frozen puddle 13 sends a repelling signal 6b. This might be appropriate, because thereby the frozen puddle 13 can be avoided with a larger security distance between the recommended track 7 and the frozen puddle 13.
  • one component points slightly in a reverse direction.
  • the repelling signal vector contains also information about deceleration.
  • the present invention claims a system for providing traffic information.
  • the system comprises a network of sensors 1 provided in and/or on one or more roads 2 or road segments, wherein the network of sensors 1 is adapted to gather information, wherein one or more of the sensors 1 of the network of sensors 1 and/or a central server 4 are adapted to process the gathered information. Further features of the system correspond to the features of the described method.
  • a server (4) is implemented for providing traffic information, wherein the server (4) is adapted to process information gathered by one or more sensors (1) of a network of sensors (1) provided in and/or on one or more roads (2) or road segments.
  • the server (4) is also referred to as a central server (4).
  • a vehicle (3), a sensor (1), a relay (5) and/or a central server (4) might comprise a computer or processor used to execute a software program adapted to execute instructions adapted to the implementation of the vehicle (3), the sensor (1), the relay (5) and/or the central server (4), respectively.
  • a vehicle (3), a sensor (1), a relay (5) and/or a central server (4) might be equipped with a memory for storing data and/or instructions, in particular program code.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • the functions of a computer may be employed in a digital signal processor, micro-controller, or general-purpose computer implemented as a single device or integrated in a computer network.
  • a computer may comprise program code embodied in tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed in the computer, the computer becomes an apparatus used for practicing the invention.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
EP14290156.0A 2014-05-28 2014-05-28 Verfahren zur Bereitstellung von Verkehrsinformationen Withdrawn EP2950290A1 (de)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP14290156.0A EP2950290A1 (de) 2014-05-28 2014-05-28 Verfahren zur Bereitstellung von Verkehrsinformationen

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP14290156.0A EP2950290A1 (de) 2014-05-28 2014-05-28 Verfahren zur Bereitstellung von Verkehrsinformationen

Publications (1)

Publication Number Publication Date
EP2950290A1 true EP2950290A1 (de) 2015-12-02

Family

ID=51063380

Family Applications (1)

Application Number Title Priority Date Filing Date
EP14290156.0A Withdrawn EP2950290A1 (de) 2014-05-28 2014-05-28 Verfahren zur Bereitstellung von Verkehrsinformationen

Country Status (1)

Country Link
EP (1) EP2950290A1 (de)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102020208189A1 (de) 2020-06-30 2021-12-30 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zur Bestimmung mindestens eines Fahrbahnkontaktparameters eines Fahrzeugs
US11454525B2 (en) 2018-10-19 2022-09-27 Robert Bosch Gmbh Vehicle sensor field calibration utilizing other vehicles
CN115565378A (zh) * 2022-12-01 2023-01-03 四川振函创新智能科技有限公司 高速公路事件情报信息动态发布方法、系统、终端及介质
JP2024026968A (ja) * 2022-08-16 2024-02-29 株式会社デンソーテン 情報処理装置、情報処理方法、およびプログラム

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070208498A1 (en) * 2006-03-03 2007-09-06 Inrix, Inc. Displaying road traffic condition information and user controls

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070208498A1 (en) * 2006-03-03 2007-09-06 Inrix, Inc. Displaying road traffic condition information and user controls

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11454525B2 (en) 2018-10-19 2022-09-27 Robert Bosch Gmbh Vehicle sensor field calibration utilizing other vehicles
DE102020208189A1 (de) 2020-06-30 2021-12-30 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zur Bestimmung mindestens eines Fahrbahnkontaktparameters eines Fahrzeugs
EP3932767A1 (de) 2020-06-30 2022-01-05 Volkswagen Ag Verfahren und vorrichtung zur bestimmung mindestens eines fahrbahnkontaktparameters eines fahrzeugs
JP2024026968A (ja) * 2022-08-16 2024-02-29 株式会社デンソーテン 情報処理装置、情報処理方法、およびプログラム
CN115565378A (zh) * 2022-12-01 2023-01-03 四川振函创新智能科技有限公司 高速公路事件情报信息动态发布方法、系统、终端及介质
CN115565378B (zh) * 2022-12-01 2023-03-07 四川振函创新智能科技有限公司 高速公路事件情报信息动态发布方法、系统、终端及介质

Similar Documents

Publication Publication Date Title
JP7048682B2 (ja) 隊列走行管理システム、隊列走行管理方法及び隊列走行管理プログラム
US8781675B2 (en) Electric power transmission reception system
JP6312304B2 (ja) 位置測定方法、自己位置測定装置及び車載器
US10422649B2 (en) Autonomous driving sensing system and method
US20190294167A1 (en) System and method for optimizing autonomous vehicle capabilities in route planning
AU2011239314B9 (en) Method for determining the distance of a vehicle from a radio beacon and radio beacon for this purpose
CN102333687B (zh) 用于轨道车辆的装置和方法
EP2950290A1 (de) Verfahren zur Bereitstellung von Verkehrsinformationen
CN108335381A (zh) 车辆传感器健康状况监测
EP3088219B1 (de) Reifensensorbasiertes laufleistungsverfolgungssystem und verfahren
RU2018125029A (ru) Карта сцепления транспортного средства для автономных транспортных средств
CN107614353A (zh) 建立和管理列车编组的系统和方法
CN109637137A (zh) 基于车路协同的交通管理系统
KR20080049082A (ko) 지능형 교통정보 시스템 및 방법
SE1351131A1 (sv) Styrenhet och metod för att reglera ett fordon i ett fordonståg
US9286798B2 (en) Speeding enforcement method of vehicle using wireless communications
CA2850287A1 (en) Train control system
SE536548C2 (sv) System och metod för reglering av fordon i ett fordonståg
CN103927870A (zh) 一种基于多个震动检测传感器的车辆检测装置
US20120200431A1 (en) Traffic monitoring system and method
JP6352010B2 (ja) 列車接近警報システムおよび携帯端末
JPH09102055A (ja) 移動体用通信制御方法
JP6600823B2 (ja) 路側装置、サーバ装置、車載装置、隊列走行判定方法及び交通情報予測システム
WO2019190818A1 (en) System and method for operating drones under micro-weather conditions
WO2012013228A1 (en) A method and a system for monitoring traffic of vehicles

Legal Events

Date Code Title Description
AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20160602

RBV Designated contracting states (corrected)

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

RAP1 Party data changed (applicant data changed or rights of an application transferred)

Owner name: ALCATEL LUCENT

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20190527

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20210727