WO2021233699A1 - Procédé de prédiction d'une probabilité d'au moins un espace de stationnement libre pour véhicules automobiles dans une installation de repos, et système de prédiction d'espace de stationnement - Google Patents

Procédé de prédiction d'une probabilité d'au moins un espace de stationnement libre pour véhicules automobiles dans une installation de repos, et système de prédiction d'espace de stationnement Download PDF

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Publication number
WO2021233699A1
WO2021233699A1 PCT/EP2021/062191 EP2021062191W WO2021233699A1 WO 2021233699 A1 WO2021233699 A1 WO 2021233699A1 EP 2021062191 W EP2021062191 W EP 2021062191W WO 2021233699 A1 WO2021233699 A1 WO 2021233699A1
Authority
WO
WIPO (PCT)
Prior art keywords
parking space
motor vehicle
probability
rest area
free parking
Prior art date
Application number
PCT/EP2021/062191
Other languages
German (de)
English (en)
Inventor
Simon Ellwanger
Friedrich Niehaus
Stefan Engelen
Ottmar Gehring
Ralf Traub
Original Assignee
Daimler Ag
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 Daimler Ag filed Critical Daimler Ag
Publication of WO2021233699A1 publication Critical patent/WO2021233699A1/fr

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
    • G08G1/146—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas where the parking area is a limited parking space, e.g. parking garage, restricted space
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • the invention relates to a method for predicting a probability of at least one free parking space for motor vehicles in a rest area by means of a parking space prediction system according to the preamble of claim 1.
  • the invention also relates to a parking space prediction system.
  • truck drivers have to take breaks at precisely defined times. For example, these truck drivers must take a corresponding break after four hours of driving. During a planning phase of the corresponding driving order, information for rest areas, which are available for the truck driver, is therefore important.
  • EP 2 953 111 A1 relates to a method and a device for determining free parking spaces for truck parking spaces and notifying truck drivers.
  • At least one central server and a large number of trucks participating in the process are connected via a wireless, bidirectional communication device by means of a server module and a truck module in each case carried in a truck.
  • Position data and information on the capacity of truck parking spaces are stored and / or generated in the central server.
  • Position data of the parking positions are recorded in a parked truck and transmitted to the server.
  • the server uses such position data from several trucks to determine the number of process-relevant trucks parked and, accordingly, the occupied parking spaces on a specific truck parking lot.
  • US 2014/0266800 A1 describes an architecture that uses crowd-sourced parking-related information to calculate the probability of finding parking spaces on certain street sections, parking lots and / or in larger geographical areas.
  • the parking-related information originating from the crowd can be obtained from geolocation traces.
  • This approach uses a location trace mining method to calculate the likelihood of finding parking spaces on specific street segments, parking lots and / or in larger geographic areas.
  • the location traces can be evaluated in order to classify parking spaces as public, private and semi-private.
  • the location traces can be evaluated in order to derive the times and dates at which a vehicle is allowed to park at a certain location.
  • DE 102016003262 A1 relates to a method for determining free parking spaces for a vehicle, in particular a truck, with information determined about free parking spaces in a predetermined parking area being output to a vehicle occupant of the vehicle.
  • Data on the specified parking area, positions of vehicles driving in the vicinity of the parking area, positions of vehicles parked on the specified parking area and an expected parking period of the parked vehicles are recorded, with currently free or potentially vacant parking options on the given depending on the recorded data Parking area are determined, possible upcoming successive parking processes of the vehicles driving in the vicinity of the parking area and vehicles parked on the parking area are determined, and the information determined is output in the vehicle.
  • the object of the present invention is a method and a
  • One aspect of the invention relates to a method for predicting a probability of at least one free parking space for a motor vehicle in a rest area by means of a parking space prediction system, in which a motor vehicle characterizing parameter is detected and transmitted to an electronic computing device of the parking space prediction system external to the vehicle, the probability of at least one free parking space for vehicles being predicted by means of the electronic computing device external to the vehicle as a function of the transmitted characterizing parameter.
  • a driving dynamics parameter of the motor vehicle is detected as a parameter characterizing the motor vehicle and the probability of at least one free parking space for motor vehicles is predicted as a function of the detected driving dynamics parameter.
  • a low probability of a free parking space is predicted if the motor vehicle has not stopped in the rest area and has left the rest area after a predetermined period of time.
  • the driver of the motor vehicle will only visit the rest area if he is also taking a break. If the motor vehicle drives through the rest area, it can be assumed that no free parking space was available at this point in time. A corresponding probability of a free parking space can therefore be classified as low.
  • a high probability of a free parking space is predicted if the motor vehicle has stopped in the rest area and has left the rest area after a further predetermined period of time.
  • the further predetermined time span can be, for example, five minutes. If the motor vehicle remains within the rest area for more than five minutes, it can be assumed that a break has been taken. This in turn is due to the fact that a free parking space was available for the motor vehicle. This information can then in turn be transmitted to the electronic computing device external to the vehicle, so that a corresponding prediction can be made for a free parking space at the rest area.
  • the driving dynamics parameters of the motor vehicle are also recorded.
  • a Speed of the motor vehicle a current position of the motor vehicle, in particular the moving motor vehicle, or so-called “driver card” information can be used.
  • this enables a detection to be carried out, for example, that a motor vehicle has driven into an area with potential parking spaces or that this will soon be the case.
  • the following three possibilities can then be determined, for example: If the motor vehicle does not stop within the rest area and leave the rest area again within a short time, it cannot be concluded that there are no free parking spaces. Should the motor vehicle stop and begin with the break time, the start of the break can be determined. If the motor vehicle has stopped and the end of the break time will soon be recorded, the end of the break can be expected.
  • the course of the speed of the motor vehicle in particular over a large number of other motor vehicles, over time and GPS position data, can determine the probability of at least one free parking space.
  • driver card information can also be used, this being collected over time, in particular from a large number of motor vehicles. This data is then used to learn, based on fleet data, which parking spaces are occupied at which times. This makes it possible to predict a probability as to whether and when a parking space will be free in a certain rest area or in a certain rest area. This data is then in turn forwarded to the individual vehicles in the fleet to optimize route planning and updated at any time with the help of further fleet data.
  • the driver card information contains, in particular, information about the driving history of the motor vehicle with the driver. In particular, the start of the journey time and corresponding breaks can be or will be stored on the driver card.
  • the motor vehicle is designed as a truck.
  • the truck driver can use the parking lot prediction system to reliably determine whether a free parking space is free for a corresponding break within the rest area that can be approached in the future.
  • a rest area is to be understood in particular as a parking lot for trucks.
  • a rest area can be used as a rest area on a motorway or be viewed as a truck stop on a country road.
  • the rest area is defined in particular by the possibility of a longer stay, for example 30 minutes.
  • a speed during a drive through of the motor vehicle within the rest area is recorded as a driving dynamics parameter. If, for example, the speed does not change noticeably when driving through, in particular if it remains high, it can be concluded that there is no free parking space available for the user of the motor vehicle.
  • the driver “drives through” the rest area and has to go to the next rest area, for example. This can be used to record that at a certain point in time the rest area was full and no free parking space was available for other vehicles. This in turn can then be evaluated accordingly and transmitted to the parking space forecast system. As a result, a reliable prediction of a probability for at least one free parking space can be realized.
  • a current position of the motor vehicle is recorded as the driving dynamics parameter while the motor vehicle is driving through the rest area.
  • the position in front of the rest area can also be recorded.
  • travel times of a user of the motor vehicle are recorded as the driving dynamics parameter.
  • the corresponding driving times or driving times of the driver of the motor vehicle are recorded. For example, this can be done using a “driver card”.
  • This data in turn, can be used to predict at which location which driver will take a break. This, in turn, can be incorporated into the corresponding algorithm, so that the free parking spaces can be determined in the future.
  • the specific probability is transmitted to a large number of other motor vehicles.
  • the large number of motor vehicles can also be referred to as a fleet.
  • the corresponding The probability of a free parking space can then be transferred to the fleet so that it can reliably plan its route and its break times along this route.
  • the certain probability is determined as a function of a time of day and / or of a weekday and / or of a location of the rest area.
  • a time of day and / or of a weekday and / or of a location of the rest area can also be taken into account.
  • vacation times or construction site times or waiting times at border crossings can also be taken into account.
  • driving ban times or driving ban days can also be taken into account.
  • a free parking space in a respective rest area can be reliably predicted at different times of the day or weekdays.
  • Another aspect of the invention relates to a parking space prediction system for predicting a probability of at least one free parking space for motor vehicles in a rest area, with at least one electronic computing device external to the vehicle, the parking space prediction system being designed to carry out a method according to the preceding aspect.
  • the method is carried out by means of the parking space prediction system.
  • the parking space prediction system has objective features which enable the method and an advantageous embodiment thereof to be carried out.
  • Show: 1 shows a schematic plan view of an embodiment of a parking space prediction system
  • FIG. 2 shows a schematic time-speed diagram of a motor vehicle.
  • the parking space prediction system 10 is designed to predict a probability 12 for at least one free parking space 14 for motor vehicles 16, 18 in a rest area 20 and has at least one electronic computing device 22 external to the motor vehicle.
  • a parameter characterizing a motor vehicle 16 is recorded and transmitted to an electronic computing device 22 of the parking space prediction system 10 external to the vehicle 12 is predicted for at least one free parking space 14 for motor vehicles 16, 18.
  • a driving dynamics parameter v, P of the motor vehicle 16 is recorded as a parameter characterizing the motor vehicle 16 and the probability 12 of at least one free parking space 14 for motor vehicles 16, 18 is predicted as a function of the recorded driving dynamics parameter v, P .
  • a first motor vehicle 16 has already entered the rest area 20.
  • Another motor vehicle 18 is still on a street, for example a motorway.
  • v the driving dynamics parameter
  • the probability 12 of whether a free parking space 14 can be found within the rest area 20 can now be determined. This in turn can then be transmitted to the further motor vehicle 18, for example, so that the latter knows whether a free parking space 14 is available within the rest area 20 when the further motor vehicle 18 drives into the rest area 20.
  • the motor vehicles 16, 18 are designed in particular as trucks.
  • a speed v is recorded as the driving dynamics parameter v, P while the motor vehicle 16 is driving through the rest area 20.
  • a current position P of the motor vehicle 16 while the motor vehicle 16 is driving through within the rest area 20 can be recorded as the driving dynamics parameter v, P.
  • travel times of a user of motor vehicle 16 are recorded as driving dynamics parameters v, P.
  • the certain probability 12 can in particular be transmitted to a large number of other motor vehicles 18, which can in particular be referred to as a fleet, wherein, for example, the certain probability 12 also depends on a time of day and / or on a weekday and / or on a location of the rest area 20 is determined.
  • a low probability is predicted if the motor vehicle 16 has not stopped in the rest area 20 and has left the rest area 20 after a predetermined period of time t. This is also shown in particular in FIG. 2.
  • a high probability 12 of a free parking space 14 is predicted when the motor vehicle 16 has stopped in the rest area 20 and has not left the rest area 20 after a further predetermined time period t. It can further be provided that a stopping time of the motor vehicle 16 within the rest area 20 is taken into account in the prediction of the probability 12.
  • an estimation algorithm can thus be generated on the basis of fleet management, which estimates how high the probability 12 is that there is a free parking space 14 at the rest area 20 or whether the rest area 20 is overcrowded.
  • the algorithm is supplied with corresponding data from motor vehicles 16, 18, in particular in real time, so that probability 12 can also be determined in real time.
  • the speed v in particular over a period of time, is considered, as well as the position P, which in particular is a global one Navigation satellite system position can be.
  • the position P can in particular be non-personalized.
  • so-called “driver card” information can also be taken into account in order to determine that, for example, the motor vehicle 16 has driven into the rest area 20.
  • this “driver card” information can be used, for example, to estimate when the motor vehicle 16 will leave the rest area 20 again.
  • the probability prediction can be updated in real time and learned further, for example on the basis of feedback from the corresponding users of the motor vehicles 16, 18.
  • This algorithm is embodied in particular on a backend, the backend in particular representing the electronic computing device 22 external to the vehicle. The motor vehicles 16, 18 are then in turn in communication with this backend.
  • FIG. 2 shows a schematic time-speed diagram.
  • FIG. 2 shows in particular the case that the motor vehicle 16 drives into the rest system 20 at a first speed v1 at time t1.
  • the motor vehicle 16 brakes to the speed v2 within the rest area 20.
  • point in time t2 which is in particular a short time after point in time t1, for example only five minutes, motor vehicle 16 accelerates again to speed v1.
  • the motor vehicle 16 has driven into the rest area 20, which can be determined in particular on the basis of the position P, has stopped, which in turn can be determined on the basis of the speed v , and the “Driver Card” information can be used to predict that a break will soon be taken. In particular, it can be used to determine the start of the break.
  • the motor vehicle 16 is in the break and the legally required break time is about to expire.
  • the motor vehicle 16 is below a predetermined threshold value for breaks, for example that a break must be carried out within the next fifteen minutes. If the motor vehicle 16 then does not drive into a corresponding rest area 20, which can be determined, for example, on the basis of the position P and the speed v of the motor vehicle 16, a full rest area 20 can be determined.
  • the electronic computing device 22 can also be supplied with corresponding information over time, so that the estimate of the probability 12 for a free parking space 14 can be updated and transmitted to the motor vehicles 16, 18.
  • the invention shows a prediction of free parking spaces 14 in real time through the use of “on-board” data and the use of an “off-board” learning algorithm within the electronic computing device 22.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un procédé de prédiction d'une probabilité (12) d'au moins un espace de stationnement libre (14) pour véhicules automobiles (16, 18) dans une installation de repos (20) au moyen d'un système de prédiction d'espace de stationnement (10), dans lequel procédé un paramètre caractérisant un véhicule automobile (16) est acquis et transmis à un dispositif informatique électronique externe de véhicule automobile (22) du système de prédiction d'espace de stationnement (10), dans lequel, au moyen du dispositif informatique électronique externe au véhicule automobile (22), la probabilité (12) d'au moins un espace de stationnement libre (14) pour véhicules automobiles (16, 18) est prédite en fonction du paramètre de caractérisation transmis, un paramètre de dynamique de conduite (v, P) du véhicule automobile (16) est acquis en tant que paramètre caractérisant le véhicule automobile (16), et la probabilité (12) d'au moins un espace de stationnement libre (14) pour des véhicules automobiles (16, 18) est prédite en fonction du paramètre de dynamique de conduite acquis (v, P). L'invention concerne en outre un système de prédiction d'espace de stationnement (10).
PCT/EP2021/062191 2020-05-19 2021-05-07 Procédé de prédiction d'une probabilité d'au moins un espace de stationnement libre pour véhicules automobiles dans une installation de repos, et système de prédiction d'espace de stationnement WO2021233699A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020002997.3A DE102020002997A1 (de) 2020-05-19 2020-05-19 Verfahren zum Vorhersagen einer Wahrscheinlichkeit für zumindest einen freien Parkplatz für Kraftfahrzeuge in einer Rastanlage, sowie Parkplatzvorhersagesystem
DE102020002997.3 2020-05-19

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WO2021233699A1 true WO2021233699A1 (fr) 2021-11-25

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013083307A1 (fr) * 2011-12-05 2013-06-13 Skobbler Gmbh Procédé de navigation lors de la recherche d'une place de stationnement
US20140266800A1 (en) 2013-03-14 2014-09-18 Microsoft Corporation Crowd-sourced parking advisory
EP2953111A1 (fr) 2014-06-06 2015-12-09 MAN Truck & Bus AG Procédé et dispositif destinés à déterminer des aires de stationnement libres sur des parkings de camions et communication au conducteur de camion
DE102016003262A1 (de) 2016-03-16 2016-09-29 Daimler Ag Verfahren zur Ermittlung freier Parkmöglichkeiten für ein Fahrzeug

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013083307A1 (fr) * 2011-12-05 2013-06-13 Skobbler Gmbh Procédé de navigation lors de la recherche d'une place de stationnement
US20140266800A1 (en) 2013-03-14 2014-09-18 Microsoft Corporation Crowd-sourced parking advisory
EP2953111A1 (fr) 2014-06-06 2015-12-09 MAN Truck & Bus AG Procédé et dispositif destinés à déterminer des aires de stationnement libres sur des parkings de camions et communication au conducteur de camion
DE102016003262A1 (de) 2016-03-16 2016-09-29 Daimler Ag Verfahren zur Ermittlung freier Parkmöglichkeiten für ein Fahrzeug

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