DE102020002997A1 - Method for predicting a probability of at least one free parking space for motor vehicles in a rest area, as well as parking space prediction system - Google Patents

Abstract

The invention relates to a method for predicting a probability (12) for at least one free parking space (14) for motor vehicles (16, 18) in a rest area (20) by means of a parking space prediction system (10), in which a parameter characterizing a motor vehicle (16) is detected and transmitted to an electronic computing device (22) external to the vehicle in the parking space prediction system (10), the probability (12) of at least one free parking space (14) for motor vehicles being available by means of the electronic computing device (22) external to the vehicle as a function of the transmitted characterizing parameters (16, 18) is predicted, with a driving dynamics parameter (v, P) of the motor vehicle (16) being recorded as a parameter characterizing the motor vehicle (16) and the probability (12 ) for at least one free parking space (14) for motor vehicles (16, 18 ) is predicted. The invention also relates to a parking space prediction system (10).

Description

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.

It is known that, for example in the European Union, 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.

the 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. By comparing with the server data on the capacity, the server determines the probability of any free parking spaces and communicates this information about the probability of free parking spaces on this truck parking lot to drivers of trucks that are relevant to the process.

The object of the present invention is to create a method and a parking space prediction system by means of which an improved prediction for at least one free parking space within a rest area for vehicles can be realized.

This object is achieved by a method and by a parking space prediction system according to the independent patent claims. Advantageous embodiments are specified in the subclaims.

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 parameter characterizing a motor vehicle is recorded and transmitted to an electronic computing device of the parking space prediction system external to the vehicle electronic computing device, depending on the transmitted characterizing parameters, the probability of at least one free parking space for motor vehicles is predicted.

It is provided that 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.

This enables a reliable prediction of the probability of at least one parking space for further motor vehicles to be carried out on the basis of the driving dynamics parameter.

In other words, it is provided that, in addition to pure position data, the driving dynamics parameters of the motor vehicle are also recorded. For example, 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. In particular, 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, for example, 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.

In particular, 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. Further, so-called “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 can a probability can be predicted 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.

It is preferably provided that the motor vehicle is designed as a truck. In particular, should a legally prescribed rest time be provided for the driver of the 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 the present case in particular as a parking lot for trucks. For example, such a rest area can be viewed as a rest area on a motorway or 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.

According to an advantageous embodiment, 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.

It is also advantageous if a current position of the motor vehicle is recorded as the driving dynamics parameter while the motor vehicle is driving through the rest area. In particular, the position in front of the rest area can also be recorded. In particular, it can be concluded whether a motor vehicle will soon take a corresponding break. This in turn can be used to reliably predict the probability of a free parking space within the rest area.

It is also advantageous if travel times of a user of the motor vehicle are recorded as the driving dynamics parameter. In the case of trucks in particular, 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.

It has also proven to be advantageous if the specific probability is transmitted to a large number of other motor vehicles. In particular, the large number of motor vehicles can also be referred to as a fleet. The corresponding probability of a free parking space can then be transmitted to the fleet so that it can reliably plan its route and its break times along this route.

In a further advantageous embodiment, 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. Furthermore, for example, vacation times or construction site times or waiting times at border crossings can also be taken into account. Furthermore, driving ban times or driving ban days can also be taken into account. As a result, a free parking space in a respective rest area can be reliably predicted at different times of the day or weekdays.

Furthermore, it has proven to be advantageous if a low probability of a free parking space is predicted if the motor vehicle has not stopped in the rest area and after one has left the rest area for a specified period of time. In particular, 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.

It is also advantageous if a high probability of a free parking space is predicted when the motor vehicle has stopped in the rest area and has left the rest area after a further predetermined period of time. In particular, 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.

In a further advantageous embodiment, a stopping time of the motor vehicle within the rest area is taken into account when predicting the probability. In particular, corresponding rest periods that a user of the motor vehicle must take can be taken into account. If, for example, the motor vehicle drives into the rest area and takes a break, it can be taken into account for the given time break, which is for example legally prescribed, that no further motor vehicle can park there as long as this parking space is occupied.

A reliable prediction for a free parking space within the rest area can thus be carried out.

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. In particular, the method is carried out by means of the parking space prediction system.

Advantageous refinements of the method are to be regarded as advantageous refinements of the parking space prediction system. For this purpose, the parking space prediction system has objective features which enable the method and an advantageous embodiment thereof to be carried out.

Further advantages, features and details of the invention emerge from the following description of preferred exemplary embodiments and on the basis of the drawings. The features and combinations of features mentioned above in the description as well as the features and combinations of features mentioned below in the description of the figures and / or shown alone in the figures can be used not only in the respectively specified combination, but also in other combinations or on their own, without the scope of Invention to leave.

• 1 eine schematische Draufsicht auf eine Ausführungsform eines Parkplatzvorhersagesystems; und
• 2 ein schematisches Zeit-Geschwindigkeits-Diagramm eines Kraftfahrzeugs.

Show:

• 1 a schematic plan view of an embodiment of a parking space prediction system; and
• 2 a schematic time-speed diagram of a motor vehicle.

Identical or functionally identical elements are provided with the same reference symbols in the figures.

1 shows in a schematic plan view an embodiment of a parking space prediction system 10 . The parking space forecast system 10 is for predicting a probability 12th for at least one free parking space 14th for automobiles 16 , 18th in a rest area 20th and has at least one electronic computing device external to the vehicle 22nd on.

In the process of predicting the probability 12th for at least one parking space 14th for automobiles 16 , 18th of the parking space prediction system 10 becomes a a motor vehicle 16 characterizing parameters are recorded and sent to an electronic computing device external to the vehicle 22nd of the parking space prediction system 10 transmitted, by means of the electronic computing device external to the vehicle 22nd the probability 12th for at least one free parking space 14th for automobiles 16 , 18th is predicted.

It is provided that as a the motor vehicle 16 characterizing parameter a driving dynamics parameter v , P. of the motor vehicle 16 is detected and depending on the detected driving dynamics parameters v , P. the probability 12th for at least one free parking space 14th for automobiles 16 , 18th is predicted.

In particular, it is shown here that a first motor vehicle 16 already in the rest area 20th has retracted. Another motor vehicle 18th is still on a street, for example a motorway. Based on the driving dynamics parameters v , P. of the motor vehicle 16 can now the probability 12th determine whether there is a free parking space 14th within the rest area 20th is to be found. This in turn can then, for example, be applied to the further motor vehicle 18th transmitted so that it knows whether there is a free parking space 14th within the rest area 20th is available when the additional motor vehicle 18th in the rest area 20th comes in.

The motor vehicles 16 , 18th are designed in particular as trucks.

In particular, it can be provided that the driving dynamics parameter v , P. a speed v while the motor vehicle is passing through 16 within the rest area 20th is captured. It can also be used as a driving dynamics parameter v , P. a current position P. of the motor vehicle 16 while the motor vehicle is passing through 16 within the rest area 20th are recorded. It can also be provided that the driving dynamics parameters v , P. Driving times of a user of the motor vehicle 16 are recorded.

The definite probability 12th can in particular be used on a large number of other motor vehicles 18th are transmitted, which can in particular be referred to as a fleet, with, for example, also the specific probability 12th depending on a time of day and / or on a weekday and / or on a location of the rest area 20th is determined.

It can further be provided that a low probability is predicted when the motor vehicle 16 not in the rest area 20th has stopped and after a predetermined period of time t the rest area 20th has left. This is particularly also in the 2 shown.

It has also been shown to be beneficial when a high probability 12th for a free parking space 14th is predicted when the motor vehicle 16 in the rest area 20th has stopped and after a further predetermined period of time t the rest area 20th has not left. It can also be provided that a stopping time of the motor vehicle 16 within the rest area 20th in predicting the probability 12th is taken into account.

In particular, an estimation algorithm can thus be generated on the basis of fleet management, which estimates how high the probability is 12th is that a free parking space 14th at the rest area 20th is present or whether the rest area 20th is crowded. The algorithm is used in particular in real time with corresponding data from motor vehicles 16 , 18th supplied so that in real time also the probability 12th can be determined.

For this purpose, in particular, the speed v , especially over a period of time, considered, as well as the position P. which in particular may be a global navigation satellite system position. Furthermore, the position P. especially be non-personalized. Furthermore, so-called “driver card” information can also be taken into account in order to determine that, for example, the motor vehicle 16 the rest area 20th has driven on. In addition, this “driver card” information can be used, for example, to estimate when the motor vehicle will arrive 16 the rest area 20th again leaves. The probability prediction can be updated in real time and, for example, on the basis of feedback from the corresponding users of the motor vehicles 16 , 18th be learned further. This algorithm is embodied in particular on a backend, the backend in particular being the electronic computing device external to the vehicle 22nd represents. The motor vehicles 16 , 18th are then in turn in communication with this backend.

2 shows a schematic time-speed diagram. the 2 particularly shows the case that the motor vehicle 16 into the rest area at a first speed v1 at time t1 20th comes in. The car 16 brakes to speed v2 within the rest area 20th away. The motor vehicle accelerates at time t2, which is, in particular, a short time after time t1, for example only five minutes 16 back to speed v1. In particular, this can be read in such a way that there is no free parking space 14th within the rest area 20th was to be found. The car 16 was only a short time inside the rest area 20th and has a correspondingly free parking space 14th searched, but not found, which is why at time t2 it drove back onto the freeway, for example, and accelerated again.

In particular, it can thus be shown that the motor vehicle 16 although the rest area 20th has driven on but has not stopped and the rest area 20th has left again in a short time, which suggests no free parking space.

In another, not in 2 illustrated case, it can be, for example, that the motor vehicle 16 the rest area 20th has traveled, which is based in particular on the position P. can be determined who has stopped, which in turn is based on the speed v can be determined, 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.

In a further case it can be determined that, for example, the motor vehicle 16 is taking a break and the legally required break time is about to expire. When the vehicle “starts moving” 16 thing in particular based on speed v of the motor vehicle 16 can be determined, the end of the pause can be determined.

In a further case it can be provided that the motor vehicle 16 is below a predetermined threshold value for breaks, for example that within the next fifteen minutes one Break must be carried out. Should then the motor vehicle 16 not a corresponding rest area passed 20th drive on what, for example, based on the position P. and the speed v of the motor vehicle 16 can be determined, so can a full rest area 20th to be determined.

In particular if different data from different motor vehicles over a predetermined period of time 16 , 18th regarding the free parking spaces 14th were collected and to the electronic computing device external to the vehicle 22nd were transmitted, this can be learned and thus as fleet data for a fleet of motor vehicles 16 , 18th to provide. This enables an estimate of the probability 12th be given when and at what time corresponding rest areas 20th are overcrowded or free parking spaces 14th exhibit. This in turn can then be used in the route planning for the motor vehicles 16 , 18th Move in.

The electronic computing device 22nd can also be supplied with corresponding information over time, so that the estimation of the probability 12th for a free parking space 14th updated to the motor vehicles 16 , 18th can be transferred.

Overall, the invention shows a prediction of free parking spaces 14th in real time through the use of "on-board" data and the use of an "off-board" learning algorithm within the electronic computing device 22nd .

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• EP 2953111 A1 [0003]

Claims (10)

Method for predicting a probability (12) for at least one free parking space (14) for motor vehicles (16, 18) in a rest area (20) by means of a parking space prediction system (10) in which a parameter characterizing a motor vehicle (16) is recorded and on an electronic computing device (22) external to the vehicle of the parking space prediction system (10) is transmitted, the probability (12) of at least one free parking space (14) for vehicles (16, 18) being available by means of the electronic computing device (22) external to the vehicle as a function of the transmitted characterizing parameters ) is predicted, characterized in that 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) as a function of the detected driving dynamics parameter (v, P) for at least one free parking space (14) for motor vehicles (16, 18) v is predicted. Procedure according to Claim 1 , characterized in that a speed (v) while the motor vehicle (16) is driving through within the rest area (20) is recorded as the driving dynamics parameter (v, P). Procedure according to Claim 1 or 2 , characterized in that a current position (P) of the motor vehicle (16) while the motor vehicle (16) is driving through within the rest area (20) is recorded as the driving dynamics parameter (v, P). Method according to one of the preceding claims, characterized in that travel times of a user of the motor vehicle (16) are recorded as driving dynamics parameters (v, P). Method according to one of the preceding claims, characterized in that the specific probability (12) is transmitted to a large number of further motor vehicles (16, 18). Method according to one of the preceding claims, characterized in that the certain probability (12) is determined as a function of a time of day and / or of a weekday and / or of a location of the rest area (20). Method according to one of the preceding claims, characterized in that a low probability (12) for a free parking space (14) is predicted if the motor vehicle (16) has not stopped in the rest area (20) and after a predetermined period of time (t) has left the rest area (20). Method according to one of the preceding claims, characterized in that a high probability (12) for a free parking space (14) is predicted when the motor vehicle (16) has stopped in the rest area (20) and after a predetermined period of time (t) the Has not left the rest area (20). Method according to one of the preceding claims, characterized in that a stopping time of the motor vehicle (16) within the rest area (20) is taken into account when predicting the probability (12). Parking space prediction system (10) for predicting a probability (12) for at least one free parking space (14) for motor vehicles (16, 18) in a rest area (20), with at least one electronic computing device (22) external to the vehicle, the parking space prediction system (10) for Carrying out a method according to one of the Claims 1 until 9 is trained.

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