WO2017149016A1 - Procédé et dispositif de production d'au moins une information de prédiction dans un véhicule - Google Patents

Procédé et dispositif de production d'au moins une information de prédiction dans un véhicule Download PDF

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Publication number
WO2017149016A1
WO2017149016A1 PCT/EP2017/054779 EP2017054779W WO2017149016A1 WO 2017149016 A1 WO2017149016 A1 WO 2017149016A1 EP 2017054779 W EP2017054779 W EP 2017054779W WO 2017149016 A1 WO2017149016 A1 WO 2017149016A1
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WIPO (PCT)
Prior art keywords
information
vehicle
prediction information
determined
driver
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.)
Ceased
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PCT/EP2017/054779
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German (de)
English (en)
Inventor
Jan Winhuysen
Andreas Binner
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Novero GmbH
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Novero GmbH
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Publication of WO2017149016A1 publication Critical patent/WO2017149016A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle

Definitions

  • the invention relates to a method and a device for generating at least one prediction information in a vehicle.
  • eHorizon One known technology for generating prediction information in a vehicle is the so-called “electronic horizon” (eHorizon), which generally refers to a determination of desired route-related information along a stretch of road, such as inclines, bends and traffic signs along the route This information can in turn be used to control vehicle assistance systems or output to a driver.
  • Driver assistance systems or the behavior of actuators such as brake or steering can be prepared for upcoming traffic situations even before
  • Vehicle sensors capture the situation. It is also described that dynamic events such as weather, accidents or traffic jams can be taken into account.
  • the basis of such a system is usually static map data, e.g. can be processed by a navigation device of the vehicle.
  • Congestion information can be linked.
  • Prediction information in a vehicle in particular in a motor vehicle.
  • the method can be carried out by means of an evaluation device.
  • the evaluation device can be designed as a computing device, in particular as a microcontroller, or comprise such.
  • the evaluation device can be further provided by a control unit of the vehicle.
  • At least one input information is detected or determined.
  • the detection of input information may mean that the
  • Input information sensor-based is detected. Determining a
  • Input information may designate a computational determination. Also, the input information can be transmitted to the evaluation device. Exemplary input information will be explained in more detail below.
  • the initial situation may designate a situation or state from which the prediction information is determined.
  • the initial situation can be a current starting situation, ie a situation at a current time.
  • the starting situation can be determined, for example, as a function of at least one assignment from one starting situation to at least one input variable.
  • These Assignment may be previously known. For example, such an assignment can be learned or retrieved.
  • An initial situation can be described for example by one or more characteristic (s).
  • the starting situation may be given by a vector comprising one or more parameters.
  • an association of one or more characteristic (s) to at least one input information may exist by which one or more characteristic (s) of at least one
  • Input information can be assigned.
  • An assignment can also exist from an initial situation to at least one input information, by means of which the initial situation of the at least one input information can be assigned.
  • an input information is a parameter.
  • a parameter can be a current date.
  • Another parameter may, for example, be a current position in a global reference coordinate system, for example a GPS position.
  • a parameter is a quantized input variable.
  • input variables from a predetermined value interval can each be assigned the same quantized value.
  • an input variable may be the current time, with a quantized input corresponding to the time of day “morning,” “noon,” “evening,” and "night.”
  • the at least one prediction information is determined from at least one vehicle-specific assignment of at least one prediction information to at least one starting situation.
  • the at least one prediction information from at least one vehicle driver-specific assignment of at least one prediction information to at least one
  • At least one prediction information from at least one vehicle-specific and / or vehicle driver-specific assignment of at least one prediction information to the at least one input information is determined as a function of one or more input information / s.
  • the at least one vehicle-specific and / or vehicle driver-specific assignment can provide a memory.
  • the assignment can be in particular a learned assignment.
  • This information can then be assigned to the starting situation.
  • This association can also be stored. For example, it is possible to determine information about a destination that originates from an initial situation of the vehicle and / or
  • information may include information about (average) speeds along a traveled route that has been driven out of a particular starting situation.
  • the information can be determined by calculation or sensory. After learning, that is, "remembering", the learned information can be determined as prediction information depending on a starting situation, which can also be referred to as "remembering".
  • driver identity This can be done by methods known to those skilled in the art.
  • Prediction information may thus denote information associated with a future time or period. This time or period may indicate a time / period when the information becomes relevant.
  • an assignment of the future time or period to the prediction information and vice versa can be determined. Furthermore, an assignment of a future spatial position of the vehicle to the prediction information and vice versa can be determined.
  • the prediction information can be a vehicle-specific and / or
  • the driver-specific amount of data are assigned by at least one future period or time one or more information.
  • the prediction information can be information which is assigned to a future point in time / time and optionally to a future position when the vehicle is traveling along a selected route.
  • An input information and / or a prediction information can via a
  • Vehicle communication system such as a bus system, in particular a CAN bus
  • the vehicle are transmitted.
  • the information may be coded in corresponding messages.
  • prediction information may be information about a position of a predicted destination of the vehicle. Also, the prediction information may be information about a route to the destination. Also, prediction information may include information about future (average) speeds along a selected or predicted driving route.
  • different predicted speeds may be assigned to different future positions and / or future time periods.
  • the prediction information is vehicle-specific and / or
  • this can mean, for example, that the destination predicted as a function of the starting situation is a vehicle and / or vehicle driver-specific destination.
  • the (average) speeds predicted as a function of the starting situation may also affect the vehicle and / or vehicle
  • this may mean, for example, that corresponding information has been stored in advance vehicle-specific and / or vehicle driver-specific. For example, a destination selected in the vehicle and / or selected by a particular driver can be determined and stored for different starting situations. It is also possible to determine and store an average speed along a route for each starting situation, in each case one reached by the vehicle and / or by a specific driver this information is assigned to a vehicle and / or driver.
  • This vehicle-specific and / or vehicle-driver-specific speed profile is assigned to an initial situation.
  • the starting situation can be characterized, for example, by at least the parameter "selected driving route".
  • this information can be assigned to an initial situation, wherein the initial situation is characterized, for example, by the fact that a specific route has been selected.
  • the assignment of at least one prediction information to at least one starting situation can be known in advance. It is e.g. It is possible to learn this assignment or retrieve data from another, in particular external, device.
  • a vehicle identity and / or a driver identity is taken into account when generating the at least one prediction information.
  • mappings can be made for several
  • Assignment is used to generate a prediction information.
  • the prediction information can be used to control driver assistance systems.
  • the prediction information for controlling a driver assistance system For example, the prediction information for controlling a driver assistance system
  • the prediction information can also be used for
  • Headlamps and other actuators serve.
  • Travel distance known so a remaining travel time can be determined depending on the predicted speeds. Further, for example, brake actuators can also be put into standby time before a corresponding speed reduction or even activated.
  • the assignment can be stored in a memory device here.
  • Memory device may be a memory device of the vehicle or a vehicle external memory device.
  • an input information is an already generated prediction information.
  • prediction information may be generated, e.g. a position of the destination.
  • a route from a starting location to the destination can then be determined as prediction information.
  • a vehicle and / or vehicle driver-specific speed profile along the route can then be determined as prediction information depending on the route.
  • prediction information may also designate information for generating further prediction information.
  • Use as input information e.g. from a driver. This can e.g. done by operating an input device.
  • An initial situation and / or a prediction information can be determined repeatedly, in particular periodically. Alternatively or cumulatively, it is possible that an initial situation and possibly also a prediction information is determined again if an actually acquired information deviates from a previously determined prediction information by more than a predetermined amount. This is the case, for example, if the vehicle deviates from a predicted driving route. Thus, it may be possible that, after the determination of the prediction information, the correspondingly corresponding and actually setting information is determined in time to detect a deviation between the prediction information and the
  • the method advantageously makes it possible to generate prediction information which has a high quality, in particular a high accuracy, for a specific vehicle and / or a particular vehicle driver.
  • a speed gradient along a specific route be determined not only dependent on static map data, but also depending on the (recent) driving behavior of a vehicle and / or a driver.
  • the prediction information thus determined also enables better control of further driver assistance systems.
  • the input information comprises at least one time information.
  • the input information comprises at least one location information.
  • the input information comprises at least one vehicle identity information and / or at least one
  • the prediction information can be determined depending on a time information and a position information. So it is possible, for example, a
  • Prediction information in dependence on a spatio-temporal assignment are determined.
  • the vehicle and / or vehicle driver specific assignment can also be determined as a spatial-temporal assignment.
  • a vehicle and / or vehicle driver-specific destination can be determined as a function of the current time and the current position. Runs e.g. the vehicle and / or a particular driver on weekdays in a period from 07.30 to 08.30 from his place of residence with known position to his place of work, as prediction information a destination as the position of the
  • Workplace are determined when the current time in the period from 07.30 to 08.30 on a weekday and the current position is in a predetermined position range around the position of the place of residence.
  • a vehicle and / or vehicle driver-specific travel route to the thus determined destination can then be determined as further prediction information.
  • the at least one vehicle and / or vehicle driver-specific prediction information can be determined as a function of time information, but independently of location information.
  • the vehicle and / or vehicle driver specific assignment can also be referred to as time allocation.
  • Vehicle driver from an external device such as a mobile terminal of the driver or an external server device, retrieved.
  • a position assigned to the appointment can then be determined as further prediction information, for example as a destination. The following can then be considered as more
  • Prediction information to be determined a corresponding route. Subsequently, as further prediction information, e.g. a speed course along the route to be determined.
  • Prediction information can thus be determined based on one another or dependent on one another. Thus, e.g. Depending on a starting situation, at least one prediction information is determined, in which case a further starting situation is determined as a function of the starting situation and the prediction information. This can then be assigned via an association another prediction information.
  • the at least one prediction information can be determined as a function of the vehicle and / or driver identity information, but independently of a time information and independently of a location information.
  • the vehicle and / or vehicle driver specific assignment can also be referred to as a factual assignment.
  • information about a fuel card of the vehicle driver can be determined as prediction information.
  • prediction information about a mobile operator of a mobile terminal of the driver and / or the vehicle can be determined as prediction information about a mobile operator of a mobile terminal of the driver and / or the vehicle.
  • This so-called factual information can be learned, for example, or retrieved by a, in particular external, further device.
  • Further prediction information for example, a course of network coverage along the selected route.
  • positions of corresponding filling stations along the selected route can be determined as further prediction information.
  • An input information may also be real-time information. This can be detected for example by a sensor device of the vehicle. Also, real-time information can be retrieved from an external device.
  • Real-time information can be, for example, information about weather conditions
  • Traffic information information about traffic conditions (traffic information) or information about a current vehicle state be (vehicle information). For example, it may be determined whether a driver has activated a sport mode or eco mode of the vehicle.
  • real-time information can also be used to determine the starting situation and / or to determine the prediction information.
  • a determination may in particular also be made on the basis of time of day and / or weekday and / or seasonally dependent. If it is determined, for example, that a vehicle and / or driver drives from his place of residence to the work place on weekdays within a predetermined period and drives from his place of residence to a shopping location at the weekend during this predetermined period, then a weekend day has been identified as a function of the time information in that the position of the place of purchase, instead of the position of the place of work, is determined as prediction information as destination.
  • the prediction information is one
  • Trip profile information may include information about a size or designate an event that adjusts along a predicated driving route.
  • a size or an event can hereby be set along the entire or only along a section of the route or at a point of the route.
  • a trip profile information may include one or more of the following information: information about a destination, information about a duration of the journey or an arrival time, information about a route, information about one
  • Travel profile information to be information about the occurrence of congestion, fog, intersections, traffic lights along the route. Also, one can
  • Ride profile information may be information about a road category, where a road category may, for example, designate a "country lane”, “country lane”, “freeway” or another road category.
  • trip profile information may alternatively or cumulatively include further information.
  • a trip profile information may be a time-dependent and / or location-dependent and / or environment-dependent trip profile information.
  • vehicle driver specific route to be determined. Furthermore, it is also possible to determine a vehicle-specific and / or vehicle-specific mobile network coverage along the selected route.
  • prediction information may be about a position of so-called points of interest along the route. This can for example also be a position on a gas station, which can be operated with a fuel card of the driver.
  • Trip profile information may in particular be stored information. These may be in a storage device of the vehicle or an external vehicle
  • Memory device such as a storage device of a mobile terminal or an external server device to be stored.
  • Trip profile information from information generated at earlier times e.g. self-learning methods known to the person skilled in the art. This will be explained in more detail below.
  • Trip profile information may also be determined depending on real-time information, such as weather information. For example, average speeds can be determined under certain weather conditions.
  • the trip profile information can also be determined as a function of a current vehicle state. For example, it can be determined whether a sports mode or an eco mode is activated.
  • trip profile information results in an advantageous manner to the vehicle and / or the driver generation of driving-specific
  • the vehicle or vehicle driver specific assignment is a learned assignment.
  • Teaching may in particular mean that one or more input variables (n), at least one starting situation and / or at least one prediction information, in particular a trip profile information, are detected or determined and then stored, e.g. in the form of a database.
  • an assignment of at least one input variable to an initial situation and / or from an initial situation to a prediction information can then be determined, for example via so-called self-learning algorithms, e.g. Neural Networks.
  • self-learning algorithms e.g. Neural Networks.
  • assignments can also be made with alternatives
  • Be determined method for example with rule-based systems.
  • the training can of course be done before using the assignment to determine the prediction information.
  • Other aspects of learning have been explained previously. Thus, for example, continuously and / or during driving a
  • Vehicle a current position, a current time, a current driving speed, a driven route, a selected destination, a current
  • Mobile network coverage, etc. are determined and stored. At a later time, as described above, corresponding allocations can then be determined as a function of this stored data, and the at least one prediction information can be generated as a function of the allocations.
  • the vehicle or vehicle driver specific assignment may be a retrieved assignment. In this case, this may in particular be a non-learned assignment.
  • a resulting prediction information is determined as a function of a plurality of prediction information.
  • the plurality of prediction information may be similar but have different contents.
  • At least one prediction information can, as previously explained, be determined assignment based.
  • Determining the resulting prediction information is a prediction information determined non-assignment based.
  • a plurality of different destinations or a plurality of different routes can be determined as prediction information.
  • a weighting of the various partial information can take place.
  • the weighting may in particular be a priority-based weighting.
  • prediction information can be assigned different priorities.
  • a calendar entry-based prediction information may be assigned a higher priority than a weekday-based prediction information. For example, on a weekday as a destination not the position of the place of work, but the position associated with the calendar entry are determined.
  • the at least one prediction information is additionally determined as a function of at least one non-assignment-based
  • this partial information can also be a prediction information.
  • the prediction information may additionally be in
  • Real-time information e.g. Weather information or traffic information.
  • This information which is not assignment-based, denotes non-vehicle and non-driver-specific information. If, for example, an accident occurs on a route from the place of residence to the place of work for which a vehicle and / or vehicle driver-specific course of travel speeds is known, this traffic information can be taken into account in determining the remaining journey time or the arrival time.
  • driver-specific information with independent of the vehicle and the driver information to determine the prediction information can be linked. This allows a further increase in the quality of the prediction information.
  • the at least one input information is provided by a vehicle sensor or a vehicle-external device.
  • Assignment provided by a vehicle sensor or an off-vehicle device.
  • An off-vehicle device may in particular be a server device. Also, an off-vehicle device, a terminal, such as a mobile phone or a tablet. Further, an off-vehicle device another
  • vehicle-driver-specific assignment is provided by a vehicle-external device, it can be advantageously made possible for vehicle-driver-specific prediction information to be generated in various vehicles.
  • vehicle-driver-specific prediction information for example, the driver-specific assignment on a mobile phone
  • Terminal of the driver are provided, this terminal then provides this assignment in different vehicles.
  • the at least one prediction information can also be transmitted to a vehicle-external device. This can then use the prediction information for their operation.
  • information about a predicted speed profile can be transmitted via a Car2X communication to other vehicles. These can then use the speed information to control their own driving operation.
  • the at least one prediction information is output to a vehicle driver. This can, for example, via a corresponding output device, such as a display device and / or a
  • Audio signal output device done.
  • the proposed method allows the generation of a vehicle and / or vehicle driver-specific time line (personal timeline).
  • a timeline may be given, for example, in the form of a vector, by the future one
  • Time points or periods are associated with information, at least one of this information is a vehicle and / or vehicle driver specific information. This timeline can be repeated, e.g. periodically, be redetermined.
  • the method enables vehicle and / or vehicle driver specific
  • Such maps can then be used to determine prediction information at a later time.
  • a map may designate a data set by which a current and / or a current position is assigned vehicle-specific and / or vehicle-driver-specific information, for example a current speed or a current destination.
  • a device for generating at least one prediction information in a vehicle advantageously makes it possible to carry out a method according to one of the embodiments described in this disclosure.
  • the device is designed such that a corresponding method by means of the device is feasible.
  • the device comprises at least one evaluation device. Furthermore, the evaluation device comprises at least one input interface for transmitting at least one input information to the evaluation device. Next is through the evaluation device.
  • Evaluation device depending on the at least one input information an initial situation determinable.
  • the device may further comprise a memory device, wherein in the
  • the evaluation device may be a microcontroller or include such.
  • the evaluation device may be an evaluation device installed in the vehicle and thus part of the vehicle. For example, can the evaluation device
  • the evaluation device may be an evaluation device external of the vehicle, e.g. an evaluation device of a mobile terminal or a server device.
  • the storage device may also be a storage device of the vehicle and thus a component of the vehicle.
  • the storage device may be an off-board storage device.
  • Storage device can be wired or wireless. For this purpose, methods known to those skilled in the art for data and / or signal transmission can be used.
  • An input interface may be, for example, an interface for receiving data of a vehicle communication system or for communication with a vehicle communication system.
  • the vehicle communication system may include, for example, a vehicle bus system, e.g. a CAN bus or an Automotive Ethernet. About such an interface information from other control devices of the vehicle can be provided.
  • An input interface may also be an interface for receiving information from a mobile terminal or for communicating with such a terminal.
  • This interface may allow, for example, a wired and / or wireless communication with the terminal.
  • Evaluation be transferred. Furthermore, information about a vehicle and / or vehicle driver specific assignment can also be transmitted via this interface. For example, calendar information can be transmitted from a mobile terminal to the evaluation device.
  • the input interface can be an interface for receiving information from an off-vehicle server device or for communication with such a server device.
  • real-time information for example weather information or traffic information, can be transmitted to the evaluation device via such an interface.
  • the interface may be an interface of a Car2X communication. This information from other vehicles or other participants of a Car2X network can be transferred to the evaluation.
  • the input interface is an interface for receiving data of a vehicle communication system and / or a
  • Interface for receiving information from a mobile terminal and / or an interface for receiving information from a vehicle external
  • the evaluation device has at least one output interface, wherein the output interface is an interface to the
  • the output interface can also form an input interface.
  • prediction information generated according to the invention can be transmitted from the evaluation device to further systems via such an output interface. For example, over the interface for the transmission of
  • Output data are transmitted to the vehicle communication system prediction information, which serve the operation of other driver assistance systems. Also prediction information can be transmitted via this output interface, which are displayed to the driver or issued to this.
  • information can be transmitted that is used to send a message about a possible delay of the driver. Also, depending on the transmitted information, e.g. Calendar entries are moved in the mobile device.
  • information about an arrival time of the vehicle at a destination can be transmitted via the interface for the transmission of information to a vehicle-external server device. This information can then be used, for example, for a heating control in a residential building.
  • a Car2X output interface Via a Car2X output interface, as explained above,
  • Prediction information to other vehicles or participants of a Car2X network are made available.
  • FIG. 1 is a schematic block diagram of a device according to the invention
  • FIG. 2 shows an exemplary representation of a card section
  • FIG. 3 shows the map detail shown in FIG. 2 with prediction information
  • Fig. 5 shows the map detail shown in Fig. 4 with positions of a
  • Fig. 6 is a schematic flow diagram of a method according to the invention.
  • the device 1 shows a schematic block diagram of a device 1 for generating at least one prediction information PI (see FIG. 6).
  • the device 1 can be installed in particular in a vehicle, not shown.
  • the device 1 comprises at least one evaluation device 2 and a memory device 3 connected to the evaluation device 2 in a data-related manner. Furthermore, an input interface 4 for receiving information transmitted via a vehicle communication system has an input interface 5 for receiving information from a mobile terminal Input interface 6 for receiving information from an off-board server device and a Car2X input interface 7. Further, the device 1 comprises several
  • Output interfaces namely an output interface 8 for transmitting output data to a vehicle communication system, an output interface 9 for transmitting information to a mobile terminal, an output interface 10 for transmitting information to an off-board server device and a Car2X output interface 1 1.
  • the device 1 may perform a method according to the embodiment shown in FIG.
  • the input interfaces 4, 5, 6, 7 can be used in particular, but not so
  • input information El (see FIG. 6) is transmitted to the evaluation device 2.
  • the evaluation device 2 can determine an initial situation AS, wherein the
  • Starting situation AS serves as initial situation for the prediction.
  • the evaluation device 2 can determine the at least one prediction information PI from at least one vehicle-specific and / or vehicle driver-specific assignment Z of at least one prediction information PI to at least one starting situation AS.
  • This assignment Z or (partial information about this assignment Z can / can, for example, in the
  • Memory device 3 to be stored.
  • the assignment Z or (partial information about this assignment Z can be transmitted to the evaluation device 2 via one of the input interfaces 4, 5, 6, 7.
  • several assignments Z may exist, with a first set of assignments Z in the memory device 3 may be stored and a further assignment Z via the input interfaces 4, ..., 7 can be transmitted.
  • Input quantity El or an initial situation AS via an output interface 8, 9, 10, 1 1 transmits to another device, which then determines an initial situation FS or a prediction information PI.
  • FIG. 2 shows a schematic map with average speeds along different routes R1, R2, R3 from a starting point S to a destination Z.
  • An average speed v1_R1, v2_R1, v3_R1_R3, v_R1_R3, v_R2R3, v_R2R3, v_R2R3, v_R2R3, v_R2R3, v5_R2, v5_R3, v_R1_R3, v_R1_R2R3, v_R1_R2R3 may be an average of the speeds at which the vehicle and / or a particular driver drive the vehicle corresponding subsection of
  • Travel route R1, R2, R3 has passed. These speeds are e.g. for the vehicle and / or for the driver during the corresponding journeys.
  • the course of the average speeds v1_R1, v2_R1, v3_R1 R3, v4_R1 R3, v1_R2R3, v2_R2R3, v5_R2, v5_R3, v3_R1_R3, v6_R1_R2R3, v7_R1_R2R3 is vehicle or vehicle driver-specific and assigned to a specific route.
  • the average speed for a segment may vary at different times of the day.
  • the course of the average speeds v1_R1, v2_R1, v3_R1 R3, v4_R1 R3, v1_R2R3, v4_R2R3, v5_R2, v5_R3, v3_R1_R3, v6_R1_R2R3, v7_R1_R2R3 can be specific to the vehicle or vehicle driver and can be assigned to a specific travel distance and time of day be.
  • FIG. 3 shows the schematic map detail shown in FIG. 2 with prediction information PI.
  • prediction information PI For example, information about the current point in time and about the position of the starting point S. served as input information El
  • Input information El can be assigned to a specific starting situation AS or this input information El can characterize an initial situation AS.
  • this destination Z As a function of this input information El or the initial situation AS, the position of the destination Z was determined as the prediction information PI.
  • This destination Z can the said starting situation AS or the input information El assigned via a vehicle driver specific assignment Z.
  • the position of the destination Z can then serve as further input information El, in which case the input information El information about the current time, the position of the starting location S and the position of the destination Z include.
  • the second distance R2 can then be determined as prediction information PI.
  • the second distance R2 can then turn as input information El to
  • the throughput speeds v1_R2R3, v2_R2R3, v3_R2R3, v4_R2R3, v5_R2, v6_R1_R2R3, v7_R1_R2R3 can then be determined along the second distance R2 as prediction information PI. Then you can tell the driver, for example, about a
  • Display means, the average speeds or times at which the vehicle passes a corresponding section of road are displayed.
  • FIG. 4 shows a schematic map section with a mobile network coverage along a route R from a starting point S to a destination Z.
  • a vehicle or a mobile terminal may have a
  • the mobile radio network may in particular be a mobile radio network via which the
  • Vehicle and / or a specific driver communicates.
  • the course of the network coverage vehicle or vehicle driver specific and a specific route R be assigned.
  • Fig. 5 shows the map shown in Fig. 4 excerpt with a prediction information PI, namely periods T1, T2, in which for the vehicle or a vehicle disposed in the mobile terminal no sufficient network coverage will be available. If the selected route R is available as input information, it can be determined in what periods of time the vehicle will be located in the sections with zero percent coverage.
  • the periods T1, T2 may, for example, depending on known
  • Prediction information determined average speeds for determining further prediction information, in this case periods of zero percent coverage.
  • the periods T1, T2 can be displayed to the driver, for example via a display device. Also, the driver, e.g. be informed optically and / or acoustically, a predetermined period of time before the periods T1, T2, that in these periods T1, T2 no connection via a mobile network will be possible.
  • FIG. 6 shows a schematic flow diagram of a method according to the invention.
  • input information El is detected or determined. These may be, for example, time information, location information, a
  • input information may be real-time information
  • information about a current vehicle state For example, information about a current vehicle state, weather information or traffic information.
  • an initial situation AS can be classified.
  • previously known assignments Z can be used.
  • an initial situation AS may be a vector comprising one or more input information (s) El.
  • At least one prediction information PI can then be determined by means of a vehicle-specific and / or vehicle-driver-specific assignment Z.
  • a destination can be determined as the prediction information PI.
  • prediction information PI is again used as input information El.
  • a new starting situation AS and a new prediction information PI can then be determined.
  • a route R (see FIG. 3) is determined in a subsequent step as prediction information.
  • a vehicle driver-specific speed profile along the determined route R can be determined as the prediction information.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
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Abstract

L'invention concerne un dispositif et un procédé de production d'au moins une information de prédiction (PI) dans un véhicule, au moins une information d'entrée (E1) étant acquise ou déterminée, une situation de départ (AS) étant déterminée en fonction de l'information d'entrée (El), l'information de prédiction (PI) étant déterminée en fonction de la situation de départ (AS) à partir d'au moins une affectation (Z) spécifique au véhicule et/ou au conducteur du véhicule d'au moins une information de prédiction (PI) à au moins une situation de départ (AS).
PCT/EP2017/054779 2016-03-03 2017-03-01 Procédé et dispositif de production d'au moins une information de prédiction dans un véhicule Ceased WO2017149016A1 (fr)

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DE102016203500.2A DE102016203500A1 (de) 2016-03-03 2016-03-03 Verfahren und Vorrichtung zur Erzeugung von mindestens einer Prädiktions-Information in einem Fahrzeug

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EP3672287A1 (fr) * 2018-12-18 2020-06-24 ZF Friedrichshafen AG Procédé, programme informatique et système de prédiction de la disponibilité d'un réseau radiotéléphonie mobile

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