WO2020193862A1 - Amélioration de données cartographiques - Google Patents

Amélioration de données cartographiques Download PDF

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Publication number
WO2020193862A1
WO2020193862A1 PCT/FI2020/050190 FI2020050190W WO2020193862A1 WO 2020193862 A1 WO2020193862 A1 WO 2020193862A1 FI 2020050190 W FI2020050190 W FI 2020050190W WO 2020193862 A1 WO2020193862 A1 WO 2020193862A1
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WIPO (PCT)
Prior art keywords
vehicle
road
map data
data
information
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PCT/FI2020/050190
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English (en)
Inventor
Kimmo ERKKILÄ
Jarmo Leino
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Eee Innovations Oy
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Publication of WO2020193862A1 publication Critical patent/WO2020193862A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G01C21/32—Structuring or formatting of map data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3807—Creation or updating of map data characterised by the type of data
    • G01C21/3815—Road data
    • G01C21/3822—Road feature data, e.g. slope data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data

Definitions

  • the present application relates to the field of computer systems, and more particularly to a computer-implemented method and an apparatus for enhancing map data.
  • Vehicle positioning is an important service for various users.
  • the positioning may be based on utilizing satellites that provide positioning signals for a receiver installed in a vehicle. Based on the received positioning signals it is possible to establish a current location of the vehicle and to display the location for a driver, for example, by using a map application.
  • One of the problems associated with the satellite based positioning service is that the positioning is not very accurate and the accuracy varies depending on the surrounding conditions (for example, tall buildings may reduce the accuracy). Further, satellite based positioning works well only outdoors, therefore making it unusable indoors.
  • map data relating to roads is simple as it basically tells only where the roads are located. Thus, the map data fails to provide any additional information about the roads.
  • a computer-implemented method for enhancing map data comprises applying a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle; calculating, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; calculating, based on the dynamic model of the vehicle
  • a computer-implemented method for enhancing map data comprises applying a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data; calculating, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; calculating, based on the dynamic model of the vehicle, a momentary track width; calculating, based on the effective travel distance of a wheel of the vehicle and the momentary track width, a momentary direction of the vehicle; calculating, based on the effective travel
  • the status information comprises at least one of a motor power, tyre speeds, a steering wheel position, vehicle system information, traction control information, vehicle stabilization system information and anti-lock braking system information.
  • obtaining, based on vehicle identity information, vehicle dynamics information comprises obtaining initial vehicle dynamics information associated with a model or type of the vehicle from at least one external data source.
  • vehicle dynamics information comprises obtaining supplemental vehicle dynamics information based on real-time vehicle dynamics data obtained from the vehicle.
  • vehicle identity information may be the real vehicle identification number (VIN) information of the vehicle, or plain unique reference which can be used to match the vehicle history and calibration values together.
  • the road characteristics data comprises at least one of road quality data, road irregularity data and data about local deviations associated with the road.
  • the method further comprises combining the dynamic model with the road characteristics data to enhance the calculation of the position of the vehicle.
  • the three-dimensional road data comprises road inclination data
  • the method further comprises combining the dynamic model with the road inclination data to enhance the calculation of the position of the vehicle.
  • the method further comprises comparing an expected behavior of the vehicle at the position to the behavior of the vehicle at the position; calculating road inclination at the position based on the comparison; and updating road inclination at the position in the map data based on the calculated road inclination.
  • the method further comprises dividing a road section of the map data into road segments; comparing an expected behavior of the vehicle at positions within a road segment to the behavior of the vehicle at the positions within the road segment; calculating an average inclination for the road segment based on the comparison; and updating the average road inclination for the road segment in the map data based on the calculated average road inclination.
  • the method further comprises determining an average roughness for a road segment based on an amplitude of momentary speed changes of wheels of the vehicle; and updating the map data for the road segment with the average roughness of the road segment.
  • the method further comprises detecting amplitude changes of momentary speed changes of wheels of the vehicle in a road segment; determining, based on the amplitude changes, degree of at least one road surface irregularity associated with the road segment; and updating the map data based on the degree of at least one irregularity associated with the road segment.
  • the method further comprises calculating a sensitivity dependency for the vehicle, the sensitivity dependency being dependent on speed and mass of the vehicle; comparing the sensitivity dependency of the vehicle to a sensitivity dependency of at least one other vehicle; and calibrating the sensitivity dependencies of the vehicles based on the comparison.
  • an apparatus for enhancing map data comprises at least one processor and at least one memory connected to the at least one processor.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to apply a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two- dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle; calculate,
  • an apparatus for enhancing map data comprises at least one processor and at least one memory connected to the at least one processor.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to apply a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data; calculate, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; calculate, based on the dynamic model of the vehicle, a momentary track width; calculate, based on the effective travel distance of
  • the status information comprises at least one of a motor power, tyre speeds, a steering wheel position, vehicle system information, traction control information, vehicle stabilization system information and anti-lock braking system information.
  • obtaining, based on vehicle identity information, vehicle dynamics information comprises obtaining initial vehicle dynamics information associated with a model or type of the vehicle from at least one external data source.
  • vehicle dynamics information comprises obtaining supplemental vehicle dynamics information based on real-time vehicle dynamics data obtained from the vehicle.
  • vehicle identity information may be the real vehicle identification number (VIN) information of the vehicle, or plain unique reference which can be used to match the vehicle history and calibration values together.
  • the road characteristics data comprises at least one of road quality data, road irregularity data and data about local deviations associated with the road.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to combine the dynamic model with the road characteristics data to enhance the calculation of the position of the vehicle.
  • the three-dimensional road data comprises road inclination data
  • the method further comprises combining the dynamic model with the road inclination data to enhance the calculation of the position of the vehicle.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to compare an expected behavior of the vehicle at the position to the behavior of the vehicle at the position; calculate road inclination at the position based on the comparison; and update road inclination at the position in the map data based on the calculated road inclination.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to divide a road section of the map data into road segments; compare an expected behavior of the vehicle at positions within a road segment to the behavior of the vehicle at the positions within the road segment; calculate an average inclination for the road segment based on the comparison; and update average road inclination for the road segment in the map data based on the calculated average road inclination.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to determine an average roughness for a road segment based on an amplitude of momentary speed changes of wheels of the vehicle; and update the map data for the road segment with the average roughness of the road segment.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to detect amplitude changes of momentary speed changes of wheels of the vehicle in a road segment; determine, based on the amplitude changes, degree of at least one road surface irregularity associated with the road segment; and update the map data based on the degree of at least one irregularity associated with the road segment.
  • the at least one memory stores program instructions that, when executed by the at least one processor, cause the apparatus to calculate a sensitivity dependency for the vehicle, the sensitivity dependency being dependent on speed and mass of the vehicle; compare the sensitivity dependency of the vehicle to a sensitivity dependency of at least one other vehicle; and calibrate the sensitivity dependencies of the vehicles based on the comparison.
  • a computer program comprises instructions which, when the program is executed by a computer, cause the computer to carry out the method of any the first aspect.
  • a computer-readable medium comprises instructions which when, executed by a computer, cause the computer to carry out the method of the first aspect.
  • an apparatus for enhancing map data comprises at least one processor configured to apply a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two- dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle; calculate, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; calculate, based on the dynamic model of the vehicle,
  • an apparatus for enhancing map data comprises at least one processor configured to apply a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data; calculate, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; calculate, based on the dynamic model of the vehicle, a momentary track width; calculate, based on the effective travel distance of a wheel of the vehicle and the momentary track width, a momentary direction of the vehicle; calculate, based on the effective travel distance of the wheel and
  • an apparatus for enhancing map data comprises means for applying a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three- dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle; means for calculating, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; means for calculating, based on the dynamic model of the
  • an apparatus for enhancing map data comprises means for applying a dynamic model associated with a vehicle, the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three- dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data; means for calculating, based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; means for calculating, based on the dynamic model of the vehicle, a momentary track width; means for calculating, based on the effective travel distance of a wheel of the vehicle and the momentary track width, a momentary direction of the vehicle; means for calculating
  • FIG. 1 illustrates a block diagram representing information based on which it is possible to determine and model behavior of a vehicle.
  • FIG. 2 illustrates a flow diagram of a method for modelling dynamics of a vehicle.
  • FIG. 3 illustrates calculation of the change of wheel normal load caused by the road longitudinal inclination in front and rear axles.
  • FIG. 4 illustrates a computer-implemented method for positioning a vehicle.
  • FIGS. 5A and 5B illustrate graphs associated with a turning vehicle.
  • FIG. 6 illustrates a computer-implemented method for enhancing map data.
  • FIG. 7 illustrates examples of possible irregularities associated with a road.
  • FIG. 8 illustrates a system diagram depicting an exemplary apparatus according to an aspect.
  • a disclosure in connection with a described method may also hold true for a corresponding device or system configured to perform the method and vice versa.
  • a corresponding device may include a unit or other means to perform the described method step, even if such unit is not explicitly described or illustrated in the figures.
  • a corresponding method may include a step performing the described functionality, even if such step is not explicitly described or illustrated in the figures.
  • FIG. 1 illustrates a block diagram representing information based on which it is possible to determine and model behavior of a vehicle.
  • Vehicle status information 100 refers to information that can be obtained or can originate from the vehicle itself, for example, via one or more information buses of the vehicle while the vehicle is in use.
  • the vehicle status information comprises, for example, at least one of motor power, tyre speeds, a steering wheel position, vehicle system information, traction control information, vehicle stabilization system information and anti-lock braking system information.
  • Vehicle dynamics information 102 may be obtained based on vehicle identity information, for example, from one or more external data sources.
  • the vehicle dynamics information 102 may provide characteristics associated with the vehicle. The characteristics may comprise initial values, for example, for a mass of the vehicle, powertrain efficiency coefficient or map, a track width, a wheelbase, location of the center of gravity, and the moment of inertia along different axes of the vehicle and the moment of inertia associated with rotating masses. If no vehicle specific dynamics information is not available, it is possible to use vehicle dynamics information that is common for this vehicle type. When the vehicle is used, the vehicle dynamics information may be updated based on analysis of the behavior of the vehicle. Further, vehicle dynamics information determined for a specific vehicle may be used as preliminary knowledge for other similar vehicles.
  • the vehicle identity information may be the real vehicle identification number (VIN) information of the vehicle, or plain unique reference which can be used to match the vehicle history and calibration values together.
  • Map data 104 may represent road characteristics of roads of a geographical area, and the map data 104 may comprise two-dimensional road map data, three-dimensional road map data associated with the roads, and/or road characteristics data.
  • the three-dimensional road map data may comprise information, for example, about road height data and road inclination data.
  • the road characteristics data may comprise at least one of road quality data, road irregularity data and data about local deviations associated with the road.
  • the road characteristics data may comprise, for example, other typical dynamic behaviour changes that can be statistically recognized by various vehicles at the same locations. For example, if a road is rutted, a weight transfer of a vehicle can be considered as“normal” if several different vehicles experience the same weight transfer. Similarly, pits, rail locations or other road imperfections of a road are characteristics that have an effect on how a vehicle should behave based on a current dataset associated with the vehicle.
  • vehicle dynamics information 102 and the map data 104 it is possible to determine a dynamic model for the vehicle.
  • the dynamic model may then enable an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle.
  • updated map data may be subsequently received and the updated map data may be used in determining or updating the dynamic model of the vehicle.
  • the dynamic model may be calculated and/or updated by a vehicle-mounted apparatus.
  • the dynamic model may be calculated and/or updated by a network-based service that may receive information from one or more vehicles.
  • the illustrated solution enables a fusion of the vehicle status information 100 obtained from the vehicle and the map data.
  • the map data can be enriched based on the vehicle status information 100.
  • FIG. 2 illustrates a computer-implemented method for modelling dynamics of a vehicle.
  • the vehicle may be any vehicle travelling on a road, for example, an autonomous vehicle, a passenger car, a truck, a motorcycle etc.
  • status information is obtained from at least one information bus of the vehicle, the status information providing status information or real-time status information about the vehicle.
  • vehicle dynamics information representing vehicle dynamics is obtained based on vehicle identity information.
  • map data representing road characteristics of roads of a geographical area comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data is obtained.
  • behavior of the vehicle is analyzed based on the status information and the vehicle dynamics information.
  • a dynamic model for the vehicle is calculated by comparing the behavior of the vehicle to the map data.
  • the dynamic model of the vehicle can be used in various applications in which an accurate vehicle representation is needed. For example, based on the dynamic model, it is possible to calculate a presumed behavior of the vehicle in various driving conditions. If an actual behavior of the vehicle deviates from the presumed behavior of the vehicle, this is caused either by changes in driving conditions or changes in the vehicle.
  • the dynamic model of the vehicle may define a set of vehicle-specific calibration parameters. These calibration parameters may represent how the vehicle behaves in various driving situations.
  • the dynamic model of the vehicle may involve a set of various parameters or calibration parameters, for example, one or more of the following: 1) a mass of the vehicle, 2) powertrain efficiency coefficient or map, 3) a track width, 4) a wheelbase, 5) location of the center of gravity, 6) the moment of inertia along different axes of the vehicle and the moment of inertia associated with rotating masses, 7) effective rolling circumferences of the wheels, 8) an effect of the circumferential force of a wheel to the rolling circumferences, and 9) overall flexibility in longitudinal and lateral directions in weight of the transition situations caused, for example, by the wheels, the car chassis structure and the car body.
  • various parameters or calibration parameters for example, one or more of the following: 1) a mass of the vehicle, 2) powertrain efficiency coefficient or map, 3) a track width, 4) a wheelbase, 5) location of the center of gravity, 6) the moment of inertia along different axes of the vehicle and the moment of inertia associated with rotating masses,
  • the mass of the vehicle may be obtained from an external data source, it can be manually input or a mass estimation may be used based on the vehicle type.
  • the mass of the vehicle can be made more accurate by comparing the effect of the energy produced by a motor of the vehicle to state of motion of the vehicle, taking into account also, for example, at least one of changes in vehicle speed, altitude changes, the amount of work performed by the motor of the vehicle, efficiencies, driving resistances and road quality factors.
  • A frontal area
  • V2 speed at point 2
  • the mass of the vehicle may be estimated as follows:
  • Powertrain efficiency information may be obtained from one or more external data sources, manually or based on the vehicle type. Later, the information may be made more accurate by multivariate analysis or statistical analysis.
  • Track width information may be obtained from one or more external data sources, manually or based on the vehicle type. Later, the information may be made more accurate by multivariate analysis or statistical analysis.
  • Wheelbase information may be obtained from one or more external data sources, manually or based on the vehicle type. Later, the information may be made more accurate by multivariate analysis or statistical analysis.
  • Information about the location of the center of gravity may be obtained from one or more external data sources, manually or based on the vehicle type. Later, the information may be made more accurate by multivariate analysis or statistical analysis. Information about the moment of inertia along different axes of the vehicle and the moment of inertia associated with rotating masses may be obtained from one or more external data sources, manually or based on the vehicle type. Later, the information may be made more accurate by multivariate analysis or statistical analysis.
  • Effective rolling circumferences of the wheels may be calculated for each wheel by comparing the travelled distance to each other and to map data in chosen periods. For each wheel a computational dependency tied to the normal force of the wheel is generated. A momentary normal force of the wheel takes into account a dynamic mass shift of the vehicle, also taking into account road characteristics, for example, road inclinations to different directions. The effect of speed may also be taken into account.
  • the effect of the circumferential force of a wheel to the rolling circumferences of the wheels is estimated by comparing speed of rotation differences with each other with different tractive forces.
  • the estimation may also take into account road quality characteristics and friction.
  • Flexibility of the wheels and the chassis in longitudinal and lateral directions in weight transition situations may be determined for each and for each wheel type by using initial values or by searching the values from a library for a similar vehicle and wheel type.
  • the effects of the weight transition in longitudinal and lateral directions are estimated separately because a wheel of the vehicle and chassis structures act differently with forces having different directions.
  • Coefficients representing the dynamics indicate both the change caused by the weight shift to the effective travelling device of each wheel and changes caused by the flexibility to the effective travelling distance of the wheel.
  • the location of the effective contact point affects, for example, to the track width and wheelbase.
  • the map data is utilized, for example, by comparing a turning degree of the vehicle according to the dynamic model in a curve to an expected turning degree based on the map data.
  • lateral inclination and longitudal and lateral accelerations ay be calculated similarly.
  • FIG. 4 illustrates a computer-implemented method for positioning a vehicle.
  • the vehicle may be any vehicle travelling on a road, for example, an autonomous vehicle, a passenger car, a truck, a motorcycle etc.
  • a dynamic model associated with a vehicle discussed above is applied.
  • the dynamic model has been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle.
  • an effective travel distance of a wheel of the vehicle is calculated based on the dynamic model of the vehicle.
  • a momentary track width is calculated based on the dynamic model of the vehicle.
  • a momentary direction of the vehicle is calculated based on the effective travel distance of a wheel of the vehicle and the momentary track width.
  • a position of the vehicle is calculated based on the effective travel distance of the wheel and the momentary direction of the vehicle.
  • the three-dimensional map data may comprise also road inclination.
  • the position determination can be made more accurate. For example, an effect of the road inclination to a projection of the track width can be taken into account.
  • Equations for the rear axle may be formed similarly.
  • a starting value for a wheel speed difference between left and right sides by a wheel travel is
  • VRCOT VR ⁇ VR * SA%
  • the mass detection may be performed using the previously presented equations for the mass of the vehicle.
  • TFC type and suspension flex in z direction (compression flex)
  • TW dyn TW + ATW dyn
  • TWdyn dynamic track width
  • V Rdyn right wheel speed after dynamic corrections
  • the illustrated solution for determining the position accurately may be used in vehicle navigation and especially in real-time navigation and in real-time three-dimensional navigation.
  • the illustrated solution can also be utilized in locations where a satellite position based signal, for example, a GPS signal, is weak on non-existent. These locations may include, for example, closed parking lots, blind spots due to buildings, tunnels and quarries. Further, the illustrated solution may provide a location service in applications in which a single location signal is not sufficient, for example, in autonomous vehicles, metros and trains.
  • FIG. 6 illustrates a computer-implemented method for enhancing map data.
  • a dynamic model associated with a vehicle discussed above is applied.
  • the vehicle may be any vehicle travelling on a road, for example, an autonomous vehicle, a passenger car, a truck, a motorcycle etc.
  • the dynamic model has been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle.
  • an effective travel distance of a wheel of the vehicle is calculated based on the dynamic model of the vehicle.
  • a momentary track width is calculated based on the dynamic model of the vehicle.
  • a momentary direction of the vehicle is calculated based on the effective travel distance of a wheel of the vehicle and the momentary track width.
  • a position of the vehicle is calculated based on the effective travel distance of the wheel and the momentary direction of the vehicle.
  • the behavior of the vehicle at the position is associated to the map data at the position.
  • the map data at the position is updated based on the behavior of the vehicle at the position.
  • the updated map data may be used as an enhanced map with all other vehicles travelling the same road or road segment later.
  • the updated map data may be transmitted to a network-based service that may receive updates from one or more vehicles. The network-based service may then use these updates to enhance map data and to transmit the enhanced map data to other vehicles.
  • an expected behavior based on the dynamic model of the vehicle at the calculated position may be compared to the actual behavior of the vehicle at the position.
  • road inclination causes a weight shift for the vehicle. This, in turn, has an effect for the effective travel distance of a wheel.
  • Road inclination at the position of the vehicle can be calculated based on the comparison. Further, now that the accurate position of the vehicle is known and the road inclination at the position of the vehicle are known, road inclination at the position in the map data can be updated based on the calculated road inclination.
  • a road or a road section of the map data may be divided into road segments.
  • the division may be based on, for example, the fact how a dynamic state of the vehicle changes.
  • the road or road sections may be divided into bends, straight parts, inclination changes etc.
  • An expected behavior of the vehicle at positions within a road segment may be compared to the behavior of the vehicle at the positions within the road segment, and an average inclination may be calculated for the road segment based on the comparison. Now that the average inclination for the road segment is known, the average road inclination for the road segment may be updated in the map data.
  • momentary road inclinations may be calculated similarly. Due to this, accurate inclination data can be calculated for the road section at all travelled positions.
  • the map data may be enhanced even more to over a more complete part of the road or road section. Further, as different vehicles travel similar paths along the road or road section, road inclination data provided by different vehicles is verified. In one embodiment, the map data may be enhanced with the calculated road inclination data after a predetermined number of vehicles have travelled the road or road section and the analysis results have been verified.
  • an average roughness for a road segment may be determined based on an amplitude of momentary speed changes of wheels of the vehicle obtained based on analyzing the behavior of the vehicle when it has travelled along the road segment.
  • the map data for the road segment may then be updated with the average roughness of the road segment.
  • the average roughness may give, for example, valuable information about the overall condition of the road segment.
  • the behavior of the vehicle and/or the dynamic model of the vehicle ca also used in determining quality attributes associated with a road or road segment.
  • Amplitude changes of momentary speed changes of wheels of the vehicle in a road segment may be detected.
  • degree of at least one road surface irregularity associated with the road segment may be determined.
  • the map data may then be updated based on the degree of at least one irregularity associated with the road segment. In other words, potholes and bumps and their degree and/or severity may be determined based on the amplitude changes.
  • a sensitivity dependency maybe calculated for the vehicle, the sensitivity dependency being dependent on speed and mass of the vehicle.
  • Different vehicles can then act as“road sensors”, where each sensor may provide a different speed change response for a same pothole or bump of a specific road position.
  • the sensitivity dependencies of the vehicles may be calibrated based on the comparison. After the calibration, results of different vehicles can be compared against each other.
  • vehicle behavior analysis may be performed by wheel basis of a single vehicle. By analyzing each wheel separately, it is possible to determine which wheels of the vehicle, i.e. which side of the vehicle, travelled over an irregularity associated with the road segment. This also enables determination of an accurate position of the irregularity in the road segment.
  • FIG. 7 illustrates examples of possible irregularities associated with a road.
  • the y axis represents wheel speed percentual impulse.
  • the x axis represents a wheel speed message count.
  • the wheel speed percentual impulse represents a relative speed change as a percentage. This may mean, for example, that the larger speed change experienced by a wheel in a pothole or other road irregularity of a road, the larger the pothole or other road irregularity is in the road.
  • References 700 and 704 identify specific holes or potholes in an analyzed position in a road or road segment. A higher reading in the y axis indicated a more severe hole or pothole.
  • References 702 and 712 identifies a more general section of the road or road segment, for example, gravel.
  • a reference 706 identifies a normal asphalt section of the road or road segment. As can be seen from FIG. 7, values in the y axis are the lowest in the asphalt section. This means that the asphalt section is the smoothest surface in this example.
  • a reference 706 identifies a wide turn out of the road.
  • a reference 710 identifies a minor bump in a repaired asphalt on the right side of the vehicle.
  • a reference 714 identifies a bumpy home road section.
  • FIG. 8 illustrates a system diagram depicting an exemplary apparatus 800 including a variety of optional hardware and software components, shown generally at 812. Any components 812 in the apparatus can communicate with any other component, although not all connections are shown, for ease of illustration.
  • the apparatus 800 can be any of a variety of computing devices (for example, a computer, a cloud based server etc.) and can allow two-way communications with one or more communications networks, such as the Internet.
  • the illustrated apparatus 800 can include one or more controllers or processors 802 (e.g., signal processor, microprocessor, ASIC, or other control and processing logic circuitry) for performing such tasks as signal coding, data processing, input/output processing, power control, and/or other functions.
  • An operating system 808 can control the allocation and usage of the components 812 and support for one or more application programs 810.
  • the application programs can include common computing applications (e.g., server software), or any other computing application.
  • the illustrated apparatus 800 can include a memory 804.
  • the memory 804 can include non-removable memory and/or removable memory.
  • the non-removable memory can include RAM, ROM, flash memory, a hard disk, or other well-known memory storage technologies.
  • the removable memory can include flash memory or other well-known memory storage technologies.
  • the memory 804 can be used for storing data and/or code for running the operating system 808 and the applications 810.
  • Example data can include web pages, text, images, sound files, video data, or other data sets to be sent to and/or received from one or more network servers or other devices via one or more wired or wireless networks.
  • the apparatus 800 can further include at least one physical connector, which can be a USB port, IEEE 1394 (FireWire) port, and/or RS-232 port etc.
  • at least one physical connector can be a USB port, IEEE 1394 (FireWire) port, and/or RS-232 port etc.
  • the illustrated components 812 are not required or all-inclusive, as any components can deleted and other components can be added.
  • the apparatus 800 may be configured to implement the various features, examples and embodiments illustrated in FIGS. 1-4 partially or completely.
  • the functionality described herein can be performed, at least in part, by one or more computer program product components such as software components.
  • the processor 802 may be configured by the program code which when executed performs the examples and embodiments of the operations and functionality described.
  • the functionality described herein can be performed, at least in part, by one or more hardware logic components.
  • illustrative types of hardware logic components include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), Graphics Processing Units (GPUs).
  • FPGAs Field-programmable Gate Arrays
  • ASICs Program-specific Integrated Circuits
  • ASSPs Program-specific Standard Products
  • SOCs System-on-a-chip systems
  • CPLDs Complex Programmable Logic Devices
  • GPUs Graphics Processing Units
  • one or more of the disclosed elements or components of the apparatus 800 may constitute means for applying a dynamic model associated with the vehicle, the dynamic model having been determined by obtaining status information originating from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle during the use of the vehicle, obtaining, based on vehicle identity information, vehicle dynamics information providing characteristics associated with the vehicle, obtain map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three- dimensional road map associated with the roads, and road characteristics data, analyzing behavior of the vehicle based on the status information and the vehicle dynamics information, and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data, the dynamic model enabling at least one of an estimation of the performance of the vehicle, observation of changes associated with the vehicle and observation of changes in driving conditions of the vehicle; means for calculating based on the dynamic model of the vehicle, an effective travel distance of a wheel of the vehicle; means for calculating, based on the dynamic model of the vehicle, a momentary track
  • the functionality of the apparatus 800 may be implemented by program instructions stored on a computer readable medium.
  • the program instructions when executed, cause the computer, processor or the like, to perform the disclosed steps or functionality.
  • the computer readable medium can be any medium, including non-transitory storage media, on which the program is stored such as a Blu-Ray disc, DVD, CD, USB (flash) drive, hard disc, server storage available via a network, a ROM, a PROM, an EPROM, an EEPROM or a Flash memory having electronically readable control signals stored thereon which cooperate or are capable of cooperating with a programmable computer system such that an embodiment of at least one of the inventive methods is performed.
  • An embodiment of the invention comprises or is a computer program comprising program code for performing any of the methods described herein, when executed on a computer.
  • Another example of the invention comprises or is a computer readable medium comprising a program code that, when executed by a processor, causes an apparatus to perform any of the methods described herein.
  • the apparatus 800 comprises a vehicle navigator apparatus.
  • the vehicle navigator device may receive the status information from at least one information bus of the vehicle. Then, the vehicle navigator device may comprise the dynamical model of the vehicle, and the dynamical model may calculate the actual direction and speed of the vehicle based on the vehicle dynamics information and the map data.
  • the vehicle navigator apparatus always stays on a correct driving lane. Further, the illustrated solution also removes the problem of determining of a correct direction, for example, when driving in cities.

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Abstract

L'invention concerne, selon un aspect, un procédé mis en œuvre par ordinateur permettant d'améliorer des données cartographiques. Le procédé comprend l'application d'un modèle dynamique associé à un véhicule (600) ; le calcul, en fonction du modèle dynamique du véhicule, d'une distance de déplacement effective d'une roue du véhicule (602) ; le calcul, en fonction du modèle dynamique du véhicule, d'une largeur de voie momentanée (604) ; le calcul, en fonction de la distance de déplacement effective d'une roue du véhicule et de la largeur de voie momentanée, d'une direction momentanée du véhicule (606) ; le calcul, en fonction de la distance de déplacement effective de la roue et de la direction momentanée du véhicule, d'une position du véhicule (608) ; l'association du comportement du véhicule à la position aux données cartographiques à la position (610) ; et la mise à jour des données cartographiques à la position en fonction du comportement du véhicule à la position (612).
PCT/FI2020/050190 2019-03-25 2020-03-25 Amélioration de données cartographiques WO2020193862A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2541203A1 (fr) * 2011-06-30 2013-01-02 Furuno Electric Company Limited Serveur de retours d'expérience de carte routière pour navigation de véhicule avec modes à l'estime et GPS étroitement liés
US20150266455A1 (en) * 2013-12-06 2015-09-24 Christopher Kenneth Wilson Systems and Methods for Building Road Models, Driver Models, and Vehicle Models and Making Predictions Therefrom
US20170146362A1 (en) * 2015-11-19 2017-05-25 GM Global Technology Operations LLC Method and apparatus for fuel consumption prediction and cost estimation via crowd-sensing in vehicle navigation system
US20180154723A1 (en) * 2013-03-15 2018-06-07 ClearMotion, Inc. Self-driving vehicle with integrated active suspension

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2541203A1 (fr) * 2011-06-30 2013-01-02 Furuno Electric Company Limited Serveur de retours d'expérience de carte routière pour navigation de véhicule avec modes à l'estime et GPS étroitement liés
US20180154723A1 (en) * 2013-03-15 2018-06-07 ClearMotion, Inc. Self-driving vehicle with integrated active suspension
US20150266455A1 (en) * 2013-12-06 2015-09-24 Christopher Kenneth Wilson Systems and Methods for Building Road Models, Driver Models, and Vehicle Models and Making Predictions Therefrom
US20170146362A1 (en) * 2015-11-19 2017-05-25 GM Global Technology Operations LLC Method and apparatus for fuel consumption prediction and cost estimation via crowd-sensing in vehicle navigation system

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