Method for determining, based on vehicle information, a roadway cross slope

Abstract

A method (200) for determining, based on vehicle information, a roadway cross slope, the method (200) comprising the following steps: Obtaining (210) vehicle information from at least one vehicle (12), wherein the vehicle information is obtained from at least one of a global navigation satellite system, GNSS, receiver (48) and one or more vehicle-mounted sensors (42-46), and the GNSS receiver (48) and the one or more vehicle-mounted sensors (42-46) are installed in the at least one vehicle (12); performing (220) a roadway cross slope determination method using the obtained vehicle information to obtain a roadway cross slope; and Updating (230) a representative roadway cross slope based on the roadway cross slope, wherein the roadway cross slope determination method is a vehicle dynamics-based roadway cross slope determination method and the step of obtaining (210) vehicle information includes obtaining vehicle-side sensor data from one or more vehicle-side sensors (42-46) of the at least one vehicle (12), and the vehicle-side sensor data includes suspension sensor data from a plurality of suspension sensors (42) installed on the at least one vehicle (12); wherein the vehicle dynamics-based road bank determination method includes determining a frictional acceleration of the at least one vehicle (12) based on the suspension sensor data.

Description

The present description relates to determination of road characteristics such as a road bank based on information obtained from a vehicle.Vehicles include electronic control units (ECUs) that perform various tasks for the vehicle. Many vehicles today have various sensors to acquire information about the operation of the vehicle, including the position and trajectory of the vehicle. Some roads include road slopes ("slopes" for short) in which the road is laterally sloped or rolled (i.e., angled with respect to the roll axis), typically as part of an over-center turn or turn, such that friction between the vehicle wheels and the road is reduced and / or vehicle travel along the road (e.g., around the road curve) is facilitated.L. Brown and D. Bevly: Roll and Bank Estimation Using GPS / INS and Suspension Deflections. electronics, 2015, 118-149, https: / / doi.org / 10.3390 / electronics4010118 describes a method that enables estimation of the road inclination by decoupling the vehicle inclination caused by the dynamics and the inclination caused by the road inclination. Measurements of suspension deflection were used to measure the relative roll between the vehicle frame and the axle frame and between the sprung and unsprung masses, respectively. A scaling parameter for the compression was determined via the suspension geometry and the dynamic analysis. The relative roll measurement was then integrated into two different kinematic navigation models based on Extended Kalman Filter Architectures, EKF. Each algorithm was tested on the Prowler ATV experimental platform at the National Center for Asphalt Technology, NCAT, and then verified. The experimental data showed that both the cascaded and coupled approaches provided good results in estimating the current vehicle grade and the current road grade.It is therefore an object of the invention to provide a method for determining a roadway cross slope of a roadway based on information obtained on one or more vehicles.According to one aspect, a method for determining a roadway cross slope based on vehicle information is provided. The method includes the steps of: obtaining vehicle information from at least one vehicle, wherein the vehicle information is obtained from at least one of a global navigation satellite system, GNSS, receiver and one or more vehicle-side sensors, and the GNSS receiver and the one or more vehicle-side sensors are installed in the at least one vehicle; performing a lane inclination determination method using the obtained vehicle information to obtain a lane inclination; and updating a representative lane inclination based on the lane inclination. The method further comprises the roadway roll determination method being a vehicle dynamics-based roadway roll determination method, and the step of obtaining vehicle information includes obtaining vehicle-side sensor data from one or more vehicle-side sensors of the at least one vehicle. The vehicle-side sensor data includes the suspension sensor data from a plurality of suspension sensors installed on the at least one vehicle. The vehicle dynamics-based road roll determination method includes determining a frictional acceleration of the at least one vehicle based on the suspension sensor data.According to one embodiment, the vehicle dynamics-based roadway roll determination method includes determining a roll angle of the at least one vehicle. The roll angle is determined based on the suspension sensor data using a suspension distance roll angle function.According to another embodiment, the vehicle dynamics-based roadway lateral slope determination method includes determining the frictional acceleration using a roll angle-to-frictional acceleration function or a suspension distance-to-frictional acceleration function, and determining a lateral acceleration of the at least one vehicle based on the vehicle-side sensor data;According to a further embodiment, the following equation is used for determining the road bank.alpha.utilization of the frictional acceleration a f and the lateral accelerationAccording to another embodiment, the roll angle-to-friction acceleration function is a modified roll angle-to-friction acceleration function that takes into account a weight distribution index and a road roughness index.According to another embodiment, the roadway bank determination method is a height-based roadway bank determination method. The vehicle information includes GNSS data used to obtain a geographic position of the at least one vehicle, and wherein the GNSS data includes a height of the at least one vehicle and is obtained from the GNSS receiver installed in the at least one vehicle.According to another embodiment, the elevation-based roadway bank determination method includes obtaining roadway map data of an area that includes the geographic location of the at least one vehicle and that includes a portion of a roadway having a roadway curve that is elevated.According to another embodiment, a curvature extraction method is performed using the road map data to extract road curve information regarding the road curve, wherein the road curve information includes a geographical point representing a center of curvature of the road curve.According to another embodiment, the elevation-based road roll determination method includes performing a linear regression on a plurality of radial distance data points derived from a plurality of vehicles traveling along the road curve.According to a further embodiment, a linear regression result is obtained from the linear regression, and wherein the roadway transverse inclination is determined based on the linear regression result.According to another embodiment, the method is performed at a remote facility located remote from the at least one vehicle, wherein the at least one vehicle includes a first plurality of vehicles and a second plurality of vehicles, wherein the elevation-based roadway roll determination process is performed for the first plurality of vehicles and for the second plurality of vehicles, and wherein the updating step includes summarizing results of the elevation-based roadway roll determination processes for the first plurality of vehicles and for the second plurality of vehicles to obtain the representative roadway roll; and / or the at least one vehicle is a plurality of vehicles, the method including performing a plurality of roadway roll processes for the plurality of vehicles, wherein the plurality of roadway roll processes includes a vehicle dynamics-based roadway roll determination method and an elevation-based roadway roll determination method, and wherein the updating step includes merging or otherwise combining results of each of the plurality of roadway roll processes to obtain the representative roadway roll.According to another aspect, a method for determining a roadway cross slope based on vehicle information is provided. The method includes the steps of: obtaining vehicle sensor data from a vehicle using vehicle sensors installed on the vehicle, the vehicle sensor data including the suspension sensor data; deriving a lateral acceleration and a frictional acceleration from the vehicle sensor data; and determining a roadway lateral slope based on the lateral acceleration and the frictional acceleration.According to another aspect, a method for determining a roadway cross slope based on vehicle information is provided. The method includes the steps of: obtaining global navigation satellite system, GNSS, data from a GNSS receiver installed in the vehicle for each of a plurality of vehicles, wherein the GNSS data includes a geographic position of the vehicle, and wherein the geographic position includes an altitude; obtaining road map data of an area including a road curve along which the plurality of vehicles travel or have traveled; extracting, using the obtained road map data, road curve information about the road curve; mapping the geographic positions of the plurality of vehicles to the extracted roadway curve information to determine a radial distance of each geographic position as taken from a center of curvature of the roadway curve and derive a representative roadway lateral slope based on the radial distances and the height of each of the plurality of vehicles using a linear regression technique.One or more embodiments of the description will be described below in connection with the accompanying figures, wherein like reference numerals designate like elements, and wherein: FIG. 1 is a block diagram of a communication system capable of utilizing the method described herein; FIG. 2 is a flow diagram of a method for determining a road grade based on vehicle information; FIG. 3 is a flow diagram of a vehicle dynamics-based road roll determination method that may be performed as part of a method for determining road roll based on vehicle information; FIG. 4 is a diagram illustrating a vehicle traveling around a road curve inclined at an optimal road bank; FIG. 5 is a diagram illustrating a vehicle traveling around a road curve inclined at a cross-slope of the road and in which the vehicle receives side friction; FIG. 6 is a machine learning flowchart for improving a roll angle-to-friction acceleration function that may be used with various embodiments of the vehicle dynamic roadway roll determination method of FIG. 3 ; FIG. 7 is a flowchart of a height-based road roll determination method that may be performed as part of a method for determining road roll based on vehicle information; FIG. 8 is a diagram illustrating a plurality of vehicles traveling around a curved lane that is elevated; and FIG. 9 is a diagram illustrating a linear regression result representing a lane inclination along a portion of a lane curve obtained based on processing of GNSS data from a plurality of vehicles.The following system and method enable a roadway cross slope to be determined based on information from a vehicle. The information obtained from a vehicle may be referred to as vehicle information and may include global navigation satellite system (GNSS) data and / or sensor data of the vehicle. In at least one embodiment, vehicle information may be used as input to a roadway roll determination method, which may be performed as part of a method for determining a roadway roll based on vehicle information. Once the roadway bank is determined, it may be added to various types of navigation maps and may be useful for vehicle dynamics control, particularly in the context of autonomous vehicles.According to one embodiment, the roadway roll determination method is a vehicle dynamics-based roadway roll determination method in which a roadway roll is determined based on vehicle-side sensor data, such as landing gear sensor data from a plurality of landing gear sensors installed on the vehicle. According to another embodiment, the roadway bank determination method is an elevation-based roadway bank determination method in which a roadway bank is determined based on GNSS data from a plurality of vehicles, which may include mapping the geographic position of the vehicles to extracted curvature information of a roadway curve. In a particular embodiment of the elevation-based road roll determination method, a radial distance and elevation of each vehicle may be derived from the GNSS data and road map data, and this information may then be used together with a linear regression technique to determine a road roll corresponding to the slope of the linear regression line, as discussed in more detail below. The results of these various embodiments of the roadway roll determination method may be combined, merged, and / or otherwise associated with one another to compensate for one another and thereby increase the accuracy of the roadway roll estimate.FIG. 1 illustrates an operating environment that includes a communication system 10 and with which the method described herein may be implemented. The communication system 10 generally includes at least one vehicle 12 with vehicle electronics 20, a plurality of global navigation satellite system (GNSS) satellites 60, a wireless carrier system 70, a land network 76, and a remote facility 80. The following sections thus provide only a brief overview of such a communication system 10; however, other systems not shown here could also apply the described method.The vehicle 12 is shown as passenger cars in the illustrated embodiment, but it should be appreciated that any other vehicle including motor bicycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), etc. may also be used. Portions of the vehicle electronics 20 are generally shown in FIG. 1 and include an onboard computer 22, a wireless communication device 30, a communication bus 40, vehicle-side sensors 42- 46, and a GNSS receiver 48. some or all of the various vehicle electronics may be connected for communication with one another via one or more communication buses, such as the communication bus 40. The communication bus 40 provides the vehicle electronics 20 with network connections via one or more network protocols and may use a serial data communication architecture. Examples of suitable network connections include a controller area network (CAN), a media oriented system transfer (MOST), a local area network (LIN), a local area network (LAN), and other suitable connections such as Ethernet or others conforming to known ISO, SAE, and IEEE standards and specifications, to name a few.Those skilled in the art will understand that the schematic block diagram of the vehicle electronics 20 is intended merely to illustrate some of the more relevant hardware components used with the present method, and is not an accurate or complete representation of the vehicle hardware that would typically be found in such a vehicle. Moreover, the structure or architecture of the vehicle electronics 20 may deviate substantially from that illustrated in FIG. 1. Thus, for the purposes of brevity and clarity, the vehicle electronics 20 will be described in connection with the illustrated embodiment of FIG. 1 due to the incounteduous possible arrangements, and it should be appreciated that the present system and method is not limited thereto.The onboard computer 22 is part of the vehicle electronics 20 and includes a processor 24 and a memory 26. In one embodiment, the onboard computer 22 may be configured to perform one or more steps of the method / s described below. Also in embodiments where the onboard computer 22 performs one or more method steps, the onboard computer 22 may do so with the processor 24. According to various embodiments, the onboard computer 22 may be integrated into other devices or components of the vehicle electronics 20. Moreover, in at least some embodiments, the onboard computer 22 may be (or integrated with) an infotainment unit (e.g., infotainment head unit, in-car entertainment (ICE) unit, in-car infotainment (IVI) unit, a vehicle head unit, a center stack module (CSM), or a vehicle navigation module.The wireless communication device 30 provides long range wireless communication capabilities to the vehicle such that the vehicle may communicate and communicate with other devices or systems that are not part of the vehicle electronics 20, such as the remote computer 82 of the remote facility 80. In the illustrated embodiment, the wireless communication device 30 includes a cellular chipset 32, antenna 34, processor 36, and memory 38. The cellular chipset 32 may be a cellular chipset that enables cellular wireless communication, such as used with the wireless carrier system 70. The antenna 34 of the wireless communication device 30 may be used to transmit and receive this wireless communication. In some embodiments, the wireless communication device 30 may include (or be communicatively coupled to) a short-range radio communication circuit (SRWC) that enables short-range radio communication (e.g., Bluetooth™ other IEEE 802.15 communication, vehicle-to-vehicle communication (V2V), vehicle-to-infrastructure communication (V2I), Wi-Fi™ other IEEE 802.11 communication, etc.) with any number of nearby devices. This SRWC circuitry may be provided in addition to the cellular chipset and may be part of the same module. In other embodiments, the SRWC circuitry and the cellular chipset 32 may be part of various modules - for example, the SRWC circuitry may be part of an infotainment unit and the cellular chipset 32 may be part of a telematics unit that is separate from the infotainment unit.The vehicle-side sensors 42- 46 may capture or receive information about the vehicle, which may then be sent to one or more other portions of the vehicle electronics 20 and / or external systems or devices, such as the remote facility 80. The sensor signals obtained from the vehicle-side vehicle sensors 42- 46 may be associated with a time indicator (e.g., a timestamp) as well as other metadata or information. The sensor data of the vehicle-side sensor 42- 46 may be retrieved from the vehicle-side sensors 42- 46 in a raw format and / or processed by the sensors, for example, for purposes of compression, filtering, and / or other formatting. Moreover, the vehicle-side sensor data may be sent (in raw or formatted form) over the communication bus 40 to one or more other portions of the vehicle electronics 20, such as the wireless communication device 30 and / or the onboard computer 22.The suspension sensors 42 are used to provide the suspension sensor data, which is a type of sensor data in the vehicle. The suspension sensors 42 may be any type of sensor that can acquire this suspension sensor data. The suspension sensor data may be used to determine a suspension distance, which is a distance between an associated vehicle wheel (or the ground) and a reference point of the vehicle body. In one embodiment, the suspension sensors 42 may include a strain gauge that may generate suspension sensor data used to determine a suspension distance. Those skilled in the art will appreciate that various suspension sensors may be used to provide the suspension sensor data, with which a suspension distance may then be determined. In one example, the suspension distance represents a distance between a reference point on the vehicle wheel and a reference point on the vehicle body, and / or may represent the change (or difference) between a resting suspension distance and a measured suspension distance. In one embodiment, the vehicle includes four wheels and four suspension sensors 42, each associated with one of the vehicle wheels. Of course, in other embodiments, the vehicle may include a different number of wheels and / or suspension sensors.The inertial sensor 44 is a motion sensor installed on the vehicle as a vehicle-side sensor. Although only a single inertial sensor 44 is illustrated and described, it should be appreciated that the vehicle 12 may include any number of inertial sensors. The inertial sensor 44 may be used to obtain inertial sensor data that is a type of vehicle-side sensor data that may be used to determine the acceleration and direction of acceleration of the vehicle, or a portion thereof. The inertial sensor data is a type of motion sensor data and also a type of vehicle-side sensor data. The inertial sensor 44 may be a microelectromechanical system (MEMS) sensor or accelerometer and may be part of an inertial measurement unit (IMU). The inertial sensor 44 may be used to detect collisions based on detection of relatively high deceleration as well as other events, for example, when the vehicle enters a segment of a roadway having a roadway cross slope above a predetermined threshold. In one embodiment, inertial sensor data may be continuously collected and sent to the onboard computer 22 (or other portions of the vehicle electronics 20), which may then process the inertial sensor data, for example, by using this data in road roll determination, which may also be continuously performed. In another embodiment, upon detection of an event, inertial sensor data may be sent from the inertial sensor 44 to the onboard computer 22 (or other portions of the vehicle electronics 20 (e.g., wireless communication device 30)), which may then process the inertial sensor data and / or send the inertial sensor data (or information based thereon or derived therefrom) to the remote facility 80. In one embodiment, the vehicle 12 may include a plurality of inertial sensors located throughout the vehicle. And in some embodiments, each of the one or more inertial sensors may be a multi-axis accelerometer that can measure acceleration or inertial force along a plurality of axes. The plurality of axes may be orthogonal or perpendicular to each other, respectively, and in addition, one of the axes may extend in the direction from the front side to the rear side of the vehicle 12. Other embodiments may use single axis acceleration sensors or a combination of single and multiple axis acceleration sensors. Other types of sensors may be used, including other accelerometers, gyroscope sensors, and / or other inertial sensors that are known or that may become known in the art.The vehicle 12 may include other motion sensors 46 that may be used to obtain motion sensor data regarding the vehicle, such as vehicle speed, acceleration, yaw (and yaw rate), pitch, turn, and various other attributes of the vehicle regarding its motion, measured locally through the use of vehicle-side sensors. The motion sensors 46 may be mounted to the vehicle in various locations, for example, within a vehicle interior cabin, a front or rear bumper of the vehicle, vehicle wheels, and / or the hood of the vehicle 12. Motion sensor data (i.e., vehicle-side sensor data obtained from the motion sensors 44, 46) may be obtained and sent to the other portions of the vehicle electronics 20, including the wireless communication device 30 and / or the vehicle-side computer 22.In one embodiment, motion sensors 46 may include wheel speed sensors that may be installed on the vehicle as vehicle-side sensors. The wheel speed sensors are each coupled to a wheel of the vehicle 12 and may determine a speed of the respective wheel. The speeds of various wheel speed sensors may then be used to obtain a linear or transverse vehicle speed. Moreover, in some embodiments, the wheel speed sensors may be used to determine the acceleration of the vehicle. In some embodiments, wheel speed sensors may be referred to as vehicle speed sensors (VSS) and may be part of an anti-lock braking system (ABS) of the vehicle 12 and / or an electronic stability control program.Alternatively or additionally, motion sensors 46 may include one or more yaw rate sensors that may be installed on the vehicle as a vehicle-side sensor. The yaw rate sensor(s) may obtain vehicle angular velocity information with respect to a vertical axis of the vehicle. The yaw rate sensors may include gyroscopic mechanisms that may determine the yaw rate and / or slip angle. Various types of yaw rate sensors may be used, including micromechanical yaw rate sensors and piezoelectric yaw rate sensors.Alternatively or additionally, the motion sensors 46 may also include a steering wheel angle sensor that may be installed on the vehicle as a vehicle-side sensor. The steering wheel angle sensor is coupled to a steering wheel of the vehicle 12 or a component of the steering wheel, which may be part of the steering column. The steering wheel angle sensor may detect the angle at which a steering wheel is turned, which may correspond to the angle of one or more vehicle wheels with respect to a longitudinal axis of the vehicle 12 that extends from rear to front.The global navigation satellite system (GNSS) receiver 48 receives radio signals (referred to as GNSS signals) from the plurality of GNSS satellites 60. the GNSS receiver 48 may be configured to comply with and / or operate according to certain regulations or laws of a certain geopolitic region (e.g., a country). The GNSS receiver 48 may be configured for use with various GNSS implementations including Global Positioning System (GPS) for the United States, BeiDou Navigation Satellite System (BDS) for China, Global Navigation Satellite System (GLONASS) for Russia, Galileo for the European Union, and various other satellite navigation systems. The GNSS receiver 48 may include at least one processor and memory, including a non-transitory computer readable memory storing instructions (software) that the processor can access to perform the processing performed by the GNSS receiver 48. The GNSS receiver 48 may be used to provide navigation and other position-related services to the vehicle operator. The navigation services may be provided using a dedicated navigation module in the vehicle (to which the GNSS receiver 48 may belong and / or may be integrated as part of the wireless communication device 30), or some or all of the navigation services may be via the wireless communication device 30 (or other telematics-enabled device) installed in the vehicle, with the position information sent to a remote location to provide the vehicle with navigation maps, points of interest (restaurant, etc.), route calculations, and the like.The GNSS receiver 48 may thus determine a geographic position of the vehicle 12 based on information included in a plurality of GNSS signals received from the plurality of GNSS satellites 60. The geographic position may include or be represented by a geographic coordinate, which may be, for example, a longitudinal / longitudinal coordinate pair. Also in at least one embodiment, geographic position may include an altitude. In some embodiments, the GNSS receiver 48 (or other portion of the vehicle electronics 20) may determine a vehicle trajectory or other position-related information regarding the vehicle 12, which may include a vehicle position, a vehicle heading, a vehicle speed (or speed), a vehicle acceleration, etc. This data received or derived from the GNSS receiver 48 (i.e., the "GNSS data") may be sent to other portions of the vehicle electronics 20, including the wireless communication device 30 and / or the onboard computer 22. The GNSS data may also be transmitted from the wireless communication device 30 to the remote facility 80 via the wireless carrier system 70 and / or the land network 76.The wireless carrier system 70 may be any suitable cellular system. The wireless carrier system 70 is illustrated as a cell tower 72; however, the wireless carrier system 70 may include one or more of the following components (e.g., depending on cellular technology): cell towers, base transceiver stations, mobile switches, base station controllers, evolved nodes (e.g., eNodeBs), mobility management entities (MMEs), serving and PGN gateways, etc., as well as any other networking components required to connect the wireless carrier system 70 to the land network 76 or connect the wireless carrier system to terminals (UEs) (e.g., wireless communication device 30 in the vehicle 12). Carrier system 70 may implement any suitable communication technology, including GSM / GPRS technology, CDMA or CDMA2000 technology, LTE technology, etc. In general, wireless carrier systems 70, their components, the arrangement of their components, the interaction between the components, etc., are well known in the art.Land network 76 may be a conventional land based telecommunications network connected to one or more land telephone sets and connecting wireless carrier system 70 to remote facility 80. For example, land network 76 may include a public switched telephone network (PSTN) as used for providing land telephony, packet switched data communication, and the Internet infrastructure. One or more segments of land network 76 could be realized through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), networks providing broadband wireless access (BWA), or any combination thereof.Remote facility 80 is a facility that is remote from vehicle 12 and includes one or more electronic computing devices, such as remote computer 82. In the illustrated embodiment, the remote facility 80 includes at least one remote computer 82 that includes a processor 84 and a memory 86. The remote facility 80 may be used for one or more purposes, for example, to provide backend vehicle services to one or more vehicles as well as to all other cloud-based services. In one embodiment, remote facility 80 includes a network of remote servers 82 hosted on the Internet in a cloud configuration to perform all or part of the method described herein. For example, processor 84 may execute computer instructions stored on memory 86, which may result in remote device 80 executing at least a portion of the method described herein.Each of the processors discussed herein (e.g., processor 24, processor 36) may be any type of device capable of processing electronic instructions, including microprocessors, microcontrollers, host processors, controllers, vehicle communication processors, general processing unit (GPU), accelerators, field programmable gated arrays (FPGA), and application specific integrated circuits (ASICs), to name a few possibilities. The processor may execute various types of electronic instructions, such as software and / or firmware programs stored in memory that enable the module to perform various functions. One or more of the memories discussed herein (e.g., memory 26, memory 38) may be a non-transitory computer readable medium; these include various types of random access memory (RAM), including various types of dynamic RAM (DRAM) and static RAM (SRAM), read only memory (ROM), solid state drives (SSDs) (including other solid state memories such as solid state hybrid drives (SSHDs)), hard drives (HDDs), magnetic or optical disk drives, or other suitable computer media that electronically store information. Although certain devices or components of the vehicle electronics 20 may be described as including a processor and / or memory, the processor and / or memory of these devices or components may be shared with other devices or components and / or packaged in (or a portion of) other devices or components of the vehicle electronics 20-for example-, each of these processors or memory may be a dedicated processor or memory used only for modules or shared with other vehicle systems, modules, devices, components, etc.Referring to FIG. 2, a flowchart depicting an example method 200 for determining a roadway cross slope based on vehicle information is shown. The method 200 may be performed by the vehicle electronics 20, the remote facility 80, or a combination thereof. In one embodiment, remote facility 80 performs method 200. In another embodiment, the onboard computer 22 and / or other portions of the vehicle electronics 20 may perform steps 210 and / or 220, and the remote facility 80 may perform step 230. Also in embodiments in which the vehicle electronics 20 perform one or more steps, the vehicle electronics 20 may do so with existing vehicle hardware. The term "roadway" as used herein generally includes any type of road (e.g., country road, pre-city road, highway, or expressway, etc.) on which the vehicle 12 may travel. As used herein, "roadway bank" refers to the sides angle or slope of an inclined portion of a roadway; typically, the sides angle of an inclined curve where the roadway slopes toward the inside of the curve.The method 200 begins with step 210 wherein vehicle information is obtained. The vehicle information is initially captured at the vehicle and may include vehicle-side sensor data obtained from one or more vehicle-side vehicle sensors or GNSS data captured by a GNSS receiver. The particular type of vehicle information obtained may be based on the particular method of determining the road grade to be performed (see step 220). For example, if a vehicle dynamic approach is used for road roll determination (see method 300 of FIG. 3 ), the vehicle information may include vehicle-side sensor data obtained from the vehicle-side vehicle sensors 42- 46 (e.g., the inertial sensor 44 may provide lateral acceleration data of the vehicle 12 and the suspension sensors 42 may provide suspension distances). In another example, where an elevation-based approach is used for the roadway roll determination process (see method 500 in FIG. 7 ), GNSS data including a geographic position of the vehicle 12 may be obtained with the GNSS receiver 48. In various embodiments, the method may obtain other types of vehicle information from the vehicle-side vehicle sensors 42- 46, the GNSS receiver 48, or other portions of the vehicle electronics 20, such as motion sensor data from other motion sensors 44, other vehicle-side sensor data, etc., as well as data that does not represent "vehicle information.".In some embodiments, step 220 described below may be performed at a remote device, such as remote device 80. In such embodiments, the vehicle information may be initially captured, derived, and / or otherwise obtained by the vehicle electronics 20 and then sent to the remote facility 80. For example, the vehicle electronics 20 may send the vehicle information to the remote facility 80 via the wireless communication device 30, the wireless carrier system 70, and / or the land network 76. Thus, the vehicle information in the remote facility 80 may be retrieved from the vehicle electronics 20 via a remote connection.In one embodiment, in response to receiving an indication that the roadway on which the vehicle is travelling is tilted, the vehicle electronics 20 may receive sensor data onboard the vehicle -- these indicators may be referred to as roadway roll indicators. It should be appreciated that in other embodiments, the sensor data of the vehicle-side sensor may be continuously retrieved, rather than in response to receiving an indication that the roadway on which the vehicle is travelling is over-elevated. Although the respective cross-lane inclination of the vehicle electronics 20 is not yet (or may not) known, the vehicle may still determine or recognize an indication that the roadway (on which the vehicle is travelling) is tilted. For example, if the inertial sensor data of the inertial sensor 44 indicates that the vehicle is inclined at a roll angle greater than a predetermined threshold (an example of a roadbank display), then the vehicle information may be retrieved. Further examples of road roll indicators are when the yaw rate is determined to exceed a predetermined threshold, when the suspension distance (as indicated by suspension sensor data from, for example, one or more suspension sensors 42) is above and / or below a predetermined threshold, or when a lateral acceleration is above a predetermined threshold. Any combination of these and other road grade indicators may be used. The method 200 then proceeds to step 220.In step 220, a method for determining the roadway bank is performed. The lane inclination determination method is a method that determines a lane inclination of a lane based on vehicle information of at least one vehicle. Two embodiments of a roadway bank determination method are discussed herein with reference to FIG. 3 (process 300) and FIG. 7 (process 500), as will be discussed in more detail below. Step 220 may be performed on the vehicle using the vehicle electronics 20 or on the remote facility 80. In some embodiments, portions of the roadway roll determination process may be performed at the vehicle electronics 20 and other portions of the roadway roll determination process may be performed at the remote facility 80. Once the roadway bank is determined, the method 200 proceeds to step 230.In step 230, a representative roadway grade is updated or otherwise determined based on the roadway grade (e.g., the roadway grade determined in step 220). Remote facility 80 may store roadway grade information for a plurality of roadway sections or sections that may be identified based on their geographical location. For example, the roadway grade information stored in the remote facility 80 may be part of the map or other navigation data and may include entries that include a roadway grade and an associated geographic position (e.g., geographic coordinates). In other embodiments, the roadway cross slope information may be stored by other devices in the remote facility 80, other computers accessible via the land network 76 and / or the wireless carrier system 70, and / or the vehicle electronics 20. Steps 210- 220 may be performed multiple times to determine the road inclination angles for different positions, and may be based on vehicle information obtained from different vehicles. Thus, many different values representing the road bank of a particular location may be determined. As used herein, "representative road grade" refers to an average, weighted, or representative value determined based on a plurality of road grade determinations. The representative roadway roll may be based on roadway rolls determined using various methods (e.g., vehicle dynamic roadway roll determination method 300 (FIG. 3 ), elevation-based roadway roll determination method 500 (FIG. 7 )), from various vehicles, and at different times. Remote facility 80 may merge or otherwise combine the various determined roadway slopes to determine the representative roadway slopes. Various weighting techniques may be used to determine the influence of any road roll on the representative road roll. The method 200 then ends.Referring to FIG. 3, a roadway roll determination method 300 is illustrated as being performed in accordance with a vehicle dynamics-based n approach. This vehicle dynamics-based road roll determination method 300 uses vehicle-side sensor data to determine the road roll slopes. In one embodiment, the onboard computer 22 may use the processor 24 to perform the vehicle dynamics-based n roadway cross slope determination method 300. The process 300 begins with step 310, wherein the sensor data of the vehicle-side sensor is obtained. In at least some embodiments, the vehicle dynamics-based approach uses chassis sensor data and inertial sensor data from the vehicle electronics 20, which may be obtained as described above in step 210 of the method 200 (FIG. 2 ). In one embodiment, the onboard computer 22 may obtain the vehicle-side sensor data from one or more vehicle-side sensors.Referring to FIGS. 4 and 5, plots are shown that respectively represent forces acting on the vehicle 12 as the vehicle travels along a roadway curve that is sloped under roadway cross slopes. FIG. 4 illustrates a theoretical scenario 140 in which there is no lateral friction between the vehicle (e.g., the tire of the vehicle) and the roadway surface 142. FIG. 5 illustrates a scenario 170 in which the friction between the vehicle (e.g., the tire of the vehicle) and the roadway surface 172 contributes to the lateral acceleration of the vehicle 12. In the theoretical scenario 140, centripetal or lateral acceleration a y may be given by a y= g×sin θ where g is gravity and θ is road lateral slope - in scenario 140, road lateral slope is the angle between road surface 142 and horizontal direction H. In addition, the relationship between vehicle speed v, road curve radius r, and road lateral slope θ may be represented by the following equations: where N is the normal force acting on the vehicle and m is the mass of the vehicle.In scenario 170 of FIG. 5, the centripetal or lateral acceleration of the vehicle is based on a normal force (due to gravity) and friction between roadway and vehicle (e.g., the tire of the vehicle). When rounding the roadway curve in scenario 170, the left front suspension distance d lf is smaller than the right front suspension distance d rf. The angle between the vehicle body and the road surface 172 is considered to be the roll angle β which can (for example) be determined from the distances of the left front suspension distance d lf, the left rear suspension distance d lr, the right front suspension distance d rf and the right rear suspension distance d rr and the lateral (or horizontal) distance(s) between these measurement points, which is represented by 1. The lateral acceleration a y may be given by a y= a f cos β+g sin(β-α), where α is the roadway lateral slope in scenario 170, the roadway lateral slope is the angle between the roadway surface 172 and the horizontal direction H. As will be explained in more detail below, the roll angle β may be determined using a suspension roll angle function. h(ΔD), where ΔD={Δd lf, Δd rf, Δd lr, Δd rr}. A left front suspension distance may be represented as Δd lf a right front suspension distance may be represented as Δd rf a left rear suspension distance may be represented as Δd lr and a right rear suspension distance may be represented as Δd rr. As mentioned above, these suspension distances may represent a distance between a vehicle wheel (or the ground) and a reference point on the vehicle body and / or may be represented as the change in distance between a resting suspension distance and a measured suspension distance, to name a few possibilities.The roll angle β may then be used to determine a frictional acceleration a f by using a roll angle frictional acceleration function. H (β)=a f. The following equation can be used to solve the road transverse inclination α:Thus, once the suspension distances of the vehicle 12 are obtained using the suspension sensors 42 and the lateral acceleration a y is obtained using the inertial sensor 44 (and / or other motion sensors 46), the roadway lateral slope α may be determined. Referring to step 310 of FIG. 3, vehicle-side sensor data may be collected from the suspension sensors 42 and the inertial sensor 44 or otherwise obtained from the vehicle 12. The method 300 then proceeds to step 320.In step 320, the frictional acceleration a f, the lateral acceleration a y and the roll angle β are determined. At least in one embodiment, inertial sensor data from the inertial sensor 44 is used to determine the magnitude of the lateral acceleration a y. The frictional acceleration a f may be determined using the equations above, including the suspension to roll angle function and / or the roll angle to frictional acceleration function. In one embodiment, the suspension-to-roll angle function h(ΔD) and / or the roll-to-friction acceleration function H(β) may be mapping functions and may be refined by machine learning techniques such as neural networks, regression, etc. The roll angle β may be determined by the suspension-to-roll angle function. h(ΔD), or may be determined or estimated using other known methods. The method 300 continues with step 330.In step 330, the road surface lateral inclination α is determined based on the frictional acceleration a f and the lateral acceleration a y. In at least one embodiment, equation (2) identified above may be used to derive roadway slopes α based on friction acceleration a f, lateral acceleration a y and roll angle β.In one embodiment, the method 300 may further include determining the frictional acceleration a f based on one or more suspension-related parameters. These suspension-related parameters may include all parameters that may affect the suspension distances determined above, which in turn may affect the friction acceleration estimate. Exemplary suspension-related parameters that may affect suspension distances and / or friction acceleration include weight distribution (e.g., weight distribution of objects including passengers in the vehicle), road roughness (e.g., coefficient of kinetic friction between the tires of the vehicle and the roadway), and other factors. Additionally or alternatively, the method 300 may further include accounting for random sensor noise in determining the frictional acceleration a f. The differences in friction acceleration due to the random sensor noise may be represented as ε 1 and the differences in friction acceleration due to the suspension-related parameters may be represented as ε 2. At least in one embodiment, the following equation may be used to represent the roll angle friction acceleration function H as it is affected by the random sensor noise and suspension related parameters:The roll angle-to-friction acceleration function H is modified to take into account the weight distribution, the road roughness, and other factors. This modified roll angle to friction acceleration function may be referred to as H' and may be represented by: where w is a weight distribution index, R is a road roughness index, and A represents other suspension related parameter values. The weight distribution index w may be based on the suspension distances. ΔD can be determined using a trained classifier W(·) such that the weight distribution index w=W(ΔD). The weight distribution index may be determined at a time prior to performing the method 300 (or another method described herein), for example, at a time when vehicle ignition is started. Thus, in one embodiment, suspension distances may be achieved at a time the vehicle is at a standstill and / or on a planar (and / or levelled) surface. The road roughness index R may be obtained based on the type of roadway (e.g., dirt, concrete) and / or measured by vehicle-side sensors (e.g., wheel speed sensors, suspension sensors) and stored in a remote facility, such as the remote facility 80, on the vehicle electronics 20, or a combination thereof. Since the modified roll angle friction acceleration function H' takes these suspension-related parameters into account, the differences in the friction acceleration by the suspension-related parameters (represented by ε 2) can be represented as:Of course, this represents a possibility to take one or more suspension-related parameters into account and other methods can certainly also be used.In some embodiments, the method 300 may also include performing online learning to improve the suspension-to-roll angle function h(ΔD). and / or the roll angle-to-friction acceleration function H(β). Referring to FIG. 6, a process 400 for performing online learning to refine the modified roll angle-to-friction acceleration function is illustrated. H'(β). Although the process 400 becomes H'(β)beschrieben with respect to the modified roll angle-to-friction acceleration function, the method 400 may also be used to refine the roll angle-to-friction acceleration function H(β). In addition, this method can refine a suspension-to-friction acceleration function, which is a function that maps the suspension displacements to a friction acceleration. This spring travel friction acceleration function may be developed based on the combination of the spring travel-to-roll angle function h(ΔD) and the roll angle-to-friction acceleration function H(β) (or modified roll angle-to-friction acceleration function) H'(β)). In this way, the suspension path friction acceleration function can map the suspension paths directly to a friction acceleration. This process 400 begins with step 410 in which a roll angle β, a lateral acceleration a y and a known pitch angle α K are determined or otherwise obtained. The known inclination angle α K is a known or estimated inclination angle of the road on which the vehicle travels at the time of measuring the suspension distances. The known angle of inclination α K may be obtained from a database or memory of the remote facility 80 and may be determined based on the above method 300 with respect to other vehicles. In another embodiment, the method 500 (FIG. 7 ) discussed below may be used to provide a known angle of inclination α K. And in another embodiment, road construction plans (or information) and / or map or navigation data may be used to provide the known angle of inclination α K. The roll angle β and the lateral acceleration a y may be determined as described above with respect to the method 300. The process 400 continues to step 420.In step 420, the roll angle β, the lateral acceleration a y and the known pitch angle α K are used to infer or otherwise determine a friction acceleration a f. The following equation can be used to determine a frictional acceleration a fAfter step 420, the friction acceleration a f may be combined or adjusted based on feedback friction acceleration information represented as a' f. The frictional acceleration a f may then be transferred to a machine learning process, as shown in FIG. 6. The process 400 continues to step 430.In step 430, the sensor data of the vehicle-side sensor is determined. This vehicle-side sensor data may include suspension distances ΔD, and in one embodiment may include suspension-related parameter values such as a weight distribution index w, a road roughness index R, and other suspension-related parameter values A.In step 440, machine learning is performed to improve the rolling angle friction acceleration function H. As mentioned above, machine learning may take into account the friction acceleration a f( step 420) determined above, as well as sensor data of the vehicle and / or chassis-related parameter values. Various machine learning techniques may be employed, such as neural networks, regression, etc. The machine learning output is presented at step 450, which may be a modified roll angle friction acceleration function H' that takes into account certain chassis-related parameter values and / or sensor data in the vehicle. Then, the modified roll angle-to-friction acceleration function H' may be used to determine feedback friction acceleration information a' f that may be used in subsequent iterations of the process 400. The process 400 may perform repeated iterations to continue or end learning.Referring to FIG. 7, a roadway bank determination method 500 is illustrated as being performed according to a height-based approach. The height-based approach uses GNSS data to determine the angle of inclination of the roadway. The height-based approach may be performed by one or more remote device computers or servers, such as one or more remote computers (e.g., remote computer 82) in remote device 80. However, in some embodiments, one or more of steps 510- 560 may be performed by vehicle electronics 20.Referring to FIG. 8, a diagram illustrating multiple vehicles traveling around a curved road that is elevated is illustrated. A plurality of vehicles 12A-C travel along roadway 600 inclined at roadway cross slopes α. These vehicles 12A-C may be the same as or similar to the vehicle 12 described above. The first vehicle 12A travels along the roadway 600 (on the roadway surface 602) at a radial distance d 1 from the center of curvature C of the roadway curve, the second vehicle 12B travels along the roadway 600 at a radial distance d 2 from the center of curvature C of the roadway curve, and the third vehicle 12C travels along the roadway 600 at a radial distance d 3 from the center of curvature C of the roadway curve. In addition, the first vehicle 12A is at a first height e 1, the second vehicle 12B is at a second height e 2, the third vehicle 12C is at a third height e 3.Referring to FIG. 7, process 500 begins with step 510, where GNSS data is obtained. In at least some embodiments, the elevation-based approach uses GNSS data from the GNSS receiver 48 of the vehicle electronics 20, which may be obtained as described above in step 210 of the method 200 (FIG. 2 ). This GNSS data may include a geographic position of the vehicle 12 that may be determined by the GNSS receiver 48 based on information included in a plurality of GNSS signals received from the plurality of GNSS satellites 60. The geographic position may include an elevation (or elevation coordinate or elevation data from barometer sensors (which may be incorporated as part of vehicle electronics)), and in one embodiment, the geographic position includes an elevation, a latitude, and a longitudinal coordinate. In some embodiments, the GNSS data may be used to determine trajectory information of the vehicle, including a vehicle speed and a vehicle direction. In embodiments where the subsequent process steps are performed at a remote facility, all or part of the GNSS data may be sent from the vehicle to the remote computer 82 (or other portion of the remote facility 80) using the wireless communication device 30. In at least one embodiment, remote facility 80 may receive GNSS data from a plurality of vehicles (e.g., vehicles 12A-C), and then this GNSS data from plurality of vehicles may be used to determine a roadway cross slope, as will be discussed in more detail below. The process 500 continues to step 520.In step 520, schedule data is obtained. The travel plan data includes maps and / or other types of navigation information digitally representing geographic areas of the earth, including lanes. The travel plan data may include roadway boundary information, roadway dimensions, roadway attributes (e.g., speed boundary, allowable travel direction, lane information, traffic light data), roadway conditions (e.g., current or estimated traffic conditions, predicted and / or observed weather conditions between the roadway), and various other information. In one embodiment, the schedule data may also include or be based on topographical map information. In embodiments where process 500 is performed by remote device 80, the schedule data may be retrieved from a remote server or computer that is separate from remote device 80 or from internal map data acquisitions. This separate remote server or computer may be a third party server that provides open source map (OSM) data, which may also include schedule data. Remote facility 80 may obtain this schedule data by downloading the schedule data to the separate remote server or computer over a remote connection, for example, through the use of land network 76. process 500 continues to step 530.In step 530, a curvature extraction process is performed using the road map data. In at least one embodiment, curvature extraction process extracts (or determines) a representative radius and center of curvature from schedule data. This process may identify a roadway curve of a roadway by reviewing the roadway map data and then obtaining roadway curve information. The roadway curve information may be any roadway curve information, such as geometric roadway curve information including a position of a center of curvature of the roadway curve, distance(s) (or radius(s)) between the roadway and the center of curvature, arc length of the roadway curve, etc. According to the non-limiting example in FIG. 8, the center of curvature C yields the radii d 1, d 2, and d 3. The travel plan data may include a plurality of reference points corresponding to the lane curve, which may be fitted to a circle using a least squares fitting method. A representative radius of the roadway may be measured from the center of curvature to the center of the roadway along the roadway curve, an inner edge of the roadway, an outer edge of the roadway, etc. In at least one embodiment, representative radius may be extracted or otherwise determined once reference points of schedule data are attached to a circle.In one embodiment, steps 520 and / or 530 may be performed in response to receiving GNSS data from one or more vehicles. In such embodiments, a general area in which the vehicle(s) are located may be determined and then the schedule data may be obtained based on the location of the vehicle(s). In further embodiments, steps 520 and / or 530 may be performed prior to receiving the GNSS data. In one embodiment, process 500 may further include a step of identifying regions where there is a roadway curve that may be defined as a path along a roadway in which the degree of curvature is above a predetermined threshold; however, in other embodiments, this step may be omitted. The process 500 continues to step 540.In step 540, the geographic position(s) (included in or determined from the GNSS data) are converted to a suitable coordinate system, such as polar coordinates. In one embodiment, the GNSS data obtained in step 510 includes a geographic position of the vehicle. The geographic position may include GNSS coordinates that may be represented by Cartesian coordinates, such as a latitude coordinate, a longitude coordinate, and an elevation coordinate. These cartesian coordinates are converted to polar coordinates by known methods, which are estimated by those skilled in the art. The coordinates of the geographical position may be transformed by calculating the distance and angle from the center of curvature for each of the geographical positions. These geographic coordinates, which are converted to polar coordinates, may be referred to as transformed geographic coordinates. In some embodiments, this transformation step may not be necessary or desirable, such that, for example, all references herein to "geographic coordinates" or "geographic location" may refer to Cartesian coordinates or polar coordinates. The polar coordinates may represent the radial distances of the vehicles from the center of curvature (e.g., radial distances d 1, d 2, d 3 (FIG. 8 )) as well as the height of the vehicle (e.g., heights e 1, e 2, e 3 (FIG. 8 )). The process 500 continues to step 550.In step 550, the transformed geographic(s) position(s) are adjusted to a slope using a slope adjustment technique to determine a roadway cross slope. In at least some embodiments, a linear regression technique is used in which the transformed geographic(s) position(s) are fitted to a linear regression line, as shown in FIG. 9. FIG. 9 illustrates a diagram 700 illustrating radial distance data points representing a portion of a roadway curve. The x-axis 702 represents a distance from the center of curvature and the y-axis 704 represents a height. The small points (or radial distance data points) 712 represent geographic coordinates of various vehicles, the large points 714, which are also radial distance data points, represent representative geographic coordinates for a particular radial distance (or range of radial distances) from the center of curvature, and the line 720 is the linear regression line determined from the transformed geographic positions (or radial distance data points). In one embodiment, each representative geographic coordinate 714 may be provided for a particular radial distance (or range of radial distances) from the center of curvature, and in such a case, each representative geographic coordinate 714 may include a representative height value, which may be an average or mean value of the height taken from points at the respective radial distance (or range of radial distances). These points may be determined based on the radial distances of the vehicles from the center of curvature (e.g., radial distances d 1, d 2, d 3 (FIG. 8 )) as well as the elevation of the vehicle (e.g., elevation e 1, e 2, e 3 (FIG. 8 )), which may be represented by or based on information contained in (or derived from) the transformed geographic position(s). From these representative geographical coordinates 714, the linear regression line 720 can then be determined with the aid of a linear regression technique. The slope of the linear regression line 720 may be used to determine the roadway transverse slope α. The roadway slopes α correspond to the angle between the linear regression line 720 and a reference line that is parallel to the x-axis. This linear regression line 720 can be seen as representing the road surface, such as road surface 602, as shown in FIG. 8. Of course, other regression and slope adjustment techniques may be used in other embodiments. The process 500 then ends or may be re-performed for repeated execution and continuous updating of the roadway cross slope.As used in this specification and claims, the terms "for example," "for example," "for example," "such as," and "like," and the verbs "comprising," "having," "including," and their other verb forms, when used in connection with a listing of one or more components or other items, are each to be construed as open-ended, meaning that the listing is not to be viewed as excluding other, additional components or items. Other terms are to be interpreted with their broadest reasonable meaning unless they are used in a context that requires a different interpretation. Moreover, the term "and / or" is to be construed as inclusive or inclusive. By way of example, the phrase "A, B, and / or C": "A"; "B"; "C"; "A and B"; "A and C"; "B and C"; and "A, B, and C".

Claims

A method (200) for determining, based on vehicle information, a roadway roll, the method (200) comprising the steps of: obtaining (210) vehicle information from at least one vehicle (12), wherein the vehicle information is obtained from at least one of a global navigation satellite system, GNSS, receiver (48) and one or more vehicle-side sensors (42-46), and the GNSS receiver (48) and the one or more vehicle-side sensors (42-46) are installed in the at least one vehicle (12); performing (220) a roadway roll determination method using the obtained vehicle information to obtain a roadway roll; An update (230) of a representative roadway roll based on the roadway roll, wherein the roadway roll determination method is a vehicle dynamics-based roadway roll determination method, and the step of obtaining (210) vehicle information includes obtaining vehicle-side sensor data from one or more vehicle-side sensors (42-46) of the at least one vehicle (12), and the vehicle-side sensor data includes suspension sensor data from a plurality of suspension sensors (42) installed on the at least one vehicle (12); wherein the vehicle dynamics-based roadway roll determination method includes determining a friction acceleration of the at least one vehicle (12) based on the suspension sensor data.The method (200) of claim 1, wherein the vehicle dynamics-based road roll determination method comprises determining a roll angle of the at least one vehicle, and the roll angle is determined based on the suspension sensor data using a suspension-to-roll angle function.The method (200) of claim 1, wherein the vehicle dynamics-based roadway lateral slope determination method includes determining the frictional acceleration using a roll angle-to-frictional acceleration function or a suspension distance-to-frictional acceleration function, and determining a lateral acceleration of the at least one vehicle (12) based on the vehicle-side sensor data.The method (200) of claim 1, wherein the roadway roll determination method is an elevation-based roadway roll determination method and the vehicle information comprises GNSS data used to obtain a geographic position of the at least one vehicle (12), and wherein the GNSS data includes an elevation of the at least one vehicle (12) and is obtained from the GNSS receiver (48) installed in the at least one vehicle (12).The method (200) of claim 4, wherein the elevation-based roadway cross slope determination method includes obtaining roadway map data of an area that includes the geographic location of the at least one vehicle and that includes a portion of a roadway (600) having a roadway curve that is elevated.The method (200) of claim 5, wherein a curvature extraction method is performed using the roadway map data to extract roadway curve information regarding the roadway curve, wherein the roadway curve information includes a geographic point representing a center of curvature of the roadway curve.The method (200) of claim 6, wherein the elevation-based roadway roll determination method comprises performing a linear regression on a plurality of radial distance data points derived from a plurality of vehicles (12A-C) traveling along the roadway curve.A method (500) of determining a roadway cross slope based on vehicle information, the method (500) comprising the steps of: obtaining (510) global navigation satellite system, GNSS, data from a GNSS receiver (48) installed in the vehicle (12) for each of a plurality of vehicles (12A-C), wherein the GNSS data includes a geographic position of the vehicle (12), and wherein the geographic position includes an altitude; obtaining (520) travel plan data of an area including a roadway curve along which the plurality of vehicles (12A-C) travels or has traveled; extracting (530), using the obtained roadway map data, roadway curve information about the roadway curve; mapping (540) the geographic positions of the plurality of vehicles (12A-C) to the extracted roadway curve information to determine a radial distance of each geographic position taken from a center of curvature of the roadway curve; and deriving (550) a representative roadway cross slope based on the radial distances and the height of each of the plurality of vehicles (12A-C) using a linear regression technique.

Images