Method and apparatus for predicting height using compressed road information

Abstract

The invention provides a method and a device for predicting height by using compressed road information. The method includes generating, based on two-dimensional raw road information, two-dimensional compressed road information associated with a specified reference point for each of one or more sections of a road distinguished in a predetermined manner. The method further includes generating compressed height information corresponding to the specified reference point based on map information including the original height information. The method additionally includes predicting forward gradient information based on the three-dimensional compressed road information having the two-dimensional compressed road information and the compressed height information, a position of the vehicle, and a driving direction of the vehicle.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to Korean Patent Application No. 10-2023-0133530, filed on October 6, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to a method of predicting altitude by using compressed road information and an apparatus using the same, and more particularly, to a method of predicting altitude by estimating a slope of forward travel. Background Art

[0004] Forward height prediction is an important component of the vehicle's driving dynamics control and accurate calculation of the remaining drivable distance (distance to empty, DTE). In this article, the vehicle can be an electrified vehicle driven by electric energy. In the case of an electrified vehicle, the vehicle can use a battery as a buffer. The vehicle can charge the battery by absorbing gravitational potential energy through regenerative braking in a downhill section of the road, or it can operate a drive motor in an uphill section of the road and increase the potential energy by utilizing the energy stored in the battery. In addition, in the case of a vehicle that charges the battery based on the power generation of a fuel cell, the vehicle can generate electricity from the fuel cell according to the state of the uphill section of the road and thereby charge the battery using the electricity required for uphill driving.

[0005] Since a large amount of energy is exchanged between the energy accumulated in the battery and the potential energy in high energy areas such as high slope sections, it may become necessary to predict DTE and control the fuel cell prediction. If the fuel cell prediction is not controlled, the continuous drop in the battery charge level during driving in high weight and high slope sections of the road may make it impossible to drive normally. As a result, it may be necessary to forcibly charge the high voltage battery after stopping.

[0006] If the slope ahead is not taken into account when predicting DTE, the battery of the electric energy-based vehicle may be exhausted earlier than expected in the uphill section of the road, which may lead to a situation where it is impossible to drive. In addition, in the case of a fuel cell-based vehicle, regenerative braking in the downhill section of the road can store electrical energy from gravitational potential energy, thereby improving fuel efficiency. However, during downhill driving, when the battery is overcharged without considering the slope ahead, the gravitational potential energy cannot be recovered. Therefore, when the slope ahead is not taken into account, fuel efficiency may decrease. On the other hand, when there is a downhill road ahead on the expected driving route, fuel efficiency can be improved if the amount of chargeable energy of the battery is ensured and the gravitational potential energy is recovered as electrical energy as much as possible on the downhill road.

[0007] Therefore, for electrified vehicles, forward altitude prediction may be required for fuel cell power generation control and prediction, high voltage battery output control, regenerative braking, DTE prediction, etc.

[0008] Existing altitude prediction methods provide information on the slope of the road ahead at a limited distance from the current driving position on the route to the destination set by the navigation system. According to the existing methods, it is impossible to predict the altitude ahead at a long distance. This is because a large amount of altitude information is not efficient for on-board storage in the vehicle. Moreover, such a large amount of altitude information is also a burden for real-time transmission. Summary of the invention

[0009] Various approaches are being tried to obtain the long-range forward slope information required to effectively control driving dynamics and accurately calculate DTE.

[0010] An embodiment of the present disclosure provides a method for predicting altitude by estimating a slope of forward travel based on a small amount of compressed road information having compressed external three-dimensional information including a large amount of altitude information, and a device using the same.

[0011] It should be understood by those skilled in the art that the objectives to be achieved by the present disclosure are not limited to the contents described above. Other objectives not mentioned above should be clearly understood from the following detailed description.

[0012] According to an embodiment of the present disclosure, a method for predicting altitude by using compressed road information is provided. The method includes: generating two-dimensional compressed road information associated with a specified reference point of each of one or more sections of a road distinguished in a predetermined manner based on two-dimensional original road information. The method also includes: generating compressed altitude information corresponding to the specified reference point based on map information including original altitude information. The method further includes: predicting forward slope information based on three-dimensional compressed road information having two-dimensional compressed road information and compressed altitude information, the position of the vehicle, and the driving direction of the vehicle.

[0013] According to an embodiment, the two-dimensional original road information may be configured in a format including reference point data associated with reference points allocated along the road. Moreover, the reference point data may include an identifier of the reference point, reference state data represented by a distance from a specific point on the road, and reference position data having two-dimensional position data of the reference point described in a preset coordinate system.

[0014] According to an embodiment, the method may further include: when the two-dimensional original road information is not configured in the format, converting the two-dimensional original road information into a format including reference point data.

[0015] According to an embodiment, the two-dimensional compressed road information may be generated through numerical optimization based on the two-dimensional original road information.

[0016] According to an embodiment, the two-dimensional compressed road information may include a first parameter applied to a first approximation function that approximates the two-dimensional original road information by numerical optimization. The first parameter may include specified reference point data for each of one or more intervals corresponding to the number of intervals determined by numerical optimization.

[0017] According to an embodiment, generating two-dimensional compressed road information may include: setting the number of intervals in the entire range of reference point data present in the two-dimensional original road information; searching for the first parameter by applying numerical optimization of the number of intervals. Generating two-dimensional compressed road information may also include: when the maximum distance error among the distance errors at each first test point according to the first parameter exceeds the tolerable distance error and the number of intervals is less than the tolerable number of intervals, re-searching the first parameter by increasing the number of intervals. Generating two-dimensional compressed road information may further include: when the maximum distance error is equal to or less than the tolerable distance error or the number of intervals is equal to or greater than the tolerable number of intervals, determining the first parameter according to the number of intervals as the designated reference point data.

[0018] According to an embodiment, the compressed height information may be generated through numerical optimization based on the two-dimensional original road information and the map information.

[0019] According to an embodiment, the compressed height information may include a second parameter applied to a second approximation function that approximates the original height information by numerical optimization. The second parameter may include coefficient data associated with a configuration vector function having an order for each of the one or more intervals determined by numerical optimization.

[0020] According to an embodiment, generating compressed height information may include: smoothing the height information extracted from the map information in response to the position data of the reference point in the two-dimensional original road information. Generating compressed height information may also include: setting the order for each of one or more intervals of the two-dimensional compressed road information. Generating compressed height information may further include: searching for the second parameter by applying numerical optimization of the order. Generating compressed height information may further include: when the maximum height error among the height errors at each second test point according to the second parameter exceeds the tolerable height error and the order is less than the tolerable order, re-searching the second parameter by increasing the order. Generating compressed height information may further include: adopting the second parameter when the maximum height error is equal to or less than the tolerable height error or the order is equal to or greater than the tolerable order.

[0021] According to an embodiment, predicting the front slope information may include: determining whether the vehicle's travel is estimated in at least one interval of the intervals of the three-dimensional compressed road information. Predicting the front slope information may also include: determining the nearest interval of the vehicle based on the allowable range of at least one interval. Predicting the front slope information may further include: estimating the front slope information based on the three-dimensional compressed road information of the nearest interval and the driving direction.

[0022] According to another embodiment of the present disclosure, a device for implementing height prediction by utilizing compressed road information is provided. The device includes: a memory storing at least one instruction; and a processor executing at least one instruction based on data obtained from the memory. The processor is configured to generate two-dimensional compressed road information associated with a specified reference point of each section of a road distinguished in a predetermined manner based on the two-dimensional original road information. The processor is also configured to generate compressed height information corresponding to the specified reference point based on map information including original height information. The processor is further configured to provide three-dimensional compressed road information having two-dimensional compressed road information and compressed height information for controlling a vehicle, so as to predict the front slope information based on the three-dimensional compressed road information, the position of the vehicle, and the driving direction of the vehicle.

[0023] The features briefly summarized above are only illustrative aspects of the present disclosure. These features are not intended to limit the scope of the present disclosure.

[0024] An embodiment of the present disclosure provides a method for predicting altitude by estimating a slope of forward travel based on a small amount of compressed road information having compressed external three-dimensional information including a large amount of altitude information, and a device using the same.

[0025] The effects that can be achieved by the present disclosure are not limited to the above-mentioned effects. Other effects not described herein should be clearly understood by those skilled in the art from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a diagram showing transmission and reception of data when a vehicle communicates with another device.

[0027] Figure 2 is a view showing modules constituting a vehicle according to an embodiment of the present disclosure.

[0028] Figure 3 is a view illustrating modules constituting an external device according to an embodiment of the present disclosure.

[0029] Figure 4 is a flowchart of a method for predicting height using compressed road information according to an embodiment of the present disclosure.

[0030] Figure 5is a flowchart of a process of converting two-dimensional original road information into a predetermined format according to an embodiment of the present disclosure.

[0031] Figure 6 is a flowchart of a process of generating two-dimensional compressed road information according to an embodiment of the present disclosure.

[0032] Figure 7 is a view illustrating processing of two-dimensional original road information into two-dimensional compressed road information according to an embodiment of the present disclosure.

[0033] Figure 8 is a flowchart of a process of generating compressed height information according to an embodiment of the present disclosure.

[0034] Fig. 9 is a view illustrating smoothed original height information according to an embodiment of the present disclosure.

[0035] Fig.10 is a view illustrating processing original height information into compressed height information according to an embodiment of the present disclosure.

[0036] Fig.11 is a flowchart of a process of predicting forward slope information according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] Hereinafter, embodiments of the present disclosure are described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement the embodiments of the present disclosure. However, the present disclosure can be implemented in various different ways, so the present disclosure is not limited to the embodiments described herein.

[0038] In the following description, when it is considered that a detailed specific description of a well-known feature, function or configuration may unnecessarily obscure the gist of the present disclosure, a detailed description thereof is omitted. In addition, the same constituent elements in the drawings are represented by the same reference numerals, and repeated description of the same elements is omitted.

[0039] In the present disclosure, when an element is simply referred to as being "connected to," "coupled to," or "linked to" another element, this may mean that the element is "directly connected to," "directly coupled to," or "directly linked to" another element, or is connected to, coupled to, or linked to another element with one or more intermediate elements between the two elements. In addition, when an element is described as "including" or "having" another element, this means that the element may further include other elements. Unless otherwise specifically stated, these terms do not exclude the possibility that an element "includes" or "has" one or more additional elements or components.

[0040] In the present disclosure, the terms first, second, etc. are only used to distinguish one element from another element. Unless specifically mentioned, these terms do not limit the order or importance between elements. Therefore, without departing from the scope of the present disclosure, the first element in one embodiment can be referred to as the second element in another embodiment. Similarly, without departing from the scope of the present disclosure, the second element in one embodiment can be referred to as the first element in another embodiment.

[0041] In the present disclosure, the elements that are distinguished from each other are distinguished only for the purpose of clearly describing each feature. These distinctions do not necessarily mean that the elements are separate. For example, multiple elements can be integrated in one hardware or software unit, or one element can be distributed and formed in multiple hardware or software units. Therefore, even if not mentioned otherwise, these integrated or distributed embodiments are also included in the scope of the present disclosure.

[0042] In the present disclosure, the elements described in the various embodiments are not necessarily meant to be essential elements. Some elements may be optional elements. Therefore, the embodiments consisting of a subset of the elements described in the embodiments are also included in the scope of the present disclosure. In addition, the embodiments including other elements in addition to the elements described in the various embodiments are also included in the scope of the present disclosure.

[0043] The advantages and features of the present disclosure and the methods for achieving these advantages and features will become more apparent with reference to the embodiments described in detail below in conjunction with the accompanying drawings. However, the embodiments may be implemented in many different forms and should not be construed as being limited to the example embodiments set forth herein. Instead, these embodiments are provided to ensure that the present disclosure is complete and to fully convey the scope of the present disclosure to those of ordinary skill in the art.

[0044] In the present disclosure, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", "at least one of A, B or C", and "at least one of A, B, C or a combination thereof" may include any or all possible combinations of items listed together in a corresponding one of these phrases.

[0045] When a component, device, element, etc. of the present disclosure is described as having a purpose or performing an operation, function, etc., the component, device or element should be considered herein as being “configured to” satisfy the purpose or perform the operation or function.

[0046] In the following, reference is made to Figures 1 to 3 , describing a vehicle and an external device constituting a system using compressed road information according to an embodiment of the present disclosure. Figure 1is a view illustrating transmission and reception of data when a vehicle communicates with another device according to an embodiment of the present disclosure.

[0047] Reference Figure 1 , the vehicle 100 can be driven based on electrical energy or fossil energy. The vehicle 100 based on electrical energy can be a pure battery-based vehicle driven only by a high-voltage battery, or the vehicle 100 can also use a gas-based fuel cell as an energy source. In the case of a fuel cell, the vehicle 100 can charge the high-voltage battery through the power generation of the fuel cell, and perform various functions required by the modules of the vehicle 100 through the output power of the high-voltage battery. In addition, the fuel cell can use various types of gases that can generate electrical energy. For example, the gas can be hydrogen. However, the gas is not limited to this. Various other gases can be used. The vehicle 100 based on fossil energy can be driven by an internal combustion engine that uses petroleum as an energy source.

[0048] The present disclosure describes a fuel cell-based vehicle 100 as an example of an electric vehicle. However, the present disclosure can be applied to a vehicle in which the high-voltage battery and the battery are heterogeneous. In this case, the power generation of the battery charges the high-voltage battery, and the high-voltage battery outputs power for starting, driving, and auxiliary machinery of the vehicle 100.

[0049] The vehicle 100 may refer to a device that is capable of moving. For example, the vehicle 100 may be a typical passenger vehicle or a commercial vehicle, a mobile office, or a mobile hotel. The vehicle 100 may be a four-wheeled vehicle, such as a car, a sport utility vehicle (SUV), a small truck, etc. In some examples, the vehicle 100 may be a vehicle with more than four wheels, such as a bus, a large truck, a container truck, a heavy vehicle, etc. The vehicle 100 may be a manned or unmanned robot using multiple batteries, such as a robot system for construction machinery. The vehicle 100 may be implemented by manual operation or automatic driving (including semi-automatic driving and fully automatic driving).

[0050] The vehicle 100 can communicate with another device or another vehicle under the control of a communication control unit (CTU) mounted thereon. For example, the other device may include a support server 400 for supporting various controls, state management, and driving, an ITS device for receiving information from an intelligent transportation system (ITS), various types of user devices, and the like.

[0051] The vehicle 100 may communicate with another vehicle or another device based on cellular communication, Wireless Access to Vehicular Environment (WAVE) communication, Dedicated Short Range Communication (DSRC) or short range communication, or any other communication scheme.

[0052] For example, the vehicle 100 may communicate with the support server 400 and another vehicle using Long Term Evolution (LTE) as a cellular communication network, a communication network such as 5G, a WiFi communication network, a WAVE communication network, etc. In addition, the DSRC used in the vehicle 100 may be used for vehicle-to-vehicle communication. The communication scheme between the vehicle 100 and another device is not limited to the above-described embodiment.

[0053] In an embodiment, the processor 118 can be used in the vehicle 100 to generate three-dimensional compressed road information based on map information including road information and altitude information, and store and manage the map information and / or three-dimensional compressed road information in the memory 116. For example, the memory 166 can be a non-volatile memory (NVM). The map information can be integrated in the memory 116. Additionally or alternatively, map information can be received from an external device 200. The map information received from the external device 200 can be map data of a neighboring area at least associated with the driving route. In an embodiment of the present disclosure, the device for implementing altitude prediction using compressed road information can be the vehicle 100 or can be another device configured to include at least the processor 118 and the memory 116 of the vehicle 100.

[0054] The external device 200 may communicate with the vehicle 100 to exchange various types of data required to drive the vehicle 100. The external device 200 may process information and provide the information to the vehicle 100. In an example, the external device 200 may include a data server 300 and a support server 400.

[0055] The data server 300 can manage map information including road information and altitude information and three-dimensional compressed road information. The data server 300 can send at least one of the above information in response to receiving a request from the vehicle 100 and / or the support server 400. The above example describes that the vehicle 100 generates three-dimensional compressed road information. In another example, the data server 300 can generate three-dimensional compressed road information and send it to the vehicle 100, etc. In an embodiment of the present disclosure, the generation of three-dimensional compressed road information can be performed by a device that implements altitude prediction. Such a device can be the data server 300 or another device configured to include at least the processor 206 and the memory 204 of the data server 300.

[0056] Map information may include numerical elevation information and map data represented by a two-dimensional position, and the numerical elevation information includes height data corresponding to the two-dimensional position. Numerical elevation maps can be generated by measuring the surface of the ground, rather than the height of an artifact. A digital elevation map (DEM) capable of identifying the height of corresponding surface points can be used as a numerical elevation map. Surface points can be represented by latitude and longitude. For example, a numerical elevation map can be an open map data recorded by NASA (USA), JAXA (Japan), etc. through remote radiometric measurement of numerical elevation. NASA SRTM maps have a resolution of 90 meters on the entire earth and can be generated to have a resolution of 30 meters for a specific area. JAXA AW3D30 maps can be generated to have a resolution of 30 meters on the entire earth. For areas without observation data such as elevation, i.e., blank areas, numerical elevation maps can be generated with reference to blank area-related data of other maps. The data of the numerical elevation map is represented by h (φ, λ), where φ and λ are latitude and longitude, respectively.

[0057] The support server 400 may send various information and software modules for controlling the vehicle 100 to the vehicle 100 in response to requests and data received from the vehicle 100. For example, the support server 400 may be a connected car service server. In an embodiment, the support server 400 may provide the vehicle 100 with map information, road information, traffic information, and / or weather information required for driving.

[0058] Figure 2 is a view showing modules constituting a vehicle according to an embodiment of the present disclosure. Figure 2 The vehicle 100 shown may be a mobile object driven by electric energy. The vehicle 100 may charge a battery 102 by generating electricity from a fuel cell 104 installed therein.

[0059] The vehicle 100 may include a battery 102 , a fuel cell 104 , and a wheel drive unit 106 .

[0060] The battery 102 can be charged by the power generation of the fuel cell 104. The battery 102 can thus provide the power required by the modules of the vehicle 100. For example, the battery 102 can be a high-voltage battery composed of secondary batteries. The battery 102 can provide energy for starting and driving the vehicle 100 and the operation of the auxiliary machinery 110. For example, the battery 102 can provide energy applied from the fuel cell 104 for starting, driving, air conditioning and / or many other electrical devices of the vehicle 100. The battery 102 can output a higher voltage than the fuel cell 104, and can provide energy to, for example, the wheel drive unit 106 and the high-power electrical module.

[0061] The fuel cell 104 may include a hydrogen fuel cell that generates electrical energy by reacting hydrogen supplied from a tank (not shown) with oxygen from the outside. The fuel cell 104 may generate an amount of electricity determined based on the power required for starting, driving, and auxiliary machinery 110, and the battery 102 may be charged with the electricity generated thereby. In addition, according to the design specifications, the fuel cell 104 may supply energy to a low-power electrical module installed in the vehicle 100. Hereinafter, for ease of explanation, the battery 102 and the fuel cell 104 are described as examples. However, the present disclosure may also be applied to a vehicle 100 that does not have any battery such as a fuel cell 104 for charging the battery 102 inside the vehicle 100. Such a vehicle 100 may be operated only by a battery 102 charged by an external device, except for regenerative braking.

[0062] The vehicle 100 may also include a converter (not shown) or other modules used as a step-up / step-down transformer. The converter or other modules may charge the battery 102 by converting the voltage from the fuel cell 104 and supplying it to the battery 102. Depending on the operating conditions, the converter or other modules may supply power to the wheel drive unit 106 and various electronic devices at the converted voltage. For example, the electronic device may be an auxiliary machine 110.

[0063] The wheel drive unit 106 may be a module that receives power from the battery 102 and drives the wheel. The wheel drive unit 106 may include a motor unit and a wheel unit. For example, the wheel unit may be connected to the motor unit and driven by the motor unit. As another example, only a portion of the wheel unit may be connected to the motor unit, and any wheel unit that is not connected to the motor unit may be driven by the wheel unit driven by the motor. The wheel unit may have a wheel and a wheel brake module. The wheel brake module may be a module that transmits a braking force to the wheel under a deceleration control request of the processor 118. The wheel brake module may thereby decelerate the wheel.

[0064] The motor unit can receive power from the battery 102 and can generate a driving force. The motor unit can transfer the driving force to the wheel unit so that the wheel unit can be driven to rotate. For example, the motor unit can have a motor and a motor control module for controlling the motor torque, the motor rotation direction and the braking. The motor unit can be driven by receiving the power applied from the battery 102 via an inverter (not shown). The inverter can convert a specific form of power such as alternating current of the battery 102 into another form such as direct current and / or reduce the voltage. The inverter can convert the reverse power of the predetermined form of the motor unit generated by regenerative braking into a form suitable for the battery 102, and can provide it to the battery 102.

[0065] The vehicle 100 may include a sensor unit 108 , an auxiliary machine 110 , a transceiver 112 , a display 114 , a memory 116 , and a processor 118 .

[0066] The sensor unit 108 may be equipped with various types of sensor modules for detecting various states and situations occurring in the internal and external environments of the vehicle 100. For example, the sensor unit 108 may be equipped with a positioning sensor 108a, an acceleration sensor 108b, and a speed sensor 108c.

[0067] The positioning sensor 108a can measure the two-dimensional position and height of the vehicle 100 during driving in order to detect the position of the vehicle 100. For example, the positioning sensor 108a can be a global navigation satellite system (GNSS) sensor, a global positioning system (GPS) sensor, etc. The GPS sensor can measure the position of the vehicle 100 based on information sent from multiple satellites. The positioning sensor 108a is not limited to a GPS sensor. For example, the positioning sensor 108a can be configured as a plurality of sensors, and the plurality of sensors include a GPS sensor and another sensor combined therewith. The acceleration sensor 108b can be equipped with a gyro sensor and / or a three-axis acceleration sensor that detects the attitude and direction of the vehicle 100. The acceleration sensor 108b can estimate or measure the slope of the vehicle 100 by a combination of the above sensors. For example, the speed sensor 108c can be configured as a wheel speed sensor connected to an electric brake system (EBS). The speed sensor 108c can measure the vehicle speed.

[0068] although Figure 2 Although not shown in the figure, the sensor unit 108 may include an image sensor, a lidar sensor, a laser sensor, a distance sensor, etc. The present disclosure mainly describes the sensors referred to in the embodiments of the present disclosure. The sensor unit 108 may further include one or more other sensors for detecting various situations not listed herein.

[0069] The auxiliary machinery 110 may be installed on the vehicle 100. The auxiliary machinery 110 may be an auxiliary device that consumes power supplied from the battery 102 when used by an occupant or other user of the auxiliary device. In an embodiment of the present disclosure, the auxiliary machinery 110 may be an electrical device of a type used for non-driving other than a driving power system such as a wheel drive unit 106. The auxiliary machinery 110 may include various devices installed in the vehicle 100, such as an air conditioning system, a lighting system, a seat system, and the like.

[0070] The transceiver 112 may support mutual communication with the support server 400 , nearby vehicles, roadside base stations, or user devices.

[0071] In an embodiment of the present disclosure, under the control of the CTU, the transceiver 112 may transmit data generated or stored during driving to the support server 400, and receive data and software modules transmitted from the support server 400. In an embodiment of the present disclosure, the vehicle 100 may receive map information including a numerical elevation map or information about a high energy area from the outside through the transceiver 112.

[0072] The display 114 may be used as a user interface. The processor 118 may cause the display 114 to display the operating status and control status of the vehicle 100, route / traffic information, battery status, information on the remaining amount of gas, content requested by the driver, etc. The display 114 may be configured as a touch screen that is configured to sense driver input and provide the processor 118 with requests from the driver or other users.

[0073] The memory 116 may store applications and various data for controlling the vehicle 100, load applications at the request of the processor 118, and read or record data. In an embodiment of the present disclosure, the memory 116 may store three-dimensional compressed road information sent from the data server 300 or the support server 400. The processor 118 may use the three-dimensional compressed road information to predict long-distance forward slope information based on the three-dimensional compressed road information. The processor 118 may also estimate the DTE based on the predicted forward slope information. The memory 116 may store an application and at least one instruction for controlling the power required for driving, such as driving power. The memory 116 may store road information, map information, three-dimensional compressed road information, and various information and applications required for driving.

[0074] The processor 118 may perform overall control of the vehicle 100. The processor 118 may be configured to run applications and instructions stored in the memory 116. In an embodiment of the present disclosure, the processor 118 may utilize the applications, instructions, and data stored in the memory 116 to predict long-distance front slope information based on three-dimensional compressed road information, estimate DTE based on the predicted front slope information, and / or perform processing to control the power required for driving, such as driving power. The processing may be performed in at least a portion of the processor 118, such as at least one processing module and at least a portion of the memory 116.

[0075] When the vehicle 100 generates three-dimensional compressed road information, the processor 118 can generate two-dimensional compressed road information associated with a specified reference point of each section of the road distinguished in a predetermined manner based on the two-dimensional original road information. Based on the map information including the original height information, the processor 118 can perform processing to generate compressed height information corresponding to the specified reference point. In addition, the processor 118 can construct the three-dimensional compressed road information from the two-dimensional compressed road information and the compressed height information. Then, the processor 118 can predict the front slope information based on the three-dimensional compressed road information, the position of the vehicle 100, and the driving direction of the vehicle 100.

[0076] As another example, processing may be performed in multiple processing modules and memory embedded in each module. The multiple processing modules and embedded memory may include a processor 118 and a memory 116 according to the present disclosure.

[0077] For example, the plurality of processing modules may be composed of a separate processing module for controlling each element of the vehicle 100 and a high-level processing module for managing the separate processing modules at a higher level. The high-level processing module for managing each separate processing module may be a vehicle control unit (VCU). The VCU may predict long-distance forward slope information based on three-dimensional compressed road information. The VCU may also estimate DTE or process drive power control based on the predicted forward slope information.

[0078] According to an embodiment, the VCU may perform the described process. However, in the present disclosure, for ease of explanation, the processor 118 including such a processing module is described as performing the described process. Although the description of the detailed process of the above-mentioned processing is mainly focused on the processor 118, the processing module that performs the detailed process can be clearly inferred from the above description. Therefore, in the present disclosure, the processor refers to a conceptual controller including a single processing module or a plurality of processing modules.

[0079] Figure 3 is a view illustrating modules constituting an external device according to an embodiment of the present disclosure.

[0080] The data server 300 or the support server 400 constituting the external device 200 may generally include a communication unit 202, a memory 204, and a processor 206. The communication unit 202 may be a communication module for exchanging data with the vehicle 100 or another device.

[0081] In an example where three-dimensional compressed road information is generated by a data server 300 as an external device 200, the memory 204 may store information managed by each server. For example, the memory 204 may store road information, map information, three-dimensional compressed road information, and various information and applications required for driving. The processor 206 may perform overall control of the communication unit 202 and the memory 204, and may perform predetermined processing based on data obtained from each unit. Based on the original road information, the processor 206 of the data server 300 may generate two-dimensional compressed road information associated with a specified reference point of each section of the road distinguished in a predetermined manner. Based on the map information including the original height information, the processor 206 may perform processing to generate compressed height information corresponding to the specified reference point. In addition, the processor 206 may control the data server 300 to send three-dimensional compressed road information having two-dimensional compressed road information and compressed height information to the vehicle 100 or the server 400 at the request of the vehicle 100 or the support server 400.

[0082] Reference Figures 4 to 11 The above-described processes of the data server 300 and the processor 206 according to an embodiment are described in more detail.

[0083] Reference Figure 4 , a vehicle control method utilizing height prediction based on high energy region identification according to an embodiment of the present disclosure is described in detail. Figure 4 1 is a flow chart of a method for predicting height using compressed road information according to an embodiment of the present disclosure. The present disclosure mainly describes an example of generating three-dimensional compressed road information by vehicle 100. However, in some embodiments, Figure 4 The process may be performed by the external device 200 to generate the three-dimensional compressed road information. In the case of the external device 200, a process of transmitting the three-dimensional compressed road information from the external device 200 to the vehicle 100 may be added to operation or step S125.

[0084] Reference Figure 4 In operation or step S105, the processor 118 of the vehicle 100 may obtain the two-dimensional original road information stored in the memory 116. Specific modules of the vehicle 100 are described in detail only for processes that are not executed by the processor 118 of the vehicle 100. Hereinafter, the processor 118 of the vehicle 100 is sometimes referred to as the vehicle 100.

[0085] The two-dimensional original road information may be information generated in a predetermined format. Specifically, the two-dimensional original road information may be constructed in a format including reference point data associated with reference points allocated along a specific road.

[0086] The reference point may be a point of a road that is actually or virtually given at intervals of a predetermined distance from a specific point in a specific direction of the road. For example, the specific point may be the starting point of the road, and the intervals of the distances may be the same or different depending on the road conditions. For example, for the reference point, a point associated with a milestone installed along the road may be adopted, but is not limited to the above example and may be set in various ways. The reference point data may include an identifier of the reference point, reference state data represented by a distance from a specific point of the road, and reference position data having two-dimensional position data of the reference point represented by a preset coordinate system. For example, the reference state data is data associated with a distance from the starting point of the road in a specific direction of the road. The reference state data may be data represented based on a distance from a reference point of the road. For example, the reference state data may be composed of a milepost distance, milepost coordinates, and the like. The reference position data may include reference latitude data and reference longitude data, and the coordinate system of each data set may be marked with, for example, the latitude and longitude of the earth ellipsoid. In addition, the two-dimensional original road information may have original road information corresponding to each of the plurality of roads.

[0087] Reference Figure 5 DETAILED DESCRIPTION Two-dimensional original road information is output in a predetermined format according to an embodiment. Figure 5 is a flowchart of a process of converting two-dimensional original road information into a predetermined format according to an embodiment. Figure 5 It is shown that the two-dimensional original road information is processed according to whether the two-dimensional original road information is constructed in a predetermined format.

[0088] In step or operation S205 , the vehicle 100 may obtain two-dimensional original road information. In operation S210 , the processor 118 of the vehicle 100 may check whether the two-dimensional original road information satisfies a predetermined format.

[0089] The predetermined format may be a format including reference point data associated with the reference point. For example, the processor 118 may check whether the two-dimensional original road information is composed of reference point data having an identifier of the reference point, reference state data, and reference position data.

[0090] For example, the two-dimensional original road information constructed in a predetermined format may have [ID, milepost coordinates (distance from the road reference point), latitude, longitude]. Here, the ID may be an identifier of the reference point, the distance from the reference point represented by the milepost coordinates may be reference state data represented by the milepost distance, and the latitude and longitude may be reference position data showing reference latitude data and reference longitude data, respectively. Specifically, a vector having ID, milepost coordinates, and the distance from the reference point to the road may be generated. Latitude vector and the longitude vector The road data is used as the two-dimensional original road information.

[0091] If the obtained two-dimensional original road information is constructed in a required format ("yes" in step or operation S210), in conjunction with step or operation S245, the two-dimensional original road information may be output and used without preprocessing described below.

[0092] In Korea, the Korea Expressway Corporation operates a system that provides road data in the above format and outputs Korean road data without preprocessing.

[0093] On the other hand, if the two-dimensional original road information does not satisfy the predetermined format, the processor 118 may perform a preprocessing process according to steps or operations S215 to S240 to convert the obtained two-dimensional original road information into the above-mentioned format.

[0094] The two-dimensional original road information may not be data of each road point specified on the road, but road information described in vector form according to the road. For geospatial data such as the United States National Network (US NN), the road information is described in vector form and may be represented by a coordinate system of the Universal Transverse Mercator Coordinate System instead of the latitude and longitude coordinate system of the reference location data.

[0095] If the processor 118 confirms that the two-dimensional original road information is described in vector form (“Yes” in step or operation S215 ), then in step or operation S220 , the processor 118 may extract road point data based on the vector form road information.

[0096] On the other hand, if the processor 118 confirms that the two-dimensional original road information is described as data for each road point ("No" in step or operation S215), the processor 118 can identify the road point as road point data, and thus, the process can proceed to step or operation S225.

[0097] In step or operation S225 , the processor 118 may check whether the road point data is described in a predetermined coordinate system with reference to the latitude and longitude coordinates of the position data.

[0098] If the data is not described in a predetermined coordinate system, then in step or operation S230, the processor 118 may convert the coordinate system of the road point data to a latitude and longitude coordinate system according to the reference point data. For example, the latitude and longitude coordinate system according to the reference point data may be the latitude and longitude of the WGS84 earth ellipsoid, and the road point data may be pre-processed to convert it to the above latitude and longitude. When the road point data is described by the latitude and longitude coordinates of the reference location data, the process may proceed to step or operation S235.

[0099] In step or operation S235, the processor 118 may check whether there is reference identification data associated with the road point data. The reference identification data may include a reference point and reference status data, and the reference status data may be a milepost distance or a milepost coordinate.

[0100] If the reference identification data does not exist, then in step or operation S240, the processor 118 may calculate the reference point and the milepost distance / milepost coordinates based on the latitude and longitude of the road point data. In step or operation S245, the processor 118 may output the two-dimensional original road information in a predetermined format. When the road point associated with the road point data is extracted from the vector data, since there is no road reference point and the distance information from the reference point, the reference point and the milepost distance / milepost coordinates may be calculated. As a result of the above processing, the two-dimensional original road information may be generated in a format such as [ID, milepost coordinates (distance from the road reference point), latitude, longitude]. Specifically, a vector having ID, milepost coordinates, and the like may be generated. Latitude vector Longitude vector The road data is used as the preprocessed two-dimensional original road information.

[0101] Refer again Figure 4 In step or operation S110, the data server 300 may generate two-dimensional compressed road information through numerical optimization based on the two-dimensional original road information.

[0102] The two-dimensional compressed road information may be generated by numerical optimization based on the two-dimensional original road information. The two-dimensional compressed road information may include a first parameter of a first approximation function applied to approximate the two-dimensional original road information in the numerical optimization. The first parameter may include reference point data for each interval corresponding to the number of intervals determined by the numerical optimization.

[0103] Refer to the following Figure 6 and Figure 7 Step S110 according to an embodiment is described in more detail. Figure 6 is a flowchart of a process of generating two-dimensional compressed road information according to an embodiment. Figure 7is a view illustrating processing of two-dimensional original road information into two-dimensional compressed road information according to an embodiment.

[0104] Reference Figure 6 In step or operation S305, the processor 118 may receive two-dimensional original road information in a predetermined format. In step or operation S310, the processor 118 may set an initial number of intervals for dividing the entire range where the road reference data exists.

[0105] In the present disclosure, the problem of numerical optimization is solved so that a piecewise cubic hermite approximation curve (PCHAC) approximating the original latitude and longitude can be obtained with a data set whose number is significantly smaller than that of the original data set, i.e., the number of reference point data sets. The cubic Hermite curve is a cubic function generated based on the function value and the derivative value of each point. When the derivative value is given, the function can be used for interpolation, and even when there is no derivative value, the function can be used for interpolation in order to approximate the derivative by finite difference. In the present disclosure, the function can be used not for interpolation but for generating an approximate curve. When the derivative value is given, the function can be used for interpolation. 0 and x 0 +h gives the value and derivative of the function f When the value of , the interpolation function g by the cubic Hermite is the first approximation function and is calculated as shown in the following equation 1.

[0106]

[0107] In the embodiments of the present disclosure, the problem of numerical optimization is solved, so that a piecewise cubic Hermite curve approximating the original latitude and longitude can be obtained with a data set whose number is significantly smaller than the original data set, that is, the reference point data set of the two-dimensional original road information. 0 , x 1 , ..., x M Given the original data f(x), the main goal may be to obtain g(x) that approximates the original data with a significantly smaller number of data points N (<<M) of milepost coordinates. To this end, by giving the reference point data (x) that divides the existing road 0 , x 1 , ..., x M ), the process of calculating the minimum number of intervals N can be started.

[0108] The embodiment of the present disclosure provides a method for obtaining a reference data set from an initial hypothesis N. 0 Focus on finding the minimum interval N that meets the maximum error requirement * Method.0 is the number of initial intervals that divide the entire range where reference point data exists. N 0 It can be set differently according to the terrain characteristics of each area. As an example, in Korea, N 0 Can be set to In the United States, since roads are often straight, 0 When N 0 When set to 1, the optimal N * , but the time required for road compression may increase.

[0109] In step or operation S315 , the processor 118 may search for a first parameter of the first approximation function by applying numerical optimization of the number of intervals.

[0110] The numerical optimization for obtaining the first parameter may be implemented by the following method. The numerical optimization is not limited thereto and may be implemented in various methods.

[0111] To determine the PCHAC of a single function, the following 3N+3 parameters are required: N+1 point vectors of input data [z 0 , z 1 , ..., z N ] T , N+1 function vectors [g(z 0 ), g(z 1 ), ..., g(z N )] T and N+1 derivative vectors Here, the point vector of the input data may be a point vector of the milestone coordinates of the reference point corresponding to the initial interval number. The function g may be the function shown in Equation 1.

[0112] In order to compress the two-dimensional original road information, since the latitude function and longitude should be approximated respectively, it is necessary to determine 3(N 1 +N 2 )+6 parameters. If the changes in latitude and longitude on the road are not uniform, determining a separate point vector for the input data according to each axis is desirable for data compression.

[0113] Since the embodiments of the present disclosure compress data of roads with many altitude changes such as mountains, it is less likely that the roads have constant longitude and change only in latitude or have constant latitude and change only in longitude. Therefore, the following 2D PCHAC can be generated using 5N+5 parameters.

[0114] The number of point vectors of milestone coordinates that can correspond to the current state data of the reference point can be The number of latitude function vectors associated with the reference latitude data may be The number of latitudinal derivative vectors used here can be The number of longitude function vectors used and associated with the reference longitude data may be The number of longitude derivative vectors used here can be

[0115] When using a numerical optimization algorithm, since the number of intervals is a factor that greatly affects the computation time, the numerical optimization problem can be defined as follows. The following problem statement simplifies the dimensionality of the problem to a 3N+3 form using known latitude and longitude information. For a given N, a known numerical optimization algorithm (e.g., Mathworks' fmicon) is used to determine the 3N+3 variables, and the 2D optimal PCHAC can be obtained.

[0116] The decision vector according to Equation 2 may be an object to be obtained in the corresponding process.

[0117]

[0118] As shown in Equation 3, the initial hypothesis can be the point vector z of the input data 0 , z 1 , ..., z N are isometric, and the derivative can be assumed to be 0. In addition, and They can be the longitude derivative vector and the latitude derivative vector respectively.

[0119]

[0120] As shown in Equation 4, for the linear assumption, it can be assumed that when given by linear interpolation When and

[0121]

[0122] The constraint can be z 0 =x 0 and z N =x M (boundary conditions) and z i >z i-1 +δ min (spacing condition), and δ min It may be a minimum spacing (eg 5 km).

[0123] The objective function as in Equation 5 may be provided.

[0124]

[0125] In equation 5, and From the decision vector Generated PCHAC x i The longitude and latitude of the coordinate.

[0126] The process can be directed to search for a decision vector that minimizes the objective function under given conditions and constraints. Numerical optimization algorithms can be used to start from the initial value under the above premises and assumptions. Start calculating the local optimum of the objective function In other words, the 2D PCHAC most similar to the original 2D route may be calculated as the 2D original road information.

[0127] In step or operation S320, the processor 118 may generate a distance error at each first test point according to the searched first parameter. In step or operation S325, a maximum distance error among the distance errors is calculated.

[0128] The first test point may be a point or a section corresponding to the reference latitude data and the reference longitude data searched in step or operation S315.

[0129] Distance error i Can be In this equation, the distance error may occur using the input of the first test point. The maximum distance error can be calculated by e max =max(e 1 , e 2 , ..., e M )calculate.

[0130] In step or operation S330 , the processor 118 may determine whether the maximum distance error is equal to or less than the allowable distance error or whether the number of intervals is equal to or greater than the allowable number of intervals.

[0131] For example, the maximum permissible distance error e tol It can be 1km. If e tol If it is set large, the compression rate increases, but errors may occur when identifying vehicles on the road. On the other hand, if e tol If the value is set small, the compression rate decreases. Therefore, it is desirable to set the maximum allowable distance error appropriately according to regional characteristics. In the United States, the maximum allowable distance error can be set to a larger value than in Korea.

[0132] The number of admissible intervals N max is an upper limit applied when it seems that excessive data is being used to simulate a road, and can be, for example,

[0133] If the maximum distance error exceeds the allowable distance error and the set number of intervals is less than the allowable number of intervals, the processor 118 may increase the number of intervals in step or operation S335. The processor 118 may then perform steps or operations S315 to S330 again.

[0134] On the other hand, if the maximum distance error is equal to or less than the allowable distance error, then in step or operation S340, the processor 118 may determine the set number of intervals and the corresponding designated reference point data of the first parameter as a first approximate element. In step or operation S345, two-dimensional compressed road information may be generated based on the first approximate element and the first approximate function.

[0135] The reference point data is specified as the 5N+5 compressed road information matrix R, which can be determined as In the case of the Yeongdong Expressway in South Korea, the 2D route of the expressway can be compressed into 115 datasets at a 1km error level, compared to 2344×2 datasets of 2D original road information. This means that at a 1km error level, the entire Yeongdong Expressway can be represented with only 2.45% of the data volume. In addition, Figure 7 As shown, through the above process, the two-dimensional compressed road information 504 can be approximated to the two-dimensional original road information 502 with low error.

[0136] Refer again Figure 4 In step or operation S115, the processor 118 may receive map information stored in the memory 116. The map information may include original altitude information.

[0137] When a range according to two-dimensional original road information is given, DEM map data (map data according to a numerical elevation model) as an example of original height information may have a data form described below. Here, φ i It can be latitude data. In addition, φ 1 Can be the lower latitude limit, φ n Can be an upper latitude limit. λ i It can be longitude data. In addition, λ 1 It can be the lower limit of longitude, λ m Can be an upper longitude limit.

[0138] For a given latitude or longitude upper limit, the number of data points n can be determined as shown in Equation 6.

[0139]

[0140] The DEM of the earth's surface can include the height information h(φ i ,λ i ). Specifically, And invalid values ​​can be based on map definitions (e.g., -32768).

[0141] From the DEM, the height matrix can be obtained as shown in Equation 7.

[0142]

[0143] In step or operation S120, the processor 118 may generate compressed altitude information corresponding to the designated reference point based on the map information including the original altitude information.

[0144] The compressed height information may be generated by numerical optimization based on the two-dimensional original road information and the map information. The compressed height information may include a second parameter applied to a second approximation function that approximates the original height information by numerical optimization. For example, the second parameter may include coefficient data associated with a configuration vector function having an order according to each interval determined by numerical optimization.

[0145] Combine the following Figures 8 to 10 The step or operation S120 according to an embodiment is described in more detail. Figure 8 is a flow chart of a process for generating compressed height information according to an embodiment. Fig. 9 is a view illustrating smoothed original height information according to an embodiment. Fig.10 is a view illustrating processing of original height information into compressed height information according to an embodiment.

[0146] Reference Figure 8 In step or operation S405, the processor 118 may receive map information including original altitude information and two-dimensional original road information having a predetermined format.

[0147] In step or operation S410, the processor 118 may extract altitude information corresponding to the reference position data of the two-dimensional original road information from the map information. In step or operation S415, the processor 118 may smooth the extracted altitude information.

[0148] As a specific example, since the positions on the real road do not correspond to the data points of the DEM map, a two-dimensional linear interpolation method can be used to extract the height of the road.

[0149]

[0150] In equation 8, and H, f lonand f lat Same variables and functions as above, and x i May be reference position data.

[0151] Here, the two-dimensional original road information can be used instead of the two-dimensional compressed information. Since the data obtained from this process is a grid of average heights on the earth's surface, it may not correspond to the height of the real road because roads are usually built on terrains with gentle slopes. Therefore, according to Equation 9, smoothing is performed by applying a one-dimensional Gaussian filter, and it can be obtained Obtained by filtering There may be M height data sets. Then, as with the latitude and longitude data for roads, the filtered height data may be subjected to non-conservative compression by numerical optimization. Fig. 9 As shown, filtered altitude data 510 appears smoother along the milestone distance than raw altitude data 508 .

[0152]

[0153] In step or operation S420 , the processor 118 may set an initial order assigned to each designated section in the two-dimensional compressed road information.

[0154] When using Figure 8 When the numerical optimization method at the step or operation S420 is used, the order is the order of the configuration vector function according to each interval, and the configuration vector function can be a function expressing the height at each interval. The overall vector function including all configuration vectors can correspond to the second approximate function, and the present disclosure solves the problem of numerical optimization, so that the second approximate function of the piecewise polynomial curve having an approximate original height can be obtained through a data set with a number significantly less than the number of original height information.

[0155] The following process may be performed by setting the initial order applied to the interval to 1. In addition, the optimal coefficient of the highly approximating polynomial function and the highly approximating polynomial function may be obtained in the following process.

[0156] In step or operation S425 , the processor 118 may search for a second parameter applied to each interval of the second approximation function by applying numerical optimization of an order.

[0157] In the example of the second parameter search using numerical optimization, when the polynomial order N is given according to each latitude and longitude interval, p When N is determined, it is necessary to determine the N that minimizes the error of the highly approximate curve. p +1 parameter. Utilization Figure 6 The process can determine the milepost coordinate interval And for each interval Solve the following optimization problem.

[0158] As shown in Equation 10, a decision vector is assigned to each interval j of each latitude and longitude approximation curve, which may be the coefficient of a polynomial.

[0159]

[0160] As shown in Equation 11, the initial assumption may be that each coefficient is zero.

[0161]

[0162] For the linearization assumption, the start and end points of each interval can be obtained by numerical optimization as shown in Equation 12. Therefore, N, which defines the predicted height t A new test point is created and the filtered height value at the test point can be defined by linear interpolation.

[0163]

[0164] In equation 12, i=0, 1, 2, ..., N t -1,

[0165] The filtered height values ​​obtained by linear interpolation can be obtained by calculate.

[0166] As shown in Equation 13, the approximate function is defined on interval j and can be a typical polynomial.

[0167]

[0168] For the constraints, each approximation function is And may correspond to data at the boundary of the milepost coordinate interval.

[0169] For the objective function, under the above premises and assumptions, a numerical optimization algorithm can be used to calculate (Equation 14), where the target is from the initial value Starts out with a local optimum.

[0170]

[0171] In step or operation S430, the processor 118 may generate a height error of each second test point according to the searched second parameter. In step or operation S435, the processor 118 may calculate a maximum height error among the height errors.

[0172] The second test point may be the same as in step or operation S315 ( Figure 6 ) The point or interval corresponding to the reference latitude data and the reference longitude data searched. The second test point can be the starting point and the end point of each interval.

[0173] In step or operation S440 , the processor 118 may determine whether the maximum height error is equal to or less than the allowable height error or whether the set order is equal to or greater than the allowable order.

[0174] If the maximum height error exceeds the allowable height error and if the set order is less than the allowable order, the processor 118 may increase the order in step or operation S445. The processor 118 may then perform steps S425 to S440 again.

[0175] On the other hand, if the maximum height error is equal to or less than the allowable height error, the processor 118 may determine the set order and the corresponding second parameter as the second approximate element in step or operation S450. Therefore, compressed height information may be generated based on the second approximate element and the second approximate function in step or operation S455.

[0176] In the embodiment of the present disclosure, in order to improve the compression ratio, the optimal order can be determined as follows: (j is an interval). The embodiment of the present disclosure uses as little data as possible to represent the three-dimensional compressed road information within a given error limit. To this end, it is necessary to simulate the height by using the least number of coefficients of the polynomial function in each milepost coordinate interval, and the error limit must also be met. ∈ tol is the allowable height error used to determine whether improvement is needed in the interval, and may be given as 30 meters, for example.

[0177] If no improvement is needed in the interval or if the current order is equal to or greater than the upper limit of the predetermined order (For example, ), the current order can be determined as the optimal order.

[0178] If the interval needs improvement (i.e., ∈ max >∈ tol ), the order can be increased, so the process of steps or operations S425 to S440 can be performed again. The admissible order can be introduced This prevents too much data from being used for a high approximation of a specific interval.

[0179] like Fig.10 As shown, a height approximation curve 514 that is close to the original height curve 512 of the road can be obtained. The error is within a predetermined error limit ∈ tol= 30m, 9 intervals are determined by linear functions (2 variables), 8 intervals are determined by quadratic functions, 3 intervals are determined by cubic functions, and 2 intervals are determined by quartic functions. It can be seen that all the heights of Yongdong Expressway are compressed with an accuracy of 30 meters through a total of 64 variables. This means that about 2.7% of the 2344 original height data sets are sufficient to approximate the entire range.

[0180] Refer again Figure 4 In step or operation S125, the above process may generate three-dimensional compressed road information using the two-dimensional compressed road information and the compressed height information.

[0181] In step or operation S130 , the processor 118 of the vehicle 100 may predict long-distance forward slope information based on the three-dimensional compressed road information, the position of the vehicle 100 , and the driving direction of the vehicle 100 .

[0182] Combine the following Fig.11 Step S130 according to an embodiment is described in more detail. Fig.11 is a flowchart of a process of predicting forward slope information according to an embodiment.

[0183] In step or operation S505 , the processor 118 of the vehicle 100 may check whether the vehicle 100 is traveling on a specific road based on the location information of the vehicle, and identify the specific road on which the vehicle 100 is traveling.

[0184] In step or operation S510 , the processor 118 may determine whether the travel of the vehicle is estimated in at least one of the intervals of the three-dimensional compressed road information.

[0185] In step or operation S515, the processor 118 may determine the nearest interval of the vehicle 100 based on the allowable range of each determined interval. Since the interval of the three-dimensional compressed road information may not correspond to the road on which the vehicle 100 is traveling, the vehicle 100 wants to use the three-dimensional compressed road information of the interval closest to the driving position of the vehicle 100.

[0186] In step or operation S520, the processor 118 may determine a driving direction based on at least two past driving locations that the vehicle 100 has traveled before the current driving location and the current driving location.

[0187] In step or operation S525 , the processor 118 may estimate long-distance forward slope information based on the three-dimensional compressed road information of the nearest section and the driving position.

[0188] Although for the sake of clarity of description, the example methods and processes of the present disclosure are generally described above as a series of steps or operations, it is not intended to limit the order in which the steps are performed. These steps or operations can be performed simultaneously or in different orders as needed. In order to implement the method or process according to an embodiment of the present disclosure, the described steps or operations may further include other steps or operations, may include other steps or operations that replace some of the described steps or operations, or may include other additional steps or operations in addition to the described steps or operations.

[0189] The various embodiments of the present disclosure are not a list of all possible combinations, but are intended to describe representative aspects of the present disclosure. The subject matter described in the various embodiments may be applied alone or in combination of two or more.

[0190] In addition, various embodiments of the present disclosure may be implemented in hardware, firmware, software, or a combination thereof. In the case of implementing the present disclosure in hardware, the present disclosure may be implemented using: an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a general purpose processor, a controller, a microcontroller, a microprocessor, etc.

[0191] The scope of the present disclosure includes: software or machine executable commands (e.g., operating systems, applications, firmware, programs, etc.) for enabling the operations of the methods according to various embodiments to be executed on a device or computer; non-transitory computer-readable media on which these software or commands are stored and can be executed on a device or computer.

Claims

1. A method for predicting altitude by utilizing compressed road information, comprising: generating two-dimensional compressed road information associated with a designated reference point of each of one or more sections of a road distinguished in a predetermined manner based on the two-dimensional original road information; generating compressed altitude information corresponding to the designated reference point based on map information including original altitude information; as well as The forward slope information is predicted based on the three-dimensional compressed road information having the two-dimensional compressed road information and the compressed height information, the position of the vehicle, and the traveling direction of the vehicle.

2. The method according to claim 1, wherein: The two-dimensional original road information is configured in a format including reference point data associated with reference points allocated along the road, and The reference point data includes an identifier of the reference point, reference state data represented by a distance from a specific point on the road, and reference position data having two-dimensional position data of the reference point described in a preset coordinate system.

3. The method according to claim 2, further comprising: When the two-dimensional original road information is not configured in the format, the two-dimensional original road information is converted into a format including the reference point data.

4. The method according to claim 1, wherein: The two-dimensional compressed road information is generated through numerical optimization based on the two-dimensional original road information.

5. The method according to claim 4, wherein: The two-dimensional compressed road information includes a first parameter applied to a first approximation function, the first approximation function approximates the two-dimensional original road information through the numerical optimization, and The first parameters include designated reference point data of one or more intervals corresponding to the number of intervals determined by the numerical optimization.

6. The method according to claim 5, wherein: Generating two-dimensional compressed road information includes: Setting the number of intervals in the entire range of reference point data existing in the two-dimensional original road information; searching for said first parameter by applying a numerical optimization of said number of intervals; When a maximum distance error among distance errors at each first test point according to the first parameter exceeds a tolerable distance error and the number of intervals is less than the tolerable number of intervals, re-searching the first parameter by increasing the number of intervals; and When the maximum distance error is equal to or smaller than the allowable distance error or the number of intervals is equal to or larger than the allowable number of intervals, a first parameter according to the number of intervals is determined as designated reference point data.

7. The method according to claim 1, wherein: The compressed height information is generated through numerical optimization based on the two-dimensional original road information and the map information.

8. The method according to claim 7, wherein: The compressed height information includes a second parameter applied to a second approximation function, the second approximation function approximating the original height information by the numerical optimization, and The second parameters include coefficient data associated with a configuration vector function having an order of one or more intervals determined by the numerical optimization.

9. The method according to claim 8, wherein: Generating compressed height information includes: smoothing the height information extracted from the map information in response to the position data of the reference point in the two-dimensional original road information; setting an order for each of the one or more intervals of the two-dimensional compressed road information; searching for the second parameter by applying numerical optimization of the order; When a maximum height error among height errors at each second test point according to the second parameter exceeds a tolerable height error and the order is smaller than the tolerable order, re-searching the second parameter by increasing the order; and When the maximum height error is equal to or less than the allowable height error or the order is equal to or greater than the allowable order, the second parameter is adopted.

10. The method according to claim 1, wherein: Predicted forward slope information includes: determining whether travel of the vehicle is estimated in at least one of the intervals of the three-dimensional compressed road information; determining a nearest interval of the vehicle based on an allowable range of the at least one interval; and The front slope information is estimated based on the three-dimensional compressed road information of the nearest section and the traveling direction.

11. An apparatus for implementing height prediction by utilizing compressed road information, comprising: A memory storing at least one instruction; as well as a processor, executing the at least one instruction based on data obtained from the memory, Wherein, the processor is configured to: generating two-dimensional compressed road information associated with a designated reference point of each of one or more sections of a road distinguished in a predetermined manner based on the two-dimensional original road information, generating compressed altitude information corresponding to the designated reference point based on map information including original altitude information, and Three-dimensional compressed road information having the two-dimensional compressed road information and the compressed height information is provided for controlling a vehicle so as to predict front slope information based on the three-dimensional compressed road information, the position of the vehicle, and the driving direction of the vehicle.

12. The device according to claim 11, wherein: The two-dimensional original road information is configured in a format including reference point data associated with reference points allocated along the road, and The reference point data includes an identifier of the reference point, reference state data represented by a distance from a specific point on the road, and reference position data having two-dimensional position data of the reference point described in a preset coordinate system.

13. The device according to claim 12, wherein: The processor is further configured to: when the two-dimensional original road information is not configured in the format, convert the two-dimensional original road information into a format including the reference point data.

14. The device according to claim 11, wherein: The two-dimensional compressed road information is generated through numerical optimization based on the two-dimensional original road information.

15. The device according to claim 14, wherein: The two-dimensional compressed road information includes a first parameter applied to a first approximation function, the first approximation function approximates the two-dimensional original road information through the numerical optimization, and The first parameters include designated reference point data for each of one or more intervals corresponding to the number of intervals determined by the numerical optimization.

16. The device according to claim 15, wherein: The processor is configured to generate the two-dimensional compressed road information at least in the following manner: Setting the number of sections in the entire range of reference point data existing in the two-dimensional original road information, searching for said first parameter by applying a numerical optimization of said number of intervals, when a maximum distance error among distance errors at each first test point according to the first parameter exceeds a tolerable distance error and the number of intervals is less than the tolerable number of intervals, re-searching the first parameter by increasing the number of intervals, and When the maximum distance error is equal to or smaller than the allowable distance error or the number of intervals is equal to or larger than the allowable number of intervals, a first parameter according to the number of intervals is determined as designated reference point data.

17. The device according to claim 11, wherein: The compressed height information is generated through numerical optimization based on the two-dimensional original road information and the map information.

18. The device according to claim 17, wherein: The compressed height information includes a second parameter applied to a second approximation function, the second approximation function approximating the original height information by the numerical optimization, and The second parameters include coefficient data associated with a configuration vector function having an order for each of the one or more intervals determined by the numerical optimization.

19. The device according to claim 18, wherein The processor is configured to generate the compressed height information at least in the following manner: smoothing the height information extracted from the map information in response to the position data of the reference point in the two-dimensional original road information, setting an order for each of the one or more intervals of the two-dimensional compressed road information, searching for said second parameter by applying a numerical optimization of said order, when a maximum height error among height errors at each second test point according to the second parameter exceeds a tolerable height error and the order is less than the tolerable order, re-searching the second parameter by increasing the order, and When the maximum height error is equal to or less than the allowable height error or the order is equal to or greater than the allowable order, the second parameter is adopted.

20. The device according to claim 11, wherein The processor is configured to predict the forward slope information at least in the following manner: determining whether the travel of the vehicle is estimated in at least one section of the sections of the three-dimensional compressed road information, determining a nearest interval of the vehicle based on an allowable range of the at least one interval, and The front slope information is estimated based on the three-dimensional compressed road information of the nearest section and the traveling direction.

Images