System for building a vehicle driving environment database

Abstract

It complements information such as regional characteristics on roads without requiring the addition of special sensors. [Solution] This relates to a system for constructing a vehicle driving environment database. A digital twin model capable of reproducing the vehicle state and driving environment of a specific vehicle is constructed (S11), nominal driving data for a specific region obtained from the constructed digital twin model is acquired (S14), actual driving data from the actual driving of a standard vehicle corresponding to the specific vehicle in the specific region is acquired (S15), and the regional characteristics of the specific region are calculated and stored by comparing the obtained nominal driving data with the actual driving data (S16).

Description

Technical Field

[0001] The present disclosure relates to a system for constructing a vehicle driving environment database that accumulates information on the driving energy required according to the road environment when the vehicle is driving.

Background Art

[0002] Conventionally, navigation devices that assist vehicle driving have been widely spread. In this navigation device, route search and route guidance to a destination are performed, and traffic jam information and the like are acquired and used for route search.

[0003] In Patent Document 1, a sensor installed on a main road detects the speed of a vehicle traveling at that point, and for a non-main road, the actual speed of a vehicle traveling there is acquired from when leaving the main road until returning, and a technique for associating these with map information is described.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the patent document, the information obtained for non-main roads is limited to vehicle speed, and there is no disclosure regarding the handling of other information. Therefore, information other than vehicle speed cannot be supplemented.

Means for Solving the Problems

[0006] The vehicle driving environment database construction system relating to this disclosure is a vehicle driving environment database construction system that constructs a digital twin model capable of reproducing the vehicle state and driving environment of a specific vehicle, acquires nominal driving data for a specific region obtained from the constructed digital twin model, acquires actual driving data from the actual driving of a standard vehicle corresponding to the specific vehicle in the specific region, and calculates and stores the regional characteristics of the specific region by comparing the obtained nominal driving data with the actual driving data.

[0007] It is advisable to obtain actual driving data for individual user-owned vehicles corresponding to a specific vehicle type in a specific region, and then calculate and store the vehicle weight of the individual vehicle by comparing the obtained nominal driving data with the actual driving data.

[0008] It is advisable to obtain driving data in other specific regions different from the designated region using individual user-owned vehicles corresponding to a specific vehicle, and then calculate the regional characteristics of the other specific region using the obtained driving data.

[0009] The vehicle driving environment database construction system described herein is a vehicle driving environment database construction system that constructs a digital twin model capable of reproducing the vehicle state and driving environment of a specific vehicle, acquires nominal driving data for a specific region obtained from the constructed digital twin model, acquires actual driving data for the actual driving of a user-owned individual vehicle corresponding to the specific vehicle in the specific region, and calculates and stores the vehicle weight of the individual vehicle by comparing the obtained nominal driving data with the actual driving data. [Effects of the Invention]

[0010] It can supplement information such as regional characteristics on roads without adding any special sensors. It can also supplement information about roads that have not actually been driven on. [Brief explanation of the drawing]

[0011] [Figure 1]This diagram shows the processing procedure of the server computer that constitutes the system for building the driving environment database. [Figure 2] This diagram explains the process for obtaining regional wind characteristics within the processing shown in Figure 1. [Figure 3] This diagram illustrates the estimation of the weight and driving tendency coefficient for individual vehicles. [Figure 4] This is a diagram illustrating the expansion of the target area. [Modes for carrying out the invention]

[0012] The embodiments of this disclosure will be described below with reference to the drawings. The embodiments described below are not limiting to this disclosure, and configurations formed by selectively combining multiple examples are also included in this disclosure.

[0013] "Digital Twin Model" In the vehicle driving environment database construction system according to this embodiment, a digital twin model is used. A digital twin is a technology that virtually reproduces various environments and devices in the real physical space digitally based on IoT technology, etc., and creates a twin on a computer. In this embodiment, a digital twin model of a vehicle, that is, a digital twin model of the driving energy of a standard vehicle, is constructed.

[0014] Figure 1 shows the processing procedure of the server computer that constitutes the system for building the driving environment database.

[0015] First, as a model, the required driving force P of the vehicle is set as follows. P={a(V+Vwind)^2+bV+c}+Mgsinθ+Mα

[0016] Here, V: vehicle speed, Vwind: wind speed, M: vehicle mass, g: acceleration due to gravity, θ: road gradient (angle of inclination), α: vehicle acceleration, a, b, c: coefficients. a(V+Vwind)^2 is the term for air resistance proportional to the square of the velocity, bV is the term for rolling resistance proportional to the velocity, Mgsinθ is the force term based on the road gradient (vehicle weight Mg, road gradient θ), and Mα is the term for the force in the vehicle's acceleration and deceleration states (vehicle mass M, acceleration α).

[0017] Tests are conducted under various conditions using a chassis dynamometer, etc., and the model is identified so that the obtained test results match the output of the digital twin model. For example, a driving test is conducted under conditions such as Vwind=0, vehicle weight Mg, sinθ: gradient (known), α: acceleration (known), etc., and the driving data is obtained to identify the model. In other words, the model is identified by determining the coefficients of the digital twin model so that the driving data and the calculation results of the model match. In this way, a digital twin model for the vehicle is constructed (S11). The vehicle specifications are those of a standard vehicle that represents a certain number of vehicle types.

[0018] Furthermore, various measuring instruments, including anemometers and vehicle speedometers, are installed at designated representative points, and by acquiring measurement values ​​from these instruments as appropriate, data on environmental conditions such as road surface conditions, wind speed, average traffic volume, and road gradient at the representative points is accumulated (S12). Data can also be acquired from available map data. Representative points are points that represent a predetermined area, and there may be one point or multiple points per area. In other words, any point that represents a certain area is sufficient. Alternatively, measurements may be taken at multiple points in a corresponding area, and the average value may be used as the representative point for that area. In this way, through actual observations, factors that contribute to the vehicle's driving energy, i.e., environmental conditions, such as standard road surface conditions, weather, average wind speed, and traffic volume at the representative points, are established.

[0019] Using a standard vehicle that is an actual vehicle, drive along a predetermined driving route and measure the driving energy required for actual driving (S13). This driving energy can be calculated from the detection results of various sensors installed in the standard vehicle that is driving and various detectors provided on the road side.

[0020] Using the digital twin model of S11 with the data of the standard environmental conditions obtained in S12 and the driving route on which the standard vehicle traveled in S13 as inputs, reproduce the driving when driving the driving route with the standard vehicle and calculate the driving energy at that time (S14).

[0021] In S13, as described above, the driving energy when actually driving by the standard vehicle is obtained. That is, for the traveled route, actual driving data reflecting regional characteristics is obtained (S15).

[0022] Compare the driving data obtained by driving the driving route of S13 with the digital twin model obtained in S14 and the driving data obtained by the actual driving of the standard vehicle obtained in S15, and calculate the regional characteristics (S16). That is, the driving energy obtained in S14 is data under standard conditions (nominal conditions) based on the environmental conditions of the surface points, and the driving energy obtained in S15 is the driving energy reflecting the regional characteristics at each point traveled, and the regional characteristics can be calculated by comparing the two. For example, the value Vreal as the regional characteristic of the wind speed Vwind on the driving route can be calculated. If the environmental conditions other than the wind speed are the same as the nominal conditions, the correct wind speed Vreal as the regional characteristic can be calculated. If this process is performed under the condition that only one of the plurality of environmental conditions changes, the regional characteristics for any environmental condition at each point of the driving route can be obtained.

[0023] Thus, in the system according to this embodiment, the regional characteristics of a driving route can be calculated from the difference between the driving data of a specific driving route using a digital twin model and the actual driving data. By storing the calculated regional characteristics of the driving route in a navigation database on a server computer, it becomes possible to utilize those regional characteristics when other vehicles travel the route.

[0024] Furthermore, in this embodiment, individual vehicles are also used to build the database. Here, individual vehicles refer to vehicles that are not standard vehicles, such as vehicles owned by customers who have already purchased them.

[0025] Using the navigation database data with regional characteristics calculated in S16, the driving of a predetermined route is reproduced using a digital twin model of a standard vehicle (S17). Since regional characteristics are taken into consideration, the same driving data as when actually driving the predetermined route in a standard vehicle should be obtained.

[0026] On the other hand, driving data for individual vehicles is acquired for the same driving route (S18). In this case, the vehicle characteristics of the individual vehicle will be reflected in the acquired driving data. It is preferable that the individual vehicles have specifications that correspond to the standard vehicle to some extent. In addition, the driving data for individual vehicles may be acquired from a specific vehicle, but it can also be acquired from a CANDB or similar system that stores driving data for various vehicles.

[0027] Then, in S17, the driving data obtained by the digital twin model is compared with the driving data of individual vehicles that traveled the same route. The driving data in S17 takes regional characteristics into account, and any difference between the two sets of driving data indicates a difference in vehicles. Therefore, the vehicle weight of the individual vehicles and the change in the driving resistance coefficient (a, b, c) are calculated from the difference in driving data (S19).

[0028] The individual vehicle's running resistance coefficient calculated in this way is stored in the CANDB database as belonging to that individual vehicle (S20). This resistance coefficient is a characteristic of the individual vehicle, and by applying this characteristic to the digital twin model, a digital twin model of the individual vehicle can be formed. Therefore, by using the digital twin model of the individual vehicle, it is possible to acquire driving data in various environments, and this data can be used for driving in those environments.

[0029] Furthermore, based on the vehicle characteristics calculated in S19, individual vehicles with vehicle characteristics equivalent to those of the standard vehicle are identified (S21).

[0030] Then, driving data for the identified individual vehicle on a different driving route is acquired (S22). This driving data corresponds to the driving data obtained by driving the same different driving route with a standard vehicle. In other words, it corresponds to the driving route in S13 being replaced with the different driving route. Therefore, using the driving data from the different driving route, the regional characteristics of the different driving route can be calculated in the same manner as in S16 (S22).

[0031] This process allows us to use driving data from individual vehicles owned by people who purchased vehicles from automobile companies to obtain data on various regional factors and vehicle characteristics. This enables the construction of a more appropriate database.

[0032] "Acquiring regional characteristics of wind" Figure 2 illustrates the process for obtaining regional wind characteristics within the process described in Figure 1 above.

[0033] First, the road surface database stores data on the characteristics of road surfaces, such as rolling resistance, which are necessary for calculating vehicle energy for each region. The weather database also stores data necessary for calculating vehicle energy for each region, such as average weather conditions (temperature, humidity, frequency of sunny and rainy days) and average wind speed.

[0034] Based on the data in this database, a digital twin model of driving energy is constructed as shown in S11 of Figure 1 (S31).

[0035] Next, as shown in S13, driving data is acquired from actual driving using a standard vehicle, and noisy driving data is excluded from this (S32). Noisy driving data includes driving data from accidents and traffic jams. Furthermore, it is advisable to classify and organize the driving data according to weather and pavement type, which are factors that cause changes in the road surface resistance coefficient μ, and compare it with data under similar conditions when comparing it with actual driving data. This reduces the influence of differences in coefficients a, b, and c. Additionally, by extracting data from high-speed driving, where wind has a significant impact, the regional characteristics of wind can be grasped more appropriately.

[0036] In S32, noise data is excluded, and the required driving energy is obtained from the driving data obtained when driving the set driving route (S33). That is, the driving force P_real required for driving the set driving route is obtained from various driving data stored in CANDBs such as CAN300, which stores various driving data of the vehicle collected by CAN (Controller Area Network) communication, as well as from various instruments installed in the standard vehicle. In other words, P_real = the actual value of the motor's driving force. That is, the driving energy obtained is the driving energy obtained when actually driving the set driving route by the standard vehicle, and it is driving energy that reflects regional characteristics.

[0037] On the other hand, similar to S14, the driving energy P_normal, which is the standard (nominal) driving data for driving the same route, is calculated using the digital twin model constructed in S31 (S34). In other words, the standard driving energy P_normal is obtained when regional characteristics are not reflected.

[0038] In other words, P_normal={a(V+V_normal)^2+bV+c} +Mgsinθ+Mα The required driving force is calculated using the digital twin model. V_normal is the average wind speed at a representative location obtained from the weather database.

[0039] Then, the driving energy calculated in S33, which reflects regional characteristics based on actual driving, is compared with the driving energy obtained in S14, and based on the difference, the characteristics specific to that region, in this example, the wind speed Vwind at each point in the region, are obtained (S35). In other words, it functions as a virtual sensor of regional characteristics.

[0040] Here, the wind speed Vwindh can be calculated as follows: By optimizing V_normal so that ΔP is minimized, using ΔP = P_real - Pnormal, we can calculate V_real, which is the wind speed at the travel point.

[0041] Then, V_real / V_normal is stored in the navigation database as a wind speed conversion coefficient. That is, the conversion coefficient V_real / V_normal for wind speed, which reflects regional characteristics, is stored in the navigation database, for example, as the conversion coefficient for that region (S36).

[0042] In this way, by storing the wind speed conversion coefficient for a given area in the navigation database, it becomes possible to make more accurate estimates of wind speed when the vehicle is traveling through that area, and to provide the user with appropriate information. Furthermore, it becomes possible to predict driving energy more accurately.

[0043] <Complementing other regional characteristics> The processing described above can be used to supplement other regional characteristics besides wind speed (to obtain conversion factors). For example, it can be used to supplement changes in road surface conditions. In this case, as part of the noise reduction process in S32, it is advisable to extract driving data from the start to low speeds, where the average wind speed is close to zero and the influence of wind speed is minimal. This eliminates the influence of wind speed and allows for a more appropriate understanding of road surface conditions (conversion factors).

[0044] The optimization logic should be optimized to minimize ΔP, as in the case described above. In this case, b and c will be optimized to minimize ΔP.

[0045] "Regarding weight and rolling resistance coefficient" Figure 3 illustrates the estimation of the weight and driving tendency coefficient for individual vehicles.

[0046] The navigation database stores data for various locations, including regional characteristics such as wind speed obtained through the processing shown in Figure 2. For example, it stores the latitude and longitude of a given location, the link length (distance between locations), the travel time between locations, the average gradient for each link, the weather at each location, the average wind speed, and a regional characteristic conversion factor (for example, the wind speed conversion factor obtained in S36 of Figure 2).

[0047] Furthermore, CANDB stores driving records for individual vehicles. For example, it stores the time-series location (latitude, longitude), vehicle speed history, and powertrain operating status such as gear ratios for each individual vehicle.

[0048] With these premises in mind, first, we will prepare a digital twin model for driving, similar to the S11.

[0049] Next, actual driving data for a specific route, such as the route traveled by a standard vehicle, is read from the data stored in CANDB, and driving data containing noise is excluded (S41). The purpose of this process is to understand the characteristics of the vehicle's weight and rolling resistance coefficient. For example, when towing, both the vehicle weight and rolling resistance coefficient change, so it is good to exclude this data. Therefore, the data can be stratified and extracted as follows. For example, by extracting data during acceleration to optimize the vehicle mass M, extracting data from starting to low-speed driving to optimize terms b and c, and extracting data during high-speed driving to optimize term a, accuracy can be improved.

[0050] Once noise-free driving data is obtained from CANDB, this data is used to acquire the driving energy required when driving a specific route, under conditions that reflect the vehicle weight and terms a, b, and c for that individual vehicle (S42). In other words, the actual motor driving force P_real during actual driving is acquired.

[0051] On the other hand, the driving energy for the same route is calculated using a digital twin model constructed based on the data from the navigation database (S43).

[0052] In other words, P_normal={a_normal(V+Vreal)^2+b_normalV+c_normal}+(M_normal)gsinθ+(M_normal)α, The nominal driving energy, taking into account regional characteristics obtained in S36, is calculated using a digital twin model. Here, M_normal / a_normal / b_normal / c_normal are certified values, and V_real is the value obtained by multiplying the average wind speed by a regional characteristic conversion factor.

[0053] In this way, the vehicle mass M and the coefficient terms a, b, and c are nominal, and driving energy that takes into account regional characteristics such as wind speed is obtained.

[0054] Then, calculations are performed to minimize the difference ΔP between the driving energy obtained in S42 and the driving energy obtained in S43, and the vehicle mass M and coefficients a, b, and c are calculated (S44), and written to CANDB (S45). In other words, it functions as a virtual sensor for the individual vehicle state. In other words, ΔP = P_real - Pnormal as, Optimize M_normal, a_normal, and b_normal / c_normal to minimize ΔP.

[0055] In this way, we can obtain the vehicle states M_normal, a_normal, and b_normal / c_normal for each individual vehicle.

[0056] In this embodiment, individual vehicles are used as virtual sensors to calculate the vehicle mass M and coefficients a, b, and c for each vehicle.

[0057] Furthermore, the obtained vehicle status can be used to update the CAN signal during vehicle operation and utilize it for onboard control. In addition, it can be linked with individual vehicle information data from CANDB and used for personalization such as acceleration / deceleration control and fuel efficiency control in autonomous driving, as well as as a parameter for predicting remaining driving range by the navigation system.

[0058] In this example, the processing shown in Figure 2 reflected environmental conditions that incorporated regional characteristics such as wind speed. However, if data with relatively few regional characteristics as shown in Figure 2 is used, data on vehicle characteristics can be obtained through the processing shown in Figure 3 without going through the processing shown in Figure 2.

[0059] "Expansion of target areas" Figure 4 is a diagram illustrating the enlargement of the target area.

[0060] CANDB stores driving records for individual vehicles. For example, it stores the time-series position (latitude, longitude), vehicle speed history, and powertrain operating status such as gear ratios for individual vehicles, as well as vehicle condition data such as the vehicle mass M and coefficients a, b, and c for individual vehicles, which were written in S45 of Figure 3.

[0061] In CANDB, vehicles are extracted from the standard vehicle's driving route data that are estimated to have a vehicle mass M and terms a, b, and c equivalent to the standard condition (S51). Then, using the VIN code (identification code that identifies individual vehicles) of the extracted vehicles, driving data other than the standard vehicle's driving route is searched for and extracted (S52). In this case, it is preferable that the driving data to be extracted is data that is included in the same trip or single journey as the standard vehicle's driving route. This is because the vehicle weight Mg and terms a, b, and c are considered to have not changed.

[0062] Then, by performing the processing steps S11 to S16 in Figure 2 on the individual vehicle's driving data, regional characteristics outside the standard vehicle's driving route are obtained (S53). In other words, regional characteristics can be obtained by replacing the standard vehicle's driving data with the individual vehicle's driving data without having the standard vehicle run. Therefore, individual vehicles can be used as virtual sensors for Chigira characteristics.

[0063] Then, by writing the acquired regional characteristics into the navigation database, the number of locations where regional characteristics are recorded can be increased. Furthermore, by repeating this process with the same individual vehicle, and then performing it with multiple vehicles, the target locations can be expanded.

[0064] "Effects of the Embodiment" Advanced functions and control in navigation systems require an understanding of the unique driving environment, road structure, and vehicle condition at each location. However, obtaining accurate information frequently has required costly measures such as actual road measurements and the addition of sensors to commercially available vehicles.

[0065] In this embodiment, a standard vehicle with known vehicle conditions and a digital twin model capable of reproducing the vehicle conditions and driving environment are prepared. By utilizing the driving data of the standard vehicle and comparing it with standard driving data (nominal driving data) obtained by the digital twin model, various location characteristics (such as wind between buildings and changes in road surface friction coefficient) can be acquired and stored.

[0066] Furthermore, by comparing the driving data of individual user-owned vehicles after purchase with standard driving data obtained from the digital twin model, it becomes possible to understand changes in the vehicle's weight and changes in driving resistance due to towing. This allows for the calculation of road conditions and other factors from the user's individual vehicle, enabling timely information updates on the characteristics of various locations and creating a database of the latest, high-value-added information at a low cost.

Claims

1. A system for building a vehicle driving environment database, We will build a digital twin model that can reproduce the vehicle condition and driving environment of a specific vehicle, By acquiring nominal driving data for a specific region obtained through the constructed digital twin model, We obtain actual driving data from standard vehicles corresponding to specific vehicles in specific regions. By comparing the obtained nominal driving data with actual driving data, the regional characteristics of a specific area are calculated and stored. A system for building a database of driving conditions.

2. A system for constructing a driving environment database according to claim 1, We acquire actual driving data for individual user-owned vehicles corresponding to specific vehicles in a specific region. By comparing the obtained nominal driving data with actual driving data, the vehicle weight of each individual vehicle is calculated and stored. A system for building a database of driving conditions.

3. A system for constructing a driving environment database according to claim 1 or 2, By acquiring driving data in a different specific region from the specified region using individual user-owned vehicles corresponding to a specific vehicle, The acquired driving data is used to calculate the regional characteristics of other specific areas. A system for building a database of driving conditions.

4. A system for constructing a driving environment database according to claim 3, An individual vehicle is a vehicle that has a vehicle weight corresponding to a specific vehicle. A system for building a database of driving conditions.

5. A system for building a vehicle driving environment database, We will build a digital twin model that can reproduce the vehicle condition and driving environment of a specific vehicle, By acquiring nominal driving data for a specific region obtained through the constructed digital twin model, We acquire actual driving data for individual user-owned vehicles corresponding to specific vehicles in a specific region. By comparing the obtained nominal driving data with actual driving data, the vehicle weight of each individual vehicle is calculated and stored. A system for building a database of driving conditions.

Images