Travel determination device

Abstract

This travel determination device comprises a processor and a storage medium on which a program to be executed by the processor is stored. The processor comprises: an environment information acquisition unit that acquires environment information on a travel route of a vehicle; a vehicle information acquisition unit that acquires ego vehicle information of the vehicle; and a travel determination unit that executes a simulation of the vehicle traveling on the travel route on the basis of the environment information and the own vehicle information.

Description

Driving judgment device

[0001] The present invention relates to the technical field of motion determination devices.

[0002] A technology has been disclosed in which a server acquires weather information related to the planned route of the vehicle and the destination, and notifies the vehicle of the equipment necessary for traveling the planned route based on the acquired weather information (for example, Patent Document 1). Patent Document 1 also describes that if the vehicle does not have the equipment necessary for traveling the planned route, a detour route is notified.

[0003] Japanese Patent Application Laid-Open No. 2003-148984

[0004] However, the technology of Patent Document 1 notifies the user of the equipment necessary for traveling along the planned travel route, but does not determine whether the vehicle can actually travel along the planned travel route. Therefore, there is a need to accurately determine whether the vehicle can travel along the planned travel route.

[0005] The present invention has been made in view of the above circumstances, and has an object to improve the accuracy of determining whether or not a vehicle is capable of traveling.

[0006] A driving determination device according to one embodiment of the present invention comprises a processor and a storage medium storing a program executed by the processor, wherein the processor comprises an environmental information acquisition unit that acquires environmental information on a driving route of a vehicle, a vehicle information acquisition unit that acquires vehicle information about the vehicle, and a driving determination unit that performs a simulation of the vehicle driving along the driving route based on the environmental information and the vehicle information.

[0007] According to the present invention, it is possible to improve the accuracy of determining whether or not a vehicle can be driven.

[0008] It is a diagram showing an outline of the configuration of a traveling determination system. It is a diagram showing the configuration of a server. It is a diagram showing the configuration of a vehicle. It is a diagram showing the functional configuration of a CPU of a server. It is a flowchart showing the flow of traveling determination processing. It is a block diagram showing the processing flow in the traveling determination processing. It is a diagram showing information sources on a traveling route.

[0009] <1. Vehicle Configuration> Fig. 1 is a diagram showing an outline of the configuration of a moving vehicle determination system 1. As shown in Fig. 1, the moving vehicle determination system 1 includes a server 2 and a vehicle 3. Note that Fig. 1 illustrates a case where one server 2 and one vehicle 3 are provided, but a plurality of servers 2 and a plurality of vehicles 3 may also be provided.

[0010] The server 2 is connected to a network 4 such as the Internet by wire or wirelessly. The vehicle 3 is connected to the network 4 by, for example, wirelessly. The server 2 and the vehicle 3 are capable of communicating with each other via the network 4.

[0011] 2. Server Configuration Fig. 2 is a diagram showing the configuration of the server 2. As shown in Fig. 2, the server 2 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input device 16, an output device 17, a storage device 18, a communication device 19, a media drive 20, and the like.

[0012] The CPU 11 executes various processes in accordance with programs loaded from the ROM 12, the storage device 18, or the removable media 21 into the RAM 13. The RAM 13 also stores data necessary for the CPU 11 to execute various processes as appropriate. The ROM 12, the storage device 18, and the removable media 21 are examples of storage media.

[0013] The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0014] The input / output interface 15 is connected to an input device 16, an output device 17, a storage device 18, a communication device 19, and a media drive 20. The input device 16 is composed of a keyboard, a mouse, a touch panel, a microphone, etc. The output device 17 is composed of a display such as an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) panel, a speaker, etc.

[0015] The storage device 18 is configured with a hard disk drive (HDD) or flash memory, and stores data such as programs.

[0016] The communication device 19 performs communication processing and inter-device communication via the network 4. Removable media 21 such as a semiconductor memory, a magnetic disk, an optical disk, or a magneto-optical disk is loaded into the media drive 20 as needed, and data is written to and read from the removable media 21.

[0017] In the server 2, data and programs are uploaded and downloaded through communication by the communication device 19. Data and programs can also be transferred via removable media 21. The CPU 11 performs processing operations based on various programs, thereby executing the information processing and communication required by the server 2.

[0018] 3 is a diagram showing the configuration of the vehicle 3. As shown in Fig. 3, the vehicle 3 includes a vehicle control device 31, a navigation device 32, a GNSS (Global Navigation Satellite System) receiver 33, an external environment recognition device 34, sensors 35, a display device 36, and a communication device 37, which are interconnected via a bus 38 so as to be able to communicate with each other.

[0019] The vehicle control device 31 is, for example, an ECU (Electronic Control Unit) including a CPU, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The vehicle control device 31 controls the entire vehicle 3 in an integrated manner.

[0020] The navigation device 32 performs processes such as recommending and presenting a driving route from the current vehicle position to a destination in response to a search operation by the user (driver). The navigation device 32 also performs various navigation processes in accordance with the driving route. In the following description, the vehicle position, which is the position of the vehicle 3, may be referred to as the current location.

[0021] The GNSS receiver 33 receives satellite signals from a plurality of navigation satellites and, based on the received satellite signals, measures the current location of the vehicle 3. Information about the measured current location is provided to the navigation device 32 and the like.

[0022] The external environment recognition device 34 includes a stereo camera that captures images ahead of the vehicle 3, and a computer that performs image processing. In the external environment recognition device 34, the computer performs predetermined image processing related to recognition of the environment outside the vehicle based on image data obtained by the stereo camera capturing images in the traveling direction of the vehicle 3.

[0023] The external environment recognition device 34 performs image processing based on the stereoscopically captured image data, for example, as follows. First, the external environment recognition device 34 generates distance information for each pixel from a pair of captured images as image data using the principle of triangulation based on the amount of deviation (parallax) between corresponding positions. The external environment recognition device 34 then performs a well-known grouping process on the distance information and compares the grouped distance information with pre-stored three-dimensional road shape data, object data, etc. As a result, the external environment recognition device 34 recognizes lane markings, road markings, guardrails along the road, side walls such as curbs, objects such as vehicles, stop lines, traffic signals, railroad crossings, crosswalks, lanes, etc.

[0024] Furthermore, the external environment recognition device 34 can recognize surrounding objects based on image data and can also recognize their behavior. For example, the external environment recognition device 34 can recognize the position, speed, acceleration, changes in direction of travel, and the on / off status of the turn signal of a preceding vehicle relative to the vehicle 3. The on / off status of the turn signal indicates whether the turn signal is on, flashing, or off.

[0025] The sensor 35 collectively represents various sensors provided in the vehicle 3. The sensors 35 include a vehicle speed sensor that detects the speed of the vehicle itself, an engine rotation speed sensor that detects the engine rotation speed, an accelerator opening sensor that detects the accelerator opening from the amount of depression of the accelerator pedal, a steering angle sensor that detects the steering angle, a yaw rate sensor that detects the yaw rate, a G sensor that detects acceleration, water temperature and oil temperature sensors that measure the temperature of the coolant and the temperature of the oil that serve as indicators for estimating the engine temperature, a fuel sensor that detects the remaining amount of fuel by measuring the up and down position of a float provided in the fuel tank, and a brake switch that is turned on or off depending on whether the brake pedal is operated or not.

[0026] The sensors 35 include an intake air volume sensor that detects the volume of intake air into the engine, a throttle opening sensor that detects the opening of a throttle valve that is installed in the intake passage and adjusts the volume of intake air supplied to each cylinder of the engine, an outside air temperature sensor that detects the air temperature outside the vehicle, various temperature sensors that detect the wheel temperature and brake temperature, a gradient sensor that detects the gradient of the road on which the vehicle is traveling, and a stroke sensor that detects the suspension stroke.

[0027] The display device 36 is a display such as an LCD or an organic EL panel, and is capable of displaying various images. The display device 36 may be provided as a part of the navigation device 32.

[0028] The communication device 37 performs communication via the network 4, so-called V2V communication (vehicle-to-vehicle communication) and road-to-vehicle communication. The vehicle control device 31 can acquire various types of information received by the communication device 37.

[0029] 4. Moving Determination Process Next, a description will be given of the moving determination process performed by the moving determination system 1. The moving determination process is mainly performed by the server 2, but notifications to the driver and the like are performed by the vehicle 3.

[0030] The driving determination process estimates the environmental conditions from the current location of vehicle 3 to the destination, and determines whether vehicle 3 can reach the destination by simulating the driving route using a digital twin that models the driving route based on the estimated environmental conditions.

[0031] Fig. 4 is a diagram showing the functional configuration of the CPU 11 of the server 2. Fig. 5 is a flowchart showing the flow of the driving determination process. Fig. 6 is a block diagram showing the processing flow of the driving determination process. Fig. 7 is a diagram showing information sources on the driving route.

[0032] As shown in FIG. 4 , the CPU 11 functions as a vehicle information acquisition unit 41 , an environmental information acquisition unit 42 , an environmental condition estimation unit 43 , and a traveling possibility determination unit 44 .

[0033] The vehicle information acquisition unit 41 acquires information about the vehicle from the vehicle 3 (hereinafter referred to as host vehicle information). The environmental information acquisition unit 42 acquires information necessary for estimating the environmental conditions of the travel route (hereinafter referred to as environmental information) from other facilities, other devices, databases, etc. via the network 4. The environmental condition estimation unit 43 estimates the environmental conditions (road friction coefficient) of the travel route along which the vehicle 3 is scheduled to travel, based on the host vehicle information and the environmental information. The travel feasibility determination unit 44 performs a travel simulation of the vehicle 3 using a digital twin that models the travel route based on the environmental conditions estimated by the environmental condition estimation unit 43, thereby determining whether the vehicle 3 can arrive at the destination.

[0034] 5 , when the driving determination process starts, in step S1 the environmental condition estimation unit 43 acquires position information of the current location and the destination from the vehicle 3. When the driver or the like inputs the destination into the navigation device 32, the vehicle 3 transmits the position information of the current location and the destination to the server 2.

[0035] In step S2, the environmental situation estimation unit 43 searches for a driving route from the current location to the destination. Here, the environmental situation estimation unit 43 may search for one driving route, or may search for multiple driving routes under different conditions. Then, in step S3, the environmental situation estimation unit 43 sets one of the searched driving routes as the driving route to be simulated. If multiple driving routes under different conditions have been searched for, the environmental situation estimation unit 43 may, for example, notify the vehicle 3 of the multiple searched driving routes to allow the driver to select one.

[0036] The travel route searched here may be searched by the same method as the travel route searched by the navigation device 32, or may be searched by another method. The travel route may also be searched by the navigation device 32 (vehicle 3). A known algorithm can be used as the search algorithm for the travel route, and therefore, a description thereof will be omitted here.

[0037] In step S4, the vehicle information acquisition unit 41 acquires host vehicle information from the vehicle 3. The host vehicle information acquired here includes information on the behavior of the vehicle 3, information on the weight of the vehicle 3, and information on maintenance of the vehicle 3.

[0038] The information relating to the behavior of the vehicle 3 includes the steering angle, accelerator opening, yaw rate, and lateral G. The information relating to the weight of the vehicle 3 includes information on the suspension stroke detected by a stroke sensor. The maintenance information of the vehicle 3 includes information on the tire brand and tire replacement history. This host vehicle information is used to estimate the CP (Cornering Power) and weight of the vehicle 3, which will be described later. Note that the host vehicle information may include information other than the above, or may not include some of this information, as long as it is information that can be used to estimate the CP and weight of the vehicle 3.

[0039] In step S5, the environmental information acquisition unit 42 acquires environmental information relating to the environmental conditions of the set travel route. Here, the environmental conditions indicate the environment of the travel route, such as traffic regulations, congestion information, weather, temperature, dangerous spots, rainfall, wind speed, presence or absence of dense fog, snow accumulation, and amount of water on the road surface, which are shown as estimated items in FIG. 6 .

[0040] The environmental information acquisition unit 42 acquires environmental information from various information sources in order to estimate the environmental conditions of the set driving route. Here, as shown in Fig. 7 , various information sources are provided along a driving route 53 from a current location 51 to a destination 52, from which the conditions of the driving route can be acquired. For example, possible information sources include traffic information 54 that provides information about road closures and congestion, a social networking service (SNS) 55 posted from a mobile device on the driving route 53 or in the vicinity of the driving route 53, a live camera 56 provided along the driving route 53, and a preceding vehicle 57 traveling along the driving route 53.

[0041] In this embodiment, an example will be described in which traffic information, weather forecast, SNS, weather information, live camera, and leading vehicle information shown in FIG. 6 are provided as information sources.

[0042] 6, the environmental information acquisition unit 42 acquires traffic information for the travel route from a predetermined traffic information center. The traffic information includes locations where traffic restrictions are in place, details of the traffic restrictions, and congestion information regarding road congestion.

[0043] The environmental information acquisition unit 42 also acquires weather forecasts for the travel route from a predetermined weather information center. The weather forecasts include forecasts (forecast values) of the weather, temperature, rainfall, wind speed, presence or absence of dense fog, and snowfall for each location or region.

[0044] Furthermore, the environmental information acquisition unit 42 acquires posts from a social networking service (SNS) that include the name of the travel route or the name of places around the travel route, location information, etc. Posts on the SNS may include images (including moving images).

[0045] The environmental information acquisition unit 42 also acquires weather information from weather observation stations installed along the travel route or in the vicinity of the travel route. The weather information includes actual measurements at the weather observation stations of the weather, rainfall, and whether or not there is dense fog.

[0046] The environmental information acquisition unit 42 also acquires images (including moving images) from live cameras installed on the travel route or around the travel route.

[0047] The environmental information acquisition unit 42 also acquires leading vehicle information from other vehicles (leading vehicles) traveling on the travel route. The leading vehicle information includes, for example, an image captured in front of the vehicle.

[0048] In step S6, the environmental condition estimation unit 43 estimates the environmental conditions along the travel route based on the environmental information acquired in step S5. The items estimated as the environmental conditions are, as described above, traffic regulations, congestion information, weather, temperature, dangerous spots, rainfall, wind speed, presence or absence of dense fog, snow accumulation, and road surface water volume. In Fig. 6, solid lines extending from the information sources to the estimation items indicate actual measured values ​​or values ​​with high estimation accuracy, and dashed lines extending from the information sources to the estimation items indicate values ​​with low estimation accuracy.

[0049] Specifically, the environmental condition estimation unit 43 identifies the locations of traffic restrictions and congestion (traffic congestion information) on the driving route based on traffic information. The environmental condition estimation unit 43 also estimates the weather, temperature, dangerous spots, rainfall, wind speed, presence or absence of dense fog, and snow accumulation on the driving route based on a weather forecast. The estimated values ​​used here are the forecast values ​​shown in the weather forecast.

[0050] Furthermore, the environmental condition estimation unit 43 estimates traffic regulations, congestion information, weather, temperature, dangerous spots, rainfall, wind speed, the presence or absence of dense fog, and snow accumulation based on the text information and images included in the SNS post. Here, if such information is included in the text information, the environmental condition estimation unit 43 acquires this information as estimated values ​​based on the text information. Furthermore, if the SNS post includes an image, the environmental condition estimation unit 43 estimates this information by performing image analysis. Note that a known method can be used for image analysis, and therefore a description thereof will be omitted here. The same applies hereinafter.

[0051] Furthermore, the environmental condition estimation unit 43 acquires the actual measured values ​​of the amount of rainfall, wind speed, and snow accumulation along the travel route as actual measured values, based on the weather information.

[0052] The environmental condition estimation unit 43 also performs image analysis on images acquired from the live camera to estimate the weather, dangerous spots, rainfall, presence or absence of dense fog, snow accumulation, and amount of water on the road surface.

[0053] Furthermore, the environmental condition estimation unit 43 estimates the weather, danger points, rainfall, presence or absence of dense fog, and amount of water on the road surface by performing image analysis on the image included in the preceding vehicle information. Note that if the preceding vehicle has acquired this information, it may be configured to acquire this information from the preceding vehicle as preceding vehicle information.

[0054] Here, when estimating one estimation item based on multiple pieces of information, the environmental condition estimation unit 43 may estimate the amount of rain by weighting values ​​obtained from each piece of information. For example, the amount of rainfall can be estimated from any of a weather forecast, SNS, weather information, a live camera, and preceding vehicle information. In such a case, the environmental condition estimation unit 43 may estimate the amount of rainfall by lowering the weighting of values ​​estimated from the weather forecast and SNS, which have low estimation accuracy, and by higher weighting of values ​​estimated from the live camera and preceding vehicle information, which have high estimation accuracy.

[0055] The environmental condition estimation unit 43 also estimates the CP and weight of the vehicle 3 based on the host vehicle information. Therefore, the CP and weight of the vehicle 3 may be treated as one of the estimation items. Here, the environmental condition estimation unit 43 estimates the degree of tire wear based on the vehicle behavior information and maintenance information included in the host vehicle information, and estimates the CP from the estimated degree of wear. The environmental condition estimation unit 43 also estimates the weight of the vehicle 3 based on the stroke amount detected by the stroke sensor. Note that these processes may be performed by the driving feasibility determination unit 44.

[0056] The items estimated by the environmental condition estimation unit 43 are merely examples, and some of these may not be included, or other items may be included. Furthermore, the estimation method for each item is merely an example, and estimation may be performed using other methods.

[0057] The traveling feasibility determination unit 44 determines whether the vehicle 3 can travel the traveling route using the digital twin based on the estimated items estimated by the environmental condition estimation unit 43.

[0058] Specifically, the traveling possibility determination unit 44 identifies dangerous points on the traveling route based on traffic regulations, congestion information, and dangerous points.

[0059] The traveling possibility determination unit 44 also refers to a database based on traffic congestion information, weather, danger points, rainfall, snowfall, and road surface water volume to predict road surface conditions at the time the vehicle 3 will reach the danger point. The database stores road surface conditions (road surface friction coefficients) for weather, rainfall, snowfall, and road surface water volume. The traveling possibility determination unit 44 then predicts road surface conditions at the danger point by searching the database for road surface conditions under conditions close to the estimated weather, rainfall, snowfall, and road surface water volume.

[0060] The method for predicting road surface conditions is not limited to this, and predictions may be made from images or information about preceding vehicles using, for example, the technology disclosed in Japanese Patent Application Laid-Open No. 2010-020430. Road surface conditions may also be predicted from road surface friction coefficient data for snow accumulation and the amount of water on the road surface.

[0061] In step S7, the traveling possibility determination unit 44 predicts the road surface conditions at the dangerous spot, and then creates a traveling route model that reproduces the predicted road surface conditions on the traveling route from the current location to the destination.

[0062] The driving feasibility determination unit 44 also generates a model of the vehicle 3 based on the CP and vehicle weight of the vehicle 3. Furthermore, the driving feasibility determination unit 44 adds, as a disturbance parameter, a change in the forward gaze point ahead of the vehicle based on the weather, the amount of rain, and the presence or absence of dense fog. The driving feasibility determination unit 44 also adds wind speed as a disturbance parameter.

[0063] The traveling feasibility determination unit 44 executes a simulation in which the modeled vehicle 3 travels on the modeled traveling route, and determines whether the vehicle 3 can travel on the traveling route and how dangerous it is.

[0064] In step S8, if the simulation result determines that the route is impossible or difficult to travel, the travel feasibility determination unit 44 determines whether to simulate another travel route. Here, if multiple travel routes have been searched, it determines whether there is another travel route that has not been determined to be impossible or difficult to travel.

[0065] If there is another driving route (Yes in step S8), the process proceeds to step S2, and the processes from step S2 to step S8 are performed for the other driving route. On the other hand, if there is no other driving route (No in step S8), in step S9 the driving feasibility determination unit 44 transmits the simulation results to the vehicle 3. In the vehicle 3 to which the simulation results have been transmitted, the vehicle control device 31 causes the display device 36 to display the simulation results.

[0066] In this way, the travel determination system 1 estimates the environmental conditions of the travel route based on environmental information from multiple information sources, and determines whether the vehicle 3 can travel the travel route using a digital twin that reproduces the estimated environmental conditions. This makes it possible to accurately determine whether the vehicle 3 can travel the travel route, taking into account the condition of the tires of the vehicle 3 and the road surface conditions.

[0067] 5. Modifications Although the present invention has been described above with reference to exemplary embodiments, the present invention is not limited to the specific examples described above and may employ various configurations. For example, in the above exemplary embodiments, the CPU 11 of the server 2 functions as the vehicle information acquisition unit 41, the environmental information acquisition unit 42, the environmental condition estimation unit 43, and the driving feasibility determination unit 44. However, the vehicle information acquisition unit 41, the environmental information acquisition unit 42, the environmental condition estimation unit 43, and the driving feasibility determination unit 44 may be functioned by one or both of the CPU 11 of the server 2 and the vehicle control device 31 of the vehicle 3. For example, the vehicle information acquisition unit 41, the environmental information acquisition unit 42, the environmental condition estimation unit 43, and the driving feasibility determination unit 44 may be functioned by the vehicle control device 31 of the vehicle 3.

[0068] In the above embodiment, the driving feasibility determination unit 44 determines whether the vehicle 3 is capable of driving by using a digital twin. However, the driving feasibility determination unit 44 may be configured to, for example, identify a preceding vehicle traveling on the driving route that has specifications closest to those of the vehicle 3 based on the vehicle information, and determine whether the vehicle 3 is capable of driving based on the behavior of the identified vehicle. The driving feasibility determination unit 44 may also determine whether the vehicle 3 is capable of driving by machine learning the estimated environmental conditions.

[0069] 6. Summary of the Embodiment As described above, the traveling determination device (server 2) of the embodiment includes a processor (CPU 11) and storage media (ROM 12, storage device 18, removable media 21) storing a program executed by the processor. The processor includes an environmental information acquisition unit 42 that acquires environmental information along the traveling route of the vehicle 3, a vehicle information acquisition unit 41 that acquires host vehicle information about the vehicle, and a traveling feasibility determination unit 44 that executes a simulation of the vehicle 3 traveling along the traveling route based on the environmental information and the host vehicle information. This enables the server 2 to accurately estimate the environmental conditions based on the environmental information. The server 2 also enables the server 2 to estimate the degree of tire wear of the vehicle 3 based on the host vehicle information. Therefore, the server 2 can generate a model that accurately reproduces the environmental conditions of the traveling route in the simulation, thereby improving the accuracy of the simulation. Thus, the server 2 can improve the accuracy of determining whether the vehicle 3 is traveling or not.

[0070] Furthermore, the traveling feasibility determination unit 44 executes a simulation using a digital twin that reproduces the traveling route based on the environmental information. By using the digital twin, the server 2 can accurately reproduce the traveling route, thereby improving the accuracy of determining whether the vehicle is traveling or not.

[0071] The traveling feasibility determination unit 44 predicts the road surface conditions of the traveling route based on the environmental information and executes a simulation using the predicted road surface conditions, thereby enabling a simulation that reflects the road surface conditions (road surface friction coefficient) to be executed, thereby improving the accuracy of determining whether traveling is possible.

[0072] The driving feasibility determination unit 44 generates a vehicle model in the digital twin based on the vehicle information and executes the simulation using the generated vehicle model. This makes it possible to execute a simulation that reflects the state of tire wear of the vehicle 3, thereby improving the accuracy of determining whether the vehicle is capable of driving.

[0073] The travel feasibility determination unit 44 determines whether the vehicle 3 can travel on a plurality of travel routes to the destination and provides guidance on travel routes that are feasible. This allows the vehicle 3 to avoid getting stuck on the road or having to make detours by avoiding travel routes that are impossible or difficult to travel and providing guidance on travel routes that are feasible.

[0074] REFERENCE SIGNS LIST 1 Travel determination system 2 Server 3 Vehicle 11 CPU 12 ROM 18 Storage device 41 Vehicle information acquisition unit 42 Environmental information acquisition unit 43 Environmental condition estimation unit 44 Travel feasibility determination unit

Claims

1. A driving determination device comprising: a processor; and a storage medium storing a program executed by the processor, wherein the processor comprises: an environmental information acquisition unit that acquires environmental information on a driving route of a vehicle; a vehicle information acquisition unit that acquires vehicle information about the vehicle; and a driving feasibility determination unit that executes a simulation of the vehicle driving the driving route based on the environmental information and the vehicle information.

2. The driving determination device according to claim 1, wherein the driving feasibility determination unit executes the simulation using a digital twin that reproduces the driving route based on the environmental information.

3. The driving determination device according to claim 2, wherein the driving feasibility determination unit predicts road surface conditions of the driving route based on the environmental information, and executes the simulation that reproduces the predicted road surface conditions.

4. A driving determination device according to any one of claims 1 to 3, wherein the driving feasibility determination unit generates a vehicle model based on the host vehicle information and executes the simulation using the generated vehicle model.

5. The driving determination device according to any one of claims 1 to 3, wherein the driving feasibility determination unit determines whether a plurality of driving routes to the destination of the vehicle are capable of driving, and provides guidance on a feasible driving route.

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