Driving assistance systems

Abstract

To prevent cargo from shifting. [Solution] The driving support device comprises an acquisition means (23a) and a presentation means (23h). The acquisition means (23a) acquires driving information including the acceleration of the moving object. The presentation means (23h) presents to the driver information regarding load shifting on the moving object, which is identified based on the distribution of acceleration in multiple axes of the moving object based on the driving information acquired by the acquisition means (23a).

Description

Technical Field

[0001] The present invention relates to a driving support device.

Background Art

[0002] Conventionally, a technique for predicting the collapse of luggage loaded on the cargo bed of a vehicle in motion by acquiring the acceleration of the vehicle or the like is known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above conventional technology, since there is room for improvement in the prediction accuracy of cargo collapse, there is room for further improvement in suppressing cargo collapse in a vehicle.

[0005] The present invention has been made in view of the above, and an object thereof is to provide, for example, a driving support device, a driving support method, and a driving support program capable of suppressing cargo collapse.

Means for Solving the Problems

[0006] The driving support device according to claim 1 includes an acquisition unit and a presentation unit. The acquisition unit acquires driving information including the acceleration of the moving body. The presentation unit presents information regarding cargo collapse in the moving body, which is specified based on the distribution of accelerations in a plurality of axes of the moving body based on the driving information acquired by the acquisition unit, to the driver.

[0007] Furthermore, the driving assistance method described in claim 8 is a driving assistance method implemented by a driving assistance device, which acquires driving information including the acceleration of a moving body, and presents to the driver information regarding load collapse on the moving body, which is identified based on the distribution of acceleration in multiple axes of the moving body based on the acquired driving information.

[0008] Furthermore, the driving assistance program described in claim 9 is a driving assistance program that causes a computer to perform a process of acquiring driving information including the acceleration of a moving body, and presenting to the driver information regarding load collapse on the moving body, which is identified based on the distribution of acceleration in multiple axes of the moving body based on the acquired driving information. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is an explanatory diagram showing an example of the configuration of the control system according to Embodiment 1. [Figure 2] Figure 2 is an explanatory diagram showing an example of the server configuration according to Embodiment 1. [Figure 3] Figure 3 is an explanatory diagram showing an example of the configuration of a vehicle according to Embodiment 1. [Figure 4] Figure 4 shows an example of an acceleration value distribution diagram according to Embodiment 1. [Figure 5] Figure 5 is a diagram illustrating an example of the road surface condition estimation process according to Embodiment 1. [Figure 6] Figure 6 shows an example of an acceleration value distribution diagram according to Embodiment 1. [Figure 7] Figure 7 is a diagram illustrating an example of the driver assistance process according to Embodiment 1. [Figure 8] Figure 8 is a diagram illustrating an example of the driver assistance process according to Embodiment 1. [Figure 9] Figure 9 is a diagram illustrating an example of the driver assistance process according to Embodiment 1. [Figure 10] Figure 10 is an explanatory diagram showing an example of the configuration of a vehicle according to Embodiment 2. [Figure 11]FIG. 11 is a flowchart showing the procedure of the road surface state estimation process according to Embodiment 1. [Figure 12] FIG. 12 is a flowchart showing the procedure of the driving support process according to Embodiment 1.

Mode for Carrying Out the Invention

[0010] Hereinafter, embodiments for carrying out the present invention (hereinafter referred to as embodiments) will be described with reference to the drawings. Note that the present invention is not limited to the embodiments described below. Further, in the description of the drawings, the same parts are denoted by the same reference numerals.

[0011] <Configuration of Control System> First, the configuration of the control system 1 according to Embodiment 1 will be described with reference to FIG. 1. FIG. 1 is an explanatory diagram showing an example of the configuration of the control system 1 according to Embodiment 1. As shown in FIG. 1, the control system 1 according to Embodiment 1 includes a server 2 and a plurality of vehicles 3. The vehicle 3 is an example of a moving body.

[0012] The server 2 manages a plurality of vehicles 3 as one control network. The plurality of vehicles 3 each have a control device 4.

[0013] The server 2 and the plurality of vehicles 3 are connected via a network (for example, the Internet) N by, for example, wireless LAN (Local Area Network) communication, WAN (Wide Area Network) communication, mobile phone communication, etc., and various information can be communicated between both.

[0014] <Configuration of Server> Next, the configuration of the server 2 according to Embodiment 1 will be described with reference to FIG. 2. FIG. 2 is an explanatory diagram showing an example of the configuration of the server 2 according to Embodiment 1. As shown in FIG. 2, the server 2 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0015] Note that the server 2 may have an input unit (e.g., a keyboard, a mouse, etc.) for receiving various operations from an administrator or the like who uses such a server 2, and a display unit (e.g., a liquid crystal display, etc.) for displaying various information.

[0016] The communication unit 11 is realized, for example, by a NIC (Network Interface Card) or the like. The communication unit 11 is connected to the network N by wire or wirelessly, and transmits and receives information to and from a plurality of vehicles 3 via the network N.

[0017] The storage unit 12 is realized, for example, by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 2, the storage unit 12 includes a road information storage unit 12a, a road surface information storage unit 12b, and a cargo collapse information storage unit 12c.

[0018] The road information storage unit 12a stores road position information indicating the position of a road, for example, map information. The road surface information storage unit 12b stores information regarding the road surface state of a road (hereinafter, also referred to as "road surface information"). In such a road surface information storage unit 12b, for example, information regarding the position of a road surface having irregularities and information regarding the details (e.g., shape, etc.) of such irregularities are stored in association with each other.

[0019] The cargo collapse information storage unit 12c stores information regarding cargo collapse in the vehicle 3 (hereinafter, also referred to as cargo collapse information). In such a cargo collapse information storage unit 12c, for example, information regarding the position where the degree of influence on cargo collapse is large and information regarding the degree of influence on cargo collapse are stored in association with each other.

[0020] The control unit 13 is a controller, and is implemented, for example, by a CPU (Central Processing Unit) or MPU (Micro Processing Unit) executing various programs stored in the internal memory of the server 2 using RAM as the working area. Alternatively, the control unit 13 can be implemented, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0021] As shown in Figure 2, the control unit 13 has an acquisition means 13a and a transmission means 13b, and realizes or executes the functions and operations of the various processes described below. Note that the internal configuration of the control unit 13 is not limited to the configuration shown in Figure 2, and other configurations are also acceptable as long as they perform the various processes described later.

[0022] The acquisition means 13a acquires road surface information estimated by the control device 4 of the vehicle 3 and transmitted from this control device 4, and stores it in the road surface information storage unit 12b. In addition, the acquisition means 13a acquires load collapse information identified by the control device 4 of the vehicle 3 and transmitted from this control device 4, and stores it in the load collapse information storage unit 12c.

[0023] The transmitting means 13b transmits road surface information stored in the road surface information storage unit 12b and load collapse information stored in the load collapse information storage unit 12c to the control device 4 of the vehicle 3 based on a command from the control device 4 of the vehicle 3.

[0024] <Road surface condition estimation process> Next, the configuration of the vehicle 3 according to Embodiment 1 and the details of the road surface condition estimation process performed by this vehicle 3 will be explained with reference to Figures 3 to 6. Figure 3 is an explanatory diagram showing an example of the configuration of the vehicle 3 according to Embodiment 1.

[0025] As shown in Figure 3, the vehicle 3 includes a control device 4, a three-axis acceleration sensor 5, a GPS (Global Positioning System) sensor 6, a speed sensor 7, and a display unit 8. The control device 4 is an example of a road surface condition estimation device and also an example of a driver assistance device.

[0026] The 3-axis acceleration sensor 5 is a sensor that detects acceleration in each of the following directions: the X-axis (for example, the longitudinal direction of the vehicle 3), the Y-axis (for example, the lateral direction of the vehicle 3), and the Z-axis (for example, the vertical direction of the vehicle 3), and supplies the detected signals to the control device 4. The 3-axis acceleration sensor 5 may also detect the angular velocity and angular acceleration of the vehicle 3's movement.

[0027] The GPS sensor 6 receives radio waves from multiple GPS satellites that carry downlink data including positioning data, and supplies this positioning data to the control device 4. The control device 4 can detect the absolute position of the vehicle 3 from the position information (e.g., latitude and longitude) contained in this positioning data.

[0028] The speed sensor 7 is, for example, a sensor that detects the speed of the vehicle 3 and supplies the detection signal to the control device 4. The display unit 8 is, for example, provided on the instrument panel of the vehicle 3 and consists of a liquid crystal display, an organic EL (Electro-Luminescence) element, etc.

[0029] The control device 4 comprises a communication unit 21, a storage unit 22, and a control unit 23. The communication unit 21 is implemented, for example, by a NIC. The communication unit 21 is connected to the network N by wire or wireless and transmits and receives information to and from the server 2 via the network N.

[0030] The memory unit 22 is implemented by, for example, semiconductor memory elements such as RAM and flash memory, or storage devices such as hard disks and optical discs.

[0031] The control unit 23 is a controller, and is implemented, for example, by a CPU or MPU executing various programs stored in the internal memory of the control device 4 using RAM as the working area. Alternatively, the control unit 23 is a controller and can be implemented by an integrated circuit such as an ASIC or FPGA.

[0032] As shown in Figure 3, the control unit 23 comprises an acquisition means 23a, a generation means 23b, a measurement means 23c, an estimation means 23d, a storage means 23e, an extraction means 23f, a specific means 23g, and a presentation means 23h, and realizes or executes the functions and operations of the various processes described below. Note that the internal configuration of the control unit 23 is not limited to the configuration shown in Figure 3, and other configurations are also acceptable as long as they perform the various processes described below.

[0033] The acquisition means 23a acquires information relating to the vehicle 3's movement (hereinafter also referred to as "movement information"). As such movement information, the acquisition means 23a acquires, for example, information relating to the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions from the 3-axis acceleration sensor 5.

[0034] Furthermore, the acquisition means 23a acquires, for example, the location information of the vehicle 3 from the GPS sensor 6 and the speed information of the vehicle 3 from the speed sensor 7 as driving information.

[0035] The generation means 23b uses the information obtained by the acquisition means 23a regarding the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions to generate an acceleration value distribution diagram, which is a diagram plotting the distribution of acceleration in three axes over a predetermined unit of time in a three-dimensional coordinate system. Details of this acceleration value distribution diagram are described below.

[0036] Figure 4 shows an example of an acceleration value distribution diagram according to Embodiment 1. For example, when vehicle 3 is stopped with its engine off, the plotted range of the acceleration value distribution diagram is the plotted range D1 near the origin of the 3D coordinate system.

[0037] Furthermore, when vehicle 3 is stopped with its engine idling, the plotted range of the acceleration value distribution diagram will be a spherical plotted range D2, centered on the origin of the 3D coordinate system, and wider than the plotted range D1 described above.

[0038] Furthermore, when vehicle 3 is traveling on a flat road surface, the plotted range of the acceleration value distribution diagram is a spherical plotted range D3 centered on the origin of the 3D coordinate system, which is wider than the plotted range D2 described above. Note that in Figure 4 or the text, the origin is set to a point that takes gravitational acceleration into account (initial value of 1g in the vertical direction).

[0039] Next, we will explain the road surface condition estimation process using this acceleration value distribution diagram. Figure 5 is a diagram illustrating an example of the road surface condition estimation process according to Embodiment 1. As shown in Figure 5, let's assume that vehicle 3 is passing through a section X of road surface deterioration where there are cracks in the asphalt, etc.

[0040] In this case, the left front wheel FL and left rear wheel RL of vehicle 3 pass through the road surface deterioration section X in sequence, while the right front wheel FR and right rear wheel RR of vehicle 3 do not pass through the road surface deterioration section X. As a result, vehicle 3 is shaken in the longitudinal direction, and shaken more to the left than to the right.

[0041] Then, in the unit time it takes to pass through the road surface deterioration section X, the generation means 23b generates an acceleration value distribution diagram as shown in Figure 6. Figure 6 is a diagram showing an example of an acceleration value distribution diagram according to Embodiment 1.

[0042] During a unit of time while passing through a road surface deterioration section X, the generation means 23b generates an acceleration value distribution map of a plot range D4 that extends in the longitudinal and vertical directions, and is more widely extended to the left than to the right, as shown in Figure 6.

[0043] Returning to the explanation of Figure 3, the measurement means 23c of the control unit 23 measures information regarding the vibration of the vehicle 3 (hereinafter also referred to as vibration information) based on the multi-axis distribution of the acceleration of the vehicle 3 (for example, the acceleration value distribution diagram generated by the generation means 23b).

[0044] Examples of vibration information measured by the measurement means 23c include the absolute value |A| of the magnitude in the longitudinal direction in the acceleration value distribution diagram of the plotted range D4 shown in Figure 6. Furthermore, examples of vibration information measured by the measurement means 23c include the absolute value |B| of the magnitude in the lateral direction in the acceleration value distribution diagram of the plotted range D4.

[0045] Furthermore, vibration information measured by the measurement means 23c includes, for example, the absolute value |B / C| of the ratio between the magnitude B in the left-right direction and the magnitude C in the lateral direction (leftward in the figure), which has a larger spread relative to the origin.

[0046] Furthermore, vibration information measured by the measurement means 23c includes, for example, the absolute value |D| of the vertical magnitude in the acceleration value distribution diagram of the plot range D4.

[0047] Returning to the explanation of Figure 3, the estimation means 23d of the control unit 23 estimates the road surface condition of the road surface on which the vehicle 3 traveled, based on the vibration information measured by the measurement means 23c.

[0048] For example, the estimation means 23d estimates that the greater the absolute value |A| measured by the measurement means 23c, the greater the degree of deterioration (e.g., unevenness) of the road surface deterioration section X.

[0049] Furthermore, the estimation means 23d estimates that the greater the absolute value |B| measured by the measurement means 23c, the greater the degree of deterioration (e.g., unevenness) of the road surface deterioration section X.

[0050] Furthermore, the estimation means 23d estimates that the greater the absolute value |B / C| measured by the measurement means 23c, the more the partial deterioration of the road surface deterioration section X is progressing.

[0051] Furthermore, the estimation means 23d can estimate the lateral position of the road surface deterioration section X (the left-right relative position of the road surface deterioration section X with respect to the vehicle 3) based on the absolute value |B / C| measured by the measurement means 23c, for example.

[0052] Furthermore, the estimation means 23d can estimate that if the fluctuation value of the absolute value |D| measured by the measurement means 23c is large, the contribution of the degree of deterioration of the road surface deterioration section X to the absolute value |B / C| is greater due to partial deterioration than to the lateral position of the road surface deterioration section X.

[0053] As described above, in Embodiment 1, vibration information of the vehicle 3 is measured based on the multi-axis distribution of the vehicle's acceleration (for example, an acceleration value distribution diagram), and the road surface condition is estimated based on this vibration information of the vehicle 3. This makes it possible to estimate the road surface condition with high accuracy.

[0054] Furthermore, in Embodiment 1, the estimation means 23d may estimate the road surface condition based on the magnitude of the vibration of the vehicle 3 (for example, absolute value |A|, absolute value |B|, absolute value |D|), the bias of the vibration position of the vehicle 3 (for example, absolute value |B / C|), and the duration of the vibration.

[0055] In this way, by estimating the degree of deterioration of the road surface deterioration section X using various parameters, the road surface condition can be estimated with even greater accuracy.

[0056] Furthermore, in Embodiment 1, the measuring means 23c may measure the magnitude of vibration of the vehicle 3 and the bias of the vibration position of the vehicle 3 based on the shape of the acceleration value distribution diagram generated by the generating means 23b.

[0057] This allows for accurate measurement of the magnitude of vibrations in vehicle 3 and the bias in the vibration location of vehicle 3. Therefore, according to Embodiment 1, the road surface condition can be estimated with even greater accuracy.

[0058] Furthermore, in Embodiment 1, the estimation means 23d may estimate the undulation of the road surface based on the magnitude of the vibration of the vehicle 3, and estimate the lateral position of the point where the road surface condition is damaged in the driving lane based on the bias in the vibration position of the vehicle 3.

[0059] This allows for the estimation of the degree of deterioration of the road surface deterioration section X using various parameters, as well as the estimation of the lateral position of the road surface deterioration section X. Therefore, according to Embodiment 1, the road surface condition can be estimated with even greater accuracy.

[0060] Furthermore, in Embodiment 1, the estimation means 23d may directly estimate the road surface condition based on the shape of the acceleration value distribution diagram generated by the generation means 23b. For example, the estimation means 23d can estimate whether there are steps or cracks in the road surface based on the shape of the acceleration value distribution diagram. Therefore, according to Embodiment 1, the road surface condition can be estimated with even greater accuracy.

[0061] Furthermore, in Embodiment 1, the acquisition means 23a may acquire driving information from a single 3-axis acceleration sensor 5 located inside the vehicle 3. This allows for the easy generation of an acceleration value distribution map of the vehicle 3, thereby enabling low-cost estimation of road surface conditions.

[0062] Furthermore, the 3-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3; for example, it may be a 3-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3.

[0063] Furthermore, in this disclosure, the number of 3-axis acceleration sensors 5 for measuring the acceleration of the vehicle 3 is not limited to one, but may be multiple. This allows for accurate measurement of the acceleration of the vehicle 3, thereby enabling more accurate estimation of the road surface condition.

[0064] Returning to the explanation of Figure 3, the storage means 23e of the control unit 23 associates the location information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d and stores it in the road surface information storage unit 12b (see Figure 2) of the server 2 (see Figure 2).

[0065] As a result, information about locations with poor road conditions is stored in the road information storage unit 12b of server 2, and this information about locations with poor road conditions can be utilized by multiple vehicles 3 connected to the network N. Examples of how this road condition information can be utilized will be described later.

[0066] Furthermore, in Embodiment 1, the shape and size of the generated acceleration value distribution map may differ depending on the speed of the vehicle 3, the type of vehicle 3, and the type of tires mounted on the vehicle 3.

[0067] Therefore, in Embodiment 1, in addition to the various vibration information described above, the road surface condition may be estimated based on information such as the speed of the vehicle 3 when passing through the road surface deterioration section X, the type of vehicle 3, and the type of tires mounted on the vehicle 3.

[0068] For example, each vehicle 3 may be calibrated by having it drive on a road surface whose uneven shape is known in advance.

[0069] Furthermore, the server 2 and the control device 4 generate a learning model based on a set of acceleration value distribution diagrams for one vehicle 3, using the acceleration value distribution diagram as input information and the degree of road surface deterioration as output information. The estimation means 23d may then use this learning model to estimate the degree of road surface deterioration each time an acceleration value distribution diagram is generated.

[0070] Furthermore, the server 2 generates a learning model based on a set of acceleration value distribution maps of multiple vehicles 3 connected to the network N, using the acceleration value distribution map as input information and the degree of road surface deterioration as output information. The estimation means 23d may then use this learning model to estimate the degree of road surface deterioration each time an acceleration value distribution map is generated.

[0071] Furthermore, in Embodiment 1, the generation means 23b may generate an acceleration value distribution diagram for each unit time during the entire driving time, or it may generate an acceleration value distribution diagram from the point in time when the 3-axis acceleration sensor 5 detects an unusual acceleration.

[0072] <Driving support processing> Next, the details of the driving support process according to Embodiment 1 will be explained with reference to Figures 3 and 6 to 9.

[0073] The acquisition means 23a of the control unit 23 shown in Figure 3 acquires driving information of the vehicle 3. As such driving information, the acquisition means 23a acquires, for example, information regarding the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions from the 3-axis acceleration sensor 5.

[0074] Furthermore, the acquisition means 23a acquires, for example, the location information of the vehicle 3 from the GPS sensor 6 and the speed information of the vehicle 3 from the speed sensor 7 as driving information.

[0075] The generation means 23b uses the information on the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions acquired by the acquisition means 23a to generate an acceleration value distribution map plotting the distribution of acceleration in three axes over a predetermined unit of time in a three-dimensional coordinate system.

[0076] The measuring means 23c measures vibration information of the vehicle 3 based on the multi-axis distribution of the vehicle's acceleration (for example, an acceleration value distribution diagram generated by the generating means 23b).

[0077] The measuring means 23c measures, for example, the magnitude of vibration of the vehicle 3 (e.g., absolute value |A|, absolute value |B|, absolute value |D|) and the bias of the vibration position of the vehicle 3 (e.g., absolute value |B / C|), similar to the road surface condition estimation process described above.

[0078] The identification means 23g identifies information regarding cargo collapse in the vehicle 3 (i.e., cargo collapse information) based on various types of information. Such cargo collapse information may include, for example, the probability of cargo collapse occurring and the extent of any cargo collapse that has occurred.

[0079] In the following explanation, the probability of cargo collapse occurring and the extent of cargo collapse that has occurred, as included in the cargo collapse information, will be collectively referred to as "the degree of impact on cargo collapse."

[0080] The identification means 23g identifies information about the load shifting of the vehicle 3, for example, using vibration information of the vehicle 3 measured by the measuring means 23c.

[0081] For example, the identifying means 23g identifies that the larger the absolute value of |A| (see Figure 6) of the magnitude in the longitudinal direction in the acceleration value distribution diagram, the greater the degree of influence on cargo collapse. In this case, the identifying means 23g may also take into account the duration of the longitudinal vibration in the vehicle 3 when determining the degree of influence on cargo collapse.

[0082] Furthermore, the identification means 23g may have a dead zone against longitudinal vibrations in the vehicle 3. This allows for accurate determination of the degree of impact on cargo collapse.

[0083] Furthermore, the identifying means 23g identifies, for example, that the larger the absolute value of |B| (see Figure 6) of the magnitude in the lateral direction in the acceleration value distribution diagram, the greater the degree of influence on cargo collapse. In this case, the identifying means 23g may also take into account the duration of the lateral vibration in the vehicle 3 when determining the degree of influence on cargo collapse.

[0084] Furthermore, the identifying means 23g identifies, for example, that the larger the absolute value |B / C| (see Figure 6) mentioned above, which indicates the bias in the vibration position of the vehicle 3, the greater the degree of influence on cargo collapse. In this case, the identifying means 23g may also take into account the duration of the bias in the vibration position of the vehicle 3 when determining the degree of influence on cargo collapse.

[0085] Furthermore, the identification means 23g may identify the load collapse information of the vehicle 3 using information different from the vibration information measured by the measurement means 23c. Figure 7 is a diagram illustrating an example of the driving support process according to Embodiment 1, and shows the changes in each acceleration at each time in a three-dimensional coordinate system in which the distribution of acceleration along three axes is plotted.

[0086] Specifically, in Figure 7, the distribution of acceleration along the three axes is first plotted at plot P1, and then the distribution of acceleration along the three axes is plotted in the order of plots P2, P3, P4, P5, and P6.

[0087] In this case, the extraction means 23f of the control unit 23 extracts information regarding the lateral component of the acceleration of the vehicle 3 from, for example, the changes in the acceleration of the three axes of the vehicle 3. For example, the extraction means 23f extracts the absolute value |E| of the magnitude of the lateral component in each plot (for example, plot P5) shown in Figure 7.

[0088] The identifying means 23g then identifies that the larger the absolute value |E| of the magnitude of the lateral component, the greater the degree of influence on load collapse.

[0089] Furthermore, the extraction means 23f extracts a value obtained by adding the distance between plot P5 and the origin to the absolute value |E| of the magnitude of the horizontal component in each plot shown in Figure 7 (for example, plot P5).

[0090] The identifying means 23g then identifies that the larger the absolute value |E| of the magnitude of the lateral component, taking into account the distance from the origin, the greater the degree of influence on load collapse.

[0091] Furthermore, the extraction means 23f extracts, for example, the absolute value |F| of the deviation (the difference from the previous plot, for example, the difference between plot P6 and the previous plot P5) in each plot shown in Figure 7.

[0092] The identifying method 23g then identifies that the larger the absolute value of the deviation |F|, the greater the degree of influence on load collapse.

[0093] Furthermore, in Embodiment 1, an acceleration value distribution diagram for one run of vehicle 3 may be generated, and the degree of influence on cargo collapse may be identified based on this acceleration value distribution diagram for one run. Figure 8 is a diagram illustrating an example of the driving support process according to Embodiment 1, and shows an example of an acceleration value distribution diagram for one run of vehicle 3.

[0094] The acceleration value distribution diagram (plot range D5) for one run of vehicle 3 shown in Figure 8 is generated by the generation means 23b of the control unit 23. The extraction means 23f then extracts the degree of distortion of this plot range D5 (for example, the bias relative to the origin).

[0095] The identifying means 23g then identifies that the greater the degree of distortion in the acceleration value distribution diagram during such a single run, the greater the degree of impact on cargo collapse.

[0096] Furthermore, in Embodiment 1, the moment applied to the cargo loaded on the vehicle 3 may be extracted from the vehicle 3 driving information acquired by the acquisition means 23a, and the degree of influence on cargo collapse may be identified based on the moment applied to the cargo. Figure 9 is a diagram illustrating an example of the driving support process according to Embodiment 1, and is a diagram illustrating the moment applied to the cargo.

[0097] In the example shown in Figure 9, the initial position of the center of gravity G of the cargo coincides with the origin (for example, the center of gravity of vehicle 3). Next, consider the case where vehicle 3 turns to the right, and the center of gravity G of the cargo moves from the origin to the left rear.

[0098] In this case, the extraction means 23f can extract the moment M applied to the load based on the following equation (1). M∝F C ×L ···(1) F C : Centrifugal force L: Distance between the origin and the center of gravity G

[0099] The identification means 23g then identifies that the greater the value of the moment M applied to the load (larger L, i.e., larger difference in the initial value of the center of gravity, and / or larger centrifugal force F, i.e., higher speed / sharper curve), the greater the degree of influence on load collapse. The position of the center of gravity G of the load can be determined from the acceleration, angular velocity, angular acceleration, etc., detected by the 3-axis acceleration sensor 5.

[0100] Returning to the explanation of Figure 3, the presentation means 23h of the control unit 23 presents the cargo collapse information of the vehicle 3 identified by the identification means 23g to the driver of the vehicle 3. For example, the presentation means 23h presents the cargo collapse information of the vehicle 3 to the driver by displaying it on the display unit 8.

[0101] For example, the notification means 23h informs the driver that there is a high probability of cargo collapse occurring when the degree of impact on cargo collapse is greater than a given threshold. This allows the control device 4 to suppress driving practices that are likely to cause cargo collapse.

[0102] Furthermore, the presentation means 23h may, for example, present to the driver the degree of cargo collapse that has occurred when the degree of impact on cargo collapse is greater than a given threshold. This also allows the control device 4 to suppress driving that is likely to result in an even greater degree of cargo collapse.

[0103] Therefore, according to Embodiment 1, it is possible to suppress cargo collapse in the vehicle 3.

[0104] Furthermore, in Embodiment 1, the driver may be individually presented with the factors that have a significant impact on cargo collapse from among the various factors mentioned above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, moment M).

[0105] Furthermore, in Embodiment 1, the various factors mentioned above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, moment M) may be comprehensively considered, and the magnitude of this comprehensively considered influence may be presented to the driver.

[0106] Furthermore, in Embodiment 1, information on locations that have a significant impact on cargo collapse may be stored in the server 2. For example, the storage means 23e associates the location information of the vehicle 3 acquired by the acquisition means 23a with the cargo collapse information identified by the identification means 23g and stores it in the cargo collapse information storage unit 12c of the server 2.

[0107] As a result, information regarding locations where cargo collapse is likely to occur is stored in the cargo collapse information storage unit 12c of server 2, and this information regarding locations where cargo collapse is likely to occur can be utilized by multiple vehicles 3 connected to the network N.

[0108] For example, the control device 4 may present the driver with route guidance for the vehicle 3 based on the information stored in the road surface information storage unit 12b and the cargo collapse information storage unit 12c of the server 2.

[0109] Specifically, the control device 4 may, for example, provide the driver with route guidance that avoids locations with poor road surface conditions stored in the road surface information storage unit 12b, and locations where cargo collapse is likely to occur (for example, locations where the degree of impact on cargo collapse is greater than a given threshold) stored in the cargo collapse information storage unit 12c.

[0110] This prevents vehicle 3 from entering areas with poor road conditions (for example, areas with large bumps or uneven surfaces) or areas where cargo is likely to shift (for example, curves with a small curvature). Therefore, according to Embodiment 1, cargo shifting in vehicle 3 can be effectively suppressed.

[0111] Furthermore, the control device 4, by referring to the road surface information storage unit 12b, estimates that if the degree of impact on cargo collapse becomes greater than a given threshold while driving on a road surface that is known to be in good condition, the increase in the degree of such impact is due to the driver's driving style rather than the road surface condition.

[0112] In this case, the presentation means 23h should present (advise) the driver on driving methods to reduce the degree of impact on cargo collapse. For example, if the degree of distortion in the acceleration value distribution diagram for one run shown in Figure 8 is large, the presentation means 23h should present the driver on driving methods to reduce the degree of distortion in the acceleration value distribution diagram during a break after the run is completed.

[0113] As a result, the control device 4 can suppress operations that are prone to causing cargo collapse. Therefore, according to Embodiment 1, cargo collapse in the vehicle 3 can be suppressed.

[0114] Furthermore, in Embodiment 1, if the position of the center of gravity G of the cargo is gradually shifting, guidance may be provided to the driver. In addition, if it is anticipated that the balance of the cargo is significantly disrupted, the notification means 23h may prompt the driver to stop the vehicle 3 and check the cargo.

[0115] <Embodiment 2> Next, the configuration of vehicle 3A according to Embodiment 2 will be described with reference to Figure 10. Figure 10 is an explanatory diagram showing an example of the configuration of vehicle 3A according to Embodiment 2. Vehicle 3A in this Embodiment 2 is a standalone vehicle that is not connected to network N (see Figure 1).

[0116] As shown in Figure 10, the vehicle 3A includes a control device 4A, a three-axis acceleration sensor 5, a GPS sensor 6, a speed sensor 7, and a display unit 8. The control device 4A is another example of a road surface condition estimation device, and also another example of a driver assistance device.

[0117] The 3-axis acceleration sensor 5 is a sensor that detects acceleration in the X, Y, and Z axes, for example, and supplies its detection signals to the control device 4A. The 3-axis acceleration sensor 5 may also detect the angular velocity and angular acceleration of the vehicle 3A's movement.

[0118] The GPS sensor 6 receives radio waves from multiple GPS satellites that carry downlink data including positioning data, and supplies this positioning data to the control device 4A. The control device 4A can detect the absolute position of the vehicle 3A from the position information (e.g., latitude and longitude) contained in this positioning data.

[0119] The speed sensor 7 is, for example, a sensor that detects the speed of the vehicle 3A and supplies the detection signal to the control device 4A. The display unit 8 is, for example, provided on the instrument panel of the vehicle 3A and consists of a liquid crystal display, an organic EL element, or the like.

[0120] As shown in Figure 10, the control device 4A comprises a storage unit 31 and a control unit 32. The storage unit 31 is implemented by, for example, a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disc. As shown in Figure 10, the storage unit 31 has a road information storage unit 31a, a road surface information storage unit 31b, and a load collapse information storage unit 31c.

[0121] The road information storage unit 31a stores road location information, such as map information, indicating the location of a road. The road surface information storage unit 31b stores road surface information. The load collapse information storage unit 31c stores load collapse information.

[0122] Since the road information storage unit 31a, the road surface information storage unit 31b, and the load collapse information storage unit 31c have the same configuration as the road information storage unit 12a, the road surface information storage unit 12b, and the load collapse information storage unit 12c of Embodiment 1 shown in Figure 2, a detailed explanation will be omitted.

[0123] The control unit 32 is a controller, and is implemented, for example, by a CPU or MPU executing various programs stored in the memory device inside the control device 4A using RAM as the working area. Alternatively, the control unit 32 is a controller and can be implemented by an integrated circuit such as an ASIC or FPGA.

[0124] As shown in Figure 10, the control unit 32 comprises an acquisition means 32a, a generation means 32b, a measurement means 32c, an estimation means 32d, a storage means 32e, an extraction means 32f, a specific means 32g, and a presentation means 32h, and realizes or executes the functions and operations of the various processes described below. Note that the internal configuration of the control unit 32 is not limited to the configuration shown in Figure 10, and other configurations are also possible as long as they perform the various processes described below.

[0125] The acquisition means 32a acquires driving information of the vehicle 3A. As such driving information, the acquisition means 32a acquires, for example, information regarding the acceleration of the vehicle 3A in the longitudinal, lateral, and vertical directions from the 3-axis acceleration sensor 5.

[0126] Furthermore, the acquisition means 32a acquires, for example, the location information of the vehicle 3A from the GPS sensor 6 and the speed information of the vehicle 3A from the speed sensor 7 as driving information.

[0127] The generation means 32b uses information on the longitudinal, lateral, and vertical acceleration of the vehicle 3A obtained by the acquisition means 32a to generate an acceleration value distribution map plotting the distribution of acceleration in three axes over a predetermined unit of time in a three-dimensional coordinate system.

[0128] The measuring means 32c measures vibration information of the vehicle 3A based on the multi-axis distribution of the vehicle 3A's acceleration (for example, an acceleration value distribution diagram generated by the generating means 32b). The estimation means 32d estimates the road surface condition of the road surface on which the vehicle 3A traveled based on the vibration information measured by the measuring means 32c.

[0129] The storage means 32e associates the location information of the vehicle 3A acquired by the acquisition means 32a with the road surface condition estimated by the estimation means 32d, and stores it in the road surface information storage unit 31b of the storage unit 31.

[0130] Since the acquisition means 32a, generation means 32b, measurement means 32c, estimation means 32d, and storage means 32e have the same configuration as the acquisition means 23a, generation means 23b, measurement means 23c, estimation means 23d, and storage means 23e of Embodiment 1 shown in Figure 3, a detailed explanation will be omitted.

[0131] Thus, in Embodiment 2, similar to Embodiment 1 described above, vibration information of vehicle 3A is measured based on the multi-axis distribution of acceleration of vehicle 3A (for example, an acceleration value distribution diagram), and the road surface condition is estimated based on this vibration information of vehicle 3A. This makes it possible to estimate the road surface condition with high accuracy.

[0132] Furthermore, in Embodiment 2, the estimation means 32d may estimate the road surface condition based on the magnitude of the vibration of the vehicle 3A (for example, absolute value |A|, absolute value |B|, absolute value |D|) and the bias of the vibration position of the vehicle 3A (for example, absolute value |B / C|).

[0133] In this way, by estimating the degree of deterioration of the road surface deterioration section X using various parameters, the road surface condition can be estimated with even greater accuracy.

[0134] Furthermore, in Embodiment 2, the measuring means 32c may measure the magnitude of vibration of the vehicle 3A and the bias of the vibration position of the vehicle 3A based on the shape of the acceleration value distribution diagram generated by the generating means 32b.

[0135] This allows for accurate measurement of the magnitude of vibration of vehicle 3A and the bias in the vibration location of vehicle 3A. Therefore, according to Embodiment 2, the road surface condition can be estimated with even greater accuracy.

[0136] Furthermore, in Embodiment 2, the estimation means 32d may estimate the undulation of the road surface based on the magnitude of the vibration of the vehicle 3A, and estimate the lateral position of the point where the road surface condition is damaged in the driving lane based on the bias in the vibration position of the vehicle 3A.

[0137] This allows for the estimation of the degree of deterioration of the road surface deterioration section X using various parameters, as well as the estimation of the lateral position of the road surface deterioration section X. Therefore, according to Embodiment 2, the road surface condition can be estimated with even greater accuracy.

[0138] Furthermore, in Embodiment 2, the estimation means 32d may directly estimate the road surface condition based on the shape of the acceleration value distribution diagram generated by the generation means 32b. For example, the estimation means 32d can estimate whether there are steps or cracks in the road surface based on the shape of the acceleration value distribution diagram. Therefore, according to Embodiment 2, the road surface condition can be estimated with even greater accuracy.

[0139] Furthermore, in the second embodiment, the acquisition means 32a may acquire driving information from a single 3-axis acceleration sensor 5 located inside the vehicle 3A. This makes it possible to easily generate an acceleration value distribution diagram of the vehicle 3A, thereby enabling low-cost estimation of road surface conditions.

[0140] Furthermore, the 3-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3A; for example, it may be a 3-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3A.

[0141] Furthermore, in this disclosure, the number of 3-axis acceleration sensors 5 that measure the acceleration of the vehicle 3A is not limited to one, but may be multiple. This allows for accurate measurement of the acceleration of the vehicle 3A, and thus enables more accurate estimation of the road surface condition.

[0142] Furthermore, in the second embodiment, the storage means 32e may associate the location information of the vehicle 3A acquired by the acquisition means 32a with the road surface condition estimated by the estimation means 32d and store it in the road surface information storage unit 31b of the storage unit 31.

[0143] As a result, locations with poor road surface conditions are stored in the road surface information storage unit 31b, allowing the information regarding the road surface conditions estimated by vehicle 3A to be utilized during subsequent runs of vehicle 3A.

[0144] Furthermore, in Embodiment 2, similar to Embodiment 1 described above, the shape and size of the generated acceleration value distribution map may differ depending on the speed of the vehicle 3A, the type of vehicle 3A, and the type of tires mounted on the vehicle 3A.

[0145] Therefore, in Embodiment 2, in addition to the various vibration information described above, the road surface condition may be estimated based on information such as the speed of the vehicle 3A when passing through the road surface deterioration section X, the type of vehicle 3A, and the type of tires mounted on the vehicle 3A.

[0146] For example, the vehicle 3A may be calibrated by having it drive on a road surface whose uneven shape is known in advance.

[0147] Furthermore, the control device 4A generates a learning model based on a set of acceleration value distribution diagrams of the vehicle 3A, using the acceleration value distribution diagram as input information and the degree of road surface deterioration as output information. The estimation means 32d may then use this learning model to estimate the degree of road surface deterioration each time an acceleration value distribution diagram is generated.

[0148] Furthermore, in Embodiment 2, the generation means 32b may generate an acceleration value distribution diagram for each unit time during the entire driving time, or it may generate the acceleration value distribution diagram from the point in time when the 3-axis acceleration sensor 5 detects an unusual acceleration.

[0149] The extraction means 32f extracts information such as the lateral component of the acceleration of vehicle 3A from the changes in the acceleration of the three axes of vehicle 3A. The identification means 32g identifies information about the load shift of vehicle 3A based on the various pieces of information.

[0150] The presentation means 32h presents the cargo collapse information of vehicle 3A, which has been identified by the identification means 32g, to the driver of vehicle 3A. For example, the presentation means 32h presents the cargo collapse information of vehicle 3A to the driver by displaying it on the display unit 8.

[0151] Since the extraction means 32f, the identification means 32g, and the presentation means 32h have the same configuration as the extraction means 23f, the identification means 23g, and the presentation means 23h of Embodiment 1 shown in Figure 3, a detailed explanation will be omitted.

[0152] Thus, in Embodiment 2, similar to Embodiment 1 described above, the cargo collapse information of the vehicle 3A identified by the identification means 32g is presented to the driver of the vehicle 3A.

[0153] For example, the notification means 32h informs the driver that there is a high probability of cargo collapse occurring when the degree of impact on cargo collapse is greater than a given threshold. This allows the control device 4A to suppress driving practices that are likely to cause cargo collapse.

[0154] Furthermore, the presentation means 32h may, for example, present to the driver the degree of cargo collapse that has occurred when the degree of impact on cargo collapse is greater than a given threshold. This also allows the control device 4A to suppress driving that is likely to result in an even greater degree of cargo collapse.

[0155] Therefore, according to Embodiment 2, it is possible to suppress cargo collapse in vehicle 3A.

[0156] Furthermore, in Embodiment 2, the driver may be individually presented with the factors that have a significant impact on cargo collapse from among the various factors shown in Embodiment 1 (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, moment M).

[0157] Furthermore, in Embodiment 2, the various factors mentioned above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, moment M) may be comprehensively considered, and the magnitude of this comprehensively considered influence may be presented to the driver.

[0158] In the second embodiment, information on locations that have a significant impact on cargo collapse may be stored in the storage unit 31. For example, the storage means 32e associates the location information of the vehicle 3A acquired by the acquisition means 32a with the cargo collapse information identified by the identification means 32g and stores it in the cargo collapse information storage unit 31c of the storage unit 31.

[0159] As a result, information regarding locations where cargo is likely to shift is stored in the road surface information storage unit 31b, and this information regarding locations where cargo is likely to shift can be utilized when the vehicle 3A travels in the future.

[0160] For example, the control device 4A may present the driver with route guidance for the vehicle 3A based on the information stored in the road surface information storage unit 31b and the cargo collapse information storage unit 31c of the storage unit 31.

[0161] Specifically, the control device 4A may, for example, provide the driver with route guidance that avoids locations with poor road surface conditions stored in the road surface information storage unit 31b, and locations where cargo collapse is likely to occur (for example, locations where the degree of impact on cargo collapse is greater than a given threshold) stored in the cargo collapse information storage unit 31c.

[0162] This prevents vehicle 3A from entering areas with poor road conditions (for example, areas with large bumps or uneven surfaces) or areas where cargo is likely to shift (for example, curves with a large curvature). Therefore, according to Embodiment 2, cargo shifting in vehicle 3A can be effectively suppressed.

[0163] Furthermore, the control device 4A, by referring to the road surface information storage unit 31b, estimates that if the degree of impact on cargo collapse becomes greater than a given threshold while driving on a road surface that is known to be in good condition, the increase in the degree of such impact is due to the driver's driving style rather than the road surface condition.

[0164] In this case, the presentation means 32h should present (advise) the driver on driving methods to reduce the degree of impact on cargo collapse. For example, if the degree of distortion in the acceleration value distribution diagram during one run is large, the presentation means 32h should present the driver on driving methods to reduce the degree of distortion in the acceleration value distribution diagram during a break after the run is completed.

[0165] As a result, the control device 4A can suppress operations that are prone to causing cargo collapse. Therefore, according to Embodiment 2, cargo collapse in the vehicle 3A can be suppressed.

[0166] Furthermore, in Embodiment 2, if the position of the center of gravity G of the cargo is gradually shifting, guidance may be provided to the driver. In addition, if it is anticipated that the balance of the cargo is significantly disrupted, the notification means 32h may prompt the driver to stop the vehicle 3A and check the cargo.

[0167] <Processing Procedure> Next, the procedures for various processes according to Embodiment 1 will be explained with reference to Figures 11 and 12. Figure 11 is a flowchart showing the procedure for road surface condition estimation according to Embodiment 1.

[0168] First, the acquisition means 23a acquires driving information of the vehicle 3 (step S101). As such driving information, the acquisition means 23a acquires, for example, information regarding the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions from the 3-axis acceleration sensor 5.

[0169] Next, the generation means 23b uses the information on the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions acquired by the acquisition means 23a to generate an acceleration value distribution map plotting the distribution of acceleration in three axes over a predetermined unit time in a three-dimensional coordinate system (step S102).

[0170] Next, the measuring means 23c measures vibration information of the vehicle 3 based on the multi-axis distribution of the vehicle's acceleration (for example, the acceleration value distribution diagram generated by the generating means 23b) (step S103).

[0171] Next, the estimation means 23d estimates the road surface condition of the road surface over which the vehicle 3 traveled, based on the vibration information measured by the measurement means 23c (step S104).

[0172] Finally, the storage means 23e associates the location information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d, stores it in the road surface information storage unit 12b of the server 2 (step S105), and ends the series of road surface condition estimation processes.

[0173] Figure 12 is a flowchart showing the procedure for the driving assistance process according to Embodiment 1. First, the acquisition means 23a acquires driving information of the vehicle 3 (step S201). As such driving information, the acquisition means 23a acquires, for example, information regarding the acceleration of the vehicle 3 in the longitudinal, lateral, and vertical directions from the 3-axis acceleration sensor 5.

[0174] Next, the identification means 23g identifies the load collapse information of the vehicle 3 based on the distribution of acceleration of the vehicle 3 along multiple axes, which is based on the driving information acquired by the acquisition means 23a (step S202).

[0175] The identification means 23g identifies information about the load collapse of the vehicle 3 based, for example, on vibration information of the vehicle 3 measured by the measuring means 23c and various information extracted by the extraction means 23f (such as the absolute value |E| and absolute value |F| mentioned above).

[0176] Finally, the presentation means 23h presents the cargo collapse information of the vehicle 3 identified by the identification means 23g to the driver of the vehicle 3 (step S203), and ends the series of driving support processes.

[0177] Although embodiments of the present invention have been described above, the present invention is not limited to the above embodiments, and various modifications are possible without departing from the spirit of the invention. For example, although the above embodiments described various processes performed on vehicle 3, the subject to which this disclosure is implemented is not limited to vehicles, but can also be applied to various types of moving objects (for example, motorcycles, trains, etc.).

[0178] Furthermore, while the above embodiment shows an example in which the presentation means 23h presents cargo collapse information to the driver of vehicle 3, the recipient of the cargo collapse information presented by the presentation means 23h is not limited to the driver of vehicle 3, but may be another driver of vehicle 3 or the administrator of server 2, etc.

[0179] Furthermore, the above embodiment describes an example in which a 3-axis acceleration sensor 5 generates an acceleration value distribution diagram, which is a plot of the distribution of acceleration in three axes over a predetermined unit of time in a three-dimensional coordinate system, and road surface condition estimation processing and driving support processing are performed based on this acceleration value distribution diagram.

[0180] However, the above embodiments are not limited to such examples. For example, an acceleration value distribution diagram may be generated by using accelerations in the X-axis and Y-axis directions measured by an acceleration sensor to plot the distribution of accelerations in two axes over a predetermined unit of time in a two-dimensional coordinate system. Road surface condition estimation processing and driving support processing may then be performed based on this acceleration value distribution diagram.

[0181] Further effects and modifications can be readily derived by those skilled in the art. Therefore, broader aspects of the present invention are not limited to the specific details and representative embodiments expressed and described above. Accordingly, various modifications are possible without departing from the spirit or scope of the overall concept of the invention as defined by the appended claims and their equivalents. [Explanation of symbols]

[0182] 1. Control System 2 servers 3. 3A Vehicle (an example of a mobile vehicle) 4.4A Control device (an example of a road surface condition estimation device and a driving assistance device) 5. 3-axis accelerometer 6 GPS sensors 7 Speed ​​sensor 8 Display 12, 22, 31 Storage section 13, 23, 32 Control Unit 23a, 32a Acquisition method 23b, 32b Generation means 23c, 32c Measuring means 23d, 32d estimation means 23e, 32e Storage means 23f, 32f extraction means 23g, 32g identification means 23h, 32h Presentation method

Claims

1. A means for acquiring driving information including the acceleration of a moving object, The system includes a presentation means for presenting to the driver information regarding load collapse on the moving body, which is identified based on the distribution of acceleration in multiple axes of the moving body based on the driving information acquired by the acquisition means. A driving assistance device characterized by the following features.

2. An extraction means for extracting information regarding the lateral component of the acceleration of the moving body from the distribution of acceleration in multiple axes of the moving body based on the driving information acquired by the acquisition means, The system further comprises: an identification means for identifying information regarding load collapse on the moving body based on information regarding the lateral component of the acceleration of the moving body extracted by the extraction means; The driving support device according to feature 1.

3. An extraction means for extracting information regarding the lateral component of the acceleration of the moving body, weighted by the longitudinal component of the acceleration of the moving body, from the distribution of acceleration in multiple axes of the moving body based on the driving information acquired by the acquisition means, The system further comprises: identification means for identifying information regarding load collapse on the moving body based on information regarding the lateral component of the acceleration of the moving body, weighted by the longitudinal component of the acceleration of the moving body extracted by the extraction means. The driving support device according to feature 1 or 2.

4. An extraction means for extracting information regarding the deviation of the acceleration of the moving body, taking into account time-series information, from the distribution of acceleration in multiple axes of the moving body based on the driving information acquired by the acquisition means, The system further comprises: identification means for identifying information regarding load collapse on the moving body based on information regarding the deviation of the acceleration of the moving body, taking into account the time-series information extracted by the extraction means; A driving assistance device according to any one of features 1 to 3.

5. An extraction means for extracting information regarding the degree of distortion of the acceleration of the moving body in the most recent predetermined time period from the distribution of acceleration in multiple axes of the moving body based on the travel information acquired by the acquisition means, The system further comprises: an identification means for identifying information regarding load collapse on the moving body based on information regarding the degree of distortion of the acceleration of the moving body in the most recent predetermined time period, which is extracted by the extraction means; A driving assistance device according to any one of features 1 to 4.

6. An extraction means for extracting information regarding the moment applied to the cargo carried by the moving body from the driving information acquired by the acquisition means, The system further comprises: identification means for identifying information regarding load collapse on the moving body based on information regarding the moment applied to the load carried by the moving body, which has been extracted by the extraction means. A driving support device according to any one of features 1 to 5.

7. The acquisition means acquires the driving information from a three-axis acceleration sensor located inside the moving body. A driving assistance device according to any one of features 1 to 6.

8. A driver assistance method implemented by a driver assistance system, Acquires driving information including the acceleration of the moving object. The driver is presented with information regarding load shifting on the moving body, which is identified based on the distribution of acceleration along multiple axes of the moving body, derived from the acquired driving information. A driving assistance method characterized by the following features.

9. Acquires driving information including the acceleration of the moving object. The driver is presented with information regarding load shifting on the moving body, which is identified based on the distribution of acceleration along multiple axes of the moving body, derived from the acquired driving information. A driver assistance program that allows a computer to perform a task.

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