Driving support device

Abstract

To restrict collapse of cargo.SOLUTION: A driving support device comprises acquisition means (23a) and presentation means (23h). The acquisition means (23a) acquires travel information containing acceleration of a mobile body. The presentation means (23h) presents information related to collapse of cargo in the mobile body specified according to distribution of a plurality of axes of acceleration of the mobile body based on the travel information acquired by the acquisition means (23a), to a driver.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a driving assistance device. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a technique for predicting the collapse of luggage loaded on the bed of a traveling vehicle by acquiring the acceleration of the vehicle or the like (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2012-224270 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional technology, there is room for improvement in the accuracy of predicting cargo shifting, and therefore there is room for further improvement in preventing cargo shifting in vehicles.

[0005] The present invention has been made in view of the above, and has an object to provide a driving assistance device, a driving assistance method, and a driving assistance program that can prevent cargo from falling, for example. [Means for solving the problem]

[0006] The driving support device according to claim 1 includes an acquisition unit and a presentation unit. The acquisition unit acquires travel information including an acceleration of a moving object. The presentation unit presents to a driver information related to a load shift in the moving object, the information being specified based on a multi-axial distribution of the acceleration of the moving object based on the travel information acquired by the acquisition unit. [Brief description of the drawings]

[0007] [Figure 1]FIG. 1 is an explanatory diagram illustrating an example of a configuration of a control system according to the first embodiment. [Diagram 2] FIG. 2 is an explanatory diagram illustrating an example of a configuration of the server according to the first embodiment. [Diagram 3] FIG. 3 is an explanatory diagram illustrating an example of the configuration of a vehicle according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of an acceleration value distribution map according to the first embodiment. [Diagram 5] FIG. 5 is a diagram for explaining an example of the road surface condition estimating process according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of an acceleration value distribution map according to the first embodiment. [Figure 7] FIG. 7 is a diagram for explaining an example of the driving assistance process according to the first embodiment. [Figure 8] FIG. 8 is a diagram for explaining an example of the driving assistance process according to the first embodiment. [Figure 9] FIG. 9 is a diagram for explaining an example of the driving assistance process according to the first embodiment. [Figure 10] FIG. 10 is an explanatory diagram illustrating an example of the configuration of a vehicle according to the second embodiment. [Figure 11] FIG. 11 is a flowchart showing the procedure of the road surface condition estimation process according to the first embodiment. [Figure 12] FIG. 12 is a flowchart showing the procedure of the driving assistance process according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, a mode for carrying out the present invention (hereinafter, referred to as an embodiment) will be described with reference to the drawings. Note that the present invention is not limited to the embodiment described below. Furthermore, in the description of the drawings, the same parts are given the same reference numerals.

[0009] <Control system configuration> First, the configuration of a control system 1 according to the first embodiment 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 the first embodiment. As shown in Fig. 1, the control system 1 according to the first embodiment includes a server 2 and a plurality of vehicles 3. The vehicles 3 are an example of moving bodies.

[0010] The server 2 manages a plurality of vehicles 3 as one control network. Each of the vehicles 3 has a control device 4.

[0011] These servers 2 and the multiple vehicles 3 are connected via a network (e.g., the Internet) N, for example, by wireless LAN (Local Area Network) communication, WAN (Wide Area Network) communication, mobile phone communication, etc., and various information can be communicated between the two.

[0012] <Server configuration> Next, the configuration of the server 2 according to the first embodiment 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 the first embodiment. As shown in Fig. 2, the server 2 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0013] The server 2 may also have an input unit (such as a keyboard or a mouse) that accepts various operations from an administrator who uses the server 2, and a display unit (such as a liquid crystal display) that displays various information.

[0014] The communication unit 11 is realized by, for example, a network interface card (NIC) etc. The communication unit 11 is connected to a network N by wire or wirelessly, and transmits and receives information to and from a plurality of vehicles 3 via the network N.

[0015] The storage unit 12 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) 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 has a road information storage unit 12a, a road surface information storage unit 12b, and a load collapse information storage unit 12c.

[0016] The road information storage unit 12a stores road position information, such as map information, that indicates the position of a road. The road surface information storage unit 12b stores information related to the road surface condition (hereinafter also referred to as "road surface information"). For example, information related to the position of a road surface where unevenness exists and information related to the details of the unevenness (such as the shape) are stored in association with each other in the road surface information storage unit 12b.

[0017] The cargo collapse information storage unit 12c stores information regarding cargo collapse in the vehicle 3 (hereinafter, also referred to as cargo collapse information). For example, the cargo collapse information storage unit 12c stores information regarding positions that have a large influence on cargo collapse and information regarding the degree of influence on cargo collapse in association with each other.

[0018] The control unit 13 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs stored in a storage device inside the server 2 using a RAM as a working area. The control unit 13 is also, for example, a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0019] 2, the control unit 13 has an acquisition means 13a and a transmission means 13b, and realizes or executes the functions and actions of various processes described below. Note that the internal configuration of the control unit 13 is not limited to the configuration shown in FIG. 2, and may be other configurations as long as they perform various processes described below.

[0020] The acquisition means 13a acquires road surface information estimated by and transmitted from the control device 4 of the vehicle 3, and stores it in the road surface information storage unit 12b. The acquisition means 13a also acquires cargo collapse information identified by and transmitted from the control device 4 of the vehicle 3, and stores it in the cargo collapse information storage unit 12c.

[0021] Based on a command from the control device 4 of the vehicle 3, the transmission means 13b transmits to the control device 4 the road surface information stored in the road surface information storage unit 12b and the cargo collapse information stored in the cargo collapse information storage unit 12c.

[0022] <Road surface condition estimation process> Next, the configuration of the vehicle 3 according to the first embodiment and the road surface condition estimation process performed by the vehicle 3 will be described in detail 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 the first embodiment.

[0023] 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 driving assistance device.

[0024] The three-axis acceleration sensor 5 is a sensor that detects acceleration in each direction of the X-axis (for example, the front-rear direction of the vehicle 3), the Y-axis (for example, the left-right direction of the vehicle 3), and the Z-axis (for example, the up-down direction of the vehicle 3), and supplies the detection signals to the control device 4. The three-axis acceleration sensor 5 may further detect the angular velocity and angular acceleration of the movement of the vehicle 3.

[0025] The GPS sensor 6 receives radio waves carrying downlink data including positioning data from a plurality of GPS satellites, and supplies the 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) included in the positioning data.

[0026] 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 provided, for example, on the instrument panel of the vehicle 3, and is configured with a liquid crystal display, an organic EL (Electro Luminescence) element, or the like.

[0027] The control device 4 includes a communication unit 21, a storage unit 22, and a control unit 23. The communication unit 21 is realized by, for example, a NIC. The communication unit 21 is connected to a network N by wire or wirelessly, and transmits and receives information to and from the server 2 via the network N.

[0028] The storage unit 22 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk.

[0029] The control unit 23 is a controller, and is realized, for example, by a CPU, an MPU, or the like, executing various programs stored in a storage device inside the control device 4 using a RAM as a working area. The control unit 23 is also, for example, a controller, and is realized by an integrated circuit such as an ASIC or an FPGA.

[0030] As shown in Fig. 3, the control unit 23 includes an acquisition unit 23a, a generation unit 23b, a measurement unit 23c, an estimation unit 23d, a storage unit 23e, an extraction unit 23f, a specification unit 23g, and a presentation unit 23h, and realizes or executes the functions and actions of various processes described below. Note that the internal configuration of the control unit 23 is not limited to the configuration shown in Fig. 3, and may be other configurations as long as they perform the various processes described below.

[0031] The acquisition means 23a acquires information (hereinafter also referred to as traveling information) related to the traveling of the vehicle 3. The acquisition means 23a acquires, as such traveling information, information related to the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3 from the three-axis acceleration sensor 5, for example.

[0032] Moreover, the acquisition means 23a acquires, as the travel information, for example, position information of the vehicle 3 from the GPS sensor 6 and speed information of the vehicle 3 from the speed sensor .

[0033] The generating means 23b generates an acceleration value distribution map, which is a diagram in which the distribution of acceleration on three axes in a predetermined unit time is plotted in a three-dimensional coordinate system, using information on the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3 acquired by the acquiring means 23a. Details of such an acceleration value distribution map will be described below.

[0034] 4 is a diagram showing an example of an acceleration value distribution map according to embodiment 1. For example, when the vehicle 3 is stopped with the engine off, the plot range of the acceleration value distribution map is a plot range D1 near the origin of a three-dimensional coordinate system.

[0035] Furthermore, when the vehicle 3 is stopped with the engine idling, the plot range of the acceleration value distribution map is a spherical plot range D2 centered on the origin of the three-dimensional coordinate system and wider than the above-mentioned plot range D1.

[0036] Furthermore, when the vehicle 3 is traveling on a flat road surface, the plot range of the acceleration value distribution map is a spherical plot range D3 that is centered on the origin of the three-dimensional coordinate system and is wider than the above-mentioned plot range D2. Note that in Fig. 4 and the main text, the origin is set to a point that takes into account the gravitational acceleration (initial value is 1g in the vertical direction).

[0037] Next, a road surface condition estimation process using this acceleration value distribution map will be described. Fig. 5 is a diagram for explaining an example of the road surface condition estimation process according to the embodiment 1. As shown in Fig. 5, it is assumed that a vehicle 3 passes through a deteriorated road surface section X where cracks in the asphalt, etc. are present.

[0038] In this case, the left front wheel FL and the left rear wheel RL of the vehicle 3 pass through the deteriorated road surface section X in order, while the right front wheel FR and the right rear wheel RR of the vehicle 3 do not pass through the deteriorated road surface section X, so that the vehicle 3 is swayed in the fore-and-aft direction and more to the left than to the right.

[0039] Then, in a unit time when passing through the deteriorated road surface section X, the generating means 23b generates an acceleration value distribution map as shown in Fig. 6. Fig. 6 is a diagram showing an example of the acceleration value distribution map according to the first embodiment.

[0040] During the unit time when passing through the deteriorated road section X, the generation means 23b generates an acceleration value distribution diagram of a plot range D4 that extends in the front-to-back and up-to-down directions and is larger to the left than to the right, as shown in Figure 6.

[0041] Returning to the explanation of Fig. 3, the measuring means 23c of the control unit 23 measures information on the vibration of the vehicle 3 (hereinafter also referred to as vibration information) based on a multi-axial distribution of the acceleration of the vehicle 3 (for example, an acceleration value distribution map generated by the generating means 23b).

[0042] An example of the vibration information measured by the measuring means 23c is the absolute value |A| of the magnitude in the front-rear direction in the acceleration value distribution diagram of the plot range D4 shown in Fig. 6. Also, an example of the vibration information measured by the measuring means 23c is the absolute value |B| of the magnitude in the left-right direction in the acceleration value distribution diagram of the plot range D4.

[0043] In addition, examples of vibration information measured by the measuring means 23c include the absolute value |B / C| of the ratio between the left-right magnitude B in the acceleration value distribution diagram of the plot range D4 and the horizontal magnitude C (the left direction in the diagram) which has the largest spread with respect to the origin.

[0044] Moreover, examples of vibration information measured by the measuring means 23c include the absolute value |D| of the magnitude in the vertical direction in the acceleration value distribution diagram of the plot range D4.

[0045] Returning to the description of Fig. 3, the estimation means 23d of the control unit 23 estimates the road surface condition of the road on which the vehicle 3 has traveled, based on the vibration information measured by the measurement means 23c.

[0046] 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 (eg, unevenness, etc.) of the deteriorated road surface section X.

[0047] 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 (eg, unevenness, etc.) of the deteriorated road surface section X.

[0048] Furthermore, the estimation means 23d estimates that the greater the absolute value |B / C| measured by the measurement means 23c, the more advanced the partial deterioration of the deteriorated road surface section X is.

[0049] In addition, the estimation means 23d can estimate the lateral position of the deteriorated road section X (the left-right relative position of the deteriorated road section X with respect to the vehicle 3) based on, for example, the above-mentioned absolute value |B / C| measured by the measurement means 23c.

[0050] In addition, when the fluctuation value of the above-mentioned absolute value |D| measured by the measuring means 23c is large, the estimation means 23d can estimate that the contribution of the degree of deterioration of the road surface deterioration section X to the absolute value |B / C| is greater due to the influence of partial deterioration than the lateral position of the road surface deterioration section X.

[0051] As described above, in the first embodiment, vibration information of the vehicle 3 is measured based on a multi-axial distribution of the acceleration of the vehicle 3 (for example, an acceleration value distribution map), and the road surface condition is estimated based on the vibration information of the vehicle 3. This makes it possible to estimate the road surface condition with high accuracy.

[0052] In addition, 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|) and the bias of the vibration position of the vehicle 3 (for example, absolute value |B / C|), and the temporal length of the vibration.

[0053] In this way, by estimating the degree of deterioration of the deteriorated road section X using various parameters, it is possible to estimate the road surface condition with even greater accuracy.

[0054] In the first embodiment, 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 map generated by the generating means 23b.

[0055] This makes it possible to accurately measure the magnitude of vibration of the vehicle 3 and the deviation of the vibration position of the vehicle 3. Therefore, according to the first embodiment, it is possible to estimate the road surface condition with even greater accuracy.

[0056] In addition, in embodiment 1, the estimation means 23d estimates the undulations of the road surface based on the magnitude of the vibration of the vehicle 3, and estimates the lateral position of the point where the road surface condition is damaged in the travel lane based on the bias of the vibration position of the vehicle 3.

[0057] This makes it possible to estimate the degree of deterioration of the road surface deterioration section X using various parameters, and also to estimate the lateral position of the road surface deterioration section X. Therefore, according to the first embodiment, it is possible to estimate the road surface condition with even greater accuracy.

[0058] In addition, in the first embodiment, the estimation means 23d may directly estimate the road surface condition based on the shape of the acceleration value distribution map generated by the generation means 23b. For example, the estimation means 23d can estimate whether there is a step on the road surface or whether there is a crack on the road surface based on the shape of the acceleration value distribution map. Therefore, according to the first embodiment, the road surface condition can be estimated with even higher accuracy.

[0059] In the first embodiment, the acquiring means 23a may acquire the traveling information from one three-axis acceleration sensor 5 located inside the vehicle 3. This allows an acceleration value distribution map of the vehicle 3 to be easily generated, and therefore allows the road surface condition to be estimated at low cost.

[0060] The three-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3, but may be a three-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3.

[0061] In addition, in the present disclosure, the number of three-axis acceleration sensors 5 that measure the acceleration of the vehicle 3 is not limited to one, and may be multiple. This allows the acceleration of the vehicle 3 to be measured with high accuracy, and therefore allows the road surface condition to be estimated with even higher accuracy.

[0062] Returning to the explanation of Fig. 3, the storage means 23e of the control unit 23 associates the position information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d, and stores them in the road surface information storage unit 12b (see Fig. 2) of the server 2 (see Fig. 2).

[0063] As a result, information regarding locations with poor road surface conditions is stored in the road surface information storage unit 12b of the server 2, and the information regarding locations with poor road surface conditions can be utilized by a plurality of vehicles 3 connected to the network N. Examples of utilization of such road surface conditions will be described later.

[0064] Furthermore, in the first embodiment, the shape and size of the generated acceleration value distribution map may differ depending on the speed of the vehicle 3, the model of the vehicle 3, the type of tires mounted on the vehicle 3, and so on.

[0065] 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 deteriorated road surface section X, the type of vehicle 3, and the type of tires installed on the vehicle 3.

[0066] For example, the calibration process may be performed on each vehicle 3 by having the vehicle 3 travel on a road surface whose uneven shape is known in advance.

[0067] Furthermore, the server 2 and the control device 4 generate a learning model with the acceleration value distribution map as input information and the degree of road surface deterioration as output information based on a collection of acceleration value distribution maps of one vehicle 3. Then, the estimation means 23d may use this learning model to estimate the degree of road surface deterioration every time it generates an acceleration value distribution map.

[0068] Furthermore, the server 2 generates a learning model with the acceleration value distribution map as input information and the degree of road surface deterioration as output information based on a collection of acceleration value distribution maps of a plurality of vehicles 3 connected to the network N. Then, the estimation means 23d may estimate the degree of road surface deterioration using this learning model every time it generates an acceleration value distribution map.

[0069] In addition, in embodiment 1, the generation means 23b may generate an acceleration value distribution diagram for each unit time during the entire driving time, or may generate an acceleration value distribution diagram from the point in time when the three-axis acceleration sensor 5 detects an anomalous acceleration.

[0070] <Driving assistance processing> Next, details of the driving assistance process according to the first embodiment will be described with reference to FIG. 3 and FIGS. 6 to 9.

[0071] 3 acquires driving information of the vehicle 3. The acquisition means 23a acquires, as the driving information, information on the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3 from the three-axis acceleration sensor 5, for example.

[0072] Moreover, the acquisition means 23a acquires, as the travel information, for example, position information of the vehicle 3 from the GPS sensor 6 and speed information of the vehicle 3 from the speed sensor .

[0073] The generation means 23b uses information regarding the acceleration of the vehicle 3 in the forward / backward, left / right, and up / down directions acquired by the acquisition means 23a to generate an acceleration value distribution diagram in which the three-axis distribution of acceleration in a specified unit time is plotted in a three-dimensional coordinate system.

[0074] The measuring means 23c measures vibration information of the vehicle 3 based on a multi-axial distribution of the acceleration of the vehicle 3 (for example, an acceleration value distribution map generated by the generating means 23b).

[0075] The measurement 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.

[0076] The identification means 23g identifies information (i.e., cargo collapse information) related to cargo collapse in the vehicle 3 based on various information. Such cargo collapse information includes, for example, the probability of cargo collapse occurring, the extent of cargo collapse that has occurred, and the like.

[0077] In the following description, the probability of cargo collapse occurring and the extent of the cargo collapse that has occurred, which are included in the cargo collapse information, will be collectively referred to as the "degree of influence on cargo collapse."

[0078] The identifying means 23g identifies cargo collapse information of the vehicle 3, for example, by using vibration information of the vehicle 3 measured by the measuring means 23c.

[0079] For example, the specifying means 23g specifies that the greater the absolute value |A| (see FIG. 6) of the magnitude in the front-rear direction in the acceleration value distribution diagram, the greater the degree of influence on the collapse of the cargo. In this case, the specifying means 23g may specify the degree of influence on the collapse of the cargo by taking into account the duration of the vibration in the front-rear direction in the vehicle 3.

[0080] The specifying means 23g may be provided with a dead zone for vibrations in the front-rear direction of the vehicle 3. This makes it possible to accurately specify the degree of influence on cargo collapse.

[0081] The specifying means 23g also specifies that the greater the absolute value |B| (see FIG. 6) of the magnitude in the left-right direction in the acceleration value distribution diagram, the greater the degree of influence on the collapse of the cargo. In this case, the specifying means 23g may specify the degree of influence on the collapse of the cargo by taking into account the duration of the left-right vibration in the vehicle 3.

[0082] Furthermore, the specifying means 23g specifies that the greater the value of the above-mentioned absolute value |B / C| (see FIG. 6), which indicates the bias of the vibration position of the vehicle 3, the greater the degree of influence on the collapse of the cargo. In this case, the specifying means 23g may specify the degree of influence on the collapse of the cargo by taking into account the duration of the bias of the vibration position of the vehicle 3.

[0083] The identifying means 23g may identify the cargo collapse information of the vehicle 3 by using information other than the vibration information measured by the measuring means 23c. Fig. 7 is a diagram for explaining an example of the driving support process according to the first embodiment, in which the transition of each acceleration at each time is plotted in a three-dimensional coordinate system in which the distribution of acceleration on three axes is plotted.

[0084] Specifically, in FIG. 7, first, the distribution of acceleration along three axes is plotted at the position of plot P1, and then the distribution of acceleration along three axes is plotted in the order of plot P2, plot P3, plot P4, plot P5 and plot P6.

[0085] In this case, the extraction means 23f of the control unit 23 extracts information about the left-right component of the acceleration of the vehicle 3, for example, from the transition of the acceleration of 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 FIG.

[0086] The specifying means 23g specifies that the greater the absolute value |E| of the magnitude of the lateral component, the greater the degree of influence on cargo collapse.

[0087] Furthermore, the extraction means 23f extracts a value obtained by adding the distance between the plot P5 and the origin to the absolute value |E| of the magnitude of the horizontal component in each plot (for example, the plot P5) shown in FIG.

[0088] The specifying means 23g specifies that the greater 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 cargo collapse.

[0089] Moreover, the extraction means 23f extracts, for example, the absolute value |F| of the deviation in each plot shown in FIG. 7 (the difference from the immediately preceding plot, for example, the difference between the plot P6 and the immediately preceding plot P5).

[0090] The specifying means 23g then specifies that the greater the absolute value |F| of the deviation, the greater the degree of influence on cargo collapse.

[0091] In the first embodiment, an acceleration value distribution map for one trip of the vehicle 3 may be generated, and the degree of influence on cargo collapse may be identified based on the acceleration value distribution map for one trip. Fig. 8 is a diagram for explaining an example of the driving support process according to the first embodiment, and shows an example of the acceleration value distribution map for one trip of the vehicle 3.

[0092] 8 is generated by the generating means 23b of the control unit 23. Then, the extracting means 23f extracts the degree of distortion (for example, bias with respect to the origin) of the plot range D5.

[0093] The specifying means 23g specifies that the greater the degree of distortion in the acceleration value distribution diagram in one operation, the greater the degree of influence on cargo collapse.

[0094] In the first embodiment, the moment acting on the luggage loaded on the vehicle 3 may be extracted from the travel information of the vehicle 3 acquired by the acquisition means 23a, and the degree of influence on the luggage collapse may be identified based on the moment acting on the luggage. Fig. 9 is a diagram for explaining an example of the driving assistance process according to the first embodiment, and is a diagram for explaining the moment acting on the luggage.

[0095] 9, the position of the center of gravity G of the luggage in the initial stage coincides with the origin (for example, the position of the center of gravity of the vehicle 3). Next, consider a case where the vehicle 3 turns right and the center of gravity G of the luggage moves from the origin to the rear left.

[0096] In this case, the extraction means 23f can extract the moment M acting on the luggage based on the following formula (1). M∝F C ×L (1) F C : Centrifugal force L: Distance between the origin and the center of gravity G

[0097] The determination means 23g determines that the greater the value of the moment M applied to the luggage (the greater the L, i.e., the greater the difference in the initial value of the center of gravity, and / or the greater the centrifugal force F, i.e., the greater the speed or the sharper the curve), the greater the influence on the collapse of the luggage. The position of the center of gravity G of the luggage can be found from the acceleration, angular velocity, and angular acceleration detected by the three-axis acceleration sensor 5.

[0098] Returning to the description of Fig. 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 the cargo collapse information of the vehicle 3 on the display unit 8.

[0099] For example, when the degree of influence on the cargo collapse is greater than a given threshold, the notifying means 23h notifies the driver that the cargo collapse is likely to occur. This enables the control device 4 to suppress driving that is likely to cause the cargo collapse.

[0100] In addition, the notifying means 23h may notify the driver of the extent of the load collapse that has occurred, for example, when the degree of the influence on the load collapse is greater than a given threshold value. This also enables the control device 4 to suppress driving that is likely to cause the load collapse to become even greater.

[0101] Therefore, according to the first embodiment, collapse of the cargo in the vehicle 3 can be suppressed.

[0102] In addition, in the first embodiment, among the various factors mentioned above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, and moment M), factors that have a large influence on cargo collapse may be individually presented to the driver.

[0103] In addition, in the first embodiment, the various factors described above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, and moment M) may be taken into consideration comprehensively, and the magnitude of the overall influence taken into consideration may be presented to the driver.

[0104] Furthermore, in the first embodiment, information on points that have a large influence on cargo collapse may be stored in the server 2. For example, the storage means 23e associates the position information of the vehicle 3 acquired by the acquisition means 23a with the cargo collapse information identified by the identification means 23g, and stores them in the cargo collapse information storage unit 12c of the server 2.

[0105] As a result, information regarding locations where cargo shifting is likely to occur is stored in the cargo shift information storage unit 12c of the server 2, and such information regarding locations where cargo shifting is likely to occur can be utilized by multiple vehicles 3 connected to the network N.

[0106] For example, the control device 4 may provide the driver with route guidance along which the vehicle 3 is scheduled to travel, based on the information stored in the road surface information storage unit 12b and the load shift information storage unit 12c of the server 2.

[0107] Specifically, the control device 4 may provide the driver with route guidance that avoids locations where road conditions are poor and that are stored in the road surface information memory unit 12b, and locations where cargo collapse is likely to occur and that are stored in the cargo collapse information memory unit 12c (for example, locations whose impact on cargo collapse is greater than a given threshold value).

[0108] This makes it possible to prevent the vehicle 3 from entering places where the road surface is poor (for example, places where the road surface is significantly uneven) or places where cargo is likely to shift (for example, curves with a small curvature). Therefore, according to the first embodiment, cargo shifting on the vehicle 3 can be effectively suppressed.

[0109] In addition, the control device 4 refers to the road surface information memory unit 12b, and when the degree of the impact on cargo collapse becomes greater than a given threshold while traveling on a road surface that has been previously determined to be in good condition, it estimates that the increase in the degree of the impact is due to the driver's driving style and not the road surface conditions.

[0110] In this case, the presenting means 23h may present (advise) the driver with a driving method for reducing the degree of influence on the collapse of the load. For example, when the degree of distortion of the acceleration value distribution diagram in one trip shown in Fig. 8 is large, the presenting means 23h may present to the driver, during a break after one trip, a driving method for reducing the degree of distortion of the acceleration value distribution diagram.

[0111] This allows the control device 4 to suppress operations that are likely to cause cargo to shift. Therefore, according to the first embodiment, cargo shifting in the vehicle 3 can be suppressed.

[0112] In the first embodiment, when the position of the center of gravity G of the luggage is gradually shifting, a guidance may be presented to the driver. Furthermore, when it is expected that the balance of the load is significantly lost, the presenting means 23h may present the driver with a request to temporarily stop the vehicle 3 and check the load.

[0113] <Embodiment 2> Next, the configuration of a vehicle 3A according to the second embodiment will be described with reference to Fig. 10. Fig. 10 is an explanatory diagram showing an example of the configuration of a vehicle 3A according to the second embodiment. The vehicle 3A according to the second embodiment is a stand-alone vehicle that is not connected to the network N (see Fig. 1).

[0114] 10, a vehicle 3A has 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 driving assistance device.

[0115] The three-axis acceleration sensor 5 is a sensor that detects acceleration in each of the X-axis, Y-axis, and Z-axis directions, for example, and supplies the detection signals to the control device 4 A. The three-axis acceleration sensor 5 may further detect the angular velocity and angular acceleration of the movement of the vehicle 3 A.

[0116] The GPS sensor 6 receives radio waves carrying downlink data including positioning data from a plurality of GPS satellites and supplies the 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) included in the positioning data.

[0117] 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 is configured with a liquid crystal display, an organic EL element, or the like.

[0118] As shown in Fig. 10, the control device 4A includes a storage unit 31 and a control unit 32. The storage unit 31 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in Fig. 10, the storage unit 31 includes a road information storage unit 31a, a road surface information storage unit 31b, and a load collapse information storage unit 31c.

[0119] The road information storage unit 31a stores road position information indicating the location of a road, such as map information. The road surface information storage unit 31b stores road surface information. The load collapse information storage unit 31c stores load collapse information.

[0120] In addition, since the road information storage unit 31a, the road surface information storage unit 31b, and the cargo 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 cargo collapse information storage unit 12c of embodiment 1 shown in Figure 2, detailed explanations will be omitted.

[0121] The control unit 32 is a controller, and is realized, for example, by a CPU, an MPU, or the like, executing various programs stored in a storage device inside the control device 4A using a RAM as a working area. The control unit 32 is also, for example, a controller, and is realized by an integrated circuit such as an ASIC or an FPGA.

[0122] As shown in Fig. 10, the control unit 32 includes an acquisition unit 32a, a generation unit 32b, a measurement unit 32c, an estimation unit 32d, a storage unit 32e, an extraction unit 32f, a specification unit 32g, and a presentation unit 32h, and realizes or executes the functions and actions of various processes described below. Note that the internal configuration of the control unit 32 is not limited to the configuration shown in Fig. 10, and may be other configurations as long as they perform the various processes described below.

[0123] The acquiring means 32a acquires driving information of the vehicle 3A. For example, the acquiring means 32a acquires information on the acceleration in the forward / backward direction, the left / right direction, and the up / down direction of the vehicle 3A from the three-axis acceleration sensor 5 as the driving information.

[0124] Moreover, the acquisition means 32a acquires, as the travel information, for example, position information of the vehicle 3A from the GPS sensor 6 and speed information of the vehicle 3A from the speed sensor .

[0125] The generation means 32b uses information regarding the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3A acquired by the acquisition means 32a to generate an acceleration value distribution diagram in which the three-axis distribution of acceleration in a specified unit time is plotted in a three-dimensional coordinate system.

[0126] The measuring means 32c measures vibration information of the vehicle 3A based on a multi-axial distribution of the acceleration of the vehicle 3A (for example, an acceleration value distribution map generated by the generating means 32b). The estimating means 32d estimates the road surface condition of the road on which the vehicle 3A has traveled based on the vibration information measured by the measuring means 32c.

[0127] The storage means 32e stores the position information of the vehicle 3A acquired by the acquisition means 32a and the road surface condition estimated by the estimation means 32d in the road surface information storage section 31b of the storage unit 31 in association with each other.

[0128] Since the acquiring means 32a, the generating means 32b, the measuring means 32c, the estimating means 32d, and the memory means 32e have the same configurations as the acquiring means 23a, the generating means 23b, the measuring means 23c, the estimating means 23d, and the memory means 23e of the first embodiment shown in FIG. 3, respectively, detailed explanations thereof will be omitted.

[0129] In this manner, in the second embodiment, similarly to the first embodiment described above, vibration information of the vehicle 3A is measured based on a multi-axial distribution of the acceleration of the vehicle 3A (for example, an acceleration value distribution map), and the road surface condition is estimated based on the vibration information of the vehicle 3A. This makes it possible to estimate the road surface condition with high accuracy.

[0130] In addition, 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|).

[0131] In this way, by estimating the degree of deterioration of the deteriorated road section X using various parameters, it is possible to estimate the road surface condition with even greater accuracy.

[0132] In the second embodiment, the measuring means 32c may measure the magnitude of vibration of the vehicle 3A and the deviation of the vibration position of the vehicle 3A based on the shape of the acceleration value distribution map generated by the generating means 32b.

[0133] This makes it possible to accurately measure the magnitude of vibration of the vehicle 3A and the deviation of the vibration position of the vehicle 3A. Therefore, according to the second embodiment, it is possible to estimate the road surface condition with even greater accuracy.

[0134] In addition, in embodiment 2, the estimation means 32d estimates the undulations of the road surface based on the magnitude of the vibration of the vehicle 3A, and estimates the lateral position of the point where the road surface condition is damaged in the driving lane based on the bias of the vibration position of the vehicle 3A.

[0135] This makes it possible to estimate the degree of deterioration of the road surface deterioration section X using various parameters, and also to estimate the lateral position of the road surface deterioration section X. Therefore, according to the second embodiment, it is possible to estimate the road surface condition with even greater accuracy.

[0136] In the second embodiment, the estimation means 32d may directly estimate the road surface condition based on the shape of the acceleration value distribution map generated by the generation means 32b. For example, the estimation means 32d can estimate whether there is a step on the road surface or whether there is a crack on the road surface based on the shape of the acceleration value distribution map. Therefore, according to the second embodiment, the road surface condition can be estimated with even higher accuracy.

[0137] In the second embodiment, the acquiring means 32a may acquire the traveling information from one three-axis acceleration sensor 5 located inside the vehicle 3A. This allows an acceleration value distribution map of the vehicle 3A to be easily generated, and therefore allows the road surface condition to be estimated at low cost.

[0138] The three-axis acceleration sensor 5 is not limited to being mounted on the vehicle 3A, but may be a three-axis acceleration sensor mounted on an information terminal such as a smartphone and placed inside the vehicle 3A.

[0139] In addition, in the present disclosure, the number of three-axis acceleration sensors 5 that measure the acceleration of the vehicle 3A is not limited to one, and may be more than one. This allows the acceleration of the vehicle 3A to be measured with high accuracy, and therefore the road surface condition can be estimated with even higher accuracy.

[0140] In addition, in embodiment 2, the memory means 32e may associate the position information of the vehicle 3A acquired by the acquisition means 32a with the road surface condition estimated by the estimation means 32d and store them in the road surface information memory unit 31b of the memory unit 31.

[0141] As a result, locations where the road surface condition is poor are stored in the road surface information storage unit 31b, and the information on the road surface condition estimated by the vehicle 3A can be utilized the next time and thereafter when the vehicle 3A travels.

[0142] In addition, in the second embodiment, as in the first embodiment 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, etc.

[0143] 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 vehicle 3A when passing through the deteriorated road surface section X, the vehicle model of vehicle 3A, and the type of tires installed on vehicle 3A.

[0144] For example, the vehicle 3A may be subjected to the calibration process by having the vehicle 3A travel on a road surface whose unevenness is known in advance.

[0145] Furthermore, the control device 4A generates a learning model based on a set of acceleration value distribution maps of the vehicle 3A, with the acceleration value distribution map as input information and the degree of road surface deterioration as output information. Then, the estimation means 32d may estimate the degree of road surface deterioration using this learning model every time an acceleration value distribution map is generated.

[0146] In addition, in the second embodiment, the generation means 32b may generate an acceleration value distribution diagram for each unit time during the entire driving time, or may generate an acceleration value distribution diagram from the point in time when the three-axis acceleration sensor 5 detects an anomalous acceleration.

[0147] The extraction means 32f extracts information on the left and right components of the acceleration of the vehicle 3A from the transition of the acceleration of three axes of the vehicle 3A, for example. The identification means 32g identifies cargo collapse information of the vehicle 3A based on various information.

[0148] The presentation means 32h presents the cargo collapse information of the vehicle 3A identified by the identification means 32g to the driver of the vehicle 3A. For example, the presentation means 32h presents the cargo collapse information of the vehicle 3A to the driver by displaying the cargo collapse information of the vehicle 3A on the display unit 8.

[0149] The extraction unit 32f, the identification unit 32g, and the presentation unit 32h have the same configurations as the extraction unit 23f, the identification unit 23g, and the presentation unit 23h of the first embodiment shown in FIG. 3, respectively, and therefore detailed description thereof will be omitted.

[0150] In this manner, in the second embodiment, similarly to the first embodiment 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.

[0151] For example, when the degree of influence on the load shift is greater than a given threshold, the notifying means 32h notifies the driver that the load shift is likely to occur. This allows the control device 4A to suppress driving that is likely to cause the load shift.

[0152] In addition, the notifying means 32h may notify the driver of the extent of the load collapse when the degree of the influence on the load collapse is greater than a given threshold value. This also enables the control device 4A to suppress driving that is likely to cause the load collapse to become even greater.

[0153] Therefore, according to the second embodiment, it is possible to prevent the load from shifting in the vehicle 3A.

[0154] In addition, in the second embodiment, among the various factors shown in the first embodiment described above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, and moment M), factors that have a large influence on cargo collapse may be individually presented to the driver.

[0155] In addition, in the second embodiment, the various factors described above (for example, absolute value |A|, absolute value |B|, absolute value |B / C|, absolute value |E|, absolute value |F|, and moment M) may be taken into consideration comprehensively, and the magnitude of the overall influence taken into consideration may be presented to the driver.

[0156] In the second embodiment, information on points that have a large influence on the collapse of cargo may be stored in the storage unit 31. For example, the storage unit 32e associates the position information of the vehicle 3A acquired by the acquisition unit 32a with the collapse of cargo information identified by the identification unit 32g, and stores the associated information in the collapse of cargo information storage unit 31c of the storage unit 31.

[0157] As a result, information regarding locations where cargo is likely to fall is stored in the road surface information storage unit 31b, and the information regarding locations where cargo is likely to fall can be used the next time and thereafter when the vehicle 3A travels.

[0158] For example, the control device 4A may provide the driver with route guidance along which the vehicle 3A is scheduled to travel, based on the information stored in the road surface information storage unit 31b and the load shift information storage unit 31c of the storage unit 31.

[0159] Specifically, the control device 4A may provide the driver with route guidance that avoids locations where road conditions are poor and that are stored in the road surface information memory unit 31b, and locations where cargo collapse is likely to occur and that are stored in the cargo collapse information memory unit 31c (for example, locations where the degree of impact on cargo collapse is greater than a given threshold value).

[0160] This makes it possible to prevent the vehicle 3A from entering places where the road surface is poor (for example, places where the road surface is significantly uneven) or places where cargo collapse is likely to occur (for example, curves with a large curvature). Therefore, according to the second embodiment, it is possible to effectively prevent cargo collapse on the vehicle 3A.

[0161] In addition, the control device 4A refers to the road surface information memory unit 31b, and when the degree of the impact on cargo collapse becomes greater than a given threshold while driving on a road surface that has been previously determined to be in good condition, it estimates that the increase in the degree of the impact is due to the driver's driving style rather than the road surface conditions.

[0162] In this case, the presenting means 32h may present (advise) the driver with a driving method for reducing the degree of influence on the collapse of the load. For example, if the degree of distortion of the acceleration value distribution chart during one trip is large, the presenting means 32h may present to the driver, during a break after one trip, a driving method for reducing the degree of distortion of the acceleration value distribution chart.

[0163] This allows the control device 4A to suppress operations that are likely to cause cargo to shift, and therefore, according to the second embodiment, cargo shifting in the vehicle 3A can be suppressed.

[0164] In the second embodiment, when the position of the center of gravity G of the luggage is gradually shifting, a guidance may be presented to the driver. Furthermore, when it is expected that the balance of the load is significantly lost, the presentation means 32h may present the driver with a message to stop the vehicle 3A and check the load.

[0165] <Processing procedure> Next, procedures of various processes according to the first embodiment will be described with reference to Fig. 11 and Fig. 12. Fig. 11 is a flowchart showing the procedures of the road surface condition estimation process according to the first embodiment.

[0166] First, the acquisition means 23a acquires (step S101) driving information of the vehicle 3. For example, the acquisition means 23a acquires information on the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3 from the three-axis acceleration sensor 5 as the driving information.

[0167] Next, the generation means 23b uses information regarding the acceleration of the vehicle 3 in the forward / backward, left / right and up / down directions acquired by the acquisition means 23a to generate an acceleration value distribution diagram in which the three-axis distribution of acceleration in a specified unit time is plotted in a three-dimensional coordinate system (step S102).

[0168] Next, the measuring means 23c measures vibration information of the vehicle 3 based on the multi-axial distribution of the acceleration of the vehicle 3 (for example, the acceleration value distribution map generated by the generating means 23b) (step S103).

[0169] Next, the estimation means 23d estimates the road surface condition of the road surface on which the vehicle 3 has traveled, based on the vibration information measured by the measurement means 23c (step S104).

[0170] Finally, the memory means 23e associates the position information of the vehicle 3 acquired by the acquisition means 23a with the road surface condition estimated by the estimation means 23d and stores them in the road surface information memory unit 12b of the server 2 (step S105), thereby completing the series of road surface condition estimation processes.

[0171] 12 is a flowchart showing the procedure of the driving assistance process according to the embodiment 1. First, the acquisition means 23a acquires driving information of the vehicle 3 (step S201). The acquisition means 23a acquires, as the driving information, information on the acceleration in the forward / backward, left / right, and up / down directions of the vehicle 3 from the three-axis acceleration sensor 5, for example.

[0172] Next, the identifying means 23g identifies cargo collapse information of the vehicle 3 based on the multi-axial distribution of the acceleration of the vehicle 3 based on the travel information acquired by the acquiring means 23a (step S202).

[0173] The identification means 23g identifies cargo collapse information of the vehicle 3, for example, based on vibration information of the vehicle 3 measured by the measurement means 23c and various information extracted by the extraction means 23f (such as the above-mentioned absolute value |E| and absolute value |F|).

[0174] Finally, the presentation unit 23h presents the cargo collapse information of the vehicle 3 identified by the identification unit 23g to the driver of the vehicle 3 (step S203), and the driving support process ends.

[0175] Although the embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the spirit of the present invention. For example, in the above embodiment, various processes performed in the vehicle 3 are shown, but the subject to which the present disclosure is implemented is not limited to vehicles, and the present disclosure can be applied to various moving objects (for example, motorcycles, trains, etc.).

[0176] In addition, in the above embodiment, an example is shown in which the presentation means 23h presents cargo collapse information to the driver of the vehicle 3, but the recipient to whom the presentation means 23h presents the cargo collapse information is not limited to the driver of the vehicle 3, and may be the driver of another vehicle 3, the administrator of the server 2, etc.

[0177] In addition, in the above embodiment, an example has been shown in which an acceleration value distribution diagram, which is a diagram in which a three-axis distribution of acceleration in a predetermined unit time is plotted in a three-dimensional coordinate system, is generated using the three-axis acceleration sensor 5, and a road surface condition estimation process and a driving assistance process are performed based on the acceleration value distribution diagram.

[0178] However, the above embodiment is not limited to such an example. For example, an acceleration value distribution diagram may be generated by using two-axis acceleration in the X-axis direction and the Y-axis direction measured by an acceleration sensor, in which a two-axis distribution of acceleration in a predetermined unit time is plotted in a two-dimensional coordinate system, and a road surface condition estimation process or a driving assistance process may be performed based on the acceleration value distribution diagram.

[0179] Further advantages and modifications may readily occur to those skilled in the art. Thus, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and equivalents thereof. [Explanation of symbols]

[0180] 1. Control System 2 Server 3. 3A Vehicle (an example of a moving object) 4, 4A Control device (an example of a road condition estimation device and driving support device) 5 3-axis acceleration sensor 6 GPS Sensor 7 Speed ​​Sensor 8 Display 12, 22, 31 Storage section 13, 23, 32 Control section 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

[Claim 1] An acquisition means for acquiring travel information including an acceleration of a moving object; and a presentation means for presenting to a driver information regarding a load shift in the moving body, the information being specified based on a multi-axial distribution of the acceleration of the moving body based on the travel information acquired by the acquisition means. A driving assistance device comprising:

Images