Driving assistance device, vehicle, and driving assistance method

Abstract

A driving assistance device according to an embodiment of the present disclosure comprises: an information acquisition unit configured to acquire observation information including one or a plurality of pieces of first event information; a hypothesis inference unit configured to infer a plurality of hypotheses each including one or a plurality of pieces of second event information by applying background knowledge information including a plurality of pieces of evidence information to the observation information and performing hypothesis inference; a derivation unit configured to derive the cost for each of the pieces of first event information included in the observation information; and a determination unit configured to determine a maximum likelihood hypothesis, which has the lowest total cost among the plurality of hypotheses, on the basis of the cost for each of the pieces of first event information included in the observation information and the cost for each of the pieces of second event information included in each of the hypotheses. When reliability information for each of the pieces of first event information in the observation information is obtained, the derivation unit derives the cost for each of the pieces of first event information on the basis of the reliability information. When the reliability information for each of the pieces of first event information in the observation information is unable to be obtained, the derivation unit is configured to derive the cost for each of the pieces of first event information on the basis of a prescribed parameter pertaining to recognition of a recognition target object by the information acquisition unit.

Description

Driving assistance device, vehicle, and driving assistance method

[0001] The present disclosure relates to a driving assistance device, a vehicle, and a driving assistance method.

[0002] BACKGROUND ART Various techniques have been disclosed as driving assistance devices and driving assistance methods that use abductive reasoning to provide driving assistance for a target vehicle (see, for example, Patent Document 1).

[0003] JP 2016-91039 A

[0004] A driving assistance device according to one embodiment of the present disclosure is a device that provides driving assistance for a target vehicle using abductive reasoning, and includes: an information acquisition unit configured to acquire observation information including one or more pieces of first event information; abductive reasoning unit configured to infer a plurality of hypotheses, each of which includes one or more pieces of second event information, by applying background knowledge information including a plurality of pieces of evidence information to the observation information to perform abductive reasoning; a derivation unit configured to derive a cost for each piece of first event information included in the observation information; and a determination unit configured to determine a maximum likelihood hypothesis among the plurality of hypotheses that has the lowest total cost based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses. The derivation unit is configured to, when reliability information for each piece of first event information in the observation information is obtained, derive the cost for each piece of first event information based on the reliability information; and, when reliability information for each piece of first event information in the observation information is not obtained, derive the cost for each piece of first event information based on predetermined parameters related to recognition of the recognition target object by the information acquisition unit.

[0005] A vehicle according to an embodiment of the present disclosure is equipped with the driving assistance device according to the embodiment of the present disclosure.

[0006] A driving assistance method according to an embodiment of the present disclosure is a method for providing driving assistance to a target vehicle using abductive reasoning, the method including: acquiring observation information including one or more pieces of first event information; applying background knowledge information including a plurality of pieces of evidence information to the observation information to perform abductive reasoning to infer a plurality of hypotheses, each of which includes one or more pieces of second event information; deriving a cost for each piece of first event information included in the observation information; and determining a maximum likelihood hypothesis among the plurality of hypotheses that has the lowest total cost based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses. The deriving the cost for each piece of first event information includes, when reliability information for each piece of first event information in the observation information is obtained, deriving the cost for each piece of first event information based on the reliability information; and, when reliability information for each piece of first event information in the observation information is not obtained, deriving the cost for each piece of first event information based on predetermined parameters related to recognition of the recognition target object.

[0007] The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate one embodiment and, together with the description, serve to explain the principles of the disclosure.

[0008] FIG. 1 is a schematic diagram illustrating an example of a schematic configuration of a vehicle equipped with a driving assistance device according to an embodiment of the present disclosure. FIG. 2 is a block diagram illustrating an example of a detailed configuration of the vehicle illustrated in FIG. 1. FIG. 3 is a flowchart illustrating an example of a driving assistance process according to the embodiment. FIG. 4 is a schematic diagram for explaining an overview of the abductive reasoning illustrated in FIG. 3. FIG. 5 is another schematic diagram for explaining an overview of the abductive reasoning illustrated in FIG. 3. FIG. 6 is a flowchart illustrating a detailed example of a process for deriving the cost of the first event information illustrated in FIG. 3. FIG. 7 is a diagram illustrating specific examples of predetermined parameters illustrated in FIG. 6. FIG. 8 is a schematic diagram for explaining the shielding rate illustrated in FIG. 7. FIG. 9 is another schematic diagram for explaining the shielding rate illustrated in FIG. 7. FIG. 10 is a schematic diagram for explaining the relative distance illustrated in FIG. 7. FIG. 11 is another schematic diagram for explaining the relative distance illustrated in FIG. 7. FIG. 12 is a schematic diagram for explaining the determination duration illustrated in FIG. 7. FIG. 13 is another schematic diagram for explaining the determination duration illustrated in FIG. 7. FIG. 14 is another schematic diagram for explaining the determination duration illustrated in FIG. 7.

[0009] In a driving assistance device or the like that provides driving assistance for a target vehicle using abductive reasoning, it is desired to improve convenience when determining the most likely hypothesis, for example. It is desirable to provide a driving assistance device, a vehicle, and a driving assistance method that can improve convenience when determining the most likely hypothesis.

[0010] Some exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the following description illustrates one specific example of the present disclosure and should not be construed as limiting the present disclosure. For example, each element, including numerical values, shapes, materials, parts, the position of each part, and the connection method of each part, is merely an example and should not be construed as limiting the present disclosure. Furthermore, in the following exemplary embodiments, components not described in independent claims based on the highest concept of the present disclosure are optional and may be provided as needed. The drawings are schematic and are not intended to be drawn to scale. Throughout this specification and the drawings, components having substantially the same function and configuration are designated by the same reference numerals, and redundant description will be omitted. Furthermore, components not directly related to one embodiment of the present disclosure are not shown in the drawings.

[0011] 1. Embodiment> [Configuration] Fig. 1 is a schematic diagram illustrating an example of the overall configuration of a vehicle 1 equipped with a driving assistance device (a driving assistance device 111 described later) according to an embodiment of the present disclosure. Fig. 2 is a block diagram illustrating an example of the detailed configuration of the vehicle 1 illustrated in Fig. 1.

[0012] 1 and 2, the vehicle 1 includes a vehicle control unit 11, a battery 12, a communication device 13, and a camera 14. As shown in FIG. 1, the vehicle 1 has an occupant 9 (such as a driver) on board.

[0013] (A. Vehicle Control Unit 11) The vehicle control unit 11 is a component (control unit) that controls various operations in the vehicle 1 and performs various arithmetic processing. Specifically, the vehicle control unit 11 includes, for example, one or more processors (CPU: Central Processing Unit) that execute programs, and one or more memories communicatively connected to these processors. Furthermore, such memories include, for example, RAM (Random Access Memory) that temporarily stores processing data, and ROM (Read Only Memory) that stores programs.

[0014] In the example shown in FIG. 2, the vehicle control unit 11 includes a driving assistance device 111, a battery control unit 112, a communication control unit 113, and a camera control unit 114.

[0015] (A-1. Driving assistance device 111) The driving assistance device 111 is a device that provides driving assistance to a target vehicle (vehicle 1) using hypothetical reasoning, which will be described later, and in the example shown in FIG. 2, has an information acquisition unit 21, a hypothetical reasoning unit 22, a derivation unit 23, a determination unit 24, a notification unit 25, and a driving control unit 26.

[0016] The notification unit 25 and the driving control unit 26 each correspond to a specific example of a "control unit" in an embodiment of the present disclosure.

[0017] The information acquisition unit 21 is a unit that acquires various types of information related to the vehicle 1. Specifically, in the present embodiment, the information acquisition unit 21 acquires, as one type of such various information, observation information Im including one or more pieces of first event information Ie1, which will be described later, using the camera 14, various sensors, etc. Details of such observation information Im will be described later.

[0018] The hypothetical reasoning unit 22 is a unit that performs hypothetical reasoning by applying background knowledge information Ibk, which includes a plurality of pieces of evidence information Ir (described later), to the above-mentioned observation information Im, to infer a plurality of hypotheses H, each of which includes one or a plurality of pieces of second event information Ie2 (described later). Details of such hypothetical reasoning processing will be described later.

[0019] The derivation unit 23 is a unit that derives the cost C for each piece of first event information Ie1 included in the observation information Im. Details of the process of deriving the cost C for each piece of first event information Ie1 will be described later.

[0020] The determination unit 24 is a unit that determines a most likely hypothesis Hm, which is the hypothesis H with the lowest total cost among the multiple hypotheses H, based on the cost C for each piece of first event information Ie1 included in the above-mentioned observation information Im and the cost C for each piece of second event information Ie2 included in each of the above-mentioned hypotheses H. Details of the process of determining such a most likely hypothesis Hm will be described later.

[0021] The notification unit 25 is a unit that performs a predetermined notification operation (notification processing) for the occupant 9 (such as the driver) of the vehicle 1. The notification unit 25 is configured to include, for example, a display unit (such as a display of various types) that performs a predetermined display operation as such a notification operation, and a tone output unit (such as a speaker of various types) that performs a predetermined audio output operation as such a notification operation.

[0022] The driving control unit 26 is a unit that controls the driving operation of the vehicle 1 (performs driving control processing), and performs overall control regarding the driving of the vehicle 1. Specifically, the driving control unit 26 controls, for example, the drive system, braking system, steering system, and the like of the vehicle 1.

[0023] Here, the notification unit 25 and the driving control unit 26 of the present embodiment each use the maximum likelihood hypothesis Hm determined by the determination unit 24 (based on the second event information Ie2 included in the maximum likelihood hypothesis Hm) to provide driving assistance for the vehicle 1. Specifically, the notification unit 25 performs notification processing using such maximum likelihood hypothesis Hm, and the driving control unit 26 performs driving control processing using such maximum likelihood hypothesis Hm.

[0024] (A-2. Battery control unit 112, communication control unit 113, camera control unit 114) The battery control unit 112 is a unit that controls the operation (charging operation, discharging operation, etc.) of the battery 12. The communication control unit 113 is a unit that controls the communication operation of the communicator 13. The camera control unit 114 is a unit that controls the imaging operation of the camera 14.

[0025] (B. Battery 12, Communicator 13, Camera 14) The battery 12 is a component that functions as a power source for the vehicle 1, and is configured using various types of secondary batteries such as lithium ion batteries. The communicator 13 is a device that performs various types of communication with the outside of the vehicle 1 (for example, an information processing device such as a server provided outside the vehicle 1, or other vehicles other than the vehicle 1).

[0026] The camera 14 is a component that acquires imaging data relating to the outside (such as the surrounding environment of the vehicle 1) and the inside (such as the occupants of the vehicle 1) of the vehicle 1. The imaging data acquired in this manner is supplied to, for example, the driving assistance device 111 (such as the information acquisition unit 21 described above) in the vehicle control unit 11.

[0027] [Operation, Function, and Effect] Next, the operation, function, and effect of this embodiment will be described in detail.

[0028] Fig. 3 is a flowchart showing an example of driving assistance processing according to this embodiment (a processing example in the driving assistance device 111). Figs. 4 and 5 are each a schematic diagram for explaining an outline of the hypothetical reasoning (step S3, described later) shown in Fig. 3. Note that Fig. 5 shows a situation in which, on a road having road signs St (green signs) on each of three driving lanes L1 to L3, a preceding vehicle 7 and a stopped vehicle 8 (stopped ahead of the preceding vehicle 7) are present ahead of vehicle 1 (traveling in driving lane L2), which is the target vehicle of the driving assistance processing.

[0029] (A. Overview of Hypothetical Reasoning) Before describing the overall driving support process shown in FIG. 3, an overview of hypothetical reasoning will be described.

[0030] First, abductive reasoning is a method of inferring premises by applying rules to a conclusion. On the other hand, deduction is a method of inferring a conclusion from rules and premises. As such, abductive reasoning and deduction are characterized by different directions of reasoning, with abductive reasoning also being called backward reasoning and deduction also being called forward reasoning. When such abductive reasoning is applied to vehicle driving assistance processing (hazard prediction), it is as follows. That is, the above-mentioned hypothesis H (corresponding to a "premises") is inferred by applying the above-mentioned previously set background knowledge information Ibk (corresponding to a "rule") to the above-mentioned observation information Im and events indicating a hazard (corresponding to a "conclusion").

[0031] Furthermore, the degree to which the hypothesis H inferred in this way matches the observation information Im (goodness of proof) can be output as a cost C. In other words, the lower this cost C is, the better the proof is. When outputting this cost C, the following two types of "weights" are used:

[0032] The "weight" indicating the importance of each piece of evidence information Ir is registered in the background knowledge information Ibk, and the more important the piece of evidence information Ir is, the higher the "weight" is set. The "weight" indicating the reliability R of each piece of information (first event information Ie1) included in the observation information Im is registered in the observation information Im, and the higher the reliability R of the first event information Ie1 is, the lower the "weight" is set.

[0033] Here, a specific example of such a hypothesis overview processing will be described with reference to Figures 4 and 5. Note that the contents of each piece of information and the value of cost C shown in Figure 4 are merely examples, and any settings are possible. Also, the symbol "^" shown in Figure 4 indicates a logical product ("AND").

[0034] First, in the observation information Im obtained by various sensors, etc., a cost C is assigned to each piece of first event information Ie1 according to a weight indicating the reliability R described above. In the example shown in FIG. 4, a cost C of $10 is assigned to each piece of first event information Ie1 indicating "vehicle ahead" and "green sign ahead." In addition, in the example shown in FIG. 4, the first event information Ie1 indicating "occurrence of danger" is not directly observed and is therefore uncertain (has low reliability R), and therefore its cost C is set to a value 10 times the above (cost C = $100). In the example shown in FIG. 5, this "occurrence of danger" refers to an event in which vehicle 1 comes into contact with a preceding vehicle 7 (see arrow P1 in FIG. 5).

[0035] Next, a hypothesis H is inferred by applying background knowledge information Ibk to such observation information Im and performing abductive reasoning. In the example shown in FIG. 4 , the background knowledge information Ibk includes a plurality of pieces of evidence information Ir (evidence information Ir indicating "there is a preceding vehicle," "there is a stopped vehicle," and "there is a green sign"), and the weights (weights Wr) indicating the above-mentioned importance are set to 0.3, 0.6, and 0.3, respectively. Also, in the example shown in FIG. 4 , hypothesis H includes a plurality of pieces of second event information Ie2 (second event information Ie2 indicating "there is a preceding vehicle," "there is a stopped vehicle," and "there is a green sign").

[0036] Here, in the example shown in FIG. 4, the total cost (total value of costs C) for hypothesis H obtained in this way is $120 (= $30 + $60 + $30), which is 1.2 times the value before inference (cost C for "risk occurrence" = $100). In other words, because hypothesis H is not observational information, the total cost increases. The total cost thus increased is allocated to each of the multiple pieces of second event information Ie2, which are factors (literals) that make up hypothesis H. At this time, the importance (weight Wr) of each piece of evidence information Ir described above is reflected.

[0037] Next, if the same type of information (literal) exists between the first event information Ie1 included in the observation information Im and the second event information Ie2 included in the hypothesis H, the following unification process is performed. That is, in this unification process, between the first event information Ie1 and the second event information Ie2, the information with the relatively larger cost C is deleted, and only the information with the relatively smaller cost C is adopted. In the example shown in FIG. 4 , the unification process indicated by symbol U1 adopts the information with the relatively smaller cost C (first event information Ie1 indicating "there is a preceding vehicle"). Furthermore, the unification process indicated by symbol U2 adopts the information with the relatively smaller cost C (first event information Ie1 indicating "there is a green traffic sign").

[0038] In this way, the most likely hypothesis Hm is determined, which is the hypothesis H with the lowest total cost (the best evidence). In the example shown in Figure 4, the most likely hypothesis Hm includes multiple pieces of second event information Ie2 (second event information Ie2 indicating "vehicle ahead," "green sign," and "parked vehicle"), and has a total cost of $80 (= $10 + $10 + $60).

[0039] (B. Driving Assistance Processing) Next, based on the outline of the abductive reasoning described above, the driving assistance processing of this embodiment shown in FIG. 3 will be described in detail.

[0040] 3, first, the information acquisition unit 21 acquires (step S1) observation information Im including the first event information Ie1 as one of various pieces of information related to the vehicle 1. Note that the observation information Im may include not only current information but also, for example, past information (such as information from the past few seconds).

[0041] (S2: Deriving Process of Cost C of First Event Information Ie1) Next, the derivation unit 23 derives the cost C for each piece of first event information Ie1 included in the observation information Im (step S2). Note that the derivation unit 23 may update the value of the cost C for each piece of first event information Ie1, for example, periodically (daily, etc.) (see FIG. 6 described below).

[0042] FIG. 6 is a flowchart showing a detailed example of the process (step S2) for deriving the cost C of the first event information Ie1 shown in FIG.

[0043] In the processing example shown in Fig. 6, the derivation unit 23 first determines whether or not information on the reliability R for each piece of first event information Ie1 in the observation information Im has been obtained (step S21). If it is determined that information on the reliability R for each piece of first event information Ie1 has been obtained (step S21: Y), the derivation unit 23 derives a cost C for each piece of first event information Ie1 based on the information on the reliability R for each piece of first event information Ie1 (step S22). After that, the process proceeds to step S3 in Fig. 3, which will be described later.

[0044] On the other hand, if it is determined that information on the reliability R for each piece of first event information Ie1 has not been obtained (step S21: N), the derivation unit 23 derives the cost C for each piece of first event information Ie1 as follows. That is, in this case, the derivation unit 23 derives the cost C for each piece of first event information Ie1 based on a predetermined parameter Pr related to the recognition of the recognition target object 6 by the information acquisition unit 21 (step S23). Specific examples of such a predetermined parameter Pr will be described later ( FIGS. 7 to 14 ). After that, the process proceeds to step S3 in FIG. 3 , which will be described below.

[0045] (S3: Hypothetical Reasoning Process) After the process of deriving the cost C of the first event information Ie1 (step S2 in FIG. 3 ), the hypothetical reasoning unit 22 then performs the above-mentioned hypothetical reasoning to infer multiple hypotheses H (step S3 in FIG. 3 ). Specifically, the hypothetical reasoning unit 22 performs hypothetical reasoning by applying background knowledge information Ibk to the observation information Im acquired in step S1, thereby inferring multiple hypotheses H including the second event information Ie2.

[0046] (S4: Process for Determining Maximum Likelihood Hypothesis Hm) Next, the determiner 24 performs the process for determining the maximum likelihood hypothesis Hm described above (step S4). Specifically, the determiner 24 determines the maximum likelihood hypothesis Hm, which is the hypothesis H with the lowest total cost among the multiple hypotheses H, based on the cost C for each piece of first event information Ie1 included in the observation information Im and the cost C for each piece of second event information Ie2 included in the multiple hypotheses H obtained in step S3.

[0047] (S5: Driving Assistance Processing) Next, the notification unit 25 and the driving control unit 26 each use the maximum likelihood hypothesis Hm determined in step S4 to provide driving assistance for the vehicle 1 (step S5). Specifically, the notification unit 25 uses such maximum likelihood hypothesis Hm to perform the various notification processing described above, and the driving control unit 26 uses such maximum likelihood hypothesis Hm to perform the various driving control processing described above.

[0048] This completes the series of processing steps shown in FIG.

[0049] (C. Specific Examples of Parameter Pr) Next, specific examples (specific embodiments) of the above-mentioned predetermined parameter Pr will be described in detail.

[0050] Fig. 7 shows a specific example of the predetermined parameter Pr shown in Fig. 6. Figs. 8 and 9 are each a schematic diagram for explaining the later-described shielding rate px shown in Fig. 7. Figs. 10 and 11 are each a schematic diagram for explaining the later-described relative distance dx shown in Fig. 7. Figs. 12 to 14 are each a schematic diagram for explaining the later-described determination duration tx shown in Fig. 7.

[0051] 7, in this embodiment, the predetermined parameter Pr includes at least one of the following: the occlusion rate px of the recognition target object 6, the relative distance dx, and the determination duration tx. The occlusion rate px of the recognition target object 6, the relative distance dx between the vehicle 1 (target vehicle) and the recognition target object 6, and the determination duration tx (the length of the period during which the recognition target object 6 is continuously determined to be of the same type).

[0052] (C-1. Shielding Ratio px) First, the above-mentioned shielding ratio px will be described with reference to FIG. 7 as well as FIG. 8 and FIG.

[0053] The derivation unit 23 increases the cost C=Cx of the first event information Ie1 (for example, in a proportional relationship) as the occlusion rate px of the recognition target object 6 increases (see FIG. 7 ). Specifically, in the example of FIG. 8 , as the occlusion rate px increases from 25%, to 50%, to 100%, the cost Cx increases from $25, to $50, to $100 (in a proportional relationship).

[0054] Here, the procedure for deriving the cost Cx in accordance with the coverage rate px in the deriving unit 23 is, for example, as follows (see steps S31 to S35 in FIG. 9).

[0055] S31: Determine the type of the recognition target object 6 recognized by the information acquisition unit 21. S32: Estimate the target area a (all) when the entire area of ​​the recognition target object 6 is recognizable according to the type determination result (for example, a predetermined table may be used). S33: Calculate the recognition area a (vis), which is the area of ​​the area of ​​the recognition target object 6 that is recognizable (by the information acquisition unit 21). S34: Based on the target area a (all) and the recognition area a (vis), calculate the occlusion rate px of the recognition target object 6 by using the following formula (1-1). S35: Calculate the above-mentioned cost Cx by using the following formula (1-2).

[0056]

[0057] (C-2. Relative Distance dx) Next, the above-mentioned relative distance dx will be described with reference to FIGS. 10 and 11 in addition to FIG.

[0058] As the relative distance dx between the vehicle 1 and the recognition target object 6 increases (see the regions of relative distance dx=d1, d2, d3 in the cases of short distance, medium distance, and long distance shown in FIG. 10 ), the derivation unit 23 performs the following calculation. That is, as the relative distance dx increases, the derivation unit 23 increases the cost C=Cx of the first event information Ie1 (for example, in an exponential relationship) (see FIG. 7 ). Specifically, in the example of FIG. 10 , as the relative distance dx increases from 10 [m] to 100 [m] to 125 [m], the cost Cx increases from $1 to $37 to $100 (in an exponential relationship). Note that the example of FIG. 10 is a numerical example in which dmin = 10 and α = 25 in equation (2-1) described below.

[0059] Here, the procedure for deriving the cost Cx in accordance with the shielding rate px in the deriving unit 23 is, for example, as follows (steps S41 and S42 below).

[0060] S41: Determine the relative distance dx of the recognition target object 6. S42: Calculate the cost Cx using the following equations (2-1) and (2-2) (see FIG. 11) (dmin, dmax: minimum and maximum values ​​of the relative distance dx recognizable by the information acquisition unit 21, α: constant calculated by equation (2-2)).

[0061]

[0062] (C-3. ​​Determination duration tx) Next, the above-mentioned determination duration tx will be described with reference to FIGS. 12 to 14 in addition to FIG.

[0063] First, as described above, the determination duration tx is the length of time during which the recognition target object 6 is continuously determined to be of the same type. Specifically, in the example of FIG. 12 (an example of the correspondence relationship between time t and the type determination value J), ​​the determination duration tx is defined as the period during which the type determination value J of the recognition target object 6 continuously indicates the same value (the period during which the recognition target object 6 is continuously determined to be of the same type). The derivation unit 23 decreases the cost C=Cx of the first event information Ie1 (e.g., in an inversely proportional relationship) as the determination duration tx increases (see FIG. 7 ). Specifically, in the examples of FIGS. 13 and 14 , as the determination duration tx increases from 0.1 s to 0.5 s to 1.0 s, the cost Cx decreases from $100 to $20 to $10 (inversely proportional relationship). The examples in FIGS. 13 and 14 are numerical examples in which β=10 in equation (3-1) described below.

[0064] Here, the procedure for deriving the cost Cx in accordance with the coverage rate px in the deriving unit 23 is, for example, as follows (steps S51 to S53 below).

[0065] S51: Determine the type of the recognition target object 6 (obtain the above-mentioned type determination value J). S52: Determine the determination duration time tx using the above-mentioned definition. S53: Calculate the above-mentioned cost Cx using the following equations (3-1) and (3-2) (tmin: minimum value of the determination duration time tx that can be calculated by the derivation unit 23, β: constant obtained by equation (3-2)).

[0066]

[0067] (D. Actions and Effects) In this embodiment, by performing abductive inference by applying background knowledge information Ibk to observation information Im including first event information Ie1, a plurality of hypotheses H, each of which includes second event information Ie2, are inferred. Furthermore, the most likely hypothesis Hm from the plurality of hypotheses H is determined based on the cost C for each piece of first event information Ie1 and the cost C for each piece of second event information Ie2. At this time, if information on the reliability R for each piece of first event information Ie1 is obtained, the cost for each piece of first event information Ie1 is derived based on the information on the reliability R. On the other hand, if information on the reliability R for each piece of first event information Ie1 is not obtained, the cost C for each piece of first event information Ie1 is derived based on a predetermined parameter Pr related to the recognition of the recognition target object 6.

[0068] As a result, even when information on the reliability R for each piece of first event information Ie1 in the observation information Im cannot be obtained, the cost C for each piece of first event information Ie1 in the observation information Im can be derived using the predetermined parameter Pr. As a result, in this embodiment, it is possible to improve the convenience when determining the maximum likelihood hypothesis Hm.

[0069] Furthermore, in this embodiment, the predetermined parameter Pr includes at least one of the above-mentioned shielding rate px, relative distance dx, and determination duration tx, resulting in the following: By utilizing such various parameters Pr, the cost C for each piece of first event information Ie1 can be more appropriately derived. As a result, it is possible to further improve the convenience of determining the maximum likelihood hypothesis Hm.

[0070] 2. Modifications The present disclosure has been described above by giving several embodiments and examples, but the present disclosure is not limited to these embodiments and can be modified in various ways.

[0071] For example, the configuration (type, arrangement, number, etc.) of each component in a vehicle or the like is not limited to that described in the above embodiment. That is, the configuration of each component may be of a different type, arrangement, number, etc. Specifically, for example, in the above embodiment, an example has been described in which the driving assistance device in one embodiment of the present disclosure is provided in a target vehicle for driving assistance, but this example is not limiting. That is, at least some of the components in the driving assistance device (such as the information acquisition unit, the abductive reasoning unit, the derivation unit, and the determination unit) may be provided, for example, inside a server (information processing device) or the like provided outside the target vehicle.

[0072] Furthermore, in the above embodiments, various processing examples (such as the overall driving assistance processing described above, the processing for deriving the cost C of the first event information Ie1, the abductive reasoning processing, and the processing for determining the maximum likelihood hypothesis Hm) have been specifically described, but the present invention is not limited to the methods described in the above embodiments, and other methods may be used, for example. In addition, in the above embodiments, specific examples (examples) of the predetermined parameter Pr have been described in detail, but the present invention is not limited to these specific examples, and other types of parameters (such as the color, material, or shape of the object to be recognized) may be used as the predetermined parameter Pr.

[0073] Furthermore, the values, ranges, magnitude relationships, etc. of the various parameters described in the above embodiments, etc. are not limited to those described in the above embodiments, etc., and may be other values, ranges, magnitude relationships, etc.

[0074] Additionally, in the above-described embodiment, the vehicle 1 is provided with a battery 12 as a power source. That is, in the above-described embodiment, the vehicle 1 is an electric vehicle (EV) or a hybrid electric vehicle (HEV), but the present invention is not limited to this example. That is, the vehicle 1 may be, for example, a gasoline-powered vehicle or a fuel-powered vehicle.

[0075] Furthermore, the series of processes described in the above embodiments may be performed by hardware (circuits), software (programs), or a combination of hardware and software. When performed by software, the software is composed of a group of programs for causing a computer to execute each function. Each program may be, for example, pre-installed in the computer, or may be installed on the computer from a network or recording medium.

[0076] Furthermore, the various examples described above may be applied in any combination.

[0077] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0078] The present disclosure may also be configured as follows: (1) A driving assistance device that uses abductive reasoning to provide driving assistance for a target vehicle, comprising: an information acquisition unit configured to acquire observation information including one or more pieces of first event information; abductive reasoning unit configured to perform the abductive reasoning by applying background knowledge information including a plurality of pieces of evidence information to the observation information, thereby inferring a plurality of hypotheses, each of which includes one or more pieces of second event information; a derivation unit configured to derive a cost for each piece of first event information included in the observation information; and a determination unit configured to determine a maximum likelihood hypothesis among the plurality of hypotheses, the maximum likelihood hypothesis being the hypothesis with the lowest total cost, based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses, wherein the derivation unit is configured to: when reliability information for each piece of first event information in the observation information is obtained, derive the cost for each piece of first event information based on the reliability information; and when reliability information for each piece of first event information in the observation information is not obtained, derive the cost for each piece of first event information based on a predetermined parameter related to recognition of a recognition target object by the information acquisition unit. (2) The driving assistance device according to (1), wherein the predetermined parameter includes at least one of: an occlusion rate of the recognition target object, a relative distance between the target vehicle and the recognition target object, and a determination duration that is the length of a period during which the recognition target object is continuously determined to be of the same type. (3) The driving assistance device according to (2), wherein the predetermined parameter includes the occlusion rate, and the derivation unit is configured to increase the cost of the first event information as the occlusion rate increases. (4) The driving assistance device according to (2) or (3), wherein the predetermined parameter includes the relative distance, and the derivation unit is configured to increase the cost of the first event information as the relative distance increases.(5) The driving assistance device according to any one of (2) to (4) above, wherein the predetermined parameter includes the determination duration, and the derivation unit is configured to reduce the cost of the first event information as the determination duration becomes longer. (6) The driving assistance device according to any one of (1) to (5) above, wherein the derivation unit is configured to periodically update the cost value for each piece of first event information. (7) The driving assistance device according to any one of (1) to (6) above, further comprising: a control unit configured to provide driving assistance for the target vehicle by using the maximum likelihood hypothesis. (8) A vehicle equipped with the driving assistance device according to any one of (1) to (7). (9) A method for providing driving assistance for a target vehicle using abductive reasoning, comprising: acquiring observation information including one or more pieces of first event information; applying background knowledge information including a plurality of pieces of evidence information to the observation information to perform the abductive reasoning, thereby inferring a plurality of hypotheses, each of which includes one or more pieces of second event information; deriving a cost for each piece of first event information included in the observation information; and determining a maximum likelihood hypothesis among the plurality of hypotheses, which is the hypothesis with the lowest total cost, based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses; wherein deriving the cost for each piece of first event information comprises: when reliability information for each piece of first event information in the observation information is obtained, deriving the cost for each piece of first event information based on the reliability information; and when reliability information for each piece of first event information in the observation information is not obtained, deriving the cost for each piece of first event information based on predetermined parameters related to recognition of a recognition target object.

[0079] The vehicle control unit 11 shown in FIGS. 1 and 2 can be implemented by circuitry including at least one semiconductor integrated circuit, such as at least one processor (e.g., a central processing unit (CPU)), at least one application-specific integrated circuit (ASIC), and / or at least one field-programmable gate array (FPGA). The at least one processor can be configured to perform all or some of the various functions of the vehicle control unit 11 shown in FIGS. 1 and 2 by reading instructions from at least one non-transitory, tangible computer-readable medium. Such medium can take various forms, including, but not limited to, various magnetic media such as hard disks, various optical media such as CDs or DVDs, and various semiconductor memories (i.e., semiconductor circuits) such as volatile or non-volatile memories. Volatile memories can include DRAM and SRAM. Non-volatile memories can include ROM and NVRAM. An ASIC is an integrated circuit (IC) specialized to perform all or some of the various functions of the vehicle control unit 11 shown in FIGS. 1 and 2. The FPGA is an integrated circuit that is designed to be configurable after manufacture so as to execute all or part of the various functions of the vehicle control unit 11 shown in FIGS.

Claims

1. A driving assistance device that uses abductive reasoning to provide driving assistance to a target vehicle, comprising: an information acquisition unit configured to acquire observation information including one or more pieces of first event information; abductive reasoning unit configured to perform the abductive reasoning by applying background knowledge information including a plurality of pieces of evidence information to the observation information, thereby inferring a plurality of hypotheses, each of which includes one or more pieces of second event information; a derivation unit configured to derive a cost for each piece of first event information included in the observation information; and a determination unit configured to determine a maximum likelihood hypothesis among the plurality of hypotheses, which is the hypothesis with the lowest total cost, based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses, wherein the derivation unit is configured: when reliability information for each piece of first event information in the observation information is obtained, to derive the cost for each piece of first event information based on the reliability information; and when reliability information for each piece of first event information in the observation information is not obtained, to derive the cost for each piece of first event information based on predetermined parameters related to the recognition of a recognition target object by the information acquisition unit.

2. The driving assistance device of claim 1, wherein the predetermined parameters include at least one of the following: an occlusion rate of the object to be recognized; a relative distance between the target vehicle and the object to be recognized; and a determination duration, which is the length of time during which the object to be recognized is continuously determined to be of the same type.

3. The driving assistance device according to claim 2, wherein the predetermined parameter includes the shading rate, and the derivation unit is configured to increase the cost of the first event information as the shading rate increases.

4. The driving assistance device according to claim 2, wherein the predetermined parameter includes the relative distance, and the derivation unit is configured to increase the cost of the first event information as the relative distance increases.

5. The driving assistance device according to claim 2, wherein the predetermined parameters include the determination duration, and the derivation unit is configured to reduce the cost of the first event information as the determination duration becomes longer.

6. The driving assistance device according to any one of claims 1 to 5, wherein the derivation unit is configured to periodically update the cost value for each piece of first event information.

7. The driving assistance device according to any one of claims 1 to 5, further comprising a control unit configured to provide driving assistance for the target vehicle using the maximum likelihood hypothesis.

8. A vehicle equipped with a driving assistance device according to any one of claims 1 to 5.

9. A method for providing driving assistance for a target vehicle using abductive reasoning, comprising: acquiring observation information including one or more pieces of first event information; applying background knowledge information including a plurality of pieces of evidence information to the observation information to perform the abductive reasoning, thereby inferring a plurality of hypotheses, each of which includes one or more pieces of second event information; deriving a cost for each piece of first event information included in the observation information; and determining a maximum likelihood hypothesis among the plurality of hypotheses, which is the hypothesis with the lowest total cost, based on the cost for each piece of first event information included in the observation information and the cost for each piece of second event information included in each of the hypotheses; wherein deriving the cost for each piece of first event information comprises: if reliability information for each piece of first event information in the observation information is obtained, deriving the cost for each piece of first event information based on the reliability information; and if reliability information for each piece of first event information in the observation information is not obtained, deriving the cost for each piece of first event information based on predetermined parameters related to recognition of a recognition target object.

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