Information processing device, vehicle control device, and vehicle

Abstract

An information processing device according to one embodiment of the present disclosure can execute the following three steps. (1) A traffic context is interpreted on the basis of acquired map data and situation data, whereby a plurality of surrounding traffic scenes in time series are generated, and the generated plurality of surrounding traffic scenes in time series are integrated to create a surrounding traffic scenario; (2) for each known traffic scenario included in a knowledge space, a degree of similarity to the surrounding traffic scenarios is calculated, and, among the plurality of known traffic scenarios included in the knowledge space, the known traffic scenario having the highest degree of similarity is determined to be a dangerous traffic scenario; and (3) the intensity of at least one of a danger notification control and a danger avoidance control is set in accordance with the magnitude of the degree of similarity of the dangerous traffic scenario.

Description

Information processing device, vehicle control device, and vehicle

[0001] The present disclosure relates to an information processing device, a vehicle control device, and a vehicle.

[0002] A driving assistance method is known that predicts dangerous events and warns the driver by combining knowledge data such as ontology and knowledge graphs with logical inference.

[0003] JP 2016-091039 A

[0004] An information processing device according to a first aspect of the present disclosure includes a storage unit, a data acquisition unit, and a processing unit. The storage unit stores a knowledge space that spatially represents a plurality of known traffic scenarios, each of which is composed of a plurality of integrated time-series known traffic scenes. The data acquisition unit is capable of acquiring map data and situation data around the vehicle. The processing unit is capable of performing information processing using the plurality of known traffic scenarios read from the storage unit and the map data and situation data acquired by the data acquisition unit. The processing unit is capable of performing the following three operations: (1) Interpreting the traffic context based on the map data and situation data acquired by the data acquisition unit, thereby generating a plurality of time-series surrounding traffic scenes, and integrating the generated plurality of time-series surrounding traffic scenes to create a surrounding traffic scenario; (2) For each known traffic scenario included in the knowledge space, calculating the similarity with the surrounding traffic scenario created by the creation unit, and determining that the known traffic scenario with the highest similarity among the plurality of known traffic scenarios included in the knowledge space is the dangerous traffic scenario that the vehicle is likely to face; (3) Setting the strength of at least one of the danger warning control and the danger avoidance control according to the degree of similarity of the dangerous traffic scenario.

[0005] A vehicle control device relating to a second aspect of the present disclosure includes an intensity acquisition unit capable of acquiring an intensity from an information processing device relating to the first aspect of the present disclosure, and a control unit capable of performing at least one of danger warning control and danger avoidance control in accordance with the magnitude of the intensity acquired by the acquisition unit.

[0006] A vehicle relating to a third aspect of the present disclosure includes an alarm device, a running device, and a control unit capable of performing at least one of danger alarm control for the alarm device and danger avoidance control for the running device, based on the magnitude of the intensity obtained from the information processing device relating to the first aspect of the present disclosure.

[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 diagram illustrating an example of functional blocks of a vehicle according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an example of the concept of a known traffic scenario. FIG. 3 is a diagram illustrating an example of a knowledge graph embedded in a knowledge space. FIG. 4 is a diagram illustrating an example of a known traffic situation A. FIG. 5 is a diagram illustrating an example of a traffic context for the known traffic situation A in FIG. 4. FIG. 6 is a diagram illustrating an example of a known traffic situation B. FIG. 7 is a diagram illustrating an example of a traffic context for the known traffic situation B in FIG. 6. FIG. 8 is a diagram illustrating an example of a known traffic situation C. FIG. 9 is a diagram illustrating an example of a traffic context for the known traffic situation C in FIG. 8. FIG. 10 is a diagram illustrating an example of a known traffic situation D. FIG. 11 is a diagram illustrating an example of a traffic context for the known traffic situation D in FIG. 10. FIG. 12 is a diagram illustrating an example of a surrounding traffic situation X. FIG. 13 is a diagram illustrating an example of a traffic context for the surrounding traffic situation X in FIG. 12. FIG. 14 is a diagram illustrating an example of a surrounding traffic situation Y. FIG. 15 is a diagram illustrating an example of a traffic context for the surrounding traffic situation Y in FIG. 14. FIG. 16 is a diagram showing an example of the concept of a surrounding traffic scenario. FIG. 17 is a diagram showing an example of vector values ​​of a surrounding traffic scene (scene 1) included in a surrounding traffic scenario and an example of vector values ​​of multiple traffic elements included in the surrounding traffic scene (scene 1). FIG. 18 is a diagram showing an example of vector values ​​of a surrounding traffic scene (scene 2) included in a surrounding traffic scenario and an example of vector values ​​of multiple traffic elements included in the surrounding traffic scene (scene 2). FIG. 19 is a diagram showing an example of a driving assistance procedure for the vehicle of FIG. 1. FIG. 20 is a diagram showing a modified example of the functional blocks of the vehicle of FIG. 1. FIG. 21 is a diagram showing an example of the importance levels of FIG. 20. FIG. 22 is a diagram showing a modified example of a knowledge graph embedded in a knowledge space. FIG. 23 is a diagram showing an example of the TTC order of other vehicles as seen from the host vehicle and an intersecting vehicle in the observed traffic scene (scene 2) of FIG. 16. FIG. 24 is a diagram showing a modified example of the functional blocks of the vehicle of FIG. 20. FIG. 25 is a diagram showing a modified example of the concept of a known traffic scenario. FIG. 26 is a diagram showing a modified example of a knowledge graph embedded in a knowledge space. FIG. 27A is a diagram showing an example of the value of importance before change included in the knowledge graph embedded in the knowledge space.FIG. 27B is a diagram showing an example of the changed importance values ​​included in the knowledge graph of FIG. 27A.

[0009] 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.

[0010] <1. Background> A driving assistance method is known that predicts dangerous events and warns the driver by combining knowledge data such as ontologies and knowledge graphs with logical inference. In such a driving assistance method, logical inference works when the observed traffic situation is included in the traffic situation described in the knowledge data, but does not work when the observed traffic situation is not included in the traffic situation described in the knowledge data.

[0011] Therefore, as a result of intensive research, the inventors of the present application have come up with a technology that enables logical inference to be performed even when the observed traffic situation is not included in the traffic situation described in the knowledge data. The information processing device, vehicle control device, and vehicle that realize this technology will be described in detail below.

[0012] 2. Embodiments [Configuration Example] A vehicle 1 according to an embodiment of the present disclosure will be described. Fig. 1 shows a schematic configuration example of the vehicle 1 according to an embodiment of the present disclosure. The vehicle 1 corresponds to a specific example of a "vehicle" according to an embodiment of the present disclosure.

[0013] The vehicle 1 is capable of traveling by being driven by a prime mover 50 (engine or motor). As shown in Fig. 1 , the vehicle 1 includes, for example, a sensor unit 10, a communication unit 20, a control unit 30, a storage unit 40, the prime mover 50, a brake 60, an EPS (Electric Power Steering) motor 70, and a display unit 80.

[0014] The sensor unit 10 is configured to include various sensors mounted on the vehicle 1. The sensor unit 10 is configured to include, for example, an accelerator opening sensor, a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, a steering torque sensor, and a brake torque sensor. The sensor unit 10 may also include sensors other than those described above.

[0015] The accelerator position sensor is capable of detecting the accelerator position from the amount of depression of the accelerator pedal, and is capable of outputting time-series data (accelerator position data) regarding the detected accelerator position to the control unit 30.

[0016] The vehicle speed sensor is capable of detecting the speed (vehicle speed) of the vehicle 1. The vehicle speed sensor is capable of outputting time series data (vehicle speed data) about the detected vehicle speed to the control unit 30. The acceleration sensor is capable of detecting acceleration applied to the vehicle 1. The acceleration sensor is capable of outputting time series data (acceleration data) about the detected acceleration in three directions to the control unit 30. The angular velocity sensor is capable of detecting the angular velocity of the vehicle 1. The angular velocity sensor is capable of outputting time series data (angular velocity data) about the detected three angular velocities (yaw angular velocity, roll angular velocity, and pitch angular velocity) to the control unit 30.

[0017] The steering angle sensor is capable of detecting the steering angle of the steering wheel of the vehicle 1 (steering angle). The steering angle sensor is capable of outputting time-series data (steering angle data) about the detected steering angle to the control unit 30. The steering torque sensor is capable of detecting the steering torque generated by the driver's steering wheel operation. The steering torque sensor is capable of outputting time-series data (steering torque data) about the detected steering torque to the control unit 30. The brake torque sensor is capable of detecting the braking force (torque of the brakes 60) relative to the brake pressure of the vehicle 1. The brake torque sensor is capable of outputting time-series data (braking force data) about the detected braking force to the control unit 30.

[0018] The sensor unit 10 further includes a stereo camera mounted on the vehicle 1 and a driving environment detection unit. The stereo camera is an autonomous sensor that senses the real space around the vehicle 1. The stereo cameras are, for example, arranged at symmetrical positions on either side of the central part in the width direction of the vehicle 1, and are capable of capturing stereo images of the area in front of the vehicle 1 from different viewpoints. The stereo cameras are capable of outputting image data Da (a pair of stereo image data) obtained by capturing images to the control unit 30.

[0019] The stereo camera is capable of generating distance image data Db calculated from the amount of displacement between corresponding objects based on image data Da (a pair of stereo image data) obtained by capturing images. The driving environment detection unit is capable of, for example, calculating lane markings that divide the road around the vehicle 1 based on the distance image data Db. The driving environment detection unit is also capable of calculating the road curvature of the markings that divide the left and right sides of the road (driving lane) on which the vehicle 1 is traveling, and the width between the left and right markings (vehicle width). The driving environment detection unit is also capable of performing predetermined pattern matching on the distance image data Db to detect lanes and three-dimensional objects such as structures present around the vehicle 1.

[0020] Here, the detection of a three-dimensional object by the driving environment detection unit includes, for example, detecting the type of the three-dimensional object, the distance to the three-dimensional object, the speed of the three-dimensional object, and the relative speed between the three-dimensional object and the vehicle (host vehicle). Examples of three-dimensional objects to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, bicycles, and buildings. Examples of buildings include detached houses, apartment complexes (condominiums), commercial facilities, factories, and signs. The driving environment detection unit is capable of outputting driving environment information around the vehicle 1, including the thus acquired information on the three-dimensional object, to the control unit 30.

[0021] The communication unit 20 can acquire data to supplement data that cannot be obtained from the image data Da and the distance image data Db, for example, through vehicle-to-vehicle communication, road-to-vehicle communication, and satellite communication. The communication unit 20 can output the acquired data to the control unit 30.

[0022] The communication unit 20 can acquire data (e.g., vehicle position and vehicle speed) obtained by other vehicles through, for example, vehicle-to-vehicle communication. The communication unit 20 can receive positioning signals transmitted from multiple positioning satellites through, for example, satellite communication.

[0023] The communication unit 20 is capable of, for example, acquiring road map data around the vehicle 1 from a control device that is capable of successively integrating and updating road map data transmitted from each vehicle via road-to-vehicle communication and transmitting the updated road map data to each vehicle. The road map data is, for example, made up of high-precision road map data (dynamic map) and includes static information and quasi-static information that mainly constitute road information, and quasi-dynamic information and dynamic information that mainly constitute traffic information.

[0024] Static information that makes up road information includes, for example, information that requires updates within one month, such as roads, structures on roads, structures around roads, lane information, road surface information, and permanent traffic regulations. "Roads" include, for example, road locations and shapes, intersections, and road attributes (e.g., national roads, prefectural roads, city roads, private roads, priority roads, non-priority roads, general roads, and expressways). "Structures on roads" include, for example, traffic signs, traffic lights, convex mirrors, pedestrian bridges, bus stops, and garbage collection stations. "Structures around roads" include, for example, various buildings and parks.

[0025] The semi-static information that constitutes the road information is composed of information that needs to be updated every hour or less, such as traffic regulation information due to road construction or events, wide-area weather information, and traffic congestion forecasts.

[0026] The semi-dynamic information that constitutes traffic information is composed of information that requires updating within one minute, such as the actual traffic congestion situation at the time of observation, driving restrictions, temporary driving obstructions such as fallen objects and obstacles, actual accident conditions, and narrow-area weather information.

[0027] The dynamic information that constitutes the traffic information is composed of information that needs to be updated every second, such as information sent and exchanged between mobile bodies, information on currently displayed traffic signals, information on pedestrians and bicycles at intersections, information on vehicles traveling on roads, etc. Such road map data is maintained and updated periodically until the next information is received from each vehicle, and the updated road map data is transmitted to each vehicle as appropriate via the communication unit 20.

[0028] The storage unit 40 is configured, for example, by a non-volatile memory, such as an EEPROM (Electrically Erasable Programmable Read-Only Memory), a flash memory, a resistance change memory, etc. For example, as shown in Fig. 1, the storage unit 40 stores a road map DB 41, a knowledge graph DB 42, and a knowledge space DB 43. For example, as shown in Fig. 1, the storage unit 40 stores data (similarity 44, danger content 45, and intensity 46) obtained by calculations performed by the danger prediction unit 31 (described later).

[0029] The road map DB 41 is a large-capacity storage medium such as an HDD, and stores high-precision road map data (dynamic map). Similar to the road map data included in the road map data integration ECU 201, this high-precision road map data includes static information and quasi-static information that mainly constitute road information, and quasi-dynamic information and dynamic information that mainly constitute traffic information.

[0030] The knowledge graph DB 42 and knowledge space DB 43 are knowledge data that structure traffic rules, inference rules, and general knowledge, respectively, in a manner that can be used to analyze traffic conditions. Traffic conditions represent the relationship between the driving environment and the behavior of each traffic participant. Traffic rules refer to rules that traffic participants must follow in order to participate in traffic in a compliant manner. Inference rules refer to rules that can be used to derive conclusions about unknown things based on known facts. General knowledge refers to the experience and collective knowledge that people implicitly know.

[0031] The knowledge graph DB 42 stores knowledge data that describes multiple known traffic scenarios in a graph structure. A known traffic scenario refers to an expected traffic scenario or a past traffic scenario. A traffic scenario refers to a summary of traffic conditions that change from moment to moment. In the knowledge graph DB 42, known traffic scenarios are described as condition terms, and possible events (e.g., dangerous events) are described as result terms. In a traffic scenario, an identifier (ID) is assigned to each traffic participant, and the position and speed of each traffic participant are associated with each traffic participant. Traffic participants may include, for example, the subject vehicle (SbjCar), other vehicles (ObjCar), bicycles (Bicycles), and pedestrians (Pedestrians). In a traffic scenario, the lane and lane type of each vehicle are also associated with each vehicle.

[0032] A known traffic scenario includes multiple known traffic scenes in an integrated time series. A known traffic scene refers to an expected traffic scene or a past traffic scene. A traffic scene refers to an organized traffic situation at a certain moment. In a traffic scenario, multiple traffic scenes are associated with a temporal order. A known traffic scenario includes multiple traffic elements. Traffic elements may include, for example, object types, road structures, object speeds, or object positions. Object types may include, for example, vehicles (passenger cars), motorcycles, bicycles, and pedestrians. Road structures may include, for example, intersections, traffic lights, and driving lanes. Object speeds may include, for example, a constant speed, deceleration, and acceleration. Object positions may include, for example, identifiers indicating that an object is moving in a certain location, creating a blind spot in a certain location, approaching a certain location or object, stopping in a certain location, indicating a positional relationship with a certain location or object, being on the verge of collision with an object, and entering a certain location. The traffic elements may further include, for example, a transition time between scenes, which refers to the difference between the time of a second scene and the time of a second scene between two consecutive scenes (a first scene and a second scene) in time.

[0033] A certain known traffic scenario (hereinafter referred to as "known traffic scenario I") in the knowledge graph DB 42 includes a plurality of known traffic scenes (scene a, scene b, scene c, and scene d) in an integrated time series, as shown in FIG. 2, for example. In the known traffic scenario I, the plurality of known traffic scenes (scene a, scene b, scene c, and scene d) are linked to a time sequence. In the known traffic scenario I, each known traffic scene (scene a, scene b, scene c, and scene d) includes a plurality of traffic elements. For example, as shown in FIG. 2, the known traffic scene (scene a) includes a plurality of traffic elements (traffic elements a1, a2, a3, a4, etc.). For example, as shown in FIG. 2, the known traffic scene (scene b) includes a plurality of traffic elements (traffic elements b1, b2, b3, b4, etc.). A known traffic scene (scene c) includes a plurality of traffic elements (traffic elements c1, c2, c3, c4, etc.) as shown in Fig. 2. A known traffic scene (scene d) includes a plurality of traffic elements (traffic elements d1, d2, d3, d4, etc.) as shown in Fig. 2.

[0034] In the knowledge graph DB 42, each known traffic scenario is composed of a directed graph including multiple nodes (entities) and multiple edges (relationships) that associate two nodes (entities). A node (entity) corresponds to a known traffic scene or traffic element. In each known traffic scenario, each entity and each relation is associated with a label, and two entities and one relation that associates these two entities form a sentence (subject, predicate, object).

[0035] The knowledge space DB 43 has knowledge data in which a plurality of known traffic scenarios contained in the knowledge graph DB 42 are spatially expressed. The knowledge space DB 43 has a knowledge graph 43A in which a known traffic scenario I contained in the knowledge graph DB 42 is expressed as a spatial vector, as shown in FIG. 3, for example. The knowledge graph 43A includes a plurality of identifiers (IDs), as shown in FIG. 3, for example, and is table data in which attributes, labels, and spatial vectors are associated with each identifier (ID). The identifiers (IDs) are used to identify each entity and each relation in the known traffic scenario I, and one ID is assigned to each entity and each relation.

[0036] Examples of labels that can be assigned to entities and relations include a label indicating an object type, a label indicating a road structure, a label indicating an object speed, and a label indicating an object position.

[0037] Examples of labels indicating object types include the labels shown below. Label indicating the main vehicle (SbjCar) Label indicating the secondary vehicle (ObjCar1, ObjCar2, ...) Label indicating a motorcycle (Motorcycle1, Motorcycle2, ...) Label indicating a pedestrian (Pedestrian1, Pedestrian12, ...) Label indicating a bicycle (Bicycle1, Bicycle2, ...) Label indicating a vehicle to be monitored from the perspective of the main vehicle (tgtCar) Label indicating a motorcycle to be monitored from the perspective of the main vehicle (tgtMotorcycle) Label indicating a pedestrian to be monitored from the perspective of the main vehicle (tgtPedestrian) Label indicating a bicycle to be monitored from the perspective of the main vehicle (tgtBicycle)

[0038] Examples of labels indicating road structures include the following: A label indicating an intersection (Intersection) A label indicating an intersection with a traffic light (SignalIntersection) A label indicating an intersection without a traffic light (noSignalIntersection) A label indicating a traffic light (TrafficSignal) A label indicating the lane in which the main vehicle is traveling (SubjectLane) A label indicating the lane whose traveling direction is opposite to that of the main vehicle (OppositeDirectionLane) A label indicating the lane that intersects with the lane in which the main vehicle is traveling (CrossRoad)

[0039] The labels indicating the object speed include, for example, the following labels: A label indicating a constant speed (Constant) A label indicating deceleration (Deceleration)

[0040] Examples of labels that indicate object positions include the following: A label that indicates where an object is moving (IsRunningOn) A label that indicates that an object has speed (HasSpeed) A label that indicates that an object is creating a blind spot (CreateBlindIn) A label that indicates that an object is approaching (ApproachTo) A label that indicates that an object is stopped (stopAt) A label that indicates the relative position of an object or road structure (nextRoadSegment) A label that indicates that an object is on the verge of collision (NearCrachTo) A label that indicates that an object is entering (EnterIn)

[0041] In the knowledge space DB 43, a set of (subject, predicate, object) is converted into a numerical expression using an "embedding algorithm" so that a specific operation is established for the set of (subject, predicate, object) defined in the knowledge graph DB 42. Examples of the "embedding algorithm" include TransE and RESCAL. Here, the numerical expression is, for example, a space vector as shown in FIG. 3. When the numerical expression is a space vector, the "specific operation" means that when the entity corresponding to the subject is a first vector value, the relation corresponding to the predicate is a second vector value, and the entity corresponding to the object is a third vector value, then "first vector value + second vector value = third vector value."

[0042] For example, in a set (subject, predicate, object) called (SbjCar IsRunningOn subjectLane), the vector value of SbjCar is [0,1,2], the vector value of IsRunningOn is [1,1,1], and the vector value of subjectLane is [1,2,3]. In this case, [0,1,2] + [1,1,1] = [1,2,3] holds.

[0043] The "embedded algorithm" is stored in, for example, the storage unit 40. The calculation process for deriving a numerical expression using the "embedded algorithm" is executed, for example, in the risk prediction unit 31 described below. The numerical expression may be, for example, a two-dimensional vector or a vector of four or more dimensions. The numerical expression may also be an expression other than a vector, for example, a matrix.

[0044] The knowledge space DB 44 may contain a set of (subject, predicate, object) indicating the transition time of a certain known traffic scene. In FIG. 3, an example of a set of (subject, predicate, object) indicating the transition time of a certain known traffic scene is (Scene a, HasTime, 0 sec), which indicates the transition time of a known traffic scene (Scene a). "Scene a, HasTime, 0 sec" means that "the transition time of Scene a is 0 seconds."

[0045] 4 shows an example of a known traffic situation (hereinafter referred to as "known traffic situation A"). The known traffic scene (scene a) in the knowledge graph DB 42 is generated based on a traffic context (see FIG. 5) obtained by interpreting data acquired by a data acquisition unit 311 (described later) in the known traffic situation A shown in FIG. 4 (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1).

[0046] In known traffic situation A, it is assumed that vehicle (host vehicle) 100a is traveling on road La with one lane in each direction. This one-lane road La is composed of a driving lane L1 in which vehicle 100a is traveling and an oncoming lane L2 that runs parallel to driving lane L1 via a center line. An intersection IS without traffic lights is located ahead of vehicle 100a on this one-lane road La. No traffic lights are installed at the intersection IS without traffic lights. This one-lane road La is a priority road in relation to road Lb (crossing road) that intersects with this one-lane road La at the intersection IS without traffic lights. In other words, vehicle 100a is traveling on the priority road. Meanwhile, road Lb that intersects with the priority road at the intersection IS without traffic lights is a non-priority road in relation to the priority road. On the non-priority road, vehicle 100e (crossing vehicle) is traveling slowly just before the intersection IS without traffic lights.

[0047] When viewed from the driver of vehicle 100a, vehicle 100e is located in a blind spot BA of vehicle 100c (oncoming vehicle) traveling in the oncoming lane L2, and the driver of vehicle 100a cannot see vehicle 100e. This is because vehicle 100e is hidden behind vehicle 100c, and the image data Da and distance image data Db obtained by vehicle 100a do not contain data indicating the presence of vehicle 100e.

[0048] The driver of vehicle 100a recognizes that vehicle 100a is traveling on a priority road. Therefore, vehicle 100a is heading toward the untraffic intersection IS without slowing down. At this time, vehicle 100b (a preceding vehicle) traveling in front of vehicle 100a begins to slow down. Furthermore, from the driver of vehicle 100a's perspective, vehicle 100d (an oncoming vehicle) is stopped in the oncoming lane L2, behind the untraffic intersection IS.

[0049] Fig. 5 shows an example of a traffic context obtained by interpreting known traffic situation A. The traffic context of known traffic situation A is composed of a sentence (subject, predicate, object), for example, as shown in Fig. 5 .

[0050] "Scenea, time, 0sec" means "the transition time of scene a is 0 seconds." "SbjCar, isRunningOn, subjectLane" means "vehicle 100a is traveling on traveling lane L1." "subjectLane, nextRoadSegment, noSignalIntersection" means "traveling lane L1 has an unsignalized intersection IS as a road segment ahead of vehicle 100a." "ObjCar1, HasAct, Deceleration" means "vehicle 100b is decelerating." "ObjCar2,creatBlindIn,noSignalIntersection" means that "vehicle 100c is generating a blind spot area BA at the unsignalized intersection IS." "ObjCar3,stopAt,OppositeDirectionLane" means that "vehicle 100d is stopped in the oncoming lane L2."

[0051] 6 shows an example of a known traffic situation (hereinafter referred to as "known traffic situation B") following known traffic situation A. Known traffic situation B is, for example, a traffic situation one second after known traffic situation A. A known traffic scene (scene b) in the knowledge graph DB 42 is generated based on a traffic context (see FIG. 7) obtained by interpreting data acquired by a data acquisition unit 311 (described later) in known traffic situation B shown in FIG. 6 (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1).

[0052] In known traffic situation B, vehicle 100a travels in driving lane L1 and is therefore slightly closer to the intersection IS without a traffic light than in scene a. Vehicle 100b travels in driving lane L1 and is therefore entering the intersection IS without a traffic light. Vehicle 100c remains stopped in oncoming lane L2. Vehicle 100d remains traveling in oncoming lane L2. Vehicle 100e travels slowly on road Lb and is therefore slightly closer to the intersection IS without a traffic light than in scene a.

[0053] 7 shows an example of a traffic context obtained by interpreting known traffic situation B. The traffic context of known traffic situation B is composed of a sentence (subject, predicate, object), for example, as shown in FIG.

[0054] "Sceneb,time,1sec" means "the transition time of scene b is 1 second." "SbjCar,isRunningOn,subjectLane" means "vehicle 100a is traveling on traveling lane L1." "subjectLane,nextRoadSegment,noSignalIntersection" means "traveling lane L1 has an unsignalized intersection IS as a road segment ahead of vehicle 100a." "sbjCar,ApproachTo,noSignalIntersection" means "vehicle 100a is approaching an unsignalized intersection IS."

[0055] "ObjCar1, HasSpeed, Constant" means that "vehicle 100b is traveling at a constant speed." "ObjCar2, HasSpeed, Constant" means that "vehicle 100c is traveling at a constant speed." "ObjCar3, stopAt, OppositeDirectionLane" means that "vehicle 100d is stopped in the oncoming lane L2." "tgtCar, isRunningOn, crossRoad" means that "vehicle 100e is traveling on road Lb." "crossRoad, nextRoadSegment, noSignalIntersection" means that "road Lb has an unsignalized intersection IS as a road segment ahead of the vehicle 100e." "tgtCar, ApproachTo, noSignalIntersection" means that "vehicle 100e is approaching an unsignalized intersection IS."

[0056] 8 shows an example of a known traffic situation (hereinafter referred to as "known traffic situation C") following known traffic situation B. Known traffic situation C is, for example, a traffic situation one second after known traffic situation B. A known traffic scene (scene c) in the knowledge graph DB 42 is generated based on a traffic context (see FIG. 9) obtained by interpreting data (various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1) acquired by a data acquisition unit 311 (described later) in known traffic situation C shown in FIG. 8, for example.

[0057] In known traffic situation C, vehicle 100a travels along lane L1 and enters intersection IS without a traffic light. Vehicle 100c remains stopped in oncoming lane L2. Vehicle 100d remains traveling along oncoming lane L2. Vehicle 100e travels along road Lb for another second and enters intersection IS near the center.

[0058] 9 shows an example of a traffic context obtained by interpreting a known traffic situation C. The traffic context of the known traffic situation C is composed of a sentence (subject, predicate, and object), for example, as shown in FIG.

[0059] "Scenec,time,2sec" means "the transition time of scene c is 2 seconds." "SbjCar,EnterIn,noSignalIntersection" means "vehicle 100a is entering an intersection IS without a traffic light." "ObjCar2,HasSpeed,Constant" means "vehicle 100c is traveling at a constant speed." "ObjCar3,stopAt,OppositeDirectionLane" means "vehicle 100d is stopped in the oncoming lane L2." "tgtCar,EnterIn,noSignalIntersection" means "vehicle 100e is entering an intersection IS without a traffic light."

[0060] 10 shows an example of a known traffic situation (hereinafter referred to as "known traffic situation D") following known traffic situation C. Known traffic situation D is, for example, a traffic situation one second after known traffic situation C. Scene d in the knowledge graph DB 42 is generated based on a traffic context (see FIG. 11) obtained by interpreting data acquired by a data acquisition unit 311 (described later) in known traffic situation D shown in FIG. 10 (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1).

[0061] In known traffic situation D, vehicle 100a has entered the center of the untraffic intersection IS and is about to collide with vehicle 100e. Vehicle 100d is still stopped in the oncoming lane L2. Vehicle 100e has entered the untraffic intersection IS beyond the center and is about to collide with vehicle 100a.

[0062] 11 shows an example of a traffic context obtained by interpreting a known traffic situation D. The traffic context of the known traffic situation D is composed of a sentence (subject, predicate, and object), for example, as shown in FIG.

[0063] "Scene, time, 3sec" means that "the transition time of scene d is 3 seconds." "SbjCar, NearCrachTo, tgtCar" means that "vehicle 100a is about to collide with vehicle 100e." "ObjCar3, stopAt, OppositeDirectionLane" means that "vehicle 100d is stopped in the oncoming lane L2." "tgtCar, NearCrachTo, SbjCar" means that "vehicle 100e is about to collide with vehicle 100a."

[0064] 1, the storage unit 40 stores data (e.g., similarity 44, danger content 45, and intensity 46) obtained as a result of the calculation process in the danger prediction unit 31. The similarity 44, danger content 45, and intensity 46 will be described in detail later.

[0065] The control unit 30 is capable of controlling the entire vehicle 1. The control unit 30 is, for example, a so-called ECU (Electronic Control Unit) and is configured to include, for example, one or more processors and one or more memories. The control unit 30 may be configured to include, for example, a CPU (Central Processing Unit). In this case, the control unit 30 is capable of controlling the entire vehicle 1 by, for example, executing a program stored in a storage unit. The control unit 30 corresponds to a specific example of a "vehicle control device" according to an embodiment of the present disclosure.

[0066] The control unit 30 includes, for example, a locator unit. The locator unit is capable of acquiring the position coordinates of the vehicle 1 based on the positioning signal received through the communication unit 20. The locator unit is capable of estimating the vehicle's position on a road map by map-matching the acquired position coordinates with route map data. Based on the acquired position coordinates of the vehicle 1, the locator unit is capable of acquiring map data of a predetermined range including the vehicle 1 from map data stored in a road map DB (database) 41 (described later).

[0067] In an environment where it is not possible to receive valid positioning signals from positioning satellites due to reduced sensitivity, such as when driving inside a tunnel, the locator unit can switch to autonomous navigation, which estimates the vehicle's position based on the vehicle speed, angular velocity, and longitudinal acceleration detected by sensor unit 10, and estimate the vehicle's position on a road map.

[0068] As described above, the locator unit estimates the position of vehicle 1 (vehicle position) on a road map based on the positioning signal received through communication unit 20 or information detected by sensor unit 10, and is then able to determine the road type, etc. of the road on which vehicle 1 is traveling based on the estimated vehicle position on the road map.

[0069] The locator unit is capable of updating the road map data stored in the road map DB 41 to the latest version using road map data acquired through external communication (roadside-to-vehicle communication and vehicle-to-vehicle communication) via the communication unit 20. This information update is performed not only for static information but also for quasi-static information, quasi-dynamic information, and dynamic information. As a result, the road map data includes road information and traffic information acquired through communication with the outside of the vehicle, and information on moving objects such as vehicles traveling on roads is updated in approximately real time.

[0070] The locator unit verifies road map data based on the traveling environment information recognized as described above, and is able to update the road map data stored in the road map DB 41 to the latest version. This information update is performed not only on static information, but also on semi-static information, semi-dynamic information, and dynamic information. As a result, information on moving objects such as vehicles traveling on roads recognized as described above is updated in real time.

[0071] The control unit 30 includes a risk prediction unit 31, as shown in FIG. 1 , for example. The risk prediction unit 31 is capable of estimating whether or not the vehicle 1 is in a dangerous traffic situation. If the estimation result indicates that the vehicle 1 is in a dangerous traffic situation, the risk prediction unit 31 is capable of predicting risk based on a dangerous traffic scenario that the vehicle 1 is likely to be facing. The risk prediction unit 31 is capable of outputting the content of the predicted risk (risk content 45 described below) to the driving control unit 32. The risk prediction unit 31 corresponds to a specific example of an "information processing device" according to an embodiment of the present disclosure.

[0072] 1, the risk prediction unit 31 includes a data acquisition unit 311, a scenario creation unit 312, a similarity determination unit 313, and an intensity setting unit 314. The data acquisition unit 311 corresponds to a specific example of a "data acquisition unit" according to an embodiment of the present disclosure. The scenario creation unit 312, the similarity determination unit 313, and the intensity setting unit 314 correspond to a specific example of a "control unit" according to an embodiment of the present disclosure.

[0073] The data acquisition unit 311 is capable of periodically acquiring data on the situation or state of the vehicle 1. Specifically, the data acquisition unit 311 is capable of acquiring various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1. The data acquisition unit 311 is further capable of acquiring map data of the surroundings of the vehicle 1 from the road map DB 41 of the storage unit 40. The various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1 correspond to a specific example of "surrounding situation data of the vehicle" according to an embodiment of the present disclosure. The map data of the surroundings of the vehicle 1 acquired from the road map DB 41 corresponds to a specific example of "surrounding map data of the vehicle" according to an embodiment of the present disclosure.

[0074] The scenario creation unit 312 , the similarity determination unit 313 , and the intensity setting unit 314 are capable of performing information processing using the data acquired by the data acquisition unit 311 .

[0075] The scenario creation unit 312 is capable of interpreting a traffic context based on the map data and situation data acquired by the data acquisition unit 311. Assume that the vehicle 1 (vehicle 100a) is in a certain traffic situation (hereinafter referred to as "surrounding traffic situation X"), for example, as shown in FIG. 12. At this time, the scenario creation unit 312 is capable of interpreting a traffic context based on data acquired by the data acquisition unit 311 in the surrounding traffic situation X (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1). The scenario creation unit 312 is capable of generating a traffic context such as that shown in FIG. 13, for example.

[0076] 13, "Scene1, time, 0sec" means that "the transition time of scene 1 is 0 seconds." "SbjCar, isRunningOn, subjectLane" means that "vehicle 100a is traveling in traveling lane L1." "subjectLane, nextRoadSegment, noSignalIntersection" means that "traveling lane L1 has an unsignalized intersection IS as a road segment ahead of vehicle 100a." "ObjCar1, createBlindIn, noSignalIntersection" means that "vehicle 100c is generating a blind spot area BA at the unsignalized intersection IS." "ObjCar2, stopAt, OppositeDirectionLane" means that "the vehicle 100d is stopped in the oncoming lane L2."

[0077] The scenario creation unit 312 is capable of interpreting a new traffic context based on the map data and situation data acquired by the data acquisition unit 311, for example, one second after the surrounding traffic situation X. For example, as shown in FIG. 14 , assume that the vehicle 1 (vehicle 100 a) is in a certain traffic situation (hereinafter referred to as “surrounding traffic situation Y”) one second after the surrounding traffic situation X. At this time, the scenario creation unit 312 is capable of interpreting a traffic context based on the data acquired by the data acquisition unit 311 in the surrounding traffic situation Y (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1). The scenario creation unit 312 is capable of generating a traffic context such as that shown in FIG. 15 , for example.

[0078] In Figure 15, "Scene2, time, 1sec" means "The transition time of scene 2 is 1 second." "SbjCar, isRunningOn, subjectLane" means "Vehicle 100a is traveling on traveling lane L1." "SubjectLane, nextRoadSegment, noSignalIntersection" means "Traveling lane L1 has an unsignalized intersection IS as a road segment ahead of vehicle 100a." "sbjCar, ApproachTo, noSignalIntersection" means "Vehicle 100a is approaching an unsignalized intersection IS."

[0079] "ObjCar1, HasSpeed, Constant" means that "vehicle 100c is traveling at a constant speed." "ObjCar2, stopAt, OppositeDirectionLane" means that "vehicle 100d is stopped in the oncoming lane L2." "tgtCar, isRunningOn, crossRoad" means that "vehicle 100e is traveling on road Lb." "crossRoad, nextRoadSegment, noSignalIntersection" means that "road Lb has an unsignalized intersection IS as a road segment ahead of vehicle 100e." "tgtCar, ApproachTo, noSignalIntersection" means that "the vehicle 100e is approaching an intersection IS with no traffic lights."

[0080] The scenario creation unit 312 is capable of generating two time-series surrounding traffic scenes (scene 1 and scene 2) having a graph structure from the two time-series traffic contexts obtained in this manner. Surrounding traffic scene 1 is a traffic scene corresponding to surrounding traffic situation X. Surrounding traffic scene 2 is a traffic scene corresponding to surrounding traffic situation Y. The scenario creation unit 312 is further capable of integrating the two generated time-series surrounding traffic scenes (scene 1 and scene 2) to create surrounding traffic scenario II, for example, as shown in FIG. 16 .

[0081] The surrounding traffic scenario II includes a plurality of integrated time-series surrounding traffic scenes (scene 1, scene 2), as shown in FIG. 16 . In the surrounding traffic scenario II, the surrounding traffic scenes (scene 1, scene 2) are associated with a time sequence. In the surrounding traffic scenario II, each surrounding traffic scene (scene 1, scene 2) includes a plurality of traffic elements. For example, as shown in FIG. 16 , the surrounding traffic scene (scene 1) includes a plurality of traffic elements (traffic elements α1, α2, α3, α4, etc.). For example, as shown in FIG. 16 , the surrounding traffic scene (scene 2) includes a plurality of traffic elements (traffic elements β1, β2, β3, β4, etc.).

[0082] A surrounding traffic scenario is composed of a directed graph including multiple nodes (entities) and multiple relations (edges) that associate two nodes (entities). In the surrounding traffic scenario, each entity and each relation is associated with a label, and two entities and one relation that associates these two entities form a sentence (subject, predicate, object).

[0083] The scenario creation unit 312 is capable of, for example, reading out, for each traffic element (e.g., α1, α2, α3, α4, etc.) included in the surrounding traffic scene (scene 1) from the knowledge graph 43A, etc. included in the knowledge space DB 43, a numerical representation (e.g., a spatial vector V1) associated with the same label as the label of each traffic element (e.g., α1, α2, α3, α4, etc.) included in the surrounding traffic scene (scene 1). The scenario creation unit 312 is capable of, for example, reading out from the knowledge space DB 43 a plurality of vector values ​​([1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], etc.) as shown in FIG. 17 . The scenario creation unit 312 is capable of, for example, calculating a new numerical representation (e.g., a spatial vector V2) based on the plurality of read numerical representations (e.g., a spatial vector V1) and setting the newly calculated numerical representation (e.g., a spatial vector V2) as a numerical representation (e.g., a spatial vector V3) corresponding to the surrounding traffic scene (scene 1). 17, the scenario creation unit 312 can calculate the centroid vector ([2.5, 2.5, 2.5]) of the read multiple vector values ​​([1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], etc.) and use the calculated centroid vector ([2.5, 2.5, 2.5]) as a spatial vector V3 corresponding to the surrounding traffic scene (scene 1). The spatial vector V3 corresponds to a specific example of a "second vector value" according to an embodiment of the present disclosure.

[0084] The scenario creation unit 312 is capable of, for example, reading out, for each traffic element, a numerical representation (e.g., a space vector V4) associated with the same label as the label of each traffic element (e.g., β1, β2, β3, β4, etc.) included in the surrounding traffic scene (scene 2) from the knowledge graph 43A or the like included in the knowledge space DB 43. The scenario creation unit 312 is capable of, for example, calculating a new numerical representation (e.g., a space vector V5) based on the plurality of read numerical representations (e.g., a space vector V4) and setting the newly calculated numerical representation (e.g., a space vector V5) as a numerical representation (e.g., a space vector V6) corresponding to the surrounding traffic scene (scene 2). For example, as shown in FIG. 18 , the scenario creation unit 312 calculates the centroid vector ([2.5, 0, 2.5]) of the read multiple vector values ​​([1, 0, 1], [2, 0, 2], [3, 0, 3], [4, 0, 4], etc.), and can use the calculated centroid vector ([2.5, 0, 2.5]) as a space vector V6 corresponding to the surrounding traffic scene (scene 2). The space vector V6 corresponds to a specific example of a "second vector value" according to an embodiment of the present disclosure. The surrounding traffic scenes (scene 1, scene 2) are represented by space vectors common to the space vectors in the knowledge space DB 43.

[0085] The similarity determination unit 313 is capable of calculating the similarity S between the created surrounding traffic scenario and each known traffic scenario included in the knowledge graph DB 42 or the knowledge space DB 43. The similarity S corresponds to a specific example of “similarity” according to an embodiment of the present disclosure.

[0086] The similarity determination unit 313 is capable of reading, for example, from the knowledge space DB 43, numerical representations (e.g., spatial vectors Va, Vb, Vc, and Vd) of each known traffic scene (scene a, scene b, scene c, and scene d) constituting the known traffic scenario A. The spatial vectors Va, Vb, Vc, and Vd correspond to a specific example of a "first vector value" according to an embodiment of the present disclosure. The spatial vector Va is the spatial vector of the known traffic scene (scene a) and is the spatial vector of the first scene in the known traffic scenario A. The spatial vector Vb is the spatial vector of the known traffic scene (scene b) and is the spatial vector of the second scene in the known traffic scenario A. The spatial vector Vc is the spatial vector of the known traffic scene (scene c) and is the spatial vector of the third scene in the known traffic scenario A. The spatial vector Vd is the spatial vector of the known traffic scene (scene d) and is the spatial vector of the fourth scene in the known traffic scenario.

[0087] The similarity determination unit 313 is capable of calculating a similarity S1 between each known traffic scene and each surrounding traffic scene by comparing the numerical representation of each known traffic scene read from the knowledge space DB 43 with the numerical representation of each surrounding traffic scene created by the scenario creation unit 312. The similarity S1 corresponds to a specific example of a "first similarity" according to an embodiment of the present disclosure.

[0088] The similarity determination unit 313 is capable of calculating the similarity S1 between each of the known traffic scenes (scenes a, b, c, and d) and the surrounding traffic scenes (scenes 1 and 2) by comparing, for example, the numerical representations (e.g., spatial vectors Va, Vb, Vc, and Vd) of each of the known traffic scenes (scenes a, b, c, and d) read from the knowledge space DB 43 with the numerical representations (e.g., spatial vectors V3 and V6) of each of the surrounding traffic scenes (scenes 1 and 2) created by the scenario creation unit 312. The similarity determination unit 313 is capable of calculating, for example, the similarity S1 between each of the spatial vectors Va, Vb, Vc, and Vd and the spatial vector V3, and the similarity S1 between each of the spatial vectors Va, Vb, Vc, and Vd and the spatial vector V6.

[0089] The similarity determination unit 313 is capable of calculating the similarity S1 by, for example, applying a first-order norm, a second-order norm, or cosine similarity to the spatial vectors Va, Vb, Vc, and Vd and the spatial vectors V2 and V4. The similarity determination unit 313 is capable of, for example, deriving a difference between the first-order norm of each of the spatial vectors Va, Vb, Vc, and Vd and the first-order norm of each of the spatial vectors V2 and V4, and setting the derived difference as the similarity S1. The similarity determination unit 313 may be capable of, for example, deriving a difference between the second-order norm of each of the spatial vectors Va, Vb, Vc, and Vd and the second-order norm of each of the spatial vectors V2 and V4, and setting the derived difference as the similarity S1. The similarity determination unit 313 may be capable of, for example, deriving the difference between the cosine similarity of each spatial vector Va, Vb, Vc, and Vd and the cosine similarity of each spatial vector V2 and V4, and setting the derived difference as the similarity S1.

[0090] The similarity determination unit 313 calculates the similarity S2 based on the calculated similarity S1 for each known traffic scene, and can use the calculated similarity S2 as the similarity S of the surrounding traffic scenario to the known traffic scene. The similarity S2 corresponds to a specific example of a "second similarity" according to an embodiment of the present disclosure.

[0091] The similarity determination unit 313, for example, calculates a similarity S1 between each known traffic scene (scene a, scene b, scene c, scene d) in known traffic scenario A and a surrounding traffic scene (scene 1) in surrounding traffic scenario II, determines the largest similarity S1 among the calculated similarities S1 as similarity Sm1, and identifies the known traffic scene having similarity Sm1 as scene m1. Scene m1 is, for example, scene a. The similarity determination unit 313, for example, calculates a similarity S1 between each known traffic scene (scene a, scene b, scene c, scene d) in known traffic scenario A and a surrounding traffic scene (scene 2) in surrounding traffic scenario II, determines the largest similarity S1 among the calculated similarities S1 as similarity Sm2, and identifies the known traffic scene having similarity Sm2 as scene m2. Scene m2 is, for example, scene b. The similarity determination unit 313 can calculate the similarity S2 using, for example, the similarity Sm1 and the similarity Sm2. For example, the similarity determination unit 313 can set the average value of the similarity Sm1 and the similarity Sm2 as the similarity S2. For example, the similarity determination unit 313 can set the similarity S2 as the similarity S of the surrounding traffic scenario II to the known traffic scene A. For example, the similarity determination unit 313 can calculate the similarity S2 for other known traffic scenarios using the same method as above, and set the calculated similarity S2 as the similarity S of the surrounding traffic scenario to the known traffic scene.

[0092] The similarity determination unit 313 is capable of determining that the known traffic scenario with the highest similarity S value among the multiple known traffic scenarios included in the knowledge graph DB 42 or the knowledge space DB 43 is the dangerous traffic scenario that the vehicle 1 (vehicle 100a) is likely to be facing. The similarity determination unit 313 is capable of storing the dangerous traffic scenario in the memory unit 40, and storing the similarity S of the dangerous traffic scenario (hereinafter referred to as the "maximum similarity Smax") in the memory unit 40 as the similarity 44.

[0093] The intensity setting unit 314 is capable of predicting the risk that the vehicle 1 (vehicle 100a) is likely to face based on the content of the result term (e.g., hazardous event) of the dangerous traffic scenario obtained by the judgment. The intensity setting unit 314 is capable of storing the content of the risk obtained by the prediction as a risk content 45 in the memory unit 40. The intensity setting unit 314 is further capable of calculating the degree of risk obtained by the prediction based on the magnitude of the maximum similarity Smax of the dangerous traffic scenario. The intensity setting unit 314 is capable of setting the calculated degree of risk as the intensity of at least one of the danger warning control and the danger avoidance control. The intensity setting unit 314 is capable of storing the set intensity as an intensity 46 in the memory unit 40. The intensity setting unit 314 is capable of outputting the risk content 45 and the intensity 46 to the driving control unit 32, which will be described later.

[0094] 1, the control unit 30 further includes a driving control unit 32. The driving control unit 32 is capable of controlling the driving of the vehicle 1 (for example, the torque of the motor 50, the amount of brake depression, and the steering angle of the steering wheel) and the notification to the driver of the vehicle 1. The driving control unit 32 is capable of performing driving control and notification control using data acquired by the data acquisition unit 311 (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals to various devices of the vehicle 1) and data acquired by the intensity setting unit 314 (hazard details 45 and intensity 46).

[0095] The driving control unit 32 is capable of calculating a correction torque for correcting a required torque to be applied to an accelerator control unit 321 (described later) based on the data acquired by the data acquisition unit 311 and the data (hazard type 45 and intensity 46) obtained by the intensity setting unit 314. The driving control unit 32 is capable of calculating a correction torque for correcting a required torque to be applied to a brake control unit 322 (described later) based on the data acquired by the data acquisition unit 311 and the data (hazard type 45 and intensity 46) obtained by the intensity setting unit 314. The driving control unit 32 is capable of calculating a correction torque for correcting a steering assist torque generated by a steering control unit 323 (described later) based on the data acquired by the data acquisition unit 311 and the data (hazard type 45 and intensity 46) obtained by the intensity setting unit 314. The driving control unit 32 is capable of generating notification data to alert the driver of the vehicle 1 based on the data acquired by the data acquisition unit 311 and the data (hazard content 45 and intensity 46) obtained by the intensity setting unit 314.

[0096] The driving control unit 32 includes, for example, an accelerator control unit 321, a brake control unit 322, a steering control unit 323, and a notification control unit 324, as shown in FIG.

[0097] Accelerator control unit 321 is capable of controlling the torque of prime mover 50 based on a required torque corresponding to the amount of accelerator pedal depression by the driver of vehicle 1. Accelerator control unit 321 is further capable of controlling the torque of prime mover 50 based on a target torque obtained by adding a correction torque to the required torque. Prime mover 50 is configured to drive the steered wheels of vehicle 1, and is capable of driving the steered wheels of vehicle 1 in accordance with the required torque or target torque input from accelerator control unit 321.

[0098] The brake control unit 322 is capable of controlling the torque of the brake 60 based on a required torque corresponding to the amount of brake pedal depression by the driver of the vehicle 1. The brake control unit 322 is further capable of controlling the torque of the brake 60 based on a target torque obtained by adding a correction torque to the required torque. The brake 60 is configured to brake the steered wheels of the vehicle 1, and is capable of braking the steered wheels of the vehicle 1 in accordance with the required torque or target torque input from the brake control unit 322.

[0099] The steering control unit 323 is capable of deriving a steering assist torque that assists the steering torque generated by the driver's steering wheel operation and setting an EPS torque corresponding to the derived steering assist torque. The steering control unit 323 is capable of outputting a control signal to the EPS motor 70 so that the output torque of the EPS motor 70 becomes the set EPS torque. The steering control unit 323 is capable of outputting a control signal to the EPS motor 70 so that the output torque of the EPS motor 70 becomes the EPS torque taking into account the correction torque. The EPS motor 70 generates an output torque based on the input control signal and is capable of controlling the steering angle of the steering wheel.

[0100] The accelerator control unit 321, the brake control unit 322, and the steering control unit 323 may be configured to include, for example, a CPU, etc. In this case, the accelerator control unit 321, the brake control unit 322, and the steering control unit 323 are able to perform the various driving controls described above by, for example, executing control software stored in a storage unit.

[0101] The notification control unit 324 is capable of outputting notification data to the notification unit 80 for notifying the driver of the vehicle 1. The notification control unit 324 is capable of, for example, generating a video signal including the notification data and outputting it to the notification unit 80. The notification control unit 324 is capable of, for example, generating an audio signal including the notification data and outputting it to the notification unit 80. The notification unit 80 is configured to include, for example, a display panel and a speaker. When the video signal is input to the notification unit 80 from, for example, the control unit 30 (notification control unit 324), the notification unit 80 is capable of displaying an image corresponding to the input video signal on a display screen. When the audio signal is input to the notification unit 80 from, for example, the control unit 30 (notification control unit 324), the notification unit 80 is capable of outputting audio corresponding to the input audio signal from the speaker.

[0102] (Driving Assistance Procedure) Next, a driving assistance procedure in the vehicle 1 will be described with reference to Fig. 19. Fig. 19 shows an example of a driving assistance procedure in the vehicle 1.

[0103] The vehicle 1 (risk prediction unit 31) first acquires various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1. Based on the acquired various data and various control signals, the risk prediction unit 31 acquires position information of the vehicle 1 and surrounding map data including the acquired position of the vehicle 1 from the road map data DB 41 in the storage unit 40. The risk prediction unit 31 also acquires stereo images Da or distance images Db from the stereo camera. The risk prediction unit 31 acquires attribute information and position information of each structure constituting the road around the vehicle 1 from the map data and the stereo images Da or distance images Db, etc. In this manner, the risk prediction unit 31 acquires the map data and situation data (step S101).

[0104] Next, the risk prediction unit 31 interprets the traffic context based on the acquired map data and situation data (step S102). The risk prediction unit 31 newly interprets the traffic context based on, for example, periodically acquired map data and situation data. The risk prediction unit 31 generates multiple time-series surrounding traffic scenes having a graph structure from the multiple traffic contexts thus obtained (step S103). Next, the risk prediction unit 31 integrates the generated time-series surrounding traffic scenes to create a surrounding traffic scenario (step S104).

[0105] Next, the risk prediction unit 31 calculates the similarity S between each known traffic scenario included in the knowledge space DB 43 and the created surrounding traffic scenario (step S105). The risk prediction unit 31 determines that the known traffic scenario with the highest similarity S among the multiple known traffic scenarios included in the knowledge space DB 43 is the dangerous traffic scenario that the vehicle 1 is likely to be facing (step S106). The risk prediction unit 31 stores the dangerous traffic scenario in the memory unit 40, and also stores the similarity S of the dangerous traffic scenario (hereinafter referred to as the "maximum similarity Smax") in the memory unit 40 as the similarity 44.

[0106] The risk prediction unit 31 predicts the risk that the vehicle 1 is likely to face based on the content of the result term of the dangerous traffic scenario obtained by the judgment (e.g., dangerous events). The risk prediction unit 31 stores the content of the risk obtained by the prediction as risk content 45 in the memory unit 40. The risk prediction unit 31 further calculates the degree of risk obtained by the prediction based on the magnitude of the maximum similarity Smax of the dangerous traffic scenario. The risk prediction unit 31 sets the calculated degree of risk as the strength of at least one of the risk warning control and the risk avoidance control (step S107). The risk prediction unit 31 outputs the risk content 45 and strength 46 to the driving control unit 32 (step S108).

[0107] The driving control unit 32 performs driving control and notification control using data acquired by the data acquisition unit 311 (various data acquired from the sensor unit 10, various data acquired from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1) and data acquired by the intensity setting unit 314 (hazard details 45 and intensity 46). The driving control unit 32 calculates a correction torque for correcting the required torque to be applied to the accelerator control unit 321, based on the data acquired by the data acquisition unit 311 and the data acquired by the intensity setting unit 314 (hazard details 45 and intensity 46). The driving control unit 32 calculates a correction torque for correcting the required torque to be applied to the brake control unit 322, based on the data acquired by the data acquisition unit 311 and the data acquired by the intensity setting unit 314 (hazard details 45 and intensity 46). The driving control unit 32 corrects the steering assist torque generated by the steering control unit 323 based on the data acquired by the data acquisition unit 311 and the data (hazard type 45 and hazard strength 46) obtained by the strength setting unit 314. The driving control unit 32 generates notification data for notifying the driver of the vehicle 1 based on the data acquired by the data acquisition unit 311 and the data (hazard type 45 and hazard strength 46) obtained by the strength setting unit 314.

[0108] The accelerator control unit 321 controls the torque of the prime mover 50 based on a target torque obtained by adding a correction torque to the required torque. The prime mover 50 drives the steered wheels of the vehicle 1 in accordance with the target torque input from the accelerator control unit 321. The brake control unit 322 controls the torque of the brake 60 based on the target torque obtained by adding a correction torque to the required torque. The brake 60 brakes the steered wheels of the vehicle 1 in accordance with the target torque input from the brake control unit 322. The steering control unit 323 outputs a control signal to the EPS motor 70 so that the output torque of the EPS motor 70 becomes EPS torque taking the correction torque into consideration. The EPS motor 70 generates an output torque based on the input control signal and controls the steering angle of the steering wheel.

[0109] The notification control unit 324 generates a video signal including the notification data and outputs it to the notification unit 80. The notification control unit 324 further generates an audio signal including the notification data and outputs it to the notification unit 80. When the video signal is input from, for example, the control unit 30 (notification control unit 324), the notification unit 80 displays an image corresponding to the input video signal on the display screen. When the audio signal is input from, for example, the control unit 30 (notification control unit 324), the notification unit 80 outputs audio corresponding to the input audio signal from a speaker. In this manner, driving assistance in the vehicle 1 is performed.

[0110] [Effects] Next, effects of the vehicle 1 according to the embodiment of the present disclosure will be described.

[0111] In this embodiment, the traffic context is interpreted based on the acquired map data and situation data, thereby generating multiple time-series surrounding traffic scenes, and the generated multiple time-series surrounding traffic scenes are integrated to create a surrounding traffic scenario. For each known traffic scenario included in the knowledge space DB 43, a similarity S with the surrounding traffic scenario is calculated, and among the multiple known traffic scenarios included in the knowledge space DB 43, the known traffic scenario with the highest similarity S is determined to be the dangerous traffic scenario that the vehicle 1 is likely to be facing. As a result, even if the observed surrounding traffic scenario is not included in the known traffic scenarios described in the knowledge space DB 43, logical inference can be performed using a known traffic scenario similar to the surrounding traffic scenario. Then, using the risk content 45 and risk intensity 46 obtained as a result of performing the logical inference, at least one of risk warning control and risk avoidance control can be performed. From the above, logical inference can be performed even if the observed traffic situation is not included in the traffic situations described in the knowledge graph DB 42 and the knowledge space DB 43.

[0112] In this embodiment, each traffic element, each known traffic scene, and each known traffic scenario is represented by a spatial vector in the knowledge space DB 43. This allows the calculation of the similarity S described above to be performed using the spatial vector, making it possible to derive a dangerous traffic scenario with a small amount of calculation.

[0113] In this embodiment, the surrounding traffic scenario and each of the surrounding traffic scenes are represented by a space vector common to the space vector in the knowledge space. A first vector value of each known traffic scene constituting the known traffic scenario is compared with a second vector value of each surrounding traffic scene constituting the surrounding traffic scenario to calculate a first similarity for each known traffic scene. A second similarity is calculated based on the calculated first similarity for each known traffic scene, and the calculated second similarity becomes the similarity S. This allows deriving a dangerous traffic scenario with a small amount of calculation.

[0114] In this embodiment, the first similarity is calculated by applying the linear norm, the quadratic norm, or the cosine similarity to the first vector value and the second vector value, thereby making it possible to derive a dangerous traffic scenario with a small amount of calculation.

[0115] 3. Modifications Next, modifications of the vehicle 1 according to the above embodiment will be described.

[0116] [Variation 3-1] In the above embodiment, the vehicle 1 may further include an importance 47 in the storage unit 40, as shown in FIG. 20, for example. The importance 47 defines the importance of known traffic scenes and traffic elements included in known traffic scenes, which contribute to the risk of a scenario, for each type of label. In the importance 47, as shown in FIG. 21, for example, an importance is associated with each type of label assigned to a node (entity). In the importance 47, an importance of, for example, 0.2 is assigned to an object type, an importance of, for example, 0.5 is assigned to a road structure, an importance of, for example, 0.1 is assigned to an object speed, and an importance of, for example, 0.2 is assigned to an object position.

[0117] In each known traffic scenario included in the knowledge graph DB 42, each node (entity) is assigned an importance value according to the definition of importance 47. Furthermore, in each known traffic scenario embedded (spatially expressed) in the knowledge space DB 43, each node (entity) is assigned an importance value according to the definition of importance 47. For example, as shown in FIG. 22 , in a known traffic scenario 43A embedded (spatially expressed) in the knowledge space DB 43, each entity is assigned an importance value according to the definition of importance 47.

[0118] In this modification, the similarity determination unit 313 can calculate the vector value of a known traffic scene based on the vector value of each traffic element constituting the known traffic scene and the importance assigned to each traffic element or each known traffic scene. For example, the similarity determination unit 313 can calculate the vector value of a known traffic scene by a weighted average using the importance.

[0119] For example, in scene a of known traffic scenario I, assume that the vector value of traffic element a1 is [1,1,1], the vector value of traffic element a2 is [2,2,2], the vector value of traffic element a3 is [3,3,3], and the vector value of traffic element a4 is [4,4,4]. Furthermore, for example, in scene a of known traffic scenario I, if the label of traffic element a1 is SbjCar, the importance of traffic element a1 is the importance of object type (e.g., 0.2), if the label of traffic element a2 is SubjectLane, the importance of traffic element a2 is the importance of road structure (e.g., 0.5), if the label of traffic element a3 is SbjCar1, the importance of traffic element a3 is the importance of road structure (e.g., 0.2), and if the label of traffic element a4 is SbjCar2, the importance of traffic element a4 is the importance of road structure (e.g., 0.2). At this time, the similarity determination unit 313 can calculate the vector value [Gx, Gy, Gz] of scene a of known traffic scenario I, for example, by the following calculation.

[0120] Gx = (1 x 0.2 + 2 x 0.5 + 3 x 0.2 + 4 x 0.2) / (0.2 + 0.5 + 0.2 + 0.2) = 2.6 Gy = (1 x 0.2 + 2 x 0.5 + 3 x 0.2 + 4 x 0.2) / (0.2 + 0.5 + 0.2 + 0.2) = 2.6 Gz=(1×0.2+2×0.5+3×0.2+4×0.2) / (0.2+0.5+0.2+0.2)=2.6

[0121] In this way, in this modification, the vector value of a known traffic scene is calculated by a weighted average using the importance, which allows the vector value to be set according to the risk of the known traffic scenario, thereby enabling at least one of risk warning control and risk avoidance control to be performed according to the actual risk level.

[0122] [Variation 3-2] In Variation 3-1, the importance level was a predetermined value. However, in Variation 3-1, the similarity determination unit 313 may be able to update the importance level assigned to each traffic element or each known traffic scene based on the map data and situation data acquired by the data acquisition unit 311. In this case, a vector value according to the actual risk level can be set, making it possible to perform at least one of risk warning control and risk avoidance control according to the actual risk level.

[0123] [Variation 3-3] In the above-described variation 3-2, when another vehicle is present in the situation data acquired by the data acquisition unit 311, the similarity determination unit 313 may be capable of updating the importance assigned to each traffic element or each known traffic scene based on the time to collision (TTC (Time To Collision)).

[0124] The similarity determination unit 313 may be capable of updating the importance related to other vehicles among the multiple importance levels in the knowledge graph DB 42 or the knowledge space DB 43 to a larger value when the priority (first priority) of other vehicles in terms of time to collision (TTC) as seen from vehicle 1 and the priority (second priority) of vehicle 1 in terms of time to collision (TTC) as seen from the other vehicles are different from each other.

[0125] Assume that the priority (first priority) of another vehicle in terms of time to collision (TTC) from the perspective of vehicle 1 is lower than the priority (second priority) of vehicle 1 in terms of time to collision (TTC) from the perspective of the other vehicle. In this case, the driver of the other vehicle pays relatively less attention to vehicle 1 than the driver of vehicle 1 pays to the other vehicle. As a result, the driver of the other vehicle may be late in noticing the presence of vehicle 1, which may result in the other vehicle colliding with vehicle 1.

[0126] On the other hand, it is assumed that the priority (first priority) of the other vehicle in terms of time to collision (TTC) as seen from vehicle 1 matches the priority (second priority) of vehicle 1 in terms of time to collision (TTC) as seen from the other vehicle. In this case, the attention of the driver of the other vehicle to vehicle 1 is roughly equal to the attention of the driver of vehicle 1 to the other vehicle. As a result, the driver of vehicle 1 drives while paying attention to the movements of the other vehicle, and the driver of the other vehicle drives while paying attention to the movements of vehicle 1, so there is a low possibility that vehicle 1 and the other vehicle will come into contact with each other.

[0127] 23, in a certain scene, the priority of vehicle 100e is first from the viewpoint of time to collision (TTC) as seen from vehicle 1 (vehicle 100a), and the priority of vehicle 1 (vehicle 100a) is third from the viewpoint of time to collision (TTC) as seen from vehicle 100e. In this case, the priorities of both vehicles are different from each other, so the similarity determination unit 313 can update the importance related to vehicle 100e to a larger value among the multiple importance levels in the knowledge graph DB 42 or the knowledge space DB 43.

[0128] In this way, in this modification, based on the priority in terms of time to collision (TTC), the importance related to other vehicles is updated among the multiple importance levels in the knowledge graph DB 42 or knowledge space DB 43. This allows a vector value to be set according to the actual risk level, making it possible to perform at least one of risk warning control and risk avoidance control according to the actual risk level.

[0129] [Variation 3-4] In the above-described variation 3-2, the vehicle 1 may further include a risk knowledge graph DB 48 and a risk knowledge space DB 49 in the storage unit 40, as shown in, for example, FIG. 24. The risk knowledge graph DB 48 includes knowledge data that describes, in a graph structure, a plurality of specific traffic scenes in time series that have a high accident occurrence frequency, among the plurality of specific traffic scenes included in the knowledge graph DB 42. The risk knowledge space DB 49 includes knowledge data (for example, knowledge data expressed as spatial vectors) that spatially represents a plurality of specific traffic scenes in time series that have a high accident occurrence frequency, among the plurality of specific traffic scenes included in the knowledge graph DB 43.

[0130] The similarity determination unit 313 is capable of determining whether the plurality of time-series surrounding traffic scenes includes a plurality of known time-series traffic scenes included in the risk knowledge graph DB 48 or the risk knowledge space DB 49. If the determination result shows that the plurality of time-series surrounding traffic scenes includes a plurality of known time-series traffic scenes (hereinafter referred to as "specific traffic scenes") included in the risk knowledge graph DB 48 or the risk knowledge space DB 49, the similarity determination unit 313 is capable of updating the importance assigned to each traffic element constituting each surrounding traffic scene corresponding to the plurality of specific traffic scenes in time series, from among the plurality of importance levels in the knowledge graph DB 42 or the knowledge graph DB 43, to a larger value.

[0131] In this modification, when a plurality of specific traffic scenes in a time series with a high accident frequency are included among a plurality of surrounding traffic scenes in a time series, the importance level assigned to each traffic element constituting each surrounding traffic scene corresponding to the plurality of specific traffic scenes in the time series is updated to a larger value among the plurality of importance levels in the knowledge graph DB 42 or the knowledge graph DB 43. This makes it possible to set a vector value according to the actual risk level, thereby making it possible to perform at least one of risk warning control and risk avoidance control according to the actual risk level.

[0132] [Variation 3-5] In the above-described variation 3-2, an edge (relation) relating two chronologically consecutive traffic scenes among the known traffic scenarios included in the knowledge space DB 43 may be expressed as a matrix. For example, as shown in Fig. 25, in a known traffic scenario I included in the knowledge space DB 43, the relation relating the known traffic scene (scene a) to the known traffic scene (scene b) may be expressed as a matrix A, the relation relating the known traffic scene (scene b) to the known traffic scene (scene c) may be expressed as a matrix B, and the relation relating the known traffic scene (scene c) to the known traffic scene (scene d) may be expressed as a matrix C.

[0133] Matrices A, B, and C have values ​​that satisfy the following relational expressions. That is, by using matrix A, the vector value of scene b can be obtained. By using matrix B, the vector value of scene c can be obtained. By using matrix C, the vector value of scene d can be obtained. For example, the scenario creation unit 312 may use matrix A to predict the vector value of the scene (e.g., scene 2) that follows the scene (e.g., scene 1) that is most similar to scene m1 (e.g., scene a) in surrounding traffic scenario II. For example, the scenario creation unit 312 may use matrix B to predict the vector value of the scene (e.g., scene 3) that follows the scene (e.g., scene 2) that is most similar to scene m2 (e.g., scene b) in surrounding traffic scenario II.

[0134] Vector value of scene b = matrix A * vector value of scene a Vector value of scene c = matrix B * vector value of scene b Vector value of scene d = matrix C * vector value of scene a Vector value of scene 2 (predicted value) = matrix A * vector value of scene 1 Vector value of scene 3 (predicted value) = matrix B * vector value of scene 2

[0135] In this modification, in the knowledge graph 43A, the relation associating a known traffic scene (scene a) with a known traffic scene (scene b) is represented by a matrix A, as shown in FIG. 26 . In this way, by representing the edge (relation) associating two chronologically consecutive traffic scenes among the known traffic scenarios contained in the knowledge space DB 43 as a matrix, future scenes can be predicted. As a result, future scenes can be predicted and at least one of danger warning control and danger avoidance control can be performed.

[0136] [Variation 3-6] In the above-described variation 3-2, the similarity determination unit 313 may be configured to analyze the plurality of time-series situation data or the plurality of time-series surrounding traffic scenes acquired by the data acquisition unit 311, and detect the presence or absence of a shielded space that is not visible to the driver of the vehicle 1. When the similarity determination unit 313 detects a shielded space that is not visible to the driver of the vehicle 1, and when the plurality of surrounding traffic scenes or the plurality of traffic elements constituting each surrounding traffic scene include a surrounding traffic scene or traffic element in the shielded space, the similarity determination unit 313 is capable of updating the importance assigned to the surrounding traffic scene or traffic element in the shielded space, among the plurality of importance levels in the knowledge graph DB 42 or the knowledge space DB 43, to a larger value.

[0137] For example, the similarity determination unit 313 may detect that the vehicle 100e is located in a blind spot area BA (obstructed space) as a result of analyzing two time-series surrounding traffic scenes obtained from surrounding traffic conditions X and Y. In this case, the similarity determination unit 313 may update the importance assigned to the ID (16, 17) related to the vehicle 100e in the knowledge graph 43A to a value that is doubled, as shown in Figures 27(A) and 27(B).

[0138] In this way, in this modification, the importance level assigned to the surrounding traffic scene or traffic hazard in the shielded space is updated to a larger value among the multiple importance levels in the knowledge space DB 43. This makes it possible to perform at least one of danger notification control and danger avoidance control according to the actual danger level.

[0139] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0140] Furthermore, for example, the present disclosure can be configured as follows. (1) A vehicle navigation system includes a storage unit that stores a knowledge space that spatially represents a plurality of known traffic scenarios, each of which is composed of a plurality of integrated time-series known traffic scenes; a data acquisition unit that can acquire map data and situation data around a vehicle; and a processing unit that can perform information processing using the plurality of known traffic scenarios read from the storage unit and the map data and situation data acquired by the acquisition unit, wherein the processing unit: interprets a traffic context based on the map data and the situation data acquired by the data acquisition unit, thereby generating a plurality of time-series surrounding traffic scenes, and creates a surrounding traffic scenario by integrating the generated time-series surrounding traffic scenes; calculates a similarity between each of the known traffic scenarios included in the knowledge space and the surrounding traffic scenario created by the creation unit, and determines that the known traffic scenario with the highest similarity among the plurality of known traffic scenarios included in the knowledge space is a dangerous traffic scenario that the vehicle is likely to encounter; and sets the strength of at least one of danger warning control and danger avoidance control according to the magnitude of the similarity of the dangerous traffic scenario. and outputting the intensity. (2) The information processing device according to (1), wherein each of the known traffic scenes is composed of a plurality of traffic elements, and each of the traffic elements, each of the known traffic scenes, and each of the known traffic scenarios is represented by a spatial vector in the knowledge space. (3) The information processing device according to (2), wherein the surrounding traffic scenario and each of the surrounding traffic scenes are represented by a spatial vector common to the spatial vector in the knowledge space, and the processing unit is capable of calculating a first similarity for each of the known traffic scenes by comparing a first vector value of each of the known traffic scenes constituting the known traffic scenario with a second vector value of each of the surrounding traffic scenes constituting the surrounding traffic scenario, calculating a second similarity based on the calculated first similarity for each of the known traffic scenes, and setting the calculated second similarity as the similarity.(4) The information processing device according to (3), wherein the processing unit is capable of calculating the first similarity by applying a linear norm, a quadratic norm, or a cosine similarity to the first vector value and the second vector value. (5) The information processing device according to (3), wherein the storage unit further stores knowledge data describing the plurality of known traffic scenarios in a graph structure, wherein an importance is assigned to each of the traffic elements or each of the known traffic scenes in the knowledge data or the knowledge space, and wherein the processing unit is capable of calculating the first vector value based on the vector value of each of the traffic elements constituting the known traffic scene and the importance assigned to each of the traffic elements or each of the known traffic scenes. (6) The information processing device according to (5), wherein the processing unit is capable of updating the importance in the knowledge data or the knowledge space based on the map data and the situation data acquired by the acquisition unit. (7) The information processing device according to (5), wherein, when another vehicle is present in the situation data, and a priority (first priority) of the other vehicle in terms of time to collision as seen from the vehicle and a priority (second priority) of the vehicle in terms of time to collision as seen from the other vehicle are different from each other, the processing unit is capable of updating the importance related to the other vehicle among the plurality of importances in the knowledge data or the knowledge space to a larger value. (8) The information processing device according to (5), wherein, when the plurality of time-series surrounding traffic scenes includes a plurality of specific traffic scenes in time series with a high frequency of accidents, the processing unit is capable of updating the importance assigned to each of the surrounding traffic scenes corresponding to the plurality of specific traffic scenes or to each of the traffic elements constituting each of the surrounding traffic scenes corresponding to the plurality of specific traffic scenes to a larger value among the plurality of importances in the knowledge data or the knowledge space.(9) The information processing device according to (5), wherein the processing unit is capable of updating the importance assigned to the surrounding traffic scene or the traffic element in a shielded space not visible from the vehicle, among the plurality of importance levels in the knowledge data or the knowledge space, to a larger value when the plurality of surrounding traffic scenes or the plurality of traffic elements constituting each of the surrounding traffic scenes includes the surrounding traffic scene or the traffic element in a shielded space not visible from the vehicle. (10) A vehicle control device comprising: an intensity acquisition unit capable of acquiring the intensity from the information processing device according to any one of (1) to (9), and a control unit capable of performing at least one of danger notification control and danger avoidance control according to the magnitude of the intensity acquired by the acquisition unit. (11) A vehicle comprising: an alarm device and a controlled device; and a control unit capable of performing at least one of danger alarm control for the alarm device and danger avoidance control for the controlled device according to the magnitude of the intensity acquired from the information processing device according to any one of (1) to (9).

[0141] The control unit 30 shown in FIGS. 1, 20, and 24 may 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 may be configured to perform all or a portion of the various functions of the control unit 30 shown in FIGS. 1, 20, and 24 by reading instructions from at least one non-transitory, tangible computer-readable medium. Such medium may 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 nonvolatile memories. Volatile memories may include DRAM and SRAM. Non-volatile memories may include ROM and NVRAM. An ASIC is an integrated circuit (IC) specialized to perform all or a portion of the various functions of the control unit 30 shown in FIGS. 1, 20, and 24. An FPGA is an integrated circuit that is designed to be configurable after manufacture to perform all or part of the various functions of the control unit 30 shown in FIGS.

Claims

1. A vehicle navigation system comprising: a memory unit that stores a knowledge space that spatially represents a plurality of known traffic scenarios, each of which is composed of a plurality of integrated time-series known traffic scenes; a data acquisition unit that can acquire map data and situation data around a vehicle; and a processing unit that can perform information processing using the plurality of known traffic scenarios read from the memory unit and the map data and situation data acquired by the data acquisition unit, wherein the processing unit: interprets a traffic context based on the map data and the situation data acquired by the data acquisition unit, thereby generating a plurality of time-series surrounding traffic scenes, and creates a surrounding traffic scenario by integrating the generated time-series surrounding traffic scenes; calculates, for each of the known traffic scenarios included in the knowledge space, a similarity between the known traffic scenario included in the knowledge space and the surrounding traffic scenario created by the creation unit, and determines that the known traffic scenario with the highest similarity among the plurality of known traffic scenarios included in the knowledge space is a dangerous traffic scenario that the vehicle is likely to encounter; and sets the strength of at least one of danger warning control and danger avoidance control according to the magnitude of the similarity of the dangerous traffic scenario. and outputting the intensity.

2. The information processing device according to claim 1, wherein each of the known traffic scenes is composed of a plurality of traffic elements, and each of the traffic elements, each of the known traffic scenes, and each of the known traffic scenarios is represented by a spatial vector in the knowledge space.

3. The information processing device according to claim 2, wherein the surrounding traffic scenario and each of the surrounding traffic scenes are represented by a spatial vector common to the spatial vector in the knowledge space, and the processing unit is capable of calculating a first similarity for each of the known traffic scenes by comparing a first vector value of each of the known traffic scenes constituting the known traffic scenario with a second vector value of each of the surrounding traffic scenes constituting the surrounding traffic scenario, calculating a second similarity based on the calculated first similarity for each of the known traffic scenes, and setting the calculated second similarity as the similarity.

4. The information processing device according to claim 3, wherein the processing unit is capable of calculating the first similarity by applying a first-order norm, a second-order norm, or a cosine similarity to the first vector value and the second vector value.

5. The information processing device according to claim 3, wherein the memory unit further stores knowledge data describing the plurality of known traffic scenarios in a graph structure, and in the knowledge data or the knowledge space, an importance is assigned to each of the traffic elements or each of the known traffic scenes, and the processing unit is capable of calculating the first vector value based on the vector value of each of the traffic elements constituting the known traffic scene and the importance assigned to each of the traffic elements or each of the known traffic scenes.

6. The information processing device according to claim 5, wherein the processing unit is capable of updating the importance of the knowledge data or the importance of the knowledge space based on the map data and the situation data acquired by the acquisition unit.

7. The information processing device of claim 5, wherein when another vehicle is present in the situation data, if the priority (first priority) of the other vehicle in terms of the time to collision as seen from the vehicle and the priority (second priority) of the vehicle in terms of the time to collision as seen from the other vehicle are different from each other, the processing unit is capable of updating the importance related to the other vehicle among the multiple importances in the knowledge data or the knowledge space to a larger value.

8. The information processing device according to claim 5, wherein, when the plurality of surrounding traffic scenes in the time series include a plurality of specific traffic scenes in the time series with a high frequency of accidents, the processing unit is capable of updating the importance levels in the knowledge data or the knowledge space assigned to each of the surrounding traffic scenes corresponding to the plurality of specific traffic scenes, or to each of the traffic elements constituting each of the surrounding traffic scenes corresponding to the plurality of specific traffic scenes, to a larger value.

9. The information processing device according to claim 5, wherein the processing unit is capable of updating the importance assigned to the surrounding traffic scene or traffic element in a shielded space that is not visible from the vehicle, among the multiple surrounding traffic scenes or the multiple traffic elements that make up each surrounding traffic scene, to a larger value, among the multiple importance levels in the knowledge data or the knowledge space.

10. A vehicle control device comprising: an intensity acquisition unit capable of acquiring the intensity from the information processing device described in any one of claims 1 to 9; and a control unit capable of performing at least one of danger warning control and danger avoidance control in accordance with the magnitude of the intensity acquired by the acquisition unit.

11. A vehicle comprising: an alarm device and a controlled device; and a control unit capable of performing at least one of danger alarm control for the alarm device and danger avoidance control for the controlled device, in accordance with the magnitude of the intensity obtained from the information processing device described in any one of claims 1 to 9.

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