Information processing device, vehicle control device, and vehicle

Abstract

An information processing device according to one embodiment of the present disclosure is capable of performing the following four steps. (1) Interpreting a traffic context on the basis of map data and situation data acquired by a 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) On the basis of the time difference between a first surrounding traffic scene and a second surrounding traffic scene that are consecutive in time series in the created surrounding traffic scenario, calculating a correction value for correcting a risk level which is associated with a similar traffic scenario or with each known traffic scene constituting the similar traffic scenario, the similar traffic scenario being a known traffic scenario having high similarity with respect to the surrounding traffic scenario among a plurality of known traffic scenarios included in a knowledge space. (3) Setting the intensity of hazard notification control or hazard avoidance control on the basis of the risk level and the correction value. (4) Outputting the intensity.

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 four functions: (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 time-series surrounding traffic scenes to create a surrounding traffic scenario. (2) Based on the time difference between the first surrounding traffic scene and the second surrounding traffic scene, which are consecutive in time series in the created surrounding traffic scenario, calculating a similar traffic scenario, which is a known traffic scenario that is highly similar to the surrounding traffic scenario among the plurality of known traffic scenarios included in the knowledge space, or a correction value for correcting the risk level associated with each known traffic scene that constitutes the similar traffic scenario. (3) Setting the strength of danger warning control or danger avoidance control based on the risk level and the correction value. (4) Outputting the strength.

[0005] A vehicle control device relating to a second aspect of the present disclosure includes an intensity acquisition unit capable of acquiring 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 intensity 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, depending 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 importance levels in FIG. 1. FIG. 3 is a diagram illustrating an example of the concept of a known traffic scenario. FIG. 4 is a diagram illustrating an example of a knowledge graph embedded in a knowledge space. FIG. 5 is a diagram illustrating an example of a known traffic situation A. FIG. 6 is a diagram illustrating an example of a traffic context for the known traffic situation A in FIG. 5. FIG. 7 is a diagram illustrating an example of a known traffic situation B. FIG. 8 is a diagram illustrating an example of a traffic context for the known traffic situation B in FIG. 7. FIG. 9 is a diagram illustrating an example of a known traffic situation C. FIG. 10 is a diagram illustrating an example of a traffic context for the known traffic situation C in FIG. 9. FIG. 11 is a diagram illustrating an example of a known traffic situation D. FIG. 12 is a diagram illustrating an example of a traffic context for the known traffic situation D in FIG. 11. FIG. 13 is a diagram illustrating an example of a surrounding traffic situation X. FIG. 14 is a diagram illustrating an example of a traffic context for the surrounding traffic situation X in FIG. 13. FIG. 15 is a diagram illustrating an example of a surrounding traffic situation Y. FIG. 16 is a diagram showing an example of the traffic context of the surrounding traffic situation Y in FIG. 15 . FIG. 17 is a diagram showing an example of the concept of a surrounding traffic scenario. FIG. 18 is a diagram showing an example of vector values ​​of a surrounding traffic scene (scene 1) included in the surrounding traffic scenario and an example of vector values ​​of multiple traffic elements included in the surrounding traffic scene (scene 1). FIG. 19 is a diagram showing an example of vector values ​​of a surrounding traffic scene (scene 2) included in the surrounding traffic scenario and an example of vector values ​​of multiple traffic elements included in the surrounding traffic scene (scene 2). FIG. 20 is a diagram showing an example of a driving assistance procedure for the vehicle of FIG. 1 .

[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> Driving assistance methods are known that combine knowledge data such as ontologies and knowledge graphs with logical inference to estimate dangerous events and warn the driver. In such driving assistance methods, 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. Therefore, after extensive research, the inventors of the present application have come up with a technology that enables logical inference to work even when the observed traffic situation is not included in the traffic situation described in the knowledge data.

[0011] It is also conceivable to predefine in the knowledge data a "risk level" indicating the risk of the traffic conditions described in the knowledge data, thereby performing notification control and driving control according to the defined risk level. However, in this case, as described above, if the observed traffic conditions are not included in the traffic conditions described in the knowledge data and logical inference does not work, notification control and driving control according to the defined risk level cannot be performed. Therefore, after extensive research, the inventors of the present application have come up with a technology that enables notification control and driving control according to the risk of the traffic conditions even when the observed traffic conditions are not included in the traffic conditions described in the knowledge data. The following describes in detail an information processing device, a vehicle control device, and a vehicle for realizing this technology.

[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, 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, or a resistance-change memory. As shown in FIG. 1, the storage unit 40 stores, for example, a road map DB 41, an importance DB 42, a knowledge graph DB 43, and a knowledge space DB 44. As shown in FIG. 1, the storage unit 40 stores, for example, data (risk content 45, risk level 46, correction value 47, and strength 48) obtained by calculations performed by the risk 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 importance 42 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 42, for example, as shown in FIG. 2, an importance is assigned to each type of label assigned to a node (entity). In the importance 42, 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.

[0031] The knowledge graph DB 43 and knowledge space DB 44 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.

[0032] The knowledge graph DB 43 stores knowledge data in which multiple known traffic scenarios are described 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 constantly changing traffic conditions. In the knowledge graph DB 43, 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.

[0033] 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.

[0034] The traffic elements may further include, for example, a transition time between scenes or a risk level indicating the risk of a scene. The transition time between scenes refers to the difference between the time of a first scene and the time of a second scene in two consecutive scenes (a first scene and a second scene) over time. The risk level indicating the risk of a scene refers to an index indicating the possibility of future interference (contact or collision) between a main vehicle and a traffic participant (another vehicle, a motorcycle, a bicycle, or a pedestrian) that is being monitored from the main vehicle's perspective in a certain scene. The risk level is defined, for example, by the energy at the time of interference (contact or collision) or statistical frequency.

[0035] A certain known traffic scenario in the knowledge graph DB 43 (hereinafter referred to as "known traffic scenario I") includes, for example, a plurality of known traffic scenes (scene a, scene b, scene c, and scene d) in an integrated time series, as shown in FIG. 3 . 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. 3 , 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. 3 , 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. 3. A known traffic scene (scene d) includes a plurality of traffic elements (traffic elements d1, d2, d3, d4, etc.) as shown in Fig. 3.

[0036] In the knowledge graph DB 43, 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 a 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).

[0037] The knowledge space DB 44 has knowledge data that spatially represents multiple known traffic scenarios contained in the knowledge graph DB 43. The knowledge space DB 44 has a knowledge graph 44A that represents known traffic scenarios I contained in the knowledge graph DB 43 using spatial vectors, as shown in FIG. 4, for example. The knowledge graph 44A includes multiple identifiers (IDs), as shown in FIG. 4, 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.

[0038] 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.

[0039] 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)

[0040] 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)

[0041] The labels indicating the object speed include, for example, the following labels: A label indicating a constant speed (Constant) A label indicating deceleration (Deceleration)

[0042] 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)

[0043] In the knowledge space DB 44, 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 43. Examples of the "embedding algorithm" include TransE and RESCAL. Here, the numerical expression is, for example, a space vector as shown in FIG. 4. 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."

[0044] For example, in a set (subject, predicate, object) of (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.

[0045] 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.

[0046] The knowledge space DB 44 may include a set of (subject, predicate, object) indicating the transition time of a certain known traffic scene, a set of (subject, predicate, object) indicating the risk level of a certain known traffic scene, and a set of (subject, predicate, object) indicating the risk level of a certain known traffic scenario. Figure 4 shows examples of (subject, predicate, object) sets indicating the transition time of a certain known traffic scene, such as (Scene a, HasTime, 0 sec) indicating the transition time of a known traffic scene (Scene a) and (Scene c, HasTime, 2 sec) indicating the transition time of a known traffic scene (Scene b). "Scene a, HasTime, 0 sec" means that "the transition time of Scene a is 0 seconds." "Scene c, HasTime, 2 sec" means that "the transition time of scene c is 2 seconds."

[0047] 4 also shows, as examples of sets (subject, predicate, object) indicating the risk level of a certain known traffic scene, (Scene a, HasRisk, RiskLevel_a) indicating the risk level of a known traffic scene (Scene a) and (Scene c, HasRisk, RiskLevel_c) indicating the risk level of a known traffic scene (Scene c). Also, FIG. 4 shows, as an example of a set (subject, predicate, object) indicating the risk level of a certain known traffic scenario, (Scenario I, HasRisk, RiskLevel_I) indicating the risk level of a known traffic scenario I. "Scene a, HasRisk, RiskLevel_a" means that "the risk level of scene a is RiskLevel_a." "Scene c, HasRisk, RiskLevel_c" means that "the risk level of Scene c is RiskLevel_c." "Scenario I, HasRisk, RiskLevel_I" means that "the risk level of Scenario I is RiskLevel_I." Note that RiskLevel_a, RiskLevel_c, and RiskLevel_I are actually expressed as specific numerical values.

[0048] 5 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 43 is generated based on a traffic context (see FIG. 6) obtained by interpreting data acquired by a data acquisition unit 311 (described later) in the known traffic situation A shown in FIG. 5 (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).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Fig. 6 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. 6.

[0053] "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."

[0054] 7 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, the traffic situation one second after known traffic situation A. A known traffic scene (scene b) in the knowledge graph DB 43 is generated based on a traffic context (see FIG. 8) 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 B shown in FIG. 7, for example.

[0055] 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.

[0056] Fig. 8 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. 8.

[0057] "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."

[0058] "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."

[0059] 9 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 43 is generated based on a traffic context (see FIG. 10) obtained by interpreting data acquired by a data acquisition unit 311 (described later) in known traffic situation C shown in FIG. 9 (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).

[0060] 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.

[0061] 10 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.

[0062] "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."

[0063] 11 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 43 is generated based on a traffic context (see FIG. 12) 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 the known traffic situation D shown in FIG. 11, for example.

[0064] 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.

[0065] 12 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.

[0066] "Scene, time, 3sec" means that "the transition time of scene c 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."

[0067] 1, the storage unit 40 stores data (e.g., danger content 45, danger level 46, correction value 47, and intensity 48) obtained as a result of the calculation process in the danger prediction unit 31. The danger content 45, danger level 46, correction value 47, and intensity 48 will be described in detail later.

[0068] 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.

[0069] 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).

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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 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 prediction results (risk content 45, risk level 46, correction value 47, and strength 48) 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.

[0075] 1 , the risk prediction unit 31 includes a data acquisition unit 311, a scenario creation unit 312, a similarity determination unit 313, a risk correction unit 314, and an intensity setting unit 315. 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, the risk correction unit 314, and the intensity setting unit 315 correspond to a specific example of a "control unit" according to an embodiment of the present disclosure.

[0076] 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.

[0077] The scenario creation unit 312 , the risk correction unit 313 , and the intensity setting unit 314 are capable of performing information processing using the data acquired by the data acquisition unit 311 .

[0078] 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. 13. 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. 14, for example.

[0079] 14, "Scene1, HasTime, 0 sec" 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."

[0080] The scenario creation unit 312 is capable of determining whether a change has occurred in the surrounding traffic conditions, for example, based on the map data and situation data acquired by the data acquisition unit 311 periodically (for example, every 0.1 seconds) from the surrounding traffic conditions X. The scenario creation unit 312 is capable of generating a new traffic context, for example, when it determines, as a result of the determination, that a change has occurred in the surrounding traffic conditions. The scenario creation unit 312 is capable of interpreting a new traffic context, for example, based on the map data and situation data acquired by the data acquisition unit 311 0.5 seconds after the surrounding traffic conditions X.

[0081] Assume that, for example, as shown in FIG. 15 , 0.5 seconds after the vehicle 1 (vehicle 100a) is in a traffic condition different from the surrounding traffic condition X (hereinafter referred to as "surrounding traffic condition Y"). 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 condition 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. 16 , for example.

[0082] In FIG. 16 , "Scene2, HasTime, 0.5sec" means that "the transition time of scene 2 is 0.5 seconds." "SbjCar, EnterIn, noSignalIntersection" means that "vehicle 100a is entering an intersection IS without a traffic light." "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, EnterIn, noSignalIntersection" means that "vehicle 100e is entering an intersection IS without a traffic light."

[0083] 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 generated two time-series surrounding traffic scenes (scene 1 and scene 2) to create surrounding traffic scenario II, for example, as shown in FIG. 17 .

[0084] The surrounding traffic scenario II includes a plurality of integrated time-series surrounding traffic scenes (scene 1, scene 2), as shown in FIG. 17 . 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. 17 , 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. 17 , the surrounding traffic scene (scene 2) includes a plurality of traffic elements (traffic elements β1, β2, β3, β4, etc.).

[0085] 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).

[0086] 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 44, 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.). The scenario creation unit 312 is capable of, for example, reading out from the knowledge space DB 44 a plurality of vector values ​​([1, 0, 1], [2, 2, 2], [3, 3, 3], [4, 3, 4], etc.) as shown in FIG. 18 . 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). For example, as shown in Figure 18, the scenario creation unit 312 can calculate the center of gravity vector ([2.5, 2.0, 2.5]) of the multiple vector values ​​([1, 0, 1], [2, 2, 2], [3, 3, 3], [4, 3, 4], etc.) that have been read out, and use the calculated center of gravity vector ([2.5, 2.0, 2.5]) as the spatial vector V3 corresponding to the surrounding traffic scene (scene 1).

[0087] The scenario creation unit 312 is capable of, for example, reading out, for each traffic element (e.g., β1, β2, β3, β4, etc.) included in the surrounding traffic scene (scene 2) from the knowledge graph 43A, etc. included in the knowledge space DB 44, a numerical representation (e.g., a spatial vector V4) associated with the same label as the label of each traffic element (e.g., β1, β2, β3, β4, etc.). The scenario creation unit 312 is capable of, for example, reading out from the knowledge space DB 44 a plurality of vector values ​​([1, 1, 1], [2, 3, 2], [3, 4, 3], [4, 4, 4], etc.) as shown in FIG. 19 . The scenario creation unit 312 is capable of, for example, calculating a new numerical representation (e.g., a spatial vector V5) based on the plurality of read numerical representations (e.g., a spatial vector V4) and setting the newly calculated numerical representation (e.g., a spatial vector V5) as a numerical representation (e.g., a spatial vector V6) corresponding to the surrounding traffic scene (scene 2). 19, the scenario creation unit 312 can calculate the centroid vector ([2.5, 3.0, 2.5]) of the read multiple vector values ​​([1, 1, 1], [2, 3, 2], [3, 4, 3], [4, 4, 4], etc.) and use the calculated centroid vector ([2.5, 3.0, 2.5]) as the space vector V6 corresponding to the surrounding traffic scene (scene 2). The surrounding traffic scenes (scene 1, scene 2) are expressed by the same space vectors as those in the knowledge space DB 44.

[0088] 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 43 or the knowledge space DB 44. The similarity S corresponds to a specific example of “similarity” according to an embodiment of the present disclosure.

[0089] The similarity determination unit 313 is capable of reading, for example, from the knowledge space DB 44, 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 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.

[0090] The similarity determination unit 313 may be capable of calculating a 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. The similarity determination unit 313 may be capable of calculating a vector value of a known traffic scene using the importance when a vector value is not assigned to the known traffic scene in the knowledge space DB 44. The similarity determination unit 313 may be capable of calculating a vector value of a known traffic scene taking into account the risk of the known traffic scenario using the importance even when a vector value is assigned to the known traffic scene in the knowledge space DB 44.

[0091] The similarity determination unit 313 may be capable of calculating the vector values ​​of the known traffic scenes by, for example, a weighted average using the importance. For example, in scene a of known traffic scenario I, 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 traffic element a2 is labeled SbjCar, the importance of traffic element a1 is the importance of the object type (e.g., 0.2), if the traffic element a2 is labeled SubjectLane, the importance of traffic element a2 is the importance of the road structure (e.g., 0.5), if the traffic element a3 is labeled SbjCar1, the importance of traffic element a3 is the importance of the road structure (e.g., 0.2), and if the traffic element a4 is labeled SbjCar2, the importance of traffic element a4 is the importance of the road structure (e.g., 0.2). In this case, the similarity determination unit 313 may be capable of calculating the vector value [Gx, Gy, Gz] of scene a of known traffic scenario I, for example, by the following calculation.

[0092] 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

[0093] The similarity determination unit 313 may be capable of, for example, converting the vector values ​​obtained using the numerical representations (e.g., spatial vector values) of each traffic element of each known traffic scene (scene a, scene b, scene c, scene d) and the above-mentioned importance into numerical representations (e.g., spatial vectors Va, Vb, Vc, Vd) of each known traffic scene (scene a, scene b, scene c, scene d) that constitute the known traffic scenario A.

[0094] The similarity determination unit 313 is capable of calculating the 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 44 with the numerical representation of each surrounding traffic scene created by the scenario creation unit 312.

[0095] 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 44 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.

[0096] 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 V3 and V6. 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 V3 and V6, 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 V3 and V6, 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 V3 and V6, and setting the derived difference as the similarity S1.

[0097] 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.

[0098] 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, and 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, and 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 c.

[0099] 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.

[0100] The similarity determination unit 313 is capable of determining that the known traffic scenario having the highest value of similarity S among the multiple known traffic scenarios contained in the knowledge graph DB 43 or the knowledge space DB 44 is the traffic scenario (hereinafter referred to as the "similar traffic scenario") that the vehicle 1 (vehicle 100a) is likely to be facing. The similarity determination unit 313 is capable of reading out the contents of the result terms of the similar traffic scenario obtained by the determination (e.g., hazardous events) from the knowledge graph DB 43 and storing the read contents in the storage unit 40 as hazard contents 45.

[0101] The risk correction unit 314 is capable of reading out the risk levels associated with the similar traffic scenario or each known traffic scene constituting the similar traffic scenario from the knowledge graph DB 43 or the knowledge space DB 44. The risk correction unit 314 is capable of storing one or more risk levels read out from the knowledge graph DB 43 or the knowledge space DB 44 as risk levels 46 in the storage unit 40.

[0102] The risk correction unit 314 is capable of deriving the time difference Δtx between each of a plurality of surrounding traffic scenes in a time series. The time difference Δtx corresponds to a specific example of a "first difference" according to an embodiment of the present disclosure. For example, the risk correction unit 314 is capable of deriving the time difference between each of two surrounding traffic scenes (scene 1, scene 2) in a time series in surrounding traffic scenario II, and setting the derived time difference as the time difference Δtx. For example, the risk correction unit 314 is capable of reading out the transition time of scene 2 as the time difference Δtx from the knowledge graph DB 43 or the knowledge space DB 44.

[0103] The risk level correction unit 314 is capable of calculating a correction value for correcting the risk level 46 based on the time difference Δtx. The risk level correction unit 314 is capable of reading out numerical expressions (e.g., space vectors) of multiple time-series surrounding traffic scenes from the knowledge space DB 44, and calculating a correction value for correcting the risk level 46 based on the read-out numerical expressions (e.g., space vectors) and the time difference Δtx. The risk level correction unit 314 is capable of storing the calculated correction value in the storage unit 40 as a correction value 47.

[0104] The risk level correction unit 314 is capable of calculating a vector time change (dVx / dt) based on the time difference Δtx between each of the multiple surrounding traffic scenes in time series and the numerical representation (vector value) of each of the multiple surrounding traffic scenes in time series. The vector time change (dVx / dt) corresponds to a specific example of a “first vector time change” according to an embodiment of the present disclosure.

[0105] The risk correction unit 314 can derive the vector time change amount (dVx / dt) based on, for example, the following formula: dVx / dt = (Vx2 - Vx1) / Δtx = (e.g., [0, 2, 0]) Vx1: Space vector corresponding to the surrounding traffic scene (scene 1) (e.g., [2.5, 2.0, 2.5]) Vx2: Space vector corresponding to the surrounding traffic scene (scene 2) (e.g., [2.5, 3.0, 2.5]) Δtx: Transition time of the surrounding traffic scene (scene 2) (e.g., 0.5 seconds)

[0106] The risk correction unit 314 is capable of calculating a numerical representation (vector value) of a known traffic scene (scene m1) that has a high similarity in relation to a surrounding traffic scene (scene 1) among the multiple known traffic scenes that make up the similar traffic scenario. The risk correction unit 314 is capable of calculating a numerical representation (vector value) of a known traffic scene (scene m2) that has a high similarity in relation to a surrounding traffic scene (scene 2) among the multiple known traffic scenes that make up the similar traffic scenario.

[0107] The risk level correction unit 314 is capable of calculating a vector time change (dVy / dt) based on the time difference Δty between two known traffic scenes (scene m1, scene m2), the numerical representation (vector value) of the known traffic scene (scene m1), and the numerical representation (vector value) of the known traffic scene (scene m2). The time difference Δty corresponds to a specific example of a "second difference" according to an embodiment of the present disclosure. The numerical representation (vector value) of the known traffic scene (scene m1) corresponds to a specific example of a "first vector value of the first known traffic scene" according to an embodiment of the present disclosure. The numerical representation (vector value) of the known traffic scene (scene m2) corresponds to a specific example of a "second vector value of the second known traffic scene" according to an embodiment of the present disclosure. The vector time change (dVy / dt) corresponds to a specific example of a "second vector time change" according to an embodiment of the present disclosure.

[0108] The risk correction unit 314 can derive the vector time change amount (dVy / dt) based on, for example, the following formula: dVy / dt = (Vy2 - Vy1) / Δty = (e.g., [0, 0.75, 0]) Vy1: spatial vector corresponding to known traffic scene (scene m1) (e.g., [2.5, 2, 5, 2.5]) Vy2: spatial vector corresponding to known traffic scene (scene 2) (e.g., [2.5, 4.0, 2.5]) Δty: transition time of known traffic scene (scene 2) (e.g., 2 seconds)

[0109] The risk correction unit 314 is capable of calculating a correction value for correcting the risk level 46 based on the vector time change amount (dVx / dt) and the vector time change amount (dVy / dt). The risk correction unit 314 is capable of calculating a correction value for correcting the risk level 46, for example, by the ratio between the first-order norm of the vector time change amount (dVx / dt) and the first-order norm of the vector time change amount (dVy / dt). The risk correction unit 314 is capable of calculating a correction value C for correcting the risk level 46, for example, based on the following equation: C = {first-order norm of (dVx / dt)} / {first-order norm of (dVy / dt)} = (for example, 2 / 0.75 = 8 / 3)

[0110] The risk level correction unit 314 is capable of determining whether the surrounding traffic scenario has a value of the traffic element that increases the risk to the vehicle 1 compared to the similar traffic scenario. For example, the risk level correction unit 314 compares the similar traffic scenario with the surrounding traffic scenario with respect to the number of traffic participants, which is one of the traffic elements, and determines that the surrounding traffic scenario has a larger number of traffic participants than the similar traffic scenario. In this case, the risk level correction unit 314 is capable of determining that the surrounding traffic scenario has a value of the traffic element that increases the risk to the vehicle 1 compared to the similar traffic scenario. On the other hand, the risk level correction unit 314 is capable of comparing the similar traffic scenario with the surrounding traffic scenario with respect to the number of traffic participants, which is one of the traffic elements, and determines that the surrounding traffic scenario has a smaller number of traffic participants than the similar traffic scenario. In this case, the risk level correction unit 314 is capable of determining that the surrounding traffic scenario has a value of the traffic element that decreases the risk to the vehicle 1 compared to the similar traffic scenario.

[0111] Furthermore, the risk level correction unit 314 may, for example, compare the similar traffic scenario with the surrounding traffic scenario with respect to the speed of the traffic participants, which is one of the traffic elements, and determine that the speed of the traffic participants in the surrounding traffic scenario is higher than that in the similar traffic scenario. At this time, the risk level correction unit 314 is able to determine that the surrounding traffic scenario has a value that increases the risk to the vehicle 1 in the traffic element compared to the similar traffic scenario. On the other hand, the risk level correction unit 314 may, for example, compare the similar traffic scenario with the surrounding traffic scenario with respect to the speed of the traffic participants, which is one of the traffic elements, and determine that the speed of the traffic participants in the surrounding traffic scenario is lower than that in the similar traffic scenario. At this time, the risk level correction unit 314 is able to determine that the surrounding traffic scenario has a value that decreases the risk to the vehicle 1 in the traffic element compared to the similar traffic scenario.

[0112] Furthermore, the risk level correction unit 314 may, for example, compare the similar traffic scenario with the surrounding traffic scenario regarding the behavior of a traffic participant, which is one of the traffic elements, and determine that the behavior of the traffic participant in the surrounding traffic scenario is more abrupt than in the similar traffic scenario. At this time, the risk level correction unit 314 is able to determine that the surrounding traffic scenario has a value that increases the risk to the vehicle 1 in the traffic element compared to the similar traffic scenario. On the other hand, the risk level correction unit 314 may, for example, compare the similar traffic scenario with the surrounding traffic scenario regarding the behavior of a traffic participant, which is one of the traffic elements, and determine that the behavior of the traffic participant in the surrounding traffic scenario is more gradual than in the similar traffic scenario. At this time, the risk level correction unit 314 is able to determine that the surrounding traffic scenario has a value that decreases the risk to the vehicle 1 in the traffic element compared to the similar traffic scenario.

[0113] When the risk level correction unit 314 determines that the surrounding traffic scenario has a value in a traffic element that increases the risk to the vehicle 1 compared to the similar traffic scenario, it is possible to set the correction value 47 for correcting the risk level 46 to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is greater than the risk level 46. When the risk level correction unit 314 determines that the surrounding traffic scenario has a value in a traffic element that decreases the risk to the vehicle 1 compared to the similar traffic scenario, it is possible to set the correction value 47 for correcting the risk level 46 to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is smaller than the risk level 46.

[0114] The intensity setting unit 315 is capable of calculating the degree of danger of the danger content 45 based on the danger level 46 and the correction value 47. The intensity setting unit 315 is capable of setting the calculated danger level as the intensity of at least one of the danger warning control and the danger avoidance control. The intensity setting unit 315 is capable of storing the set intensity as intensity 48 in the memory unit 40. The intensity setting unit 315 is capable of outputting the danger content 45, the danger level 46, the correction value 47, and the intensity 48 to the driving control unit 32, which will be described later.

[0115] 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 (e.g., the torque of the prime mover 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 for various devices of the vehicle 1) and data acquired by the intensity setting unit 314 (hazard details 45, hazard level 46, correction value 47, and intensity 48).

[0116] 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, hazard level 46, correction value 47, and intensity 48) 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, hazard level 46, correction value 47, and intensity 48) 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, hazard level 46, correction value 47, and intensity 48) 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 obtained by the intensity setting unit 314 (hazard content 45, hazard level 46, correction value 47 and intensity 48).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] (Driving Assistance Procedure) Next, a driving assistance procedure in the vehicle 1 will be described with reference to Fig. 20. Fig. 20 shows an example of a driving assistance procedure in the vehicle 1.

[0124] 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).

[0125] 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).

[0126] Next, the risk prediction unit 31 calculates the similarity S between each known traffic scenario included in the knowledge space DB 44 and the created surrounding traffic scenario (step S105). The risk prediction unit 31 determines that, among the multiple known traffic scenarios included in the knowledge space DB 44, the known traffic scenario with the highest value of similarity S is the similar traffic scenario that the vehicle 1 is likely to be facing (step S106). The risk prediction unit 31 reads out the content of the result term of the similar traffic scenario obtained by the determination (e.g., hazardous events) from the knowledge graph DB 43, and stores the read content in the storage unit 40 as hazard content 45.

[0127] The risk prediction unit 31 reads out the risk levels associated with the similar traffic scenario or each known traffic scene constituting the similar traffic scenario from the knowledge graph DB 43 or the knowledge space DB 44. The risk prediction unit 31 stores one or more risk levels read out from the knowledge graph DB 43 or the knowledge space DB 44 as a risk level 46 in the storage unit 40. The risk prediction unit 31 derives the time difference Δtx between each of the multiple surrounding traffic scenes in time series. The risk prediction unit 31 calculates a correction value for correcting the risk level 46 based on the time difference Δtx (step S107). The risk prediction unit 31 stores the calculated correction value in the storage unit 40 as a correction value 47.

[0128] The risk prediction unit 31 determines whether the surrounding traffic scenario has values ​​in the traffic elements that increase the risk to the vehicle 1 compared to the similar traffic scenario. When the risk prediction unit 31 determines that the surrounding traffic scenario has values ​​in the traffic elements that increase the risk to the vehicle 1 compared to the similar traffic scenario, it sets the correction value 47 for correcting the risk level 46 to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is greater than the risk level 46. When the risk prediction unit 31 determines that the surrounding traffic scenario has values ​​in the traffic elements that decrease the risk to the vehicle 1 compared to the similar traffic scenario, it sets the correction value 47 for correcting the risk level 46 to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is smaller than the risk level 46.

[0129] The risk prediction unit 31 calculates the degree of risk of the risk content 45 based on the risk level 46 and the correction value 47. The risk prediction unit 31 sets the calculated risk level as an intensity 48 of at least one of the risk notification control and the risk avoidance control (step S107). The risk prediction unit 31 stores the set intensity 48 in the memory unit 40. The risk prediction unit 31 outputs the risk content 45, the risk level 46, the correction value 47, and the intensity 48 to the driving control unit 32 (step S108).

[0130] 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, hazard level 46, correction value 47, and intensity 48). The driving control unit 32 calculates a correction torque for correcting a 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, hazard level 46, correction value 47, and intensity 48). The driving control unit 32 calculates a correction torque for correcting a 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, hazard level 46, correction value 47, and intensity 48). 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, hazard level 46, correction value 47, and intensity 48) obtained by the intensity 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, hazard level 46, correction value 47, and intensity 48) obtained by the intensity setting unit 314.

[0131] 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.

[0132] 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.

[0133] [Effects] Next, effects of the vehicle 1 according to the embodiment of the present disclosure will be described.

[0134] In this embodiment, a traffic context is interpreted based on the acquired map data and situation data, thereby generating multiple time-series surrounding traffic scenes, and a surrounding traffic scenario is created by integrating the generated multiple time-series surrounding traffic scenes. For each known traffic scenario included in the knowledge space DB 44, a similarity S with the surrounding traffic scenario is calculated. Among the multiple known traffic scenarios included in the knowledge space DB 44, the known traffic scenario with the highest similarity S is determined to be the similar traffic scenario that the vehicle 1 is likely to be facing. As a result, even when the observed surrounding traffic scenario is not included in the known traffic scenarios described in the knowledge space DB 44, logical inference can be performed using a known traffic scenario similar to the surrounding traffic scenario. Then, using the data obtained as a result of performing logical inference, at least one of hazard warning control and hazard avoidance control can be performed. From the above, logical inference can be performed even when the observed traffic situation is not included in the traffic situations described in the knowledge graph DB 43 and the knowledge space DB 44.

[0135] Furthermore, in this embodiment, based on the time difference Δtx between the first surrounding traffic scene and the second surrounding traffic scene, which are consecutive in time series in the surrounding traffic scenario, a similar traffic scenario, which is a known traffic scenario having a high similarity to the surrounding traffic scenario among the multiple known traffic scenarios contained in the knowledge space DB 44, or a correction value 47 for correcting the risk level 46 associated with each known traffic scene constituting the similar traffic scenario is calculated. As a result, even when the observed surrounding traffic scenario is not included in the known traffic scenarios described in the knowledge space DB 44, at least one of hazard warning control and hazard avoidance control can be executed using the risk level 46 associated with the known traffic scenario similar to the surrounding traffic scenario or each known traffic scene constituting the similar traffic scenario, or the correction value 47 for correcting the risk level 46. As described above, even when the observed traffic situation is not included in the traffic situations described in the knowledge graph DB 43 and the knowledge space DB 44, at least one of hazard warning control and hazard avoidance control can be executed in accordance with the actual risk.

[0136] In this embodiment, the correction value 47 is calculated based on the time difference Δtx between the first and second surrounding traffic scenes, which are consecutive in time series, and the numerical representations of the first and second surrounding traffic scenes, which are consecutive in time series. This provides the correction value 47 according to the magnitude of the time difference Δtx, making it possible to execute at least one of the danger warning control and the danger avoidance control depending on the actual danger.

[0137] In this embodiment, each of the known traffic scenarios, known traffic scenes, and traffic elements is represented by a spatial vector in the knowledge space DB 44. This allows the correction value 47 to be obtained with a small amount of calculation, making it possible to execute at least one of danger warning control and danger avoidance control in real time according to the actual danger.

[0138] In this embodiment, each surrounding traffic scenario and each surrounding traffic scene are represented by a space vector common to the space vectors in the knowledge space DB 44. Furthermore, a vector time change (dVx / dt) is calculated based on the time difference Δtx between the first surrounding traffic scene and the second surrounding traffic scene, which are consecutive in time series, and the vector values ​​of the first surrounding traffic scene and the second surrounding traffic scene. Furthermore, a numerical representation (vector value) of a known traffic scene (scene m1) that is highly similar to the first surrounding traffic scene among the multiple known traffic scenes constituting the similar traffic scenario, and a numerical representation (vector value) of a known traffic scene (scene m2) that is highly similar to the second surrounding traffic scene among the multiple known traffic scenes constituting the similar traffic scenario are calculated. Furthermore, a vector time change (dVy / dt) is calculated based on the time difference Δty between the first known traffic scene and the second known traffic scene, the numerical expression (vector value) of the known traffic scene (scene m1), and the numerical expression (vector value) of the known traffic scene (scene m2). Furthermore, a correction value 47 is calculated based on the vector time change (dVx / dt) and the vector time change (dVy / dt). This allows the correction value 47 to be obtained with a small amount of calculation, making it possible to execute at least one of hazard warning control and hazard avoidance control in real time in accordance with the actual hazard.

[0139] Furthermore, in this embodiment, when it is determined that the surrounding traffic scenario has a value in a traffic element that increases the risk to the vehicle 1 compared to the similar traffic scenario, the correction value 47 that corrects the risk level 46 is set to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is greater than the risk level 46. This makes it possible to execute at least one of risk warning control and risk avoidance control depending on the actual risk.

[0140] Furthermore, in this embodiment, when it is determined that the surrounding traffic scenario has a value that reduces the risk to the vehicle 1 in the traffic element compared to the similar traffic scenario, the correction value 47 that corrects the risk level 46 is set to a value such that the risk level obtained by multiplying the risk level 46 by the correction value 47 is smaller than the risk level 46. This makes it possible to execute at least one of the risk warning control and the risk avoidance control depending on the actual risk.

[0141] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0142] Furthermore, for example, the present disclosure can be configured as follows. (1) A system for managing a vehicle comprising: 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 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; and calculates a similar traffic scenario, which is a known traffic scenario with a high similarity to the surrounding traffic scenario among the plurality of known traffic scenarios included in the knowledge space, or a correction value that corrects a risk associated with each of the known traffic scenes that constitute the similar traffic scenario, based on a first difference between the times of a first surrounding traffic scene and a second surrounding traffic scene that are consecutive in time series in the created surrounding traffic scenario; (2) The information processing device according to (1), wherein the processing unit calculates the correction value based on the first difference and numerical representations of the first surrounding traffic scene and the second surrounding traffic scene. (3) The information processing device according to (2), 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.(4) The information processing device according to (3), 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 vector time change based on the first difference and vector values ​​of the first surrounding traffic scene and the second surrounding traffic scene; calculating a first vector value of a first known traffic scene in the plurality of known traffic scenes constituting the similar traffic scenario that has a high similarity in relation to the first surrounding traffic scene, and a second vector value of a second known traffic scene in the plurality of known traffic scenes constituting the similar traffic scenario that has a high similarity in relation to the second surrounding traffic scene; calculating a second vector time change based on a second difference between the time of the first known traffic scene and the second known traffic scene, the first vector value, and the second vector value; and calculating the correction value based on the first vector time change and the second vector time change. (5) The information processing device according to any one of (1) to (4), wherein the processing unit is capable of setting the correction value to a value that increases the degree of risk when the surrounding traffic scenario has a traffic element value that increases the risk to the vehicle compared to the similar traffic scenario. (6) The information processing device according to any one of (1) to (4), wherein the processing unit is capable of setting the correction value to a value that decreases the degree of risk when the surrounding traffic scenario has a traffic element value that decreases the risk to the vehicle compared to the similar traffic scenario. (7) 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 (6), and a control unit capable of performing at least one of danger warning control and danger avoidance control according to the magnitude of the intensity acquired by the intensity acquisition unit.(8) A vehicle comprising: an alarm device and a driving 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 driving device, based on the content corresponding to the magnitude of the intensity obtained from the information processing device described in any one of (1) to (6).

[0143] The control unit 30 shown in FIG. 1 can be implemented by circuitry including at least one semiconductor integrated circuit, such as at least one processor (e.g., a central processing unit (CPU)), at least one application-specific integrated circuit (ASIC), and / or at least one field-programmable gate array (FPGA). The at least one processor can be configured to perform all or a portion of the various functions of the control unit 30 shown in FIG. 1 by reading instructions from at least one non-transitory, tangible computer-readable medium. Such medium can take various forms, including, but not limited to, various magnetic media such as hard disks, various optical media such as CDs or DVDs, and various semiconductor memories (i.e., semiconductor circuits) such as volatile or non-volatile memories. Volatile memory can include DRAM and SRAM. Non-volatile memory can 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 FIG. 1. An FPGA is an integrated circuit designed to be configurable after manufacture to perform all or part of the various functions of control unit 30 shown in FIG.

Claims

1. A 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; and calculates a similar traffic scenario, which is a known traffic scenario that has a high similarity to the surrounding traffic scenario among the plurality of known traffic scenarios included in the knowledge space, or a correction value that corrects the risk associated with each of the known traffic scenes that constitute the similar traffic scenario, based on a first difference between the times of a first surrounding traffic scene and a second surrounding traffic scene that are consecutive in time series in the created surrounding traffic scenario; An information processing device capable of setting an intensity of danger warning control or danger avoidance control based on the degree of danger and the correction value, and outputting the intensity.

2. The information processing device according to claim 1, wherein the processing unit calculates the correction value based on the first difference and numerical representations of the first surrounding traffic scene and the second surrounding traffic scene.

3. The information processing device according to claim 2, 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.

4. The information processing device according to claim 3, wherein the surrounding traffic scenario and each of the surrounding traffic scenes are represented by a spatial vector common to the spatial vectors in the knowledge space, and the processing unit is capable of: calculating a first vector time change based on the first difference and the vector values ​​of the first surrounding traffic scene and the second surrounding traffic scene; calculating a first vector value of a first known traffic scene in the plurality of known traffic scenes constituting the similar traffic scenario that has a high similarity in relation to the first surrounding traffic scene, and a second vector value of a second known traffic scene in the plurality of known traffic scenes constituting the similar traffic scenario that has a high similarity in relation to the second surrounding traffic scene; calculating a second vector time change based on a second difference between the time of the first known traffic scene and the second known traffic scene, the first vector value, and the second vector value; and calculating the correction value based on the first vector time change and the second vector time change.

5. The information processing device of claim 1, wherein the processing unit is capable of setting the correction value to a value that increases the risk when the surrounding traffic scenario has a value in a traffic element that increases the risk to the vehicle compared to the similar traffic scenario.

6. The information processing device according to claim 1, wherein the processing unit is capable of setting the correction value to a value that reduces the risk to the vehicle when the surrounding traffic scenario has a value in a traffic element that reduces the risk to the vehicle compared to the similar traffic scenario.

7. A vehicle control device comprising: an intensity acquisition unit capable of acquiring the intensity from the information processing device according to any one of claims 1 to 6; 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 intensity acquisition unit.

8. A vehicle comprising: an alarm device and a driving 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 driving device, in accordance with the magnitude of the intensity obtained from the information processing device described in any one of claims 1 to 6.

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