Information processing device, vehicle control device, and vehicle

Abstract

An information processing device according to one aspect of the present disclosure can interpret traffic context on the basis of map data and situation data around a vehicle, thereby generating a plurality of time-series surrounding traffic scenes, and integrate the generated plurality of time-series surrounding traffic scenes to create a surrounding traffic scenario. The information processing device can calculate a correction value for correcting the degree of risk associated with each surrounding traffic scene constituting the surrounding traffic scenario by using a threshold value or rule of a region-dependent parameter corresponding to location data of the vehicle, and set the strength of risk notification control or risk avoidance control on the basis of the degree of risk and the correction value.

Description

Information processing apparatus, vehicle control apparatus, and vehicle

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

[0002] There is known a driving support method that estimates a dangerous event by combining knowledge data such as an ontology or a knowledge graph with logical inference and warns a driver.

[0003] Japanese Unexamined Patent Application Publication No. 2016-091039

[0004] The information processing apparatus according to the first aspect of the present disclosure includes an acquisition unit and a processing unit. The acquisition unit acquires position data of a vehicle, map data and situation data around the vehicle, and can also acquire a threshold value or a rule of a region-dependent parameter corresponding to the position data from an external device. The processing unit interprets a traffic context based on the map data and the situation data, thereby generating a plurality of surrounding traffic scenes in time series, and can create a surrounding traffic scenario by integrating the plurality of generated surrounding traffic scenes in time series. The processing unit calculates a correction value for correcting a risk level associated with each surrounding traffic scene constituting the surrounding traffic scenario using the threshold value or the rule of the region-dependent parameter, and can set the intensity of risk notification control or risk avoidance control based on the risk level and the correction value.

[0005] The information processing device relating to the second aspect of this disclosure comprises an acquisition unit and a processing unit. The acquisition unit is capable of acquiring vehicle location data and map data and situational data of the area surrounding the vehicle. The processing unit is capable of interpreting the traffic context based on the map data and situational data, thereby generating multiple time-series surrounding traffic scenes, and integrating the generated time-series surrounding traffic scenes to create a surrounding traffic scenario. Each time a surrounding traffic scenario is created, the processing unit stores the created surrounding traffic scenario in a storage unit, associating it with regional identification data corresponding to the vehicle location data, and generates thresholds or rules for region-dependent parameters based on multiple surrounding traffic scenarios that share common regional identification data among the multiple surrounding traffic scenarios stored in the storage unit. Using the generated thresholds or rules for region-dependent parameters, the processing unit calculates a correction value to correct the risk level associated with each surrounding traffic scene constituting the surrounding traffic scenario, and sets the intensity of risk warning control or risk avoidance control based on the risk level and correction value.

[0006] The information processing device relating to the third aspect of this disclosure comprises a storage unit, a receiving unit, a processing unit, and a transmitting unit. The storage unit stores thresholds or rules for a plurality of region-dependent parameters defined for each region. The receiving unit is capable of receiving region identification data. When the receiving unit receives region identification data from a vehicle, the processing unit is capable of reading from the storage unit the thresholds or rules for region-dependent parameters corresponding to the received region identification data. The transmitting unit is capable of transmitting to the vehicle the thresholds or rules for region-dependent parameters read from the storage unit by the processing unit as a response to the receipt of region identification data.

[0007] A vehicle control device relating to the fourth aspect of this disclosure comprises a scenario acquisition unit capable of acquiring a new surrounding traffic scenario from any of the information processing devices relating to the first to third aspects of this disclosure, and a control unit capable of performing at least one of notification control and driving control based on the new surrounding traffic scenario acquired by the scenario acquisition unit.

[0008] A vehicle relating to the fifth aspect of this disclosure comprises a notification device, a running gear, and a control unit. The control unit is capable of performing at least one of notification control for the notification device and running control for the running gear based on a novel surrounding traffic scenario obtained from any of the information processing devices relating to the first to third aspects of this disclosure.

[0009] The accompanying drawings are provided for further understanding of this disclosure and are incorporated herein and constitute part of this specification. The drawings illustrate one embodiment and, together with the specification, serve to illustrate the principles of this disclosure.

[0010] Figure 1 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation Ta). Figure 2 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation Tb). Figure 3 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation U). Figure 4 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation V). Figure 5 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation W). Figure 6 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation X). Figure 7 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation Y). Figure 8 is a diagram showing an example of the traffic situation in front of the vehicle (surrounding traffic situation Z). Figure 9 is a diagram showing an example of the schematic configuration of a driving control system including a vehicle according to one embodiment of the present disclosure. Figure 10 is a diagram showing an example of a functional block of a driving control device provided in the vehicle of Figure 9. Figure 11 is a diagram showing an example of the concept of a known traffic scenario. Figure 12 is a diagram showing an example of a knowledge graph embedded in the knowledge space. Figure 13 is a diagram showing an example of known traffic situation A. Figure 14 is a diagram showing an example of the traffic context for known traffic situation A in Figure 13. Figure 15 is a diagram showing an example of known traffic situation B. Figure 16 is a diagram showing an example of the traffic context for known traffic situation B in Figure 15. Figure 17 is a diagram showing an example of known traffic situation C. Figure 18 is a diagram showing an example of the traffic context for known traffic situation C in Figure 17. Figure 19 is a diagram showing an example of external recognition data obtained from sensors, etc., in the surrounding traffic situation Ta in Figure 1. Figure 20 is a diagram showing an example of the traffic context for surrounding traffic situation Ta in Figure 1. Figure 21 is a diagram showing an example of the traffic context for surrounding traffic situation Tb in Figure 2. Figure 22 is a diagram showing an example of the concept of a surrounding traffic scenario. Figure 23 is a diagram showing an example of the functional block of the server device in Figure 9. Figure 24 is a diagram showing an example of a driving assistance procedure in a vehicle equipped with the driving control device in Figure 10. Figure 25 is a diagram showing an example of a driving assistance procedure following Figure 24. Figure 26 is a diagram showing a modified example of the functional block of the driving control device in Figure 10. Figure 27 shows a modified example of the functional block of the driving control device shown in Figure 10. Figure 28(A) shows an example of the rear-end collision priority list shown in Figure 27. Figure 28(B) shows an example of the intersection collision priority list shown in Figure 27.Figure 28(C) is a diagram showing an example of the priority list for interruption accidents in Figure 27. Figure 28(D) is a diagram showing an example of the priority list for right-turn accidents in Figure 27. Figure 29 is a diagram showing an example of the accident statistics frequency table in Figure 27. Figure 30 is a diagram showing an example of the write list in Figure 27. Figure 31 is a diagram showing one modification of the driving assistance procedure following Figure 24. Figure 32 is a diagram showing one modification of the function block of the driving control device in Figure 26. Figure 33 is a diagram showing one modification of the function block of the driving control device in Figure 10. Figure 34 is a diagram showing one modification of the function block of the driving control device in Figure 33.

[0011] Hereinafter, several exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The following description is intended to illustrate specific examples of the present disclosure and should not be construed as limiting the disclosure. For example, elements such as numerical values, shapes, materials, parts, the location of each part, and the method of connecting each part are merely examples and should not be construed as limiting the disclosure. Furthermore, in the following exemplary embodiments, components not described in separate sections based on the highest-level concepts of the present disclosure are optional and may be provided as needed. The drawings are schematic and are not intended to be to scale. Throughout this specification and the drawings, components having substantially the same function and substantially the same configuration are denoted by the same reference numerals, and redundant descriptions are omitted. Furthermore, components not directly related to an embodiment of the present disclosure are not shown in the drawings.

[0012] <1. Background> Driving assistance methods are known that estimate dangerous events and warn drivers by combining knowledge data such as ontologs and knowledge graphs with logical reasoning. However, in such driving assistance methods, if the interpretation of surrounding traffic conditions differs from region to region, there is a risk that logical reasoning that does not reflect reality may be in operation.

[0013] Therefore, after careful consideration, the inventor of this application conceived of a technology that enables logical reasoning operations that are in line with reality, even when the interpretation of surrounding traffic conditions differs from region to region. Below, we will give an example of traffic conditions that are interpreted differently from region to region and explain the background of the technology that we have newly conceived.

[0014] (Traffic conditions may vary depending on the region) Figures 1 to 8 show an example of the traffic conditions in front of vehicle 100g (the vehicle itself) (surrounding traffic conditions Ta to Z). Figures 1 to 8 illustrate the surrounding traffic conditions Ta to Z as seen from above the road traveled by vehicle 100g.

[0015] (Surrounding traffic conditions Ta) In Figure 1, vehicle 100g is assumed to be traveling on a road La with one lane in each direction. Road La consists of a driving lane La1 on which vehicle 100g is traveling, and an opposing lane La2 provided along driving lane La1 via a center line. An unsignaled intersection ISb is provided on road La ahead of vehicle 100g. Road La intersects with road Lb at intersection ISb. Road Lb is, for example, a road with one lane in each direction.

[0016] In the opposing lane La2, vehicle 100c is traveling towards intersection ISb. In the driving lane La1, vehicle 100g is traveling towards intersection ISb. Vehicle 100g is slowing down and activating its right turn signal in order to turn right at intersection ISb. Meanwhile, vehicle 100c is slowing down just before intersection ISb and flashing its high beams momentarily. At this time, it is generally known that the interpretation of the surrounding traffic situation Ta differs depending on the region. Specifically, for example, in region α, vehicle 100c's flashing means yielding to vehicle 100g, but in region β, it means that vehicle 100c will pass intersection ISb before vehicle 100g. Assume that the driver of vehicle 100g is from region α and is driving in region β without knowing the customs of region β. At this time, the driver of vehicle 100g mistakenly interprets vehicle 100c's flashing headlights as vehicle 100c yielding the right of way to vehicle 100g. As a result, vehicle 100g enters intersection ISb and begins to turn right, while vehicle 100c attempts to pass through intersection ISb. Consequently, vehicle 100g may come into contact with vehicle 100c within intersection ISb, as shown in Figure 2, for example.

[0017] (Surrounding traffic conditions U) In Figure 3, vehicle 100g is assumed to be traveling on a road La with one lane in each direction. Road La consists of a driving lane La1 on which vehicle 100g is traveling, and an opposing lane La2 provided along the driving lane La1 via a center line. An intersection ISa is provided on road La in front of vehicle 100g. Road La intersects with road Lb at intersection ISa. Road Lb is, for example, a road with one lane in each direction. Pedestrian crossings CW1 are provided on road La, both before and after intersection ISa in relation to vehicle 100g. Traffic lights TL1 are also provided on road La, both before and after intersection ISa in relation to vehicle 100g. Pedestrian crossings CW2 are provided on road Lb, both to the left and to the right of intersection ISa. Road Lb is further equipped with traffic lights TL2 on both the left and right sides of intersection ISa.

[0018] Traffic light TL1 is red (indicating entry is not permitted), and vehicles 100g and 100c are stopped before the stop line. The driver of vehicle 100g intends to proceed straight through intersection ISa when traffic light TL1 turns green (indicating entry is permitted). Meanwhile, the driver of vehicle 100c has activated its right turn signal in order to turn right at intersection ISa. At this time, it is generally known that the interpretation of the surrounding traffic conditions U shown in Figure 3 differs depending on the region. Specifically, one of the criteria for deciding whether vehicle 100c will immediately begin turning right at intersection ISa when traffic light TL1 turns green (indicating entry is permitted) is the distance Da between the two stop lines flanking intersection ISa (the size of intersection ISa). In region α, it is known that when the distance Da is D1 or greater, vehicle 100c will immediately begin turning right when traffic light TL1 turns green (indicating entry is permitted). In area β, it is known that when the distance Da is D2 (>D1) or greater, and the traffic light TL1 turns green (indicating permission to enter), vehicle 100c immediately begins to turn right. In area γ, it is known that when the distance Da is D3 (>D2) or greater, and the traffic light TL1 turns green (indicating permission to enter), vehicle 100c immediately begins to turn right.

[0019] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that vehicle 100c will not immediately begin to turn right even if traffic light TL1 turns green (permission to enter) at intersection ISa where the distance Da is less than or equal to D1. Therefore, vehicle 100g starts moving when traffic light TL1 turns green (permission to enter) at intersection ISa where the distance Da is less than or equal to D1, and proceeds straight through intersection ISa. On the other hand, vehicle 100c starts moving when traffic light TL1 turns green (permission to enter) and attempts to turn right at intersection ISa. As a result, there is a possibility that vehicle 100g will collide with vehicle 100c within intersection ISa.

[0020] (Surrounding Traffic Conditions V) In Figure 4, vehicle 100g is assumed to be traveling on a road La with one lane in each direction. Road La consists of a driving lane La1 on which vehicle 100g is traveling, and an opposing lane La2 provided along driving lane La1 via a center line. An unsignaled intersection ISb is provided on road La ahead of vehicle 100g. Road La intersects with road Lb at intersection ISb. Road Lb is, for example, a road with one lane in each direction.

[0021] Vehicles 100g and 100c are traveling towards intersection ISb. Vehicle 100g is traveling at a constant speed in order to pass straight through intersection ISb. Vehicle 100c, on the other hand, is slowing down before intersection ISb and flashing its right turn signal in order to turn right at intersection ISb. At this time, it is generally known that the interpretation of the surrounding traffic conditions V differs depending on the region. Specifically, one of the criteria for deciding whether or not vehicle 100c will start to turn right at intersection ISb, which is an unsignaled intersection, is the distance Db from vehicle 100c to vehicle 100g. In region α, it is known that vehicle 100c will turn right at intersection ISb when the distance Db is D1 or greater. In region β, it is known that vehicle 100c will turn right at intersection ISb when the distance Db is D2 (>D1) or greater. In region γ, it is known that vehicle 100c will turn right at intersection ISb when the distance Da is D3 (>D2) or greater.

[0022] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that vehicle 100c will not begin to turn right at intersection ISb where the distance Db is less than or equal to D1. Therefore, vehicle 100g proceeds straight through intersection ISb without slowing down. Meanwhile, vehicle 100c attempts to turn right at intersection ISb. As a result, there is a possibility that vehicle 100g will come into contact with vehicle 100c within intersection ISb.

[0023] (Surrounding traffic conditions W) In Figure 5, vehicle 100g is assumed to be traveling on a road Lc with two lanes in each direction. The road Lc consists of a driving lane Lc1 on which vehicle 100g is traveling, and an overtaking lane Lc2 provided along the driving lane Lc1 via lane markings. In the driving lane Lc1, vehicle 100d is traveling in front of vehicle 100g. In the overtaking lane Lc2, vehicle 100c is traveling diagonally in front of vehicle 100g.

[0024] Vehicle 100g is traveling in lane Lc1 at a constant speed with a following distance Dc. Following distance Dc is the distance from vehicle 100g to vehicle 100d. Meanwhile, vehicle 100c is accelerating slightly in the overtaking lane Lc2 and flashing its left turn signal in order to change lanes. At this time, it is generally known that the interpretation of the surrounding traffic conditions W differs depending on the region. Specifically, one of the criteria for deciding whether or not vehicle 100c will start changing lanes is the following distance Dc. In region α, it is known that vehicle 100c will start changing lanes when the following distance Dc is D1 or greater. In region β, it is known that vehicle 100c will start changing lanes when the following distance Dc is D2 (>D1) or greater. In region γ, it is known that vehicle 100c will start changing lanes when the following distance Dc is D3 (>D2) or greater.

[0025] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that vehicle 100c will not begin changing lanes when the distance Dc between vehicles is less than or equal to D1. Therefore, vehicle 100g continues to drive at a constant speed without slowing down when the distance Dc is less than or equal to D1. Meanwhile, vehicle 100c begins to change lanes. As a result, there is a possibility that vehicle 100g will come into contact with vehicle 100c.

[0026] (Surrounding traffic conditions X) In Figure 6, vehicle 100g is assumed to be traveling on a road Ld with one lane in each direction. Road Ld consists of the driving lane Ld1 on which vehicle 100g is traveling, and the opposing lane Ld2 which is provided along the driving lane Ld1 via a center line. In front of vehicle 100g, a store entrance / exit passage Le connects to the driving lane Ld1. Vehicle 100c is stopped in the store entrance / exit passage Le and has its left turn signal flashing.

[0027] Vehicle 100g is traveling at a constant speed at a distance Dd from vehicle 100c. Distance Dd is the distance between vehicle 100g and the point in the driving lane Ld1 closest to vehicle 100c. Meanwhile, vehicle 100c is stopped with its left turn signal flashing in order to move from the store entrance / exit aisle Le to the driving lane Ld1. At this time, it is generally known that the interpretation of the surrounding traffic conditions X differs depending on the region. Specifically, one of the criteria for deciding whether or not vehicle 100c will start moving from the store entrance / exit aisle Le to the driving lane Ld1 is distance Dd. In region α, it is known that vehicle 100c will start moving from the store entrance / exit aisle Le to the driving lane Ld1 when distance Dd is D1 or greater. In region β, it is known that vehicle 100c will start moving from the store entrance / exit aisle Le to the driving lane Ld1 when distance Dd is D2 (>D1) or greater. In region γ, it is known that when the distance Dd is D3 (>D2) or greater, vehicle 100c begins moving from the store entrance passage Le to the driving lane Ld1.

[0028] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that when the distance Dd is less than or equal to D1, vehicle 100c will not begin moving from the store entrance / exit aisle Le to the driving lane Ld1. Therefore, when the distance Dd is less than or equal to D1, vehicle 100g will continue to drive at a constant speed without decelerating. Meanwhile, vehicle 100c will begin moving from the store entrance / exit aisle Le to the driving lane Ld1. As a result, there is a possibility that vehicle 100g will come into contact with vehicle 100c.

[0029] (Surrounding traffic conditions Y) In Figure 7, vehicle 100g is assumed to be traveling on a road Ld with one lane in each direction. Road Ld consists of the driving lane Ld1 on which vehicle 100g is traveling, and the opposing lane Ld2 which is provided along the driving lane Ld1 via a center line. In front of vehicle 100g, a store entrance passage Lf connects to the opposing lane Ld2. Vehicle 100c is stopped in the store entrance passage Lf and has its right turn signal flashing.

[0030] Vehicle 100g is traveling at a constant speed at a distance De from vehicle 100c. Distance De is the distance between vehicle 100g and the point in the driving lane Ld1 closest to vehicle 100c. Meanwhile, vehicle 100c is stopped with its right turn signal flashing in order to move from the store entrance passage Lf to the oncoming lane Ld2. At this time, it is generally known that the interpretation of the surrounding traffic conditions Y differs depending on the region. Specifically, one of the criteria for deciding whether or not vehicle 100c will start moving from the store entrance passage Lf to the oncoming lane Ld2 is distance De. In region α, it is known that vehicle 100c will start moving from the store entrance passage Lf to the oncoming lane Ld2 when distance De is D1 or greater. In region β, it is known that vehicle 100c will start moving from the store entrance passage Lf to the oncoming lane Ld2 when distance De is D2 (>D1) or greater. In region γ, it is known that when the distance De is D3 (>D2) or greater, vehicle 100c begins moving from the store entrance passage Lf to the opposing lane Ld2.

[0031] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that when the distance De is less than or equal to D1, vehicle 100c will not begin moving from the store entrance passage Lf to the oncoming lane Ld2. Therefore, when the distance De is less than or equal to D1, vehicle 100g will continue to drive at a constant speed without decelerating. Meanwhile, vehicle 100c will begin moving from the store entrance passage Lf to the oncoming lane Ld2. As a result, there is a possibility that vehicle 100g will come into contact with vehicle 100c.

[0032] (Surrounding traffic conditions Z) In Figure 8, vehicle 100g is assumed to be traveling on a road La with one lane in each direction. Road La consists of a driving lane La1 on which vehicle 100g is traveling, and an opposing lane La2 provided along driving lane La1 via a center line. An unsignaled intersection ISb is provided on road La ahead of vehicle 100g. Road La intersects with road Lb at intersection ISb. Road Lb is, for example, a road with one lane in each direction.

[0033] Vehicles 100g and 100d are traveling towards intersection ISb. Vehicle 100g is traveling at a constant speed in lane La1 in order to pass straight through intersection ISb. Meanwhile, vehicle 100d is traveling at a constant speed in the oncoming lane La2 in order to pass straight through intersection ISb. Vehicle 100c is traveling on road Lb. Vehicle 100c is traveling towards intersection ISb and is slowing down before intersection ISb in order to either go straight through intersection ISb or turn right at intersection ISb. At this time, it is generally known that the interpretation of the surrounding traffic conditions Z differs depending on the region. Specifically, one of the criteria for deciding whether or not vehicle 100c enters the unsignaled intersection ISb is the distance Df between vehicle 100g and vehicle 100d. In region α, it is known that vehicle 100c enters intersection ISb when the distance Df is D1 or greater. In region β, it is known that vehicle 100c enters intersection ISb when the distance Df is D2 (>D1) or greater. In region γ, it is known that vehicle 100c enters intersection ISb when the distance Df is D3 (>D2) or greater.

[0034] Assume that the driver of vehicle 100g is from region γ and is driving in region α without knowing the customs of regions α and β. In this case, the driver of vehicle 100g mistakenly believes that vehicle 100c will not enter intersection ISb when the distance Df is less than or equal to D1. Therefore, vehicle 100g proceeds straight through intersection ISb without slowing down, where the distance Df is less than or equal to D1. Meanwhile, vehicle 100c enters intersection ISb. As a result, there is a possibility that vehicle 100g will come into contact with vehicle 100c within intersection ISb.

[0035] Thus, in surrounding traffic conditions Ta to Z, where interpretations differ depending on the region, there is a possibility that vehicle 100g may come into contact with vehicle 100c. Therefore, the inventors of the present invention conceived of a technology that can perform logical reasoning operations that are in line with the actual situation when the interpretation of surrounding traffic conditions differs depending on the region. Below, the information processing device, vehicle control device, and vehicle for realizing this will be described in detail in the following embodiments.

[0036] <2. Embodiments> [Configuration Example] Vehicles 100g, 100h, and 100i according to one embodiment of the present disclosure will be described. Figure 9 shows a schematic configuration example of a driving control system 2 including vehicles 100g, 100h, and 100i according to this embodiment. Vehicles 100g, 100h, and 100i correspond to one specific example of "vehicles" according to one embodiment of the present disclosure. The driving control system 2 includes, for example, a plurality of vehicles (for example, vehicles 100g, 100h, and 100i) and a server device 300, as shown in Figure 9. The server device 300 corresponds to one specific example of "external devices" according to one embodiment of the present disclosure. Each vehicle (for example, vehicles 100g, 100h, and 100i) and the server device 300 are configured to communicate with each other via a network NW. The network NW is, for example, the Internet, a wireless LAN such as Wi-Fi, or a mobile phone line. Vehicles 100g, 100h, and 100i share a common configuration. Vehicle 100g travels in region α. ​​Vehicle 100h travels in region β. Vehicle 100i travels in region α. ​​The following describes vehicle 100g.

[0037] Vehicle 100g is capable of moving by the drive of a prime mover (engine or motor). Vehicle 100g includes a driving control device 1000, for example, as shown in Figure 9. The driving control device 1000 includes a sensor unit 10, a communication unit 20, a storage unit 30, a control unit 40, a prime mover 50, a brake 60, an EPS motor 70, and a notification unit 80, for example, as shown in Figure 10. The communication unit 20 corresponds to one specific example of the "communication unit" according to one embodiment of the present disclosure.

[0038] The sensor unit 10 is composed of various sensors mounted on the vehicle 100g. For example, the sensor unit 10 is composed of 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 listed above.

[0039] The accelerator pedal position sensor can detect the accelerator pedal position from the amount the accelerator pedal is pressed. The accelerator pedal position sensor can output time-series data (accelerator pedal position data) about the detected accelerator pedal position to the control unit 40.

[0040] The vehicle speed sensor is capable of detecting the speed of the vehicle (vehicle speed) of 100g. The vehicle speed sensor is capable of outputting time-series data (vehicle speed data) of the detected vehicle speed to the control unit 40. The acceleration sensor is capable of detecting the acceleration applied to the vehicle (vehicle speed). The acceleration sensor is capable of outputting time-series data (acceleration data) of the detected acceleration in three directions to the control unit 40. The angular velocity sensor is capable of detecting the angular velocity of the vehicle (vehicle speed). The angular velocity sensor is capable of outputting time-series data (angular velocity data) of the detected three angular velocities (yaw angular velocity, roll angular velocity, and pitch angular velocity) to the control unit 40.

[0041] The steering angle sensor is capable of detecting the steering angle of the vehicle's 100g steering wheel. The steering angle sensor can output time-series data (steering angle data) of the detected steering angle to the control unit 40. The steering torque sensor is capable of detecting the steering torque generated by the driver's steering wheel operation. The steering torque sensor can output time-series data (steering torque data) of the detected steering torque to the control unit 40. The brake torque sensor is capable of detecting the braking force (torque of the brake 60) in relation to the brake pressure of the vehicle 1. The brake torque sensor can output time-series data (braking force data) of the detected braking force to the control unit 40.

[0042] The sensor unit 10 further includes a stereo camera mounted on the vehicle 100g and a driving environment detection unit. The stereo camera is an autonomous sensor that senses the real space around the vehicle 100g. The stereo camera is arranged, for example, at symmetric positions sandwiching the central portion in the width direction of the vehicle 100g, and can perform stereo imaging of the front of the vehicle 100g from different viewpoints. The stereo camera can output the image data Ia (a pair of stereo image data) of the front of the vehicle 100g obtained by imaging to the control unit 40.

[0043] The stereo camera can generate distance image data Ib obtained from the displacement amount of the position of the corresponding object based on the image data Ia (a pair of stereo image data) obtained by imaging. The driving environment detection unit can, for example, obtain lane dividing lines that divide the road around the vehicle 100g based on the distance image data Ib. The driving environment detection unit can further obtain the road curvature of the dividing lines that divide the left and right of the driving lane (driving lane) on which the vehicle 1 travels, and the width (vehicle width) between the left and right dividing lines. The driving environment detection unit can further perform predetermined pattern matching or the like on the distance image data Ib to detect three-dimensional objects such as lanes and structures existing around the vehicle 100g.

[0044] Here, in the detection of three-dimensional objects by the driving environment detection unit, for example, detection of the type of three-dimensional object, the distance to the three-dimensional object, the speed of the three-dimensional object, the relative speed between the three-dimensional object and the vehicle (own vehicle), etc. is performed. Examples of the three-dimensional object to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, bicycles, buildings, etc. Examples of buildings include detached houses, apartment houses (condominiums), commercial facilities, factories, billboards, etc. The driving environment detection unit can output the driving environment information around the vehicle 100g including the information of the three-dimensional objects obtained in this way to the control unit 40.

[0045] The communication unit 20 can transmit the data obtained by the control unit 40 to the server device 200 via the network NW, receive data from the server device 200 via the network NW, and output the received data to the control unit 40. The communication unit 20 can further obtain data for supplementing data that cannot be obtained from the image data Ia and the distance image data Ib, for example, by vehicle-to-vehicle communication, road-to-vehicle communication, and satellite communication. The communication unit 20 can output the obtained data to the control unit 40.

[0046] The communication unit 20 can, for example, obtain data (e.g., vehicle position, vehicle speed) obtained by other vehicles by vehicle-to-vehicle communication. The communication unit 20 can, for example, receive positioning signals transmitted from a plurality of positioning satellites by satellite communication.

[0047] The communication unit 20 can, for example, obtain road map data around the vehicle 1 from a control device that can sequentially integrate and update the road map data transmitted from each vehicle by road-to-vehicle communication and transmit the updated road map data to each vehicle. The road map data consists of, for example, high-precision road map data (dynamic map), and has static information and quasi-static information mainly constituting road information, and quasi-dynamic information and dynamic information mainly constituting traffic information.

[0048] The static information constituting the road information is composed of, for example, information that requires an update frequency within one month, such as roads, structures on the road, structures around the road, lane information, road surface information, permanent regulation information, etc. The "road" includes, for example, the position and shape of the road, intersections, and the attributes of the road (e.g., national road, prefectural road, municipal road, private road, priority road, non-priority road, general road, highway). The "structures on the road" includes, for example, traffic signs, crosswalks, traffic lights, curve mirrors, pedestrian bridges, bus stops, garbage collection points, etc. The "structures around the road" includes, for example, various buildings, parks, etc.

[0049] The quasi-static information that makes up road information consists of information that needs to be updated within an hour, such as traffic restriction information due to road construction or events, wide-area weather information, and congestion forecasts.

[0050] The semi-dynamic information that makes up traffic information consists of information that needs to be updated within one minute, such as actual traffic congestion and driving restrictions at the time of observation, temporary driving obstructions such as fallen objects and obstacles, actual accident conditions, and local weather information.

[0051] The dynamic information that constitutes traffic information consists of information that requires updates every second, such as information transmitted and exchanged between moving objects, information on currently displayed traffic signals, information on pedestrians and cyclists at intersections, and information on vehicles traveling on roads. This road map data is maintained and updated in cycles 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.

[0052] The storage unit 30 is composed of, for example, non-volatile memory, such as EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, or resistive random-access memory. The storage unit 30 stores, for example, a road map DB 31, a knowledge graph DB 32, and a knowledge space DB 33, as shown in Figure 10.

[0053] The road map DB31 is a large-capacity storage medium such as an HDD, and stores high-precision road map data (dynamic map). This high-precision road map data includes, for example, static and quasi-static information that mainly constitutes road information, and quasi-dynamic and dynamic information that mainly constitutes traffic information.

[0054] Knowledge graph DB32 and knowledge space DB33 are knowledge data structured in a manner that can be used for analyzing traffic conditions, including traffic rules, reasoning rules, and common sense, respectively. Traffic conditions represent the relationship between the driving environment and the behavior of each traffic participant. Traffic rules refer to the rules that traffic participants must follow in order to participate in traffic in compliance with the rules. Reasoning rules refer to rules that allow conclusions about unknown matters to be drawn based on known matters. Common sense refers to the experience and collective intelligence that people implicitly know.

[0055] Knowledge Graph DB32 contains knowledge data that describes multiple known traffic scenarios in a graph structure. Known traffic scenarios refer to anticipated traffic scenarios or past traffic scenarios. A traffic scenario refers to a summary of traffic conditions that change moment by moment. In Knowledge Graph DB32, known traffic scenarios are described as conditional terms, and possible events (e.g., dangerous events) are described as resultal terms. In a traffic scenario, each traffic participant is assigned an identifier (ID), and the position and speed of each traffic participant are associated with each participant. Traffic participants may include, for example, the vehicle itself (SbjCar), as well as other vehicles (ObjCar), motorcycles (ObjMotorcycle), bicycles (ObjBicycle), and pedestrians (ObjPedestrian) that are present around the vehicle. In a traffic scenario, the lane each vehicle is traveling in and its type are also associated with each vehicle.

[0056] A known traffic scenario consists of multiple known traffic scenes in an integrated time series. A known traffic scene refers to a hypothetical traffic scene or a past traffic scene. A traffic scene refers to a compilation of traffic conditions at a given moment. In a traffic scenario, multiple traffic scenes are linked in a temporal order. A known traffic scenario consists of multiple traffic elements. Traffic elements may include, for example, object type, road structure, object speed, object position, or object state. Object types may include traffic participants (e.g., vehicles (passenger cars), motorcycles, bicycles, and pedestrians). Road structure may include, for example, intersections, traffic lights, and driving lanes. Object speed may include, for example, constant speed, deceleration, and acceleration. Object position may include, for example, identifiers indicating that an object is moving in a certain place, creating a blind spot in a certain place, approaching a certain place or object, stopping in a certain place, indicating a positional relationship with a certain place or object, being in a position to collide with an object, and entering a certain place. The state of an object may include, for example, the state of a traffic light. Traffic elements may further include, for example, the transition time between scenes. The transition time between scenes refers to the difference between the time of the first scene and the time of the second scene in two consecutive scenes (the first scene and the second scene) over time.

[0057] Traffic elements may also include, for example, a hazard level indicating the danger of a scene. A hazard level indicating the danger of a scene refers to an indicator of the likelihood that, in a given scene, the main vehicle and the traffic participants being monitored by the main vehicle (other vehicles, motorcycles, bicycles, or pedestrians) will interfere with each other (contact, collision). The hazard level is defined, for example, by the energy at the time of interference (contact, collision) or by the statistical frequency.

[0058] In the knowledge graph DB32, a certain known traffic scenario (hereinafter referred to as "Known Traffic Scenario I") is composed of multiple known traffic scenes (scene a, scene b, scene c) in an integrated time series, as shown in Figure 11, for example. In Known Traffic Scenario I, the multiple known traffic scenes (scene a, scene b, scene c) are linked in a temporal order. In Known Traffic Scenario I, each known traffic scene (scene a, scene b, scene c) is composed of multiple traffic elements. Known traffic scene (scene a) is composed of multiple traffic elements (traffic elements a1, a2, a3, a4, etc.), as shown in Figure 11, for example. Known traffic scene (scene b) is composed of multiple traffic elements (traffic elements b1, b2, b3, b4, etc.), as shown in Figure 11, for example. A known traffic scene (scene c) is composed of multiple traffic elements (traffic elements c1, c2, c3, c4, etc.), as shown in Figure 11, for example.

[0059] In the knowledge graph DB32, each known traffic scenario is composed of a directed graph containing multiple nodes (entities) and multiple edges (relationships) that connect two nodes (entities). Each node (entity) corresponds to a known traffic scene or traffic element. In each known traffic scenario, each entity and each relation is associated with a label, and a sentence (subject, predicate, object) is formed by two entities and one relation that connects these two entities.

[0060] The knowledge space DB 33 contains knowledge data that numerically represents (e.g., spatially represents) multiple known traffic scenarios included in the knowledge graph DB 32. The knowledge space DB 33 also contains a knowledge graph 33A that represents the known traffic scenario I included in the knowledge graph DB 32 as spatial vectors, as shown in Figure 12, for example. The knowledge graph 33A contains multiple identifiers (IDs), as shown in Figure 12, and each identifier (ID) is associated with attributes, labels, and spatial vectors, forming a table data. The identifiers (IDs) are used to identify each entity and each relation in the known traffic scenario I, and one is assigned to each entity and each relation.

[0061] Examples of labels assigned to entities and relationships include labels indicating object type, road structure, object speed, object location, and object state.

[0062] Examples of labels used to indicate the type of object include the following: • Labels indicating the primary vehicle (SbjCar) • Labels indicating secondary vehicles (ObjCar1, ObjCar2, ...) • Labels indicating motorcycles (ObjMotorcycle1, ObjMotorcycle2, ...) • Labels indicating pedestrians (ObjPedestrion1, ObjPedestrion12, ...) • Labels indicating bicycles (ObjBicycle1, ObjBicycle2, ...)

[0063] Examples of labels indicating road structure include the following: • Intersection label • Signal Intersection label • No Signal Intersection label • Traffic Signal label • Subject Lane label • Opposite Direction Lane label • Crossroad label

[0064] Examples of labels that indicate object velocity include the following: • A label indicating constant velocity (Constant) • A label indicating deceleration (Deceleration) • A label indicating acceleration (Acceleration)

[0065] Examples of labels that indicate the position of an object include the following: • Labels indicating the location where the object is moving (IsRunningOn) • Labels indicating that the object has velocity (HasSpeed) • Labels indicating that the object is creating a blind spot (CreateBlindIn) • Labels indicating that the object is approaching (ApproachTo) • Labels indicating that the object is stationary (stopAt) • Labels indicating the relative position of the object or road structure (nextRoadSegment) • Labels indicating that the object is in a position to collide (NearCrashTo) • Labels indicating that the object is entering (EnterIn) • Labels indicating that the object is moving away (Leave)

[0066] Examples of labels that indicate the state of an object include the following: • Labels indicating the lighting status of a traffic light (Light_turns_green, Light_turns_yellow, Light_turns_red)

[0067] In the knowledge space DB33, sets of (subject, predicate, object) are converted into numerical representations using an "embedding algorithm" so that specific operations can be performed on the sets of (subject, predicate, object) defined in the knowledge graph DB32. Examples of "embedding algorithms" include TransE or RESCAL. Here, the numerical representation is a spatial vector, as shown in Figure 12. When the numerical representation is a spatial vector, the "specific operation" means that when the entity corresponding to the subject is the first vector value, the relation corresponding to the predicate is the second vector value, and the entity corresponding to the object is the third vector value, then "the first vector value + the second vector value = the third vector value".

[0068] For example, in the subject-predicate-object set (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 true.

[0069] The "embedding algorithm" is stored, for example, in the memory unit 30. The calculation process for deriving the numerical representation using the "embedding algorithm" is performed, for example, in the risk prediction unit 41 described later. The numerical representation may be, for example, a two-dimensional vector or a vector of four or more dimensions. The numerical representation may also be a representation other than a vector, for example, a matrix.

[0070] The knowledge space DB33 may include sets of (subject, predicate, object) indicating the transition time of a known traffic scene, sets of (subject, predicate, object) indicating the risk level of a known traffic scene, and sets of (subject, predicate, object) indicating the risk level of a known traffic scenario. Figure 12 illustrates, for example, a set of (subject, predicate, object) indicating the transition time of a known traffic scene (scene a): (Scenea, HasTime, 0Sec). "Scenea, HasTime, 0Sec" means "the transition time of scene a is 0 seconds."

[0071] Furthermore, Figure 12 illustrates, for example, a set of (subject, predicate, object) indicating the risk level of a known traffic scene, namely (Scene a, HasRisk, RiskLevel_a). "Scene a, HasRisk, RiskLevel_a" means "The risk level of Scene a is a." Also, Figure 12 illustrates, for example, a set of (subject, predicate, object) indicating the risk level of a known traffic scenario, namely (Scenario_I, HasRisk, RiskLevel_I). "Scenario_I, HasRisk, RiskLevel_I" means "The risk level of Scenario I is RiskLevel_I." Note that RiskLevel_a and RiskLevel_I are actually expressed as specific numerical values.

[0072] Figure 13 shows an example of a known traffic situation (hereinafter referred to as "Known Traffic Situation A"). A known traffic scene (scene a) in the knowledge graph DB32 is generated, for example, in Known Traffic Situation A shown in Figure 13, based on the traffic context (see Figure 14) obtained by interpreting the data acquired by the data acquisition unit 411 (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 100g).

[0073] In known traffic conditions A, vehicle 100g is traveling on a road La with one lane in each direction. Road La consists of a driving lane La1 on which vehicle 100g is traveling, and an opposing lane La2 that runs alongside driving lane La2 via a center line. An intersection ISb is located in front of vehicle 100g on road La. Intersection ISb is an unsignalized intersection. Road La intersects with road Lb at intersection ISb. Road Lb is, for example, a road with one lane in each direction.

[0074] Vehicle 100g is flashing its right turn signal in order to turn right at intersection ISb. Vehicle 100c is traveling in the oncoming lane La2. Vehicle 100c is slowing down and flashing its headlights to give way to vehicle 100g at intersection ISb. The driver of vehicle 100g can see vehicle 100c traveling towards intersection ISb in the oncoming lane La2.

[0075] Figure 14 shows an example of a traffic context obtained by interpreting known traffic situation A. The traffic context of known traffic situation A consists of multiple sentences (subject, predicate, object), as shown in Figure 14.

[0076] In Figure 14, "Scene a, HasTime, 0sec" means "The transition time for scene a is 0 seconds." "SbjCar, isRunningOn, subjectLane" means "Vehicle 100g is traveling in lane La1." "subjectLane, nextRoadSegment, SignalIntersection" means "Lane La1 has a signalized intersection ISb as a road segment ahead of vehicle 100g."

[0077] "SbjCar, HasSpeed, Deceleration" means "Vehicle 100g is traveling at a reduced speed." "SbjCar, ApproachTo, NoSignalIntersection" means "Vehicle 100g is approaching intersection ISb." "SbjCar, Lighting, RightDirectionIndicator" means "Vehicle 100g has its right-turn indicator flashing."

[0078] "ObjCar,isRunningOn,OppositeDirectionLane" means "Vehicle 100c is traveling in the opposing lane La2." "ObjCar,HasSpeed,deceleration" means "Vehicle 100c is traveling at a reduced speed." "ObjCar,ApproachTo,NoSignalIntersection" means "Vehicle 100c is approaching intersection ISb." "ObjCar,isPassing,HeadLight" means "Vehicle 100c is flashing its headlights." "ObjCar,isPassing,HeadLight" is an example of the region-dependent parameter RD, which will be described later.

[0079] Figure 15 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. Known traffic scenes (scene b) in the knowledge graph DB 32 are generated based on the traffic context (see Figure 16) obtained by interpreting the data acquired by the data acquisition unit 411 in Known Traffic Situation B shown in Figure 15. In Known Traffic Situation B, vehicle 100g has started to enter intersection ISb as a result of traveling in lane La1. Vehicle 100c is stopped before intersection ISb.

[0080] Figure 16 shows an example of a traffic context obtained by interpreting known traffic situation B. The traffic context of known traffic situation B consists of multiple sentences (subject, predicate, object), as shown in Figure 16.

[0081] In Figure 16, "Scene b, HasTime, 1sec" means "The transition time for scene b is 1 second." "SbjCar, isRunningOn, subjectLane" means "Vehicle 100g is running in lane La1."

[0082] "SbjCar, HasSpeed, Deceleration" means "Vehicle 100g is traveling at a reduced speed." "SbjCar, EnterIn, NoSignalIntersection" means "Vehicle 100g is entering intersection ISb." "SbjCar, Islighting, RightDirectionIndicator" means "Vehicle 100g has its right turn signal flashing." "ObjCar, StopAt, OppositeDirectionLane" means "Vehicle 100c is stopped in the opposite lane La2." "ObjCar, isRightBefore, NoSignalIntersection" means "Vehicle 100c is stopped before intersection ISb."

[0083] Figure 17 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, the traffic situation one second after Known Traffic Situation B. The Known Traffic Scene (Scene C) in the Knowledge Graph DB 32 is generated based on the traffic context (see Figure 18) obtained by interpreting the data acquired by the data acquisition unit 411 in Known Traffic Situation C shown in Figure 17. In Known Traffic Situation C, vehicle 100c is stopped before intersection ISb, and vehicle 100g is turning right at intersection ISb before vehicle 100c.

[0084] In Figure 18, "Scene c, HasTime, 2sec" means "The transition time for scene c is 2 seconds." "SbjCar, isRunningOn, subjectLane" means "Vehicle 100g is running in lane La1."

[0085] "SbjCar, HasSpeed, Acceleration" means "Vehicle 100g is accelerating." "SbjCar, isTurningRightAt, NoSignalIntersection" means "Vehicle 100g is turning right at intersection ISb." "SbjCar, islighting, RightDirectionIndicator" means "Vehicle 100g has its right turn signal flashing." "ObjCar, StopAt, OppositeDirectionLane" means "Vehicle 100c is stopped in the opposite lane La2." "ObjCar, isRightBefore, NoSignalIntersection" means "Vehicle 100c is stopped before intersection ISb."

[0086] The control unit 40 is capable of controlling the entire vehicle 100g. The control unit 40 is, for example, a so-called ECU (Electronic Control Unit) and is composed of, for example, one or more processors and one or more memories. The control unit 40 may also be composed of, for example, a CPU (Central Processing Unit). In this case, the control unit 40 is capable of controlling the entire vehicle 100g by, for example, executing a program stored in a memory unit. The control unit 40 corresponds to one specific example of a "vehicle control device" according to one embodiment of the present disclosure.

[0087] The control unit 40 includes, for example, a locator unit. The locator unit is capable of acquiring the position coordinates of the vehicle 100g based on the positioning signal received through the communication unit 20. The locator unit is capable of estimating the vehicle's position on the road map by map matching the acquired position coordinates onto route map data. Based on the acquired position coordinates of the vehicle 100g, the locator unit is capable of acquiring map data for a predetermined range including the vehicle 100g from the map data stored in the road map DB (database) 31 described later.

[0088] The locator unit can switch to autonomous navigation, which estimates the vehicle's position on a road map based on vehicle speed, angular velocity, and longitudinal acceleration detected by the sensor unit 10, in environments where it is not possible to receive effective positioning signals from positioning satellites due to reduced sensitivity, such as when driving in a tunnel.

[0089] As described above, the locator unit estimates the position of the vehicle 100g on the road map (vehicle position) based on the positioning signal received through the communication unit 20 or the information detected by the sensor unit 10. Based on the estimated vehicle position on the road map, it is possible to determine the type of road the vehicle 100g is traveling on.

[0090] The locator unit can update the road map data stored in the road map DB 31 to the latest state using road map data acquired through external communication (vehicle-to-infrastructure communication and vehicle-to-vehicle communication) via the communication unit 20. This information update is performed not only on static information but also on quasi-static, quasi-dynamic, and dynamic information. As a result, the road map data is composed of road information and traffic information acquired through communication with the outside of the vehicle, and information on moving objects such as vehicles traveling on the road is updated in near real time.

[0091] The locator unit verifies the road map data based on the driving environment information recognized as described above, and can update the road map data stored in the road map DB31 to the latest state. This information update is performed not only on static information, but also on quasi-static information, quasi-dynamic information, and dynamic information. As a result, information on moving objects such as vehicles traveling on the road, as recognized as described above, is updated in real time.

[0092] The control unit 40 includes, for example, a hazard prediction unit 41, as shown in Figure 10. The hazard prediction unit 41 is capable of estimating whether or not the vehicle 100g is in a dangerous traffic situation. If, as a result of the estimation, the vehicle 100g is in a dangerous traffic situation, the hazard prediction unit 41 is capable of predicting hazards based on the most likely dangerous traffic scenarios that the vehicle 100g is facing. The hazard prediction unit 41 is capable of outputting the predicted hazard (hazardous event) to the driving control unit 42. The hazard prediction unit 41 corresponds to one specific example of the "information processing device" according to one embodiment of the present disclosure.

[0093] The risk prediction unit 41 includes, for example, a data acquisition unit 411, a scenario creation unit 412, a similarity determination unit 413, and a knowledge acquisition unit 414, as shown in Figure 10. The data acquisition unit 411 corresponds to one specific example of the "acquisition unit" according to one embodiment of the present disclosure. The scenario creation unit 412, the similarity determination unit 413, and the knowledge acquisition unit 414 correspond to one specific example of the "processing unit" according to one embodiment of the present disclosure.

[0094] The data acquisition unit 411 is capable of periodically acquiring data about the status or condition of the vehicle 100g. Specifically, the data acquisition unit 411 is capable of acquiring 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 100g. Furthermore, the data acquisition unit 411 is capable of acquiring map data of the area around the vehicle 100g from the road map DB 31 in the storage unit 30.

[0095] The road data surrounding the vehicle 100g, which is included in the various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, various control signals for various devices of the vehicle 100g, and the map data of the area around the vehicle 100g obtained from the road map DB 31, includes, for example, the names (labels), locations, number of lanes and presence or absence of a median strip of roads La and Lb, and the names (labels), locations and types of intersections ISb.

[0096] Traffic data about vehicle 100g and traffic participants around vehicle 100g, included in various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, various control signals for various devices of vehicle 100g, and map data of the area around vehicle 100g obtained from the road map DB 31, includes, for example, the name (label), location and speed of vehicle 100g, and the name (label), location and speed of traffic participants around vehicle 100g. This data corresponds to one specific example of "traffic data about the vehicle and traffic participants around the vehicle." The data including the "road data around the vehicle" and the "traffic data about the vehicle and traffic participants around the vehicle" described above corresponds to one specific example of "surrounding conditions data for the vehicle" according to one embodiment of this disclosure.

[0097] The scenario creation unit 412, the similarity determination unit 413, and the knowledge acquisition unit 414 are capable of processing multiple known traffic scenarios read from the storage unit 30 and data acquired by the data acquisition unit 411. Here, the data acquired by the data acquisition unit 411 includes, for example, image data of the area in front of the vehicle 100g (image data Ia and distance image data Ib), map data and situational data of the area around the vehicle 100g.

[0098] The scenario creation unit 412 is capable of generating external environment recognition data, as shown in Figure 19, based on the map data and situation data acquired by the data acquisition unit 411. Furthermore, the scenario creation unit 412 is capable of interpreting the traffic context, as shown in Figure 20, based on the generated external environment recognition data.

[0099] The scenario creation unit 412 can, for example, generate external environment recognition data based on map data and situation data acquired by the data acquisition unit 411 in the surrounding traffic conditions Ta, and interpret a new traffic context based on the newly generated external environment recognition data. For example, suppose a vehicle 100g is in the surrounding traffic conditions Ta as shown in Figure 1. In this case, the scenario creation unit 412 can interpret a traffic context (see Figure 20) based on data acquired by the data acquisition unit 411 in the surrounding traffic conditions Ta. The scenario creation unit 412 can, for example, generate external environment recognition data based on map data and situation data acquired by the data acquisition unit 411 in the surrounding traffic conditions Ta, and interpret a traffic context based on the generated external environment recognition data.

[0100] The scenario creation unit 412 can, for example, generate new external environment recognition data based on the situation data acquired by the data acquisition unit 411 one second after the surrounding traffic situation Ta, and interpret a new traffic context based on the newly generated external environment recognition data. For example, suppose that vehicle 100g is in a certain traffic situation (hereinafter referred to as "surrounding traffic situation Tb") one second after the surrounding traffic situation Ta, as shown in Figure 2. At this time, the scenario creation unit 412 can interpret the traffic context based on the data acquired by the data acquisition unit 411 in the surrounding traffic situation Tb. For example, the scenario creation unit 412 can generate new external environment recognition data based on the situation data acquired by the data acquisition unit 411 one second after the surrounding traffic situation Tb, and interpret a new traffic context (see Figure 21) based on the newly generated external environment recognition data.

[0101] The scenario creation unit 412 is capable of creating numerical representations of the obtained traffic contexts using the knowledge space DB 33. The scenario creation unit 412 is capable of generating multiple time-series surrounding traffic scenes having a graph structure from multiple time-series traffic contexts. For example, the scenario creation unit 412 is capable of generating two time-series surrounding traffic scenes (Scene 1, Scene 2) having a graph structure from two time-series traffic contexts. Surrounding traffic scene (Scene 1) is a traffic scene corresponding to surrounding traffic condition Ta. Surrounding traffic scene (Scene 2) is a traffic scene corresponding to surrounding traffic condition Tb. Furthermore, the scenario creation unit 412 is capable of creating a surrounding traffic scenario by integrating the multiple time-series surrounding traffic scenes that have been generated. For example, as shown in Figure 22, the scenario creation unit 412 is capable of creating surrounding traffic scenario II by integrating the two time-series surrounding traffic scenes (Scene 1, Scene 2) that have been generated.

[0102] Surrounding traffic scenario II is composed of multiple integrated time-series surrounding traffic scenes (Scene 1, Scene 2), as shown in Figure 22, for example. In surrounding traffic scenario II, the surrounding traffic scenes (Scene 1, Scene 2) are linked in a temporal order. In surrounding traffic scenario II, each surrounding traffic scene (Scene 1, Scene 2) is composed of multiple traffic elements. Surrounding traffic scene (Scene 1) is composed of multiple traffic elements (traffic elements α1, α2, α3, α4, etc.), as shown in Figure 22, for example. Surrounding traffic scene (Scene 2) is composed of multiple traffic elements (traffic elements β1, β2, β3, β4, etc.), as shown in Figure 22, for example.

[0103] The Peripheral Traffic Scenario II is composed of a directed graph containing multiple nodes (entities) and multiple relations (edges) that connect two nodes (entities). In Peripheral Traffic Scenario II, each entity and each relation is associated with a label, and a sentence (subject, predicate, object) is formed by two entities and one relation that connects these two entities.

[0104] The scenario creation unit 412 can, for example, read out a numerical representation (e.g., spatial vector V1) associated with the same label as each traffic element (e.g., α1, α2, α3, α4, etc.) included in the surrounding traffic scene (scene 1) from the knowledge graph 33A, etc., included in the knowledge space DB 33, for each traffic element. The scenario creation unit 412 can, for example, calculate a new numerical representation (e.g., spatial vector V2) based on the read-out numerical representations (e.g., spatial vector V1), and make the newly calculated numerical representation (e.g., spatial vector V2) the numerical representation (e.g., spatial vector V3) corresponding to the surrounding traffic scene (scene 1). The scenario creation unit 412 can, for example, calculate the centroid vector of the read-out vector values, and make the calculated centroid vector the spatial vector V3 corresponding to the surrounding traffic scene (scene 1).

[0105] The scenario creation unit 412 can, for example, read out numerical representations (e.g., spatial vector V4) associated with the same labels as each traffic element (e.g., β1, β2, β3, β4, etc.) included in the surrounding traffic scene (scene 2) from the knowledge graph 33A, etc., included in the knowledge space DB 33, for each traffic element. The scenario creation unit 412 can, for example, calculate a new numerical representation (e.g., spatial vector V5) based on the read-out numerical representations (e.g., spatial vector V4), and make the newly calculated numerical representation (e.g., spatial vector V5) the numerical representation (e.g., spatial vector V6) corresponding to the surrounding traffic scene (scene 2). The scenario creation unit 412 can, for example, calculate the centroid vector of the read-out vectors, and make the calculated centroid vector the spatial vector V6 corresponding to the surrounding traffic scene (scene 2). The surrounding traffic scenes (scene 1, scene 2) are represented by spatial vectors common to the spatial vectors in the knowledge space DB 33.

[0106] The similarity determination unit 413 is capable of calculating the similarity Sm between each known traffic scenario included in the knowledge graph DB 32 or knowledge space DB 33 and the surrounding traffic scenario created by the scenario creation unit 412.

[0107] The similarity determination unit 413 can, for example, read numerical representations (e.g., spatial vectors Va, Vb, Vc) of each known traffic scene (scene a, scene b, scene c) that constitutes the known traffic scenario I from the knowledge space DB 33. 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 I. 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 I. 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 I.

[0108] The similarity determination unit 413 can calculate 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 33 with the numerical representation of each surrounding traffic scene created by the scenario creation unit 412.

[0109] The similarity determination unit 413 can calculate the similarity S1 between each known traffic scene (scene a, scene b, scene c) and each surrounding traffic scene (scene 1, scene 2) by, for example, comparing the numerical representation (e.g., spatial vectors Va, Vb, Vc) of each known traffic scene (scene a, scene b, scene c) read from the knowledge space DB 33 with the numerical representation (e.g., spatial vectors V3, V6) of each surrounding traffic scene (scene 1, scene 2) created by the scenario creation unit 412. The similarity determination unit 413 can calculate the similarity S1 between each spatial vector Va, Vb, Vc and spatial vector V3, and the similarity S1 between each spatial vector Va, Vb, Vc and spatial vector V6.

[0110] The similarity determination unit 413 can calculate 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 the spatial vectors V2, V4. The similarity determination unit 413 can, for example, derive the difference between the first-order norm of each spatial vector Va, Vb, Vc and the first-order norm of each spatial vector V2, V4, and use the derived difference as the similarity S1. The similarity determination unit 413 may also, for example, derive the difference between the second-order norm of each spatial vector Va, Vb, Vc and the second-order norm of each spatial vector V2, V4, and use the derived difference as the similarity S1. The similarity determination unit 413 may, for example, derive the difference between the cosine similarity of each spatial vector Va, Vb, and Vc and the cosine similarity of each spatial vector V2 and V4, and use the derived difference as the similarity S1.

[0111] The similarity determination unit 413 calculates a similarity S2 based on the calculated similarity S1 for each known traffic scene, and the calculated similarity S2 can be used as the similarity Sm of the surrounding traffic scenario to the known traffic scene.

[0112] The similarity determination unit 413 can, for example, calculate the similarity S1 between each known traffic scene (scene a, scene b, scene c) in known traffic scenario I and the surrounding traffic scene (scene 1) in surrounding traffic scenario II, and set the largest similarity among the multiple calculated similarity S1s as similarity Sm1, and set the known traffic scene having similarity Sm1 as scene m1. Scene m1 is, for example, scene a. The similarity determination unit 413 can, for example, calculate the similarity S1 between each known traffic scene (scene a, scene b, scene c) in known traffic scenario I and the surrounding traffic scene (scene 2) in surrounding traffic scenario II, and set the largest similarity among the multiple calculated similarity S1s as similarity Sm2, and set the known traffic scene having similarity Sm2 as scene m2. Scene m2 is, for example, scene b. The similarity determination unit 413 can, for example, calculate similarity S2 using similarity Sm1 and similarity Sm2. The similarity determination unit 413 can, for example, use the average of similarity Sm1 and similarity Sm2 as similarity S2. The similarity determination unit 413 can, for example, use similarity S2 as the similarity Sm for known traffic scenes in surrounding traffic scenario II. The similarity determination unit 413 can, for example, calculate similarity S2 in the same manner as above for other known traffic scenarios, and use the calculated similarity S2 as the similarity Sm for known traffic scenes in surrounding traffic scenarios.

[0113] The similarity determination unit 413 is capable of determining that the known traffic scenario with the highest similarity Sm value among multiple known traffic scenarios included in the knowledge graph DB 32 or knowledge space DB 33 is the dangerous traffic scenario that vehicle 100g is most likely to face. The similarity determination unit 413 is also capable of storing the dangerous traffic scenario in the storage unit 40, as well as the similarity Sm of the dangerous traffic scenario (hereinafter referred to as "maximum similarity Smax") in the storage unit 40.

[0114] The knowledge acquisition unit 414 is capable of determining whether or not the region-dependent parameter RD is included in the traffic context corresponding to each surrounding traffic scene that constitutes the surrounding traffic scenario. The region-dependent parameter RD may include multiple elements, such as those shown below.

[0115] - Flashing headlights from oncoming vehicles (see Figure 1) - Size of signalized intersection (distance Da) (see Figure 3) - Distance Db, Df between your vehicle and oncoming vehicles across unsignalized intersections (see Figures 4 and 8) - Distance Dc to a preceding vehicle traveling in the same lane as your vehicle (see Figure 5) - Distance Dd to other vehicles in the left-hand store entrance / exit lane (see Figure 6) - Distance De to other vehicles in the right-hand store entrance / exit lane (see Figure 7)

[0116] The knowledge acquisition unit 414 can determine whether the surrounding traffic situation is a region-dependent traffic situation based on the obtained traffic context, if the region-dependent parameter RD is included in the traffic context corresponding to each surrounding traffic scene that constitutes the surrounding traffic scenario. If the surrounding traffic situation is not a region-dependent traffic situation, the knowledge acquisition unit 414 can make the dangerous traffic scenario a driver assistance traffic scenario. If the surrounding traffic situation is a region-dependent traffic situation, the knowledge acquisition unit 414 can generate region identification data RI, which includes the region-dependent parameter RD and the position data of vehicle 100g or map data corresponding to the position of vehicle 100g. The knowledge acquisition unit 414 can output the generated region identification data RI to the communication unit 20. When the communication unit 20 receives the region identification data RI from the knowledge acquisition unit 414, it can transmit the acquired region identification data RI to the server device 300. In response to this transmission, the communication unit 20 can receive a threshold or rule for the region-dependent parameter RD from the server device 300. The communication unit 20 is capable of outputting the threshold or rule of the received region-dependent parameter RD to the knowledge acquisition unit 414.

[0117] The knowledge acquisition unit 414 can calculate a correction value to correct the degree of danger associated with each surrounding traffic scene that constitutes a dangerous traffic scenario, using a threshold or rule of the region-dependent parameter RD obtained from the server device 300 via the communication unit 20. For example, the knowledge acquisition unit 414 can calculate a correction value to correct the degree of danger associated with a known traffic scene (scene b) included in the dangerous traffic scenario to a larger value. As a result, the knowledge acquisition unit 414 can determine, for example, that in a known traffic scene (scene b) corresponding to known traffic situation B, vehicle 100c may not stop before intersection ISb, enter intersection ISb, and vehicle 100g may come into contact with vehicle 100c.

[0118] The knowledge acquisition unit 414 is capable of creating new surrounding traffic scenarios by replacing the risk level associated with each surrounding traffic scene constituting a dangerous traffic scenario with a new risk level obtained by correcting the risk level using a threshold or rule of the region-dependent parameter RD. In other words, the knowledge acquisition unit 414 is capable of generating new surrounding traffic scenarios in which the existing risk levels have been replaced with new risk levels. The knowledge acquisition unit 414 is capable of using the generated new surrounding traffic scenarios as driving assistance traffic scenarios.

[0119] The knowledge acquisition unit 414 is capable of setting the intensity of at least one of the hazard warning control and hazard avoidance control based on the hazard level of the driving assistance traffic scenario. The knowledge acquisition unit 414 is capable of outputting the set intensity to the driving control unit 42, which will be described later.

[0120] The control unit 40 further includes a driving control unit 42, as shown in Figure 10, for example. The driving control unit 42 is capable of controlling the movement of the vehicle 100g (for example, the torque of the prime mover 50, the amount of brake pedal depression, and the steering angle of the steering wheel) and providing notifications to the driver of the vehicle 100g. The driving control unit 42 is capable of performing driving control and notification control using data acquired by the data acquisition unit 411 and data obtained by the knowledge acquisition unit 414.

[0121] The driving control unit 42 is capable of calculating a correction torque to correct the requested torque given to the accelerator control unit 421 (described later) based on the data acquired by the data acquisition unit 411 and the data obtained from the knowledge acquisition unit 414. The driving control unit 42 is capable of calculating a correction torque to correct the requested torque given to the brake control unit 422 (described later) based on the data acquired by the data acquisition unit 411 and the data obtained from the knowledge acquisition unit 414. The driving control unit 42 is capable of calculating a correction torque to correct the steering assist torque generated by the steering control unit 423 (described later) based on the data acquired by the data acquisition unit 411 and the data obtained from the knowledge acquisition unit 414. The driving control unit 42 is capable of generating notification data to notify the driver of the vehicle 100g based on the data acquired by the data acquisition unit 411 and the data obtained from the knowledge acquisition unit 414.

[0122] The driving control unit 42 includes, for example, an accelerator control unit 421, a brake control unit 422, a steering control unit 423, and a notification control unit 424, as shown in Figure 8.

[0123] The accelerator control unit 421 is capable of controlling the torque of the prime mover 50 based on the required torque corresponding to the amount the driver of the vehicle 100g presses the accelerator pedal. The accelerator control unit 421 is also capable of controlling the torque of the prime mover 50 based on a target torque which is the required torque plus a correction torque. The prime mover 50 is configured to drive the steering wheels of the vehicle 100g and is capable of driving the steering wheels of the vehicle 1 according to the required torque or target torque input from the accelerator control unit 421.

[0124] The brake control unit 422 is capable of controlling the torque of the brake 60 based on the required torque corresponding to the amount the driver of the vehicle 100g presses the brake pedal. The brake control unit 422 is also capable of controlling the torque of the brake 60 based on a target torque which is the required torque plus a correction torque. The brake 60 is configured to brake the steering wheels of the vehicle 100g and is capable of braking the steering wheels of the vehicle 100g according to the required torque or target torque input from the brake control unit 422.

[0125] The steering control unit 423 can derive a steering assist torque to assist the steering torque generated by the driver's steering wheel operation, and set an EPS torque corresponding to the derived steering assist torque. The steering control unit 423 can output 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 423 can output a control signal to the EPS motor 70 so that the output torque of the EPS motor 70 becomes the EPS torque considering the correction torque. The EPS motor 70 generates an output torque based on the input control signal and can control the steering angle of the steering wheel.

[0126] The accelerator control unit 421, brake control unit 422, and steering control unit 423 may include, for example, a CPU. In this case, the accelerator control unit 421, brake control unit 422, and steering control unit 423 can perform the various driving controls described above by, for example, executing control software stored in a memory unit.

[0127] The notification control unit 424 is capable of outputting notification data to the notification unit 80 for notifying the driver of the vehicle 100g. The notification control unit 424 is capable of generating a video signal including the above notification data and outputting it to the notification unit 80. The notification control unit 424 is capable of generating an audio signal including the above notification data and outputting it to the notification unit 80. The notification unit 80 is composed of, for example, a display panel and a speaker. When the notification unit 80 receives the above video signal from the control unit 40 (notification control unit 424), it is capable of displaying an image on the display screen corresponding to the input video signal. When the notification unit 80 receives the above audio signal from the control unit 40 (notification control unit 424), it is capable of outputting an audio from the speaker corresponding to the input audio signal.

[0128] Next, the server device 300 will be described. The server device 300 includes, for example, a communication unit 310, a storage unit 320, and a signal processing unit 330, as shown in Figure 23.

[0129] The communication unit 310 is a communication interface capable of receiving data from the driving control device 1000 via the network NW and transmitting data obtained by the signal processing unit 330 to the driving control device 1000. The communication unit 310 can receive regional identification data RI from the driving control device 1000 and output the received regional identification data RI to the signal processing unit 330. The communication unit 310 can obtain a threshold or rule for the region-dependent parameter RD from the signal processing unit 330 and transmit the obtained threshold or rule for the region-dependent parameter RD to the driving control device 1000 as a response to the reception of the regional identification data RI.

[0130] The storage unit 320 is composed of, for example, non-volatile memory, such as EEPROM, flash memory, or resistive random-access memory. The storage unit 320 stores multiple thresholds / rules 321, for example, as shown in Figure 23. The multiple thresholds / rules 321 describe thresholds or rules for parameters (region-dependent parameters RD) that exhibit regional characteristics due to customs and practices in various regions (e.g., regions α, β, ...).

[0131] The signal processing unit 330 includes a threshold rule acquisition unit 331. When the threshold rule acquisition unit 331 acquires regional identification data RI, it is possible to identify one or more threshold rules 321 for a region corresponding to the location data or map data included in the acquired regional identification data RI from among a plurality of threshold rules 321 read from the storage unit 320. The threshold rule acquisition unit 331 is also possible to extract the threshold rule 321 corresponding to the region-dependent parameter RD included in the acquired regional identification data RI from among the identified one or more threshold rules 321. In other words, when the threshold rule acquisition unit 331 acquires regional identification data RI, it is possible to read the threshold rule 321 corresponding to the acquired regional identification data RI from the storage unit 320. The threshold rule acquisition unit 331 is also possible to output the extracted threshold rules 321 to the communication unit 310.

[0132] (Driving Assistance Procedure) Next, the driving assistance procedure for vehicle 100g will be explained with reference to Figures 24 and 25. Figures 24 and 25 show an example of the driving assistance procedure for vehicle 100g.

[0133] Vehicle 100g (hazard prediction unit 41) 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 vehicle 100g. Based on the acquired data, vehicle 100g acquires location data of vehicle 100g and traffic participants around vehicle 100g. Vehicle 100g further acquires surrounding map data including the acquired location of vehicle 100g from the road map data DB 31 of the storage unit 30. Vehicle 100g further acquires image data Ia or distance image data Ib from the stereo camera. From the map data and image data Ia or distance image data Ib, etc., vehicle 100g acquires attribute data and location data of each structure constituting the road around vehicle 100g, and attribute data and location data of structures on the road around vehicle 100g. In this way, image data of the area in front of the vehicle 100g (image data Ia or distance image data Ib), map data of the area around the vehicle 100g, and situational data of the area around the vehicle 100g are acquired (step S201).

[0134] Next, vehicle 100g generates external recognition data based on acquired image data, map data, and situation data, and interprets the traffic context based on the generated external recognition data (step S202). Vehicle 100g reinterprets the traffic context based on periodically obtained image data, map data, and situation data, for example. From the multiple time-series traffic contexts, vehicle 100g generates multiple time-series surrounding traffic scenes having a graph structure (step S203). Subsequently, vehicle 100g integrates the generated time-series surrounding traffic scenes to create a surrounding traffic scenario (step S204).

[0135] Next, vehicle 100g calculates the similarity Sm between each known traffic scenario in the knowledge space DB33 and the surrounding traffic scenario it created (step S205). Vehicle 100g determines that the known traffic scenario with the highest similarity Sm value among the multiple known traffic scenarios in the knowledge graph DB32 or the knowledge space DB33 is the dangerous traffic scenario that vehicle 100g is most likely to face (step S206). Vehicle 100g is capable of determining whether or not the dangerous traffic scenario is region-dependent (step S207). Specifically, vehicle 100g determines whether or not the region-dependent parameter RD is included in each traffic context that constitutes the dangerous traffic scenario. If the region-dependent parameter RD is included in the traffic context, vehicle 100g determines, based on the obtained traffic context, whether or not the surrounding traffic situation is a region-dependent traffic situation.

[0136] If the surrounding traffic conditions are not area-dependent (step S207; N), vehicle 100g sets the dangerous traffic scenario as a driver assistance traffic scenario (step S215). If the surrounding traffic conditions are area-dependent (step S207; Y), vehicle 100g requests the server device 300 to provide thresholds and rules for the area-dependent parameter RD (step S208). Specifically, vehicle 100g generates area identification data RI, which includes the area-dependent parameter RD and the location data of vehicle 100g or map data corresponding to the location of vehicle 100g, and transmits the generated area identification data RI to the server device 300. The transmission of the area identification data RI constitutes a request for thresholds and rules for the area-dependent parameter RD.

[0137] The server device 300 receives a request for thresholds and rules for the region-dependent parameter RD from the vehicle 100g (step S209). The server device 300 receives region identification data RI from the vehicle 100g as a request for thresholds and rules for the region-dependent parameter RD. The server device 300 identifies one or more thresholds and rules 321 from among a plurality of thresholds and rules 321 for the region corresponding to the location data or map data included in the received region identification data RI. The server device 300 extracts the threshold or rule 321 corresponding to the region-dependent parameter RD from among the identified thresholds and rules 321. The server device 300 transmits the extracted threshold or rule 321 to the vehicle 100g (step S210).

[0138] Vehicle 100g receives thresholds and rules 321 from server device 300 (step S211). Vehicle 100g generates a new surrounding traffic scenario using the received thresholds and rules 321 (step S212). Specifically, vehicle 100g calculates a correction value to correct the degree of danger associated with each surrounding traffic scene constituting the dangerous traffic scenario using the received thresholds and rules 321. Vehicle 100g creates a new surrounding traffic scenario by replacing the degree of danger associated with each surrounding traffic scene constituting the dangerous traffic scenario with a new degree of danger obtained by correcting the degree of danger using the threshold or rule of the region-dependent parameter RD.

[0139] Vehicle 100g stores the new surrounding traffic scenario in the memory unit 30 (step S213). Vehicle 100g uses the new surrounding traffic scenario as a driving assistance traffic scenario (step S214). Based on the degree of danger of the driving assistance traffic scenario defined in steps S214 and S215, vehicle 100g sets the intensity of at least one of the danger notification control and danger avoidance control (step S216). Vehicle 100g implements the danger notification control and danger avoidance control according to the set intensity (step S217).

[0140] Vehicle 100g performs driving control and notification control using various data acquired by the data acquisition unit 411, a driving support traffic scenario, and the intensity set in the driving support traffic scenario. Based on the various data acquired by the data acquisition unit 411, the driving support traffic scenario, and the intensity set in the driving support traffic scenario, vehicle 100g calculates a correction torque to correct the requested torque given to the accelerator control unit 421. Based on the various data acquired by the data acquisition unit 411, the driving support traffic scenario, and the intensity set in the driving support traffic scenario, vehicle 100g calculates a correction torque to correct the requested torque given to the brake control unit 422. Based on the various data acquired by the data acquisition unit 411, the driving support traffic scenario, and the intensity set in the driving support traffic scenario, vehicle 100g corrects the steering assist torque generated by the steering control unit 423. Vehicle 100g generates notification data to be sent to the driver of vehicle 100g based on various data acquired by the data acquisition unit 411, a driving assistance traffic scenario, and the intensity set for the driving assistance traffic scenario.

[0141] The accelerator control unit 421 controls the torque of the prime mover 50 based on a target torque which is the required torque plus a correction torque. The prime mover 50 drives the steering wheels of the vehicle 100g according to the target torque input from the accelerator control unit 421. The brake control unit 422 controls the torque of the brake 60 based on a target torque which is the required torque plus a correction torque. The brake 60 brakes the steering wheels of the vehicle 100g according to the target torque input from the brake control unit 422. The steering control unit 423 outputs a control signal to the EPS motor 70 so that the output torque of the EPS motor 70 becomes the EPS torque which takes the correction torque into account. The EPS motor 70 generates an output torque based on the input control signal and controls the steering angle of the steering wheel.

[0142] The notification control unit 424 generates a video signal including the notification data and outputs it to the notification unit 80. The notification control unit 424 further generates an audio signal including the notification data and outputs it to the notification unit 80. When the notification unit 80 receives the video signal from the control unit 40 (notification control unit 424), for example, it displays an image on the display screen corresponding to the input video signal. When the notification unit 80 receives the audio signal from the control unit 40 (notification control unit 424), for example, it outputs an audio from the speaker corresponding to the input audio signal. In this way, driving assistance is performed in the vehicle 100g.

[0143] [Effects] Next, the effects of a vehicle 100g according to one embodiment of the present disclosure will be described.

[0144] In this embodiment, a correction value is calculated using the threshold rule 321 to correct the degree of danger associated with each surrounding traffic scene constituting the surrounding traffic scenario (dangerous traffic scenario). Based on the degree of danger and the calculated correction value, the intensity of danger warning control or danger avoidance control is set. As a result, if the traffic context corresponding to each surrounding traffic scene constituting the surrounding traffic scenario (dangerous traffic scenario) includes the region-dependent parameter RD, the degree of danger can be set to take into account the region in which vehicle 100g is traveling. Consequently, for example, if the driver of vehicle 100g faces a traffic situation that is interpreted differently depending on the region, driving assistance can be provided that takes into account the region in which vehicle 100g is traveling.

[0145] In this embodiment, regional identification data RI is transmitted to the server device 300, and thresholds and rules 321 are received from the server device 300 in response to the transmission of regional identification data RI. As a result, if the traffic context corresponding to each surrounding traffic scene constituting the surrounding traffic scenario (dangerous traffic scenario) includes a region-dependent parameter RD, the thresholds and rules 321 obtained from the server device 300 can be used to provide driving assistance that takes into account the regional characteristics of the area in which the vehicle 100g is traveling.

[0146] In this embodiment, when regional identification data RI is acquired by the server device 300, thresholds and rules 321 corresponding to the acquired regional identification data RI are read from the storage unit 320. The read thresholds and rules 321 are transmitted to the vehicle 100g as a response to the acquisition of the identification data RI. As a result, if the traffic context corresponding to each surrounding traffic scene constituting the surrounding traffic scenario (dangerous traffic scenario) includes a region-dependent parameter RD, the thresholds and rules 321 obtained from the server device 300 can be used to provide driving assistance that takes into account the region in which the vehicle 100g is traveling.

[0147] In this embodiment, a new surrounding traffic scenario is created by replacing the risk level associated with each surrounding traffic scene constituting the dangerous traffic scenario with a new risk level obtained by correcting the risk level using the threshold rule 321. As a result, if the traffic context corresponding to each surrounding traffic scene constituting the dangerous traffic scenario includes the region-dependent parameter RD, the newly obtained new risk level can be used to provide driving assistance that takes into account the regional characteristics of the area in which the vehicle 100g is traveling.

[0148] <3. Modified Examples> Next, modified examples of the above embodiment will be described.

[0149] [Modification 3-1] In the above embodiment, the control unit 40 may further include a region-dependent parameter management unit 43 having a threshold rule acquisition unit 231, as shown in Figure 26. Furthermore, the storage unit 30 may further store threshold rules 321, as shown in Figure 26.

[0150] In this modified example, the threshold rule acquisition unit 231 is capable of identifying one or more threshold rules 321 for a region corresponding to location data or map data included in the regional identification data RI generated by the knowledge acquisition unit 414, from among the multiple threshold rules 321 read from the storage unit 30. The threshold rule acquisition unit 331 is capable of extracting the threshold rule 321 corresponding to the region-dependent parameter RD included in the acquired regional identification data RI from among the identified one or more threshold rules 321. In other words, the threshold rule acquisition unit 331 is capable of reading the threshold rules 321 corresponding to the regional identification data RI from the storage unit 30. The threshold rule acquisition unit 331 is capable of outputting the extracted threshold rules 321 to the knowledge acquisition unit 414.

[0151] In this modified version, the functions of the server 30 are provided in the driving control device 1000. This allows for driving assistance that takes into account the regional characteristics of the area in which the vehicle 100g is traveling, using thresholds and rules 321, without the need for a server device 300.

[0152] [Modification 3-2] In the above embodiment, the storage unit 30 may further store, for example, a rear-end collision priority list 34, a head-on collision priority list 35, a cutting-in accident priority list 36, a right-turn accident priority list 37, an accident statistics frequency table 38, and a write list 39, as shown in Figure 27. In the rear-end collision priority list 34, the head-on collision priority list 35, the cutting-in accident priority list 36, and the right-turn accident priority list 37, for example, as shown in Figures 28(A), 28(B), 28(C), and 28(D), the multiple traffic scenarios stored in the storage unit 30 are classified according to the type of accident.

[0153] The rear-end collision priority list 34 includes multiple traffic scenarios related to rear-end collisions. The intersection collision priority list 35 includes multiple traffic scenarios related to intersection collisions. The cut-in accident priority list 36 includes multiple traffic scenarios related to cut-in accidents. The right-turn accident priority list 37 includes multiple traffic scenarios related to right-turn accidents. In each of the rear-end collision priority list 34, intersection collision priority list 35, cut-in accident priority list 36, and right-turn accident priority list 37, multiple traffic scenarios are arranged in descending order of abstraction, as shown, for example, in Figures 28(A), 28(B), 28(C), and 28(D).

[0154] Here, the level of abstraction refers to an indicator that shows how well a given accident type covers multiple traffic scenarios. For example, the element most commonly found among multiple hazardous situation patterns included in a particular accident type is designated as the first common element, the second most commonly found element among these hazardous situation patterns is designated as the second common element, and the nth most commonly found element among these hazardous situation patterns is designated as the nth common element. In this case, a traffic scenario that contains a high proportion of high-ranking common elements is assigned a high level of abstraction. On the other hand, a traffic scenario that contains only a low proportion of high-ranking common elements is assigned a low level of abstraction. Theoretically, a traffic scenario consisting only of the first common element is assigned the highest level of abstraction, and a traffic scenario that contains no common elements at all is assigned the lowest level of abstraction.

[0155] In the accident statistics frequency table 38, for example, as shown in Figure 29, the proportion of accident occurrences is assigned to each type of accident. In the write list 39, if there is a limit to the capacity that can be written to the memory unit 30 for traffic scenarios, the number of traffic scenarios that can be written within that limit and the number of traffic scenarios for each type of accident that can be written are specified. In the write list 39, for example, as shown in Figure 30, two traffic scenarios for rear-end collisions are assigned, two traffic scenarios for intersection collisions are assigned, and one traffic scenario for cutting in is assigned.

[0156] The knowledge acquisition unit 414 is capable of identifying accident patterns in new surrounding traffic scenarios. For example, the knowledge acquisition unit 414 can identify accident patterns in new surrounding traffic scenarios by analyzing them. Furthermore, the knowledge acquisition unit 414 is capable of deriving the level of abstraction of new surrounding traffic scenarios. For example, the knowledge acquisition unit 414 can determine the extent to which common elements are included in new surrounding traffic scenarios, and thereby derive the level of abstraction of new surrounding traffic scenarios.

[0157] The knowledge acquisition unit 414 is capable of setting a write capacity limit for each hazard type according to the frequency of occurrence of each hazard type. Specifically, the knowledge acquisition unit 414 is capable of setting a write capacity limit for each hazard type based on the priority list for each accident type (rear-end collision priority list 34 to accident statistics frequency table 38) and the write list 39 stored in the memory unit 30. The knowledge acquisition unit 414 is capable of storing new surrounding traffic scenarios in the memory unit 30 based on the level of abstraction of the hazard type, as long as the set write capacity limit for each hazard type is not exceeded. Specifically, the knowledge acquisition unit 414 is capable of comparing the level of abstraction of each scenario included in the priority list corresponding to the accident type of the new surrounding traffic scenario with the level of abstraction of the new surrounding traffic scenario, and determining the rank of the new surrounding traffic scenario in terms of abstraction. The knowledge acquisition unit 414 is capable of determining whether the rank obtained by the determination exceeds the maximum number of scenarios with the same accident type as the accident type of the new surrounding traffic scenario that can be included in the write list 39. The knowledge acquisition unit 414 can store a new surrounding traffic scenario in the memory unit 30 if the ranking obtained by the determination does not exceed the maximum number mentioned above. The knowledge acquisition unit 414 can not store a new surrounding traffic scenario in the memory unit 30 if the ranking obtained by the determination exceeds the maximum number mentioned above.

[0158] (Driving Assistance Procedure) Next, the driving assistance procedure for vehicle 100g will be explained with reference to Figure 31. Figure 31 shows an example of the driving assistance procedure following Figure 24.

[0159] After generating a new surrounding traffic scenario, the vehicle 100g determines whether it is possible to write the generated new surrounding traffic scenario to the storage unit 30 (step S218). If the vehicle 100g cannot write the new surrounding traffic scenario to the storage unit 30 (step S218; N), it sets the dangerous traffic scenario as a driver assistance traffic scenario (step S215). If the vehicle 100g can write the new surrounding traffic scenario to the storage unit 30 (step S218; Y), it stores the new surrounding traffic scenario in the storage unit 30 (step S213). The vehicle 100g sets the new surrounding traffic scenario as a driver assistance traffic scenario (step S214).

[0160] Vehicle 100g sets the intensity of at least one of the hazard warning control and hazard avoidance control based on the hazard level of the driving assistance traffic scenario defined in steps S214 and S215 (step S216). Vehicle 100g implements hazard warning control and hazard avoidance control according to the set intensity (step S217). Vehicle 100g implements hazard warning control and hazard avoidance control according to the set intensity (step S122). Vehicle 100g performs driving control and warning control in the same manner as in the above embodiment. In this way, driving assistance is performed in vehicle 100g.

[0161] In this modified version, a write capacity limit is set for each hazard type according to the frequency of occurrence of each hazard type, and new surrounding traffic scenarios are stored in the memory unit 30 based on the level of abstraction of the hazard type, within the limits of the set write capacity limit for each hazard type. This makes it possible to provide driving assistance that takes into account the characteristics of the area in which the vehicle 100g is traveling, even if there is a limit to the write capacity of the memory unit 30.

[0162] [Modification 3-3] In modification 3-1 described above, the memory unit 30 may further store, for example, a rear-end collision priority list 34, an intersection collision priority list 35, a cutting-in collision priority list 36, a right-turn collision priority list 37, an accident statistics frequency table 38, and a write list 39, as shown in Figure 32. In this case, similar to modification 3-2 described above, even if there is a limit to the write capacity of the memory unit 30, driving assistance that takes into account the characteristics of the area in which the vehicle 100g travels can be provided.

[0163] [Modification 3-4] In the above embodiment, the control unit 40 may further include a threshold rule generation unit 44, for example, as shown in Figure 33. The threshold rule generation unit 44 is capable of storing the created dangerous traffic scenario in the storage unit 30 in association with the created regional identification data RI each time the similarity determination unit 413 and the knowledge acquisition unit 414 create a dangerous traffic scenario and regional identification data RI. The threshold rule generation unit 44 is capable of generating thresholds or rules (thresholds / rules 321) for parameters (region-dependent parameters RD) that exhibit regional characteristics due to regional customs and habits, etc., corresponding to location data or map data included in the regional identification data RI, based on a plurality of dangerous traffic scenarios among the plurality of dangerous traffic scenarios stored in the storage unit 30 to which a common regional identification data RI is associated. The threshold rule generation unit 44 is capable of storing the generated thresholds or rules (thresholds / rules 321) in the storage unit 30 each time it generates thresholds or rules for the regional-dependent parameters RD. In other words, the threshold rule generation unit 44 is capable of generating threshold rules 321.

[0164] The threshold rule generation unit 44 is capable of identifying one or more threshold rules 321 from among the multiple threshold rules 321 it has generated that correspond to the location data or map data included in the identification data RI newly acquired by the knowledge acquisition unit 414. The threshold rule generation unit 44 is capable of extracting the threshold rule 321 from among the one or more threshold rules 321 that correspond to the region-dependent parameter RD. The threshold rule generation unit 44 is capable of outputting the extracted threshold rules 321 to the knowledge acquisition unit 414 communication unit 310. The knowledge acquisition unit 414 is capable of using the threshold rules 321 (thresholds or rules for the region-dependent parameter RD) acquired from the threshold rule generation unit 44 to calculate a correction value that corrects the degree of danger associated with each surrounding traffic scene that constitutes the dangerous traffic scenario.

[0165] In this modified version, each time a dangerous traffic scenario and regional identification data RI are created, the created dangerous traffic scenario is stored in the storage unit 30 in association with the created regional identification data RI. Based on the multiple dangerous traffic scenarios stored in the storage unit 30 that share a common regional identification data RI, thresholds or rules (thresholds / rules 321) are generated for parameters (region-dependent parameters RD) that exhibit regional characteristics based on local customs and practices corresponding to the location data or map data included in the regional identification data RI. This allows for the calculation of correction values ​​to adjust the degree of danger associated with each surrounding traffic scene constituting the dangerous traffic scenario using the generated thresholds or rules. Furthermore, the intensity of danger warning control or danger avoidance control can be set based on the degree of danger and the calculated correction value. Thus, in this modified version, driving assistance that takes into account the regional characteristics of the area in which the vehicle 100g travels can be performed even if multiple thresholds / rules 321 are not prepared in advance.

[0166] [Modification 3-5] In modification 3-2 described above, the control unit 40 may further include a threshold rule generation unit 44, for example, as shown in Figure 34. In this case, similar to modification 3-4, driving assistance that takes into account the regional characteristics of the area in which the vehicle 100g travels can be provided even if a plurality of thresholds / rules 321 are not prepared in advance.

[0167] Although this disclosure has been described above with reference to several embodiments and their modifications, this disclosure is not limited to these embodiments, and various modifications are possible. The effects described herein are merely illustrative, and the effects of this disclosure are not limited to those described herein. Therefore, other effects may be obtained with respect to this disclosure.

[0168] The first and second embodiments and their modifications were based on the premise of a country or region with traffic regulations in which vehicle 100g etc. travels in the left lane. However, if the premise is a country or region with traffic regulations in which vehicle 100g etc. travels in the right lane, then in the first and second embodiments and their modifications, "right" shall be read as "left" and "left" as "right".

[0169] Furthermore, for example, this disclosure can take the following configuration: (1) An information processing device comprising: an acquisition unit capable of acquiring vehicle location data, map data and situation data of the area around the vehicle, and acquiring thresholds or rules for region-dependent parameters corresponding to the location data from an external device; and a processing unit capable of interpreting a traffic context based on the map data and the situation data, 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, wherein the processing unit is capable of calculating a correction value to correct the degree of risk associated with each of the surrounding traffic scenes constituting the surrounding traffic scenario using the thresholds or rules for the region-dependent parameters, and setting the intensity of risk warning control or risk avoidance control based on the degree of risk and the correction value. (2) The information processing device according to (1), further comprising: a communication unit capable of transmitting region identification data to the external device, and receiving thresholds or rules for region-dependent parameters from the external device as a response to the transmission of the region identification data.(3) An information processing device comprising: an acquisition unit capable of acquiring vehicle location data, map data and situation data of the area surrounding the vehicle; a processing unit capable of interpreting a traffic context based on the map data and the situation data, 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, wherein each time the surrounding traffic scenario is created, the processing unit stores the created surrounding traffic scenario in a storage unit in association with regional identification data corresponding to the location data; generates a threshold or rule for region-dependent parameters based on a plurality of surrounding traffic scenarios stored in the storage unit to which the common regional identification data is associated; calculates a correction value to correct the degree of risk associated with each of the surrounding traffic scenes constituting the surrounding traffic scenario using the generated threshold or rule for region-dependent parameters; and sets the intensity of risk warning control or risk avoidance control based on the degree of risk and the correction value. (4) An information processing device comprising: a storage unit that stores thresholds or rules for a plurality of region-dependent parameters defined for each region; a receiving unit that can receive region identification data; a processing unit that, when the receiving unit receives the region identification data from a vehicle, can read the thresholds or rules for the region-dependent parameters corresponding to the received region identification data from the storage unit; and a transmitting unit that, as a response to the receipt of the region identification data, can transmit the thresholds or rules for the region-dependent parameters read from the storage unit by the processing unit to the vehicle. (5) An information processing device according to any one of (1) to (3), wherein the processing unit is capable of creating a new traffic scenario in which the degree of risk associated with each of the surrounding traffic scenes constituting the surrounding traffic scenario is replaced with a new degree of risk obtained by correcting the degree of risk using the thresholds or rules for the region-dependent parameters.(6) The information processing device according to (5), wherein the processing unit sets a write capacity frame for each of the hazard types according to the frequency of occurrence of each hazard type, and stores the new traffic scenario in the storage unit based on the level of abstraction of the hazard type, within the range that does not exceed the set write capacity frame for each of the hazard types. (7) A vehicle control device comprising: a scenario acquisition unit capable of acquiring the new traffic scenario from the information processing device according to any one of (1) to (6); and a control unit capable of performing at least one of notification control and driving control based on the new traffic scenario acquired by the scenario acquisition unit. (8) A vehicle comprising: a notification device and a driving device; and a control unit capable of performing at least one of notification control for the notification device and driving control for the driving device based on the new traffic scenario acquired from the information processing device according to any one of (1) to (6).

[0170] The control unit 40 shown in Figures 10, 26, 27, 32, 33, and 34, and the signal processing unit 330 shown in Figure 23 (hereinafter referred to as "control unit 40, etc.") can be implemented by a circuit 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). At least one processor can be configured to execute all or part of the various functions of the control unit 40, etc. by reading instructions from at least one non-temporary and tangible computer-readable medium. Such a medium can take various forms, including, but is not limited to, various magnetic media such as hard disks, various optical media such as CDs or DVDs, and various semiconductor memories such as volatile memory or non-volatile memory (i.e., semiconductor circuits). Volatile memory may include DRAM and SRAM. Non-volatile memory may include ROM and NVRAM. An ASIC is an integrated circuit (IC) specialized to perform all or part of the various functions in the control unit 40, etc. An FPGA is an integrated circuit designed to be configurable after manufacturing to perform all or part of the various functions in the control unit 40, etc.

Claims

1. An information processing device comprising: an acquisition unit capable of acquiring vehicle location data, map data and situation data of the area surrounding the vehicle, and obtaining thresholds or rules for region-dependent parameters corresponding to the location data from an external device; and a processing unit capable of interpreting a traffic context based on the map data and situation data, thereby generating multiple time-series surrounding traffic scenes, and integrating the generated time-series surrounding traffic scenes to create a surrounding traffic scenario, wherein the processing unit can calculate a correction value to correct the degree of risk associated with each of the surrounding traffic scenes constituting the surrounding traffic scenario using the thresholds or rules for the region-dependent parameters, and set the intensity of risk warning control or risk avoidance control based on the degree of risk and the correction value.

2. The information processing apparatus according to claim 1, further comprising a communication unit capable of transmitting regional identification data to the external device and receiving a threshold or rule for the region-dependent parameter from the external device as a response to the transmission of the regional identification data.

3. An information processing device comprising: an acquisition unit capable of acquiring vehicle location data, map data and situation data of the area surrounding the vehicle; a processing unit capable of interpreting a traffic context based on the map data and the situation data, thereby generating multiple time-series surrounding traffic scenes, and integrating the generated multiple time-series surrounding traffic scenes to create a surrounding traffic scenario, wherein each time the surrounding traffic scenario is created, the processing unit stores the created surrounding traffic scenario in a storage unit in association with regional identification data corresponding to the location data; generates thresholds or rules for regional-dependent parameters based on multiple surrounding traffic scenarios among the multiple surrounding traffic scenarios stored in the storage unit that have common regional identification data associated with them; calculates a correction value to correct the degree of risk associated with each surrounding traffic scene constituting the surrounding traffic scenario using the generated thresholds or rules for regional-dependent parameters; and sets the intensity of risk warning control or risk avoidance control based on the degree of risk and the correction value.

4. An information processing device comprising: a storage unit that stores thresholds or rules for a plurality of region-dependent parameters defined for each region; a receiving unit capable of receiving region identification data; a processing unit that, when the receiving unit receives the region identification data from a vehicle, can read the thresholds or rules for the region-dependent parameters corresponding to the received region identification data from the storage unit; and a transmitting unit that, as a response to the receipt of the region identification data, can transmit the thresholds or rules for the region-dependent parameters read from the storage unit by the processing unit to the vehicle.

5. The processing unit is capable of creating a new traffic scenario by replacing the risk level associated with each of the surrounding traffic scenes constituting the surrounding traffic scenario with a new risk level obtained by correcting the risk level using the threshold or rule of the region-dependent parameter. The information processing device according to any one of claims 1 to 3.

6. The information processing apparatus according to claim 5, wherein the processing unit sets a write capacity limit for each hazard type according to the frequency of occurrence of each hazard type, and stores the new traffic scenario in the storage unit based on the level of abstraction of the hazard type, within a range that does not exceed the set write capacity limit for each hazard type.

7. A vehicle control device comprising: a scenario acquisition unit capable of acquiring the new traffic scenario from the information processing device described in any one of claims 1 to 4; and a control unit capable of performing at least one of notification control and driving control based on the new traffic scenario acquired by the scenario acquisition unit.

8. A vehicle comprising a notification device and a running device, and a control unit capable of performing at least one of notification control to the notification device and running control to the running device based on the new traffic scenario obtained from the information processing device according to any one of claims 1 to 4.

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