Driving assistance device and vehicle

Abstract

A driving assistance device according to an embodiment of the present disclosure is capable of: predicting whether a second vehicle traveling ahead of a first vehicle will commit a traffic violation, on the basis of travel information of the second vehicle and traffic-related information ahead of the second vehicle; if the second vehicle is predicted to commit a traffic violation, predicting the action of the second vehicle to avoid the traffic violation once the driver of the second vehicle notices the traffic violation; and performing travel control or notification control to prevent the first vehicle from colliding with the second vehicle, on the basis of travel information of the first vehicle, the travel information of the second vehicle, and the predicted action of the second vehicle.

Description

Driving Support Device and Vehicle

[0001] The present disclosure relates to a driving support device mounted on a vehicle and a vehicle equipped with such a driving support device.

[0002] Technologies for predicting traffic violations have been proposed, for example, in Patent Documents 1, 2, etc.

[0003] International Publication WO2021 / 063006 Gazette of Japanese Patent Laid-Open No. 2007-080216

[0004] The driving support device according to an embodiment of the present disclosure includes an acquisition unit and a control unit. The acquisition unit can acquire the driving information of a second vehicle traveling in front of a first vehicle and traffic-related information in front of the second vehicle. The control unit can perform driving support for the first vehicle based on the driving information and traffic-related information acquired by the acquisition unit. The control unit can execute the following three processes. (A1) Predicting a traffic violation of the second vehicle based on the driving information and traffic-related information (A2) When a traffic violation of the second vehicle is predicted, predicting the operation of the second vehicle for the driver of the second vehicle to notice the traffic violation and avoid the traffic violation (A3) Performing driving control or notification control to avoid the first vehicle from colliding with the second vehicle based on the driving information of the first vehicle, the driving information of the second vehicle, and the predicted operation of the second vehicle

[0005] A vehicle according to one embodiment of the present disclosure comprises a driver assistance device and a controlled vehicle controlled by the driver assistance device. The driver assistance device has an acquisition unit capable of acquiring driving information of a target vehicle traveling in front of the vehicle and traffic-related information in front of the target vehicle, and a control unit capable of providing driving assistance to the vehicle based on the driving information and traffic-related information acquired by the acquisition unit. The control unit is capable of performing the following three processes: (B1) predicting a traffic violation by the target vehicle based on the driving information and traffic-related information; (B2) predicting the actions of the target vehicle to avoid the traffic violation when a traffic violation by the target vehicle is predicted, so that the driver of the target vehicle will notice the traffic violation; (B3) performing driving control or notification control on the controlled vehicle to avoid a collision between the vehicle and the target vehicle, based on the vehicle's driving information, the target vehicle's driving information, and the predicted actions of the target vehicle.

[0006] 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.

[0007] Figure 1 is a diagram illustrating an example of traffic conditions at a T-junction with a one-way street. Figure 2 is a diagram illustrating an example of traffic conditions following Figure 1. Figure 3 is a diagram illustrating an example of traffic conditions following Figure 1. Figure 4 is a diagram illustrating an example of traffic conditions at an intersection where a priority road and a non-priority road intersect. Figure 5 is a diagram illustrating an example of traffic conditions following Figure 4. Figure 6 is a diagram illustrating an example of traffic conditions following Figure 4. Figure 7 is a diagram illustrating an example of traffic conditions at a signalized intersection. Figure 8 is a diagram illustrating an example of traffic conditions following Figure 7. Figure 9 is a diagram illustrating an example of traffic conditions following Figure 7. Figure 10 is a diagram illustrating an example of a functional block of a vehicle equipped with a control unit according to one embodiment of the present disclosure. Figure 11 is a diagram illustrating an example of a traffic violation table in Figure 10. Figure 12 is a diagram illustrating an example of a functional block of the avoidance prediction unit in Figure 10. Figure 13 is a diagram illustrating an example of a driving assistance procedure in the vehicle of Figure 10. Figure 14 is a diagram illustrating a modified example of the functional block of the vehicle of Figure 10. Figure 15 is a diagram illustrating an example of a stopping position table in Figure 14. Figure 16 is a diagram showing an example of the functional block of the avoidance prediction unit in Figure 14. Figure 17 is a diagram illustrating an example of the driving assistance procedure in the vehicle in Figure 14. Figure 18 is a diagram showing a modified example of the functional block of the vehicle in Figure 10. Figure 19 is a diagram showing an example of the functional block of the avoidance prediction unit in Figure 18. Figure 20 is a diagram showing a modified example of the functional block of the vehicle in Figure 14. Figure 21 is a diagram showing an example of the functional block of the avoidance prediction unit in Figure 20.

[0008] <1. Background> Technologies for predicting traffic violations have been proposed, for example, in Patent Documents 1 and 2. Patent Document 1 proposes predicting future location information from the vehicle's current location information, and predicting violations based on the predicted location information and the driver's dangerous driving history data. Patent Document 2 proposes determining whether a driver will commit a traffic violation based on the driver's personal characteristics and the characteristics of traffic hubs. Patent Document 1: International Publication WO2021 / 063006 Patent Document 2: Patent No. 4735153

[0009] A technology for predicting vehicle actions to avoid collisions is proposed, for example, in Patent Document 3. Patent Document 3 proposes predicting the collision damage of each avoidance action and selecting the optimal avoidance action based on the predicted collision damage. A technology for predicting collision damage is proposed, for example, in Patent Documents 4 and 5. Patent Document 4 proposes quantifying occupant damage based on the amount of cabin deformation due to the collision and the state of the occupants upon exiting the vehicle. Patent Document 5 proposes determining the degree of collision damage for each part of the vehicle, selecting the part with the minimum degree of collision damage, and controlling the vehicle's attitude so that the selected part collides with the target object. Patent Document 3: Patent No. 4760715 Patent Document 4: Patent No. 4937656 Patent Document 5: Japanese Patent Application Publication No. 2017-218011

[0010] Patent documents 1 and 2 make it possible to predict vehicle operations to avoid traffic violations, but they do not make it possible to predict vehicle operations according to the degree of damage caused by the traffic violation. Patent document 3 makes it possible to select an avoidance action that minimizes collision damage, but it does not make it possible to select an avoidance action that reflects the driver's psychology according to the degree of damage caused by the traffic violation. Patent documents 4 and 5 make it possible to select an avoidance action that minimizes collision damage, but they do not make it possible to select an avoidance action that takes into account uncertain potential dangers, such as when the operation of the colliding vehicle is unknown, or to select an avoidance action that reflects the driver's psychology according to the degree of damage caused by the traffic violation.

[0011] Based on the above, it is not possible to predict evasive maneuvers that take into account uncertain potential dangers, or evasive maneuvers that reflect the driver's psychology in relation to the severity of the damage caused by the traffic violation, using conventional technology. Therefore, using conventional technology may result in overestimation of braking and steering amounts during evasive maneuvers. An example of a traffic situation in which such evasive maneuvers may occur will be described below.

[0012] <2. Traffic Situation> Figure 1 illustrates an example of a traffic situation in which the driver of vehicle 100b (another vehicle) is about to unintentionally commit a violation of the Road Traffic Act by ignoring a one-way street. In Figure 1, it is assumed that vehicle 100b (another vehicle) is traveling ahead of vehicle 100a. Both vehicle 100a and vehicle 100b are traveling in lane L1 of road La. There is a T-junction TJ ahead of vehicle 100b, and the driver of vehicle 100b is about to turn left at T-junction TJ. At T-junction TJ, road Lb, which intersects with road La, is a one-way street, and it is prohibited by the Road Traffic Act for vehicle 100b to enter and travel on road Lb. However, the driver of vehicle 100b is unaware that road Lb is a one-way street and begins preparing to turn left at T-junction TJ by flashing the left turn signal.

[0013] Suppose the driver of vehicle 100b, for example as shown in Figure 2, begins to enter T-junction TJ with the left turn signal flashing, and at that moment realizes that road Lb is a one-way street. At this time, suppose that trees 200a near T-junction TJ obstruct the driver's view of road Lb. In this case, it is expected that when vehicle 100b turns left at T-junction TJ and enters road Lb, it will collide with another vehicle traveling on road Lb. The driver of vehicle 100b thinks that such a collision could be a life-threatening accident and applies the brakes suddenly. As a result, vehicle 100b slows down rapidly at T-junction TJ. At this time, for example as shown in Figure 2, there may actually be no other vehicles on road Lb, and there may be no possibility of vehicle 100b colliding with another vehicle. Furthermore, as shown in Figure 3, for example, if another vehicle (vehicle 100c) was actually present and the driver of the target vehicle 100b did not apply the brakes suddenly, there might have been a high probability that target vehicle 100b would have collided with vehicle 100c.

[0014] Figure 4 illustrates an example of a traffic situation in which the driver of vehicle 100b is about to unintentionally commit a violation of the Road Traffic Act by ignoring a stop sign. In Figure 4, it is assumed that vehicle 100b is traveling ahead of vehicle 100a. Both vehicle 100a and vehicle 100b are traveling on road Lc. In front of vehicle 100b is an unsignaled intersection IS, and the driver of vehicle 100b is about to pass through the unsignaled intersection IS. At the unsignaled intersection IS, road Ld, which intersects with road Lc, is a priority road in relation to road Lc, and on road Lc, there is a stop line SL before the unsignaled intersection IS. The Road Traffic Act mandates that vehicle 100b come to a stop at the stop line SL. However, the driver of vehicle 100b is unaware of the presence of the stop line SL and is about to pass through the unsignaled intersection IS without slowing down.

[0015] Suppose the driver of vehicle 100b notices the presence of a stop line SL when approaching an unsignaled intersection IS. At this time, assume that trees 200a near the unsignaled intersection IS obstruct the driver's view of road Ld. In this case, it is expected that when vehicle 100b enters the unsignaled intersection IS, it will collide with another vehicle traveling on road Ld. However, the driver of vehicle 100b compares the risk of colliding with another vehicle traveling on road Ld by braking gently and crossing the stop line SL with the risk of being rear-ended by a following vehicle 100a by braking suddenly, and judges that the former risk is smaller, so brakes gently. As a result, vehicle 100b stops a little past the stop line SL (for example, 1m past the stop line SL), as shown in Figure 5. In this case, for example, as shown in Figure 5, there may not have been any other vehicles on the road Ld, and there may have been no possibility of the target vehicle 100b colliding with any other vehicles. Alternatively, for example, as shown in Figure 6, there may have been an actual presence of another vehicle (vehicle 100c), and as a result of the driver of target vehicle 100b applying the brakes gently, there may have been a high probability that target vehicle 100b would collide with the side of vehicle 100c.

[0016] Figure 7 illustrates an example of a traffic situation in which the driver of vehicle 100b is about to unintentionally commit a violation of the Road Traffic Act, namely running a red light. In Figure 7, it is assumed that vehicle 100b is traveling ahead of vehicle 100a. Both vehicle 100a and vehicle 100b are traveling on road Lc. In front of vehicle 100b is intersection ISx, where a traffic light TLc is installed, and the driver of vehicle 100b is about to pass through intersection ISx without realizing that the traffic light TLc is showing red (indicating that proceeding is not permitted). At intersection ISx, a traffic light TLd is installed for road Ld which intersects with road Lc, and on road La, there is a stop line SL before intersection ISx. When the traffic light TLc is showing red, vehicle 100b is obligated to stop at stop line SL. However, the driver of vehicle 100b was unaware that the traffic light TLc was showing red and was attempting to pass through intersection ISx without slowing down.

[0017] Suppose the driver of vehicle 100b notices that the traffic light TLc is showing red as they approach intersection ISx. At this time, trees near the unsignalized intersection IS obstruct the driver's view of road Ld. In this case, it is expected that when vehicle 100b enters intersection ISx, it will collide with another vehicle traveling on road Ld in accordance with the green light (permission to proceed) of traffic light TLc. However, the driver of vehicle 100b thinks that they can avoid such a collision if they stop before reaching road Ld, and applies the brakes hard. As a result, vehicle 100b stops before reaching road Ld, for example, as shown in Figure 8. At this time, for example, as shown in Figure 8, there may not actually be any other vehicles on road Ld, and there may have been no possibility of vehicle 100b colliding with another vehicle. Furthermore, as shown in Figure 9, for example, if another vehicle (vehicle 100c) was actually present and the driver of the target vehicle 100b did not brake strongly, there might have been a high probability that target vehicle 100b would have collided with vehicle 100c.

[0018] As shown in Figures 1 to 9, the driver of vehicle 100b performs evasive maneuvers that take into account uncertain potential hazards and the possibility of a rear-end collision by a following vehicle, as well as evasive maneuvers that reflect the driver's psychology according to the degree of damage caused by the traffic violation. For example, in the traffic situation shown in Figure 2, the driver of vehicle 100b is likely to attempt to brake suddenly before the stop line SL on road Lb, considering that vehicle 100c may be present on road Lb after turning left at T-junction TJ, and that a major accident may occur. Also, for example, in the traffic situation shown in Figure 5, the driver of vehicle 100b is likely to attempt to brake suddenly before the stop line SL, considering that vehicle 100b may be present on road Lb at intersection IS without traffic signals, but that if they stop a little past the stop line SL, it will not result in a major accident and they can avoid a rear-end collision by a following vehicle. Furthermore, for example, in the traffic situation shown in Figure 8, the driver of vehicle 100b is likely to consider that vehicle 100c may be traveling on road Lb at intersection ISx, and that a major accident could occur, and therefore is likely to attempt to stop before road Ld.

[0019] Thus, as can be seen, in the traffic situation shown in Figure 2, for example, vehicle 100b is suddenly stopped on the oncoming lane L2 within the T-junction TJ, and does not always suddenly stop on lane L1 within the T-junction TJ. However, if vehicle 100a, which is traveling behind vehicle 100b, anticipates that vehicle 100b will suddenly stop on lane L1 within the T-junction TJ, then vehicle 100a's driver will be prompted to decelerate rapidly or emergency braking will occur through intervention control to avoid a collision with vehicle 100b which is suddenly stopped on lane L1 within the T-junction TJ. In this case, the driver of vehicle 100a may find the notification prompting rapid deceleration and the intervention control bothersome.

[0020] Furthermore, it can be seen that, for example, in the traffic situations shown in Figures 5 and 8, the target vehicle 100b does not always stop before the stop line SL. However, if vehicle 100a, which is traveling behind the target vehicle 100b, anticipates that the target vehicle 100b will suddenly stop before the stop line SL, then the system will prompt the driver of vehicle 100a to decelerate rapidly or trigger emergency braking through intervention control to avoid a collision with the target vehicle 100b that suddenly stops before the stop line SL. In this case, the driver of vehicle 100a may find the notification prompting rapid deceleration or the intervention control bothersome.

[0021] In order to reduce the inconvenience that the driver of vehicle 100a may feel regarding notification and intervention control, it is desirable to provide a driver assistance device capable of suppressing excessive prediction of braking and steering amounts during evasive maneuvers, and a vehicle equipped with such a driver assistance device.

[0022] 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.

[0023] <3. Embodiments> [Configuration Example] Figure 10 shows an example of a functional block of a vehicle 1 according to one embodiment of the present disclosure. Vehicle 1 is a vehicle corresponding to the vehicle 100a described above, and corresponds to one specific example of the "first vehicle" according to one embodiment of the present disclosure. Vehicle 1 includes, for example, a sensor unit 10, a communication unit 20, a control unit 30, a storage unit 40, a notification unit 50, a prime mover 60, a brake 70, and an EPS motor 80, as shown in Figure 10. The control unit 30 corresponds to one specific example of the "driving support device" according to one embodiment of the present disclosure.

[0024] The sensor unit 10 is composed of various sensors mounted on the vehicle 1. For example, the sensor unit 10 is composed of a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, a steering angular velocity sensor, a steering torque sensor, and a braking torque sensor. The sensor unit 10 may also include sensors other than those listed above.

[0025] The vehicle speed sensor is capable of detecting the speed of vehicle 1. The vehicle speed sensor is capable of outputting time-series data (vehicle speed data) of the detected vehicle speed to the control unit 30. The acceleration sensor is capable of detecting the acceleration applied to vehicle 1. The acceleration sensor is capable of outputting time-series data (acceleration data) of the detected acceleration in three directions to the control unit 30. The angular velocity sensor is capable of detecting the angular velocity of vehicle 1. 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 30.

[0026] The steering angular velocity sensor is capable of detecting the rotational speed of the steering angle (steering rake angle) of the steering wheel of vehicle 1. The steering angular velocity sensor is capable of outputting time-series data (steering angular velocity data) of the detected steering angular velocity to the control unit 30. The steering torque sensor is capable of detecting the steering torque generated by the driver's steering wheel operation. The steering torque sensor is capable of outputting time-series data (steering torque data) of the detected steering torque to the control unit 30. The braking torque sensor is capable of detecting the braking torque generated by the driver's brake operation. The braking torque sensor is capable of outputting time-series data (braking torque data) of the detected braking torque to the control unit 30.

[0027] The sensor unit 10 further comprises a stereo camera mounted on the vehicle 1 and a driving environment detection unit. The stereo camera is an autonomous sensor that senses the real space around the vehicle 1. The stereo camera is positioned, for example, symmetrically on either side of the central part of the vehicle 1 in the width direction, enabling stereo imaging of the area in front of the vehicle 1 from different viewpoints. The stereo camera is capable of outputting image data Ia (a pair of stereo image data) obtained by imaging to the control unit 30.

[0028] The stereo camera is capable of generating distance image data Ib, which is determined from the amount of displacement of the corresponding object's position, based on image data Ia (a pair of stereo image data) obtained by imaging. The driving environment detection unit can, for example, determine the lane markings that demarcate the road around the vehicle 1 based on the distance image data Ib. The driving environment detection unit can further determine the road curvature of the markings that demarcate the left and right sides of the road (driving lane) on which the vehicle 1 is traveling, and the width between the left and right markings (vehicle width). The driving environment detection unit can further perform predetermined pattern matching on the distance image data Ib to detect lanes and three-dimensional objects such as structures present around the vehicle 1.

[0029] In the detection of three-dimensional objects in the driving environment detection unit, for example, the type of three-dimensional object, the distance to the object, the speed of the object, and the relative speed between the object and the vehicle (the vehicle itself) are detected. Examples of three-dimensional objects to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, bicycles, buildings, and trees. Examples of buildings include detached houses, apartment buildings, commercial facilities, factories, and signs. Examples of trees include trees adjacent to three-way intersections and crossroads. The driving environment detection unit is capable of outputting driving environment data around the vehicle 1, including the data of three-dimensional objects acquired in this way, to the control unit 30.

[0030] The communication unit 20 is capable of acquiring data to supplement data that cannot be obtained from image data Ia and distance image data Ib, for example, through vehicle-to-vehicle communication, vehicle-to-infrastructure communication, and satellite communication. The communication unit 20 is capable of outputting the acquired data to the control unit 30.

[0031] The communication unit 20 can acquire data obtained from other vehicles (e.g., vehicle position, vehicle speed) through vehicle-to-vehicle communication, for example. The communication unit 20 can also receive positioning signals transmitted from multiple positioning satellites through satellite communication, for example.

[0032] The communication unit 20 is capable of acquiring road map data around the vehicle 1, for example, through vehicle-to-infrastructure communication. The road map data consists of, for example, high-precision road map information (dynamic map), and mainly comprises static and quasi-static information that constitutes road information, and quasi-dynamic and dynamic information that mainly constitutes traffic information.

[0033] The static information that constitutes road information consists of information that requires updates at a frequency of no more than one month, such as roads and structures on roads, structures surrounding roads, lane information, road surface information, and permanent regulatory information. "Roads" include, for example, the location and shape of roads, intersections, and road attributes (e.g., national roads, prefectural roads, municipal roads, private roads, priority roads, non-priority roads, general roads, expressways). "Structures on roads" include, for example, traffic signs, traffic lights, convex mirrors, pedestrian overpasses, and bus stops. "Structures surrounding roads" include, for example, various buildings and parks.

[0034] 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.

[0035] The semi-dynamic information that makes up traffic information consists of information that needs to be updated within one minute, such as actual congestion conditions and driving restrictions at the time of observation, temporary driving obstructions such as fallen objects and obstacles, actual accident conditions, and local weather information.

[0036] 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 the roads. This road map information is maintained and updated in cycles until the next information is received from each vehicle, and the updated road map information is transmitted to each vehicle as appropriate via the communication unit 20.

[0037] The storage unit 40 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 40 stores, for example, a road map DB 41 and a traffic violation table 42, as shown in Figure 10.

[0038] The road map DB 41 is high-precision road map data (dynamic map). This high-precision road map data includes, for example, static and quasi-static information that mainly constitute road information, and quasi-dynamic and dynamic information that mainly constitute traffic information. The traffic violation table 42 includes types of traffic violations and conditions for determining the likelihood of a traffic violation. The traffic violation table 42 may include, for example, running a red light, failing to stop at a stop sign, and failing to follow a one-way street as types of traffic violations, as shown in Figure 11. The traffic violation table 42 may include, for example, two conditions for each type of traffic violation as conditions for determining the likelihood of a traffic violation, as shown in Figure 11.

[0039] The traffic violation table 42 may include, for example, the following two conditions as criteria for determining the possibility of running a red light: fa(Vx) indicates that it is a function of the speed Vx of the vehicle in question 100b. The traffic violation table 42 may include data corresponding to the speed Vx of the vehicle in question 100b. (First condition) The time ta until the vehicle in question 100b reaches the stop line SL on the road Lc is less than or equal to fa(Vx) seconds. (Second condition) The brake lights of the vehicle in question 100b are not illuminated.

[0040] The traffic violation table 42 may include, for example, the following two conditions as criteria for determining the possibility of ignoring a stop sign: fb(Vx) indicates that it is a function of the speed Vx of the vehicle in question 100b. The traffic violation table 42 may include data corresponding to the speed Vx of the vehicle in question 100b. (First condition) The time tb until the vehicle in question 100b reaches the stop line SL on the road Lc is less than or equal to fb(Vx) seconds. (Second condition) The brake lights of the vehicle in question 100b are not illuminated.

[0041] The traffic violation table 42 may include, for example, the following two conditions as criteria for determining the possibility of disregarding a one-way street. fb(Vx) indicates that it is a function of the speed Vx of the vehicle in question 100b. The traffic violation table 42 may include data corresponding to the speed Vx of the vehicle in question 100b. (First condition) One of the following three conditions is met: - The turn signal on the road Lb side of the vehicle in question 100b is illuminated. - The vehicle in question 100b has slowed down to 20 km / h or less. - The road La is a narrow road with a width of 2.7 m or less, and the vehicle in question 100b is positioned on the opposite side of road Lb on road La, with the distance between the vehicle in question 100b and the edge of road La being 50 cm or less. (Second condition) - There are no other intersections within 30 m of the T-junction containing the one-way street.

[0042] The second condition described above is set to eliminate the possibility that the target vehicle 100b will turn right or left at another intersection located within 30 meters of the T-junction, including a one-way street. Therefore, if the traffic situation in front of the target vehicle 100b is such that it is not necessary to consider the second condition described above, the avoidance prediction unit 31b described later may ignore the second condition.

[0043] The control unit 30 is capable of controlling the entire vehicle 1. The control unit 30 is, for example, a so-called ECU (Electronic Control Unit) and is composed of, for example, one or more processors and one or more memories. The control unit 30 may also be composed of, for example, a CPU (Central Processing Unit). In this case, the control unit 30 is capable of controlling the entire vehicle 1 by, for example, executing a program stored in a memory unit.

[0044] The control unit 30, for example, has a locator unit. The locator unit can acquire the position coordinates of the vehicle 1 based on the positioning signal received through the communication unit 20. The locator unit can map-match the acquired position coordinates on the route map information to predict the position of the host vehicle on the road map. The locator unit acquires map information of a predetermined range including the vehicle 1 from the map information stored in the road map DB (database) 41 described later based on the acquired position coordinates of the vehicle 1.

[0045] In an environment where it is impossible to receive an effective positioning signal from the positioning satellite due to a decrease in sensitivity, such as when driving in a tunnel, the locator unit switches to an autonomous navigation method that predicts the position of the host vehicle based on the vehicle speed, angular velocity, and longitudinal and lateral accelerations detected by the sensor unit 10, and can predict the position of the host vehicle on the road map.

[0046] When the locator unit predicts the position of the vehicle 1 (the position of the host vehicle) on the road map based on the positioning signal received through the communication unit 20 or the information detected by the sensor unit 10 as described above, it can determine the road type and the like of the road on which the vehicle 1 is traveling based on the predicted position of the host vehicle on the road map.

[0047] The locator unit can use the road map information acquired by external communication (road-vehicle communication and vehicle-vehicle communication) through the communication unit 20 to update the road map information stored in the road map DB 41 to the latest state. This information update is performed not only for static information but also for quasi-static information, quasi-dynamic information, and dynamic information. As a result, the road map information is composed of road information and traffic information acquired by communication with the outside of the vehicle, and the information of moving objects such as vehicles traveling on the road is updated almost in real time.

[0048] The locator unit verifies the road map information based on the driving environment information recognized as described above, and updates the road map information stored in the road map DB 41 to the latest state. This information update is performed not only for static information but also for semi-static information, semi-dynamic information, and dynamic information. As a result, information on moving objects such as vehicles traveling on the road recognized as described above is updated in real time.

[0049] The control unit 30 further has, for example, a driving support unit 31 as shown in FIG. 10. The driving support unit 31 has, for example, a data acquisition unit 31a, an avoidance prediction unit 31b, a notification control unit 31c, and an avoidance control unit 31d as shown in FIG. 10. The data acquisition unit 31a corresponds to a specific example of the "acquisition unit" of the present disclosure. The avoidance prediction unit 31b, the notification control unit 31c, and the avoidance control unit 31d can perform driving support for the vehicle 1 based on the data obtained by the data acquisition unit 31a, and correspond to a specific example of the "control unit" of the present disclosure.

[0050] The data acquisition unit 31a can periodically acquire the driving information Da of the vehicle 1 and the vehicles around the vehicle 1 by monitoring. The driving information Da is a concept including, for example, the position, speed, acceleration, traveling direction, and lighting state of the direction indicator of the vehicle 1 and the vehicles around the vehicle 1. That is, the driving information Da may include the position, speed, acceleration, traveling direction, and lighting state of the direction indicator of the vehicle traveling in front of the vehicle 1. The data acquisition unit 31a can further periodically acquire the traffic-related information Db in front of the vehicle 1 by monitoring. The traffic-related information Db is a concept including, for example, the road structure, signs, and signal states on the traveling road. That is, the traffic-related information Db may include the road structure, signs, and signal states in front of the vehicle traveling in front of the vehicle 1. The data acquisition unit 31a can periodically acquire various data obtained from the sensor unit 10, various data obtained from the outside via the communication unit 20, and various control signals for various devices of the vehicle 1 by monitoring. The driving information Da corresponds to a specific example of the "driving information" of the present disclosure. The traffic-related information Db corresponds to a specific example of the "traffic-related information" of the present disclosure.

[0051] Hereinafter, various data obtained from the sensor unit 10, various data obtained from external sources via the communication unit 20, and various control signals to various devices of the vehicle 100a will be referred to as "data obtained from the sensor unit 10, etc." Of the various data obtained from the sensor unit 10, various data obtained from external sources via the communication unit 20, and various control signals to various devices of the vehicle 100a, the data corresponding to driving information Da will be referred to as "driving information obtained from the sensor unit 10, etc." Of the various data obtained from the sensor unit 10, various data obtained from external sources via the communication unit 20, and various control signals to various devices of the vehicle 100a, the data corresponding to traffic-related information Db will be referred to as "traffic-related information obtained from the sensor unit 10, etc."

[0052] The avoidance prediction unit 31b includes, for example, a target vehicle identification unit 311, a violation type determination unit 312, a violation possibility determination unit 313, a damage prediction unit 314, and an avoidance action prediction unit 315, as shown in Figure 12.

[0053] The target vehicle identification unit 311 is capable of identifying one or more vehicles (hereinafter referred to as "target vehicles") that need to be identified for predicting traffic violations, based on driving information Da and traffic-related information Db. The target vehicles correspond to a specific example of the "second vehicle" according to one embodiment of this disclosure. When the target vehicle identification unit 311 recognizes a preceding vehicle on the road on which vehicle 1 is traveling, it is possible to calculate the collision margin time Tcol (=Dah / Vref) by dividing the distance Dah between the recognized preceding vehicle and vehicle 1 by the relative speed Vref between the preceding vehicle and vehicle 1. The target vehicle identification unit 311 can identify a preceding vehicle as a target vehicle if the collision margin time Tcol is less than or equal to a predetermined threshold Tth. The threshold Tth is, for example, 3 seconds.

[0054] The target vehicle identification unit 311 can, for example, identify the target vehicle 100b as the target vehicle if, in the traffic situation shown in Figure 1, the collision margin time Tcol of the target vehicle 100b traveling in front of vehicle 1 (vehicle 100a) is less than or equal to a predetermined threshold Tth. The target vehicle identification unit 311 can, for example, identify the target vehicle 100b as the target vehicle if, in the traffic situation shown in Figure 4, the collision margin time Tcol of the target vehicle 100b traveling in front of vehicle 1 (vehicle 100a) is less than or equal to a predetermined threshold Tth. The target vehicle identification unit 311 can, for example, identify the target vehicle 100b as the target vehicle if, in the traffic situation shown in Figure 7, the collision margin time Tcol of the target vehicle 100b traveling in front of vehicle 1 (vehicle 100a) is less than or equal to a predetermined threshold Tth. The target vehicle 100b corresponds to one specific example of the "second vehicle" according to one embodiment of this disclosure.

[0055] The violation type determination unit 312 and the violation possibility determination unit 313 are capable of predicting the traffic violation of the target vehicle based on the driving information Da and traffic-related information Db. The violation type determination unit 312 is capable of predicting the type of traffic violation of the target vehicle based on the driving information Da and traffic-related information Db.

[0056] The violation type determination unit 312 can, for example, determine whether or not there are traffic rules that must be observed in front of the target vehicle. The violation type determination unit 312 can, for example, determine whether or not there are traffic conditions in front of the target vehicle where a traffic violation may occur. "Traffic conditions in which a traffic violation may occur" refers to traffic conditions including, for example, signalized intersections displaying red lights, unsignalized intersections with stop lines, or T-junctions with one-way roads that intersect with the target vehicle's roadway. The violation type determination unit 312 can, for example, identify the type of traffic violation if there are traffic conditions in front of the target vehicle where a traffic violation may occur. The violation type determination unit 312 can, for example, identify running a red light as the type of traffic violation if there are traffic conditions in front of the target vehicle including signalized intersections displaying red lights. The violation type determination unit 312 can, for example, identify running a stop line as the type of traffic violation if there are traffic conditions in front of the target vehicle including unsignalized intersections with stop lines. The violation type determination unit 312 can, for example, identify "disregarding a one-way street" as the type of traffic violation if there is a traffic situation in front of the vehicle that includes a T-junction with a one-way street that intersects with the vehicle's roadway.

[0057] The violation possibility determination unit 313 can determine the possibility of a traffic violation by the target vehicle based on the driving information Da and traffic-related information Db. The violation possibility determination unit 313 can determine the possibility of a traffic violation by the target vehicle based on the driving information Da and traffic-related information Db and the traffic violation table 42 in the storage unit 40. If the target vehicle meets the conditions described in the traffic violation table 42, the violation possibility determination unit 313 can determine that the target vehicle is likely to commit a traffic violation.

[0058] The violation possibility determination unit 313 assumes, for example, that a traffic situation including a T-junction TJ exists in front of the target vehicle 100b, as shown in Figure 1. In this case, the violation possibility determination unit 313 can determine whether the target vehicle 100b satisfies the following two conditions. If the target vehicle 100b satisfies the following two conditions, the violation possibility determination unit 313 can determine that the target vehicle 100b may commit a traffic violation of ignoring a one-way street. (Condition 1) One of the following three conditions must be met: - The turn signal on the road Lb side of the vehicle 100b is illuminated. - The vehicle 100b is slowing down to 20 km / h or less. - The road La is a narrow road with a width of 2.7 m or less, and the vehicle 100b is positioned on the opposite side of the road Lb, with the distance between the vehicle 100b and the edge of the road La being 50 cm or less. (Condition 2) - There are no other intersections within 30 m of the T-junction, including one-way streets.

[0059] The violation possibility determination unit 313 assumes, for example, that a traffic situation exists in front of the target vehicle 100b that includes an unsignaled intersection IS with a stop line SL, as shown in Figure 4. In this case, the violation possibility determination unit 313 is able to determine whether the target vehicle 100b satisfies the following two conditions. The violation possibility determination unit 313 is able to determine that the target vehicle 100b may commit a traffic violation of ignoring a stop sign if the target vehicle 100b satisfies the following two conditions. (First condition) The time tb until the vehicle 100b reaches the stop line SL on the road Lc is fb(Vx) seconds or less. (Second condition) The brake lights of the vehicle 100b are not illuminated.

[0060] The violation possibility determination unit 313 assumes, for example, that there is a traffic situation including a T-junction TJ in front of the target vehicle 100b, as shown in Figure 7. In this case, the violation possibility determination unit 313 is able to determine whether the target vehicle 100b satisfies the following two conditions. The violation possibility determination unit 313 is able to determine that the target vehicle 100b may commit a traffic violation of running a red light if the following two conditions are met. (First condition) The time ta until the vehicle 100b reaches the stop line SL on the road Lc is fa(Vx) seconds or less. (Second condition) The brake lights of the vehicle 100b are not illuminated.

[0061] The damage prediction unit 314 is capable of predicting the damage that may result from a predicted traffic violation by the target vehicle, based on driving information Da and traffic-related information Db. For example, when the driving information Da of the target vehicle and traffic-related information Db in front of the target vehicle are input, the damage prediction unit 314 is configured to include a learning model that can output the damage that may result from a predicted traffic violation. The learning model is a model that has been trained with the driving information Da and traffic-related information Db obtained for each type of traffic violation as explanatory variables and the damage that may result from the predicted traffic violation as the dependent variable.

[0062] For example, in a traffic situation where the vehicle in question is traveling towards a T-junction with a one-way street, if the vehicle ignores the one-way sign and enters the one-way street, and there are other vehicles on the one-way street, there is a possibility that the vehicle in question will collide head-on with the other vehicle on the one-way street. The damage prediction unit 314 can determine, for example, that if such a possibility occurs and the vehicle in question actually collides head-on with the other vehicle on the one-way street, there is a high probability that the driver of the vehicle in question will suffer serious injuries or be killed, and can output the details of that determination.

[0063] For example, in a traffic situation where a vehicle is traveling towards an unsignaled intersection with a stop line, if the vehicle ignores the stop sign and enters the unsignaled intersection, there is a possibility that the vehicle could collide with the side of another vehicle that is crossing the intersecting road at the unsignaled intersection. However, in the case of an unsignaled intersection, compared to an intersection with signals, other vehicles tend to slow down as they are wary of a vehicle ignoring a stop sign.

[0064] In the case of an intersection without traffic lights, drivers of vehicles approaching the intersection tend to slow down, thinking that "someone might ignore the stop sign and enter the intersection." Therefore, in the case of an intersection without traffic lights, the driver of the vehicle in question is likely to judge that "even if I collide with another vehicle that enters the intersection ignoring the stop sign, that vehicle will have slowed down, so the injuries will be minor." On the other hand, in the case of an intersection with traffic lights, drivers of vehicles approaching the intersection tend not to slow down, thinking that "someone might run a red light." Therefore, in the case of an intersection with traffic lights, the driver of the vehicle in question is likely to judge that "if I collide with another vehicle that enters the intersection running a red light, it will result in a serious accident."

[0065] The damage prediction unit 314, for example, in cases where the above-mentioned possibilities exist, takes into account the tendency of other vehicles to slow down and determines that if the target vehicle actually collides with the side of another vehicle entering an intersection without traffic signals, there is a high probability that the target vehicle will be damaged or the driver of the target vehicle will suffer minor injuries. The unit is capable of outputting the details of this determination.

[0066] For example, in a traffic situation where the target vehicle is traveling towards an intersection with a red light, if the target vehicle ignores the red light and enters the intersection, there is a possibility that the target vehicle will collide head-on with the other vehicle entering the intersection if another vehicle is attempting to cross the intersecting road at the intersection. The damage prediction unit 314, for example, in a situation where such a possibility exists, considers that at an intersection with other vehicles, there is no "tendency for other vehicles to slow down" as at the aforementioned intersection without signals, and determines that if the target vehicle actually collides head-on with the other vehicle entering the intersection, there is a high probability that the driver of the target vehicle will suffer serious injuries or death, and can output the details of that determination.

[0067] The avoidance action prediction unit 315 is capable of predicting the actions of the target vehicle to avoid a traffic violation, based on driving information Da and traffic-related information Db, as well as the nature of the damage that may occur to the target vehicle due to the traffic violation. The avoidance action prediction unit 315 is also capable of predicting the actions of the target vehicle according to the type of traffic violation.

[0068] The avoidance action prediction unit 315 can predict that if it is predicted that the target vehicle may violate traffic by running a red light at an intersection, it will perform emergency braking to avoid entering the intersection. For example, in a traffic situation like the one shown in Figure 7, if it is predicted that the target vehicle 100b may violate traffic by running a red light at intersection ISx, the avoidance action prediction unit 315 can predict that the target vehicle 100b will perform emergency braking to avoid entering intersection ISx. For example, as shown in Figure 8, the avoidance action prediction unit 315 can predict that the target vehicle 100b will perform emergency braking to stop at a point where it has crossed the stop line SL and before reaching road Ld.

[0069] The avoidance action prediction unit 315 can predict, for example, that if it is predicted that the target vehicle may violate traffic by ignoring a stop sign at a stop intersection, the target vehicle will brake to pass the stop line at the stop intersection at a slow speed. For example, in the traffic situation shown in Figure 4, if it is predicted that the target vehicle 100b may violate traffic by ignoring a stop sign at an unsignaled intersection IS, the avoidance action prediction unit 315 can predict that the target vehicle 100b will brake to pass the stop line SL at the unsignaled intersection IS at a slow speed. For example, as shown in Figure 5, the avoidance action prediction unit 315 can predict that the target vehicle 100b will brake suddenly to stop at a point where it is 1 meter beyond the stop line SL on the road Lc.

[0070] The avoidance action prediction unit 315 can predict, for example, that if it is predicted that the target vehicle may enter a one-way road at an intersection where one-way roads intersect as a traffic violation, the target vehicle will perform emergency braking to avoid entering the one-way road. For example, in the traffic situation shown in Figure 1, if it is predicted that the target vehicle 100b may enter road Lb at a T-junction TJ where road Lb, which is a one-way road, intersects as a traffic violation, the avoidance action prediction unit 315 can predict that the target vehicle 100b will perform emergency braking to avoid entering road Lb. For example, as shown in Figure 2, the avoidance action prediction unit 315 can predict that the target vehicle 100b will perform emergency braking to stop before the stop line SL on road Lb.

[0071] Next, the avoidance control unit 31d will be described. The avoidance control unit 31d is capable of performing driving control to avoid a collision between vehicle 1 and the target vehicle. The avoidance control unit 31d is capable of generating driving control data for controlling the driving of vehicle 1 and outputting it to the driving control unit 32. For example, based on the driving information of vehicle 1 and the target vehicle included in the driving information Da and the movement of the target vehicle predicted by the avoidance movement prediction unit 315 (predicted movement), the avoidance control unit 31d is capable of generating driving control data to avoid a collision between vehicle 1 and the target vehicle and outputting it to the driving control unit 32.

[0072] The avoidance control unit 31d can calculate a deceleration a_decc(V / s) that allows the target vehicle to stop at the stop position (predicted stop position) predicted by the avoidance operation prediction unit 315, based on, for example, the current position, speed, acceleration, and direction of travel of the target vehicle included in the driving information Da, and the stopping position of the target vehicle predicted by the avoidance operation prediction unit 315. The avoidance control unit 31d can calculate time-series data (first time-series data) of the position and speed of the target vehicle until it stops at the predicted stop position, based on, for example, the deceleration a_decc(V / s) and the current position, speed, acceleration, and direction of travel of the target vehicle included in the driving information Da. The avoidance control unit 31d can calculate time-series data (second time-series data) of the position and speed of vehicle 1 until it stops at the predicted stop position, based on, for example, the current position, speed, acceleration, and direction of travel of vehicle 1 included in the driving information Da. The avoidance control unit 31d can, for example, calculate time-series data (third time-series data) of the time until vehicle 1 collides with the target vehicle (collision margin time) based on the first time-series data and the second time-series data.

[0073] The avoidance control unit 31d can, for example, calculate the deceleration b_dec (V / s) of vehicle 1 required for the collision margin to exceed a predetermined threshold (e.g., 1.5 seconds) at all time points in the third time series data, if there is a time point in the third time series data where the collision margin is less than or equal to a predetermined threshold (e.g., 1.5 seconds). The avoidance control unit 31d can, for example, generate the driving control data necessary to achieve the calculated deceleration b_dec (V / s) and output it to the driving control unit 32.

[0074] Next, the notification control unit 31c will be described. The notification control unit 31c is capable of performing notification control to prevent vehicle 1 from colliding with the target vehicle. The notification control unit 31c is capable of generating notification data to notify the driver of vehicle 1 and outputting it to the notification unit 50. For example, the notification control unit 31c is capable of generating notification data necessary to achieve the deceleration b_dec (V / s) calculated by the avoidance control unit 31d and outputting it to the notification unit 50.

[0075] The notification control unit 31c is capable of, for example, generating a video signal including the notification data and outputting it to the notification unit 50. The notification control unit 31c is capable of, for example, generating an audio signal including the notification data and outputting it to the notification unit 50. The notification unit 50 is composed of, for example, a display panel and a speaker. When the notification unit 50 receives the video signal from the notification control unit 31c, it is capable of displaying an image on the display screen corresponding to the input video signal. When the notification unit 50 receives the audio signal from the notification control unit 31c, it is capable of outputting an audio signal corresponding to the input audio signal from the speaker.

[0076] The control unit 30 further includes a driving control unit 32, as shown in Figure 10, for example. The driving control unit 32 is capable of performing driving control using various data and various control signals acquired by the data acquisition unit 31a. The driving control unit 32 is capable of controlling the driving of the vehicle 1 (for example, the torque of the prime mover 60, the braking force of the brakes, and the operation of the steering wheel).

[0077] The driving control unit 32 is capable of calculating a correction torque to correct the requested torque supplied to the accelerator control unit 32a (described later) based on the data acquired by the data acquisition unit 31a and the driving control data obtained from the avoidance control unit 31d. The driving control unit 32 is capable of calculating a correction torque to correct the requested torque supplied to the brake control unit 32b (described later) based on the data acquired by the data acquisition unit 31a and the driving control data obtained from the avoidance control unit 31d. The driving control unit 32 is capable of calculating a correction torque to correct the steering assist torque generated by the steering control unit 32c (described later) based on the data acquired by the data acquisition unit 31a and the driving control data obtained from the avoidance control unit 31d.

[0078] The driving control unit 32 includes, for example, an accelerator control unit 32a, a brake control unit 32b, and a steering control unit 32c, as shown in Figure 10.

[0079] The accelerator control unit 32a is capable of controlling the torque of the prime mover 60 based on the requested torque corresponding to the amount the accelerator pedal is pressed by the driver of the vehicle 1. The accelerator control unit 32a is also capable of controlling the torque of the prime mover 60 based on a target torque which is the requested torque plus a correction torque. The prime mover 60 is configured to drive the steering wheels of the vehicle 1 and is capable of driving the steering wheels of the vehicle 1 according to the requested torque or target torque input from the accelerator control unit 32a. The prime mover 60 corresponds to one specific example of the "controlled device" in this disclosure.

[0080] The brake control unit 32b is capable of controlling the torque (braking force) of the brake 70 based on a requested torque corresponding to the amount the driver of the vehicle 1 presses the brake pedal. The brake control unit 32b is also capable of controlling the torque of the brake 70 based on a target torque which is the requested torque plus a correction torque. The brake 70 is configured to brake the steering wheels of the vehicle 1 and is capable of braking the steering wheels of the vehicle 1 according to the requested torque or target torque input from the brake control unit 32b. The brake 70 corresponds to one specific example of the "controlled device" in this disclosure.

[0081] The steering control unit 32c 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 32c can output a control signal to the EPS motor 80 so that the output torque of the EPS motor 80 becomes the set EPS torque. The steering control unit 32c can output a control signal to the EPS motor 80 so that the output torque of the EPS motor 80 becomes the EPS torque considering the correction torque. The EPS motor 80 generates an output torque based on the input control signal and can control the steering angle of the steering wheel. The EPS motor 80 corresponds to one specific example of the "controlled device" in this disclosure.

[0082] The accelerator control unit 32a, the brake control unit 32b, and the steering control unit 32c may include, for example, a CPU. In this case, the accelerator control unit 32a, the brake control unit 32b, and the steering control unit 32c can perform the various driving controls described above by, for example, executing control software stored in a memory unit.

[0083] [Operation] Next, the driving assistance in vehicle 1 will be described with reference to Figure 13. Figure 13 shows an example of the driving assistance procedure in vehicle 1.

[0084] First, the control unit 30 acquires various data (driving information Da and traffic-related information Db, etc.) (step S101). Next, if the control unit 30 detects a target vehicle based on the acquired data (step S102; Y), it determines the type of traffic violation that may occur in front of the detected target vehicle based on the driving information Da and traffic-related information Db (step S103). The control unit 30 determines the likelihood of a traffic violation by the target vehicle based on the driving information Da and traffic-related information Db (step S104).

[0085] If the control unit 30 detects a potential traffic violation by the target vehicle (step S104; Y), it predicts the damage that may result from the predicted traffic violation based on the driving information Da and traffic-related information Db (step S105). Based on the driving information Da and traffic-related information Db, and the nature of the damage that may result from the traffic violation, the control unit 30 predicts the actions the target vehicle will take to allow the driver of the target vehicle to notice the traffic violation and avoid it (step S106). The control unit 30 performs driving control and notification control to prevent vehicle 1 from colliding with the target vehicle (step S107). In this way, driving assistance is provided for vehicle 1.

[0086] [Effects] Next, the effects of the vehicle 1 according to this embodiment will be described.

[0087] In this embodiment, a traffic violation by the target vehicle is predicted based on driving information Da and traffic-related information Db. When a traffic violation by the target vehicle is predicted, the actions of the target vehicle to avoid the violation are predicted so that the driver of the target vehicle will notice it. Then, based on the driving information of vehicle 1 and the target vehicle included in the driving information Da, and the predicted actions of the target vehicle, driving control or notification control is performed to avoid a collision between vehicle 1 and the target vehicle. This makes it possible to predict the actions of the target vehicle in a way that, for example, allows the target vehicle to cross the stop line SL or to tolerate the possibility of an uncertain collision between the target vehicle and other vehicles. As a result, it is possible to suppress the prediction of an excessively high deceleration of the target vehicle. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notification and intervention control, it is possible to suppress excessive estimation of braking and steering amounts in avoidance actions.

[0088] In this embodiment, the vehicle's behavior is estimated according to the type of traffic violation. Furthermore, data defining the vehicle's behavior for each type of traffic violation is stored in the storage unit 40. This makes it possible to predict the vehicle's deceleration according to the type of traffic violation. Therefore, excessive estimation of braking and steering amounts during evasive maneuvers can be suppressed in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control.

[0089] In this embodiment, data defining the vehicle's behavior for each type of traffic violation is stored in the storage unit 40. This makes it possible to predict the vehicle's deceleration according to the type of traffic violation. Therefore, excessive estimation of braking and steering amounts during evasive maneuvers can be suppressed in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control.

[0090] In this embodiment, the types of traffic violations defined are running a red light at an intersection, failing to stop at a stop sign intersection, and entering a one-way street at an intersection where two one-way streets intersect. This makes it possible to predict the deceleration of the target vehicle according to the type of traffic violation. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0091] In this embodiment, based on driving information Da and traffic-related information Db, if it is predicted that the target vehicle may violate traffic laws by running a red light at an intersection or entering a one-way street at an intersection where two one-way streets intersect, then the vehicle's action is predicted to be emergency braking to avoid entering the intersection or the one-way street. This makes it possible to predict the deceleration of the target vehicle according to the type of traffic violation. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0092] In this embodiment, based on driving information Da and traffic-related information Db, if it is predicted that the target vehicle may violate traffic rules by ignoring a stop sign at a stop intersection, the vehicle's action is predicted to be braking to pass the stop line at the stop intersection at a slow speed. This makes it possible to predict the deceleration of the target vehicle according to the type of traffic violation. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0093] <4. Modified Examples> Next, a modified example of the vehicle 1 according to the above embodiment will be described.

[0094] [Modification A] In the above embodiment, the driving support unit 31 may have, for example, an avoidance prediction unit 31e instead of an avoidance prediction unit 31b, as shown in Figure 14. In this case, the storage unit 40 may further store a stopping position table 43, for example, as shown in Figure 14.

[0095] In the stopping position table 43, the stopping position of the vehicle in question is specified for each type of traffic violation. The types of traffic violations in the stopping position table 43 may include, for example, running a red light, failing to stop at a stop sign, and ignoring a one-way street, as shown in Figure 15. In the stopping position table 43, the stopping position of the vehicle in question is specified for each type of traffic violation. For example, in the stopping position table 43, as shown in Figure 15, the stopping position of the vehicle in question is specified as the location before the intersection in the case of running a red light, the location beyond 1 meter from the stop line on the road in the case of failing to stop at a stop sign, and the location before the one-way street in the case of ignoring a one-way street sign.

[0096] The avoidance prediction unit 31e includes, for example, a target vehicle identification unit 311, a violation type determination unit 312, a violation possibility determination unit 313, and an avoidance action setting unit 316, as shown in Figure 16.

[0097] The avoidance action setting unit 316 can set the action of the target vehicle according to the type of traffic violation predicted, based on the stopping position table 43, when a traffic violation by the target vehicle is predicted.

[0098] The avoidance action setting unit 316 can set the target vehicle to perform emergency braking to stop before the intersection if it is predicted that the target vehicle may violate traffic laws by running a red light at an intersection. For example, as shown in Figure 8, the avoidance action setting unit 316 can set emergency braking to stop the target vehicle 100b at the point where it has crossed the stop line SL and before the road Ld.

[0099] The avoidance action prediction unit 316 can, if it predicts that the target vehicle may violate traffic laws by ignoring a stop sign at a stop intersection, set the target vehicle to perform emergency braking to stop at a point where it exceeds the stop line at the intersection by 1 meter. For example, as shown in Figure 5, the avoidance action prediction unit 316 can set the target vehicle 100b to perform emergency braking to stop at a point where it exceeds the stop line SL on road Lc by 1 meter.

[0100] The avoidance action prediction unit 316 can, if it predicts that the target vehicle may enter a one-way street at an intersection where two one-way streets intersect as a traffic violation, set the target vehicle to perform emergency braking to stop before entering the one-way street. For example, as shown in Figure 2, the avoidance action prediction unit 316 can predict that the target vehicle 100b will perform emergency braking to stop before the road Lb (the stop line SL on road Lb).

[0101] Next, we will explain the driver assistance system in vehicle 1 with reference to Figure 17. Figure 17 shows an example of the driver assistance procedure in vehicle 1.

[0102] First, the control unit 30 acquires various data (driving information Da and traffic-related information Db, etc.) (step S101). Next, if the control unit 30 detects a target vehicle based on the acquired data (step S102; Y), it determines the type of traffic violation that may occur in front of the detected target vehicle based on the driving information Da and traffic-related information Db (step S103). The control unit 30 determines the likelihood of a traffic violation by the target vehicle based on the driving information Da and traffic-related information Db (step S104).

[0103] If the control unit 30 detects a potential traffic violation by the target vehicle (step S104; Y), it sets the target vehicle's operation according to the predicted type of traffic violation based on the stopping position table 43 (step S108). The control unit 30 performs driving control and notification control to prevent vehicle 1 from colliding with the target vehicle (step S107). In this way, driving assistance is provided for vehicle 1.

[0104] In this modified example, a stopping position table 43, in which the stopping position of the target vehicle is defined for each type of traffic violation, is stored in the storage unit 40. This allows the vehicle's actions to be set according to the predicted type of traffic violation without having to predict the vehicle's actions. As a result, it is possible to suppress the prediction of an excessively high deceleration for the target vehicle. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0105] [Modification B] In the above embodiment, the driving support unit 31 may have, for example, an avoidance prediction unit 31f instead of an avoidance prediction unit 31b, as shown in Figure 18. In this case, in the storage unit 40, for example, the traffic violation table 42 is omitted, as shown in Figure 14.

[0106] The avoidance prediction unit 31f includes, for example, a target vehicle identification unit 311, a violation type determination unit 312, a violation possibility determination unit 317, a damage prediction unit 314, and an avoidance action prediction unit 315, as shown in Figure 19.

[0107] The violation possibility determination unit 317 can determine whether the driver of the target vehicle gazed at a road sign indicating traffic rules for a predetermined time or longer, based on the data acquired by the data acquisition unit 31a, which includes the driver's gaze direction and gaze duration, and traffic-related information Db in front of the vehicle 1. The "predetermined time" is, for example, 0.5 seconds. If the driver of the target vehicle did not gaze at a road sign indicating traffic rules for a predetermined time or longer, the violation possibility determination unit 317 can determine that the target vehicle is likely to commit a traffic violation.

[0108] In this modified version, the driver's gaze information of the target vehicle is used to determine whether or not the target vehicle is likely to commit a traffic violation. Even in this case, it is possible to suppress the prediction of an excessively high deceleration of the target vehicle. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0109] [Modification C] In modification A described above, the driving support unit 31 may have an avoidance prediction unit 31g instead of an avoidance prediction unit 31e, as shown in Figure 20. The avoidance prediction unit 31g has, for example, a target vehicle identification unit 311, a violation type determination unit 312, a violation possibility determination unit 317, and an avoidance action prediction unit 316, as shown in Figure 21. In this case, the traffic violation table 42 is omitted in the storage unit 40, for example, as shown in Figure 20.

[0110] The violation possibility determination unit 317 can determine whether the driver of the target vehicle gazed at a road sign indicating traffic rules for a predetermined time or longer, based on the data acquired by the data acquisition unit 31a, which includes the driver's gaze direction and gaze duration, and traffic-related information Db in front of the vehicle 1. The "predetermined time" is, for example, 0.5 seconds. If the driver of the target vehicle did not gaze at a road sign indicating traffic rules for a predetermined time or longer, the violation possibility determination unit 317 can determine that the target vehicle is likely to commit a traffic violation.

[0111] In this modified version, the driver's gaze information of the target vehicle is used to determine whether or not the target vehicle is likely to commit a traffic violation. Even in this case, it is possible to suppress the prediction of an excessively high deceleration of the target vehicle. Therefore, in order to reduce the inconvenience felt by the driver of vehicle 1 regarding notifications and intervention control, it is possible to suppress excessive estimation of braking and steering amounts during evasive maneuvers.

[0112] The present disclosure has been described above with reference to embodiments and their modifications, but the present disclosure is not limited to these embodiments, and various modifications are possible. The effects described herein are merely illustrative, and the effects of the present disclosure are not limited to those described herein. Therefore, other effects may be obtained with respect to the present disclosure.

[0113] The above embodiments and modifications A to C were based on the premise that the country or region has traffic regulations in which vehicle 1 (vehicle 100a), etc., travels in the right lane. However, if the country or region has traffic regulations in which vehicle 100a, etc., travels in the left lane, then in the first and second embodiments and their modifications, "right" shall be read as "left" and "left" as "right".

[0114] Furthermore, the present disclosure may take the following forms: (1) A driving assistance device comprising: an acquisition unit capable of acquiring driving information of a second vehicle traveling in front of a first vehicle, and traffic-related information in front of the second vehicle; and a control unit capable of providing driving assistance for the first vehicle based on the driving information and traffic-related information acquired by the acquisition unit, wherein the control unit predicts a traffic violation of the second vehicle based on the driving information and traffic-related information; when a traffic violation of the second vehicle is predicted, predicts the actions of the second vehicle to allow the driver of the second vehicle to notice the traffic violation and avoid the traffic violation; and, based on the driving information of the first vehicle, the driving information of the second vehicle, and the predicted actions of the second vehicle, is capable of performing driving control or notification control to prevent the first vehicle from colliding with the second vehicle. (2) The driving assistance device according to (1), wherein the control unit is capable of predicting the actions of the second vehicle according to the type of traffic violation. (3) The driving support device according to (1) or (2), further comprising a storage unit that stores data defining the operation of the second vehicle for each type of traffic violation. (4) The driving support device according to (3), wherein the data includes data corresponding to the speed of the second vehicle. (5) The driving support device according to any one of (1) to (4), wherein the traffic violation is running a red light at an intersection, running a stop sign at a stop intersection, or entering a one-way road at an intersection where one-way roads intersect. (6) The driving support device according to (2), wherein, based on the driving information and the traffic-related information, the control unit can predict the possibility that the second vehicle may run a red light at an intersection or enter a one-way road at an intersection where one-way roads intersect as a traffic violation, and the control unit can predict that the second vehicle will perform emergency braking to avoid entering the intersection or the one-way road.(7) The driving support device as described in (2), wherein the control unit predicts, based on the driving information and the traffic-related information, that the second vehicle may violate the traffic by ignoring a stop sign at a stop intersection, and as an action of the second vehicle, it is possible to predict that the second vehicle will brake to pass the stop line at the stop intersection at a slow speed. (8) A vehicle comprising a driving assistance device and a controlled vehicle controlled by the driving assistance device, wherein the driving assistance device comprises an acquisition unit capable of acquiring driving information of a target vehicle traveling in front of the vehicle and traffic-related information in front of the target vehicle, and a control unit capable of providing driving assistance to the vehicle based on the driving information and traffic-related information acquired by the acquisition unit, wherein the control unit predicts a traffic violation of the target vehicle based on the driving information and traffic-related information, predicts the actions of the target vehicle to avoid the traffic violation when a traffic violation of the target vehicle is predicted, and is capable of providing driving control or notification control to the controlled vehicle to avoid a collision between the vehicle and the target vehicle based on the driving information of the vehicle, the driving information of the target vehicle, and the predicted actions of the target vehicle.

[0115] The control unit 30 shown in Figures 10, 14, 18, and 20 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 perform all or some of the various functions of the control unit 30 shown in Figures 10, 14, 18, and 20 by reading instructions from at least one non-temporary, 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 (i.e., semiconductor circuits) such as volatile memory or non-volatile memory. 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 some of the various functions of the control unit 30 shown in Figures 10, 14, 18, and 20. An FPGA is an integrated circuit designed to be configurable after manufacturing to perform all or some of the various functions of the control unit 30 shown in Figures 10, 14, 18, and 20.

Claims

1. A driving assistance device comprising: an acquisition unit capable of acquiring driving information of a second vehicle traveling in front of a first vehicle, and traffic-related information in front of the second vehicle; and a control unit capable of providing driving assistance to the first vehicle based on the driving information and traffic-related information acquired by the acquisition unit, wherein the control unit predicts a traffic violation of the second vehicle based on the driving information and traffic-related information; when a traffic violation of the second vehicle is predicted, predicts the actions of the second vehicle to avoid the traffic violation as the driver of the second vehicle becomes aware of it; and, based on the driving information of the first vehicle, the driving information of the second vehicle, and the predicted actions of the second vehicle, is capable of performing driving control or notification control to prevent the first vehicle from colliding with the second vehicle.

2. The driving assistance device according to claim 1, wherein the control unit is capable of predicting the operation of the second vehicle according to the type of traffic violation.

3. The driving assistance device according to claim 1, further comprising a storage unit for storing data that defines the operation of the second vehicle for each type of traffic violation.

4. The driving assistance device according to claim 3, wherein the data includes data corresponding to the speed of the second vehicle.

5. The driving assistance device according to claim 1, wherein the traffic violation is ignoring a red light at an intersection, ignoring a stop sign at a stop sign intersection, or entering a one-way street at an intersection where two one-way streets intersect.

6. The driving assistance device according to claim 2, wherein the control unit, based on the driving information and the traffic-related information, predicts that the second vehicle may violate traffic laws by running a red light at an intersection or entering a one-way street at an intersection where two one-way streets intersect, and the control unit is capable of predicting that the second vehicle should perform emergency braking to avoid entering the intersection or the one-way street.

7. The driving assistance device according to claim 2, wherein the control unit, based on the driving information and the traffic-related information, predicts that the second vehicle may violate traffic by ignoring a stop sign at a stop intersection, and as an action of the second vehicle, it is possible to predict that the second vehicle will brake to pass the stop line at the stop intersection at a slow speed.

8. A vehicle comprising a driver assistance device and a controlled vehicle controlled by the driver assistance device, wherein the driver assistance device comprises an acquisition unit capable of acquiring driving information of a target vehicle traveling in front of the vehicle and traffic-related information in front of the target vehicle, and a control unit capable of providing driver assistance to the vehicle based on the driving information and traffic-related information acquired by the acquisition unit, wherein the control unit predicts a traffic violation of the target vehicle based on the driving information and traffic-related information, predicts the actions of the target vehicle to avoid the traffic violation when a traffic violation of the target vehicle is predicted, and is capable of providing driving control or notification control to the controlled vehicle to avoid a collision between the vehicle and the target vehicle based on the driving information of the vehicle, the driving information of the target vehicle, and the predicted actions of the target vehicle.

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