Driver assistance systems and vehicles

Abstract

The present invention provides a driver assistance system and vehicle capable of providing warning and driving control in the event that multiple vehicles suddenly appear from a blind spot in succession. [Solution] The driving assistance device according to one embodiment of the present disclosure predicts the number of second vehicles expected to be in a blind spot area based on data about sounds emitted from a plurality of second vehicles expected to be in a blind spot area. If, after predicting that there are two or more second vehicles, a third vehicle suddenly appears to be coming out of the blind spot area, it is possible to determine that there is a high probability that one or more fourth vehicles, different from the third vehicle that suddenly appeared, will also suddenly appear from the blind spot area.

Description

Technical Field

[0001] The present disclosure relates to a driving support device and a vehicle.

Background Art

[0002] There is known a driving support device that supports the driving of a host vehicle based on risks existing around the host vehicle. For example, when there is a blind spot in front of the host vehicle, it is disclosed in Non-Patent Document 1 that the possibility of a vehicle jumping out from the blind spot is estimated.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

[0004] A driving support device according to an embodiment of the present disclosure includes an acquisition unit and a control unit. The acquisition unit can acquire first data indicating that there is a blind spot area in front of a first vehicle, second data about sounds emitted from a plurality of second vehicles expected to be present in the blind spot area, and third data about one third vehicle jumping out from the blind spot area among the plurality of second vehicles. The control unit can control the driving support of the first vehicle based on the first data, the second data, and the third data. The control unit can perform the following two. (1) Based on the second data, predict the number of second vehicles expected to be in the blind spot area, and if the third data is acquired by the acquisition unit after predicting that there are two or more second vehicles, it is determined that there is a high probability that one or more fourth vehicles, different from the third vehicle that jumped out, will jump out from the blind spot area. (2) In accordance with the determined likelihood of the vehicle suddenly appearing, the system provides notification control to the driver of the first vehicle or controls the driving of the first vehicle.

[0005] A vehicle according to one embodiment of the present disclosure is equipped with a driver assistance device capable of controlling the driving assistance of the vehicle. The driver assistance device has an acquisition unit and a control unit. The acquisition unit is capable of acquiring first data indicating the existence of a blind spot area in front of the vehicle, second data about sounds emitted from multiple target vehicles expected to be present in the blind spot area, and third data about one of the multiple target vehicles, the first target vehicle, that jumps out from the blind spot area. The control unit is capable of controlling the driving assistance of the vehicle based on the first data, the second data and the third data. The control unit is capable of performing the following two actions: (1) Based on the second data, predict the number of target vehicles expected to be in the blind spot area. If, after predicting that there are two or more target vehicles, the third data acquisition unit acquires data, it is determined that there is a high probability that one or more second target vehicles, different from the first target vehicle that jumped out, will jump out from the blind spot area. (2) In accordance with the determined likelihood of the vehicle suddenly appearing, provide notification control to the vehicle driver or control the vehicle's movement. [Brief explanation of the drawing]

[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] Figure 1 is a diagram illustrating an example of traffic conditions. [Figure 2] Figure 2 shows an example of traffic conditions following those shown in Figure 1. [Figure 3] Figure 3 is a diagram showing an example of traffic conditions following those shown in Figure 2. [Figure 4] Figure 4 is a diagram showing an example of a functional block of a vehicle according to one embodiment of the present disclosure. [Figure 5] Figure 5 is a diagram illustrating an example of the driver assistance procedure in the vehicle shown in Figure 4. [Modes for carrying out the invention]

[0008] 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.

[0009] <1. Background> Driver assistance systems are known that support the driving of a vehicle based on the risks present in the vehicle's surroundings. For example, Non-Patent Document 1 discloses a system that estimates the possibility of a vehicle suddenly appearing from a blind spot in front of the vehicle.

[0010] Non-Patent Literature 1 discloses a method for determining the possibility of a vehicle suddenly appearing from a blind spot by detecting the engine or power start-up sound of a vehicle in that blind spot. However, while the method described in Non-Patent Literature 1 utilizes start-up sounds directly obtained from vehicles in the blind spot, it is difficult to determine from start-up sounds alone whether multiple vehicles are likely to appear consecutively from the blind spot when multiple vehicles are present. Here, the vehicle driver performs perception, prediction, judgment, and operation in order to drive the vehicle, and spends a great deal of effort making judgments and taking action in response to the movements of other vehicles that appear from the blind spot and cross in front of the vehicle (their own vehicle). As a result, the vehicle driver may neglect to perceive and predict the appearance of a second vehicle from the blind spot, and may not immediately notice that a second vehicle has actually appeared from the blind spot. Consequently, there is a risk that the vehicle may collide with the second vehicle that has suddenly appeared from the blind spot.

[0011] Thus, conventional technology cannot provide warning control or driving control in the event of multiple vehicles successively emerging from a blind spot. Therefore, after diligent study, the inventors of this application have conceived of a technology that can provide warning control and driving control in the event of multiple vehicles successively emerging from a blind spot. The background of this newly conceived technology will be explained below with reference to collision examples.

[0012] Figure 1 illustrates an example of a traffic situation. In Figure 1, vehicle 100a is traveling on a road La with one lane in each direction. This road La consists of a driving lane L1 on which vehicle 100a is traveling, and an opposing lane L2 that runs alongside driving lane L1 via a center line. An intersection CL is located ahead of vehicle 100a on this road La. This road La intersects with road Lb at intersection CL. Building 200a is located on road Lb before intersection CL, and when the driver of vehicle 100a looks at road Lb, the area of ​​road Lb behind building 200a is a blind spot (blind spot area BR). Two vehicles 100b and 100c are parked with their engines off in the blind spot area BR on road Lb. Therefore, the driver of vehicle 100a cannot see the two vehicles 100b and 100c, which are hidden behind building 200a.

[0013] In this situation, suppose vehicle 100b starts its engine and moves towards intersection CL, passing through intersection CL as shown in Figure 2. At this time, the driver of vehicle 100a is performing perception, prediction, judgment, and operation for the operation of vehicle 100a, and expends a great deal of effort making judgments and operations in response to the movement of vehicle 100b as it emerges from the blind spot area BR and passes through intersection CL. As a result, the driver of vehicle 100a may neglect to perceive and predict that vehicle 100c will emerge from the blind spot area BR, and may not immediately notice that vehicle 100c has actually emerged from the blind spot area BR. Consequently, there is a risk that vehicle 100a may collide with vehicle 100c as it suddenly emerges from the blind spot area BR, as shown in Figure 3.

[0014] To avoid such collisions, for example, the technology described in Non-Patent Document 1 could be applied to vehicle 100a. In this case, the driver of vehicle 100a could potentially become aware of the presence of vehicle 100b early and avoid a collision with vehicle 100b. However, with the technology described in Non-Patent Document 1, if multiple vehicles are present in a blind spot, it is difficult to determine whether multiple vehicles are likely to emerge from the blind spot in succession based solely on the sounds of multiple vehicles starting up in the blind spot. Therefore, even if the technology described in Non-Patent Document 1 is applied to vehicle 100a, there is a risk that vehicle 100a may collide with a second vehicle 100c that emerges from the blind spot area BR.

[0015] Therefore, the inventor of the present invention conceived of a technology that could perform notification control and driving control assuming that multiple vehicles would successively emerge from a blind spot in the traffic situation described above. The following describes in detail the driver assistance device and vehicle that realize this technology.

[0016] <2. Embodiments> [Example Configuration] First, a vehicle 1 equipped with a control unit 30 according to the first embodiment of this disclosure will be described. Figure 4 shows a schematic example of the configuration of vehicle 1. The control unit 30 corresponds to one specific example of the "driving support device" of this disclosure. Vehicle 1 corresponds to one specific example of vehicle 100a and corresponds to one specific example of the "vehicle" and "first vehicle" of this disclosure.

[0017] Vehicle 1 is capable of moving by the drive of a prime mover 60 (engine or motor). 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 (Electric Power Steering) motor 80, as shown in Figure 4.

[0018] The sensor unit 10 is configured to include various sensors mounted on the vehicle 1. The sensor unit 10 includes, for example, an accelerator opening sensor, a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, a steering angle sensor, and a steering torque sensor. The sensor unit 10 may include sensors other than those described above.

[0019] The accelerator opening sensor is capable of detecting the accelerator opening from the depression amount of the accelerator pedal. The accelerator opening sensor is capable of outputting time-series data (accelerator opening data) regarding the detected accelerator opening to the control unit 30.

[0020] The vehicle speed sensor is capable of detecting the speed (vehicle speed) of the vehicle 1. The vehicle speed sensor is capable of outputting time-series data (vehicle speed data) regarding the detected vehicle speed to the control unit 30. The acceleration sensor is capable of detecting the acceleration applied to the vehicle 1. The acceleration sensor is capable of outputting time-series data (acceleration data) regarding the detected accelerations in three directions to the control unit 30. The angular velocity sensor is capable of detecting the angular velocity of the vehicle 1. The angular velocity sensor is capable of outputting time-series data (angular velocity data) regarding the detected three angular velocities (yaw angular velocity, roll angular velocity, pitch angular velocity) to the control unit 30.

[0021] The steering angle sensor is capable of detecting the steering angle (steering wheel angle) of the steering wheel of the vehicle 1. The steering angle sensor is capable of outputting time-series data (steering wheel angle data) regarding the detected steering wheel angle to the control unit 30. The steering torque sensor is capable of detecting the steering torque generated by the driver's steering operation. The steering torque sensor is capable of outputting time-series data (steering torque data) regarding the detected steering torque to the control unit 30.

[0022] The sensor unit 10 further includes a directional sound collector mounted on the vehicle 1. The directional sound collector includes a microphone capable of detecting sounds generated in front of the vehicle 1. The directional sound collector is capable of outputting the detected sounds generated in front of the vehicle 1 as sound data to the control unit 30.

[0023] 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.

[0024] The stereo camera is capable of generating distance image data based on the amount of displacement of the corresponding object's position, using image data Ia (a pair of stereo image data) obtained through imaging. The driving environment detection unit can, for example, determine the lane markings that demarcate the road around vehicle 1 based on the distance image data. 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 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 to detect lanes and three-dimensional objects such as structures present around vehicle 1.

[0025] In the driving environment detection unit, the detection of three-dimensional objects includes, for example, the type of object, the distance to the object, the speed of the object, and the relative speed between the object and the vehicle (the vehicle itself). Examples of objects to be detected include traffic lights, intersections, road signs, stop lines, other vehicles, pedestrians, bicycles, and buildings. Examples of buildings include detached houses, apartment buildings, commercial facilities, factories, and signs. The driving environment detection unit can output driving environment information around vehicle 1, including the information on three-dimensional objects acquired in this way, to the control unit 30.

[0026] The communication unit 20 can acquire data to supplement data that cannot be obtained from image data Ia and distance image data, for example, through vehicle-to-vehicle communication, vehicle-to-infrastructure communication, and satellite communication. The communication unit 20 can output the acquired data to the control unit 30.

[0027] 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.

[0028] The communication unit 20 is capable of acquiring road map data around 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. The communication unit 20 is also capable of acquiring weather information around vehicle 1, for example, through vehicle-to-infrastructure communication.

[0029] 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, bus stops, and garbage collection points. "Structures surrounding roads" include, for example, various buildings and parks.

[0030] 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.

[0031] 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.

[0032] 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 through the communication unit 20.

[0033] 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.

[0034] The control unit 30 includes, for example, a locator unit. The locator unit is capable of acquiring the position coordinates of vehicle 1 based on the positioning signal received through the communication unit 20. The locator unit is capable of estimating the vehicle's position on a road map by map matching the acquired position coordinates onto route map information. Based on the acquired position coordinates of vehicle 1, the locator unit acquires map information for a predetermined range including vehicle 1 from the map information stored in the road map DB (database) 41, which will be described later.

[0035] 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.

[0036] As described above, the locator unit estimates the position of vehicle 1 on a 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 on which vehicle 1 is traveling.

[0037] The locator unit can update the road map information stored in the road map DB 41 to the latest state using road map information 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 information 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.

[0038] 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 DB41 to the latest state. This information update is performed not only on static information, but also on quasi-static, quasi-dynamic, 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.

[0039] The control unit 30 further includes a driving control unit 31, as shown in Figure 4, for example. The driving control unit 31 is capable of controlling the driving of the vehicle 1 (for example, the torque of the prime mover 60, the amount of brake pedal depression, and the steering angle of the steering wheel) and providing notifications related to the driving of the vehicle 1. The driving control unit 31 includes, for example, a data acquisition unit 32, a vehicle count prediction unit 33, a sudden appearance detection unit 34, a notification control unit 35, an avoidance control unit 36, an accelerator control unit 37, a brake control unit 38, and a steering control unit 39, as shown in Figure 4.

[0040] The data acquisition unit 32 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 1 (for example, turn signals). Based on the acquired data and various control signals, the data acquisition unit 32 is capable of acquiring first data Da, second data Db, third data Dc, and fourth data Dd.

[0041] (First data Da) The first data Da includes data indicating the existence of a blind spot in front of vehicle 1. The blind spot refers to, for example, an area of ​​"roads that intersect with the road on which vehicle 1 is traveling" as detected by the driving environment detection unit of sensor unit 10, which is obscured by at least one of "structures on the road" and "structures around the road" as detected by the driving environment detection unit of sensor unit 10.

[0042] (Second data Db) The second data set Db includes data (sound data) about sounds emitted from multiple vehicles 100w expected to be located in the blind spot area BR. Each vehicle 100w corresponds to a specific example of the "second vehicle" according to one embodiment of this disclosure. This sound data is time-series data where the peak value corresponds to the volume level. The sounds emitted from the multiple vehicles 100w are, for example, the engine start-up sounds or power start-up sounds of the multiple vehicles 100w. The engine start-up sounds or power start-up sounds of the multiple vehicles 100w can be detected, for example, by a directional sound collector mounted on vehicle 1.

[0043] (Third data Dc) The third data Dc includes data about one vehicle 100x that is the first to exit the blind spot area BR among a group of vehicles 100w. The third data Dc is, for example, data indicating that one vehicle 100x that is the first to exit the blind spot area BR has been detected based on data obtained from the sensor unit 10. Vehicle 100x corresponds to one specific example of the "third vehicle" according to one embodiment of this disclosure.

[0044] (Fourth data point Dd) The fourth data Dd may be used to set a threshold for predicting the number of vehicles 100w expected to be in the blind spot. The fourth data Dd includes data on at least one of the following: the external environment of vehicle 1, the types of multiple vehicles 100w, and whether or not the multiple vehicles 100w have been modified and the details thereof. The data on the external environment of vehicle 1 is sound data about the environment related to the ease with which sound resonates outside vehicle 1, and is, for example, data collected by a directional sound collector mounted on vehicle 1 before the second data Db is obtained. This sound data may include, for example, sound data obtained during noisy daytime, quiet nighttime, the summer season when cicadas are chirping, the windy winter season, noisy cities, or quiet rural areas. The data on the types of vehicles 100w is data about the type of vehicle 100w, and is, for example, data indicating gasoline passenger cars, gasoline trucks, electric passenger cars, or electric trucks. The data regarding whether or not a 100W vehicle has been modified and the details thereof includes, for example, data on whether or not the muffler has been modified, and if data on whether or not the muffler has been modified is included, it further includes data on the details of the modified muffler.

[0045] The vehicle count prediction unit 33, the sudden appearance detection unit 34, the notification control unit 35, and the avoidance control unit 36 ​​are capable of controlling the driving assistance of vehicle 1 based on at least the first data Da, the second data Db, and the third data Dc from the first data Da, the second data Db, the third data Dc, and the fourth data Dd. When the data acquisition unit 32 acquires at least the first data Da, the second data Db, and the third data Dc from the first data Da, the second data Db, the third data Dc, and the fourth data Dd, the vehicle count prediction unit 33, the sudden appearance detection unit 34, the notification control unit 35, and the avoidance control unit 36 ​​are capable of controlling the driving assistance of vehicle 1 based on the second data Db.

[0046] The vehicle count prediction unit 33 can predict the number of vehicles 100w expected to be present in the blind spot area BR based on the second data Db. The vehicle count prediction unit 33 can predict the number of vehicles 100w based on the number of startup sounds if the second data Db includes at least one of the startup sounds of the vehicle 100w's engine startup sound and power startup sound. The vehicle count prediction unit 33 can predict the number of vehicles 100w based on the number of startup sounds included in the time-series data of the sound data included in the second data Db if the time-series data includes at least one of the startup sounds of the vehicle 100w's engine startup sound and power startup sound. The vehicle count prediction unit 33 can determine whether or not the sound data included in the second data Db includes startup sounds emitted from the vehicle 100w by comparing the sound data included in the second data Db with the sound data 43 read from the storage unit 40.

[0047] The vehicle number prediction unit 33 may be capable of predicting the number of 100W vehicles based on the peak values ​​(sound levels) of the time-series data in the second data Db. The vehicle number prediction unit 33 can, for example, compare the peak values ​​(sound levels) of the time-series data in the second data Db with multiple (N) threshold values ​​Sthi (1≦i≦N) of different magnitudes. For example, if there is a pulse in the time-series data in the second data Db with a peak value that exceeds the smallest threshold Sth1 but does not exceed the second smallest threshold Sth2, the vehicle number prediction unit 33 can determine the number of 100W vehicles by the number of such pulses. The vehicle count prediction unit 33 determines, for example, that two 100W vehicles simultaneously emitted startup sounds when there is a pulse in the time-series data in the second data Db that exceeds the second smallest threshold Sth2 but does not exceed the third smallest threshold Sth3, and the second smallest number of such pulses is used as the number of 100W vehicles. The smallest threshold Sth1 is, for example, slightly smaller than the loudness of the startup sound emitted from one 100W vehicle. The second smallest threshold Sth2 is, for example, slightly smaller than the loudness of the startup sounds emitted simultaneously from two 100W vehicles. The third smallest threshold Sth3 is, for example, slightly smaller than the loudness of the startup sounds emitted simultaneously from three 100W vehicles.

[0048] The vehicle number prediction unit 33 may be capable of predicting the number of vehicles 100W based on the number of startup sounds included in the time series data in the second data Db, the peak value (volume level) of the time series data in the second data Db, and the result of comparing it with a plurality of thresholds of different magnitudes.

[0049] The vehicle count prediction unit 33 may set multiple (N) threshold values ​​Sthi based on the fourth data Dd, and predict the number of vehicles 100w based on the volume level included in the second data Db and the set multiple (N) threshold values ​​Sthi. For example, if the fourth data Dd includes data about the external environment of vehicle 1, the vehicle count prediction unit 33 can set the threshold value Sthi to a value greater than the volume of the sound data included in the data about the external environment of vehicle 1. For example, if the fourth data Dd includes data about the vehicle type of vehicle 100w, the vehicle count prediction unit 33 can set the threshold value Sthi to a value greater than the volume of the sound generated according to the vehicle type of vehicle 100w (for example, gasoline engine sound or motor sound). For example, if the fourth data Dd includes data about whether or not vehicle 100w has been modified and the details thereof, the vehicle count prediction unit 33 can set the threshold value Sthi to a value greater than the volume of the sound that changes with the modification of vehicle 100w.

[0050] The vehicle detection unit 34, after the vehicle number prediction unit 33 predicts that there are two or more vehicles 100w expected to be in the blind spot area BR, can determine if, when the third data Dc is acquired by the data acquisition unit 32, there is a high probability that one or more vehicles 100y, different from the vehicle 100x that jumped out earlier, will jump out of the blind spot area BR from among the multiple vehicles 100w. "When the third data Dc is acquired by the data acquisition unit 32" refers to the case where one vehicle 100x jumping out of the blind spot area BR is detected. Vehicle 100y corresponds to one specific example of the "fourth vehicle" according to one embodiment of this disclosure.

[0051] The second data Db includes the engine start sound and power start sound of at least one of the multiple vehicles 100w, and the interval between the occurrence timings of the multiple start sounds is within a predetermined threshold (e.g., 3 seconds). In this case, the vehicle departure detection unit 34 may be able to determine that there is a high probability that one or more vehicles 100y, different from the vehicle 100x that departed earlier, will depart from the blind spot area BR. The second data Db includes the engine start sound and power start sound of at least one of the multiple vehicles 100w, while the interval between the occurrence timings of the multiple start sounds is greater than a predetermined threshold (e.g., 3 seconds). In this case, the vehicle departure detection unit 34 may be able to determine that there is a relatively high probability that one or more vehicles 100y, different from the vehicle 100x that departed earlier, will depart from the blind spot area BR.

[0052] For example, suppose that in the traffic situation shown in Figure 1, it is predicted that two vehicles 100b and 100c are present in the blind spot area BR, and then, for example, in the traffic situation shown in Figure 2, a vehicle 100b is detected emerging from the blind spot area BR. At this time, the emerging vehicle determination unit 34 can determine that, of the two vehicles 100b and 100c present in the blind spot area BR, there is a high probability that a different vehicle 100c from the vehicle 100b that emerged earlier will emerge from the blind spot area BR. The two vehicles 100b and 100c correspond to a specific example of the "second vehicle" according to one embodiment of this disclosure. The vehicle 100b that emerged from the blind spot area BR corresponds to a specific example of the "third vehicle" according to one embodiment of this disclosure. The vehicle 100c corresponds to a specific example of the "fourth vehicle" according to one embodiment of this disclosure.

[0053] For example, suppose the second data Db obtained from the traffic conditions shown in Figure 1 includes the engine start sound and power start sound of at least one of the vehicles 100b and 100c, and the interval between the occurrence timings of the multiple start sounds is within a predetermined threshold (e.g., 3 seconds). In this case, the vehicle sudden-out detection unit 34 may be able to determine that there is a high probability that a vehicle 100c different from the vehicle 100b that suddenly appeared will suddenly appear from the blind spot area BR. For example, suppose the second data Db obtained from the traffic conditions shown in Figure 1 includes the engine start sound and power start sound of at least one of the vehicles 100b and 100c, but the interval between the occurrence timings of the multiple start sounds is greater than a predetermined threshold (e.g., 3 seconds). In this case, the vehicle sudden-out detection unit 34 may be able to determine that there is a low probability that a vehicle 100c different from the vehicle 100b that suddenly appeared will suddenly appear from the blind spot area BR.

[0054] For example, suppose the fourth data Dd obtained from the traffic situation shown in Figure 1 includes data on the types of vehicles 100b and 100c that are expected to be in the blind spot area BR, and the fourth data Dd includes data indicating that each of the vehicles 100b and 100c expected to be in the blind spot area BR is a truck. In this case, the vehicle departure determination unit 34 may determine that each of the vehicles 100b and 100c is a truck and that there is a very high probability that one or more vehicles 100c different from the vehicle 100b that departed earlier will depart from the blind spot area BR.

[0055] Assume that after vehicle 100b has left the blind spot area BR, it is not detected that one or more vehicles 100c, different from the vehicle 100b that left the blind spot area BR, have left the blind spot area BR. In this case, the vehicle departure detection unit 34 may determine that there is a high probability that one or more vehicles 100c will leave the blind spot area BR until it is detected that one or more vehicles 100c have left the blind spot area BR. The vehicle departure detection unit 34 may gradually decrease the probability that one or more vehicles 100c will leave the blind spot area BR until it is detected that one or more vehicles 100c have left the blind spot area BR.

[0056] The notification control unit 35 is capable of providing notification control to the driver of vehicle 1 according to the likelihood that vehicle 100c will suddenly appear on the road La from the blind spot area BR. The notification control unit 35 is capable of generating a video signal for executing notification based on the judgment result of the sudden appearance detection unit 34 and outputting it to the notification unit 50. The notification unit 50 is composed of, for example, an LCD panel, an organic EL panel, or a HUD (Head-up Display), and is capable of displaying video based on the video signal input from the notification control unit 35.

[0057] The avoidance control unit 36 ​​is capable of performing driving control based on the judgment result of the sudden obstacle detection unit 34. The avoidance control unit 36 ​​can output a control signal (for example, data on additional torque obtained based on the judgment result of the sudden obstacle detection unit 34) for performing driving control based on the judgment result of the sudden obstacle detection unit 34 to at least one of the accelerator control unit 37, brake control unit 38, and steering control unit 39.

[0058] The accelerator control unit 37 is capable of controlling the torque of the prime mover 60 based on the requested torque corresponding to the amount the driver of the vehicle 1 depresses the accelerator pedal. Furthermore, the accelerator control unit 37 is capable of deriving a target torque by adding an additional torque obtained based on the judgment result of the sudden-jump determination unit 34 to the requested torque, and controlling the torque of the prime mover 60 based on the derived target 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 37.

[0059] The brake control unit 38 is capable of controlling the torque of the brake 70 based on the requested torque corresponding to the amount the driver of the vehicle 1 presses the brake pedal. Furthermore, the brake control unit 38 is capable of deriving a target torque by adding an additional torque obtained based on the judgment result of the sudden-jump determination unit 34 to the requested torque, and controlling the torque of the brake 70 based on the derived target 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 38.

[0060] The steering control unit 39 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. Furthermore, the steering control unit 39 can derive a target torque by adding an additional torque obtained based on the judgment result of the jackpot determination unit 34 to the steering assist torque, and set an EPS torque corresponding to the derived target torque. The steering control unit 39 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 EPS motor 80 generates an output torque based on the input control signal and can control the steering angle of the steering wheel.

[0061] 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, threshold data 42, and sound data 43, as shown in Figure 4.

[0062] The road map DB41 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 constitute road information, and quasi-dynamic and dynamic information that mainly constitute traffic information. The threshold data 42 includes data on various thresholds used by the vehicle count prediction unit 33 and the sudden appearance detection unit 34. The sound data 43 is reference data for comparison with sound data obtained by a directional sound collector mounted on the vehicle 1, and includes, for example, engine start sound, power start sound, door opening and closing sound, and rearview mirror opening and closing sound.

[0063] Next, we will explain the driving assistance procedure for vehicle 1.

[0064] Figure 5 shows an example of the driving assistance procedure in vehicle 1. The driving control unit 31 acquires various data, including image data Ia (step S101). The driving control unit 31 also acquires various control signals as needed. Subsequently, the driving control unit 31 determines whether or not a blind spot exists in front of vehicle 1 based on the acquired data and various control signals (step S102). For example, if the driving control unit 31 finds an area in image data Ia that is obstructed by some structure from the perspective of the driver of vehicle 1, it identifies that area as a blind spot.

[0065] If the driving control unit 31 determines that a blind spot area BR exists in front of the vehicle 1 (step S102; Y), it determines, based on the second data Db, whether or not there are sounds emitted from multiple vehicles 100w that are expected to be in the blind spot area BR (step S103). The driving control unit 31 compares the sound data contained in the second data Db with the sound data 43 read from the storage unit 40 to determine whether or not the sound data contained in the second data Db includes startup sounds emitted from the blind spot area BR.

[0066] If the driving control unit 31 determines that there are sounds emanating from multiple vehicles 100w in the blind spot area BR (step S103; Y), it predicts the number of vehicles expected to be in the blind spot area BR based on the second data Db (step S104). If the driving control unit 31 predicts that there are two or more vehicles 100w expected to be in the blind spot area BR (step S105; Y), it determines whether or not a single vehicle 100x emerging from the blind spot area BR is detected based on the acquired data and various control signals (step S106).

[0067] If the driving control unit 31 detects one vehicle 100x emerging from the blind spot area BR (step S106; Y), it determines the possibility that one or more vehicles 100y, different from the vehicle 100x that emerged from the blind spot area BR, may emerge from the blind spot area BR (step S107). For example, the driving control unit 31 may determine that there is a high probability that one or more vehicles 100y will emerge from the blind spot area BR after predicting that there are two or more vehicles 100w expected to be in the blind spot area BR and then detecting one vehicle 100x emerging from the blind spot area BR.

[0068] The driving control unit 31 may, for example, determine that there is a high probability that one or more vehicles 100y will suddenly appear from the blind spot area BR if the second data Db includes at least one of the engine start-up sounds and power start-up sounds of multiple vehicles 100w, and the interval between the occurrence timings of the multiple start-up sounds is within a predetermined threshold (e.g., 3 seconds). The driving control unit 31 may, for example, determine that there is a low probability that one or more vehicles 100y will suddenly appear from the blind spot area BR if the second data Db includes at least one of the engine start-up sounds and power start-up sounds of multiple vehicles 100w, while the interval between the occurrence timings of the multiple start-up sounds is greater than a predetermined threshold (e.g., 3 seconds).

[0069] The driving control unit 31 may, for example, determine that there is a high probability that one or more vehicles 100y will depart from the blind spot area BR after vehicle 100x has departed from the blind spot area BR, while no detection of one or more vehicles 100y departing from the blind spot area BR is present. The driving control unit 31 may, for example, gradually reduce the probability that one or more vehicles 100y will depart from the blind spot area BR after vehicle 100x has departed from the blind spot area BR, while no detection of one or more vehicles 100y departing from the blind spot area BR is present.

[0070] If the driving control unit 31 determines that there is a low probability that one or more vehicles 100y will suddenly appear from the blind spot area BR (step S108; N), or if the probability of one or more vehicles 100y suddenly appearing from the blind spot area BR is below a predetermined value, it does not perform the above-mentioned notification control or driving control. If the driving control unit 31 determines that there is a high probability that one or more vehicles 100y will suddenly appear from the blind spot area BR (step S108; Y), or if the probability of one or more vehicles 100y suddenly appearing from the blind spot area BR is higher than a predetermined value, it performs notification control and driving control to the driver of vehicle 1 according to the likelihood that vehicle 100c will suddenly appear from the blind spot area BR onto the road La (step S109). In this way, driving assistance for vehicle 1 is performed.

[0071] [effect] Next, the effects of Vehicle 1 according to one embodiment of the present disclosure will be described.

[0072] In this embodiment, the number of vehicles 100w expected to be in the blind spot is predicted based on data about sounds emitted from multiple vehicles 100w expected to be in the blind spot. As a result, if it is predicted that there are two or more vehicles 100w, and one vehicle 100x is detected emerging from the blind spot among the multiple vehicles 100w, it is determined that there is a high probability that one or more vehicles 100y, different from the vehicle 100x that emerged, will emerge from the blind spot. Then, notification control to the driver of vehicle 1 or driving control of vehicle 1 is performed according to the determined probability of the vehicles emerging. This makes it possible to inform the driver of vehicle 1 that there is a high probability that vehicle 100y will emerge from the blind spot following vehicle 100x, or to perform driving control on vehicle 1 corresponding to the possibility that vehicle 100y will emerge from the blind spot following vehicle 100x. As a result, the possibility of vehicle 1 colliding with vehicle 100y can be reduced.

[0073] In this embodiment, if the second data Db includes the starting sounds of at least one of the engine starting sounds and power starting sounds of multiple vehicles 100w, the number of vehicles 100w present in the blind spot area is predicted based on the number of starting sounds included in the second data Db. Thus, in this embodiment, the number of vehicles 100w present in the blind spot area is predicted based on data obtained from multiple vehicles 100w actually present in the blind spot area. This reduces the possibility of mistakenly identifying the presence of vehicles 100w in the blind spot area when no vehicles 100w are actually present, judging that there is a possibility of vehicles 100w suddenly appearing from the blind spot area, and then notifying the driver of vehicle 1 of the possibility of vehicles 100w suddenly appearing from the blind spot area, or performing driving control based on the assumption that vehicles 100w will suddenly appear from the blind spot area. As a result, the driver of vehicle 1 can reduce the possibility of finding such notifications and driving control bothersome.

[0074] In this embodiment, if the second data Db includes at least one of the engine start-up sounds and power start-up sounds of multiple vehicles 100w, and the interval between the occurrence timings of the multiple start-up sounds is within a predetermined threshold (for example, 3 seconds), it may be determined that there is a high probability that one or more vehicles 100y, different from the vehicle 100x that has already emerged, will emerge from the blind spot. In this case, even if one or more vehicles 100y are actually present in the blind spot, the possibility of notifying the driver of vehicle 1 that one or more vehicles 100y may emerge from the blind spot, or of performing driving control based on the assumption that one or more vehicles 100y will emerge from the blind spot, can be reduced. As a result, the possibility of the driver of vehicle 1 finding such notifications and driving control bothersome can be reduced.

[0075] In this embodiment, the number of vehicles 100w may be predicted based on the volume level included in the second data Db. In this case, even if startup sounds are emitted simultaneously from multiple vehicles 100w, the number of vehicles 100w present in the blind spot can be predicted more accurately. As a result, for example, even if multiple vehicles 100w are actually present in the blind spot, the system may mistakenly believe that only one vehicle 100w is present, and may decide that a second vehicle 100w will not emerge from the blind spot. This can reduce the possibility of failing to notify the driver of vehicle 1 that a second vehicle 100w may emerge from the blind spot, or performing driving control based on the assumption that a second vehicle 100w will not emerge from the blind spot. As a result, the possibility of vehicle 1 colliding with a second vehicle 100w can be reduced.

[0076] In this embodiment, a threshold value for the volume level included in the second data Db may be set based on the fourth data Dd, and the number of vehicles 100w may be predicted based on the volume level included in the second data Db and the set threshold value. In this case, even if noise components included in the environment outside vehicle 1 are detected superimposed on the startup sounds of multiple vehicles 100w, the threshold value can be set considering the noise components included in the environment outside vehicle 1. As a result, notification control and driving control can be performed without being affected by noise included in the environment outside vehicle 1.

[0077] In this embodiment, the sound data included in the second data Db may be the sounds of doors opening and closing or rearview mirrors opening and closing of multiple vehicles 100w. In this case, the sounds of doors opening and closing or rearview mirrors opening and closing of multiple vehicles 100w can be detected, for example, by a directional sound collector mounted on vehicle 1. Even if the sound data included in the second data Db is the sounds of doors opening and closing or rearview mirrors opening and closing of multiple vehicles 100w, the number of vehicles 100w expected to be present in the blind spot area can be predicted in the same way as if the sound data included in the second data Db were the sounds of multiple vehicles 100w starting up. Therefore, as in the above embodiment, the possibility of vehicle 1 colliding with vehicle 100y can be reduced.

[0078] In this embodiment, if the collision margin between vehicle 1 and vehicle 100y is sufficiently large (for example, 5 seconds or more), and the driver of vehicle 1 can avoid a collision with vehicle 100y, the driving control unit 31 may refrain from performing the above-described notification control and driving control.

[0079] Furthermore, the effects described herein are merely illustrative and not limiting, and other effects may also occur.

[0080] Furthermore, for example, this disclosure can take the following configuration. (1) An acquisition unit capable of acquiring first data indicating the existence of a blind spot in front of a first vehicle, second data regarding sounds emitted from multiple second vehicles expected to be present in the blind spot, and third data regarding one third vehicle among the multiple second vehicles that emerges from the blind spot. A control unit capable of controlling the driving assistance of the first vehicle based on the first data, the second data, and the third data, Equipped with, The control unit, Based on the second data, the number of second vehicles expected to be present in the blind spot area is predicted, and if the third data is acquired by the acquisition unit after predicting that the number of second vehicles is two or more, it is determined that there is a high probability that one or more fourth vehicles, different from the third vehicle that jumped out, will jump out from the blind spot area. The system will either provide notification control to the driver of the first vehicle or control the driving of the first vehicle, in accordance with the determined probability of the vehicle suddenly appearing. It is now possible to perform this action. Driving assistance system. (2) The control unit is capable of predicting the number of second vehicles based on the number of startup sounds if the second data includes at least one of the startup sounds of the engines and power supplies of multiple second vehicles. (1) The driving assistance device described above. (3) The control unit can determine that if the second data includes at least one of the engine start-up sounds and power start-up sounds of multiple second vehicles, and the interval between the occurrence timings of the multiple start-up sounds is within a predetermined threshold, then there is a high probability that one or more fourth vehicles, different from the third vehicle that has suddenly appeared, will suddenly appear from the blind spot area. (1) or (2) the driving assistance device described above. (4) The control unit is capable of predicting the number of the second vehicle based on the magnitude of the sound volume included in the second data. (1) The driving assistance device described above. (5) The acquisition unit is capable of acquiring a fourth set of data, at least one of the following: the external environment of the first vehicle, the types of the multiple second vehicles, and whether or not the multiple second vehicles have been modified and the details thereof. The control unit sets a threshold for the volume level included in the second data based on the fourth data acquired by the acquisition unit, and can predict the number of the second vehicles based on the volume level included in the second data and the set threshold. (4) The driving assistance device described above. (6) Equipped with a driver assistance system capable of controlling the vehicle's driving assistance, The aforementioned driving support device, An acquisition unit capable of acquiring first data indicating the existence of a blind spot area in front of the vehicle, second data regarding sounds emitted from multiple target vehicles expected to be present in the blind spot area, and third data regarding one of the multiple target vehicles that jumps out from the blind spot area. A control unit capable of controlling the driving assistance of the vehicle based on the first data, the second data, and the third data, Equipped with, The control unit, Based on the second data, the number of target vehicles expected to be present in the blind spot area is predicted, and if the third data is acquired by the acquisition unit after predicting that the number of target vehicles is two or more, it is determined that there is a high probability that one or more second target vehicles, different from the first target vehicle that has jumped out, will jump out from the blind spot area. The system will either provide notification control to the driver of the vehicle or control the vehicle's movement according to the determined probability of the vehicle suddenly stepping out into the road. It is now possible to perform this action. vehicle.

[0081] In a driver assistance system according to one embodiment of the present disclosure, the number of second vehicles expected to be in a blind spot is predicted based on data about sounds emitted from multiple second vehicles expected to be in the blind spot. As a result, if a third vehicle is detected suddenly emerging from the blind spot after it has been predicted that there are two or more second vehicles, it is determined that there is a high probability that one or more fourth vehicles, different from the third vehicle that emerged, will also emerge from the blind spot. Then, notification control to the driver of the first vehicle or driving control of the first vehicle is performed according to the determined probability of the vehicle emerging. This makes it possible to inform the driver of the first vehicle that there is a high probability that the fourth vehicle will emerge from the blind spot following the third vehicle, or to perform driving control on the first vehicle in response to the possibility that the fourth vehicle will emerge from the blind spot following the third vehicle. As a result, the possibility of the first vehicle colliding with the fourth vehicle can be reduced.

[0082] In a vehicle according to one embodiment of this disclosure, the number of target vehicles expected to be in the blind spot is predicted based on data about sounds emitted from multiple target vehicles expected to be in the blind spot. As a result, if a first target vehicle is detected suddenly emerging from the blind spot after it has been predicted that there are two or more target vehicles, it is determined that there is a high probability that one or more second target vehicles, different from the first target vehicle that emerged, will also emerge from the blind spot. Then, the vehicle is notified to the driver or the vehicle is driven according to the determined probability of the vehicles emerging. This makes it possible to inform the vehicle driver that there is a high probability that a second target vehicle will emerge from the blind spot following the first target vehicle, or to perform driving control on the vehicle in response to the possibility that a second target vehicle will emerge from the blind spot following the first target vehicle. As a result, the possibility of the vehicle colliding with the second target vehicle can be reduced.

[0083] The control unit 30 shown in Figure 4 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). The at least one processor can be configured to perform all or some of the functions of the control unit 30 shown in Figure 4 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 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 functions of the control unit 30 shown in Figure 4. An FPGA is an integrated circuit designed to be configurable after manufacturing to perform all or some of the functions of the control unit 30 shown in Figure 4. [Explanation of symbols]

[0084] 1,100a,100b,100c,100w,100x,100y…Vehicle, 10…Sensor unit, 20…Communication unit, 30…Control unit, 31…Driving control unit, 32…Data acquisition unit, 33…Vehicle count prediction unit, 34…Sudden obstacle detection unit, 35…Notification control unit, 36…Avoidance control unit, 37…Accelerator control unit, 38…Brake control unit, 39…Steering control unit, 40…Storage unit, 41…Road map DB, 4 2...Threshold data, 43...Sound data, 50...Notification unit, 60...Motor, 70...Brake, 80...EPS motor, 200a...Building, BR...Blind spot area, CL...Intersection, Da...First data, Db...Second data, Dc...Third data, Dd...Fourth data, Ia...Image data, L1...Driving lane, L2...Opponent lane, La,Lb...Road, Sth1,Sth2,Sth3,Sthi...Threshold.

Claims

1. An acquisition unit capable of acquiring first data indicating the existence of a blind spot in front of a first vehicle, second data regarding sounds emitted from a plurality of second vehicles expected to be present in the blind spot, and third data regarding one third vehicle among the plurality of second vehicles that jumps out from the blind spot. A control unit capable of controlling the driving assistance of the first vehicle based on the first data, the second data, and the third data, Equipped with, The control unit, Based on the second data, the number of second vehicles expected to be present in the blind spot area is predicted, and if the third data is acquired by the acquisition unit after predicting that there are two or more second vehicles, it is determined that there is a high probability that one or more fourth vehicles, different from the third vehicle that has suddenly appeared, will suddenly appear from the blind spot area. The system will perform notification control to the driver of the first vehicle or control the driving of the first vehicle according to the determined probability of the vehicle suddenly appearing. It is now possible to perform this action. Driving assistance system.

2. The control unit is capable of predicting the number of second vehicles based on the number of startup sounds, if the second data includes at least one of the startup sounds of the engines and power supplies of multiple second vehicles. The driving support device according to claim 1.

3. The control unit can determine that if the second data includes at least one of the engine start-up sounds and power start-up sounds of multiple second vehicles, and the interval between the occurrence timings of the multiple start-up sounds is within a predetermined threshold, then there is a high probability that one or more fourth vehicles, different from the third vehicle that suddenly appeared, will suddenly appear from the blind spot area. The driving support device according to claim 1.

4. The control unit is capable of predicting the number of the second vehicles based on the magnitude of the sound volume included in the second data. The driving support device according to claim 1.

5. The acquisition unit is capable of acquiring a fourth piece of data concerning at least one of the following: the external environment of the first vehicle, the types of the multiple second vehicles, and whether or not the multiple second vehicles have been modified and the details thereof. The control unit sets a threshold value for the volume level included in the second data based on the fourth data acquired by the acquisition unit, and can predict the number of the second vehicles based on the volume level included in the second data and the set threshold value. The driving support device according to claim 4.

6. Equipped with a driver assistance system capable of controlling the vehicle's driving assistance, The aforementioned driving support device, An acquisition unit capable of acquiring first data indicating the existence of a blind spot area in front of the vehicle, second data regarding sounds emitted from multiple target vehicles expected to be present in the blind spot area, and third data regarding one of the multiple target vehicles that jumps out from the blind spot area. A control unit capable of controlling the driving assistance of the vehicle based on the first data, the second data, and the third data, Equipped with, The control unit, Based on the second data, the number of target vehicles expected to be present in the blind spot area is predicted, and if the third data is acquired by the acquisition unit after predicting that the number of target vehicles is two or more, it is determined that there is a high probability that one or more second target vehicles, different from the first target vehicle that has jumped out, will jump out from the blind spot area. The system will either provide notification control to the driver of the vehicle or control the vehicle's movement according to the determined probability of the vehicle suddenly stepping out into the road. It is now possible to perform this action. vehicle.

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