control device

Abstract

Control device achieving improvement of safety. Control device (100) is provided with a processor capable of acquiring output information of a radar device (12) capable of detecting an object based on a reflected wave from the object located in the periphery of a vehicle (M), the processor acquires first detection point group data of the object in a periphery area (DA1) of the vehicle (M) based on the output information of the radar device (12), performs identification of a stationary object existing in the periphery of the vehicle (M) based on the first detection point group data, and in a case where it is identified that a stationary object exists, performs identification of a traffic cone existing in the periphery of the vehicle (M) based on the first detection point group data in a periphery area (DA2) narrower than the periphery area (DA1) in the periphery of the vehicle (M).

Description

Technical Field

[0001] This invention relates to a control device. Background Technology

[0002] In recent years, the adoption of autonomous and assisted driving technologies for vehicles has developed rapidly. As part of these technologies, technologies such as Auto Lane Changing (ALC) have been developed, allowing vehicles to change lanes even without the driver performing steering or other maneuvers.

[0003] Patent Document 1 discloses a radar device for detecting objects within a scanning range, characterized by comprising: a detection unit that detects objects within the scanning range based on reflected waves of a transmitted wave and outputs a detection result of the object; and an adjustment unit that, if the detection result includes objects other than those detected within the scanning range within a certain time, narrows the scanning range by not detecting objects other than those detected, wherein the adjustment unit determines objects with speeds less than a first value as objects other than those detected.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2014-199221 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] Identifying restricted lane zones on the road, such as those caused by construction or accidents (defined as restricted driving zones in this specification), is crucial for improving vehicle safety during driving control. When a vehicle passes through or approaches a restricted driving zone, multiple objects such as traffic cones may be present around the vehicle. Therefore, if such objects are detected over a large area, the probability of false detection increases, potentially affecting the accuracy of restricted driving zone identification.

[0009] The purpose of this invention is to improve safety. Furthermore, it contributes to further improving traffic safety and enabling the development of sustainable transportation systems.

[0010] Methods for solving problems

[0011] One aspect of the present invention is a control device for driving control of a vehicle, comprising a processor capable of acquiring output information from a sensor that detects an object based on reflected waves from an object located around the vehicle. The processor performs the following actions: acquiring first detection point group data of the object in a first region around the vehicle based on the output information from the sensor; identifying a first specific object present around the vehicle based on the first detection point group data; and, if the first specific object is identified, identifying a second specific object present around the vehicle based on the first detection point group data in a second region around the vehicle that is narrower than the first region.

[0012] Invention Effects

[0013] According to the present invention, vehicle safety can be improved. Attached Figure Description

[0014] Figure 1 This is a block diagram showing the overall structure of the vehicle system 1 equipped with the control device 100.

[0015] Figure 2 This is a schematic diagram showing an example of the external structure of the vehicle M included in the vehicle system 1.

[0016] Figure 3 This is a diagram showing an example of the structure of the first control unit 120 and the second control unit 160.

[0017] Figure 4 This is a diagram showing a specific example of a driving mode.

[0018] Figure 5 This is a schematic diagram showing the area around vehicle M where objects can be detected by radar device 12.

[0019] Figure 6 This is a schematic diagram illustrating an example of detection point group data acquired by the identification unit 130.

[0020] Figure 7 This example illustrates how the presence of a given object OB can be identified based on a cluster of detection points within the surrounding area DA2.

[0021] Figure 8 It is a schematic diagram used to illustrate the detection point group data corresponding to multiple cycles.

[0022] Figure 9 This is a flowchart used to explain the operation of the control device 100.

[0023] Explanation of reference numerals in the attached figures

[0024] 100 Control device

[0025] 10 cameras

[0026] 12 Radar Devices

[0027] M vehicle. Detailed Implementation

[0028] Hereinafter, a vehicle system 1 including a control device 100 as an embodiment of the present invention will be described with reference to the accompanying drawings. The drawings should be viewed along the directions of the reference numerals. In this specification, for the sake of simplicity and clarity, the front-back and left-right directions are indicated by... Figure 2 The direction observed by the driver of vehicle M is recorded. In the attached diagram, the front of vehicle M is denoted as Fr, the rear as Rr, the left as L, and the right as R.

[0029] <Overall Structure of Vehicle System 1>

[0030] Figure 1 This is a block diagram showing the overall structure of the vehicle system 1 equipped with the control device 100. Figure 2 This is a schematic diagram illustrating the external structure of a vehicle M included in vehicle system 1. Vehicle M is, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle, and its drive source is an internal combustion engine such as a diesel engine or gasoline engine, an electric motor, or a combination thereof. The electric motor operates using electricity generated by a generator connected to the internal combustion engine, or by discharge electricity from a secondary battery or fuel cell. Figure 2 The example shown is a four-wheeled vehicle, M.

[0031] Vehicle system 1 includes, for example, a camera 10, a radar device 12, a LiDAR (Light Detection and Range) system 14, a communication device 20, an HMI (Human Machine Interface) 30, vehicle sensors 40, a driver monitoring camera 50, a navigation device 60, an MPU (Map Positioning Unit) 70, driving controls 80, a control device 100, a driving force output device 200, a braking device 210, and a steering device 220. These devices and equipment are interconnected via CAN (Controller Area Network) communication lines, serial communication lines, wireless communication networks, etc.

[0032] Camera 10 is, for example, a digital camera utilizing imaging elements such as CCD (Charge Coupled Device) or CMOS (Complementary Metal Oxide Semiconductor). Camera 10 is mounted anywhere on the vehicle M. For example, as... Figure 2 As shown, camera 10 is located near the rearview mirror (not shown) inside the vehicle M, and at the front of the right door and the front of the left door outside the vehicle M. The image information (output information of camera 10) of the vehicle M in the direction of travel in front, to the right rear, and to the left rear, captured by camera 10, is sent to control device 100.

[0033] The radar device 12 emits millimeter-wave or other radio waves around the vehicle M, detects the radio waves reflected by objects (reflected waves), and outputs information about the location (hereinafter also referred to as the detection point) of a portion of the object determined by the reflected waves (hereinafter also referred to as the detection point data). As radio waves, lasers, microwaves, millimeter waves, ultrasound, etc., can be appropriately used. The radar device 12 can be installed at any location on the vehicle M. For example, as... Figure 2 As shown, there are three radar devices 12 on the front and two on the rear, for a total of five. The output information of the radar devices 12 is sent to the control device 100.

[0034] The LIDAR14 illuminates the periphery of vehicle M with light (or electromagnetic waves of a wavelength close to light) and measures the scattered light. The LIDAR14 detects the presence or absence of an object and its distance based on the time from the emission of light to the reception of light. The illuminated light can be, for example, a pulsed laser. The LIDAR14 can be mounted at any location on vehicle M. For example, as... Figure 2 As shown, there are two LIDAR14s on the front and three on the rear, for a total of five. The output information of the LIDAR14s is sent to the control device 100.

[0035] The communication device 20 communicates with other vehicles in the vicinity of vehicle M, such as using cellular networks, Wi-Fi networks, Bluetooth, DSRC (Dedicated Short Range Communication), etc., or communicates with various server devices via wireless base stations.

[0036] The HMI30 provides various information to the occupants of vehicle M and accepts their input. The HMI30 includes various display devices, speakers, buzzers, touch panels, switches, buttons, etc.

[0037] The vehicle sensor 40 includes a vehicle speed sensor for detecting the speed of the vehicle M, an acceleration sensor for detecting acceleration, a yaw rate sensor for detecting angular velocity about a vertical axis, and an orientation sensor for detecting the orientation of the vehicle M.

[0038] The driver monitoring camera 50 is, for example, a digital camera that utilizes imaging elements such as a CCD image sensor or a CMOS image sensor. The driver monitoring camera 50 is installed at any location in the vehicle M to capture the position and orientation of the head of the occupant (hereinafter referred to as the driver) sitting in the driver's seat of the vehicle M from the front (with the face facing the camera).

[0039] The navigation device 60 includes, for example, a GNSS (Global Navigation Satellite System) receiver 61, a navigation HMI 62, and a path determination unit 63. The navigation device 60 stores the first map information 64 in a storage device such as an HDD (Hard Disk Drive) or flash memory.

[0040] The GNSS receiver 61 determines the position of vehicle M based on signals received from GNSS satellites. The position of vehicle M can also be determined or supplemented by using the INS (Inertial Navigation System) output from vehicle sensor 40.

[0041] The navigation HMI 62 includes a display device, speakers, a touch panel, buttons, etc. The navigation HMI 62 can also share some or all of the components with the aforementioned HMI 30.

[0042] The route determination unit 63, for example, refers to the first map information 64 to determine the route (hereinafter referred to as the map path) from the position of the vehicle M determined by the GNSS receiver 61 (or any input position) to the destination input by the occupant using the navigation HMI 62. The first map information 64 represents road shape information, for example, by indicating road segments and nodes connecting those segments. The first map information 64 may also include road curvature, POI (Point of Interest) information, etc. The map path is output to the MPU 70.

[0043] The navigation device 60 can also provide route guidance using the navigation HMI 62 based on the path on the map. The navigation device 60 can also send its current location and destination to the navigation server via the communication device 20, and obtain the path from the navigation server that is equivalent to the path on the map.

[0044] MPU 70 includes, for example, a lane recommendation unit 71 that stores second map information 72 in a storage device such as an HDD or flash memory. The lane recommendation unit 71 divides the path on the map provided by the navigation device 60 into multiple segments (e.g., every 100m in the vehicle's direction of travel), and determines a recommended lane for each segment by referring to the second map information 72. The lane recommendation unit 71, for example, determines which lane to drive in from the left. When the path on the map has branching points, the lane recommendation unit 71 determines the recommended lane in a way that allows the vehicle M to travel on a reasonable path to the branch destination.

[0045] The second map information 72 is higher precision map information than the first map information 64. The second map information 72 may include, for example, information about the center of a lane or the boundaries of a lane. Furthermore, the second map information 72 may also include road information, traffic control information, residential information, facility information, telephone number information, etc. The second map information 72 can be updated in real time by communicating with other devices through the communication device 20.

[0046] In addition to the steering wheel 82, the driving control unit 80 also includes, for example, an accelerator pedal, a brake pedal, a gear shift lever, turn signals, and other control components. Sensors are installed in the driving control unit 80 to detect the amount or presence of operation, and the detection results are output to some or all of the control device 100, the driving force output device 200, the braking device 210, and the steering device 220.

[0047] The steering wheel 82 does not necessarily have to be circular; it can also be an irregularly shaped steering wheel, a lever, a button, or other similar form. A steering wheel grip sensor 84 is installed on the steering wheel 82. The steering wheel grip sensor 84 is implemented using an electrostatic capacitive sensor or the like, and outputs a signal to the control device 100 that can detect whether the driver is gripping the steering wheel 82.

[0048] The control device 100 includes at least a processor such as a CPU (Central Processing Unit) and a storage medium required for the operation of the processor. The processor functions as a first control unit 120 and a second control unit 160 by executing programs stored in the storage medium. The control device 100 is not limited to processing by a single processor; it may also utilize multiple processors to distribute the processing.

[0049] <Structure of the first control unit 120 and the second control unit 160>

[0050] Figure 3This diagram illustrates an example of the structure of the first control unit 120 and the second control unit 160. The first control unit 120, for example, includes an identification unit 130, an action plan generation unit 140, and a pattern determination unit 150. The first control unit 120, for example, implements AI (Artificial Intelligence) based functions and functions based on pre-provided models in parallel.

[0051] For example, the function of "intersection recognition" can be achieved by performing intersection recognition based on deep learning and other methods in parallel, as well as recognition based on pre-given conditions (such as the existence of signals and road signs that can be pattern matched), and then comprehensively evaluating both by adding scores to both. In this way, the reliability of autonomous driving can be ensured.

[0052] The recognition unit 130 identifies, for example, the driving environment in which vehicle M is traveling. For instance, the recognition unit 130 identifies the driving lane of vehicle M by comparing the pattern of road markings (e.g., the arrangement of solid and dashed lines) obtained from the second map information 72 with the pattern of road markings surrounding vehicle M identified from the image captured by camera 10. Furthermore, the recognition unit 130 is not limited to recognizing road markings; it can also identify driving lanes by recognizing driving road boundaries (road boundaries) including road markings, shoulders, curbs, median strips, guardrails, etc. In this recognition, the position of vehicle M obtained from navigation device 60 and the processing results of INS (In-Site Management) can also be incorporated. In addition, the recognition unit 130 identifies temporary stop lines, obstacles, red lights, toll booths, and other road features.

[0053] When identifying a driving lane, the identification unit 130 identifies the position and orientation of the vehicle M relative to the driving lane. For example, the identification unit 130 may identify the deviation of the vehicle M's reference point from the center of the lane, and the angle formed by the vehicle M's direction of travel and the line connecting the center of the lane, as the relative position and orientation of the vehicle M relative to the driving lane. Alternatively, the identification unit 130 may identify the position of the vehicle M's reference point relative to either end of the driving lane (road markings or road boundaries) as the relative position of the vehicle M relative to the driving lane.

[0054] The recognition unit 130 identifies the surrounding environment of the vehicle M based on some or all of the output information from the camera 10, radar device 12, and LIDAR 14. For example, the recognition unit 130 identifies the position and type of objects located around the vehicle M (whether they are moving or stationary). The position of an object is, for example, identified as the absolute coordinates with a representative point of the vehicle M (center of gravity, drive shaft center, etc.) as the origin (derived from the Y-axis passing through the representative point of the vehicle M and parallel to the left-right direction). Figure 6 The Y-axis (Ay) and the X-axis (Ay) passing through the representative point of vehicle M and parallel to the front-rear direction. Figure 6The X-axis (Ax) represents the position on the XY plane and is used for various controls.

[0055] As objects located around vehicle M, examples include dynamic objects (other vehicles traveling around the perimeter, etc.) and stationary objects (objects that form the road boundary, such as plantings, walls, and central dividers, as well as structures specific to construction or accidents, such as traffic cones, guardrails, signs, and temporary traffic lights). These structures include specific objects that can be distributed along the road (specifically, traffic cones).

[0056] The recognition unit 130, based on some or all of the output information from the camera 10, radar device 12, and LIDAR 14, processes the information to determine whether the surrounding environment of vehicle M is within a restricted driving zone. "The surrounding environment of vehicle M is within a restricted driving zone" refers to either the situation where vehicle M is driving within a restricted driving zone, or the situation where a restricted driving zone exists at a predetermined distance ahead of vehicle M. "The surrounding environment of vehicle M is not within a restricted driving zone" refers to the situation where vehicle M is not driving within a restricted driving zone and there is no restricted driving zone ahead of vehicle M. Based on the output information from radar device 12, the recognition unit 130 aggregates the data from detection points at approximately equal distances from vehicle M into detection point group data for processing.

[0057] The action plan generation unit 140 generates a target track on which vehicle M will automatically (independent of driver operation) travel, enabling vehicle M to travel in the recommended lane determined by the recommended lane determination unit 71, and to cope with the surrounding conditions of vehicle M. The target track includes, for example, speed elements. For instance, the target track is represented as a track arranging the locations (track points) that vehicle M should reach sequentially. Track points are locations that vehicle M should reach at predetermined travel distances (e.g., several meters), and in addition, target speed and target acceleration are generated as part of the target track at predetermined sampling times (e.g., a few tenths of a second). Alternatively, track points can be positions that vehicle M should reach at each predetermined sampling time. In this case, the target speed and target acceleration information are represented by the intervals of the track points.

[0058] The action plan generation unit 140 can also set automatic driving events each time a target track is generated. Automatic driving events include constant speed driving events, low-speed following events, lane change events, branching events, merging events, and takeover events. The action plan generation unit 140 generates the target track corresponding to the initiated events.

[0059] The mode determination unit 150 determines the driving mode of the vehicle M as any one of multiple driving modes with different tasks applied to the driver. Furthermore, if the task of the determined driving mode (hereinafter referred to as the current driving mode) is not performed by the driver, the mode determination unit 150 changes the driving mode of the vehicle M to a more demanding driving mode. The mode determination unit 150 is an example of a control state setting unit that selects and sets at least one of the automatic modes controlling the vehicle M's speed and steering from multiple driving modes.

[0060] <Specific examples of driving modes>

[0061] Figure 4 This diagram illustrates specific examples of driving modes. Vehicle M has five driving modes, for example, from driving mode 1 to driving mode 5. In terms of the degree of automation of the driving control of vehicle M, driving mode 1 is the highest, followed by driving modes 2, 3, and 4 in that order, decreasing to driving mode 5 being the lowest. Conversely, the workload imposed on the driver is lightest in driving mode 1, increasing to driving modes 2, 3, and 4 in that order, with driving mode 5 being the most demanding. Furthermore, since driving modes other than driving mode 1 are not automatic driving control states, the control device 100 is obligated to terminate automatic driving control and transfer to assisted driving or manual driving. The contents of each driving mode are illustrated below.

[0062] In the first driving mode, the vehicle is in an automated driving state, and the driver is not required to monitor the road ahead or hold the steering wheel 82. However, even in the first driving mode, the driver is required to be able to quickly switch to a manual driving posture according to the request from the control device 100. In addition, automated driving here means controlling steering, acceleration and deceleration without relying on the driver's operation. The road ahead refers to the space in which the vehicle M is traveling, as visually confirmed through the windshield. The first driving mode is, for example, a driving mode that can be executed when conditions are met, such as the vehicle M traveling at a speed below a certain speed (e.g., about 60 km / h) on a dedicated road for automobiles, such as a highway, and there is a vehicle following ahead.

[0063] In the second driving mode, which is a driver assistance mode, the driver is tasked with monitoring the area ahead of vehicle M (hereinafter referred to as monitoring the area ahead), but not with holding the steering wheel 82. In the third driving mode, which is also a driver assistance mode, the driver is tasked with both monitoring the area ahead and holding the steering wheel 82. The fourth driving mode requires some degree of driver input regarding at least one of the vehicle M's steering or acceleration / deceleration. For example, in the fourth driving mode, driver assistance features such as ACC (Adaptive Cruise Control) and LKAS (Lane Keeping Assist System) are used. In the fifth driving mode, which is a manual driving mode requiring driver input for both steering and acceleration / deceleration. Both the fourth and fifth driving modes require the driver to monitor the area ahead of vehicle M.

[0064] return Figure 3 The second control unit 160 controls the vehicle M to pass through the target track generated by the action plan generation unit 140 at a predetermined time. The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166.

[0065] The acquisition unit 162 acquires information about the target track (track point) generated by the action plan generation unit 140 and stores it in a memory (not shown). The speed control unit 164 controls the driving force output device 200 (see reference 164) based on the speed elements associated with the target track stored in the memory. Figure 1 ) or braking device 210 (refer to Figure 1 The steering control unit 166 controls the steering device 220 (see reference) based on the curvature of the target track stored in the memory. Figure 1 The processing of the speed control unit 164 and the steering control unit 166 is achieved, for example, through a combination of feedforward control and feedback control.

[0066] In the control device 100, the structure combining the action plan generation unit 140 and the second control unit 160 constitutes the driving control unit 170. The driving control unit 170 performs automatic lane change control of the vehicle M based on the recognition results of the driving environment or surrounding environment of the vehicle M identified by the recognition unit 130. Furthermore, the driving control unit 170 detects the driver's intention to change lanes based on the driver's operation of the driving control element 80 (e.g., the turn signal lever).

[0067] The driving control unit 170 selects a lane-changing method from multiple lane-changing methods with varying levels of driver involvement in vehicle M, and performs driving control (also known as lane-changing control) according to the selected lane-changing method. These multiple lane-changing methods with varying levels of driver involvement in vehicle M can also be referred to as multiple lane-changing methods with varying degrees of automation. The lower the driver's involvement, the higher the degree of automation; conversely, the higher the driver's involvement, the lower the degree of automation.

[0068] For example, multiple lane-changing methods may include the following three automatic lane-changing methods. The first automatic lane-changing method is intentional automatic lane change (ALC - Category C), in which the driver of vehicle M intentionally initiates a lane change, and the driver of vehicle M instructs the start of the lane change. In intentional automatic lane change, the driver of vehicle M considers the driving conditions of other vehicles, the route to the destination, etc., to determine whether a lane change should be made. If the driver of vehicle M determines that a lane change should be made, the driver of vehicle M instructs the start of the lane change by operating the driving control unit 80. Based on this instruction, the driving control unit 170, while considering the surrounding driving conditions, initiates the automatic lane change at the appropriate time.

[0069] The second type of automatic lane change is recommended automatic lane change (ALC - Category D), in which the driving control unit 170 recommends a lane change, and the driver of vehicle M approves the lane change. In recommended automatic lane change, the driving control unit 170 determines whether a lane change should be performed based on the driving conditions of other vehicles, the route to the destination, etc. If the driving control unit 170 determines that a lane change should be performed, it recommends the lane change to the driver. If the driver of vehicle M approves the lane change recommendation, he / she instructs vehicle M to begin the lane change by operating an approval switch. The approval switch can be a dedicated approval switch or an operating device that also has other functions (e.g., driving control device 80). Based on this instruction, the driving control unit 170, while considering the surrounding driving conditions, initiates the automatic lane change at the appropriate time. Therefore, if the driver does not approve the lane change recommendation, i.e., does not operate the driving control device 80, the automatic lane change is not performed.

[0070] The third type of automatic lane change is Automated Lane Change Decision (ALC - Category E), in which the driving control unit 170 determines whether a lane change should be made. In the automatic lane change decision process, the driving control unit 170 assesses the driving conditions of other vehicles and the route to the destination to determine whether a lane change is appropriate. If a lane change is deemed necessary, the driving control unit 170 considers the surrounding driving conditions and initiates the automatic lane change at a feasible moment. In the case of an automatic lane change decision, the driver of vehicle M does not participate in the lane change.

[0071] The control device 100 performs automatic lane changes corresponding to the driving mode. For example, the control device 100 can perform a decision-based automatic lane change in the first driving mode. The control device 100 can perform a suggested automatic lane change in the second, third, and fourth driving modes. The control device 100 can perform an intentional automatic lane change in the third and fourth driving modes. The control device 100 does not perform any automatic lane changes in the fifth driving mode.

[0072] return Figure 1 The driving force output device 200 outputs driving force (torque) to the drive wheels to propel the vehicle. The driving force output device 200 may include, for example, a combination of an internal combustion engine, an electric motor, and a transmission, and an ECU (Electronic Control Unit) that controls them. The ECU controls the aforementioned components according to information input from the second control unit 160 or from the driving operation unit 80.

[0073] The braking device 210 includes, for example, a brake caliper, a hydraulic cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the hydraulic cylinder, and a braking ECU. The braking ECU controls the electric motor according to information input from the second control unit 160 or from the driving operation unit 80, and outputs braking torque corresponding to the braking operation to each wheel.

[0074] The steering system 220 includes, for example, a steering ECU and an electric motor. The electric motor, for example, applies force to a rack and pinion mechanism to change the direction of the steering wheels. The steering ECU drives the electric motor to change the direction of the steering wheels according to information input from the second control unit 160 or from the driving control unit 80.

[0075] <Detectable range of the object>

[0076] Figure 5 This is a schematic diagram showing the area around vehicle M where objects can be detected by radar device 12. Figure 5The diagram shows a range 12A within which the radar device 12 can detect objects. In this embodiment, for example, a peripheral region DA1 is defined that is contained within the range 12A and where the radar device 12 has sufficiently high object detection resolution. The identification unit 130 acquires detection point group data within this peripheral region DA1. That is, if some objects exist in the peripheral region DA1, the radar device 12 outputs detection point data for those objects, and based on this detection point data, the identification unit 130 acquires detection point group data corresponding to those objects. The position of the detection point group data is determined by... Figure 5 The X-axis Ax and Y-axis Ay are shown as coordinates on the XY plane. Figure 5 The origin O shown represents the point on which vehicle M is located.

[0077] <Identification and processing of driving restriction zones>

[0078] Figure 6 This is a schematic diagram illustrating an example of detection point group data acquired by the identification unit 130. Figure 6 An example is shown where five data points, namely Da, Db, Dc, Dd, and De, are obtained as the detection point group data of an object existing in the surrounding area DA1.

[0079] When the identification unit 130 acquires detection point group data within the surrounding area DA1, it performs stationary object identification processing based on this detection point group data to determine whether the object corresponding to the detection point group data is a first specific object (specifically, a stationary object that does not move involuntarily). Examples of stationary objects include walls, guardrails, or traffic cones. If the identification unit 130 identifies the presence of a first specific object (stationary object) within the surrounding area DA1, it sets up a surrounding area DA2 that is narrower than the surrounding area DA1 around the vehicle M, and identifies a second specific object existing around the vehicle M based on the detection point group data within the surrounding area DA2. Specifically, the second specific object is an object that can be dispersed within the driving restriction zone, such as a traffic cone. That is, if the identification unit 130 identifies the presence of a first specific object (stationary object) based on the detection point group data within the surrounding area DA1, it narrows the scope of the detection point group data from the surrounding area DA1 to the surrounding area DA2.

[0080] like Figure 6 As shown, the surrounding area DA2 is a region that includes the origin O (the representative point of vehicle M) and whose width in the front-to-back direction and its width in the left-to-right direction are both smaller than the surrounding area DA1. The recognition unit 130 is based on... Figure 6 The detection point group data within the surrounding area DA2 shown is used to determine whether the object corresponding to the detection point group data is a second specific object for specific object recognition processing.

[0081] Furthermore, the recognition unit 130 also performs predetermined object recognition processing, which determines whether the object corresponding to the detection point group data within the surrounding area DA2 is a predetermined object. A predetermined object is an object continuously arranged in the front-to-back direction, such as a guardrail or wall. Figure 6 In the example, when the detection point group data Da, Db, Dc, Dd, De are used to identify the existence of a first specific object (stationary object) in the surrounding area DA1, the detection point group data Dc and Dd outside the surrounding area DA2 are not used for the identification of the second specific object. Only the detection point group data Da, Db, De within the surrounding area DA2 are used for the identification of the second specific object.

[0082] When the identification unit 130 identifies the presence of a second specific object in the surrounding area DA2 through the specific object identification process, it identifies the surrounding environment of the vehicle M as a driving restriction zone.

[0083] In addition, after narrowing the scope of the detection point group data utilization from the peripheral area DA1 to the peripheral area DA2, the preferred identification unit 130 identifies a predetermined object in the peripheral area DA2 based on the detection point group data in the peripheral area DA2. Then, it identifies a second specific object based on the detection point group data of the area in the peripheral area DA2 that is inside the predetermined object in the left-right direction (the side where the vehicle M exists).

[0084] Figure 7 This illustrates an example of identifying the presence of a given object OB based on a cluster of detection points within the surrounding region DA2. Figure 7 In the example, the detection point group data Db in the surrounding area DA2 located to the right of the given object OB is not used for the identification of the second specific object; only the detection point group data Da and De in the surrounding area DA2 located to the left of the given object OB are used for the identification of the second specific object.

[0085] Furthermore, the identification unit 130 preferably acquires detection point group data periodically in sync with the output cycle of the radar device 12. Figure 8The diagram shows the latest detection point group data D4 acquired by the recognition unit 130, the detection point group data D3 acquired in the previous cycle of detection point group data D4, the detection point group data D2 acquired in the previous cycle of detection point group data D3, and the detection point group data D1 acquired in the previous cycle of detection point group data D2, as detection point group data for the same object located within the surrounding area DA2. Based on these detection point group data D1, D2, D3, and D4 acquired at different times, the recognition unit 130 determines whether the object corresponding to the group of detection point group data D1, D2, D3, and D4 is a second specific object. In this way, by combining the latest detection point group data and the past detection point group data to identify the second specific object, the recognition accuracy of the second specific object can be improved.

[0086] From the viewpoint of reducing processing load and effectively utilizing memory, it is preferable to set an upper limit on the total number of detection point data used in the recognition of the second specific object. That is, it is preferable to set an upper limit on the total number of detection point data contained in the groups of detection point data D1, D2, D3, and D4. With such an upper limit set, if the total number of detection point data contained in the groups of detection point data D1, D2, D3, and D4 exceeds the upper limit, the recognition unit 130 deletes the earliest detection point group data D1 in the groups of detection point data D1, D2, D3, and D4, and performs the recognition of the second specific object based on the remaining detection point group data D2, D3, and D4.

[0087] The operation of the control device 100 will be explained below with reference to the flowchart. Figure 9 This is a flowchart used to explain the operation of the control device 100. Figure 9 The process shown is, for example, in Figure 4 The execution of any of the first to fourth driving modes shown is carried out.

[0088] The identification unit 130 acquires detection point group data within the surrounding area DA1 based on the output information of the radar device 12 (step S1). Based on the acquired detection point group data, it identifies the presence of a stationary object (a first specific object) in the surrounding area DA1 (step S2). In step S2, if a stationary object is identified as existing in the surrounding area DA1 (step S2: Yes), the process in step S3 is performed. In step S2, if a stationary object is identified as not existing in the surrounding area DA1 (step S2: No), the process returns to step S1.

[0089] In step S3, the identification unit 130 narrows the scope of the detection point group data to the surrounding area DA2. Then, based on the detection point group data within the surrounding area DA2, the identification unit 130 identifies the presence of a predetermined object in the surrounding area DA2 (step S4). If the identification unit 130 determines that no predetermined object exists in the surrounding area DA2 (step S4: No), it identifies a second specific object (traffic cone) in the surrounding area DA2 based on all the detection point group data within the surrounding area DA2 (step S10).

[0090] If the identification unit 130 identifies that a predetermined object exists in the surrounding area DA2 (step S4: Yes), it excludes the detection point group data of the area in the surrounding area DA2 that is outside the predetermined object in the left and right direction from the detection point group data used in the identification of the traffic cone (step S5).

[0091] After step S5, the identification unit 130 determines whether there exists a specific group whose total number of detection point data exceeds an upper limit, as a group of multiple detection point group data acquired at different times corresponding to the same object in the surrounding area DA2 (step S6). If the determination in step S6 is "yes", the identification unit 130 excludes the earliest detection point group data among the multiple detection point group data contained in the specific group from the detection point group data used in traffic cone identification (step S7). If the determination in step S7 and step S6 is "no", the process in step S8 is performed. In step S8, the identification unit 130 identifies the traffic cones in the surrounding area DA2 based on the remaining detection point group data in the detection point group data of the surrounding area DA2 that has not been excluded.

[0092] In either step S8 or S10, if a traffic cone is detected in the surrounding area DA2, the identification unit 130 identifies the surrounding environment of vehicle M as a restricted driving zone. When the identification unit 130 identifies the driving environment as a restricted driving zone, the driving control unit 170 restricts the driving control of vehicle M. Specifically, the driving control unit 170 restricts lane change control. Restricting lane change control means prohibiting lane change control, or performing lane change control but prohibiting lane changes as part of a process. In this way, when vehicle M is driving in a restricted driving zone or approaching a restricted driving zone, restricting lane change control allows vehicle M to drive safely.

[0093] If, in either step S8 or step S10, it is determined that there are no traffic cones in the surrounding area DA2, the process returns to step S2.

[0094] As described above, since the presence of stationary objects around vehicle M increases the likelihood that the surrounding environment of vehicle M is within a restricted driving zone, in step S3, narrowing the identifiable range of the traffic cone can prevent misidentification of the traffic cone or reduce the processing load. For example, when approaching a restricted driving zone, the detection point group data of objects outside the surrounding area DA2 (objects farther from vehicle M) is also acquired by the identification unit 130. Since the detection point group data of such objects is likely to contain errors, by not using the detection point group data of such objects for traffic cone identification, misidentification of the traffic cone or reduction of the processing load can be prevented.

[0095] The following are at least described in this specification. Furthermore, although the components corresponding to those in the above embodiments are shown in parentheses, the present invention is not limited thereto. (1)

[0097] A control device (control device 100) for driving control of a vehicle (vehicle M) includes a processor capable of acquiring output information from a sensor (radar device 12) that can detect objects based on reflected waves from objects located around the vehicle.

[0098] The processor described above performs the following actions:

[0099] Based on the output information of the aforementioned sensors, the first detection point group data of the aforementioned objects in the first area (surrounding area DA1) around the aforementioned vehicle is obtained, and based on the aforementioned first detection point group data, the identification of the first specific object (stationary object) existing around the aforementioned vehicle is performed.

[0100] If the presence of the first specific object is detected, the second specific object (traffic cone) existing around the vehicle is identified based on the data of the first detection point group in the second region (peripheral region DA2) that is narrower than the first region around the vehicle.

[0101] According to (1), since the presence of a stationary object or other specific object around the vehicle increases the likelihood that the vehicle's surrounding environment is within a restricted driving zone, narrowing the identifiable range of the second specific object can prevent misidentification and reduce the processing load. By making it possible to prevent misidentification and reduce the processing load, when the identification result of the second specific object is used for driving control, driving control can be performed with high precision, thereby improving safety. (2)

[0103] According to the control device described in (1), wherein,

[0104] Compared with the first region, the second region is smaller in width in all directions of the vehicle, including the left-right and front-back directions.

[0105] According to (2), it is possible to identify only the second specific object that is closer to the vehicle, thus preventing false detection of the second specific object and reducing the processing load. (3)

[0107] According to the control device described in (2), wherein,

[0108] When the processor identifies a predetermined object (such as a wall or guardrail) extending in the longitudinal direction of the vehicle based on the first detection point group data in the second region, it identifies the second specific object based on the first detection point group data in the second region that is inside the predetermined object in the lateral direction of the vehicle.

[0109] According to (3), when there are established objects such as walls or railings in the second region, the first detection point group data in the area outside the wall or railing in the second region is not used for the identification of the second specific object. Therefore, it is possible to narrow down to the necessary area for the identification of the second specific object, and the identification of the second specific object can be performed quickly and with high accuracy. (4)

[0111] According to any one of (1) to (3), the control device, wherein,

[0112] The processor described above performs the following actions:

[0113] Data from the first group of detection points mentioned above is periodically acquired.

[0114] The second specific object is identified based on the first group of detection points data corresponding to multiple periods.

[0115] According to (4), based on the first detection point group data of the continuously acquired object, it is possible to make a high-precision judgment on whether the object is a second specific object and the position of the object. (5)

[0117] According to the control device described in (4), wherein,

[0118] If the total number of detection point data contained in the first detection point group data corresponding to the multiple periods detected from the same object exceeds a threshold, the processor will use the detection point group data after removing the detection point group data obtained at the earlier time from the first detection point group data corresponding to the multiple periods for the identification of the second specific object.

[0119] According to (5), the processing load can be reduced. (6)

[0121] The control device according to any one of (1) to (5), wherein,

[0122] The processor described above performs the following actions:

[0123] Perform lane change control for the aforementioned vehicles.

[0124] Based on the identification results of the second specific object, the lane change control is restricted.

[0125] According to (6), for example, when the presence of a second specific object is detected, safety can be improved by restricting lane change control, thereby preventing lane changes in the driving restriction zone, for example. (7)

[0127] According to any one of (1) to (6), the control device, wherein,

[0128] The second specific object mentioned above is a traffic cone.

Claims

1. A control device for controlling the movement of a vehicle, wherein, The control device includes a processor capable of acquiring output information from a sensor that can detect an object based on reflected waves from an object located around the vehicle. The processor performs the following actions: Based on the output information of the sensor, a first group of detection points of the object in a first area surrounding the vehicle is obtained, and based on the first group of detection points, a first specific object existing in the vicinity of the vehicle as a stationary object is identified. If the presence of the first specific object is detected, the data of the first detection point group outside the second area that is narrower than the first area around the vehicle is excluded, and the identification of the second specific object existing in the driving restriction zone around the vehicle is performed only based on the data of the first detection point group within the second area.

2. The control device according to claim 1, wherein, Compared to the first region, the second region is smaller in width in both the left-right and front-back directions of the vehicle.

3. The control device according to claim 2, wherein, When the processor identifies a predetermined object extending in the longitudinal direction of the vehicle based on the first detection point group data in the second region, it identifies the second specific object based on the first detection point group data in the second region in the lateral direction of the vehicle, which is inside the predetermined object.

4. The control device according to any one of claims 1 to 3, wherein, The processor performs the following actions: Data from the first detection point group is periodically acquired. The second specific object is identified based on the first group of detection points data corresponding to multiple periods.

5. The control device according to claim 4, wherein, If the total number of detection point data contained in the first detection point group data corresponding to the multiple periods detected from the same object exceeds a threshold, the processor uses the detection point group data after removing the detection point group data obtained at the earlier time from the first detection point group data corresponding to the multiple periods for the identification of the second specific object.

6. The control device according to any one of claims 1 to 3, wherein, The processor performs the following actions: Perform lane change control on the vehicle. The lane change control is limited based on the identification result of the second specific object.

7. The control device according to any one of claims 1 to 3, wherein, The second specific object is a traffic cone.

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