Information processing apparatus, information processing method, and storage medium

Abstract

The present application provides an information processing device, an information processing method and a storage medium capable of appropriately generating a travel track of a mobile body without excessively depending on the recognition accuracy of a travel road based on a captured image. The information processing device that generates a track of a mobile body includes: a recognition unit that recognizes a travel environment around the mobile body; a risk calculation unit that calculates a risk value set around an object target located within the recognized travel environment; and a track generation unit that generates a travel track of the mobile body, i.e., a travel track in which a plurality of track points are arranged, the track generation unit repeatedly performing processing, setting a last travel track generated at a processing timing earlier than a current processing timing as a reference line, and generating a current travel track by shifting the plurality of track points constituting the reference line in a normal direction of the reference line based on the risk value.

Description

Technical Field

[0001] This invention relates to information processing apparatus, information processing method, and storage medium. Background Technology

[0002] Previously, techniques for generating travel tracks for moving objects were known. For example, International Publication No. 2020 / 116265 describes a technique that determines the travel track and speed of a vehicle based on the width of the travel path. Summary of the Invention

[0003] However, in existing technologies, the generation of a moving vehicle's trajectory tends to rely on the accuracy of the road recognition in images captured by onboard cameras. For example, existing technologies may identify the dividing lines on both sides of the road based on captured images and derive the center line of the identified dividing lines to generate the trajectory. However, when the moving vehicle is traveling on a curve or S-curve, the recognition accuracy of these dividing lines may be insufficient, making it impossible to generate a trajectory.

[0004] The present invention was made in consideration of such circumstances, and one of its objectives is to provide an information processing apparatus, information processing method, and storage medium capable of appropriately generating the travel trajectory of a moving body without overly relying on the recognition accuracy of the travel path based on captured images.

[0005] The information processing apparatus, information processing method, and storage medium of the present invention adopt the following structure.

[0006] (1): An information processing apparatus of the present invention generates a track for a moving body, wherein the information processing apparatus comprises: an identification unit that identifies the driving environment surrounding the moving body; a risk calculation unit that calculates a risk value set around an object target located in the identified driving environment; and a track generation unit that generates a driving track for the moving body, i.e., a driving track composed of multiple track points. The track generation unit repeatedly performs processing, sets the previous driving track generated at a processing time earlier than the current processing time as a baseline, and based on the risk value, shifts the multiple track points constituting the baseline along the normal direction of the baseline to generate the current driving track.

[0007] (2): Based on the above (1) scheme, the track generation unit generates the current travel track by shifting the plurality of track points constituting the baseline in the direction of the normal to the baseline in the direction of the reduced risk value.

[0008] (3): Based on the above (1) scheme, the object target includes the boundary line of the area where the mobile body can travel or an object that may come into contact with the mobile body.

[0009] (4): Based on the above (1) scheme, when the moving body is traveling at a speed below a specified speed, the track generation unit generates the travel track.

[0010] (5): Based on the above (3) scheme, when the identification unit identifies a width less than a specified width on the travel path of the moving body, the track generation unit generates the travel track.

[0011] (6): Based on the above schemes (1) to (5), the information processing device further includes a driving control unit that enables the moving body to travel along the generated driving track.

[0012] (7): Another aspect of the information processing method of the present invention enables a computer to perform the following processing in order to generate the trajectory of a moving body: identify the driving environment around the moving body, calculate the risk value set around the object target located in the identified driving environment, generate the driving trajectory of the moving body, that is, a driving trajectory composed of multiple track points, repeat the processing, set the previous driving trajectory generated at a processing time earlier than the current processing time as a baseline, and based on the risk value, offset the multiple track points constituting the baseline along the normal direction of the baseline, thereby generating the current driving trajectory.

[0013] (8): In another embodiment of the present invention, the storage medium stores a program that causes a computer to perform the following processing for generating the trajectory of a moving body: identifying the driving environment around the moving body, calculating a risk value set around an object target located in the identified driving environment, generating the driving trajectory of the moving body, i.e., a driving trajectory composed of multiple track points, repeating the processing, setting the previous driving trajectory generated at a processing time earlier than the current processing time as a baseline, and based on the risk value, offsetting the multiple track points constituting the baseline along the normal direction of the baseline, thereby generating the current driving trajectory.

[0014] According to the schemes (1) to (8), the travel trajectory of the moving body can be appropriately generated without over-relying on the recognition accuracy of the travel path based on the captured image. Attached Figure Description

[0015] Figure 1 This is a structural diagram of a vehicle system utilizing a vehicle control device according to an implementation method.

[0016] Figure 2 This is a functional structure diagram of the first control unit and the second control unit.

[0017] Figure 3 This is a diagram illustrating the method by which the first target trajectory generation unit generates the target trajectory.

[0018] Figure 4 This is a diagram illustrating the method by which the second target orbit generation unit generates the target orbit.

[0019] Figure 5 This is another diagram illustrating the method by which the second target orbit generation unit generates the target orbit.

[0020] Figure 6 This is a flowchart illustrating an example of the process executed by the vehicle control device of the embodiment.

[0021] Figure 7 This is a flowchart illustrating an example of the process performed by the risk calculation unit and the second target trajectory generation unit.

[0022] Figure 8 This is a graph showing the execution results of a simulation of the target trajectory generation in the implementation method. Detailed Implementation

[0023] Hereinafter, embodiments of the information processing apparatus, information processing method, and storage medium of the present invention will be described with reference to the accompanying drawings. In the following description, a vehicle is used as a representative example of a mobile body, but the mobile body is not limited to a vehicle, and can also be applied to all autonomous mobile bodies such as micro-mobility and robots (including robots with wheels, multi-legged robots, etc.).

[0024] [Overall Structure]

[0025] Figure 1 This is a structural diagram of a vehicle system utilizing the vehicle control device of the embodiment. The vehicle equipped with vehicle system 1 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 a 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.

[0026] Vehicle system 1 includes, for example, a camera 10, a radar device 12, a LiDAR (Light Detection and Ranging) system 14, an object recognition device 16, a communication device 20, an HMI (Human Machine Interface) 30, vehicle sensors 40, a navigation device 50, an MPU (Map Positioning Unit) 60, driving controls 80, an automatic driving 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 multiple communication lines such as CAN (Controller Area Network) communication lines, serial communication lines, and wireless communication networks. It should be noted that... Figure 1 The structure shown is just one example; a part of the structure can be omitted, or other structures can be added.

[0027] Camera 10 is, for example, a digital camera utilizing a solid-state imaging element such as CCD (Charge Coupled Device) or CMOS (Complementary Metal-Oxide Semiconductor). Camera 10 is mounted anywhere on the vehicle (hereinafter referred to as the vehicle M) on which vehicle system 1 is installed. When taking pictures of the front, camera 10 is mounted on the upper part of the windshield, the back of the rearview mirror inside the vehicle, etc. Camera 10, for example, periodically and repeatedly takes pictures of the surroundings of the vehicle M. Camera 10 can also be a stereo camera.

[0028] Radar device 12 radiates millimeter-wave or other radio waves around the vehicle M and detects the radio waves reflected by objects (reflected waves) to detect at least the position (distance and orientation) of the objects. Radar device 12 can be installed at any location on the vehicle M. Radar device 12 can also detect the position and speed of objects using FM-CW (Frequency Modulated Continuous Wave) method.

[0029] The LIDAR 14 illuminates the periphery of the vehicle M and measures the scattered light. The LIDAR 14 detects the distance to an object 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 LIDAR 14 can be mounted at any location on the vehicle M.

[0030] The object recognition device 16 performs sensor fusion processing on the detection results from some or all of the cameras 10, radar device 12, and LIDAR 14 to identify the position, type, speed, etc. of objects. The object recognition device 16 outputs the recognition results to the autonomous driving control device 100. Alternatively, the object recognition device 16 can directly output the detection results from the cameras 10, radar device 12, and LIDAR 14 to the autonomous driving control device 100. The object recognition device 16 can also be omitted from the vehicle system 1.

[0031] The communication device 20 communicates with other vehicles in the vicinity of the vehicle M, for example, using cellular networks, Wi-Fi networks, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), etc., or communicates with various server devices via wireless base stations.

[0032] The HMI30 provides various information to the occupants of vehicle M and accepts input operations performed by the occupants. The HMI30 includes various display devices, speakers, buzzers, touch panels, switches, buttons, etc.

[0033] 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 the vertical axis, and an orientation sensor for detecting the orientation of the vehicle M.

[0034] The navigation device 50 includes, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a route determination unit 53. The navigation device 50 stores first map information 54 in a storage device such as an HDD (Hard Disk Drive) or flash memory. The GNSS receiver 51 determines the position of the vehicle M based on signals received from GNSS satellites. The position of the vehicle M can also be determined or supplemented using INS (Inertial Navigation System) output from the vehicle sensors 40. The navigation HMI 52 includes a display device, a speaker, a touch panel, buttons, etc. The navigation HMI 52 can also be partially or entirely shared with the aforementioned HMI 30. The route determination unit 53, for example, refers to the first map information 54 to determine the route (hereinafter referred to as the map path) from the position of the vehicle M determined by the GNSS receiver 51 (or any input position) to the destination input by the occupant using the navigation HMI 52. The first map information 54, for example, represents road shape information by displaying road segments and nodes connecting the road segments. The first map information 54 may also include road curvature, POI (Point of Interest) information, etc. The path on the map is output to the MPU 60. The navigation device 50 may also provide route guidance using the navigation HMI 52 based on the path on the map. The navigation device 50 may also be implemented through the functions of a terminal device such as a smartphone or tablet held by the occupant. The navigation device 50 may also send its current location and destination to the navigation server via the communication device 20, and obtain a path equivalent to the path on the map from the navigation server.

[0035] MPU 60, for example, includes a lane recommendation unit 61 that stores the second map information 62 in a storage device such as an HDD or flash memory. The lane recommendation unit 61 divides the path on the map provided by the navigation device 50 into multiple blocks (e.g., every 100 [m] in the vehicle's direction of travel) and determines a recommended lane for each block with reference to the second map information 62. The lane recommendation unit 61 determines which lane to drive in from the left. When the path on the map has branching points, the lane recommendation unit 61 determines the recommended lane in a manner that allows the vehicle M to travel on a reasonable path to the branch destination.

[0036] The second map information 62 is map information with higher precision compared to the first map information 54. The second map information 62 may include, for example, information about the center of a lane or the boundaries of a lane. Furthermore, the second map information 62 may include road information, traffic restriction information, address information (address / postal code), facility information, telephone number information, etc. The second map information 62 can be updated in real time by communicating with other devices through the communication device 20.

[0037] The driving control unit 80 includes, for example, an accelerator pedal, a brake pedal, a gear shift lever, a steering wheel, a non-standard steering wheel, a joystick, and other control components. Sensors are installed on the driving control unit 80 to detect the amount of operation or whether operation has occurred, and the detection results are output to some or all of the automatic driving control unit 100, the driving force output device 200, the braking device 210, and the steering device 220.

[0038] The autonomous driving control device 100 is an example of a vehicle control device. The autonomous driving control device 100 includes, for example, a first control unit 120 and a second control unit 160. The first control unit 120 and the second control unit 160 are implemented by executing programs (software) using hardware processors such as a CPU (Central Processing Unit). Furthermore, some or all of these components can be implemented using hardware (including circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit), or through the coordinated use of software and hardware. The program can be pre-stored in a storage device such as an HDD or flash memory (a storage device with a non-transitory storage medium) of the autonomous driving control device 100, or stored in a removable storage medium such as a DVD or CD-ROM, and installed in the HDD or flash memory of the autonomous driving control device 100 by mounting the storage medium (non-transitory storage medium) to the drive unit.

[0039] Figure 2This is a functional structure diagram of the first control unit and the second control unit. The first control unit 120, for example, includes a recognition unit 130 and an action plan generation unit 140. The first control unit 120, for example, implements AI (Artificial Intelligence) based functions and functions based on pre-given models in parallel. For example, the function of "recognizing intersections" can be achieved by simultaneously executing intersection recognition based on deep learning and other methods, and recognition based on pre-given conditions (the existence of signals capable of pattern matching, road signs, etc.), and then comprehensively evaluating both. This ensures the reliability of autonomous driving.

[0040] The identification unit 130 identifies the position, speed, acceleration, and other states of objects surrounding the vehicle M based on information input from the camera 10, radar device 12, and LIDAR 14 via the object recognition device 16. Objects include both stationary and moving objects. The position of an object is identified, for example, as its absolute coordinate position with a representative point of the vehicle M (center of gravity, drive shaft center, etc.) as the origin, for control purposes. The position of an object can be represented by representative points such as its center of gravity or corners, or by a defined area.

[0041] In this embodiment, the recognition unit 130 identifies, at least based on an image captured by the camera 10 representing the surrounding conditions of the vehicle M, the boundary lines of the area where the vehicle M can travel (e.g., road markings, shoulders, curbs, median strips, guardrails, etc.) and objects that the vehicle M may come into contact with (e.g., pedestrians, other vehicles, etc.). The recognition unit 130 identifies the width by recognizing, for example, the left and right road markings as multiple sets of point data and calculating the distance between the sets of points constituting the left and right road markings.

[0042] The action plan generation unit 140 may include, for example, a first target track generation unit 142, a switching determination unit 144, a risk calculation unit 146, and a second target track generation unit 148. Some or all of the first target track generation unit 142, the switching determination unit 144, the risk calculation unit 146, and the second target track generation unit 148 may also be included in the identification unit 130. The second target track generation unit 148 is an example of a "track generation unit". Combining the switching determination unit 144, the risk calculation unit 146, and the second target track generation unit 148 constitutes an example of an "information processing device". Furthermore, the "information processing device" may include a second control unit 160; in this case, the "information processing device" becomes a "vehicle control device".

[0043] The action plan generation unit 140 generates a target track for the future travel of the vehicle M in a manner that, in principle, it travels within the recommended lane determined by the recommended lane determination unit 61 and is able to adapt to the surrounding conditions of the vehicle M. The action plan generation unit 140 generates a target track using either the first target track generation unit 142 or the second target track generation unit 148, based on the state of the vehicle M and the surrounding conditions identified by the identification unit 130. Details of this will be described later.

[0044] The target track can be represented, for example, by arranging (connecting) representative points (e.g., the center of the front end, the center of gravity, the center of the rear axle, etc.) that the vehicle M should reach at predetermined intervals (e.g., every few meters) along the length of the road. The target track is assigned a target velocity and a target acceleration at predetermined sampling times (e.g., a fraction of a second). Alternatively, a track point can be a position that the vehicle M should reach at a predetermined sampling time. In this case, the target velocity and target acceleration information are represented by the intervals between the track points.

[0045] The second control unit 160 controls the driving force output device 200, braking device 210 and steering device 220 so that the vehicle M passes through the target track generated by the action plan generation unit 140 at a predetermined time.

[0046] The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166. 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 or the braking device 210 based on the speed elements associated with the target track stored in the memory. The steering control unit 166 controls the steering device 220 according to the curvature of the target track stored in the memory. 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. As an example, the steering control unit 166 combines feedforward control corresponding to the curvature of the road ahead of the vehicle M with feedback control based on deviation from the target track.

[0047] The driving force output device 200 outputs driving force (torque) for vehicle movement to the drive wheels. The driving force output device 200 may include, for example, a combination of an internal combustion engine, an electric motor, and a transmission, as well as an ECU (Electronic Control Unit) that controls them. The ECU controls the above structure based on information input from the second control unit 160 or from the driving operation unit 80.

[0048] 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 brake ECU. The brake ECU controls the electric motor based on information input from the second control unit 160 or from the driving control unit 80, outputting brake torque corresponding to the braking operation to each wheel. The braking device 210 may include, as a backup, a mechanism for transmitting hydraulic pressure generated by the operation of the brake pedal included in the driving control unit 80 via the master hydraulic cylinder to the hydraulic cylinder. It should be noted that the braking device 210 may also be an electronically controlled hydraulic braking device that controls the actuator based on information input from the second control unit 160 and transmits hydraulic pressure from the master hydraulic cylinder to the hydraulic cylinder.

[0049] 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 based on information input from the second control unit 160 or from the driving control unit 80.

[0050] [Target orbit generation]

[0051] The method for generating the target orbit will be explained in more detail below.

[0052] When the vehicle speed of the vehicle M detected by the vehicle sensor 40 is above a predetermined speed, and the width of the travel path of the vehicle M detected by the identification unit 130 is above a predetermined width, the first target track generation unit 142 generates a target track for the vehicle M. These conditions are one example; for instance, the first target track generation unit 142 may also generate a target track for the vehicle M if any one of these conditions is met. Furthermore, for example, the first target track generation unit 142 may also generate a target track for the vehicle M if the identification unit 130 does not detect a specific type of travel path (curve, S-curve, etc.) in the direction of travel of the vehicle M.

[0053] Figure 3 This diagram illustrates the method by which the first target trajectory generation unit 142 generates the target trajectory. Figure 3 In the attached figures, reference numerals LL and RL represent the left and right road dividing lines identified by the identification unit 130, respectively; reference numerals LLP and RLP represent the point groups constituting the left road dividing line LL and the right road dividing line RL, respectively; and reference numerals P and B represent pedestrians and motorcycles present in the driving lane L1 of the vehicle M, respectively. Figure 3 As shown, the point group LLP constituting the left road dividing line LL and the point group RLP constituting the right road dividing line RL are respectively arranged at a specified interval from the starting point LLP(1) of the left road dividing line LL and the starting point RLP(1) of the right road dividing line RL.

[0054] The first target track generation unit 142 first establishes a correspondence (pairing) between each point LLP(k) of the point group RLP constituting the left road dividing line LL and each point RLP(k) of the point group RLP constituting the right road dividing line RL, based on the order from the starting point, and calculates its center point CLP(k). The first target track generation unit 142 obtains the center line CL (reference line) used to generate the target track by connecting the calculated center point CLP(k).

[0055] Next, the first target trajectory generation unit 142 derives an index value (risk value) for each point CLP(k) constituting the baseline CL, indicating that the closer the point is to the object target identified by the identification unit 130, the more negative the value becomes. Figure 3 In this diagram, darker colors indicate higher risk values, while lighter colors indicate lower risk values. In this implementation, a positive value signifies "negative," and a value close to zero signifies "positive," but this relationship can also be reversed. For example, in... Figure 3 In the case of a motorcycle, which is one of the objects identified by the identification unit 130, the point CLP(4) will have a higher risk value compared to other points CLP(k).

[0056] When the first target trajectory generation unit 142 calculates the risk value for the point CLP(k) constituting the baseline CL, if the sum of the calculated risk values ​​(or the maximum value among the calculated risk values) is above a threshold, it corrects the baseline CL in a way that makes the sum of the risk values ​​lower than the threshold. More specifically, the first target trajectory generation unit 142 corrects the baseline CL by shifting the point CLP(k) constituting the baseline CL along the normal direction of the baseline CL, and then calculates the risk value again. The first target trajectory generation unit 142 sets the corrected baseline CL obtained at the time point when the sum of the calculated risk values ​​is lower than the threshold as the target trajectory TJ1.

[0057] It should be noted that, in order to search for offsets where the sum of the calculated risk values ​​is lower than a threshold, the first target trajectory generation unit 142 can also use the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which is a type of probabilistic gradient method using random variables. Alternatively, it can also be considered in conjunction with... Figure 3The situation differs. For example, the baseline CL obtained by connecting point CLP(k) is a polyline. However, in this case, the normal direction is not uniquely determined. Therefore, the first target trajectory generation unit 142 can, for example, use the least squares method to fit the curve to a polyline and offset point CLP(k) based on the normal direction of the fitted curve. Alternatively, for example, half the angle formed by the segments constituting the polyline can be defined as the normal direction.

[0058] In this way, the first target track generation unit 142 pairs the points LLP(k) and RLP(k) of the point group of the left road dividing line LL and the right road dividing line RL identified by the identification unit 130, calculates the center point CLP(k), connects the calculated center point CLP(k) to obtain the baseline CL, and corrects the baseline CL based on the risk value of the surrounding area of ​​the vehicle M, thereby obtaining the target track TJ1.

[0059] However, generating the target track using this method requires prior pairing of points along the left road dividing line LL and the right road dividing line RL, and the recognition of the driving road based on the image captured by camera 10 requires high accuracy. However, in driving road areas with abrupt changes in road structure, such as curves and S-curves (in other words, low-speed driving areas), or in narrow driving road areas, the recognition accuracy required for the above method cannot be obtained. As a result, the target track may sometimes fail to be obtained.

[0060] In this context, the switching determination unit 144, based on the output of the vehicle sensor 40, determines whether the speed of the vehicle M is below a predetermined speed while the vehicle M is in motion, or determines whether a width below a predetermined width is detected on the road surface of the vehicle M based on the width detected by the recognition unit 130. If, while the vehicle M is in motion, it is determined that the speed of the vehicle M is below a predetermined speed, or if the width detected by the recognition unit 130 indicates that a width below a predetermined width is detected on the road surface of the vehicle M, the switching determination unit 144 replaces the first target track generation unit 142, causing the second target track generation unit 148 to generate the target track.

[0061] Figure 4 This diagram illustrates the method by which the second target trajectory generation unit 148 generates the target trajectory. Figure 4 In the figure, reference numeral TJ2 indicates the target track generated by the second target track generation unit 148, reference numeral TJ2P indicates the track point constituting the target track TJ2, and reference numeral RR indicates the recognition range of the driving environment of the vehicle M as identified by the recognition unit 130 based at least on the captured image taken by the camera 10. Figure 4The target orbit TJ2(t-1) in the text represents the target orbit generated one cycle before the target orbit TJ2(t) generated in this instance.

[0062] First, when generating the current target trajectory TJ2(t), the second target trajectory generation unit 148 sets the target trajectory TJ2(t-1) generated in the previous cycle as the baseline. In the initial case of the second target trajectory generation unit 148 generating the target trajectory, the target trajectory TJ1(t-1) generated by the first target trajectory generation unit 142 can be used as the previous target trajectory TJ2(t-1), or the target trajectory can be generated using the same method as the first target trajectory generation unit 142 only for the initial time.

[0063] The risk calculation unit 146 calculates the risk value of an object target that exists within the recognition range RR and is set along the normal direction of each trajectory point TJ2P(t-1) on the baseline set by the second target trajectory generation unit 148. The method for calculating and using the risk value is as follows. Figure 3 The method of the first target trajectory generation unit 142 is the same as described above; the closer the target object is to the identified object, the larger the calculated value (negative value). The second target trajectory generation unit 148 generates the target trajectory TJ2(t) for this time by shifting each trajectory point TJ2P(t-1) in the direction of decreasing risk value based on the risk value calculated by the risk calculation unit 146.

[0064] The second target track generation unit 148 sets the generated target track TJ2(t) of the current operation as the baseline for the next operation, and similarly generates the next target track TJ2(t+1). The second target track generation unit 148 repeats the above target track generation process until the switching determination unit 144 determines that the speed of the vehicle M is above a predetermined speed and the width of the travel path is above a predetermined width. When the switching determination unit 144 determines that the speed of the vehicle M is above a predetermined speed and the width of the travel path is above a predetermined width, it causes the first target track generation unit 142 to perform the target track generation process.

[0065] Figure 5 This is another diagram illustrating the method by which the second target trajectory generation unit 148 generates the target trajectory. Figure 5 In this study, the target trajectory TJ2(t) is obtained by offsetting each point TJ2P(t-1) of the baseline TJ2(t-1) along the normal direction in a manner that reduces the risk value. Figure 5It can be seen that the reference point TJ2P(t-1) is closer to the left road dividing line LL than the right road dividing line RL. Therefore, the risk calculation unit 146 calculates that the risk value to the left of the reference point TJ2P(t-1) in the normal direction is greater than the risk value to the right, and the second target track generation unit 148 shifts the reference point TJ2P(t-1) to the right to obtain the current track point TJ2P(t). It should be noted that in Figure 5 In the diagram, the dashed line TL represents the curve used to define the turning angle of the vehicle M. Curve TL is defined as an arc along several track points on the side of the vehicle M, within the point TJ2P(t) of the target track TJ2P(t). The second control unit 160 causes the vehicle M to turn along curve TL.

[0066] As described above, unlike the first target track generation unit 142, the second target track generation unit 148 does not pair the points LLP(k) of the left road dividing line LL with the points RLP(k) of the right road dividing line RL, but uses the previous target track TJ2(t-1) as a reference line to generate the current target track TJ2(t). That is, even on driving roads where it is difficult to identify the road dividing lines on both sides with equal accuracy, such as curves or S-curves, the second target track generation unit 148 can still generate the current target track TJ2(t). Therefore, the driving track of the moving body can be appropriately generated without overly relying on the recognition accuracy of the driving road based on the captured image.

[0067] It should be noted that the above description describes the case where the second target trajectory generation unit 148 shifts the baseline TJ2(t-1) once, but the present invention is not limited to this structure. For example, if the second target trajectory generation unit 148 calculates the risk value of each trajectory point on the current target trajectory TJ2(t) obtained by shifting the baseline TJ2(t-1), and the sum of the calculated risk values ​​(or the maximum value among the calculated risk values) is above a threshold, the current target trajectory TJ2(t) may be shifted again until it falls below the threshold.

[0068] Figure 6 This is a flowchart illustrating an example of the process executed by the vehicle control device of the embodiment. Figure 6 The process shown in the flowchart is repeated in a prescribed loop during the driving of this vehicle M.

[0069] First, the switching determination unit 144 determines whether the speed of the vehicle M is below a specified speed or whether a width below a specified width is detected in the direction of travel (step S100). If it is determined that the speed of the vehicle M is below a specified speed or that a width below a specified width is not detected in the direction of travel, the first target track generation unit 142 generates a target track (step S102).

[0070] On the other hand, if it is determined that the speed of the vehicle M is below a specified speed, or if a width below a specified width is detected in the direction of travel, the second target track generation unit 148 generates a target track (step S104). Then, the second control unit 160 drives the vehicle M along the generated target track (step S106). Thus, the processing of this flowchart ends.

[0071] Figure 7 This is a flowchart illustrating an example of the process performed by the risk calculation unit 146 and the second target trajectory generation unit 148. Figure 7 The flowchart shown illustrates the processing in... Figure 6 The process is executed in step S104 of the flowchart.

[0072] First, the second target track generation unit 148 obtains the travel track generated in the previous cycle and sets it as a baseline (step S200). Next, the risk calculation unit 146 calculates the risk value in the normal direction of the baseline for the track points constituting the set baseline (step S202).

[0073] Next, the second target track generation unit 148 shifts each track point in the direction of decreasing calculated risk value along the normal direction of the baseline (step S204). The second target track generation unit 148 sets the shifted baseline as the current travel track (step S206). Thus, the processing of this flowchart ends.

[0074] It should be noted that the inventors of this application have simulated the operation of the present invention. Figure 8 This is a graph showing the execution results of a simulation of the target trajectory generation in the implementation method. Figure 8 This illustrates the scenario where a mobile body M1 equipped with the second target track generation unit 148 travels on an S-shaped curve. As shown in the figure, it can be seen that even on an S-shaped curve where it is difficult to match the points of the left and right road dividing lines by using the target track generated by the first target track generation unit 142, the mobile body M1 equipped with the second target track generation unit 148 can travel smoothly.

[0075] According to the embodiment described above, the previous travel trajectory generated at a processing time earlier than the current processing time is set as a baseline. Based on the risk value set around the object target located within the identified driving environment, multiple trajectory points constituting the baseline are offset along the normal direction of the baseline to generate the current travel trajectory. Therefore, the travel trajectory of the moving object can be appropriately generated without overly relying on the recognition accuracy of the travel path based on the captured image.

[0076] The implementation methods described above can also be performed as follows.

[0077] An information processing device is configured to include:

[0078] Storage medium, which stores computer-readable instructions; and

[0079] The processor, which is connected to the storage medium,

[0080] The processor executes computer-readable instructions to perform the following processing for orbit generation of the moving body:

[0081] Identify the driving environment surrounding the moving body.

[0082] Calculate the risk value set around the object target located within the identified driving environment.

[0083] Generate the travel track of the moving body, that is, a travel track composed of multiple track points.

[0084] The process is repeated, and the previous travel track generated at a processing time earlier than the current processing time is set as the baseline. Based on the risk value, the multiple track points that constitute the baseline are offset along the normal direction of the baseline to generate the current travel track.

[0085] The above description illustrates specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. An information processing device for generating the trajectory of a moving body, wherein, The information processing device includes: The identification unit identifies the driving environment surrounding the moving body; The risk calculation unit calculates a risk value set around an object target located within the identified driving environment; as well as The track generation unit generates the travel track of the moving body, that is, a travel track composed of multiple track points. The orbit generation unit selects to execute either the target orbit generation process based on the first target orbit generation unit or the target orbit generation process based on the second target orbit generation unit. When the moving body is traveling at a speed greater than a predetermined speed, and the identification unit detects a width greater than a predetermined width on the moving body's travel path, the target track generation process based on the first target track generation unit is selected for execution. In the target track generation process based on the first target track generation unit, a correspondence is established between the points constituting the left road dividing line identified by the identification unit and the points constituting the right road dividing line identified by the identification unit. The center point of each point with the established correspondence is calculated, and the calculated center points are connected to obtain a baseline. The baseline is then corrected based on the risk value to generate the driving track. If the moving body is traveling at a speed below a predetermined speed, or if the identification unit detects a width below a predetermined width on the moving body's travel path, the target track generation process based on the second target track generation unit is selected to be executed. In the target track generation process based on the second target track generation unit, the process is repeated, and the previous travel track generated at a processing time earlier than the current processing time is set as the baseline. Based on the risk value, the plurality of track points constituting the baseline are offset along the normal direction of the baseline, thereby generating the current travel track.

2. The information processing apparatus according to claim 1, wherein, In the target track generation process based on the second target track generation unit, the current travel track is generated by shifting the plurality of track points constituting the baseline in the direction of the normal to the baseline in the direction of the reduced risk value.

3. The information processing apparatus according to claim 1, wherein, The target object includes the boundary line of the area where the mobile body can travel or an object that may come into contact with the mobile body.

4. The information processing apparatus according to any one of claims 1 to 3, wherein, The information processing device also includes a driving control unit that enables the moving body to travel along the generated driving track.

5. An information processing method, wherein, The information processing method causes the computer to perform the following processing in order to generate the orbit of the moving body: Identify the driving environment surrounding the moving body. Calculate the risk value set around the object target located within the identified driving environment. Generate the travel track of the moving body, that is, a travel track composed of multiple track points. Choose to execute either the target orbit generation process based on the first target orbit generation unit or the target orbit generation process based on the second target orbit generation unit. When the moving body is traveling at a speed greater than a predetermined speed, and a width greater than a predetermined width is detected on the moving body's travel path, the target track generation process based on the first target track generation unit is selected for execution. In the target track generation process based on the first target track generation unit, a correspondence is established between the points constituting the identified left road dividing line and the points constituting the identified right road dividing line. The center point of each point with the established correspondence is calculated. The calculated center points are connected to obtain a baseline. The baseline is corrected based on the risk value, thereby generating the driving track. If the moving body is traveling at a speed below a specified speed, or if a width below a specified width is detected on the moving body's travel path, then the target track generation process based on the second target track generation unit is selected to be executed. In the target track generation process based on the second target track generation unit, the process is repeated, and the previous travel track generated at a processing time earlier than the current processing time is set as the baseline. Based on the risk value, the plurality of track points constituting the baseline are offset along the normal direction of the baseline, thereby generating the current travel track.

6. A storage medium, wherein, The storage medium stores a program that causes a computer to perform the following processes in order to generate the orbit of a moving object: Identify the driving environment surrounding the moving body. Calculate the risk value set around the object target located within the identified driving environment. Generate the travel track of the moving body, that is, a travel track composed of multiple track points. Choose to execute either the target orbit generation process based on the first target orbit generation unit or the target orbit generation process based on the second target orbit generation unit. When the moving body is traveling at a speed greater than a predetermined speed, and a width greater than a predetermined width is detected on the moving body's travel path, the target track generation process based on the first target track generation unit is selected for execution. In the target track generation process based on the first target track generation unit, a correspondence is established between the points constituting the identified left road dividing line and the points constituting the identified right road dividing line. The center point of each point with the established correspondence is calculated. The calculated center points are connected to obtain a baseline. The baseline is corrected based on the risk value, thereby generating the driving track. If the moving body is traveling at a speed below a specified speed, or if a width below a specified width is detected on the moving body's travel path, then the target track generation process based on the second target track generation unit is selected to be executed. In the target track generation process based on the second target track generation unit, the process is repeated, and the previous travel track generated at a processing time earlier than the current processing time is set as the baseline. Based on the risk value, the plurality of track points constituting the baseline are offset along the normal direction of the baseline, thereby generating the current travel track.

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