Route generation device and method

Abstract

The goal is to enable a moving body to avoid obstacles in a natural way in more situations by generating a path for the moving body using information indicating the possibility of collision with an obstacle, rather than relying solely on information obtained from sensors. a movement state estimation unit that estimates a movement state of a moving object using information acquired from the sensor; a collision risk determination unit that outputs determination information that is information indicating a determination result for the moving body to avoid an obstacle based on the moving state and collision risk information that is information indicating a possibility of a collision between the moving body and an obstacle; and a route generating unit that generates a travel route for the moving object based on the determination information.

Description

[Technical field]

[0001] The present disclosure relates to a route generation device and method. [Background technology]

[0002] A path generation device has been proposed that generates a path for a moving body, such as an automobile, ship, or aircraft, to move autonomously. The autonomous moving body detects obstacles around the moving body using sensors. If an obstacle that may cause a collision with the moving body is present on the pre-planned moving path, the path generation device changes the moving path so that the moving body can avoid the obstacle.

[0003] For example, Patent Document 1 discloses a method in which a moving body avoids obstacles in a natural manner by performing finely tuned, natural-feeling braking based on the detection reliability, which is the reliability of the obstacle detection results by a sensor (the length of time the same obstacle is detected). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2012-183868 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the conventional technique described in Patent Document 1 has the following problems. For example, in an environment where the detection sensitivity of a sensor is reduced, such as at night or in fog, the detection distance of an obstacle by the sensor becomes shorter. As the detection distance of an obstacle becomes shorter, the accuracy of the detection reliability decreases. Therefore, even if braking is performed based on the detection reliability, there is a limit to how naturally the moving body can avoid the obstacle.

[0006] The present disclosure has been made to solve the problems described above, and aims to enable a moving body to avoid obstacles in a natural manner in more situations by generating a movement path for the moving body using information indicating the possibility of a collision with an obstacle, rather than relying solely on information obtained from sensors. [Means for solving the problem]

[0007] The route generation device of the present disclosure comprises: a movement state estimation unit that estimates a movement state of a moving object using information acquired from the sensor; 、 before The moving state and the moving body and obstacles a collision risk determination unit that outputs determination information that is information indicating a determination result for the moving body to avoid the obstacle, based on collision risk information that is information indicating a possibility of a collision with the obstacle; Based on the judgment information, is the acceleration generated when the moving body avoids the obstacle. a path generating unit that generates a moving path of the moving object so that the avoidance acceleration does not exceed a certain value; Equipped with.

[0008] The route generation method of the present disclosure includes: A moving state estimating unit estimates a moving state of the moving object using information acquired from the sensor, Collision risk assessment unit ,before The moving state and the moving body and obstacles outputting judgment information which is information indicating a judgment result for the moving body to avoid the obstacle based on collision risk information which is information indicating a possibility of a collision with an obstacle; A route generating unit, based on the determination information is the acceleration generated when the moving body avoids the obstacle. A movement path for the moving body is generated so that the avoidance acceleration does not exceed a certain value. Effect of the Invention

[0009] According to the present disclosure, in many situations, a moving object can avoid obstacles in a natural manner. [Brief description of the drawings]

[0010] [Figure 1] 1 is a functional block diagram showing a configuration of a path generating device according to a first embodiment. [Diagram 2] 1 is a diagram illustrating a hardware configuration of a path generating device according to a first embodiment. [Diagram 3] 1 is a diagram for explaining a specific operation of a conventional route generation device. [Figure 4] 1 is a diagram for explaining a specific operation of a conventional route generation device. [Diagram 5] FIG. 11 is a diagram for explaining a specific operation of route generation and leaving according to the first embodiment. [Figure 6] FIG. 11 is a diagram for explaining a specific operation of route generation and leaving according to the first embodiment. [Figure 7] 4 is a flowchart showing the operation of the path generating device according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment will be described with reference to the drawings. The following embodiment is merely an example, and various modifications are possible within the scope of the present invention. In the description of the embodiment and the drawings, the same elements and corresponding elements are given the same reference numerals. Descriptions of elements given the same reference numerals will be omitted or simplified as appropriate. In the following embodiment, "part" may be read as "circuit", "step", "procedure" or "process" as appropriate.

[0012] Embodiment 1 ***Configuration Description*** Fig. 1 is a functional block diagram showing a configuration of a route generation device according to the present embodiment. The route generation device 10 is a device capable of implementing a route generation method according to the embodiment. In Fig. 1, a moving body 100 includes a route generation device 10, various sensors 11, and a control unit 21. The route generation device 10 includes a movement state estimation unit 12, a collision risk determination unit 13, a route generation unit 16, a map information storage unit 17, an incident information storage unit 18, a risk map information storage unit 19, and a movement route information storage unit 20. The collision risk determination unit 13 includes an estimation unit 14 and a determination unit 15.

[0013] The moving body 100 is a moving body capable of autonomous movement. For example, the moving body 100 is an autonomous driving vehicle, personal mobility, an autonomous robot, a ship, a train, an aircraft, a drone, etc. The moving body 100 is also equipped with various sensors 11 necessary for autonomous movement. For example, the various sensors 11 are devices such as a global positioning system (GPS), an inertial measurement unit (IMU), a light detection and ranging (LiDAR), a millimeter wave radar, an ultrasonic sonar, a camera, and a beacon. Note that the various sensors 11 do not need to be equipped with all of these devices. The devices of the various sensors 11 may be appropriately selected or omitted depending on the type of the moving body 100, the moving environment, the device cost, etc.

[0014] The various sensors 11 do not necessarily have to be provided in the moving body 100. For example, a sensor (e.g., a surveillance camera, a beacon, a roadside device, etc.) provided outside the moving body 100 may acquire information necessary for the autonomous movement of the moving body 100. In other words, if information necessary for the autonomous movement of the moving body 100 can be provided to the moving body 100 from outside, the various sensors 11 of the moving body 100 may be omitted.

[0015] The moving body 100 includes a power source and a power unit required for movement, and a driven unit driven by the power unit. For example, the power source is battery power or fuel. For example, the power unit is a motor or an engine. For example, the driven unit is a wheel, a propeller, a screw, etc. The driven unit may include a steering unit for changing direction or braking the moving body 100. Note that the power source, the power unit, the driven unit, and the steering unit are not shown in FIG. 1.

[0016] The various sensors 11 acquire moving object information D1 indicating information on the state of the moving object 100 itself. For example, the moving object information D1 is information on the moving object 100's moving speed, moving direction, posture, and the like.

[0017] In addition, the various sensors 11 acquire obstacle information D2 indicating information on obstacles present around the moving body 100. For example, the obstacle information D2 is information on the type, position, size, shape, color, and whether the obstacle is moving or not of the obstacle. The obstacle is an object or state that impedes the movement of the moving body 100. For example, when the moving body 100 is an autonomous vehicle, the obstacle is a fallen object, a hole, a puddle, another moving body, a person, an animal, etc. For example, when the moving body 100 is a ship, the obstacle is a drifting object, a reef, an animal, another moving body, etc. For example, when the moving body 100 is an aircraft, the obstacle is a turbulence, a thundercloud, an animal, another moving body, etc. The obstacle information D2 may be acquired by various sensors provided in external infrastructure devices such as roadside devices, surveillance cameras, beacons, and radars, or other moving bodies. The obstacle information D2 acquired by an external infrastructure device or another moving body may be provided to the route generation device 10 of the moving body 100 via a wireless communication device (not shown).

[0018] The map information storage unit 17 is a storage unit that stores map information D3 indicating the environment of the movement route of the mobile object 100. For example, the map information D3 may be information on the condition of the ground or space including roads (paved, unpaved, road inclination, etc.), railroad tracks, waterways, intersections, multi-level intersections, the number and width of lanes, road markings, signs, and traffic lights. Note that the map information D3 may also include information on the type, position, width, size, etc. of static features that may hinder the movement of the mobile object 100.

[0019] The moving state estimation unit 12 estimates the moving state of the moving body 100 using the moving body information D1 and the map information D3. The estimated moving state is output as moving state information D4. For example, the moving state is the current position, speed, acceleration, moving direction, posture, etc. of the moving body 100 on the map.

[0020] The incident information storage unit 18 is a storage unit that stores incident information D5 indicating information on the situation in which an accident such as a collision or an event that almost caused an accident (a so-called near miss) occurred in the past. For example, the incident information D5 may be the date and time when the accident occurred, the coordinates of the location, information about the surroundings of the location, the weather, the type of moving body that caused the accident, information about obstacles, etc. The incident information D5 may be information that has been determined in advance before the moving body 100 moves, or may be changed in real time during the moving body 100 by an information update operation from outside the moving body 100.

[0021] The risk map information storage unit 19 is a storage unit that stores risk map information D6 indicating information on the potential danger level around the moving body 100. More specifically, the risk map information D6 can be information indicating the occurrence frequency of the risk of a collision between the moving body and an obstacle and the impact of the risk on a map according to, for example, the blind spot of the moving body 100, the position of features around the moving body 100, and the shape of the features. Note that the risk map information D6 may be predetermined before the moving body 100 moves, or may be changed in real time during the moving body 100 by an information update operation from outside the moving body 100.

[0022] The moving path information storage unit 20 is a storage unit that stores moving path information D7 indicating information on a moving path including a moving speed and a moving direction of the moving object 100. Note that the moving path information D7 may be determined in advance before the moving object 100 moves, or may be appropriately changed during the moving object 100 by an information update operation from outside the moving object 100.

[0023] The collision risk judgment unit 13 uses the obstacle information D2, the movement state emotion D4, the incident information D5, the risk map information D6, and the movement route information D7 to estimate the risk of collision between the moving body 100 and an obstacle, and judges whether the moving body 100 needs to avoid the obstacle, and outputs judgment information D9, which is information indicating the judgment result for the moving body 100 to avoid the obstacle. Next, the internal configuration of the collision risk determination unit 13 will be described.

[0024] The estimation unit 14 estimates collision risk information D8 indicating the possibility of collision with an obstacle when the moving body 100 moves according to the moving path information D7, using the obstacle information D2, the incident information D5, the risk map information D6, and the moving path information D7. More specifically, for example, the collision risk information D8 can be a numerical value indicating the probability of the moving body 100 colliding with an obstacle at any position on the moving path represented by the moving path information D7. For example, the collision risk information D8 may be expressed as a percentage numerical value ranging from 0% to 100%. Alternatively, the collision risk information D8 may be expressed as a normalized numerical value ranging from 0 to 1. For example, when the collision risk information D8 is expressed as a percentage numerical value, a larger numerical value indicates a higher risk of the moving body 100 colliding with an obstacle.

[0025] The estimation unit 14 can estimate the collision risk information D8, for example, by using a trained model. When the obstacle information D2, the incident information D5, the risk map information D6, and the travel route information D7 are input to the trained model, the trained model outputs the collision risk information D8, which is a response to the input information. The trained model can be created using a machine learning method such as deep learning or a support vector machine. The collision risk information D8 can also be estimated using a rule-based method such as a method of referring to a table of a predetermined risk map.

[0026] Both the incident information D5 and the risk map information D6 indicate the possibility of collision with an obstacle (e.g., another moving body, a fallen object, etc.) on the travel route. Therefore, the estimation unit 14 may estimate the collision risk information D8 using only the incident information D5. Alternatively, the estimation unit 14 may estimate the collision risk information D8 using only the risk map information D6.

[0027] The determination unit 15 uses the movement state information D4 and the collision risk information D8 to determine whether or not the moving body 100 needs to avoid an obstacle, and outputs determination information D9 which is information indicating a determination result for the moving body 100 to avoid the obstacle. More specifically, for example, the determination unit 15 can compare the collision risk information D8 at the position indicated by the movement state information D4 with a predetermined threshold, and when the probability of the possibility of a collision is higher than the threshold, output the determination information D9 so that the moving body 100 starts an avoidance action.

[0028] In order to check safety when changing trajectories, the determination unit 15 can determine the congestion state around the moving object 100 by using the movement state information D4 and the collision risk information D8. For example, the congestion state is the number of moving objects per a given distance around the movement route.

[0029] The determination unit 15 may determine whether the moving body 100 will perform other actions in addition to determining whether the moving body 100 will avoid an obstacle. For example, the determination unit 15 may determine whether the moving body 100 will perform a preparatory action for preparing to avoid an obstacle. The preparatory action indicates an action for gently decelerating the moving body 100, such as lightly braking or stopping acceleration.

[0030] The determination unit 15 may determine whether the moving body 100 will perform an avoidance operation, such as changing the course of the moving body 100 to avoid an obstacle, or strongly decelerating or stopping the moving body 100 to prevent the moving body 100 from colliding with an obstacle. The avoidance operation indicates an operation of strongly decelerating the moving body 100 to stop the moving body 100, such as applying normal brakes, or an operation of changing the course of the moving body 100.

[0031] Hereinafter, the configuration and operation of the route generation device 10 according to this embodiment will be described in more detail, assuming that the moving object 100 is an autonomous vehicle.

[0032] First, the acceleration that occurs when the moving body 100 decelerates or changes course to avoid an obstacle will be described, and a moving route that allows the moving body 100 to avoid obstacles in a natural way will be specifically defined.

[0033] First, the acceleration (deceleration acceleration) that occurs in the moving body 100 due to deceleration caused by, for example, braking operation will be described. For example, when the brakes are applied lightly, the deceleration acceleration is approximately 0.1 G. When the brakes are applied normally, the deceleration acceleration is approximately 0.2 G. When the brakes are applied hard, the deceleration acceleration is approximately 0.3 G. Here, G is a unit representing gravitational acceleration. Generally, when the deceleration acceleration exceeds 0.3 G, people feel uncomfortable. Therefore, it is preferable that the deceleration acceleration does not exceed approximately 0.25 G, taking into account a margin.

[0034] In addition, when the moving body 100 changes course to avoid an obstacle, lateral acceleration occurs in the moving body 100. The strength of the lateral acceleration is in accordance with the numerical value of the deceleration acceleration described above. Hereinafter, the deceleration acceleration and the lateral acceleration will be collectively referred to as the avoidance acceleration.

[0035] From the above, a travel route that the moving body 100 can avoid naturally is a travel route that is made up of deceleration or course change, or deceleration and course change, in which the avoidance acceleration does not exceed approximately 0.25 G. Note that the value of the avoidance acceleration, 0.25 G, is just an example, and the value of the avoidance acceleration may be changed as appropriate depending on, for example, the type of the moving body 100 and the attributes of the occupants aboard the moving body 100 (driver, passenger, etc.).

[0036] The determination unit 15 can use, for example, a rule-based method to determine the possibility of a collision with an obstacle using the collision risk information D8 and to decide the operation of the moving body 100. The rule-based determination method will be described below by taking as an example a case where the collision risk information D8 is expressed as a percentage value ranging from 0% to 100%.

[0037] First, thresholds for judging the possibility of a collision and determining the operation of the moving body 100 are defined. The predetermined thresholds are TH1 and TH2, and for example, the values ​​of the thresholds TH1 and TH2 are TH1=20% and TH2=80%, respectively. Note that the values ​​of the thresholds TH1 and TH2 are merely examples, and the values ​​of the thresholds TH1 and TH2 can be appropriately changed depending on the distance from the moving body 100 to the obstacle, the probability that the obstacle exists, the moving state of the moving body 100, the situation of the moving route, etc.

[0038] When the collision risk information D8 is equal to or greater than the threshold value TH2, the determination unit 15 determines that there is a high possibility of a collision with an obstacle, and outputs determination information D9 so that the moving object 100 performs a given action (braking or lane change). When the collision risk information D8 is less than the threshold value TH2 and is equal to or greater than the threshold value TH1, the determination unit 15 determines that the possibility of a collision with an obstacle is medium, and outputs determination information D9 to cause the moving object 100 to perform a preparatory movement. When the collision risk information D8 is less than the threshold value TH1, the determination unit 15 determines that the possibility of a collision with an obstacle is low, and outputs determination information D9 so that the moving object 100 maintains the current course and speed. The number of predetermined thresholds for determining the possibility of a collision is not limited to two. For example, if there are multiple types of preparatory movements (for example, separating the movements of lightly braking and stopping acceleration), the number of predetermined thresholds can be set to three or more levels.

[0039] Furthermore, the likelihood of collision may be set as a continuous value, for example, using a function y=f(x) in which the collision risk information D8 is input as x and the likelihood of collision is output as y. For example, the function f(x) can be determined by a statistical method such as regression analysis using data on past accidents or events that almost caused an accident.

[0040] The judgment unit 15 may also judge the possibility of a collision using a trained model. Specifically, when the collision risk information D8 is input to the trained model, the trained model estimates the possibility of a collision and determines the judgment information D9. For example, machine learning such as deep learning and a support vector machine can be used to create the trained model.

[0041] The route generation unit 16 uses the movement state information D4, the movement route information D7, and the judgment information D9 to generate a movement route that allows the moving body 100 to avoid obstacles in a natural manner. The generated (updated) movement route is output again to the movement route information storage unit 20 as the movement route information D7, and is also output as the control information D10.

[0042] Using the control information D10, the control unit 21 controls the power source, the power unit, the driven unit, and the steering unit of the moving body 100. Then, the moving body 100 can avoid obstacles in a natural manner by moving along the generated moving path.

[0043] As described above, the collision risk judgment unit 13 does not rely solely on information acquired from various sensors (i.e., obstacle information D2) to estimate the risk of collision between the moving body 100 and an obstacle, and to determine how the moving body 100 can avoid the obstacle, but makes a comprehensive judgment using information indicating the possibility of a collision with the obstacle or another moving body (i.e., incident information D5, risk map information D6), thereby allowing the moving body 100 to avoid the obstacle in a natural manner.

[0044] ***Hardware Configuration Description*** 2 is a diagram showing a hardware configuration of the route generation device 10 according to the present embodiment. The route generation device 10 is, for example, a computer, a dedicated arithmetic device, or a device that combines a computer and a dedicated arithmetic device. The route generation device 10 includes a processor 31 which is an information processing unit, a memory 32 which is a storage unit, a storage device 33 which is a non-volatile storage unit, an interface 34, and a communication unit 35.

[0045] The processor 31 is connected to other hardware via a system bus and controls the other hardware. The processor 31 is an integrated circuit (IC) that performs processing. Specific examples of the processor 31 include a central processing unit (CPU), a digital signal processor (DSP), a graphics processing unit (GPU), or a field programmable gate array (FPGA).

[0046] The processor 31 executes programs stored in the memory 32. The programs include a route generation program according to the present embodiment. The functions of the route generation device 10 are realized by the processor 31 that executes the programs. The processor 31 is an example of a processing circuitry.

[0047] For example, the moving state estimation unit 12, the collision risk determination unit 13, and the route generation unit 14 are realized by the processor 31. For example, the map information storage unit 17, the incident information storage unit 18, the risk map information storage unit 19, and the moving route information storage unit 20 are realized by the memory 32.

[0048] The CPU performs processes such as program execution and data calculations. The DSP performs digital signal processing such as arithmetic operations and data movement. For example, it is desirable to perform high-speed processing such as sensing of sensor data obtained from millimeter wave radar by the DSP instead of by the CPU.

[0049] A GPU is a processor specialized for image processing. A GPU can perform high-speed image processing by processing multiple pixel data in parallel. A GPU can perform template matching processing, which is frequently used in image processing, at high speed. For example, it is desirable to use a GPU to process the sensing of sensor data obtained from a camera. If the sensing of sensor data obtained from a camera is processed by a CPU, the processing time becomes enormous. In addition, a GPU can be used not only as a processor for image processing, but also to perform general-purpose calculations using the computing resources of the GPU (GPGPU: General Purpose Computing on Graphics Processing Units). For example, by using a GPGPU to perform image processing using deep learning, it is possible to detect obstacles and other moving objects with higher accuracy.

[0050] FPGA is a processor that can program the configuration of logic circuits. FPGA has both characteristics of dedicated hardware arithmetic circuit and programmable software. Complex arithmetic and parallel processing can be executed at high speed by FPGA.

[0051] The memory 32 is, for example, a volatile memory. The volatile memory can move data at high speed during operation of the path generating device 10. For example, specific examples of the volatile memory include a RAM (Random Access Memory) and an SDRAM (Synchronous Dynamic Random Access Memory).

[0052] The storage device 33 is a non-volatile memory, and can continue to hold the execution program and data even while the path generating device 10 is powered off. For example, specific examples of the non-volatile memory include an Electrically Erasable Programmable Read Only Memory (EEPROM), a Hard Disk Drive (HDD), a Solid State Drive (SSD), and a flash memory. For example, the non-volatile memory may be a portable storage medium such as a Secure Digital (SD) (registered trademark) memory card, a CompactFlash (CF), a NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a Digital Versatile Disc (DVD). The route generation program according to the present embodiment may be provided by a portable recording medium. The memory 32 is connected to the processor 31 via a memory interface (not shown). The memory interface centrally manages memory access from the processor and performs efficient memory access control. The memory interface is used for data transfer in the route generation device 10.

[0053] 1 are realized by a processor 31 that executes programs. Programs that realize the functions of the moving state estimation unit 12, the collision risk determination unit 13, and the route generation unit 16 are stored in a memory 32. The processor 31 reads these programs from the memory 32 and executes these programs. The memory 32 is also used to temporarily store intermediate information of each program.

[0054] Furthermore, the functions of the travel state estimation unit 12, the collision risk determination unit 13, and the route generation unit 16 may be realized by a logic circuit, which is hardware. In this case, logic circuit information is stored in the memory 32. The logic circuit information is read and executed by the processor 31.

[0055] The processor 31 may be configured with a plurality of processors. In this case, the plurality of processors may execute programs for implementing the functions of the moving state estimation unit 12, the collision risk determination unit 13, and the route generation unit 16 in a coordinated manner.

[0056] The memory 32 stores map information D3, incident information D5, risk map information D6, and travel route information D7. The map information D3, incident information D5, risk map information D6, and travel route information D7 may be stored in a storage device external to the route generation device 10. In other words, the map information storage unit 17, the incident information storage unit 18, the risk map information storage unit 19, and the travel route information storage unit 20 may be storage devices external to the route generation device 10.

[0057] The operation and effects of the route generation device 10 according to the present embodiment will be described by comparing it with the operation of a conventional route generation device. Figs. 3 and 4 are diagrams for explaining the specific operation of the conventional route generation device. Figs. 5 and 6 are diagrams for explaining the specific operation of the route generation device 10 according to the present embodiment. In each of Figs. 3 to 6, for example, the environment (weather) in which the detection sensitivity of the sensor decreases is described as fog, but the environment is not limited to fog. For example, various environments in which the detection sensitivity of the sensor decreases can be considered, such as rain, snow, nighttime, smoke from a fire or eruption, smog, and dust.

[0058] First, a specific operation of the conventional route generation device will be described with reference to FIG. 3. In FIG. 3(a), a moving body 100 moves on a lane L1 and is located at a point P1. A store S is located on the side of the lane L1 at a point P3. At the point P1, obstacle information D2 acquired by the moving body 100 indicates that an obstacle H (e.g., a parked vehicle) may be present at a point P3, which is 100 to 120 meters away from the moving body 100, and the detection reliability of the obstacle H is 60%. A cloud-like symbol C drawn to surround the obstacle H is an approximation of the detection reliability of the obstacle, and the larger the cloud-like symbol C, the lower the detection reliability. A dotted arrow R1 coming out from the moving body 100 indicates a travel route that the moving body 100 is scheduled to travel within a certain time from the current time. The length of the arrow is proportional to the speed of the moving body 100. In FIG. 3(a), a conventional path generation device estimates, at point P1, that there is a possibility that an obstacle H is present near point P3, but decides not to take any action to avoid the obstacle because the detection reliability is low.

[0059] In FIG. 3(b), the moving body 100 moves from the point P1 shown in FIG. 3(a) in the direction of the arrow R1 and is located at the point P2. Furthermore, at the time of arriving at the position of the point P2, the obstacle information D2 acquired by the moving body 100 indicates that there is a possibility that an obstacle H is present at a point 50 meters away from the moving body 100, and the detection reliability is 99%. In FIG. 3(b), the moving body 100 does not take any evasive action while moving from the point P1 to the point P2, so the speed of the moving body 100 at the point P2 is the same as the speed at the point P1. Therefore, it is certain that the moving body 100 needs to take evasive action at the position of the point P2. Therefore, the moving body 100 needs to apply the brakes. However, since the distance to the obstacle H is close, the moving body 100 is forced to suddenly apply the brakes or suddenly change course. Therefore, the moving body 100 cannot avoid the obstacle H in a natural way.

[0060] Fig. 4 shows another example of the operation of a conventional route generation device. In Fig. 4(a), the moving body 100 is located at point P1 on lane L1, as in Fig. 3(a). Furthermore, obstacle information D2 acquired by the moving body 100 at point P1 indicates that there is a possibility that an obstacle H exists at point P3, which is 100 to 120 meters away from the moving body 100, and the detection reliability is 60%. The difference from the example shown in Fig. 3 is that in the example shown in Fig. 4, although the detection reliability is low at the stage of point P1, a decision is made to avoid the obstacle H by stopping.

[0061] 4(b) shows a case where the obstacle H is almost certainly present (for example, the detection reliability is 99% or more) when the moving body 100 arrives at the point P2. In this case, the moving body 100 has already decelerated toward a stop, and therefore can stop just before the obstacle H. On the other hand, FIG. 4(c) shows a case where the obstacle H does not exist when the moving body 100 arrives at the point P2. In this case, the moving body 100 unnecessarily decelerates. Moreover, the deceleration of the moving body 100 is intended to stop the moving body 100, and the speed of the moving body 100 drops significantly. In order to quickly return the speed of the moving body 100 to its original speed, it is necessary to accelerate the moving body 100 again, but the acceleration is rapid. In this case, the moving object 100 cannot avoid the obstacle H in a natural manner.

[0062] Next, a description will be given of the operation of the path generating device 10 according to the present embodiment. Figures 5 and 6 are diagrams for explaining the specific operation of the path generating device 10 according to the present embodiment.

[0063] In Fig. 5(a), the moving body 100 is located at point P1, as in Fig. 3(a). Also, a store S is located on the side of the road in lane L1 at point P3. At point P1, obstacle information D2 acquired by the moving body 100 indicates that an obstacle H may be present at point P3, which is 100 to 120 meters away from the moving body 100, and the detection reliability of the obstacle H is 60%. A cloud-like symbol C drawn to surround the obstacle H is an approximation of the detection reliability of the obstacle, and the larger the cloud-like symbol C, the lower the detection reliability.

[0064] 5(a), the collision risk determination unit 13 estimates a collision risk between the moving body 100 and an obstacle H at a point P1, using obstacle information D2, movement state information D4, incident information D5, risk map information D6, and movement route information D7. Then, based on the estimated collision risk, the collision risk determination unit 13 determines whether or not the moving body 100 needs to avoid the obstacle, and the contents of the preparatory movement or main movement. In the case of FIG. 5(a), the estimation unit 14 refers to the incident information D5 and the risk map information D6 and confirms that a store S is located along a road near the point P3. It is also confirmed that vehicles (i.e., obstacle H) frequently stop on the road in front of the store S to load and unload luggage, and that several rear-end collisions have occurred there in the past. From these results, the estimation unit 14 estimates that the collision risk in the vicinity of the point P3 is medium.

[0065] At the position of point P1, the probability that an obstacle H exists (detection reliability) is medium at 60%, and it is not certain whether the moving body 100 will definitely stop. However, as shown in FIG. 5(b), the collision risk determination unit 13 prepares for the case where it is determined that the collision risk is medium and therefore it is highly necessary to stop the moving body 100. In other words, the collision risk determination unit 13 determines that it is necessary to apply light braking (i.e., a preparatory movement) in advance so that the moving body 100 can stop with ease. Then, the path generation unit 16 generates a path for slowing down the moving speed of the moving body 100 as shown by the arrow R3 according to the determination information D9.

[0066] Next, FIG. 5(c) shows a case where the moving body 100 has moved to point P2 and it has been almost determined that an obstacle H exists. In this case, the collision risk determination unit 13 determines that it is necessary to stop the moving body 100 before the obstacle H. Then, in order to stop the moving body 100 before the obstacle H, the path generation unit 16 further changes the moving path as indicated by the arrow R4 according to the determination information D9. In other words, the path generation unit 16 decelerates the moving body 100 further than the speed indicated by the moving path of the arrow R4, and stops the moving body 100 before the obstacle H. By gradually decreasing the speed of the moving body 100, the deceleration / acceleration of the moving body 100 until it reaches the obstacle H becomes gentle. Therefore, the moving body 100 can avoid (stop) the obstacle H in a natural manner.

[0067] 5(d) shows an example in which it is determined that the obstacle H does not exist as a result of the moving body 100 having moved to point P2. In this case, the collision risk determination unit 13 determines that the preparatory action for avoidance has ended. Then, the path generation unit 16 generates a moving path for accelerating the moving body 100 so that the moving body 100 returns to the speed defined in the moving path. In this case, the moving body 100 is only minimally decelerated, so it can return to its original speed with a short re-acceleration, and therefore the moving body 100 can avoid the obstacle H in a natural way.

[0068] Next, another example will be described with reference to Fig. 6. The example shown in Fig. 6 shows a case where the collision risk determination unit 13 determines that there is a possibility that the obstacle H exists as a result of taking into consideration the distance to the obstacle H, the detection reliability, the traveling speed of the moving body 100, and the like, but reserves the determination until the obstacle H approaches a little closer.

[0069] In Fig. 6(a), the moving body 100 is located at point P1, as in Fig. 5(a). Also, a store S is located on the side of the road in lane L1 at point P3. At point P1, obstacle information D2 acquired by the moving body 100 indicates that an obstacle H may be present at point P3, which is 100 to 120 meters away from the moving body 100, and the detection reliability of the obstacle H is 60%. A cloud-like symbol C drawn to surround the obstacle H is an approximation of the detection reliability of the obstacle, and the larger the cloud-like symbol C, the lower the detection reliability.

[0070] 6(b) shows a case where the moving body 100 moves to point P4, the distance to the obstacle H becomes closer, and the detection reliability increases from 60% to 75%. At this point, the collision risk determination unit 13 determines that a preparatory operation for obstacle avoidance is necessary. Next, the collision risk determination unit 13 determines the congestion situation around the moving body 100 (i.e., the possibility of collision with other moving bodies) using the movement state information D4, incident information D5, risk map information D6, and movement route information D7. In Fig. 6(b), since there are no other moving bodies around the moving body 100, the collision risk determination unit 13 determines that the degree of congestion of other moving bodies around the moving body is low. Unlike the example of Fig. 5 described above, the collision risk determination unit 13 determines to avoid the obstacle by changing the moving path instead of stopping the moving body 100. The path generation unit 16 generates a moving path indicated by an arrow R6 in accordance with the determination information D9 of the collision risk determination unit 13 so that the moving body 100 changes course to the adjacent lane L2.

[0071] 6(c) shows a case where the moving object 100 has moved to point P5 and the presence of obstacle H has been almost confirmed. In this case, the moving object 100 has already changed course to the adjacent lane L2, so it continues moving and passes by the side of obstacle H.

[0072] 6(d) shows a case where there is no obstacle H. In this case, the collision risk determination unit 13 determines to end the avoidance operation. Then, the path generation unit 16 generates a path returning to the movement path before the change, as shown by an arrow R7.

[0073] As described above, the path generation device 10 selects an avoidance means in consideration of the surrounding conditions of the moving body 100, and when the obstacle H is present, the path generation device 10 avoids the obstacle H by changing the planned movement position, and when the obstacle H is not present, the path generation device 10 can quickly return to the original (pre-change) planned movement path without interrupting the movement of surrounding moving bodies. Therefore, the moving body 100 can avoid the obstacle H in a natural way.

[0074] In the examples of FIGS. 5 and 6, collision risk information D8, which is information indicating a possibility of collision between the moving body 100 and the obstacle H, is a collision risk estimated from the fact that several rear-end collisions have occurred in the past on the road in front of the store S near the point P3 because vehicles frequently stop to load and unload luggage on the road. The collision risk determination unit 13 outputs determination information D9, which is information indicating a determination result for the moving body 100 to avoid the obstacle H, based on the moving state of the moving body 100 and the collision risk information D8. Then, the path generation unit 16 can generate a movement path for the moving body 100 based on the determination information D9. Therefore, the moving body 100 can avoid the obstacle H in a natural way.

[0075] As described above, in the path generation device 10 according to the present embodiment, the collision risk determination unit 13 does not rely only on information acquired from various sensors (i.e., obstacle information D2) but also makes a comprehensive determination using information indicating the possibility of a collision with an obstacle or another moving body (i.e., incident information D5, risk map information D6), so that the moving body 100 can perform a minimum necessary preparatory action at the time when the possibility of the presence of the obstacle H arises. As a result, the moving body 100 can reliably avoid or stop the obstacle H in a natural manner. Furthermore, even if the obstacle H does not exist, the path generation device 10 according to the present embodiment can quickly return the moving body 100 to the speed defined in the movement path with only a minimum deceleration. Therefore, the path generation device 10 according to this embodiment enables the moving body 100 to avoid obstacles in a natural manner in various situations.

[0076] ***Explanation of Operation Sequence*** Next, a description will be given of the operation sequence of the path generating device 10 according to the present embodiment.

[0077] In step ST10, the movement state estimation unit 12 estimates the movement state of the moving object 100 using the moving object information D1 and the map information D3 (step ST10).

[0078] In step ST11, the estimation unit 14 uses the obstacle information D2, incident information D5, risk map information D6, and travel route information D7 to estimate collision risk information D8 indicating the possibility of a collision with an obstacle if the moving body 100 moves according to the travel route information D7 (step ST11).

[0079] In step ST12, the judgment unit 15 judges whether or not the moving body 100 needs to avoid an obstacle, using the movement state information D4 and the collision risk information D8, and outputs judgment information D9 indicating the avoidance judgment result (step ST12).

[0080] In step ST13, the route generating unit 16 uses the movement state information D4, the movement route information D7, and the judgment information D9 to generate the movement route information D7 based on the judgment information D9 (step ST13). Specifically, the route generating unit 16 generates a movement route where the moving body 100 performs a preparatory action to avoid an obstacle, such as light braking (deceleration), a main action to avoid an obstacle, such as braking (deceleration) or a course change, or a movement route where the moving body 100 maintains the current course and movement speed, based on the judgment information D9.

[0081] In step ST14, it is determined whether the moving body 100 has arrived at the destination. If the moving body 100 has arrived at the destination (Yes in step ST14), this processing flow ends. If the moving body 100 has not arrived at the destination (No in step ST14), the process proceeds again to step ST10. Then, the above-mentioned estimation of the moving state of the moving body 100, estimation of the collision risk with an obstacle, output of the judgment information D9, and generation of the moving route of the moving body 100 are each executed.

[0082] <Advantages of the First Embodiment> As described above, according to the path generation device of this embodiment, the collision risk determination unit 13 estimates the collision risk between the moving body 100 and an obstacle and determines how the moving body 100 avoids the obstacle by making a comprehensive determination using information indicating the possibility of collision with the obstacle or another moving body (i.e., incident information D5 and risk map information D6) without relying only on information acquired from the various sensors 11 (i.e., obstacle information D2), and outputs determination information D9. Then, the path generation unit 16 generates a travel path based on the determination information D9. As a result, the moving body 100 can quickly determine the possibility of a collision with an obstacle and move along a moving path that allows the moving body 100 to perform appropriate avoidance operations. Therefore, the moving body 100 can avoid obstacles in a natural way.

[0083] In the above-described embodiment, an autonomous vehicle has been described as an example of a moving body, but the moving body is not limited to an autonomous vehicle. For example, the moving body may be a train, a ship, an airplane, etc. For example, when the moving body is a train, the route generation device according to the present disclosure can generate a moving path by regarding the track as a moving path. Also, for example, when the moving body is a ship, the route generation device according to the present disclosure can generate a moving path by regarding a waterway or a sea route as a moving path. [Industrial Applicability]

[0084] The route generation device according to the present disclosure is applicable to various moving bodies, such as automobiles, personal mobility, railways, ships, aircraft, drones, etc. In particular, the route generation device according to the present disclosure is suitable for use in autonomously moving self-driving vehicles. [Explanation of symbols]

[0085] 10 Route generation device, 11 Various sensors, 12 Travel state estimation unit, 13 Collision risk judgment unit, 14 Estimation unit, 15 Judgment unit, 16 Route generation unit, 17 Map information storage unit, 18 Incident information storage unit, 19 Risk map information storage unit, 20 Travel route information storage unit, 21 Control unit, 31 processor, 32 memory, 33 storage device, 34 interface, 35 communication unit, 100 mobile units.

Claims

1. a movement state estimation unit that estimates a movement state of a moving object using information acquired from the sensor; a collision risk determination unit that outputs determination information that is information indicating a determination result for the moving body to avoid the obstacle, based on the moving state and collision risk information that is information indicating a possibility of a collision between the moving body and the obstacle; a route generating unit that generates a travel route of the moving body based on the determination information so that an avoidance acceleration, which is an acceleration generated when the moving body avoids the obstacle, does not exceed a certain value; A route generating device comprising:

2. The certain value is a value obtained by subtracting a margin of 0.05 G from the acceleration at which a passenger on the moving body feels discomfort. The route generating device according to claim 1 .

3. the collision risk determination unit determines a preparatory movement or an avoidance movement for the moving body to avoid the obstacle, based on the moving state and the collision risk information; The path generation unit generates a movement path based on the preparatory movement or the avoidance movement. The route generating device according to claim 1 .

4. The collision risk information includes at least one of incident information indicating information on a situation in which a past accident or an event that almost caused an accident occurred, and risk map information indicating information on a potential danger level around the moving body.

4. The route generating device according to claim 1 or 3.

5. A moving state estimating unit estimates a moving state of the moving object using information acquired from the sensor, a collision risk determination unit outputs determination information, which is information indicating a determination result for the moving body to avoid the obstacle, based on the moving state and collision risk information, which is information indicating a possibility of a collision between the moving body and the obstacle; A path generating unit generates a moving path of the moving body based on the determination information so that an avoidance acceleration, which is an acceleration generated when the moving body avoids the obstacle, does not exceed a certain value. Route generation method.

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