Automatic driving test method and program

Abstract

An automatic driving travel test method is characterized in that a plurality of obstacles are set on a test route, information of the obstacles is acquired, and an automatic driving travel test is performed using the plurality of acquired information and artificial intelligence (AI).

Description

Technical Field

[0001] The present disclosure relates to an automatic driving driving test method and program. Background Art

[0002] Patent Document 1 describes a vehicle having an automatic driving function.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2022-035198 Summary of the invention

[0006] Means of solving problems

[0007] According to one embodiment of the present disclosure, an autonomous driving driving test method is provided. The autonomous driving driving test method is characterized in that a plurality of obstacles are set on a test route, an information acquisition unit acquires information of the obstacles, and the autonomous driving driving test is performed using the plurality of information acquired by the information acquisition unit and artificial intelligence (AI).

[0008] According to one embodiment of the present disclosure, an autonomous driving driving test method is provided. The autonomous driving driving test method is characterized in that behavior information representing the behavior of an autonomous driving vehicle is acquired, and based on data of a virtual space corresponding to a test route provided with a plurality of obstacles and the behavior information, a driving test is performed to cause the autonomous driving vehicle to drive on the test route in the virtual space.

[0009] According to one embodiment of the present disclosure, there is provided a program for causing a computer to execute the automatic driving driving test method.

[0010] It should be noted that the above disclosed invention content does not list all the necessary features of the present disclosure. In addition, sub-combinations of these feature groups can also constitute the disclosed invention content. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 An example of a test route according to the present embodiment is schematically shown.

[0012] Figure 2 An example of the functional structure of the automatic driving vehicle 10 is schematically shown.

[0013] Figure 3 An example of a processing routine executed by the information processing apparatus is schematically shown.

[0014] Figure 4This is a diagram for explaining control used on a test course for driving tests for autonomous driving.

[0015] Figure 5 This is a diagram for explaining control used on a test course for driving tests for autonomous driving.

[0016] Figure 6 This is a diagram for explaining control used on a test course for driving tests for autonomous driving.

[0017] Figure 7 An example of the hardware configuration of a computer 1200 functioning as an information processing device is schematically shown.

[0018] Figure 8 Another example of a processing routine executed by the information processing apparatus is schematically shown.

[0019] Fig. 9 This is a block diagram schematically showing an example of a server according to a modification.

[0020] Fig.10 This is a flowchart schematically showing an example of an automatic driving test method according to a modification. DETAILED DESCRIPTION

[0021] Hereinafter, the present disclosure will be described by way of the disclosed embodiments, but the following embodiments do not limit the disclosure involved in the claims. In addition, not all combinations of features described in the embodiments are essential to the disclosed solution means.

[0022] Existing driving test routes are not designed for autonomous driving and are insufficient as test driving routes for autonomous vehicles.

[0023] Therefore, in this embodiment, if Figure 1 As shown, various obstacles X are set on the test route R, and the autonomous driving vehicle 10 for test driving travels on the test route R. This makes it possible to test whether the autonomous driving system can be designed without problems, and to perform more accurate autonomous driving vehicle testing.

[0024] It should be noted that obstacles set on the test route R may include rain, snow, typhoons, smoke, fog, truck tires, drones, footballs, baseballs, golf balls, balloons, red lights, robots, two-wheeled vehicles, oncoming vehicles, glaring lights, situations where flashlights are used instead of headlights in the dark, traffic cones (registered trademark) or signs, ambulances, fire trucks, bicycles, old robots, narrow roads, dog-shaped robots, mountain roads, beaches, muddy roads, uneven roads, roadblocks caused by fallen trees, traps, puddles, sharp objects, or tracks that can barely imitate the travel of a vehicle line (it should be noted that when the vehicle line is set as an obstacle, the autonomous driving vehicle 10 travels on the track. The width of a track is smaller than the width of the vehicle tire). These multiple obstacles are configured on the test route R.

[0025] Figure 2 1 is a schematic diagram of an example of an autonomous driving vehicle 10 according to the present embodiment. The autonomous driving vehicle 10 includes a sensor 12 and an information processing device 14 mounted on the autonomous driving vehicle 10 .

[0026] The sensor 12 sequentially acquires obstacle information representing obstacles around the autonomous driving vehicle 10. As the sensor 12, the highest performance camera, solid-state LiDAR, multi-color laser coaxial displacement meter or other various sensor groups can be used. In addition, as the sensor 12, there can also be cited a vibrometer, a thermal imager, a hardness tester, a radar, a LiDAR, a high-pixel / telescopic / ultra-wide-angle / 360-degree / high-performance camera, visual recognition, micro-sound, ultrasonic wave, vibration, infrared, ultraviolet, electromagnetic wave, temperature, humidity, point-type AI weather forecast, high-precision multi-channel GPS, low-altitude satellite information or long-tail event AI data (AI data), etc. The long-tail event AI data can be the trip data of a level 5 autonomous driving vehicle installed.

[0027] It should be noted that, in addition to detecting the above obstacle information, the sensor 12 also detects images, distance, vibration, heat, smell, color, sound, ultrasonic wave, ultraviolet or infrared rays, etc. In addition, the information detected by the sensor 12 includes the movement of the center of gravity, the detection of the road material, the detection of the outside air temperature, the detection of the outside air humidity, the detection of the inclination angle of the up and down lateral inclination of the ramp, the freezing mode of the road, the detection of the amount of water, the material of each tire, the wear condition, the detection of the air pressure, the width of the road, the presence or absence of overtaking prohibition, the vehicle type information of the oncoming vehicle, the front and rear vehicles, the cruising state of these vehicles, the surrounding conditions (birds, animals, football, accident vehicles, earthquakes, fires, wind, typhoons, heavy rain, light rain, blizzards, fog, etc.), etc.

[0028] The sensor 12 performs these detections every nanosecond.

[0029] The information processing device 14 includes an information acquisition unit 140 , a control unit 142 , and an information storage unit 144 as a functional structure.

[0030] The information acquisition unit 140 acquires information about the obstacle X detected by the sensor 12. For example, the information acquisition unit 140 may acquire position information of each of the plurality of obstacles X disposed on the test route R. In this case, a communication device and a GPS device may be mounted on each obstacle X, and the position information of the obstacle X located by the GPS device may be sent to the information processing device 14 via the communication device. If the obstacle X is a mobile object, the time series data of the position information of the obstacle X may be acquired by sending the position information of the obstacle X to the information processing device 14.

[0031] The control unit 142 performs a driving test of the autonomous driving using the plurality of information acquired by the information acquisition unit 140 and AI (Artificial Intelligence).

[0032] For example, the control unit 142 executes the following processes.

[0033] (1) 3D mapping of the entire test route

[0034] (2) Develop a strategy to reach the test route as quickly as possible

[0035] (3) Plan the vehicle's forward speed and left and right steering angles in nanoseconds

[0036] (4) The number of spins and the left and right steering axes are transmitted to the four wheel motors in nanoseconds.

[0037] (5) Correction is performed when there is a deviation due to obstacles or tire slip coefficient, etc.

[0038] For example, the control unit 142 controls the autonomous driving vehicle 10 to rotate left or right in nanoseconds, calculates the direction and optimal speed that should be advanced every nanosecond, and instructs the steering axis of the autonomous driving vehicle 10. It should be noted that such vehicle control is called perfect steering. It should be noted that if the autonomous driving vehicle 10 is a vehicle equipped with wire-controlled steering technology, since there is no steering axis, an electrical signal will be directly sent to the control unit that controls the tire angle.

[0039] The control unit 142 stores information indicating the behavior of the autonomous driving vehicle 10 when the autonomous driving driving test is performed in the information storage unit 144. As a result, the information when the autonomous driving vehicle 10 drives on the test route R is stored in the information storage unit 144.

[0040] The information processing device 14 repeatedly executes Figure 3 Flowchart shown.

[0041] In step S100 , the information acquisition unit 140 acquires information on an obstacle detected by the sensor 12 .

[0042] In step S102 , the control unit 142 performs an autonomous driving driving test by controlling the autonomous driving vehicle 10 using the plurality of information acquired in step S100 and AI.

[0043] In step S104 , the control unit 142 stores information indicating the behavior of the automatically driven vehicle 10 in the information storage unit 144 .

[0044] According to this embodiment, a test route for autonomous driving is designed with various obstacles set. In addition, in this embodiment, when the autonomous driving vehicle is driving on the test route, the left and right rotations based on perfect steering are controlled in nanoseconds, and the direction and optimal speed that should be advanced per nanosecond are indicated to the steering axis. By driving the autonomous driving vehicle on such a test route, the performance of autonomous driving can be verified. In addition, when the autonomous driving vehicle is driven on the test route, the control based on perfect steering in nanoseconds can achieve safer autonomous driving.

[0045] It should be noted that the existing test routes are designed based on human driving, not for AI-based autonomous driving. AI is also based on driving on real routes. In this regard, autonomous driving may be tripped by obstacles that humans can easily cross, and the existing test routes are not enough to verify that there are no problems with AI-based autonomous driving. In addition, the test routes for autonomous driving require more sophisticated AI-based control.

[0046] In view of such problems of the prior art, according to the present embodiment, by designing a test course equipped with various obstacles, the performance of autonomous driving can be verified with high accuracy.

[0047] As an example of a control method for an autonomous driving vehicle 10 based on AI, machine learning, more specifically deep learning, can be used to infer indexed values ​​related to the control of the autonomous driving vehicle 10 based on multiple information acquired by the information acquisition unit 140.

[0048] The control unit 142 can use the computing power of Level 6 to perform a multivariate analysis based on the integral method as shown in the following formula (1) on the data collected by multiple sensor groups in the information acquisition unit 140 for each nanosecond (for example, refer to formula (2)), thereby obtaining a correct index value. More specifically, while the integral value of various ultra-high resolution delta values ​​is obtained with the computing power of Level 6, the index value of each variable can be obtained in real time at the edge level to obtain the highest probability value of the result that will occur in the next nanosecond.

[0049] [Calculation formula 1]

[0050]

[0051] [Calculation formula 2]

[0052] V n =DL(f(A,B,C,D,…,N)(dA n / dt)) (2)

[0053] It should be noted that DL in the above formula (2) represents deep learning, and A, B, C, D, ..., N represent air resistance, road resistance, road elements (such as garbage) and sliding coefficient, etc.

[0054] The index values ​​of each variable can be further refined by increasing the number of deep learning. For example, a more accurate index value can be calculated using a large amount of data such as tires, motor rotation, steering angle, road material, weather, garbage, the impact of quadratic curve deceleration, slip, steering, and speed control methods.

[0055] The control unit 142 can perform the autonomous driving control of the autonomous driving vehicle 10 based on a plurality of specific index values. Specifically, the highest probability value of the result occurring in the next nanosecond can be obtained based on the plurality of index values, and the driving control of the vehicle can be performed taking into account the probability value.

[0056] According to the autonomous driving vehicle 10 having the control unit 142, since the Level 6 computing power, which is much greater than the Level 5 computing power, can be used to perform information analysis and reasoning, it is possible to perform sophisticated analysis at a level that cannot be matched in the past. As a result, vehicle control for safe autonomous driving can be performed.

[0057] Figures 4 to 6 This is a schematic diagram of the above content. It should be noted that Figure 4"Level 6" described in the specification refers to a level indicating autonomous driving, which is equivalent to a higher level than Level 5 indicating fully autonomous driving. Although Level 5 indicates fully autonomous driving, it is the same level as human driving, and there is still a probability of accidents. Level 6 indicates a level higher than Level 5, which is equivalent to a level where the probability of accidents is lower than Level 5. In the present embodiment, Level 6 is achieved by control at the nanosecond level.

[0058] Figure 7 An example of the hardware structure of a computer 1200 that functions as the information processing device 14 is schematically shown. The program installed in the computer 1200 can make the computer 1200 function as one or more "parts" of the device involved in this embodiment, or enable the computer 1200 to perform operations associated with the device involved in this embodiment or the one or more "parts", and / or enable the computer 1200 to perform the process involved in this embodiment or the stage of the process. Such a program can be executed by the CPU 1212 to make the computer 1200 perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described in this specification.

[0059] The computer 1200 according to the present embodiment includes a CPU 1212, a RAM 1214, and a graphic controller 1216, which are connected to each other through a host controller 1210. The computer 1200 also includes a communication interface 1222, a storage device 1224, an input / output unit such as a DVD drive and an IC card drive, which are connected to the host controller 1210 through an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid state drive, etc. The computer 1200 also includes a ROM 1230 and a conventional input / output unit such as a keyboard, which are connected to the input / output controller 1220 through an input / output chip 1240.

[0060] The CPU 1212 operates according to the program stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphic controller 1216 acquires image data generated by the CPU 1212 in a frame buffer provided in the RAM 1214 or itself, and displays the image data on the display device 1218.

[0061] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0062] The ROM 1230 stores therein a boot program or the like executed by the computer 1200 when activated, and / or a program that depends on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 through a USB port, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0063] The program is provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The program is read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which can also be an example of a computer-readable storage medium, and executed by the CPU 1212. The information processing described in these programs is read by the computer 1200, and the program is caused to cooperate with the various types of hardware resources described above. The device or method can be configured by implementing the operation or processing of information according to the use of the computer 1200.

[0064] For example, in the case where communication is performed between the computer 1200 and an external device, the CPU 1212 can execute a communication program loaded in the RAM 1214, and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. The communication interface 1222 reads transmission data under the control of the CPU 1212, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer area provided on a recording medium, etc., the transmission data being stored in a transmission buffer area provided in a recording medium such as the RAM 1214, the storage device 1224, a DVD-ROM, or an IC card.

[0065] In addition, the CPU 1212 can cause all or a necessary part of a file or database stored in an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read to the RAM 1214, and perform various types of processing on the data on the RAM 1214. Then, the CPU 1212 can write the processed data back to the external recording medium.

[0066] Various types of information such as various types of programs, data, tables, and databases can be stored in the recording medium and subjected to information processing. The CPU 1212 can perform various types of processing on the data read from the RAM 1214, and the various types of processing include various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information retrieval / replacement, etc., which are specified by the command sequence of the program and recorded in various places of this disclosure, and write the results back to the RAM 1214. In addition, the CPU 1212 can also retrieve information in files, databases, etc. in the recording medium. For example, in the case where a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 can retrieve an entry that matches the condition specifying the attribute value of the first attribute from the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that meets the predetermined condition.

[0067] The above-mentioned program or software module may be stored in a computer-readable storage medium on or near the computer 1200. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet may be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0068] The boxes in the flowcharts and block diagrams in this embodiment may represent the stages of the process for performing the operation or the "parts" of the device having the function of performing the operation. The specific stages and "parts" may be implemented by a dedicated circuit, a programmable circuit provided together with a computer-readable instruction stored on a computer-readable storage medium, and / or a processor provided together with a computer-readable instruction stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may also include an integrated circuit (IC) and / or a discrete circuit. The programmable circuit may include a reconfigurable hardware circuit such as a field programmable gate array (FPGA) and a programmable logic array (PLA), and the reconfigurable hardware circuit includes logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, triggers, registers, and storage elements.

[0069] Computer-readable storage media may include any tangible device capable of storing instructions executed by an appropriate device, with the result that a computer-readable storage medium having instructions stored in a tangible device has a product including instructions that can be executed to create a unit for performing the operations specified in the flowchart or block diagram. Examples of computer-readable storage media include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media include floppy disks (registered trademark) disks, magnetic disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), electrically erasable programmable read-only memories (EEPROM), static random access memories (SRAM), compact disc read-only memories (CD-ROM), digital versatile discs (DVD), Blu-ray discs (BLUERAY (registered trademark) disks), memory sticks, integrated circuit cards, etc.

[0070] Computer readable instructions may include assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or any of source code or object code described in any combination of one or more programming languages, wherein the one or more programming languages ​​include object-oriented programming languages ​​such as Smalltalk (registered trademark), JAVA (registered trademark), C++, etc., and traditional procedural programming languages ​​such as the "C" programming language or similar programming languages.

[0071] The computer-readable instructions may be provided to a processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device locally or through a wide area network (WAN) such as a local area network (LAN), the Internet, etc., so that the processor or programmable circuit of the general-purpose computer, the special-purpose computer, or other programmable data processing device executes the computer-readable instructions, thereby generating a unit for performing the specified operation in the flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0072] It should be noted that in the above embodiment, as Figure 3 , which illustrates an example of a processing routine executed by the information processing device 14, but is not limited thereto. Figure 8 The processing routine shown.

[0073] Figure 8 1 is a flowchart schematically showing another example of a processing routine executed by the information processing device 14. Figure 8 As shown, in step S200, the information processing device 14 acquires information of the obstacle X detected by the sensor 12. The information of the obstacle X includes position information of the obstacle X at each moment, and ID information indicating the type of the obstacle X and the identification number.

[0074] For example, the information processing device 14 can obtain the information of the obstacle X stored in the information storage unit 144 through the function of the information acquisition unit 140. In addition, when the information of the obstacle X is stored in a server or the like provided outside the autonomous driving vehicle 10, the information processing device 14 can also obtain the information of the obstacle X from the external server through the function of the information acquisition unit 140.

[0075] In step S202, the information processing device 14 controls the autonomous driving vehicle 10. Specifically, the information processing device 14 controls the autonomous driving vehicle 10 using the plurality of information and AI acquired in step S200 through the function of the control unit 142, thereby performing an autonomous driving driving test.

[0076] In step S204, the information processing device 14 stores the information in the information storage unit 144. Specifically, the information processing device 14 stores the position information of the obstacle X and the information indicating the behavior of the autonomous driving vehicle 10 in the information storage unit 144 in an associated state through the function of the control unit 142. The position information of the obstacle X is the information acquired by the information acquisition unit 140 in step S200. The information indicating the behavior of the autonomous driving vehicle 10 is the information when the autonomous driving vehicle 10 is controlled by the control unit 142 in step S202.

[0077] As described above, in step S204, the position information of the obstacle X that changes at each moment is associated with the information indicating the behavior of the autonomous driving vehicle 10 approaching the obstacle X and is stored in the information storage unit 144. Therefore, by referring to the information stored in the information storage unit 144, the behavior of the autonomous driving vehicle 10 with respect to the moving obstacle X can be analyzed.

[0078] (Variation Example)

[0079] Fig. 9 A block diagram schematically shows an example of a server 20 according to a modification. Fig. 9 As shown, the server 20 of the present modification example includes route shape information 22 , obstacle information 24 , weather information 26 , and a driving test program 28 .

[0080] The route shape information 22 includes the shape and road surface information of the test route R. For example, the route shape information 22 may be information obtained by a vehicle equipped with a sensor capable of obtaining three-dimensional information while traveling on the test route R. In addition, the road surface information may include a friction coefficient calculated based on the material of the test route R, etc.

[0081] The obstacle information 24 is information about a plurality of obstacles X provided on the test course R. The obstacle information 24 includes information including the type, size, position, behavior, and the like of the obstacles X.

[0082] The weather information 26 is information on the weather on the test route R.

[0083] The driving test program 28 stores a program for causing the automated driving vehicle 10 to drive on a test course in a virtual space.

[0084] Fig.10 1 is a flowchart schematically showing an example of an automatic driving test method according to a modified example. For example, the information processing device 14 executes the driving test program 28 stored in the server 20 to perform the automatic driving test. Fig.10 Flowchart of the process.

[0085] In step S1200, the information processing device 140 generates a test route in the virtual space. Specifically, the test route is generated in the virtual space based on the route shape information 22, the obstacle information 24, and the weather information 26. Since the generated test route corresponds to the actual test route R, even if the autonomous driving vehicle 10 is not driven on the test route R, the behavior of the autonomous driving vehicle 10 can be confirmed by simulating the autonomous driving vehicle 10 driving on the test route in the virtual space.

[0086] The positions and behaviors of the respective obstacles can be changed by setting. In addition, any weather can be set according to the information stored in the weather information 26. By changing the weather information, the road surface conditions and the field of view in the test route in the virtual space will change.

[0087] In step S1202, the information processing device 140 acquires information indicating the behavior of the autonomous driving vehicle 10. When a driving test of the autonomous driving vehicle 10 is performed on the actual test route R, the information stored in the information storage unit 144 can be acquired. In addition, even when a driving test of the autonomous driving vehicle 10 is not performed on the actual test route R, information indicating the behavior of the autonomous driving vehicle 10 when it is designed can be acquired as information indicating the behavior by storing the information in a predetermined server or the like.

[0088] In step S1204, the information processing device 140 performs a simulation of driving the autonomous driving vehicle on a test course in the virtual space based on the virtual space data generated in step S1200 and the information acquired in step S1202. Thus, a driving test of the autonomous driving vehicle 10 is carried out.

[0089] In step S1206, information indicating the behavior of the automatic driving vehicle 10 during the driving test is stored in a predetermined storage unit. For example, this information may be stored in the information storage unit 144. The information stored in the information storage unit 144 may be stored as other information in each driving test. In addition, the information of the test route may be stored in association with the information indicating the behavior.

[0090] In this variation, by performing driving tests on a test route in a virtual space, even when the physical distance between the development site of the autonomous driving vehicle 10 and the test route R is far, driving tests can be performed without transporting the autonomous driving vehicle 10 to the test route R.

[0091] In addition, since the driving test is not performed using the actual autonomous driving vehicle 10, there is no need for fuel or electricity for driving the autonomous driving vehicle 10. In addition, even in a state before the autonomous driving vehicle 10 is assembled, the driving test can be performed by setting information indicating the behavior, which can shorten the development period.

[0092] The present disclosure is described above using the embodiments, but the technical scope of the present disclosure is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. It can be clearly seen from the description of the claims that the manner in which such changes or improvements are made can also be included in the technical scope of the present disclosure.

[0093] It should be noted that as long as the execution order of each process such as actions, sequences, steps and stages in the device, system, program and method shown in the claims, specifications and drawings is not specifically indicated as "before...", "in advance", etc., and the output of the previous process is not used in the subsequent process, it can be implemented in any order. Even if the action flow in the claims, specifications and drawings is described using "first", "next", etc. for convenience, it does not mean that it must be implemented in this order.

[0094] The entire contents of Japanese Patent Application No. 2022-164371 filed on October 12, 2022, Japanese Patent Application No. 2022-176624 filed on November 2, 2022, and Japanese Patent Application No. 2022-176625 filed on November 2, 2022 are incorporated into this application by reference in their entirety.

[0095] Description of Reference Numerals

[0096] 10 autonomous driving vehicle, 12 sensor, 14 information processing device, 1200 computer, 1210 host controller, 1212 CPU, 1214 RAM, 1216 graphics controller, 1218 display device, 1220 input / output controller, 1222 communication interface, 1224 storage device, 1230 ROM, 1240 input / output chip

Claims

1. An automatic driving driving test method, It is characterized in that Set up multiple obstacles on the test route. an information acquisition unit for acquiring information of the obstacle, Using the multiple information acquired by the information acquisition unit and artificial intelligence AI, a driving test of autonomous driving of the autonomous driving vehicle is carried out.

2. The automatic driving driving test method according to claim 1, in, Information representing the behavior of the autonomous driving vehicle when the autonomous driving vehicle is driven on a test route in autonomous driving is obtained.

3. The automatic driving driving test method according to claim 2, in, The information acquisition unit acquires information including the position information of the obstacle, The position information of the obstacle is stored in association with information representing the behavior of the autonomous driving vehicle.

4. A procedure, in, The program is used to cause a computer to execute the following processing: Obtain information about multiple obstacles set up on the test route; as well as Using the acquired information and artificial intelligence AI, a driving test of autonomous driving of the vehicle is carried out.

5. A procedure, in, The program is used to cause a computer to execute the automatic driving driving test method according to claim 1.

6. An automatic driving driving test method, It is characterized in that obtaining information representing the behavior of the autonomous vehicle, Based on the data of a virtual space corresponding to a test course provided with a plurality of obstacles and the information, a driving test is performed in which the autonomous driving vehicle is driven on the test course in the virtual space.

7. The automatic driving driving test method according to claim 6, in, Information representing the behavior of the autonomous driving vehicle during the driving test is stored.

8. The automatic driving driving test method according to claim 7, in, The obstacle is stored in association with information representing the behavior of the autonomous vehicle.

9. A procedure, in, The program is used to cause a computer to execute the following processing: obtaining information indicative of behavior of the autonomous vehicle; and Based on the data of a virtual space corresponding to a test course provided with a plurality of obstacles and the information, a driving test is performed in which the autonomous driving vehicle is driven on the test course in the virtual space.

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