WO2022024569A1

WO2022024569A1 - Information processing device, information processing method, and program

Info

Publication number: WO2022024569A1
Application number: PCT/JP2021/022629
Authority: WO
Inventors: 陸也江副; 達也石川; 諒渡辺
Original assignee: ソニーグループ株式会社
Priority date: 2020-07-28
Filing date: 2021-06-15
Publication date: 2022-02-03
Also published as: JP2022024493A

Abstract

A map information processing unit 32 corrects, on the basis of map information in which gradient information is set and traveling information acquired by a mobile device, the gradient information set in the map information. The traveling information includes sensor information acquired by a sensor provided in the mobile device 20, and the gradient information is calculated on the basis of the sensor information. The map information processing unit 32 corrects, according to gradient detection information generated on the basis of the traveling information acquired by the mobile device 20 and gradient information after filtering processing using gradient detection information already generated at the same movement position, the gradient information corresponding to the movement position set in the map information. If the gradient information is not set in the map information, the map information processing unit 32 sets, in the map information, the gradient detection information generated on the basis of the traveling information acquired by the mobile device. The map information processing unit 32 can generate the map information in which correct gradient information is set.

Description

Information processing equipment and information processing methods and programs

This technology makes it possible to obtain map information with correct gradient information set for information processing equipment, information processing methods and programs.

Conventionally, in order to move a mobile device autonomously, a sensor such as LiDAR (Light Detection and Ringing, Laser Imaging Detection and Ringing) is used to measure the surrounding environment and generate map information. Further, in Patent Document 1, the cleaning robot is controlled so as not to enter the non-travelable gradient area by using the map information in which the gradient information is set.

Japanese Unexamined Patent Publication No. 2019-109845

By the way, the map information used in Patent Document 1 is the map information in the home associated with the gradient information indicating the gradient and the like capable of cleaning operation, and shows only a limited narrow area, and indicates the outdoors and the like. It is not a large area map information. Further, the easily available map information indicating a wide area generally indicates the position in a two-dimensional plane, and the gradient information is not provided.

Therefore, the purpose of this technique is to provide an information processing device, an information processing method, and a program capable of obtaining map information in which correct gradient information is set.

The first aspect of this technology is
The information processing device includes a map information processing unit that corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.

In this technology, the map information processing unit corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device. Further, when the slope information is not set in the map information, the map information processing unit sets the slope information based on the map information in which the slope information is not set and the traveling information acquired by the moving device. To generate. The traveling information includes the sensor information acquired by the sensor provided in the moving device, and the gradient information is calculated based on the sensor information. The sensor information is information that detects at least one of translational motion and rotational motion of the moving device, and is, for example, translation by an acceleration sensor using an inertial measurement unit including at least one of an acceleration sensor and an angular velocity sensor. Rotational motion is detected by motion and angular velocity sensors, respectively.

The map information processing unit uses the gradient detection information generated based on the travel information acquired by the moving device to correct the gradient information of the position of the map information corresponding to the moving position that generated the gradient detection information. For example, the map information processing unit uses the gradient detection information generated based on the travel information acquired by the moving device and the gradient information after filtering using the gradient detection information already generated at the same moving position. Correct the gradient information of the position of the map information corresponding to the movement position.

The route planning department creates a route plan to the destination based on the map information generated or modified by the map information processing department. In addition, the route planning unit creates a route plan using the gradient information set in the map information. The mobile device includes a self-position estimation unit that estimates the self-position using the gradient information set in the map information.

The second aspect of this technology is
The information processing method includes modifying the gradient information set in the map information by the map information processing unit based on the map information in which the gradient information is set and the traveling information acquired by the moving device.

The third aspect of this technology is a program that causes a computer to execute processing using information acquired by a mobile device.
There is a program for causing the computer to execute a procedure for correcting the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.

The program of the present technology is, for example, a storage medium, a communication medium, for example, a storage medium such as an optical disk, a magnetic disk, or a semiconductor memory, which is provided in a computer-readable format to a general-purpose computer capable of executing various program codes. It is a program that can be provided by a medium or a communication medium such as a network. By providing such a program in a computer-readable format, processing according to the program can be realized on the computer.

It is a figure which illustrated the structure of the system which used the information processing apparatus. It is a sequence diagram which exemplifies the operation of a system. It is a figure for demonstrating the creation operation of a route search map information. It is a figure which showed the creation example of a route plan. It is a figure for demonstrating the correction self-position information. It is a figure which showed the configuration example of the vehicle control system. It is a figure which shows the example of the sensing area.

Hereinafter, a mode for implementing the present technology will be described. The explanation will be given in the following order.
1. 1. System configuration 2. System operation 3. Application example

<1. System configuration>
FIG. 1 illustrates a configuration of a system using the information processing apparatus of the present technology. The system 10 has a mobile device 20 and a server 30, and the server 30 is provided with the function of an information processing device. The mobile device 20 performs a movement operation based on the route plan supplied from the server 30. Further, the server 30 corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device 20. Further, when the map information in which the gradient information is set is not generated, the server 30 sets the gradient information based on the map information in which the gradient information is not set and the traveling information acquired by the moving device 20. Generate map information. Further, the server 30 creates a route plan by using map information (hereinafter referred to as “route search map”) in which road surface gradient information (also referred to as road surface gradient information) is set.

The moving device 20 has a wheel odometry 21, a gradient detection unit 22, a self-position estimation unit 23, a communication unit 24, a path tracking unit 25, a drive control unit 26, and a drive unit 27.

The wheel odometry 21 measures the amount of rotation of the wheels (rotational speed and angle of rotation) and calculates the amount of movement of the moving device 20. The wheel odometry 21 outputs the movement amount information indicating the calculated movement amount to the self-position estimation unit 23 and the communication unit 24. The amount of movement is not limited to the amount of rotation of the wheels, but the amount of rotation of the drive gear or the like that rotates the wheels may be used.

The gradient detection unit 22 detects the gradient of the road surface. The gradient detection unit 22 is configured by using, for example, an inertial measurement unit (IMU), and detects the gradient of the road surface based on the sensor information generated by the IMU. The sensor information is information that detects at least one of the translational motion or the rotational motion of the moving device, and the IMU includes at least an acceleration sensor and an angular velocity sensor, detects the translational motion by the acceleration sensor, and detects the translational motion by the angular velocity sensor. Detects rotational motion. The gradient detection unit 22 generates gradient detection information indicating the gradient of the road surface on which the moving device 20 is traveling from at least one of the detected translational motion or rotational motion. For example, the gradient detection unit 22 generates gradient detection information indicating a gradient calculated by using the methods described in Patent Documents "Japanese Patent Laid-Open No. 2003-09945", "Japanese Patent Laid-Open No. 2013-044562" and the like. Output to the communication unit 24.

The self-position estimation unit 23 estimates the self-position based on the movement amount information calculated by the wheel odometry 21. Further, the self-position estimation unit 23 corrects the estimated self-position based on the road surface gradient information from the server 30 received by the communication unit 24. The self-position estimation unit 23 outputs the result of self-position estimation using the gradient information, that is, the corrected self-position information indicating the corrected self-position based on the road surface gradient information to the communication unit 24 and the route tracking unit 25. Further, the self-position estimation unit 23 may request or update the road surface gradient information (or the route search map information) from the server 30 when the road surface gradient information is not obtained.

The communication unit 24 performs wireless communication with the server 30. The wireless communication may include cellular communication using any of LTE, WCDMA (registered trademark), 5G and the like, and short-range wireless communication using any of Wi-Fi, Bluetooth (registered trademark) and the like. It may be included. The communication unit 24 wirelessly communicates with the server 30, for example, the movement amount information generated by the wheel odometry 21, the gradient detection information generated by the gradient detection unit 22, and the corrected self-position information generated by the self-position estimation unit 23. To the server 30. Further, the communication unit 24 receives the road surface gradient information (or the route search map information) and the route plan transmitted from the server 30, and uses the road surface gradient information as the self-position estimation unit 23, the route plan or the route plan and the route search map. Information is output to the path tracking unit 25, respectively.

The route following unit 25 causes the moving device 20 to follow the route indicated by the route plan supplied from the server 30. The route following unit 25 recognizes the surrounding environment and searches for a trajectory to the destination based on the route plan acquired from the server 30 and the corrected self-position information generated by the self-position estimation unit 23, and collides with an obstacle or the like. The trajectory of the moving device 20 is planned so that the moving operation can be performed while avoiding the above. The path following unit 25 outputs the planned trajectory to the drive control unit 26. The surrounding environment may be recognized by using the route search map information acquired from the server 30, and an external sensor (for example, a range sensor (for example, LiDAR (Light Detection and Ringing, Laser Imaging Detection and Ringing)) provided in the mobile device 20 may be used. ), ToF (Time of Flight), stereo camera, etc.) may be used.

The drive control unit 26 generates a drive signal so as to move in the trajectory planned by the path follower unit 25 and outputs the drive signal to the drive unit 27.

The drive unit 27 is configured by using wheels, a drive source, and the like, and drives the wheels with the drive source based on the drive signal from the drive control unit 26 to move the moving device 20.

The server 30 has a communication unit 31, a map information processing unit 32, and a route planning unit 33.

The communication unit 31 is configured to be able to perform wireless communication with the communication unit 24 of the mobile device 20. The wireless communication may include cellular communication as described above, or may include short-range wireless communication. The communication unit 31 wirelessly communicates with the mobile device 20 and receives information generated by the mobile device 20, such as movement amount information and gradient detection information. The communication unit 31 outputs the received movement amount information, the corrected self-position information, and the gradient detection information to the map information processing unit 32. Further, the communication unit 31 transmits the route plan or the like created by the route planning unit 33 to the mobile device 20.

The map information processing unit 32 generates route search map information or route search map information in which road surface gradient information is set, based on the map information acquired in advance and the gradient detection information generated by the moving device 20. Correct the road surface slope information. The map information processing unit 32 has an information storage unit 321 and a road surface gradient information generation unit 322.

The information storage unit 321 stores map information of a two-dimensional plane that is easily incident, for example, acquired from an external device or the like. Further, the information storage unit 321 stores the generated route search map information when the route search map information in which the road surface gradient information is set is generated in the map information of the two-dimensional plane. Further, the information storage unit 321 stores the gradient detection information generated by another device different from the mobile device 20 and the gradient detection information generated in the past by the mobile device 20 together with the position information indicating the gradient detection position. ..

The road surface gradient information generation unit 322 corrects the road surface gradient information set in the route search map information by using the gradient detection information generated by the moving device 20. Further, when the route search map information is not generated and the gradient detection information is not stored in the information storage unit 321, the road surface gradient information generation unit 322 uses the gradient detection information generated by the moving device 20 as the road surface gradient information. The route search map information is generated by setting the map information as.

When generating the road surface gradient information, the road surface gradient information generation unit 322 averages the gradient detection information for a predetermined traveling period, for example, in order to reduce the influence of vibration of the moving device 20 and noise in the gradient detection unit 22. .. In this case, the road surface gradient information becomes information in which the resolution in the traveling direction is lowered. Therefore, the road surface gradient information generation unit 322 performs filter processing or statistical processing using the gradient detection information generated by the moving device 20 and the gradient detection information of the same moving position that has already been generated and stored in the information storage unit 321. (Hereinafter, collectively referred to as "filter processing") is performed to generate road surface gradient information that is less affected by vibration of the moving device 20 and noise in the gradient detecting unit 22 without causing a decrease in resolution in the traveling direction. The moving position is set with the self-position estimation unit 23 of the moving device 20 by using the moving amount information acquired from the moving device 20 and the gradient detection information stored in the moving position information storage unit 321 indicated by the moving amount information. The correction is performed in the same manner, and the filter processing is performed using the gradient detection information of the self-position after the correction. Further, the road surface gradient information generation unit 322 may use the slope detection information of the position indicated by the corrected self-position information generated by the self-position estimation unit 23 of the mobile device 20.

The road surface gradient information generation unit 322 may generate road surface gradient information by performing filter processing using a plurality of gradient detection information using, for example, a Kalman filter or a particle filter, and statistically performs a plurality of gradient detection information. , The mode value, the median value, or the average value may be calculated, and the calculation result may be used as the road surface gradient information. The road surface gradient information generation unit 322 sets the generated road surface gradient information in the map information acquired from an external device or the like, or sets the road surface gradient information in the route search map information by the road surface gradient information after the filtering process. Correct. Further, the road surface gradient information generation unit 322 updates the road search map information stored in the information storage unit 321 to the route search map information in which the road surface gradient information is corrected. Further, the road surface gradient information generation unit 322 outputs the road surface gradient information to the transmission source of the gradient detection information.

The route planning unit 33 starts from the corrected self-position indicated by the corrected self-position information generated by the moving device 20 based on the road surface gradient information indicated by the route search map information whose road surface gradient information is the latest information. Create a route plan to your destination. Further, the route planning unit 33 creates a route plan showing the optimum route by using the road surface gradient information set in the route search map information. The route planning unit 33 outputs the created route plan from the communication unit 31 to the mobile device 20.

<2. System operation>
FIG. 2 is a sequence diagram illustrating the operation of the system. In step ST1, the server 30 acquires the map information provided by the external device or the like and stores it in the information storage unit 321 of the map information processing unit 32.

In step ST2, the moving device 20 outputs the moving amount information indicating the moving amount calculated by the wheel odometry 21 to the server 30.

In step ST3, the server 30 outputs the gradient detection information to the road surface gradient information generation unit 322. The information storage unit 321 of the map information processing unit 32 extracts from the gradient detection information storing the gradient detection information at the position equal to the movement position based on the movement amount information supplied from the movement device 20, and generates the road surface gradient information. Output to unit 322. When the road surface gradient information is stored in the information storage unit 321, the self-position estimated in the same manner as the self-position estimation unit 23 of the mobile device 20 based on the movement amount information and the road surface gradient information supplied from the mobile device 20. Gradient detection information at a position equal to (corresponding to the self-position indicated by the corrected self-position information described later) is extracted and output to the road surface gradient information generation unit 322.

In step ST4, the mobile device 20 outputs the gradient detection information to the server 30. The moving device 20 outputs the gradient detection information generated by the gradient detection unit 22 to the server 30 at a position based on the movement amount information and the road surface gradient information. The gradient detection unit 22 of the moving device 20 generates gradient detection information according to the posture of the moving device based on, for example, the acceleration generated by the IMU, the angular acceleration, and the like. If the posture of the mobile device changes according to the vibration of the mobile device or the loading condition of the article, the gradient detection information causes an error with respect to the gradient of the road surface.

In step ST5, the server 30 outputs the road surface gradient information to the mobile device 20. The road surface gradient information generation unit 322 of the map information processing unit 32 performs filter processing using the gradient detection information supplied from the information storage unit 321 in step ST3 and the gradient detection information supplied from the moving device 20 in step ST4, and moves. Road surface gradient information indicating the slope of the road surface at the moving position of the device 20 is generated and output to the moving device 20. Further, the road surface gradient information generation unit 322 corrects the road surface gradient information of the route search map information stored in the information storage unit 321 by using the generated road surface gradient information. Further, when the route search map information is not stored in the information storage unit 321, the road surface gradient information generation unit 322 sets the road surface gradient information in the map information acquired from an external device or the like and stores the route search map information. It is stored in the unit 321.

FIG. 3 is a diagram for explaining the operation of creating route search map information. FIG. 3A exemplifies a state in which the moving device 20 is moving on a road surface having a gradient θ of “θa”, and FIG. 3B is a gradient of the moving device 20 at the moving position Pm. The gradient detection information generated by the detection unit 22 is illustrated. (C) of FIG. 3 exemplifies the gradient detection information already acquired at the same moving position by the moving device 20 or another moving device.

The road surface gradient information generation unit 322 performs a filter process from the gradient detection information shown in FIG. 3 (b) and the gradient detection information shown in FIG. 3 (c) using the gradient detection information of the same moving position Pm. As shown in FIG. 3D, road surface gradient information for each position is generated. Note that FIG. 3 (e) shows the distribution of the gradient shown in the gradient detection information at the moving position Pm.

Further, the road surface gradient information generation unit 322 adds new gradient detection information and performs filtering processing every time new gradient detection information is supplied, and corrects the road surface gradient information. The information storage unit 321 sets the road surface gradient information generated by the road surface gradient information generation unit 322 to the movement position Pm, and generates the route search map information.

In step ST6, the moving device 20 outputs the moving amount information to the self-position estimation unit 23. The wheel odometry 21 of the moving device 20 outputs the movement amount information indicating the calculated movement amount to the self-position estimation unit 23.

In step ST7, the mobile device 20 outputs the corrected self-position information to the server 30. The self-position estimation unit 23 calculates the amount of movement on the map based on the road surface gradient information supplied from the server 30 in step ST5 and the movement amount information supplied from the wheel odometry 21 in step ST6, and the influence of the road surface gradient. Calculate the self-position excluding. Further, the self-position estimation unit 23 outputs the corrected self-position information indicating the self-position excluding the influence of the slope of the road surface to the server 30.

In step ST8, the server 30 outputs the corrected self-position information and the route search map information to the route planning unit 33. The information storage unit 321 of the map information processing unit 32 is a map of a predetermined range including the destination based on the corrected self-position information supplied from the mobile device 20 and the position indicated by the corrected self-position information from the route search map information. The information is output to the route planning unit 33.

In step ST9, the server 30 outputs the route plan to the mobile device 20. The route planning unit 33 of the server 30 generates a route plan indicating a route from the current position of the mobile device indicated by the corrected self-position information to the destination. Further, the route planning unit 33 generates a route plan showing the optimum route by using not only the distance on the map from the current position to the destination but also the road surface gradient information included in the route search map information. For example, the route planning unit 33 uses a distance cost Ca set to increase as the distance increases and a gradient cost Cb set to increase as the gradient becomes steeper, and sets a cost value for each route. Calculate and select the route with the lowest cost value.

The route planning unit 33 determines the route to be traced to the destination by using the movable route based on the route search map information. Here, for example, when the route R-1 to the route R-n are determined, the route planning unit 33 calculates the cost values CR-1 to CR-n of each route based on the cost function f shown in the equation (1). do. In the equation (1), the parameter x is "x = 1 to n". The cost Ce indicates the cost of factors related to the road surface gradient (for example, ease of operation control and energy required for movement), unlike the distance and the road surface gradient, and the cost values CR-1 to CR- It does not have to be included in the calculation of n.
CR-x = f (Ca-x, Cb-x, Ce-x) ... (1)

The route planning unit 33 selects the minimum cost value from the cost values CR-1 to CR-n, and creates a route plan for moving to the destination according to the route corresponding to the selected cost value.

FIG. 4 shows an example of creating a route plan. FIG. 4A exemplifies, for example, a route R-a traveling over a hill and a route R-b avoiding a hill as a route from the movement start position Ps to the destination Pg. Further, FIG. 4B shows the relationship between the travel distance on the map from the travel start position Ps to the destination Pg when the route R-a is selected and the slope of the road surface. c) shows the relationship between the movement distance on the map from the movement start position Ps to the destination Pg when the route R-b is selected and the slope of the road surface.

When map information that does not include road surface gradient information is used, the server 30 creates a route plan that follows route R-a, which has a short distance to the destination. However, if the route search map information including the road surface gradient information is used as in the present technology, the server 30 may have an actual travel distance longer than the distance indicated by the map information not including the road surface gradient information. It is possible to determine what will occur and how much energy will be required to move to the destination.

Therefore, the server 30 calculates the cost value based on the equation (1), and when the cost value is smaller than the route R-a, the server 30 creates a route plan for the route R-b.

In this way, the route planning unit 33 uses the current position and destination of the moving device 20 and the road surface gradient information included in the route search map information to obtain an optimum route, for example, a route that can be easily moved with few ups and downs. A route plan indicating the above is generated and output to the mobile device 20.

In step ST10, the moving device 20 outputs the corrected self-position information to the path tracking unit 25. The self-position estimation unit 23 of the moving device 20 outputs the corrected self-position information excluding the influence of the slope of the road surface to the path tracking unit 25.

FIG. 5 is a diagram for explaining the corrected self-position information. When the road surface SF is an inclined surface having a gradient θa and the movement amount calculated by the wheel odometry 21 when the moving device 20 moves on the road surface SF is “ML”, as shown in FIG. 5 (a). The self-position estimated based on the movement amount "ML" is the position P1 on the map. However, the actual position of the mobile device 20 is the position P2 (= P1 × Cosθa). Therefore, the estimated self-position causes an error (P1-P2).

Since the server 30 using the present technology in the system 10 transmits the road surface gradient information to the mobile device 20, the gradient of the road surface on which the mobile device 20 moves becomes clear as shown in FIG. 5 (b). Note that FIG. 5C exemplifies the height difference and the road surface gradient information based on the position MPa of the traveling path (position MPa to position MPb) on the map.

Therefore, since the moving device 20 can correct the self-positioning error based on the movement amount information generated by the wheel odometry 21 and the road surface gradient information of the road surface on which the moving device 20 has moved, the position on the map of the moving device 20 can be corrected. It can be detected accurately.

In step ST11, the mobile device 20 outputs control information to the drive control unit 26. The route following unit 25 of the mobile device 20 moves the route indicated by the route plan based on the current position indicated by the corrected self-position information supplied from the self-position estimation unit 23 and the route plan supplied from the server 30. Control information for performing movement control so that 20 follows is generated and output to the drive control unit 26.

According to the present technology as described above, the server 30 can accurately calculate the slope of the road surface by using the gradient detection information generated by the mobile device, and can obtain the route search map information including the correct road surface gradient information. Can be easily generated. Further, the server 30 can create an optimum route plan in consideration of the slope of the road surface by using the route search map information including the road surface gradient information. Further, since the road surface gradient information is supplied to the mobile device 20, the mobile device 20 uses the road surface gradient information of the traveled road surface to accurately determine its own position as compared with the case where the road surface gradient information is not obtained. You will be able to detect it.

<3. Application example>
Next, an application example of this technology will be described. The application example illustrates a case where the mobile device 20 is a vehicle.

FIG. 6 is a diagram showing a configuration example of a vehicle control system 111, which is an example of a mobile device control system to which the present technology is applied.

The vehicle control system 111 is provided in the vehicle 100 and performs processing related to driving support and automatic driving of the vehicle 100.

The vehicle control system 111 includes a vehicle control ECU (Electronic Control Unit) 121, a communication unit 122, a map information storage unit 123, a position information receiving unit 124, an external recognition sensor 125, an in-vehicle sensor 126, a vehicle sensor 127, a recording unit 128, and a traveling unit. It includes a support / automatic driving control unit 129, a DMS (Driver Monitoring System) 130, an HMI (Human Machine Interface) 31, and a vehicle control unit 132.

Vehicle control ECU 121, communication unit 122, map information storage unit 123, position information receiving unit 124, external recognition sensor 125, in-vehicle sensor 126, vehicle sensor 127, recording unit 128, driving support / automatic driving control unit 129, driver monitoring system ( The DMS) 130, the human machine interface (HMI) 131, and the vehicle control unit 132 are connected to each other so as to be communicable with each other via the communication network 141. The communication network 141 is in-vehicle compliant with digital bidirectional communication standards such as CAN (Controller Area Network), LIN (Local Interconnect Network), LAN (Local Area Network), FlexRay (registered trademark), and Ethernet (registered trademark). It consists of a communication network and a bus. The communication network 141 may be used properly depending on the type of information to be communicated. For example, CAN is applied for information related to vehicle control, and Ethernet is applied for large-capacity information. It should be noted that each part of the vehicle control system 111 does not go through the communication network 141, but wireless communication assuming relatively short-distance communication such as short-range wireless communication (NFC (Near Field Communication)) and Bluetooth (registered trademark). In some cases, it is directly connected using.

Hereinafter, when each part of the vehicle control system 111 communicates via the communication network 141, the description of the communication network 141 shall be omitted. For example, when the vehicle control ECU 121 and the communication unit 122 communicate with each other via the communication network 141, it is described that the processor 121 and the communication unit 122 simply communicate with each other.

The vehicle control ECU 121 is composed of various processors such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit), for example. The vehicle control ECU 121 controls the functions of the entire vehicle control system 111 or a part of the vehicle control system 111.

The communication unit 122 communicates with various devices inside and outside the vehicle, other vehicles, servers, base stations, etc., and transmits and receives various information. At this time, the communication unit 122 can perform communication using a plurality of communication methods.

The communication with the outside of the vehicle, which can be executed by the communication unit 122, will be briefly described. The communication unit 122 is mounted on an external network via a base station or an access point by a wireless communication method such as 5G (5th generation mobile communication system), LTE (Long Term Evolution), DSRC (Dedicated Short Range Communications), etc. Communicates with a server (hereinafter referred to as an external server) that exists in. The external network with which the communication unit 122 communicates is, for example, the Internet, a cloud network, a network peculiar to a business operator, or the like. The communication method for communicating with the external network by the communication unit 122 is not particularly limited as long as it is a wireless communication method capable of digital bidirectional communication at a communication speed of a predetermined value or higher and a distance of a predetermined distance or more.

Further, for example, the communication unit 122 can communicate with a terminal existing in the vicinity of the own vehicle by using P2P (Peer To Peer) technology. Terminals that exist in the vicinity of the vehicle are, for example, terminals that are attached to mobile devices that move at relatively low speeds such as pedestrians and bicycles, terminals that are fixedly installed in stores, or MTC (Machine Type Communication). ) It is a terminal. Further, the communication unit 122 can also perform V2X communication. V2X communication is, for example, vehicle-to-vehicle (Vehicle to Vehicle) communication with other vehicles, road-to-vehicle (Vehicle to Infrastructure) communication with roadside devices, and home (Vehicle to Home) communication. , And communication between the vehicle and others, such as vehicle-to-vehicle (Vehicle to Pedestrian) communication with terminals owned by pedestrians.

The communication unit 122 can receive, for example, a program for updating the software that controls the operation of the vehicle control system 111 from the outside (Over The Air). The communication unit 122 can further receive map information, traffic information, information around the vehicle 100, and the like from the outside. Further, for example, the communication unit 122 can transmit information about the vehicle 100, information around the vehicle 100, and the like to the outside. Information about the vehicle 100 transmitted by the communication unit 122 to the outside includes, for example, information indicating the state of the vehicle 100, recognition result by the recognition unit 173, and the like. Further, for example, the communication unit 122 performs communication corresponding to a vehicle emergency call system such as eCall.

The communication unit 122 can roughly explain the communication with the inside of the vehicle. The communication unit 122 can communicate with each device in the vehicle by using, for example, wireless communication. The communication unit 122 performs wireless communication with a device in the vehicle by a communication method such as wireless LAN, Bluetooth, NFC, WUSB (WirelessUSB), which enables digital bidirectional communication at a communication speed higher than a predetermined value by wireless communication. Can be done. Not limited to this, the communication unit 122 can also communicate with each device in the vehicle by using wired communication. For example, the communication unit 122 can communicate with each device in the vehicle by wired communication via a cable connected to a connection terminal (not shown). The communication unit 122 uses wired communication such as USB (Universal Serial Bus), HDMI (registered trademark) (High-Definition Multimedia Interface), and MHL (Mobile High-definition Link) to perform digital bidirectional communication at a communication speed equal to or higher than a predetermined value. It is possible to communicate with each device in the car by the communication method capable of.

Here, the device in the vehicle refers to, for example, a device that is not connected to the communication network 141 in the vehicle. As the equipment in the vehicle, for example, mobile equipment and wearable equipment possessed by passengers such as drivers, information equipment brought into the vehicle and temporarily installed, and the like are assumed.

For example, the communication unit 122 receives an electromagnetic wave transmitted by a vehicle information and communication system (VICS (registered trademark) (Vehicle Information and Communication System)) such as a radio wave beacon, an optical beacon, and FM multiplex broadcasting.

The map information storage unit 123 stores one or both of the map acquired from the outside and the map created by the vehicle 100. For example, the map information storage unit 123 stores a three-dimensional high-precision map, a global map that is less accurate than the high-precision map and covers a wide area, and the like.

High-precision maps are, for example, dynamic maps, point cloud maps, vector maps, etc. The dynamic map is, for example, a map including four layers of dynamic information, quasi-dynamic information, quasi-static information, and static information, and is provided to the vehicle 100 from an external server or the like. The point cloud map is a map composed of point clouds (point cloud information). Here, the vector map refers to a map conforming to ADAS (Advanced Driver Assistance System) in which traffic information such as lanes and signal positions are associated with a point cloud map.

The point cloud map and the vector map may be provided, for example, from an external server or the like, or may be described later based on the sensing results of the radar 152, LiDAR (Light Detection and Ringing, Laser Imaging Detection and Ringing) 153, etc., which will be described later. It may be created by the vehicle 100 as a map for matching with the map and stored in the map information storage unit 123. Further, when a high-precision map is provided from an external server or the like, in order to reduce the communication capacity, for example, map information of several hundred meters square regarding the planned route on which the vehicle 100 will travel is acquired from the external server or the like. ..

The position information receiving unit 124 receives a GNSS signal from, for example, a GNSS (Global Navigation Satellite System) satellite, and acquires the position information of the vehicle 100. The received GNSS signal is supplied to the driving support / automatic driving control unit 129. The position information receiving unit 124 is not limited to the method using the GNSS signal, and may acquire the position information by using, for example, a beacon.

The external recognition sensor 125 includes various sensors used for recognizing the external situation of the vehicle 100, and supplies sensor information from each sensor to each part of the vehicle control system 111. An external recognition sensor, for example, an external recognition sensor 125, includes a camera 151, a radar 152, a LiDAR 153, and an ultrasonic sensor 154. Not limited to this, the external recognition sensor 125 may be configured to include one or more of the camera 151, the radar 152, the LiDAR 153, and the ultrasonic sensor 154. The number of cameras 151, radar 152, LiDAR 153, and ultrasonic sensor 154 is not particularly limited as long as it can be practically installed in the vehicle 100. Further, the type of sensor included in the external recognition sensor 125 is not limited to this example, and the external recognition sensor 125 may include other types of sensors. An example of the sensing area of each sensor included in the external recognition sensor 125 will be described later.

The shooting method of the camera 151 is not particularly limited as long as it is a shooting method capable of distance measurement. For example, as the camera 151, cameras of various shooting methods such as a ToF (Time of Flight) camera, a stereo camera, a monocular camera, and an infrared camera can be applied as needed. Not limited to this, the camera 151 may be simply for acquiring a captured image regardless of the distance measurement.

Further, for example, the external recognition sensor 125 can be provided with an environment sensor for detecting the environment for the vehicle 100. The environment sensor is a sensor for detecting the environment such as weather, weather, and brightness, and may include various sensors such as a raindrop sensor, a fog sensor, a sunshine sensor, a snow sensor, and an illuminance sensor.

Further, for example, the external recognition sensor 125 includes a microphone used for detecting the position of a sound or a sound source around the vehicle 100.

The in-vehicle sensor 126 includes various sensors for detecting in-vehicle information, and supplies sensor information from each sensor to each part of the vehicle control system 111. The type and number of various sensors included in the in-vehicle sensor 126 are not particularly limited as long as they can be practically installed in the vehicle 100.

For example, the in-vehicle sensor 126 can include one or more of a camera, a radar, a seating sensor, a steering wheel sensor, a microphone, and a biosensor. As the camera included in the in-vehicle sensor 126, for example, a camera of various shooting methods capable of measuring a distance, such as a ToF camera, a stereo camera, a monocular camera, and an infrared camera, can be used. Not limited to this, the camera included in the in-vehicle sensor 126 may be simply for acquiring a captured image regardless of the distance measurement. The biosensor included in the in-vehicle sensor 126 is provided on, for example, a seat, a steering wheel, or the like, and detects various biometric information of a occupant such as a driver.

The vehicle sensor 127 includes various sensors for detecting the state of the vehicle 100, and supplies sensor information from each sensor to each part of the vehicle control system 111. The types and numbers of various sensors included in the vehicle sensor 127 are not particularly limited as long as they can be practically installed in the vehicle 100.

For example, the vehicle sensor 127 includes a speed sensor, an acceleration sensor, an angular velocity sensor (gyro sensor), and an inertial measurement unit (IMU: Inertial Measurement Unit) that integrates them. For example, the vehicle sensor 127 includes a steering angle sensor for detecting the steering angle of the steering wheel, a yaw rate sensor, an accelerator sensor for detecting the operation amount of the accelerator pedal, and a brake sensor for detecting the operation amount of the brake pedal. For example, the vehicle sensor 127 detects a rotation speed of an engine or a motor, an air pressure sensor of detecting tire pressure, a slip ratio sensor of detecting a tire slip ratio, and a wheel rotation speed and rotation speed. It is equipped with a wheel sensor. For example, the vehicle sensor 127 includes a battery sensor that detects the remaining amount and temperature of the battery, and an impact sensor that detects an impact from the outside.

The recording unit 128 includes at least one of a non-volatile storage medium and a volatile storage medium, and stores information and programs. The recording unit 128 is used as, for example, an EEPROM (Electrically Erasable Programmable Read Only Memory) and a RAM (Random Access Memory), and the storage medium includes a magnetic storage device such as an HDD (Hard Disc Drive), a semiconductor storage device, and an optical storage device. And a photomagnetic storage device can be applied. The recording unit 128 records various programs and information used by each unit of the vehicle control system 111. For example, the recording unit 128 is equipped with an EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving), and records information on the vehicle 100 before and after an event such as an accident and biometric information acquired by the in-vehicle sensor 126. ..

The driving support / automatic driving control unit 129 controls the driving support and automatic driving of the vehicle 100. For example, the driving support / automatic driving control unit 129 includes an analysis unit 161, an action planning unit 162, and an motion control unit 163.

The analysis unit 161 analyzes the vehicle 100 and the surrounding conditions. The analysis unit 161 includes a self-position estimation unit 171, a sensor fusion unit 172, and a recognition unit 173.

The self-position estimation unit 171 estimates the self-position of the vehicle 100 based on the sensor information from the external recognition sensor 125 and the high-precision map stored in the map information storage unit 123. For example, the self-position estimation unit 171 generates a local map based on the sensor information from the external recognition sensor 125, and estimates the self-position of the vehicle 100 by matching the local map with the high-precision map. The position of the vehicle 100 is based on, for example, the center of the rear wheel-to-axle.

The local map is, for example, a three-dimensional high-precision map created by using a technology such as SLAM (Simultaneous Localization and Mapping), an occupied grid map (OccupancyGridMap), or the like. The three-dimensional high-precision map is, for example, the point cloud map described above. The occupied grid map is a map that divides a three-dimensional or two-dimensional space around the vehicle 100 into a grid (grid) of a predetermined size and shows the occupied state of an object in grid units. The occupied state of an object is indicated by, for example, the presence or absence of an object and the probability of existence. The local map is also used, for example, in the detection process and the recognition process of the external situation of the vehicle 100 by the recognition unit 173.

The self-position estimation unit 171 may estimate the self-position of the vehicle 100 based on the GNSS signal and the sensor information from the vehicle sensor 127.

The sensor fusion unit 172 performs sensor fusion processing for obtaining new information by combining a plurality of different types of sensor information (for example, image information supplied from the camera 151 and sensor information supplied from the radar 152). .. Methods for combining different types of sensor information include integration, fusion, and association.

The recognition unit 173 executes a detection process for detecting the external situation of the vehicle 100 and a recognition process for recognizing the external situation of the vehicle 100.

For example, the recognition unit 173 performs detection processing and recognition processing of the external situation of the vehicle 100 based on the information from the external recognition sensor 125, the information from the self-position estimation unit 171 and the information from the sensor fusion unit 172. ..

Specifically, for example, the recognition unit 173 performs detection processing, recognition processing, and the like of objects around the vehicle 100. The object detection process is, for example, a process of detecting the presence / absence, size, shape, position, movement, etc. of an object. The object recognition process is, for example, a process of recognizing an attribute such as an object type or identifying a specific object. However, the detection process and the recognition process are not always clearly separated and may overlap.

For example, the recognition unit 173 detects an object around the vehicle 100 by performing clustering that classifies the point cloud based on the sensor information by the LiDAR 153, the radar 152, or the like into each block of the point cloud. As a result, the presence / absence, size, shape, and position of an object around the vehicle 100 are detected.

For example, the recognition unit 173 detects the movement of an object around the vehicle 100 by performing tracking that follows the movement of a mass of point clouds classified by clustering. As a result, the velocity and the traveling direction (movement vector) of the object around the vehicle 100 are detected.

For example, the recognition unit 173 detects or recognizes a vehicle, a person, a bicycle, an obstacle, a structure, a road, a traffic light, a traffic sign, a road sign, or the like with respect to the image information supplied from the camera 151. Further, the type of the object around the vehicle 100 may be recognized by performing the recognition process such as semantic segmentation.

For example, the recognition unit 173 is based on the map stored in the map information storage unit 123, the self-position estimation result by the self-position estimation unit 171 and the recognition result of the object around the vehicle 100 by the recognition unit 173. It is possible to perform recognition processing of traffic rules around the vehicle 100. By this processing, the recognition unit 173 can recognize the position and state of the signal, the content of the traffic sign and the road marking, the content of the traffic regulation, the lane in which the vehicle can travel, and the like.

For example, the recognition unit 173 can perform recognition processing of the environment around the vehicle 100. As the surrounding environment to be recognized by the recognition unit 173, weather, temperature, humidity, brightness, road surface condition, and the like are assumed.

The action planning unit 162 creates an action plan for the vehicle 100. For example, the action planning unit 162 creates an action plan by performing route planning and route tracking processing.

Note that route planning (Global path planning) is a process of planning a rough route from the start to the goal. This route plan is called a track plan, and in the route planned by the route plan, the track generation (Local) capable of safely and smoothly traveling in the vicinity of the vehicle 100 in consideration of the motion characteristics of the vehicle 100 is taken into consideration. The processing of path planning) is also included. The route plan may be distinguished from the long-term route plan and the activation generation from the short-term route plan or the local route plan. The safety priority route represents a concept similar to activation generation, short-term route planning, or local route planning.

Route tracking is a process of planning an operation for safely and accurately traveling on a route planned by route planning within a planned time. The action planning unit 162 can calculate the target speed and the target angular velocity of the vehicle 100, for example, based on the result of this route tracking process.

The motion control unit 163 controls the motion of the vehicle 100 in order to realize the action plan created by the action plan unit 162.

For example, the motion control unit 163 controls the steering control unit 181, the brake control unit 182, and the drive control unit 183, which are included in the vehicle control unit 132 described later, and the vehicle 100 controls the track calculated by the track plan. Acceleration / deceleration control and direction control are performed so as to proceed. For example, the motion control unit 163 performs coordinated control for the purpose of realizing ADAS functions such as collision avoidance or impact mitigation, follow-up running, vehicle speed maintenance running, collision warning of own vehicle, and lane deviation warning of own vehicle. For example, the motion control unit 163 performs coordinated control for the purpose of automatic driving or the like that autonomously travels without being operated by the driver.

The DMS 130 performs driver authentication processing, driver status recognition processing, and the like based on sensor information from the in-vehicle sensor 126 and input information input to HMI 131, which will be described later. In this case, as the state of the driver to be recognized by the DMS 130, for example, physical condition, arousal degree, concentration degree, fatigue degree, line-of-sight direction, drunkenness, driving operation, posture and the like are assumed.

Note that the DMS 130 may perform authentication processing for passengers other than the driver and recognition processing for the status of the passenger. Further, for example, the DMS 130 may perform the recognition processing of the situation inside the vehicle based on the sensor information from the sensor 126 in the vehicle. As the situation inside the vehicle to be recognized, for example, temperature, humidity, brightness, odor, etc. are assumed.

HMI131 inputs various information and instructions, and presents various information to the driver and the like.

The input of information by HMI131 will be outlined. The HMI 131 includes an input device for a person to input information. The HMI 131 generates an input signal based on information, instructions, and the like input by the input device, and supplies the input signal to each part of the vehicle control system 111. The HMI 131 includes an operator such as a touch panel, a button, a switch, and a lever as an input device. Not limited to this, the HMI 131 may further include an input device capable of inputting information by a method other than manual operation by voice, gesture, or the like. Further, the HMI 131 may use, for example, a remote control device using infrared rays or radio waves, or an externally connected device such as a mobile device or a wearable device corresponding to the operation of the vehicle control system 111 as an input device.

The presentation of information by HMI131 will be outlined. The HMI 131 generates visual information, auditory information, and tactile information for the passenger or the outside of the vehicle. Further, the HMI 131 performs output control for controlling the output, output content, output timing, output method, etc. of each of the generated information. As visual information, the HMI 131 generates and outputs, for example, an image such as an operation screen, a status display of the vehicle 100, a warning display, a monitor image showing a situation around the vehicle 100, or information indicated by light. Further, the HMI 131 generates and outputs as auditory information, for example, information indicated by sounds such as voice guidance, warning sounds, and warning messages. Further, the HMI 131 generates and outputs tactile information that is given to the tactile sensation of the occupant by, for example, force, vibration, movement, or the like.

As an output device for which the HMI 131 outputs visual information, for example, a display device that presents visual information by displaying an image by itself or a projector device that presents visual information by projecting an image can be applied. .. In addition to the display device having a normal display, the display device displays visual information in the passenger's field of view, such as a head-up display, a transmissive display, and a wearable device having an AR (Augmented Reality) function. It may be a device. Further, the HMI 131 can also use a display device included in a navigation device, an instrument panel, a CMS (Camera Monitoring System), an electronic mirror, a lamp, etc. provided in the vehicle 100 as an output device for outputting visual information.

As an output device for which the HMI 131 outputs auditory information, for example, an audio speaker, headphones, and earphones can be applied.

As an output device for which the HMI 131 outputs tactile information, for example, a haptics element using haptics technology can be applied. The haptic element is provided in a portion of the vehicle 100 that the occupant of the vehicle contacts contacts, such as a steering wheel and a seat.

The vehicle control unit 132 controls each part of the vehicle 100. The vehicle control unit 132 includes a steering control unit 181, a brake control unit 182, a drive control unit 183, a body system control unit 184, a light control unit 185, and a horn control unit 186.

The steering control unit 181 detects and controls the state of the steering system of the vehicle 100. The steering system includes, for example, a steering mechanism including a steering wheel, electric power steering, and the like. The steering control unit 181 includes, for example, a control unit such as an ECU that controls the steering system, an actuator that drives the steering system, and the like.

The brake control unit 182 detects and controls the state of the brake system of the vehicle 100. The brake system includes, for example, a brake mechanism including a brake pedal, an ABS (Antilock Brake System), a regenerative brake mechanism, and the like. The brake control unit 182 includes, for example, a control unit such as an ECU that controls the brake system.

The drive control unit 183 detects and controls the state of the drive system of the vehicle 100. The drive system includes, for example, a drive force generator for generating a drive force of an accelerator pedal, an internal combustion engine, a drive motor, or the like, a drive force transmission mechanism for transmitting the drive force to the wheels, and the like. The drive control unit 183 includes, for example, a control unit such as an ECU that controls the drive system.

The body system control unit 184 detects and controls the state of the body system of the vehicle 100. The body system includes, for example, a keyless entry system, a smart key system, a power window device, a power seat, an air conditioner, an airbag, a seat belt, a shift lever, and the like. The body system control unit 184 includes, for example, a control unit such as an ECU that controls the body system.

The light control unit 185 detects and controls various light states of the vehicle 100. As the light to be controlled, for example, a headlight, a backlight, a fog light, a turn signal, a brake light, a projection, a bumper display, or the like is assumed. The light control unit 185 includes a control unit such as an ECU that controls the light.

The horn control unit 186 detects and controls the state of the car horn of the vehicle 100. The horn control unit 186 includes, for example, a control unit such as an ECU that controls the car horn.

FIG. 7 is a diagram showing an example of a sensing region of the external recognition sensor 125 of FIG. 6 by a camera 151, a radar 152, a LiDAR 153, an ultrasonic sensor 154, and the like. Note that FIG. 7 schematically shows a view of the vehicle 100 from above, with the left end side being the front end (front) side of the vehicle 100 and the right end side being the rear end (rear) side of the vehicle 100.

The sensing area 201F and the sensing area 201B show an example of the sensing area of the ultrasonic sensor 154. The sensing region 201F covers the vicinity of the front end of the vehicle 100 by a plurality of ultrasonic sensors 154. The sensing region 201B covers the periphery of the rear end of the vehicle 100 by a plurality of ultrasonic sensors 154.

The sensing results in the sensing area 201F and the sensing area 201B are used, for example, for parking support of the vehicle 100 and the like.

The sensing area 202F to the sensing area 202B show an example of the sensing area of the radar 152 for a short distance or a medium distance. The sensing area 202F covers a position farther than the sensing area 201F in front of the vehicle 100. The sensing region 202B covers the rear of the vehicle 100 to a position farther than the sensing region 201B. The sensing area 202L covers the rear periphery of the left side surface of the vehicle 100. The sensing region 202R covers the rear periphery of the right side surface of the vehicle 100.

The sensing result in the sensing area 202F is used, for example, for detecting a vehicle, a pedestrian, or the like existing in front of the vehicle 100. The sensing result in the sensing region 202B is used, for example, for a collision prevention function behind the vehicle 100. The sensing results in the sensing area 202L and the sensing area 202R are used, for example, for detecting an object in a blind spot on the side of the vehicle 100.

The sensing area 203F to the sensing area 203B show an example of the sensing area by the camera 151. The sensing area 203F covers a position farther than the sensing area 202F in front of the vehicle 100. The sensing region 203B covers the rear of the vehicle 100 to a position farther than the sensing region 202B. The sensing area 203L covers the periphery of the left side surface of the vehicle 100. The sensing region 203R covers the periphery of the right side surface of the vehicle 100.

The sensing result in the sensing area 203F can be used, for example, for recognition of traffic lights and traffic signs, a lane departure prevention support system, and an automatic headlight control system. The sensing result in the sensing area 203B can be used, for example, for parking assistance and a surround view system. The sensing results in the sensing area 203L and the sensing area 203R can be used, for example, in a surround view system.

Sensing area 204 shows an example of the sensing area of LiDAR153. The sensing region 204 covers a position far from the sensing region 203F in front of the vehicle 100. On the other hand, the sensing area 204 has a narrower range in the left-right direction than the sensing area 203F.

The sensing result in the sensing area 204 is used for detecting an object such as a peripheral vehicle, for example.

The sensing area 205 shows an example of the sensing area of the radar 152 for a long distance.
The sensing region 205 covers a position farther than the sensing region 204 in front of the vehicle 100. On the other hand, the sensing area 205 has a narrower range in the left-right direction than the sensing area 204.

The sensing result in the sensing area 205 is used for, for example, ACC (Adaptive Cruise Control), emergency braking, collision avoidance, and the like.

The sensing areas of the cameras 151, the radar 152, the LiDAR 153, and the ultrasonic sensors 154 included in the external recognition sensor 125 may have various configurations other than those in FIG. 7. Specifically, the ultrasonic sensor 154 may also sense the side of the vehicle 100, or the LiDAR 153 may sense the rear of the vehicle 100. Further, the installation position of each sensor is not limited to each of the above-mentioned examples. Further, the number of each sensor may be one or a plurality.

Such a vehicle control system 111 generates movement amount information indicating the movement amount of the vehicle 100 calculated based on the rotation amount of the wheels detected by the vehicle sensor 127, and is based on the acceleration and the angular speed detected by the vehicle sensor 127. Generate gradient detection information. Further, the self-position estimation unit 171 corrects the self-position based on the road surface gradient information from the server 30 received by the communication unit 122, and generates the corrected self-position information. Further, the vehicle control system 111 transmits the generated movement amount information, the gradient detection information, and the corrected self-position information from the communication unit 122 to the server 30.

Further, the vehicle control system 111 outputs the road surface gradient information from the server 30 received by the communication unit 122 to the self-position estimation unit 171 and the route plan to the action planning unit 162, respectively.

The action planning unit 162 performs route tracking for planning an operation for safely and accurately traveling on the route planned by the route plan, and generates control information for controlling the operation of the vehicle 100 to operate. Output to control unit 163.

In this way, by supplying the road surface gradient information and the route plan generated by the information processing apparatus of the present technology to the vehicle 100, the vehicle 100 can be moved to the destination by the optimum route. Further, since the vehicle 100 can accurately calculate its own position based on the traveling amount of the vehicle and the road surface gradient information, the current position of the vehicle 100 can be accurately detected even when the position information receiving unit 124 cannot receive the position information. The technique according to the present disclosure may be realized by a mobile device such as a construction machine or an agricultural machine (tractor) limited to a vehicle such as an automobile, an electric vehicle, or a hybrid electric vehicle.

The series of processes described in the specification can be executed by hardware, software, or a composite configuration of both. When executing processing by software, the program that records the processing sequence is installed in the memory in the computer built in the dedicated hardware and executed. Alternatively, it is possible to install and execute the program on a general-purpose computer that can execute various processes.

For example, the program can be recorded in advance on a hard disk as a recording medium, SSD (Solid State Drive), or ROM (Read Only Memory). Alternatively, the program is a flexible disc, CD-ROM (Compact Disc Read Only Memory), MO (Magneto optical) disc, DVD (Digital Versatile Disc), BD (Blu-Ray Disc (registered trademark)), magnetic disc, semiconductor memory card. It can be temporarily or permanently stored (recorded) on a removable recording medium such as an optical disc. Such removable recording media can be provided as so-called package software.

In addition to installing the program on the computer from a removable recording medium, the program may be transferred from the download site to the computer wirelessly or by wire via a network such as LAN (Local Area Network) or the Internet. The computer can receive the program transferred in this way and install it on a recording medium such as a built-in hard disk.

It should be noted that the effects described in the present specification are merely examples and are not limited, and may have additional effects not described. In addition, the present technology should not be construed as being limited to the embodiments of the above-mentioned techniques. The embodiment of this technique discloses the present technology in the form of an example, and it is obvious that a person skilled in the art can modify or substitute the embodiment without departing from the gist of the present technique. That is, in order to judge the gist of this technology, the scope of claims should be taken into consideration.

In addition, the information processing device of the present technology can have the following configurations.
(1) An information processing device including a map information processing unit that corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.
(2) The map information processing unit uses the gradient detection information generated based on the travel information acquired by the moving device, and the position of the map information corresponding to the moving position that generated the gradient detection information. The information processing apparatus according to (1), which corrects the slope information of the position on the map.
(3) The map information processing unit performs a filter process using the gradient detection information already generated at the same movement position as the gradient detection information generated based on the travel information acquired by the moving device, and filters. The information processing apparatus according to (2), wherein the gradient information of the position of the map information corresponding to the moving position is corrected by using the gradient information after processing.
(4) The map information processing unit generates map information with gradient information set based on map information for which gradient information is not set and traveling information acquired by the moving device (1) to (3). The information processing device described in any of.
(5) The traveling information includes sensor information acquired by a sensor provided in the moving device.
The information processing apparatus according to any one of (2) to (4), wherein the gradient detection information is generated based on the sensor information.
(6) The information processing device according to (5), wherein the sensor information is information that detects at least one of translational motion or rotational motion of the moving device.
(7) The information processing device according to (5) or (6), which is an inertial measurement unit.
(8) The information according to (7), wherein the inertial measurement unit includes at least one of an acceleration sensor and an angular velocity sensor, the translational motion is detected by the acceleration sensor, and the rotational motion is detected by the angular velocity sensor. Processing device.
(9) The information processing apparatus according to any one of (1) to (8), further comprising a route planning unit that creates a route plan to a destination based on the map information corrected by the map information processing unit.
(10) The information processing apparatus according to (9), wherein the route planning unit creates the route plan using the gradient information set in the map information.
(11) The information processing device according to any one of (1) to (10), wherein the mobile device includes a self-position estimation unit that performs self-position estimation using the gradient information set in the map information.

10 ... System 20 ... Moving device 21 ... Wheel odometry 22 ... Gradient detection unit 23 ... Self-position estimation unit 24 ... Communication unit 25 ... Path tracking unit 26 ... Drive Control unit 27 ... Drive unit 30 ... Server 31 ... Communication unit 32 ... Map information processing unit 33 ... Route planning unit 321 ... Information storage unit 322 ... Road surface gradient information generation unit

Claims

An information processing device including a map information processing unit that corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.
The map information processing unit uses the gradient detection information generated based on the travel information acquired by the moving device to obtain the gradient information of the position of the map information corresponding to the moving position that generated the gradient detection information. The information processing apparatus according to claim 1.
The map information processing unit performs a filter process using the gradient detection information already generated at the same movement position as the gradient detection information generated based on the travel information acquired by the moving device, and after the filter processing. The information processing apparatus according to claim 2, wherein the gradient information is used to correct the gradient information of the position of the map information corresponding to the moving position.
The information processing device according to claim 1, wherein the map information processing unit generates map information in which gradient information is set based on map information in which gradient information is not set and traveling information acquired by the moving device.
The traveling information includes sensor information acquired by a sensor provided in the moving device.
The information processing apparatus according to claim 2, wherein the gradient detection information is generated based on the sensor information.
The information processing device according to claim 5, wherein the sensor information is information that detects at least one of translational motion and rotational motion of the moving device.
The information processing device according to claim 5, wherein the sensor is an inertial measurement unit.
The information processing device according to claim 7, wherein the inertial measurement unit includes at least one of an acceleration sensor and an angular velocity sensor, the translational motion is detected by the acceleration sensor, and the rotational motion is detected by the angular velocity sensor.
The information processing apparatus according to claim 1, further comprising a route planning unit that creates a route plan to a destination based on the map information modified by the map information processing unit.
The information processing device according to claim 9, wherein the route planning unit creates the route plan using the gradient information set in the map information.
The information processing device according to claim 1, wherein the mobile device includes a self-position estimation unit that performs self-position estimation using the gradient information set in the map information.
An information processing method including that the map information processing unit corrects the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.
A program that allows a computer to execute processing using information acquired by a mobile device.
A program for causing the computer to execute a procedure for correcting the gradient information set in the map information based on the map information in which the gradient information is set and the traveling information acquired by the moving device.