Patent Publication Number: US-2021179142-A1

Title: Mobile object, information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present technology relates to a mobile object capable of autonomous moving, an information processing apparatus, an information processing method, and a program. 
     BACKGROUND ART 
     The technology for achieving autonomous moving of vehicles and the like has been recently developed. For example, in the control system described in Patent Literature 1, when the reliability of estimated values of a current location and posture of a vehicle is low, the vehicle is controlled so as to alleviate the swing and inclination of the vehicle body. This achieves both the stability and the following property of autonomous traveling (paragraphs [0073] to [0076] of Patent Literature 1). 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: Japanese Patent No. 5837902 
     DISCLOSURE OF INVENTION 
     Technical Problem 
     It is conceived that the use of the autonomous moving technology becomes active for various mobile objects in the future. There is a demand for a technology capable of stably executing autonomous moving control of mobile objects. 
     In view of the above circumstances, it is an object of the present technology to provide a mobile object, an information processing apparatus, an information processing method, and a program that are capable of improving the stability of autonomous moving control. 
     Solution to Problem 
     In order to achieve the above object, a mobile object according to an embodiment of the present technology includes an acquisition unit, a detection unit, and a determination unit. 
     The acquisition unit acquires situation information regarding a situation of the mobile object. 
     The detection unit detects an instability element for autonomous traveling control of the mobile object on the basis of the acquired situation information. 
     The determination unit determines a control method for executing the autonomous traveling control on the basis of the detected instability element. 
     In such a mobile object, the instability element for the autonomous moving control of the mobile object is detected on the basis of the situation information regarding the situation of the mobile object. The control method for executing the autonomous moving control is determined on the basis of the detected instability element. This makes it possible to improve the stability of the autonomous moving control. 
     The mobile object may further include a storage unit that stores a plurality of control rules for executing the autonomous moving control. In this case, the determination unit may select a control rule to be executed from the plurality of stored control rules. 
     The determination unit may dynamically change the control rule to be executed on the basis of the detected instability element. 
     The plurality of control rules may include at least one of state-dependent Riccati equation (SDRE) control, linear quadratic regulator (LQR) control, H-infinity control theory (H ∞ ) control, adaptive control, proportional-integral-differential (PID) control with a low gain, or PID control with a high gain. 
     The detection unit may calculate an instability level for each of a plurality of instability parameters regarding the autonomous moving control on the basis of the acquired situation information. 
     The plurality of instability parameters may include at least one of an instability level regarding self-location estimation, an instability level regarding a road surface environment, an instability level regarding a control cycle, an instability level regarding a control delay, an instability level regarding modeling calculation, an instability level regarding a stationary disturbance, or an instability level regarding an impulsive disturbance. 
     The determination unit may select the control rule to be executed on the basis of evaluation information set for each of the plurality of control rules. 
     The evaluation information may include an accuracy level of each of the plurality of control rules and a tolerance level of each of a plurality of tolerance parameters corresponding to the plurality of instability parameters. 
     The plurality of tolerance parameters may include at least one of a tolerance level regarding self-location estimation, a tolerance level regarding a road surface environment, a tolerance level regarding a control cycle, a tolerance level regarding a control delay, a tolerance level regarding modeling calculation, a tolerance level regarding a stationary disturbance, or a tolerance level regarding an impulsive disturbance. 
     The mobile object further includes a drive control unit, an estimation unit, a route acquisition unit, and a calculation unit. 
     The drive control unit controls driving of the mobile object. 
     The estimation unit estimates a location and a posture of the mobile object on the basis of the acquired situation information. 
     The route acquisition unit acquires a target route of the mobile object. 
     The calculation unit calculates a control value for driving the mobile object on the basis of the estimated location and posture of the mobile object and the acquired target route, and outputs the control value to the drive control unit. 
     In this case, the determination unit may determine a method of calculating the control value by the calculation unit as a control method for executing the autonomous moving control. 
     The control value may include a control value regarding steering control of the mobile object and a control value regarding speed control. 
     The situation information may include at least one of peripheral information of the mobile object or state information of the mobile object. 
     The acquisition unit may include an external sensor and an internal sensor. 
     The determination unit may determine a control parameter for executing the autonomous moving control. 
     The determination unit may adjust a gain of PID control for executing the autonomous moving control. 
     The situation information may include a driving state of the mobile object. In this case, the determination unit may select PID control with a low gain from the plurality of control rules when the mobile object starts driving. 
     The situation information may include time information. In this case, the determination unit may select a control rule having a high tolerance level regarding self-location estimation from the plurality of control rules during a night-time period. 
     The situation information may include information regarding weather. In this case, the determination unit may select a control rule having a high tolerance level regarding self-location estimation and a high tolerance level regarding a road surface environment from the plurality of control rules in a case of rainy weather. 
     The situation information may include information regarding an irradiation state of light with respect to the mobile object. In this case, the determination unit may select a control rule having a high tolerance level regarding self-location estimation and a high tolerance level regarding an impulsive disturbance from the plurality of control rules in a backlight state. 
     The acquisition unit may include an external sensor. In this case, the determination unit may calculate the tolerance level of each of the plurality of tolerance parameters corresponding to the plurality of instability parameters on the basis of a type of the external sensor and the number of external sensors. 
     The mobile object may be a vehicle, a drone, and a robot capable of autonomous moving control. 
     An information processing apparatus according to an embodiment of the present technology includes an acquisition unit, a detection unit, and a determination unit. 
     The acquisition unit acquires situation information regarding a situation of a mobile object. 
     The detection unit detects an instability element for autonomous traveling control of the mobile object on the basis of the acquired situation information. 
     The determination unit determines a control method for executing the autonomous traveling control on the basis of the detected instability element. 
     An information processing method according to an embodiment of the present technology is an information processing method to be executed by a computer system, the information processing method including: acquiring situation information regarding a situation of a mobile object; detecting an instability element for autonomous traveling control of the mobile object on the basis of the acquired situation information; and determining a control method for executing the autonomous traveling control on the basis of the detected instability element. 
     A program according to an embodiment of the present technology is a program that causes a computer system to execute the following steps of: acquiring situation information regarding a situation of a mobile object; detecting an instability element for autonomous moving control of the mobile object on the basis of the acquired situation information; and determining a control method for executing the autonomous moving control on the basis of the detected instability element. 
     Advantageous Effects of Invention 
     As described above, according to the present technology, it is possible to improve the stability of the autonomous moving control. Note that the effects described herein are not necessarily limited and any one of the effects described in this disclosure may be produced. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is an external view illustrating a configuration example of a vehicle according to a first embodiment of the present technology. 
         FIG. 2  is a block diagram illustrating a configuration example of a vehicle control system  100  that controls a vehicle  10 . 
         FIG. 3  is a flowchart illustrating an example of a selection process of a control rule for autonomous traveling. 
         FIG. 4  is a block diagram illustrating a functional configuration example for executing the determination of a control method for autonomous traveling. 
         FIG. 5  is a diagram illustrating an example of the accuracy and stability of the control rule. 
         FIG. 6  is a diagram illustrating the stability in the control of automatic driving. 
         FIG. 7  is a diagram illustrating an element that destabilizes the control of automatic driving. 
         FIG. 8  is a diagram illustrating a correlation with the element that destabilizes the control of automatic driving. 
         FIG. 9  is a diagram illustrating an example of a method of selecting a control rule. 
         FIG. 10  is a diagram illustrating an example of a control rule to be selected. 
         FIG. 11  is a diagram illustrating another example of the method of selecting a control rule. 
         FIG. 12  is a diagram illustrating an evaluation when the control rule is selected. 
     
    
    
     MODE(S) FOR CARRYING OUT THE INVENTION 
     Embodiments according to the present technology will now be described below with reference to the drawings. 
     [Vehicle] 
       FIG. 1  is an external view illustrating a configuration example of a vehicle according to an embodiment of the present technology. A of  FIG. 1  is a perspective view illustrating a configuration example of a vehicle  10 . B of  FIG. 1  is a schematic diagram obtained when the vehicle  10  is viewed from above. The vehicle  10  has an automatic driving function capable of automatically travelling to a destination. The vehicle  10  corresponds to an embodiment of a mobile object according to the present technology. 
     The vehicle  10  includes various sensors  20  used in automatic driving. As an example, for example, A of  FIG. 1  schematically illustrates an imaging device  21  and a distance sensor  22  directed toward the front of the vehicle  10 . The imaging device  21  and the distance sensor  22  function as an external sensor  190  to be described later. Further, B of  FIG. 1  schematically illustrates wheel encoders  23  for detecting the rotation or the like of respective wheels. The wheel encoder  23  functions as an internal sensor  195  to be described later. 
     The imaging device  21  is disposed facing a forward direction of the vehicle  10 , images the front side of the vehicle  10 , and detects image information. Examples of the imaging device  21  to be used include an RGB camera including an image sensor such as a charge-coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS). The present technology is not limited thereto, and an image sensor or the like that detects infrared light or polarized light may be used as the imaging device  21 . 
     The distance sensor  22  is disposed facing a forward direction of the vehicle  10 . The distance sensor  22  detects information regarding a distance between the distance sensor  22  and an object included in a detection range of the distance sensor  22 , and detects depth information in the periphery of the vehicle  10 . Examples of the distance sensor  22  to be used include a LiDAR (Laser Imaging Detection and Ranging) sensor. 
     Use of the LiDAR sensor allows an image with depth information (depth image) or the like to be easily detected, for example. Alternatively, for example, a time-of-flight (ToF) depth sensor or the like may be used as the distance sensor  22 . In addition, the type or the like of the distance sensor  22  is not limited, and any sensor using a range finder, a millimeter-wave radar, an infrared laser, or the like may be used. 
     In addition, various sensors  20  are mounted on the vehicle  10 , and moving control of the vehicle  10  is performed on the basis of the output from the sensors  20 . For example, the sensors  20  may be disposed in any direction such as the rear or the side of the vehicle  10 . 
     [Configuration of Vehicle Control System] 
       FIG. 2  is a block diagram illustrating a configuration example of a vehicle control system  100  that controls the vehicle  10 . The vehicle control system  100  is a system that is installed in the vehicle  10  and that controls the vehicle  10  in various ways. Note that, hereinafter, the vehicle  10  is referred to as an own car or an own vehicle in the case of distinguishing the vehicle  10  from other vehicles. 
     The vehicle control system  100  includes an input unit  101 , a data acquisition unit  102 , a communication unit  103 , in-vehicle equipment  104 , an output control unit  105 , an output unit  106 , a drivetrain control unit  107 , a drivetrain system  108 , a body control unit  109 , a body system  110 , a storage unit  111 , and an automatic driving control unit  112 . The input unit  101 , the data acquisition unit  102 , the communication unit  103 , the output control unit  105 , the drivetrain control unit  107 , the body control unit  109 , the storage unit  111 , and the automatic driving control unit  112  are connected to each other via a communication network  121 . For example, the communication network  121  includes a bus or a vehicle-mounted communication network compliant with any standard such as a controller area network (CAN), a local interconnect network (LIN), a local area network (LAN), FlexRay (registered trademark), or the like. Note that sometimes the units of the vehicle control system  100  may be directly connected to each other without using the communication network  121 . 
     Note that, hereinafter, description of the communication network  121  will be omitted in the case where the units of the vehicle control system  100  communicate with each other via the communication network  121 . For example, simple description indicating that the input unit  101  and the automatic driving control unit  112  communicate with each other will be given, in the case where the input unit  101  and the automatic driving control unit  112  communicate with each other via the communication network  121 . 
     The input unit  101  includes an apparatus used by a passenger to input various kinds of data, instructions, or the like. For example, the input unit  101  includes an operation device such as a touchscreen, a button, a microphone, a switch, or a lever, an operation device capable of inputting information by sound, gesture, or the like that is different from manual operation, or the like. Alternatively, for example, the input unit  101  may be external connection equipment such as a remote control apparatus using infrared or another radio wave, or mobile equipment or wearable equipment compatible with operation of the vehicle control system  100 . The input unit  101  generates an input signal on the basis of data, an instruction, or the like input by a passenger, and supplies the generated input signal to the respective units of the vehicle control system  100 . 
     The data acquisition unit  102  includes various kinds of sensors or the like for acquiring data to be used in processes performed by the vehicle control system  100 , and supplies the acquired data to the respective units of the vehicle control system  100 . 
     For example, the data acquisition unit  102  includes various kinds of sensors for detecting a state or the like of the vehicle  10 . Specifically, for example, the data acquisition unit  102  includes a gyro sensor, an acceleration sensor, an inertial measurement unit (IMU), and sensors or the like for detecting an amount of operation of an accelerator pedal, an amount of operation of a brake pedal, an steering angle of a steering wheel, the number of revolutions of an engine, the number of revolutions of a motor, rotational speeds of wheels, and the like. 
     Further, for example, the data acquisition unit  102  includes various kinds of sensors for detecting information regarding the outside of the vehicle  10 . Specifically, for example, the data acquisition unit  102  includes an imaging apparatus such as a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, or another camera. Further, for example, the data acquisition unit  102  includes an environment sensor for detecting weather, a meteorological phenomenon, or the like, and a surrounding information detection sensor for detecting objects around the vehicle  10 . For example, the environment sensor includes a raindrop sensor, a fog sensor, a sunshine sensor, a snow sensor, or the like. The surrounding information detection sensor includes an ultrasonic sensor, a radar, a LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) sensor, a sonar, or the like. 
     Furthermore, for example, the data acquisition unit  102  includes various kinds of sensors for detecting a current location of the vehicle  10 . Specifically, for example, the data acquisition unit  102  includes a global navigation satellite system (GNSS) receiver or the like. The GNSS receiver receives satellite signals (hereinafter, referred to as GNSS signals) from a GNSS satellite serving as a navigation satellite. 
     Further, for example, the data acquisition unit  102  includes various kinds of sensors for detecting information regarding the inside of the vehicle  10 . Specifically, for example, the data acquisition unit  102  includes an imaging apparatus that captures an image of a driver, a biological sensor that detects biological information of the driver, a microphone that collects sound within the interior of the vehicle, or the like. The biological sensor is, for example, disposed on a seat surface, the steering wheel, or the like, and detects biological information of a passenger sitting in a seat or the driver holding the steering wheel. 
     The communication unit  103  communicates with the in-vehicle equipment  104 , various kinds of vehicle exterior equipment, a server, a base station, or the like, transmits data supplied by the respective units of the vehicle control system  100 , and supplies the received data to the respective units of the vehicle control system  100 . Note that a communication protocol supported by the communication unit  103  is not specifically limited. Further, it is also possible for the communication unit  103  to support a plurality of types of communication protocols. 
     For example, the communication unit  103  establishes wireless connection with the in-vehicle equipment  104  by using a wireless LAN, Bluetooth (registered trademark), near-field communication (NFC), wireless USB (WUSB), or the like. Further, for example, the communication unit  103  establishes wired connection with the in-vehicle equipment  104  by using Universal Serial Bus (USB), High-Definition Multimedia Interface (HDMI), Mobile High-Definition Link (MHL), or the like via a connection terminal (and a cable if necessary) (not illustrated). 
     Furthermore, for example, the communication unit  103  communicates with equipment (for example, an application server or a control server) present on an external network (for example, the Internet, a cloud network, or a company-specific network) via a base station or an access point. Further, for example, the communication unit  103  communicates with a terminal (for example, a terminal of a pedestrian or a store, or a machine-type communication (MTC) terminal) present in the vicinity of the vehicle  10  by using a peer-to-peer (P2P) technology. Furthermore, for example, the communication unit  103  carries out V2X communication such as vehicle-to-vehicle communication, vehicle-to-infrastructure communication, vehicle-to-home communication between the vehicle  10  and a home, or vehicle-to-pedestrian communication. Further, for example, the communication unit  103  includes a beacon receiver, receives a radio wave or an electromagnetic wave transmitted from a radio station installed on a road or the like, and acquires information regarding the current location, traffic congestion, traffic regulation, necessary time, or the like. 
     The in-vehicle equipment  104  includes mobile equipment or wearable equipment possessed by a passenger, information equipment carried into or attached to the vehicle  10 , a navigation apparatus that searches for a route to any destination, and the like, for example. 
     The output control unit  105  controls output of various kinds of information to the passenger of the vehicle  10  or to an outside of the vehicle  10 . For example, the output control unit  105  generates an output signal that includes at least one of visual information (such as image data) or audio information (such as sound data), supplies the output signal to the output unit  106 , and thereby controls output of the visual information and the audio information from the output unit  106 . Specifically, for example, the output control unit  105  combines pieces of image data captured by different imaging apparatuses included in the data acquisition unit  102 , generates a bird&#39;s-eye image, a panoramic image, or the like, and supplies an output signal including the generated image to the output unit  106 . Further, for example, the output control unit  105  generates sound data including warning sound, a warning message, or the like with regard to danger such as collision, contact, or entrance into a danger zone, and supplies an output signal including the generated sound data to the output unit  106 . 
     The output unit  106  includes an apparatus capable of outputting the visual information or the audio information to the passenger or the outside of the vehicle  10 . For example, the output unit  106  includes a display apparatus, an instrument panel, an audio speaker, headphones, a wearable device such as an eyeglass type display worn by the passenger or the like, a projector, a lamp, or the like. Instead of an apparatus including a usual display, the display apparatus included in the output unit  106  may be, for example, an apparatus that displays the visual information within a field of view of the driver such as a head-up display, a transparent display, or an apparatus having an augmented reality (AR) function. 
     The drivetrain control unit  107  generates various kinds of control signals, supplies them to the drivetrain system  108 , and thereby controls the drivetrain system  108 . Further, as necessary, the drivetrain control unit  107  supplies the control signals to structural elements other than the drivetrain system  108  and notifies them of a control state of the drivetrain system  108  or the like. 
     The drivetrain system  108  includes various kinds of apparatuses related to the drivetrain of the vehicle  10 . For example, the drivetrain system  108  includes a driving force generation apparatus for generating driving force of an internal combustion engine, a driving motor, or the like, a driving force transmitting mechanism for transmitting the driving force to wheels, a steering mechanism for adjusting the steering angle, a braking apparatus for generating braking force, an anti-lock braking system (ABS), an electronic stability control (ESC) system, an electric power steering apparatus, or the like. 
     The body control unit  109  generates various kinds of control signals, supplies them to the body system  110 , and thereby controls the body system  110 . Further, as necessary, the body control unit  109  supplies the control signals to structural elements other than the body system  110  and notifies them of a control state of the body system  110  or the like. 
     The body system  110  includes various kinds of body apparatuses provided to a vehicle body. For example, the body system  110  includes a keyless entry system, a smart key system, a power window apparatus, a power seat, the steering wheel, an air conditioner, various kinds of lamps (such as headlamps, tail lamps, brake lamps, direction-indicator lamps, and fog lamps), and the like. 
     The storage unit  111  includes read only memory (ROM), random access memory (RAM), a magnetic storage device such as a hard disc drive (HDD) or the like, a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like, for example. The storage unit  111  stores various kinds of programs, data, and the like used by respective units of the vehicle control system  100 . For example, the storage unit  111  stores map data such as a three-dimensional high-accuracy map, a global map, and a local map. The high-accuracy map is a dynamic map or the like. The global map has lower accuracy than the high-accuracy map but covers wider area than the high-accuracy map. The local map includes information regarding surroundings of the vehicle  10 . 
     The automatic driving control unit  112  performs control with regard to automatic driving such as autonomous traveling or driving assistance. Specifically, for example, the automatic driving control unit  112  performs cooperative control intended to implement functions of an advanced driver-assistance system (ADAS) which include collision avoidance or shock mitigation for the vehicle  10 , following driving based on a following distance, vehicle speed maintaining driving, a warning of collision of the vehicle  10 , a warning of deviation of the vehicle  10  from a lane, or the like. Further, for example, it is also possible for the automatic driving control unit  112  to perform cooperative control intended for automatic driving that makes the vehicle travel autonomously without depending on the operation of the driver or the like. The automatic driving control unit  112  includes a detection unit  131 , a self-location estimation unit  132 , a situation analysis unit  133 , a planning unit  134 , and a behavior control unit  135 . Note that the autonomous traveling control can be regarded as a concept included in autonomous moving control. 
     The automatic driving control unit  112  corresponds to an information processing apparatus according to the present embodiment, and includes hardware necessary for a computer such as a CPU, RAM, and ROM, for example. An information processing method according to the present technology is executed when the CPU loads a program according to the present technology into the RAM and executes the program. The program is recorded on the ROM or the like in advance. 
     The specific configuration of the automatic driving control unit  112  is not limited. For example, it is possible to use a programmable logic device (PLD) such as a field-programmable gate array (FPGA), or another device such as an application-specific integrated circuit (ASIC). 
     As illustrated in  FIG. 2 , the automatic driving control unit  112  includes a detection unit  131 , a self-location estimation unit  132 , a situation analysis unit  133 , a planning unit  134 , a behavior control unit  135 , and a control adjustment unit  136 . For example, each of the functional blocks is configured when a CPU of the automatic driving control unit  112  executes a predetermined program. 
     The detection unit  131  detects various kinds of information necessary to control automatic driving. The detection unit  131  includes a vehicle exterior information detection unit  141 , a vehicle interior information detection unit  142 , and a vehicle condition detection unit  143 . In this embodiment, the detection unit  131  and the data acquisition unit  102  correspond to an “acquisition unit”, and the acquisition unit includes an external sensor and an internal sensor. 
     The vehicle exterior information detection unit  141  performs a process of detecting information regarding an outside of the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100 . For example, the vehicle exterior information detection unit  141  performs a detection process, a recognition process, a tracking process of objects around the vehicle  10 , and a process of detecting distances to the objects. Examples of the detection target object include a vehicle, a person, an obstacle, a structure, a road, a traffic light, a traffic sign, a road sign, and the like. Further, for example, the vehicle exterior information detection unit  141  performs a process of detecting an ambient environment around the vehicle  10 . Examples of the ambient environment around the detection target include weather, temperature, humidity, brightness, a road surface condition, and the like, for example. The vehicle exterior information detection unit  141  supplies data indicating results of the detection processes to the self-location estimation unit  132 , a map analysis unit  151 , a traffic rule recognition unit  152 , and a situation recognition unit  153  of the situation analysis unit  133 , an emergency event avoiding unit  171  of the behavior control unit  135 , and the like. 
     Further, in this embodiment, the vehicle exterior information detection unit  141  generates learning data used for machine learning. Therefore, the vehicle exterior information detection unit  141  can execute each of a process of detecting information outside the vehicle  10  and a process of generating learning data. 
     The vehicle interior information detection unit  142  performs a process of detecting information regarding an inside of the vehicle on the basis of data or signals from the respective units of the vehicle control system  100 . For example, the vehicle interior information detection unit  142  performs an authentication process and a recognition process of the driver, a detection process of a state of the driver, a detection process of a passenger, a detection process of a vehicle interior environment, and the like. Examples of the state of the driver, which is a detection target, include a health condition, a degree of consciousness, a degree of concentration, a degree of fatigue, a gaze direction, and the like. Examples of the vehicle interior environment, which is a detection target, include temperature, humidity, brightness, smell, and the like. The vehicle interior information detection unit  142  supplies data indicating results of the detection processes to the situation recognition unit  153  of the situation analysis unit  133 , the emergency event avoiding unit  171  of the behavior control unit  135 , and the like. 
     The vehicle condition detection unit  143  performs a process of detecting a state of the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100 . Examples of the state of the vehicle  10 , which is a detection target, includes speed, acceleration, a steering angle, presence/absence of abnormality, a content of the abnormality, a driving operation state, a position and inclination of the power seat, a state of a door lock, states of other vehicle-mounted equipment, and the like. The vehicle condition detection unit  143  supplies data indicating results of the detection processes to the situation recognition unit  153  of the situation analysis unit  133 , the emergency event avoiding unit  171  of the behavior control unit  135 , and the like. 
     The exterior and interior information of the vehicle  10  detected by the vehicle exterior information detection unit  141  and the vehicle interior information detection unit  142  corresponds to the situation information regarding the situation of the mobile object, which includes at least one of the peripheral information of the vehicle  10  or the state information of the vehicle  10  in this embodiment. 
     The self-location estimation unit  132  performs a process of estimating a location, a posture, and the like of the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100  such as the vehicle exterior information detection unit  141  and the situation recognition unit  153  of the situation analysis unit  133 . Further, as necessary, the self-location estimation unit  132  generates a local map (hereinafter, referred to as a self-location estimation map) to be used for estimating a self-location. For example, the self-location estimation map may be a high-accuracy map using a technology such as simultaneous localization and mapping (SLAM). The self-location estimation unit  132  supplies data indicating a result of the estimation process to the map analysis unit  151 , the traffic rule recognition unit  152 , and the situation recognition unit  153  of the situation analysis unit  133 , and the like. Further, the self-location estimation unit  132  causes the storage unit  111  to store the self-location estimation map. 
     Hereinafter, the process of estimating the location, the posture, and the like of the vehicle  10  will be referred to as a self-location estimation process in some cases. Further, the information of the location and the posture of the vehicle  10  will be described as location/posture information. Therefore, the self-location estimation process executed by the self-location estimation unit  132  is a process of estimating the location/posture information of the vehicle  10 . 
     The situation analysis unit  133  performs a process of analyzing a situation of the vehicle  10  and a situation around the vehicle  10 . The situation analysis unit  133  includes the map analysis unit  151 , the traffic rule recognition unit  152 , the situation recognition unit  153 , and a situation prediction unit  154 . 
     The map analysis unit  151  performs a process of analyzing various kinds of maps stored in the storage unit  111  and constructs a map including information necessary for an automatic driving process while using data or signals from the respective units of the vehicle control system  100  such as the self-location estimation unit  132  and the vehicle exterior information detection unit  141  as necessary. The map analysis unit  151  supplies the constructed map to the traffic rule recognition unit  152 , the situation recognition unit  153 , and the situation prediction unit  154 , and to a route planning unit  161 , an action planning unit  162 , a behavior planning unit  163  of the planning unit  134 , and the like. 
     The traffic rule recognition unit  152  performs a process of recognizing traffic rules around the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100  such as the self-location estimation unit  132 , the vehicle exterior information detection unit  141 , and the map analysis unit  151 . The recognition process makes it possible to recognize locations and states of traffic lights around the vehicle  10 , contents of traffic control around the vehicle  10 , a drivable lane, and the like, for example. The traffic rule recognition unit  152  supplies data indicating a result of the recognition process to the situation prediction unit  154  and the like. 
     The situation recognition unit  153  performs a process of recognizing situations related to the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100  such as the self-location estimation unit  132 , the vehicle exterior information detection unit  141 , the vehicle interior information detection unit  142 , the vehicle condition detection unit  143 , and the map analysis unit  151 . For example, the situation recognition unit  153  performs a process of recognizing a situation of the vehicle  10 , a situation around the vehicle  10 , a situation of the driver of the vehicle  10 , and the like. Further, as necessary, the situation recognition unit  153  generates a local map (hereinafter, referred to as a situation recognition map) to be used for recognizing the situation around the vehicle  10 . For example, the situation recognition map may be an occupancy grid map. 
     Examples of a recognition target include a location, a posture, and movement (such as speed, acceleration, or a movement direction, for example) of the vehicle  10 , presence/absence of abnormality, contents of the abnormality, and the like. Examples of the situation around the vehicle  10 , which is a recognition target, include types and locations of surrounding still objects, types, locations, and movement (such as speed, acceleration, and movement directions, for example) of surrounding moving objects, structures of surrounding roads, conditions of road surfaces, ambient weather, temperature, humidity, brightness, and the like. Examples of the state of the driver, which is a recognition target, include a health condition, a degree of consciousness, a degree of concentration, a degree of fatigue, movement of gaze, driving operation, and the like. 
     The situation recognition unit  153  supplies data indicating a result of the recognition process (including the situation recognition map as necessary) to the self-location estimation unit  132 , the situation prediction unit  154 , and the like. Further, the situation recognition unit  153  causes the storage unit  111  to store the situation recognition map. 
     The situation prediction unit  154  performs a process of predicting a situation related to the vehicle  10  on the basis of data or signals from the respective units of the vehicle control system  100  such as the map analysis unit  151 , the traffic rule recognition unit  152 , and the situation recognition unit  153 . For example, the situation prediction unit  154  performs a process of predicting a situation of the vehicle  10 , a situation around the vehicle  10 , a situation of the driver, and the like. 
     Examples of the situation of the vehicle  10 , which is a prediction target, includes behavior of the vehicle, occurrence of abnormality, a drivable distance, and the like. Examples of the situation around the vehicle  10 , which is a prediction target, includes behavior of moving objects, change in states of traffic lights, change in environments such as weather, and the like around the vehicle  10 . Examples of the situation of the driver, which is a prediction target, include behavior, a health condition, and the like of the driver. 
     The situation prediction unit  154  supplies data indicating results of the prediction processes to the route planning unit  161 , the action planning unit  162 , and the behavior planning unit  163  of the planning unit  134  and the like in addition to the data from the traffic rule recognition unit  152  and the situation recognition unit  153 . 
     The route planning unit  161  plans a route to a destination on the basis of data or signals from the respective units of the vehicle control system  100  such as the map analysis unit  151  and the situation prediction unit  154 . For example, the route planning unit  161  sets a route from the current location to a specified destination on the basis of the global map. Further, for example, the route planning unit  161  appropriately changes the route on the basis of situations such as traffic congestion, accidents, traffic regulation, and constructions, and a health condition and the like of the driver. The route planning unit  161  supplies data indicating the planned route to the action planning unit  162  and the like. 
     The action planning unit  162  plans an action of the vehicle  10  for driving safely in the route planned by the route planning unit  161  within a planned time period, on the basis of data or signals from the respective units of the vehicle control system  100  such as the map analysis unit  151  and the situation prediction unit  154 . That is, a target route of the vehicle  10  is acquired. For example, the action planning unit  162  plans start, stop, a driving direction (for example, forward, backward, left turn, right turn, change of direction, etc.), a driving lane, driving speed, overtaking, and the like. The action planning unit  162  supplies data indicating the action planned for the vehicle  10  to the behavior planning unit  163  and the like. 
     The behavior planning unit  163  plans behavior of the vehicle  10  for achieving the action planned by the action planning unit  162  on the basis of data or signals from the respective units of the vehicle control system  100  such as the map analysis unit  151  and the situation prediction unit  154 . For example, the behavior planning unit  163  plans acceleration, deceleration, a driving course, and the like. The behavior planning unit  163  supplies data indicating the planed behavior of the vehicle  10  to an acceleration/deceleration control unit  172 , a direction control unit  173 , and the like of the behavior control unit  135 . 
     The behavior control unit  135  controls behavior of the vehicle  10 . The behavior control unit  135  includes the emergency event avoiding unit  171 , the acceleration/deceleration control unit  172 , and the direction control unit  173 . 
     The emergency event avoiding unit  171  performs a process of detecting collision, contact, entrance into a danger zone, or an emergency event such as abnormality in the driver or abnormality in the vehicle  10  on the basis of detection results obtained by the vehicle exterior information detection unit  141 , the vehicle interior information detection unit  142 , and the vehicle condition detection unit  143 . In the case where occurrence of an emergency event is detected, the emergency event avoiding unit  171  plans behavior of the vehicle  10  such as a quick stop or a quick turn for avoiding the emergency event. The emergency event avoiding unit  171  supplies data indicating the planned behavior of the vehicle  10  to the acceleration/deceleration control unit  172 , the direction control unit  173 , and the like. 
     The acceleration/deceleration control unit  172  controls acceleration/deceleration to achieve the behavior of the vehicle  10  planned by the behavior planning unit  163  or the emergency event avoiding unit  171 . For example, the acceleration/deceleration control unit  172  computes a control goal value of the driving force generation apparatus or the braking apparatus to achieve the planned acceleration, deceleration, or quick stop, and supplies a control instruction indicating the computed control goal value to the drivetrain control unit  107 . 
     The direction control unit  173  controls a direction to achieve the behavior of the vehicle  10  planned by the behavior planning unit  163  or the emergency event avoiding unit  171 . For example, the direction control unit  173  computes a control goal value of the steering mechanism to achieve a driving course or quick turn planned by the behavior planning unit  163  or the emergency event avoiding unit  171 , and supplies a control instruction indicating the computed control goal value to the drivetrain control unit  107 . 
     The control adjustment unit  136  adjusts the control for executing the autonomous moving control of the vehicle  10 . The control adjustment unit  136  includes an instability element monitoring system  181  and a control adjustment system  182 . 
     The instability element monitoring system  181  detects an instability element that is predicted to destabilize the control of automatic driving of the vehicle  10  on the basis of data or signals from the detection unit  131 . The detected instability element is classified and quantified for each parameter. That is, the instability element monitoring system  181  detects an instability element for the autonomous moving control of the vehicle  10  on the basis of the acquired situation information. 
     Here, the instability element is an element that is predicted to destabilize the autonomous moving control. Examples of the instability element include: dark exterior, fog, strong sunlight, a low temperature, a high usage rate of the CPU of the vehicle  10 , a high temperature of the CPU of the vehicle  10 , raining or snowing, many mobile objects around the vehicle  10 , flat surroundings, periodic impacts during travelling, gusts, and mountain roads. In this embodiment, the instability element monitoring system  181  corresponds to a “detection unit”. 
     When the above-mentioned instability element is detected, the control of the behavior of the vehicle  10  may become unstable. For example, if the exterior is dark or foggy, and the sunlight is strong, self-location estimation or obstacle estimation of the vehicle  10  may fail. If the temperature is low, the battery of the vehicle  10  may be degraded, and the bias of the gyro may change, and the control of the automatic driving may fail. If the usage rate of the CPU of the vehicle  10  is high and the temperature of the CPU of the vehicle  10  is high, the control cycle may become inconstant. If it is raining or snowing, the vehicle  10  may skid due to a failure of map matching or a change in the coefficient of friction. If there are many mobile objects around the vehicle  10  and the surroundings are flat, map matching may fail. If periodic impacts occur during travelling, the wheels may go wrong and the travelling may become unstable. In the case of gusts and mountain roads, the travelling may be disturbed. 
     In such a manner, the instability element refers to a factor that may reduce the control performance of the autonomous moving control such as automatic driving of the mobile object, among various factors including surrounding environments such as a traveling route of the mobile object and internal environments of the mobile object. It is also possible to classify the instability element as an instability element related to the external environments, an instability element related to the internal environments, or the like. 
     In this embodiment, the instability element monitoring system  181  divides the instability elements into parameters of self-location noise, road surface condition disturbance, control cycle instability, control delay, modeling error, stationary disturbance, and impulsive disturbance as a plurality of instability parameters regarding the autonomous moving control on the basis of the acquired situation information. 
     The instability element monitoring system  181  quantifies each of these instability parameters as an instability level. In other words, on the basis of the acquired situation information, an instability level is calculated for each of the plurality of instability parameters regarding the autonomous moving control of the vehicle  10 . 
     A specific method of quantifying the instability parameter will be described later with reference to  FIGS. 7 and 8 . The instability element monitoring system  181  supplies data indicating the detected instability element to the control adjustment system  182  of the control adjustment unit  136 . 
     Here, the self-location noise is an instability element that may fail to accurately detect estimation of a posture and a location of the vehicle  10  on the basis of the situation of the vehicle  10 , a surrounding situation, or the like obtained from the detection unit  131 . For example, darkness, fog, strong sunlight, rain, snow, many mobile objects around the vehicle  10 , flat surroundings, and the like cause the self-location noise. 
     The road surface condition disturbance is an instability element that may destabilize the control of the vehicle  10  due to a road surface environment in the traveling route of the vehicle  10  obtained from the vehicle exterior information detection unit  141  or the like. For example, rain, snow, and irregularities of the road surface cause the road surface condition disturbance. 
     The control cycle instability is an instability element that may make a cycle of the control to the vehicle  10  by the behavior control unit  135  inconstant. For example, a high usage rate of the CPU of the vehicle  10 , a high temperature of the CPU of the vehicle  10 , and the like cause the control cycle instability. 
     The control delay is an instability element that may delay a command of the control to the vehicle  10  by the behavior control unit  135 . For example, a command of the control to the vehicle  10 , such as many mobile objects around the vehicle  10 , irregularities of the road surface, steering control of the vehicle  10 , and acceleration and deceleration of the vehicle  10 , is complicated. Calculation of a complicated control command increases the temperature of the CPU of the vehicle  10 , for example, which causes the control delay. 
     The modeling error is an instability element that may deteriorate the accuracy for the vehicle  10  to follow the target route (kinematic model by mathematical formula) obtained from the action planning unit  162  and the behavior planning unit  163 . For example, the wheel of the vehicle  10  has low effectiveness of the brake due to a failure such as a puncture, which causes the modeling error. 
     The stationary disturbance is an instability element in which a phenomenon affecting the control of the automatic driving of the vehicle  10  constantly occurs, and such a phenomenon may destabilize the control of the automatic driving of the vehicle  10 . For example, constant strong crosswinds, unexpected friction of the road surface, and the like cause the stationary disturbance. 
     The impulsive disturbance is an instability element in which a phenomenon affecting the control of the automatic driving of the vehicle  10  instantaneously occurs, and such a phenomenon may destabilize the control of the automatic driving of the vehicle  10 . For example, gusts, inconstant irregularities of the road surface such as mountain roads, and the like cause the impulsive disturbance. 
     In this embodiment, the self-location noise corresponds to an instability level regarding the self-location estimation. The road surface condition disturbance corresponds to an instability level regarding the road surface environment. The control cycle instability corresponds to an instability level regarding the control cycle. The control delay corresponds to an instability level regarding the control delay. The modeling error corresponds to an instability level regarding the modeling calculation. The stationary disturbance corresponds to an instability level regarding the stationary disturbance. The impulsive disturbance corresponds to an instability level regarding the impulsive disturbance. 
     The control adjustment system  182  selects a control rule from the storage unit  111 , which stores a plurality of control rules for executing the control of automatic driving of the vehicle  10 , on the basis of the instability level obtained by quantifying the instability parameter by the instability element monitoring system  181 . That is, a control method for executing the autonomous moving control is determined on the basis of the detected instability element. 
     A specific method of selecting the control rule will be described later with reference to  FIGS. 3 and 5 . The control adjustment system  182  supplies data indicating the selected control rule to the behavior control unit  135 . In this embodiment, the control adjustment system  182  corresponds to a “determination unit”. 
     A control rule to be executed is selected on the basis of evaluation information set for each of the plurality of control rules. The evaluation information includes an accuracy level of each of the plurality of control rules and a tolerance level regarding each of a plurality of tolerance parameters corresponding to the plurality of instability parameters. Here, the tolerance parameters include the fact that the automatic driving of the vehicle  10  is controllable stably even under a situation in which an instability element is detected. 
     The tolerance parameters are divided into parameters of a self-location noise tolerance, a road surface condition disturbance tolerance, a control cycle robustness, a control delay tolerance, a modeling error tolerance, a stationary disturbance tolerance, and an impulsive disturbance tolerance so as to respectively correspond to the self-location noise, the road surface condition disturbance, the control cycle instability, the control delay, the modeling error, the stationary disturbance, and the impulsive disturbance, which are the instability parameters. 
     In this embodiment, the self-location noise tolerance corresponds to a tolerance level regarding the self-location estimation. The road surface condition disturbance tolerance corresponds to a tolerance level regarding the road surface environment. The control cycle robustness corresponds to a tolerance level regarding the control cycle. The control delay tolerance corresponds to a tolerance level regarding the control delay. The modeling error tolerance corresponds to a tolerance level regarding the modeling calculation. The stationary disturbance tolerance corresponds to a tolerance level regarding the stationary disturbance. The impulsive disturbance tolerance corresponds to a tolerance level regarding the impulsive disturbance. 
     The storage unit  111  stores a plurality of control rules for which the accuracy level, each tolerance parameter, and each tolerance level are set. In this embodiment, six types of control rules are stored. Note that the number of control rules and the like to be stored in the storage unit  111  are not limited. The control rules may be stored assuming the situation of the vehicle  10  and the surrounding situation. The accuracy level will be specifically described later with reference to  FIG. 6 . 
       FIG. 3  is a flowchart illustrating an example of a selection process of a control rule for autonomous traveling.  FIG. 4  is a block diagram illustrating a functional configuration example for executing the determination of a control method for autonomous traveling.  FIG. 5  is a diagram illustrating an example of the accuracy and stability of the control rules. 
     As illustrated in  FIG. 3 , the situation information regarding the situation of the vehicle  10  is acquired by the vehicle exterior information detection unit  141  and the vehicle interior information detection unit  142  (Step  201 ). The instability element monitoring system  181  detects and quantifies the instability element for the autonomous moving control of the vehicle  10  on the basis of the acquired situation information of the vehicle  10  (Step  202 ). 
     The control adjustment system  182  selects a control rule with the highest accuracy among the control rules in which the tolerance levels of all the tolerance parameters corresponding to the respective instability parameters are equal to or higher than the instability levels (Step  203 ). That is, the control adjustment system  182  stores a plurality of control rules for executing the autonomous moving control, and selects a control rules to be executed from the plurality of stored control rules. 
     The behavior control unit  135  changes the control of the automatic driving of the vehicle  10  on the basis of the most accurate control rule selected (Step  204 ). Next to Step  204 , if the driving of the vehicle  10  is terminated, the flowchart of  FIG. 3  ends. If the travelling of the vehicle  10  is continued, the process returns to Step  201  to acquire the situation of the vehicle  10  and the surrounding situation. That is, the control rule to be executed is dynamically changed on the basis of the detected instability element. Thus, it is possible to correspond to changes in the situation of the vehicle  10  and the surrounding situation, change the control rule dynamically, and improve the control accuracy. 
     The configuration example for executing the flowchart of  FIG. 3  is, as illustrated in  FIG. 4 , constituted by a control adjustment unit  140 , self-location estimation  137 , control command value calculation  138 , and vehicle drive  139 . 
     The self-location estimation  137  represents a functional configuration for estimating the self-location of the vehicle  10 . The external sensor  190 , map data  191 , and a self-location estimation unit  192  are illustrated in the self-location estimation  137 . 
     The external sensor  190  corresponds to the data acquisition unit  102  and the vehicle exterior information detection unit  141  illustrated in  FIG. 2 . The external sensor  190  determines the surrounding situation of the vehicle  10 , which is the process of Step  201 . Sensors corresponding to the external sensor  190  include imaging apparatus such as a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, or another camera, a raindrop sensor, a fog sensor, a sunshine sensor, a snow sensor, an ultrasonic sensor, a radar, a LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) sensor, and the like among various sensors of the data acquisition unit  102 . 
     The map data  191  corresponds to the storage unit  111  and the map analysis unit  151  illustrated in  FIG. 2 . The map data  191  stores various types of map data, and supplies the map data to the self-location estimation unit. 
     The self-location estimation unit  192  estimates the self-location of the vehicle  10  by matching the situation around the vehicle  10  determined from the external sensor  190  and the situation of the vehicle  10  determined from the internal sensor  195  with the map data  191 . That is, the location and posture of the vehicle  10  are estimated on the basis of the acquired situation information. The external sensor  190  supplies data indicating the determined situation around the vehicle  10  to an instability element monitoring system  198 . 
     The control command value calculation  138  corresponds to the drivetrain control unit  107  and the planning unit  134  illustrated in  FIG. 2 . The control command value calculation  138  represents a functional configuration for calculating a control command value of autonomous traveling of the vehicle  10 . That is, as a control method for executing the autonomous moving control of the vehicle  10 , a calculation method for a calculated control value is determined. A steering control unit  194  and a speed control unit  193  are illustrated in the control command value calculation  138 . 
     The control command value includes a value obtained by calculating a target speed and a target steering angle for driving the vehicle  10  to follow the target route on the basis of the situation information of the vehicle  10  and the data of the target route that are obtained from the self-location estimation unit  192  and a control adjustment system  199 . That is, a control value regarding the steering control of the vehicle  10  and a control value regarding the speed control are calculated. 
     In the control command value calculation  138 , the steering control unit  194  and the speed control unit  193  perform steering control and speed control of the vehicle  10  on the basis of the data or signals of the self-location estimation unit  192 , the internal sensor  195 , a target command value, and the control adjustment system  199 . That is, a control value for driving the vehicle  10  is calculated on the basis of the estimated location and posture of the vehicle  10  and the acquired target route, and is output to a drive control unit. In other words, the target route of the vehicle  10  is acquired, and the steering angle and acceleration/deceleration of the vehicle  10  are calculated so as to cause the vehicle  10  to follow the target route according to the location/posture information of the vehicle  10  and the control rule selected by the control adjustment system  199 . Here, the target command value includes the speed, location, track, and the like for the vehicle  10  to go to the destination. 
     Here, the internal sensor  195  corresponds to the data acquisition unit  102 , the vehicle interior information detection unit  142 , and the vehicle condition detection unit  143  illustrated in  FIG. 2 . The internal sensor  195  determines the situation of the vehicle  10 . Sensors corresponding to the internal sensor  195  include a gyro sensor, an acceleration sensor, an inertial measurement unit (IMU), and sensors or the like for detecting an amount of operation of an accelerator pedal, an amount of operation of a brake pedal, an steering angle of a steering wheel, the number of revolutions of an engine, the number of revolutions of a motor, rotational speeds of wheels, and the like. 
     The speed control unit  193  corresponds to the acceleration/deceleration control unit  172  illustrated in  FIG. 2 . The speed control unit  193  calculates the opening degree of the accelerator and the brake that is necessary to drive the vehicle  10  at a target speed. The speed control unit  193  supplies data indicating the calculated opening degree of the accelerator and the brake to an accelerator/brake unit  196 . 
     The steering control unit  194  corresponds to the direction control unit  173  illustrated in  FIG. 2 . The steering control unit  193  calculates the steering angle necessary to align the vehicle  10  along the target route in real time. The steering control unit  194  supplies data indicating the calculated steering angle to a steering actuator unit  197 . 
     The wheel drive  139  corresponds to the drivetrain control unit  107 , the drivetrain system  108 , the body control unit  109 , and the body system  110  illustrated in  FIG. 2 . The wheel drive  139  represents a functional configuration for executing a control command value of the autonomous traveling control of the vehicle  10  and controlling the driving of the vehicle  10 . The steering actuator unit  197  and the accelerator/brake unit  196  are illustrated in the wheel drive  139 . 
     The wheel drive  139  includes the vehicle  10 , the internal sensor  195 , the accelerator/brake unit  196 , and the steering actuator unit  197 . Data calculated by the speed control unit  193  and the steering control unit  194  is supplied to the accelerator/brake unit  196  and the steering actuator unit  197 , and the vehicle  10  is controlled by the calculated steering angle and acceleration/deceleration. 
     The accelerator/brake unit  196  corresponds to the drivetrain system  108  illustrated in  FIG. 2 , and adjusts the accelerator/brake of the vehicle  10  on the basis of the calculation result of the speed control unit  193  and adjusts the speed of the vehicle  10  to be matched to the target speed. 
     The steering actuator unit  197  corresponds to the drivetrain system  108  illustrated in  FIG. 2 , and adjusts a steering torque to match the steering of the vehicle  10  to the calculated angle on the basis of the calculation result of the steering control unit  194 . 
     The internal sensor  195  acquires the state of the vehicle  10  to be controlled, and supplies the data to the self-location estimation, the instability element monitoring system  198 , and the control command value calculation  138 . 
     The control adjustment  140  corresponds to the control adjustment unit  136  illustrated in  FIG. 2 . The control adjustment  140  represents a functional configuration for adjusting the control of the behavior of the vehicle  10 . The instability element monitoring system  198  and the control adjustment system  199  are illustrated in the control adjustment  140 . 
     The instability element monitoring system  198  corresponds to the instability element monitoring system  181  illustrated in  FIG. 2 . The instability element monitoring system  198  detects and quantifies an instability element that is predicted to destabilize the control of automatic driving of the vehicle  10  on the basis of data or signals of the external sensor  190 , the internal sensor  195 , or the like, which is the process of Step  202 . The instability elements are divided into parameters of self-location noise, road surface condition disturbance, control cycle instability, control delay, modeling error, stationary disturbance, and impulsive disturbance as instability parameters. 
     The instability parameters are each quantified as an instability level indicating the degree predicted to destabilize the control of automatic driving of the vehicle  10 . In this embodiment, the instability level is divided into 1 to 10 stages. 
     For example, if the self-location noise is “1”, the noise of the self-location such as rain may be small and the location of the vehicle  10  may be slightly difficult to know. If the self-location noise is “10”, the noise of the self-location may be large and the location of the vehicle  10  may be almost unknown. 
     If the road surface condition disturbance is “1”, due to less irregularities of the road surface or the like, the control of the vehicle  10  may become slightly unstable. If the road surface condition disturbance is “10”, due to many irregularities of the road surface or the like, the control of the vehicle  10  may become significantly unstable. 
     If the control cycle instability is “1”, due to an increase in usage rate of the CPU of the vehicle  10  or the like, the control cycle of the vehicle  10  may become slightly unstable. If the control cycle instability is “10”, due to approaching of the usage rate of the CPU of the vehicle  10  to 100% or the like, the control cycle of the vehicle  10  may become significantly unstable. 
     If the control delay is “1”, the control delay may be slightly delayed and the control of the vehicle  10  may be slightly delayed. If the control delay is “10”, the control delay may be significantly delayed to cause a state in which the control of the vehicle  10  is significantly delayed. 
     If the modeling error is “1”, the control of the vehicle  10  may become slightly unstable with less influence of the failure or the like of the vehicle  10  on the control of the vehicle  10 . If the modeling error is “10”, the influence of the failure or the like of the vehicle  10  on the control of the vehicle  10  is large and the control of the vehicle  10  may become significantly unstable. 
     If the stationary disturbance is “1”, a constant disturbance may be weak and the control of the vehicle  10  may become slightly unstable. If the constant disturbance is “10”, a constant disturbance is strong and the control of the vehicle  10  may become significantly unstable. 
     If the impulsive disturbance is “1”, an instantaneous disturbance may be weak and the control of the vehicle  10  may become slightly unstable. If the impulsive disturbance is “10”, an instantaneous disturbance may be strong and the control of the vehicle  10  may become significantly unstable. 
     Note that the numerical value of the instability level, the stage corresponding to the numerical value, and the like are not limited. For example, the instability level may be graded by numerical values other than 1 to 10 or by alphabet. 
     The instability element monitoring system  198  provides data to the control adjustment system  198 , the data indicating an instability level obtained by quantifying the detected instability element. 
     The control adjustment system  199  corresponds to the control adjustment system  182  illustrated in  FIG. 2 . The control adjustment system  199  selects a control rule for executing the control of automatic driving of the vehicle  10  on the basis of an instability level obtained by quantifying the instability parameter by the instability element monitoring system  198 , which is Step  203 . The data of the selected control rule is supplied to the control command value calculation  138 . 
     The tolerance parameter indicates how much the control of the vehicle  10  can be stabilized with respect to the instability level indicating the degree predicted to destabilize the control of the vehicle  10 . The tolerance parameter is quantified as a tolerance level, which is the degree of stabilizing the control of the vehicle  10 . In this embodiment, the tolerance level is divided into 1 to 10 stages. 
     For example, if the self-location noise tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the self-location noise of 2 to 10. If the self-location noise tolerance is “10”, the control of the vehicle  10  can be stabilized even with the self-location noise of 1 to 10. 
     If the road surface condition disturbance tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the road surface condition disturbance of 2 to 10. If the road surface condition disturbance tolerance is “10”, the control of the vehicle  10  can be stabilized even with the road surface condition disturbance of 1 to 10. 
     If the control cycle robustness is “1”, the control of the vehicle  10  fails to be stabilized with the control cycle instability of 2 to 10. If the control cycle robustness is “10”, the control of the vehicle  10  can be stabilized even with the control cycle instability of 1 to 10. 
     If the control delay tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the control delay of 2 to 10. If the control delay tolerance is “10”, the control of the vehicle  10  can be stabilized even with the control delay of 1 to 10. 
     If the modeling error tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the modeling error of 2 to 10. If the modeling error tolerance is “10”, the control of the vehicle  10  can be stabilized even with the modeling error of 1 to 10. 
     If the stationary disturbance tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the stationary disturbance of 2 to 10. If the stationary disturbance tolerance is “10”, the control of the vehicle  10  can be stabilized even with the stationary disturbance of 1 to 10. 
     If the impulsive disturbance tolerance is “1”, the control of the vehicle  10  fails to be stabilized with the impulsive disturbance of 2 to 10. If the impulsive disturbance tolerance is “10”, the control of the vehicle  10  can be stabilized even with the impulsive disturbance of 1 to 10. 
     Note that the numerical value of the tolerance level, the stage corresponding to the numerical value, and the like are not limited. For example, the tolerance level may be graded by numerical values other than 1 to 10 or by alphabet. Further, in this embodiment, when the tolerance level is the same as the instability level, the automatic driving of the vehicle  10  is stably controlled. 
     As illustrated in  FIG. 5 , the control rule is selected on the basis of the accuracy of the control of automatic driving of the vehicle  10  and the tolerance levels of the tolerance parameters corresponding to the instability parameters. For example, if the tolerance level is lower than the instability level, such a control rule is not selected because the control rule fails to stably control the vehicle  10  with respect to the detected instability element. 
     Here, the accuracy refers to an accuracy level, and in this embodiment, it is divided into 1 to 10 stages. For example, if the accuracy is “10”, the vehicle  10  can accurately follow the target route, the target speed, and the like. If the accuracy is “1”, the vehicle  10  follows the target route, the target speed, and the like very roughly. 
     Note that the numerical value of the accuracy, the stage corresponding to the numerical value, and the like are not limited. For example, the accuracy may be graded by numerical values other than 1 to 10 or by alphabet. 
     Hereinafter, a specific example of the method of selecting a control rule, which is the process of Step  203 , will be described with reference to  FIG. 5 . Note that, in this embodiment, a control rule having the highest accuracy is selected from a plurality of control rules in which all the tolerance levels exceed all the corresponding instability levels. 
     In this embodiment, the plurality of control rules includes a control rule 1 that is state-dependent Riccati equation (SDRE) control, a control rule 2 that is linear quadratic regulator (LQR) control, a control rule 3 that is H-infinity control theory (H ∞ ) control, a control rule 4 that is adaptive control, a control rule 5 that is PID control in which the gain is set high, and a control rule 6 that is PID control in which the gain is set low. Specific tolerance levels of these control rules will be described below. 
     For example, the control rule 1 (state-dependent Riccati equation (SDRE)) has the accuracy of “10”, the self-location noise tolerance of “3”, the road surface condition disturbance tolerance of “5”, the control cycle robustness of “2”, the control delay tolerance of “5”, the modeling error tolerance of “7”, the stationary disturbance tolerance of “4”, and the impulsive disturbance tolerance of “3”. 
     In the situation where the control rule 1 is selected, for example, the self-location noise is “2”, the road surface condition disturbance is “4”, the control cycle instability is “1”, the control delay is “4”, the modeling error is “7”, the stationary disturbance is “3”, and the impulsive disturbance is “1”. In this case, the control rule 1, the control rule 4, and the control rule 6 are candidates to be selected, but the control rule 1 with high accuracy is selected. 
     The control rule 2 (linear quadratic regulator (LQR)) has the accuracy of “9”, the self-location noise tolerance of “5”, the road surface condition disturbance tolerance of “5”, the control cycle robustness of “3”, the control delay tolerance of “6”, the modeling error tolerance of “4”, the stationary disturbance tolerance of “6”, and the impulsive disturbance tolerance of “5”. 
     In the situation where the control rule 2 is selected, for example, the self-location noise is “2”, the road surface condition disturbance is “4”, the control cycle instability is “3”, the control delay is “4”, the modeling error is “3”, the stationary disturbance is “3”, and the impulsive disturbance is “4”. In this case, the control rule 2 and the control rule 6 are candidates to be selected, but the control rule 2 with high accuracy is selected. 
     The control rule 3 (H-infinity control theory (H ∞ )) has the accuracy of “8”, the self-location noise tolerance of “4”, the road surface condition disturbance tolerance of “4”, the control cycle robustness of “1”, the control delay tolerance of “1”, the modeling error tolerance of “4”, the stationary disturbance tolerance of “5”, and the impulsive disturbance tolerance of “8”. 
     In the situation where the control rule 3 is selected, for example, the self-location noise is “4”, the road surface condition disturbance is “4”, the control cycle instability is “1”, the control delay is “1”, the modeling error is “3”, the stationary disturbance is “4”, and the impulsive disturbance is “8”. In this case, the control rule 3 and the control rule 6 are candidates to be selected, but the control rule 3 with high accuracy is selected. 
     The control rule 4 (adaptive control) has the accuracy of “7”, the self-location noise tolerance of “8”, the road surface condition disturbance tolerance of “10”, the control cycle robustness of “3”, the control delay tolerance of “6”, the modeling error tolerance of “10”, the stationary disturbance tolerance of “9”, and the impulsive disturbance tolerance of “1”. 
     In the situation where the control rule 4 is selected, for example, the self-location noise is “5”, the road surface condition disturbance is “3”, the control cycle instability is “1”, the control delay is “3”, the modeling error is “9”, the stationary disturbance is “4”, and the impulsive disturbance is “1”. In this case, the control rule 4 and the control rule 6 are candidates to be selected, but the control rule 4 with high accuracy is selected. 
     The control rule 5 (proportional-integral-differential (PID) control High gain) has the accuracy of “5”, the self-location noise tolerance of “6”, the road surface condition disturbance tolerance of “7”, the control cycle robustness of “4”, the control delay tolerance of “3”, the modeling error tolerance of “5”, the stationary disturbance tolerance of “9”, and the impulsive disturbance tolerance of “5”. 
     In the situation where the control rule 5 is selected, for example, the self-location noise is “4”, the road surface condition disturbance is “4”, the control cycle instability is “3”, the control delay is “2”, the modeling error is “3”, the stationary disturbance is “7”, and the impulsive disturbance is “4”. In this case, the control rule 5 and the control rule 6 are candidates to be selected, but the control rule 5 with high accuracy is selected. 
     The control rule 6 (PID Low gain) has the accuracy of “2”, the self-location noise tolerance of “10”, the road surface condition disturbance tolerance of “10”, the control cycle robustness of “10”, the control delay tolerance of “10”, the modeling error tolerance of “10”, the stationary disturbance tolerance of “10”, and the impulsive disturbance tolerance of “10”. 
     In the situation where the control rule 6 is selected, for example, the self-location noise is “4”, the road surface condition disturbance is “2”, the control cycle instability is “7”, the control delay is “6”, the modeling error is “7”, the stationary disturbance is “6”, and the impulsive disturbance is “10”. In this case, the control rule 6 is selected because the control rules 1 to 5 have one or more of the set tolerance parameters below the instability levels. 
     Note that the contents of the control rule, the method of selecting an executable control rule, the method of setting the tolerance parameters, and the like are not limited to the above and may be optionally set. For example, the tolerance level to be set may be changed on the basis of the type and number of sensors  20  mounted on the vehicle  10 . That is, the tolerance level for each of the plurality of tolerance parameters corresponding to the plurality of instability parameters is calculated on the basis of the type and the number of external sensors. 
     By way of example, with a LiDAR alone, the self-location noise tolerant is lowered in a situation without features, such as grasslands. With a radar alone, the self-location noise tolerance is lowered in a situation without features, such as intense light and grasslands. With a camera alone, the self-location noise tolerance is lowered when the circumference is dark. 
     On the basis of the type and the number of the sensors  20  mounted on the vehicle  10 , a dedicated control rule corresponding to the type and the number of the sensors  20  may be set in addition to the change in the tolerance level. Thus, even if a part of the sensor fails, it is possible to select a control rule suitable for the situation information. 
     Note that any algorithm other than the accuracy, the self-location noise tolerance, the road surface condition disturbance tolerance, the control cycle robustness, the control delay tolerance, the modeling error tolerance, the stationary disturbance tolerance, and the impulsive disturbance tolerance illustrated in  FIG. 5  may be employed. 
     After the control rule is selected, the control rule may be switched to another one on the basis of the situation of the vehicle  10  and the surrounding situation. For example, the control rule may be discontinuously switched, or the control rule may be changed linearly gradually. 
     Further, as an example other than the above-mentioned method of selecting a control rule, a dedicated control rule or a method of selecting a dedicated control rule may be set when the vehicle  10  is traveling in a specific scene. 
     As a first example, when the vehicle  10  starts driving (starts traveling), the control rule 6 (PID Low gain) in which all of the tolerance levels are high is selected because the initial speed is low. That is, the driving state, which is the acquired situation information of the vehicle  10 , is calculated, and at the start of driving of the vehicle  10 , the PID control in which the gain is set low is selected from the plurality of control rules. 
     As a second example, the control rule 6 (PID Low gain) having a high self-location noise tolerance is selected because the surroundings are dark in the night-time period. That is, the time information, which is the acquired situation information, is calculated, and a control rule having a high tolerance level for self-location estimation is selected from the plurality of control rules in the night-time period. 
     As a third example, in the case of rainy weather, since the visibility of the surroundings is poor and the friction of the road surface also changes, the control rule 4 (adaptive control) having a high self-location noise tolerance and a high road surface condition disturbance tolerance is selected. That is, information regarding weather, which is the acquired situation information, is calculated, and in the case of rainy weather, a control rule having a high tolerance level for self-location estimation and a high tolerance level for a road surface environment is selected from the plurality of control rules. 
     As a fourth example, in the case of a backlight state, because of the poor visibility of the surroundings and an instantaneous disturbance, the control rule 3 (H ∞ ) having a high self-location noise tolerance and a high impulsive disturbance tolerance is selected. That is, information regarding an irradiation state of light with respect to the mobile object, which is the acquired situation information, is calculated, and in the case of a backlight state, a control rule having a high tolerance level for self-location estimation and a high tolerance level for an impulsive disturbance is selected from the plurality of control rules. 
     It is needless to say that a dedicated control rule or a method of selecting a dedicated control rule may be set in accordance with various situations of the vehicle  10  and surrounding situations. 
       FIG. 6  is a diagram illustrating the stability in the control of automatic driving. A of  FIG. 6  is a diagram illustrating a state in which the control of automatic driving of the vehicle  10  is stable. B of  FIG. 6  is a diagram illustrating a state in which the control of automatic driving of the vehicle  10  is unstable. 
     By the control of automatic driving, the vehicle  10  follows a set target route and target speed. At this time, the control of automatic driving becomes unstable because of the instability element. For example, in the case of A of  FIG. 6 , the control of automatic driving of the vehicle  10  is stable, and the speed of the vehicle  10  indicated by the broken line is following the target speed indicated by the solid line. 
     Hereinafter, as illustrated in A of  FIG. 6 , the control rule that causes the vehicle  10  to accurately follow the target speed and the target route will be sometimes described as having high accuracy. Further, as illustrated in B of  FIG. 6 , a case where the following is rough with respect to the target speed and the target route of the vehicle  10  will be sometimes described as having low accuracy. That is, in this embodiment, having high accuracy means a large numerical value of the accuracy level. 
     The case illustrated in A of  FIG. 6  means that there is no instability element in the situation of the vehicle  10  and the surrounding situation, or that the instability level is small and a highly accurate control rule can be selected. For example, as illustrated in  FIG. 5 , when the vehicle  10  has a failure with a low instability level of the modeling error, the control rule 1 is selected. 
     In the case illustrated in B of  FIG. 6 , the control of automatic driving of the vehicle  10  is unstable because of the instability element, and the speed of the vehicle  10  indicated by the broken line fails to follow the target speed indicated by the solid line. Such a state is caused by, for example, the modeling error in which the wheel of the vehicle  10  has a flat tire, and an IMU or the like fails to accurately detect the speed of the wheel. 
     As an example of the unstable control, it is assumed that the vehicle  10  is travelling in the city at 40 km/h. At this time, a case where the usage rate of the CPU of the vehicle  10  is high and the control cycle is disturbed will be conceived. Assuming that the control cycle of the vehicle  10  at normal time is 1000 times per second, the vehicle  10  is assumed to be moving 1 cm in one cycle. If the control cycle is disturbed and becomes 800 times per second, the control of the vehicle  10  is shifted by 200 cm. Of course, as the speed of the vehicle  10  increases, the control of the vehicle  10  is more greatly shifted. 
     In order to prevent such control from being unstable, the instability element monitoring system  198  detects the instability element that may destabilize the control. If a correlation between the detected instability element and a factor that directly destabilizes the control is previously calculated, it is possible to predict how much the detected instability element affects the control of automatic driving of the vehicle  10 . This allows the control adjustment system  182  to switch the control rule before the control becomes unstable. 
     Hereinafter, a method of quantifying the instability element will be specifically described with reference to  FIG. 7  and  FIG. 8 . 
       FIG. 7  is a diagram illustrating an element that destabilizes the control of automatic driving.  FIG. 8  is a diagram illustrating a correlation with the element that destabilizes the control of automatic driving. 
       FIG. 7  illustrates a factor that directly destabilizes the control command value calculation  138  with respect to the control of automatic driving of the vehicle  10  (hereinafter, referred to as a direct factor) and an instability element that generates the direct factor. 
     The instability element is detected by the instability element monitoring system  198  from the entire system including all of the situations of the vehicle  10  and the surrounding situations. Although the instability element does not directly affect the control command value calculation  138 , it becomes a cause to generate a direct factor. 
     The direct factor is a factor that directly affects the control command value calculation  138  of the automatic driving of the vehicle  10  and destabilizes the control, such as the self-location, the control cycle, and disturbance of the vehicle  10 . By setting the correlation between the instability element and the direct factor in advance, it becomes possible to predict the possibility that the direct factor occurs by detecting the instability element. 
     For example, as illustrated in  FIG. 6 , if the vehicle exterior information detection unit  141  detects that there are many mobile objects around the vehicle  10 , matching with existing map data is difficult, and thus noise enters the self-location. Thus, the self-location is not accurately output, the vehicle  10  fails to follow the target speed or the like, and the speed of the vehicle  10  fluctuates (see B of  FIG. 6 ). As a result, the control of the automatic driving of the vehicle  10  becomes unstable. 
     In the case of  FIG. 8 , the number of mobile objects is assumed as A, and the value of the self-location noise is assumed as B. If the correlation between A and B is calculated in advance by experiments, simulations, or the like, the occurrence of B can be predicted by detecting A before B is detected. 
     For example, if 80% of the field of view of the camera is occupied by mobile objects, the ratio of the mobile objects in point group data where the number of mobile objects A has been observed is 80%. At that time, assuming that the value B of the self-location noise is known by an experiment carried out beforehand (for example, trace of estimated covariance matrix of the self-location, etc.), the correlation between A and B can be obtained. 
     If A is detected when the vehicle  10  is actually travelling, how much the control of automatic driving of the vehicle  10  becomes unstable is calculated from the correlation between A and B, and an appropriate control rule can be selected before the control of automatic driving of the vehicle  10  becomes unstable. 
     Note that the type of the instability element and the correlation with the direct factor are not limited to the above. The instability element may be a factor that destabilizes the control of automatic driving of the vehicle  10  other than the self-location noise, the road surface condition disturbance, the control cycle instability, the control delay, the modeling error, the stationary disturbance, and the impulsive disturbance. In addition, the stability of the correlation between the instability element and the direct factor may also be evaluated from values of a gain margin, a phase margin, and the like. 
     Examples other than the method of selecting a control rule described above will be specifically described with reference to  FIGS. 9 to 12  and the like. 
       FIG. 9  is a diagram illustrating an example of the method of selecting a control rule.  FIG. 10  is a diagram illustrating an example of a control rule to be selected.  FIG. 11  is a diagram illustrating another example of the method of selecting a control rule.  FIG. 12  is a diagram illustrating an evaluation when a control rule is selected. 
     Hereinafter, in addition to the method of selecting a control rule having the highest accuracy among the control rules in which all the tolerance levels exceed all the corresponding instability levels, a method of weighting the instability level, the tolerance level, and the accuracy, performing a predetermined calculation, and selecting a control rule having the highest score will be specifically described with reference to  FIGS. 9 to 12 . 
       FIG. 9  illustrates an equation for calculating a score of the control rule and variables. It is assumed that an accuracy weight is wa (weight accuracy), a tolerance level weight is wti (i-th weight tolerance), an accuracy of a control rule is a (accuracy), an i-th tolerance level of the control rule is ti (i-th tolerance), and a current i-th instability level is nli (i-th noise_level). The score of the control rule is expressed by the following equation. 
       Score= a*wa+Σi {( ti−nli )* wti}   
     Here, of the values of the accuracy weight and the tolerance level weight, a larger value means an element more requested for the control of automatic driving of the vehicle  10 . In other words, in the case of wa&lt;wti, higher importance is given to the self-location noise tolerance than the accuracy. 
     The i-th tolerance level of the control rule refers to the self-location noise tolerance when i=1, the road surface condition disturbance tolerance when i=2, the control cycle robustness when i=3, the control delay tolerance when i=4, the stationary disturbance tolerance when i=5, and the impulsive disturbance tolerance when i=6. 
     The current i-th instability level is an instability parameter corresponding to the tolerance parameter when i=1 to 6. In other words, the current i-th instability level refers to the self-location noise when i=1, the road surface condition disturbance when i=2, the control cycle instability when i=3, the control delay when i=4, the stationary disturbance when i=5, and the impulsive disturbance when i=6. 
       FIG. 10  illustrates the accuracies and the self-location noise tolerances of the control rule 1 and the control rule 2, and a score calculation for each control rule. When the accuracies and tolerance levels of the control rule 1 and the control rule 2 are substituted into the equation of  FIG. 9 , the accuracy (a) of “10” and the self-location noise tolerance (ti) of “3” of the control rule 1, and the accuracy (a) of “5” and the self-location noise tolerance (ti) of “8” of the control rule 2 are substituted. Here, when the current instability level (nli) of the self-location noise is set to “5”, the accuracy weight (wa) is set to “3”, and the tolerance level weight (wti) is set to “10” to perform substitution, the scores of the control rule 1 and the control rule 2 are expressed by the following equations. 
       Score of control rule 1=10*3+(3−5)*10=10
 
       Score of control rule 2=5*3+(8−5)*10=45
 
     The control rule 2 with a high score is selected according to the results of the score calculation for the control rules 1 and 2. Note that the method of calculating the scores, the accuracy weight, and the tolerance level weight, etc., may be freely set on the basis of the situation at the time of travelling of the vehicle  10 , surrounding situations, and the like. For example, when heavy rain and gusts are detected, the tolerance level weights of the self-location noise tolerance and the impulsive disturbance tolerance may be increased. 
     Alternatively, as illustrated in  FIG. 11 , the score of the control rule may be expressed by the following equation. 
       Score= a *arctan( Ca*wa )+Σ i {arctan( Ct *( ti−nli ))* wti} 
 
     Here, Ca and Ct are constants for adjusting an arctan function. This equation is an equation that enables the arctan function to suppress the value of the equation of  FIG. 9  from becoming too large.  FIG. 12  is a graph of the arctan function used in the equation for the score calculation. 
       FIG. 12  illustrates the tolerance level on the x-axis and the score on the y-axis. As illustrated in  FIG. 12 , as the absolute value of x increases, the value of y becomes constant. For example, when the instability level is very small (close to 0), if the tolerance level (x) is 5 or more, it is possible to sufficiently stabilize the control of automatic driving of the vehicle  10 . The use of the arctan function allows the score (y) for noise tolerance of 5 to 10 to be substantially the same. Thus, if the score of the control rule is matched with the situation of the vehicle  10  and surrounding situations, the control accuracy of the automatic driving of the vehicle  10  can be improved. 
     Note that the number of control rules and the selection method are not limited to the above. Appropriate control rules may be set and selected on the basis of an assumed situation of the vehicle  10  and assumed surrounding situations. 
     As described above, in the vehicle  10  according to this embodiment, the instability element for the autonomous moving control of the vehicle  10  is detected on the basis of the information outside and inside the vehicle  10 . A control method for executing the autonomous moving control is determined on the basis of the detected instability element. This makes it possible to improve the stability of the autonomous moving control. 
     OTHER EMBODIMENTS 
     The present technology is not limited to the embodiment described above and can achieve other various embodiments. 
     In the above description, a control rule to be executed is selected from a plurality of control rules. However, the control method for executing the autonomous moving control of the vehicle  10  is not limited to the above. Control parameters for executing the autonomous moving control of the vehicle  10  may be determined. For example, since a higher gain of the PID control increases the accuracy but reduces the stability of the control, the gain of the PID control may be determined according to the instability level, and the control rule may be handled as an infinite number of continuous values. That is, the gain of the PID control for executing the autonomous moving control of the vehicle  10  may be adjusted. In this embodiment, the gain of the PID control is included in the control parameter for executing the autonomous moving control. 
     Alternatively, only one control rule may be used. For example, the control parameter may be determined to change the tolerance level of only one control rule or the like on the basis of the calculated instability level. Further, the control parameter may be dynamically changed on the basis of the change in the acquired situation information regarding the situation of the vehicle  10 . 
     Of course, the control method for executing the autonomous moving control of the vehicle  10  may be a combination of the method of selecting a control rule to be executed from a plurality of control rules and the method of determining a control parameter. 
     In the above embodiment, the case where the automatic driving is used for the vehicle has been exemplified. The present technology is also applicable to all systems that control drones, indoor robots, etc. That is, the mobile object is a vehicle, a drone, and a robot capable of autonomous moving control. 
     For example, an object flying in the sky, such as a drone, is strongly affected by the air flow, and a situation where gusts are likely to blow, rainy weather, poor visibility of the surroundings by fog, low ambient temperature, etc. are considered as instability elements. A control rule suitable for these situations may be set and selected. 
     Further, for example, in the case of an indoor robot or the like, since the floor is slippery due to a carpet or the like, control to refrain from a sudden change in speed, or stable control even when the indoor robot or the like is slipped may be used. 
     In the above embodiment, the plurality of control rules is preset and stored in the storage unit. The present technology is not limited to the above. For example, a control rule assuming various types of situation information of the mobile object may be uploaded to a server or the like via a network and may be selected. The control rule is stored in the server or the like, and thus the processing load or the like of the mobile object can be suppressed. 
     Further, a computer (automatic driving control unit) installed in the mobile object and another computer (cloud server) communicable via a network or the like may work in conjunction with each other to execute the information processing method and the program according to the present technology and establish the mobile object and the information processing apparatus according to the present technology. 
     In other words, the information processing method and the program according to the present technology may be executed not only in a computer system configured by a single computer but also in a computer system in which a plurality of computers operates in conjunction with each other. Note that, in the present disclosure, the system means an aggregate of a plurality of components (such as apparatuses or modules (parts)) and it does not matter whether or not all the components are housed in the identical casing. Therefore, a plurality of apparatuses housed in separate casings and connected to one another via a network, and a single apparatus having a plurality of modules housed in a single casing are both the system. 
     The execution of the information processing method and the program according to the present technology by the computer system includes, for example, both of the case where the acquisition of the situation information regarding a situation of the mobile object, the detection of the instability element for the autonomous moving control of the mobile object, the selection of a control rule to be executed from the plurality of stored control rules, and the like are executed by a single computer and the case where those processes are executed by different computers. Further, the execution of the respective processes by a predetermined computer includes causing another computer to execute some or all of those processes and acquiring results thereof. 
     Out of the feature parts according to the present technology described above, at least two feature parts can be combined. That is, the various feature parts described in the respective embodiments may be arbitrarily combined irrespective of the embodiments. Further, various effects described above are merely examples and are not limited, and other effects may be exerted. 
     Note that the present technology may also be configured as below. 
     (1) A mobile object, including: 
     an acquisition unit that acquires situation information regarding a situation of the mobile object; 
     a detection unit that detects an instability element for autonomous moving control of the mobile object on the basis of the acquired situation information; and 
     a determination unit that determines a control method for executing the autonomous moving control on the basis of the detected instability element. 
     (2) The mobile object according to (1), further including 
     a storage unit that stores a plurality of control rules for executing the autonomous moving control, in which 
     the determination unit selects a control rule to be executed from the plurality of stored control rules. 
     (3) The mobile object according to (2), in which 
     the determination unit dynamically changes the control rule to be executed on the basis of the detected instability element. 
     (4) The mobile object according to (2) or (3), in which 
     the plurality of control rules includes at least one of state-dependent Riccati equation (SDRE) control, linear quadratic regulator (LQR) control, H-infinity control theory (H ∞ ) control, adaptive control, proportional-integral-differential (PID) control with a low gain, or PID control with a high gain. 
     (5) The mobile object according to any one of (2) to (4), in which 
     the detection unit calculates an instability level for each of a plurality of instability parameters regarding the autonomous moving control on the basis of the acquired situation information. 
     (6) The mobile object according to (5), in which 
     the plurality of instability parameters includes at least one of an instability level regarding self-location estimation, an instability level regarding a road surface environment, an instability level regarding a control cycle, an instability level regarding a control delay, an instability level regarding modeling calculation, an instability level regarding a stationary disturbance, or an instability level regarding an impulsive disturbance. 
     (7) The mobile object according to (5) or (6), in which 
     the determination unit selects the control rule to be executed on the basis of evaluation information set for each of the plurality of control rules. 
     (8) The mobile object according to (7), in which 
     the evaluation information includes an accuracy level of each of the plurality of control rules and a tolerance level of each of a plurality of tolerance parameters corresponding to the plurality of instability parameters. 
     (9) The mobile object according to (8), in which 
     the plurality of tolerance parameters includes at least one of a tolerance level regarding self-location estimation, a tolerance level regarding a road surface environment, a tolerance level regarding a control cycle, a tolerance level regarding a control delay, a tolerance level regarding modeling calculation, a tolerance level regarding a stationary disturbance, or a tolerance level regarding an impulsive disturbance. 
     (10) The mobile object according to any one of (1) to (9), further including: 
     a drive control unit that controls driving of the mobile object; 
     an estimation unit that estimates a location and a posture of the mobile object on the basis of the acquired situation information; 
     a route acquisition unit that acquires a target route of the mobile object; and 
     a calculation unit that calculates a control value for driving the mobile object on the basis of the estimated location and posture of the mobile object and the acquired target route, and outputs the control value to the drive control unit, in which 
     the determination unit determines a method of calculating the control value by the calculation unit as a control method for executing the autonomous moving control. 
     (11) The mobile object according to any one of (1) to (10), in which 
     the determination unit determines a control parameter for executing the autonomous moving control. 
     (12) The mobile object according to (11), in which 
     the determination unit adjusts a gain of PID control for executing the autonomous moving control. 
     (13) The mobile object according to (2), in which 
     the situation information includes a driving state of the mobile object, and 
     the determination unit selects PID control with a low gain from the plurality of control rules when the mobile object starts driving. 
     (14) The mobile object according to (2) or (13), in which 
     the situation information includes time information, and 
     the determination unit selects a control rule having a high tolerance level regarding self-location estimation from the plurality of control rules during a night-time period. 
     (15) The mobile object according to any one of (2), (13), and (14), in which 
     the situation information includes information regarding weather, and 
     the determination unit selects a control rule having a high tolerance level regarding self-location estimation and a high tolerance level regarding a road surface environment from the plurality of control rules in a case of rainy weather. 
     (16) The mobile object according to any one of (2) and (13) to (15), in which 
     the situation information includes information regarding an irradiation state of light with respect to the mobile object, and 
     the determination unit selects a control rule having a high tolerance level regarding self-location estimation and a high tolerance level regarding an impulsive disturbance from the plurality of control rules in a backlight state. 
     (17) The mobile object according to (8), in which 
     the acquisition unit includes an external sensor, and 
     the determination unit calculates the tolerance level of each of the plurality of tolerance parameters corresponding to the plurality of instability parameters on the basis of a type of the external sensor and the number of external sensors. 
     (18) An information processing apparatus, including: 
     an acquisition unit that acquires situation information regarding a situation of a mobile object; 
     a detection unit that detects an instability element for autonomous moving control of the mobile object on the basis of the acquired situation information; and 
     a determination unit that determines a control method for executing the autonomous moving control on the basis of the detected instability element. 
     (19) An information processing method to be executed by a computer system, including: 
     acquiring situation information regarding a situation of a mobile object; 
     detecting an instability element for autonomous moving control of the mobile object on the basis of the acquired situation information; and 
     determining a control method for executing the autonomous moving control on the basis of the detected instability element. 
     (20) A program that causes a computer system to execute the steps of: 
     acquiring situation information regarding a situation of a mobile object; 
     detecting an instability element for autonomous moving control of the mobile object on the basis of the acquired situation information; and 
     determining a control method for executing the autonomous moving control on the basis of the detected instability element. 
     REFERENCE SIGNS LIST 
     
         
           10  vehicle 
           20  sensor 
           112  automatic driving control unit 
           131  detection unit 
           132  self-location estimation unit 
           135  behavior control unit 
           136  control adjustment unit 
           181  instability element monitoring system 
           182  control adjustment system