Patent Publication Number: US-2022234580-A1

Title: Vehicle control device

Description:
TECHNICAL FIELD 
     The present invention relates to a vehicle control device controlling a vehicle such as a car, particularly, a vehicle having a target track generation function and a trace control function during autonomous driving travel. 
     BACKGROUND ART 
     A system is conventionally developed, which obtains information of objects around an autonomous driving vehicle, plans driving movement of the autonomous driving vehicle on the basis of the obtained object information and map information, generates a track on the basis of the plan, and controls the autonomous driving vehicle so as to trace the track (refer to, for example, the following patent literature 1). 
     A method is also developed, of obtaining, at high precision, information of the self location of an autonomous vehicle for calculating information of a center line of a lane on which the vehicle should travel, as a base of a track to be generated from a map by matching a feature point map obtained by distance sensors (a laser range finder, an ultrasonic sensor, an infrared sensor, a camera, and the like) mounted in the vehicle and a point group map (refer to, for example, the following patent literature 2). 
     CITATION LIST 
     Patent Literature 
     
         
         Patent literature 1: Japanese Unexamined Patent Application Publication No. 2018-62261 
         Patent literature 2: Japanese Unexamined Patent Application Publication No. 2017-146893 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     However, in the case where an obstacle such as a track or an advertising signboard exists between a solid object (feature) from which a feature point around an autonomous driving vehicle is obtained and a sensor for capturing the solid object, a necessary feature point cannot be obtained. Consequently, there is a case that matching with a point group map cannot be executed with high precision and, as a result, an error occurs in the self-location information. In the conventional way of thinking of autonomous driving, track generation and track tracing control based on high-precision position information are preconditions. Therefore, when an error in estimated self-location information is large, an error occurs in relative information between a road center point to be referred to and the autonomous vehicle position, and generation of a track and control of trace to a generated track become unstable. Consequently, there is a case that it is difficult to assure safety and ride comfort. 
     The present invention has been achieved in consideration of the circumstances and an object of the invention is to provide a vehicle control device capable of ensuring safety and ride comfort. 
     Solution to Problem 
     To achieve the object, a vehicle control device according to the present invention is characterized by having: a self-location estimation processing unit estimating self location of a vehicle by matching sensor information obtained from an external sensor mounted in the vehicle with map information including feature information as a point group; a solid object movement predicting unit predicting movement of an object as a factor of obstructing the matching with the map information; and a driving movement planning unit making a driving movement plan of the vehicle on the basis of a movement prediction result of the obstructive factor object, a result of estimation of a road situation at future time, and a result of estimation of a position error of the vehicle at future time. 
     Advantageous Effects of Invention 
     According to the present invention, a position error of an autonomous vehicle becomes smaller and acceleration/deceleration frequency of the autonomous vehicle and a vehicle steering amount is decreased so that deterioration in ride comfort can be prevented. 
     The other objects, configurations, operations, and effects of the present invention will become apparent by the following description of embodiments. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating the configuration of a travel drive system and sensors of an autonomous driving vehicle in which a vehicle control device according to a first embodiment of the present invention is mounted. 
         FIG. 2  is a block diagram illustrating the configuration of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 3  is a block diagram illustrating the configuration of an autonomous driving planning unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 4  is an explanatory diagram illustrating an example in which the vehicle control device according to the first embodiment of the present invention is applied. 
         FIG. 5  is an explanatory diagram illustrating a point group map used by a self-location estimation processing unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 6  is an explanatory diagram illustrating a point group map in which a feature point map and a point group database are matched. 
         FIGS. 7( a ) and 7( b )  are diagrams illustrating images in front of the vehicle, obtained by sensors. 
         FIG. 8  is a block diagram illustrating a driving movement candidate generating unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 9  is a diagram illustrating a flowchart of processes performed by a driving movement candidate generation computing unit. 
         FIG. 10  is a diagram illustrating a flowchart of generation of a position error correcting movement candidate in processes performed by the driving movement candidate generation computing unit. 
         FIG. 11  is a block diagram illustrating the configuration of a vehicle movement control unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 12  is an explanatory diagram illustrating a high-precision road map used by an autonomous driving planning unit and a vehicle motion control unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 13  is a block diagram illustrating the configuration of a track candidate generating unit of the vehicle control device according to the first embodiment of the present invention. 
         FIG. 14  is a diagram expressing the relations of the position of an autonomous driving vehicle, speed of the autonomous driving vehicle, and a position estimation error near a stop line depending on the presence/absence of an obstructive factor in the first embodiment of the present invention. 
         FIG. 15  is an explanatory diagram illustrating an example in which a vehicle control device according to a second embodiment of the present invention is applied. 
         FIG. 16  is a diagram explaining the difference of feature points which can be detected by an autonomous driving vehicle when the autonomous driving vehicle makes different movements at future time in the second embodiment of the present invention. 
         FIG. 17  is a diagram expressing the relations of the position of an autonomous driving vehicle, lanes of the autonomous driving vehicle, and a front/rear position estimation error near an intersection depending on the presence/absence of an obstructive factor in the second embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be specifically described with reference to the drawings. 
     First Embodiment 
       FIG. 1  is an explanatory diagram illustrating the general configuration of an autonomous driving vehicle (hereinafter, also simply called a vehicle or an autonomous vehicle)  401  in which a vehicle control device  1  according to a first embodiment of the present invention is mounted. In the diagram, FL wheel denotes a left front wheel, FR wheel denotes a right front wheel, RL wheel denotes a left rear wheel, and RR wheel indicates a right rear wheel. 
     The vehicle  401  of the embodiment illustrated has a steering control mechanism  10  as an actuator controlling a travel direction of the vehicle  401  on the basis of information (external information) of external sensors (hereinbelow, simply called sensors)  2 ,  3 ,  4 , and  5  recognizing the outside, a brake control mechanism  13 , and the vehicle control device  1  computing a command value to a throttle control mechanism  20 . The vehicle  401  also has a steering control device  8  controlling the steering control mechanism  10  on the basis of a command value from the vehicle control device  1 , a braking control device  15  adjusting a brake force distribution of each of the wheels (FL wheel, FR wheel, RL wheel, and RR wheel) by controlling the brake control mechanism  13  on the basis of the command value, an acceleration control device  19  adjusting a torque output of an engine (not illustrated) by controlling the throttle control mechanism  20  on the basis of the command value, and a display device  24  displaying a travel plan of the autonomous vehicle  401 , movement prediction of a moving body existing in the periphery, and the like. Although the engine is used as a driving source in the embodiment, obviously, the present invention can be also applied to an electronic vehicle using a motor as a driving source. 
     The vehicle  401  has, as sensors  2 ,  3 ,  4 , and  5  recognizing the outside, a camera  2  in the front part, laser radars  3  and  4  on the right and left sides, and a millimeter-wave radar  5  in the rear part and can detect relative distance and relative speed between the autonomous vehicle  401  and a peripheral vehicle. The vehicle  401  has a communication device  23  performing communication between a road and a vehicle or between vehicles. Although the combination of the sensors is illustrated as an example of the sensor configuration in the embodiment, the present invention is not limited to the combination but may employ a combination of an ultrasonic sensor, a stereo camera, an infrared camera, and the like. The sensor signals (output signals of the sensors) are supplied to the vehicle control device  1 . 
     Although not specifically illustrated in  FIG. 1 , the vehicle control device  1  has, for example, a CPU, a ROM, a RAM, and an input/output device. In the ROM, the flow of a vehicle travel control which will be described hereinafter is stored. As the details will be described later, the vehicle control device  1  computes command values of the actuators  10 ,  13 , and  20  for controlling vehicle travel in accordance with a generated travel plan. The control devices  8 ,  15 , and  19  of the actuators  10 ,  13 , and  20  receive command values of the vehicle control device  1  by communication and control the actuators  10 ,  13 , and  20  on the basis of the command values. 
     Next, the operation of the brake will be described. In a state where the driver drives the vehicle  401 , the pedal force on a brake pedal  12  of the driver is boosted by a brake booster (not illustrated), and hydraulic pressure according to the force is generated by a master cylinder (not illustrated). The generated hydraulic pressure is supplied to wheel cylinders  16 FL to  16 RR via the brake control mechanism  13 . Each of the wheel cylinders  16 FL to  16 RR is configured by a cylinder, a piston, a pad, and the like (which are not illustrated). The piston is propelled by an operating fluid supplied from the master cylinder, and the pad coupled to the piston is pressed against a disc rotor. The disc rotor rotates with the wheel. Consequently, the brake torque acting on the disc rotor becomes a brake force acting between the wheel and the road surface. In such a manner, according to the brake pedal operation of the driver, the brake force can be generated to each wheel. 
     Although the details are not illustrated in  FIG. 1 , the braking control device  15  has, for example, a CPU, a ROM, a RAM, and an input/output device like the vehicle control device  1 . To the braking control device  15 , a brake force instruction value from a combine sensor  14  capable of detecting longitudinal acceleration, lateral acceleration, and yaw rate, wheel speed sensors  11 FL to  11 RR mounted in the wheels, and the above-described vehicle control device  1 , and a sensor signal from a steering wheel angle detecting device  21  via the steering control device  8  are input. An output of the braking control device  15  is connected to the brake control mechanism  13  having a pump and a control valve which are not illustrated and can generate an arbitrary braking force in each wheel independently of the brake pedal operation of the driver. The brake control device  15  has the role of estimating spin and drift of the vehicle  401  and lock of the wheel on the basis of the above information, controlling the brake control mechanism  13  and the like to suppress them, and generating the brake force of the wheel to increase the driving stability of the driver. The vehicle control device  1  can generate an arbitrary brake force in the vehicle  401  by transmitting a brake force command value to the brake control device  15  and has the role of automatically applying a brake in autonomous driving in which the operation of the driver does not exist. The present invention is not limited to the brake control device  15 . Another actuator such as a brake-by-wire may be used. 
     Next, the operation of the steering will be described. In a state where the driver drives the vehicle  401 , the steering torque and the wheel angle input by the driver via a steering wheel  6  are detected by a steering torque detecting device  7  and the steering wheel angle detecting device  21 , respectively and, on the basis of the information, the steering control device  8  controls a motor  9  to generate assist torque. Although not specifically illustrated in  FIG. 1 , the steering control device  8  also has, for example, a CPU, a ROM, a RAM, and an input/output device like the vehicle control device  1 . By the resultant force of the steering torque of the driver and the assist torque by the motor  9 , the steering control mechanism  10  moves and the front wheels are steered. On the other hand, according to the steering angle of the front wheels, the reaction force from the road surface is transmitted to the steering control mechanism  10  and transferred to the driver as road surface reaction force. 
     The steering control device  8  can generate torque by the motor  9  and control the steering control mechanism  10  independently of the steering operation of the driver. 
     Therefore, the vehicle control device  1  can control the front wheels to an arbitrary steering angle by transmitting a steering force command value to the steering control device  8 , and has the role of automatically performing steering in autonomous driving in which the operation of the driver does not exist. The present invention is not limited to the steering control device  8 . Another actuator such as a steer-by-wire may be used. 
     Next, the accelerator will be described. A stroke amount of an accelerator pedal  17  of the driver is detected by a stroke sensor  18  and input to the acceleration control device  19 . Although not specifically illustrated in  FIG. 1 , like the vehicle control device  1 , the acceleration control device  19  also has, for example, a CPU, a ROM, a RAM, and an input/output device. The acceleration control device  19  adjusts the throttle angle in accordance with the accelerator pedal stroke amount to control (the torque output of) the engine. By the above, the vehicle  401  can be accelerated according to the accelerator pedal operation of the driver. The acceleration control device  19  can control the throttle angle independently of the accelerator pedal operation of the driver. Therefore, the vehicle control device  1  can generate arbitrary acceleration in the vehicle  401  by transmitting an acceleration command value to the acceleration control device  19  and has the role of automatically performing acceleration in automatic driving in which the operation of the driver does not exist. 
     Next, the configuration of the vehicle control device  1  for autonomous driving of the embodiment will be described with reference to the block diagram illustrated in  FIG. 2 . 
     The vehicle control device  1  of the embodiment illustrated basically has: an autonomous driving planning unit  201  planning operation of an autonomous vehicle which will be described later, for automatically (in a automated manner) driving the autonomous vehicle to a destination; an autonomous parking planning unit  202  planning the operation of the autonomous vehicle for automatically parking the autonomous vehicle in a parking space in a parking lot or the like; a vehicle motion control unit  203  generating a command value for controlling the vehicle motion of an autonomous driving vehicle; and an actuator control unit  204  for controlling the actuators  10 ,  13 , and  20  such as the steering, the brake, and the engine (via the control devices  8 ,  15 , and  19 ). It is assumed that those units are mounted in different controllers (CPUs). Consequently, a vehicle network  205  for performing communication among the controllers is necessary. The vehicle network  205  may be wire-connected or wireless-connected. As a method of mounting the units to the controllers, there may be a case that the autonomous parking planning unit  202  and the autonomous driving planning unit  201  are mounted in the same hardware. There is also a case that the actuator control unit  204  is mounted in different hardware such as a controller for controlling an engine and a controller for controlling a brake. 
     Next, the configuration and operation of the autonomous driving planning unit  201  included in the vehicle control device  1  of the embodiment will be described with reference to the block diagram illustrated in  FIG. 3 . 
     The autonomous driving planning unit  201  of the embodiment illustrated mainly includes a sensor information processing unit  304 , a map information processing unit  305 , a self-location estimation processing unit  306 , a solid object movement predicting unit  307 , a storage unit  308 , and a driving movement candidate generating unit (driving movement planning unit)  309 . Each of the blocks will be described hereinafter. 
     In the example illustrated in  FIG. 3 , as sensors recognizing the outside, a radar  301  emitting an electric wave toward an object and measuring the reflected wave, thereby measuring distance and direction to the object, a stereo camera  302  capable of recording information also in the depth direction of the object by simultaneously shooting the object from a plurality of different directions, and a vehicle sensor  303  as a sensor group capable of detecting a vehicle state such as sensor information measuring the speed of the vehicle and the rotating speed of the wheels, information obtained by calculating an average position of the autonomous driving vehicle using the GNSS (Global Navigation Satellite System), destination information which is input by an occupant of the autonomous driving vehicle using a navigation system as an interface, and destination information designated by an operator or the like who is in a remote place by using wireless communication such as a telephone line are mounted. Information of the ambient environment obtained on the basis of those pieces of information is input to the sensor information processing unit  304  and converted to object information of a moving object or the like existing around the autonomous vehicle. As concrete object information, attribute information of a pedestrian, a bicycle, a vehicle, and the like and present positions and present speed vectors of them are extracted. Even when the speed obtained at present time is zero, a parked vehicle which may move in future and the like is included in the moving object. There is also the storage unit  308  storing road information and traffic signal information from the point where the autonomous vehicle starts autonomous driving to a target point and their peripheral areas, route information from the present position to the destination point, a traffic rule database in a travel interval, and the like. In the storage unit  308 , a point group database used by the self-location estimation processing unit  306  is stored (the details will be described later). There is also the map information processing unit  305  for organizing light-state information of traffic lights through which the autonomous driving vehicle is supposed to pass into a form which can be used on the basis of information of center lines of lanes of roads and traffic light information necessary for performing autonomous driving on the basis of the information stored in the storage unit  308 . The self-location estimation processing unit  306  which will be described later estimates the location in which the autonomous vehicle exists (to be specific, the absolute position on the map) on the basis of the peripheral information obtained by the sensors, the point group database, the steering angle of the vehicle, the vehicle speed, and information obtained by the GNSS. 
     Subsequently, the object information and the map information is input to the solid object movement predicting unit  307  (from the sensor information processing unit  304  and the map information processing unit  305 ). In the solid object movement predicting unit  307 , the future position and speed information of each moving object (object prediction information) is computed on the basis of the input information. To predict the movement of each moving object, a position R(X(T), Y(T)) in future time T of each object is predicted on the basis of the object information. As a concrete prediction method, in the case where the present position of a moving object is Rn0(Xn(0),Yn(0)) and present speed is Vn(Vxn, Vyn), prediction computation is performed on the basis of the following linear prediction equation (1). 
       Equation (1) 
         Rn ( Xn ( T ), Yn ( T ))= Vn ( Vxn,Vyn )× T+Rn 0( Xn (0), Yn (0))  (1)
 
     In this computing method, it is assumed that each object is in uniform linear motion in which the object moves while maintaining present speed in future time. By the method, the solid object movement predicting unit  307  can perform prediction of many objects in short time. 
     After that, the solid object movement prediction result and the map information is input to the driving movement candidate generating unit  309  (from the solid object movement predicting unit  307  and the map information processing unit  305 ). As will be described later, in the driving movement candidate generating unit  309 , a driving movement candidate (also called driving movement plan) in which an autonomous driving vehicle (autonomous vehicle) does not collide another vehicle and a solid object existing around the autonomous vehicle and which is adapted to the route information and the present vehicle state (such as speed, position, and direction) is calculated and output. 
     Next, a driving movement candidate generating method based on (adding) self-location estimation information output from the self-location estimation processing unit  306  of the embodiment will be described by using a schematic diagram of a representative scene illustrated in  FIG. 4 . In the scene illustrated in  FIG. 4 , the autonomous vehicle  401  makes an autonomous driving travel. The road has one oncoming lane, and a vehicle  402  ahead (hereinafter, also called forward vehicle) travels in front of the autonomous vehicle  401 . It is assumed that the vehicle  402  ahead has features of relatively large height and width like a track or trailer. There are fences  405  each disposed as a border between the road and a sidewalk. It is also assumed that the autonomous vehicle  401  can obtain the information of road end point sequences  407  as ends of the road in which the vehicle can park safely and a road center line  406  by a method of obtaining detection information of the stereo camera  302  ( FIG. 3 ) or an autonomous vehicle peripheral map based on the position information of the autonomous vehicle  401 . Prediction track information  404  of the vehicle  402  ahead as an output result of the solid object movement predicting unit  307  is also obtained. The result of computing a prediction track until five seconds later of the vehicle  402  ahead is illustrated. 
     The details of processes in the self-location estimation processing unit  306  in the embodiment will now be described.  FIG. 5  is a point group map which is output from a point-group database (stored in the storage unit  308 ) at the time of travel in the road area illustrated in  FIG. 4 . At the time of estimating the self location by the self-location estimation processing unit  306 , by matching feature point information obtained by the sensors in the autonomous vehicle position and the point group associated with the position information by using an ICP (Iterative Closest Point) as one of methods of adjusting the relations of position postures so as to match two point groups (ICP matching method), the self location can be estimated with high precision. For example, in  FIG. 4 , edge information of the fences  405  and solid objects  411   a  and  411   b  can be detected by performing image conversion in a sensor obtaining an image. Consequently, by preliminarily forming a map in which the edge information is associated with the position information by using a network-type RTK measuring method and a distance sensor (for example, a lidar), a feature-point map can be obtained by using a distance sensor (for example, a stereo camera) and an image sensor (a monocular camera) which are relatively cheap. In the point group map (map including feature information as a point group) illustrated in  FIG. 5 , feature points ( 501   a ,  501   b ,  502   a , and  502   b ) between the solid objects  411   a  and  411   b  and the fences  405  are associated with positions and recorded. 
     Next, matching between a feature point map obtained by in-vehicle sensors and the point group map will be described with reference to  FIG. 6 . In the case of using the above-described ICP matching method, when the number of feature points of the feature-point map obtained by the sensors is small, matching with the point group map (point group database) may not be accomplished. It is the case such that, although distances among point groups corresponding to feature points are calculated to perform enlargement, reduction, rotation, or the like so that the sum of the distances becomes the minimum in the above-described ICP matching method, since the number of feature points is small, distance sums in a plurality of places become almost equal, and the position cannot be specified. Consequently, when the number of feature points obtained by the sensor is large,  601   b  is output as a candidate (matching candidate map) of a place a feature-point map  701   b  obtained from the image illustrated in  FIG. 6  matches, and variation in a matching center point  602  is small. When the number of feature points obtained by the sensor is small,  601   a  to  601   c  are output as candidates (matching candidate maps) of the place a feature point map  701   a  obtained by the image illustrated in  FIG. 6  matches. Variation in the matching center point  602  becomes a position estimation error. 
     Particularly, when a track or the like having large height or width travels as the vehicle  402  ahead in front of the autonomous vehicle  401  as illustrated in  FIG. 4 , since a sensor (for example, a stereo camera) capable of shooting only the forward of the vehicle has a narrow view angle (sensor detection range  412 ) and, in addition, the vehicle  402  ahead occupies a major region in an image obtained, there is a case that desired feature points cannot be obtained.  FIG. 7( a )  illustrates a result (image) obtained by an in-vehicle sensor in the circumstance illustrated in  FIG. 4 .  FIG. 7( a )  corresponds to the feature point map  701   a  in  FIG. 6 . Although the feature point map  701   a  is obtained from this result, there is a case that since the number of feature points is small, the above-described matching fails. 
     In the embodiment, therefore, feature point acquisition prediction at future time is performed on the basis of a movement prediction result of an object (the vehicle  402  ahead in the scene of  FIG. 4 ) which is a factor of obstructing the matching in the sensor detection range and a road situation presumption result of the circumstance and, on the basis of the result, a driving movement candidate (driving movement plan) of the autonomous vehicle  401  is generated. 
     Next, the driving movement candidate generating unit  309  of the embodiment will be described more specifically with reference to  FIG. 8 . The driving movement candidate generating unit  309  of the embodiment illustrated in the diagram is configured by including, mainly, a road situation presuming unit  901 , a feature point acquisition result predicting unit  902 , and a driving movement candidate generation computing unit  903 . Each of the blocks will be described hereinafter. 
     In the road situation presuming unit  901 , on the basis of a solid object movement prediction result from the solid object movement predicting unit  307  and road information and traffic light information from the map information processing unit  305 , a road situation in future (at future time) is predicted. For example, in the situation illustrated in  FIG. 4 , a future road situation is presumed from future traffic light prediction information based on present light-on information of the traffic light  409 , network communication, information at the time of passage in the past, and the like, presence/absence information of pedestrians around an Intersection  410  obtained from a solid object movement prediction result, and travel patterns up to the present time of the vehicle  402  ahead (disturbance factor). As an example, in a state after five seconds from now, a situation is presumed that no vehicle exists in front and rear of the autonomous vehicle and the traffic light  409  indicates red (stop). 
     Subsequently, in the feature point acquisition result predicting unit  902 , from the road situation presumption result from the road situation presuming unit  901 , the solid object movement prediction result from the solid object movement predicting unit  307 , the point group database from the storage unit  308 , and the like, a result of acquiring the feature points obtained in a future position is predicted. As a predicting method, an autonomous vehicle peripheral situation when the autonomous vehicle  401  travels in some travel patterns (in other words, drives in a plurality of driving movement patterns) can be predicted on the basis of the road situation prediction result. On the basis of angle-view information of the sensor in that position (in each of the travel patterns), whether a feature (solid object) registered in the point-group database exists in the sensor detection range or not is predicted, and a feature point acquisition result obtained from the result is predicted. For example, in the situation illustrated in  FIG. 4 , at future time (for example, after four seconds), since the vehicle  402  ahead moves far, it is predicted that the number of feature points obtained by the sensor of the autonomous vehicle  401  increases.  FIG. 7( b )  illustrates the example.  FIG. 7( b )  corresponds to the feature point map  701   b  in the above-described  FIG. 6 . 
     The (process details of) driving movement candidate generation computing unit  903  will now be described with reference to the flowchart of  FIG. 9 . 
     In basic movement candidate generation in step S 801 , context recognition of the peripheral environment is performed on the basis of the peripheral sensor, the map information, and the like, and (a plurality of) basic movement candidates are generated. For example, in the situation of  FIG. 4 , first, it is considered that the autonomous vehicle travels so as to follow the vehicle  402  ahead on the present lane. It is also considered that the autonomous vehicle passes the vehicle  402  ahead. As an example of another scene, when there are a plurality of travel lanes, the autonomous vehicle may change the lane. When there is a parked vehicle, the autonomous vehicle may pass the parked vehicle. Movement candidates generated here are stored in the memory. 
     In the future position error estimation of step S 802 , a position error in the case of making a target movement for a movement candidate stored in the memory is estimated on the basis of the result obtained by the feature point acquisition result predicting unit  902 . For example, in the situation of  FIG. 4 , when a basic movement of travelling on the present lane so as to follow the vehicle  402  ahead is taken, it is predicted that the number of feature points obtained in feature positions is small, so that it is estimated that the amount of the position error information increases. 
     In error condition determination of step S 803 , for each movement candidate stored in the memory, an error condition determination is performed by comparing a feature position error estimation result of step  3802  and an error condition necessary for the movement candidate. For example, in the situation of  FIG. 4 , in the case of taking the basic movement of travelling so as to follow the vehicle  402  ahead on the present lane, at the time of stop at a stop line  408 , as a lengthwise position error increases, the autonomous vehicle stops suddenly just before the stop line  408 , and the ride comfort deteriorates. Consequently, when there is a movement candidate which does not satisfy an error condition (which will be described later) regarding ride comfort and the like in movement candidates stored in the memory, the computing unit advances to step S 804 . When all of the movement candidates satisfy the error condition regarding ride comfort and the like, the computing unit breaks through the loop process and ends the flow. 
     In position-error-corrected movement candidate generation of step S 804 , an attempt is carried out to decrease a future position error by making a change to a movement candidate which does not satisfy the condition in the error condition determination of step S 803 . As a changing method, first, the case of changing a movement in the time (speed) axis direction (front-rear direction) is examined and, next, the case of changing a movement in the lateral direction is examined. In the case of changing the movement in the time (speed) axis direction (front-back direction), in the situation of  FIG. 4 , a movement candidate of travel while keeping a distance on the present lane without tracking the vehicle  402  ahead is generated. For this, there is a method of searching a movement candidate with a smaller error exploratorily in the time axis direction on the basis of the feature point acquisition result prediction result. Next, in the case of changing the movement in the lateral direction, a method of attempting a lane change or the like is considered. 
     The position-error-corrected movement candidate generation in step S 804  will be described with reference to the flowchart of  FIG. 10 . As illustrated in  FIG. 10 , in step S 1801 , whether it is avoidable in the front-rear direction or not is determined. In the case where it is determined that the error condition is satisfied only by an avoidance movement in the front-rear direction with respect to an obstructive factor object (for example, the vehicle  402  ahead) in the repeating process by the error condition determination of S 803 , generation of a front-rear direction avoiding movement candidate of S 1802  is performed. On the other hand, the avoiding movement in the front-rear direction is rejected in the repeating process by the error condition determination of  3803  and it is determined that the error condition cannot be satisfied only by the avoiding movement in the front-rear direction with respect to the obstructive factor object (for example, the vehicle  402  ahead), generation of a right-left direction avoiding movement of S 1803  is performed. In such a manner, a movement of avoiding a vehicle ahead in the front-rear direction or the right-left direction (driving movement candidate) is generated. 
     In the movement candidate memory exchanging process of step S 805 , the position-error-corrected movement candidate generated in step  3804  replaces the movement candidate which does not satisfy the error condition in the memory storing the movement candidates. The computing unit moves again to the step S 802 . 
     In such a manner, (the driving movement candidate generating unit  309  of) the autonomous driving planning unit  201  predicts a feature point acquisition result at future time when the autonomous vehicle  401  travels in some travel patterns on the basis of the road situation presumption result from the road situation presuming unit  901  and the solid object movement prediction result from the solid object movement predicting unit  307 , estimates a future position error of the autonomous vehicle  401  on the basis of the prediction result, and generates a driving movement candidate (driving movement plan) in which safety and ride comfort are considered on the basis of the estimation result. 
     The autonomous parking planning unit  202  included in the vehicle control device  1  of the embodiment is basically almost the same as the above-described autonomous driving planning unit  201  except for generating (computing) a driving movement candidate in autonomous parking (to a parking space) in a parking space in a parking lot or the like as a destination. 
     Next, the configuration and operation of the vehicle motion control unit  203  included in the vehicle control device  1  of the embodiment will be described with reference to  FIG. 11 . 
     The vehicle motion control unit  203  computes command values (also called actuator target values and track tracing control command values) of the actuators  10 ,  13 , and  20  on the basis of driving movement candidates, peripheral map information, and self-location estimation information output from the autonomous driving planning unit  201  and the autonomous parking planning unit  202 . In the vehicle motion control unit  203 , a lane center line generating unit  1101 , a track candidate generating unit  1102 , a track evaluating unit  1103 , a target track determining unit  1104 , and a track trace control command value computing unit  1105  exist. Each of the blocks will be described hereinafter. 
     In the lane center line generating unit  1101 , on the basis of self-location presumption information and peripheral map information, a center point sequence (expressed by latitude and longitude or (X_ 1 , Y_ 1 ), . . . , (X_N, Y_N) on plane coordinates) of a lane in which the autonomous vehicle exists is calculated. For example, in the example illustrated in  FIG. 12 , based on pedestrian crossing information  1003 , traffic light information  1004 , stop line position information  1005 , lane center point sequence (oncoming lane)  1002 , and the like, a lane center point sequence (autonomous vehicle line)  1001  is calculated. 
     The track candidate generating unit  1102  generates a track candidate on the basis of a drivable area and peripheral map information. The drivable area denotes an area in which an autonomous driving vehicle can travel and stop without colliding against another vehicle and a solid object existing around the autonomous vehicle and which is adapted to route information and present vehicle states (speed, position, orientation, and the like). 
     For example, as illustrated in  FIG. 13 , there is a method that the track candidate generating unit  1102  includes a path candidate generating unit  1201  and a speed candidate generating unit  1202 . In this method, first, the path candidate generating unit  1201  computes a path (a point sequence of passage of the autonomous vehicle, which does not have time-series information) on the basis of the drivable area and the peripheral map information. A method of making a path satisfying the following variational equation (2) in the drivable area as a candidate is considered. 
       Equation (2) 
       Min.∫ t1   t2 [ w 1(acceleration in lateral direction of autonomous vehicle) 2   +w 2(distance from target lane) 2   +w 3(vehicle head angle with respect to tangent of lane center line) 2 ] dt   (2)
 
     As the method of computing the future driving state of the autonomous vehicle, a method of using a plant model of a vehicle is considered. As a plant model, for example, a bicycle model, a four-wheeled model, or the like may be used. The behavior (longitudinal acceleration, lateral acceleration, vehicle head angle, and the like) of a vehicle in the case of travel on a path computed by the variational equation (2) can be obtained from the model. Also in the speed candidate generating unit  1202  which will be described hereinafter, a future vehicle behavior can be predicted by using a similar model. 
     By changing weights (w 1 , w 2 , w 3 ) of coefficients or changing an item to be evaluated, from the output of the equation (2), a plurality of paths can be generated. Other than an output of the equation (2), there is also a method of using a lane center point sequence of an autonomous line in the drivable area. For a plurality of path candidates (a single path candidate is also possible), the speed candidate generating unit  1202  may set speed sequence information satisfying the following equation (3) as a speed candidate. w 4 , w 5 , and w 6  in the equation (3) are weights of coefficients. 
       Equation (3) 
       Min.∫ t1   t2 [ W 4(acceleration in front-rear direction of autonomous vehicle) 2   +w 5(acceleration in lateral direction of autonomous vehicle) 2   +w 6(speed limit−autonomous vehicle speed) 2 ] dt   (3)
 
     Since no vehicle exists rearward of the autonomous vehicle  401  in the scene illustrated in  FIG. 4  in a range which can be taken as a speed candidate, a method of searching in a range equal to or lower than the legal speed specified for a road may be used. 
     Subsequently, the track evaluating unit  1103  evaluates a track candidate generated by the track candidate generating unit  1102 . As the evaluating method, there is considered a method of setting each of track candidates as an evaluation item indicating ride comfort of the following equation (4) (for example, square of acceleration in the vehicle lateral direction, square of acceleration in the vehicle longitudinal direction, and linear sum of square of jerk obtained by differentiating the accelerations). v 1 , v 2 , v 3 , and v 4  are weight coefficients. 
       Equation (4) 
       ride comfort evaluation value= v 1×(vehicle lateral direction acceleration) 2   +v 2×(vehicle longitudinal direction acceleration) 2   +v 3×(vehicle lateral direction jerk) 2   +v 4×(vehicle vertical direction jerk) 2   (4)
 
     Subsequently, the target track determining unit  1104  selects a candidate track having a high evaluation value on ride comfort from track candidates evaluated by the track evaluating unit  1103 . At this time, a method is considered that the target track determining unit  1104  makes adjustment by adding weight to an evaluation value obtained by the track evaluating unit  1103  so as not to change a candidate track which is selected once for a predetermined time for the following reason. When there is a case that, for example, in the following computation cycle of selecting a candidate track intending to change a lane, the target track determining unit  1104  selects a candidate track intending the present lane without changing the lane, there is the possibility that an occupant feels anxiety about the motion of the vehicle. 
     The track trace control command value computing unit  1105  computes a steering command value, a brake operation amount, an engine output value, and the like so that the vehicle  401  traces the target track selected and determined by the target track determining unit  1104 . In this method, command values (track trace control command values) of the steering, the brake, and the engine so as to trace the target track are computed for the target track determined by the target track determining unit  1104 . As a method of computing a command value of the steering, a method of determining a steering amount so as to decrease an error from the target track is known. As a method of computing command values to the engine and the brake realizing target speed, a cruise control computing method capable of controlling a vehicle at conventional predetermined speed may be used. 
     The effect in the situation illustrated in  FIG. 4  with respect to the motion of the vehicle motion control unit  203  of the vehicle control device  1  of the embodiment will now be described with reference to  FIG. 14 . In  FIG. 14 , the solid line indicates the autonomous vehicle speed and position estimation error information in the case where a vehicle ahead as an obstructive factor of matching with map information exists, and the broken line indicates the autonomous vehicle speed and position estimation error information in the case where an obstructive factor of matching with map information does not exist. 
     When an obstructive factor exists, an action of stopping trace to a vehicle ahead and decreasing speed is selected on the basis of information of the vehicle ahead and the circumstance and information registered in a point group database at time T=T 1 . It is seen that, after that, as the obstructive factor disappears from the field of view, matching with the map information becomes possible, and the position error starts decreasing at time T=T 2 . Consequently, no sudden deceleration occurs for a stop line at time T=T 3 , so that autonomous driving which does not deteriorate ride comfort can be performed. 
     The actuator control unit  204  included in the vehicle control device  1  of the embodiment arbitrarily controls the actuators  10 ,  13 , and  20  such as the steering, the brake, the engine, and the like (via the control devices  8 ,  15 , and  19 ) on the basis of command values (track trace control command values) output from (the track trace control command value computing unit  1105 ) of the vehicle motion control unit  203  as described above, so that the vehicle control device  1  realizes the above-described autonomous driving. 
     As described above, the vehicle control device  1  of the embodiment has: the self-location estimation processing unit  306  estimating the self location (absolute position information on the map) of the vehicle by matching with map information including feature information as a point group from sensor information obtained from the external sensors  2  to  5  mounted in the vehicle; the solid object movement predicting unit  307  predicting movement of an object as an obstructive factor of matching with the map information; and the driving movement candidate generating unit (driving action planning unit)  309  making a driving action plan of the vehicle on the basis of the movement prediction result of the obstructive factor object, the result of presuming the road situation at future time, and an estimation result of a position error of the vehicle at future time. The driving movement candidate generating unit (driving action planning unit)  309  predicts a feature point acquisition result at future time on the basis of a movement prediction result of the obstructive factor object and a road situation presumption result in the sensor detection range of the external sensors  2  to  5 , estimates a position error of the vehicle at future time on the basis of the prediction result, and makes a driving action plan of the vehicle on the basis of the estimation result. 
     As a result, the position error of the autonomous vehicle decreases, so that deterioration in ride comfort caused by decreasing the acceleration/deceleration frequency and the vehicle steering amount of the autonomous vehicle can be prevented. 
     Second Embodiment 
     Subsequently, with reference to  FIGS. 15 to 17 , a second embodiment of the present invention, particularly, a driving motion candidate generation computing unit (refer to  FIG. 8 ) in the second embodiment will be described. The configuration itself of a vehicle control device in the second embodiment is similar to that in the foregoing first embodiment. 
     For example, in a road in which a lane is specified by a white line as in the scene illustrated in  FIG. 4 , lane center information is obtained by detection of a white line or the like, and the lane of a target path can be kept or changed. On the other hand, in the case of passing through an intersection, since there is no white line, self-location estimation is performed from the sensor information, and a target lane (lane center information) (passage line) to be traced has to be calculated from the self location and a map. 
       FIG. 15  illustrates an example of a scene to which the embodiment is applied. The vehicle  402  ahead exists in front of the autonomous vehicle  401 . The travel road is a road having two lanes each way, and the autonomous vehicle  401  and the vehicle  402  ahead travel on the left lane. The intersection  410  exists forward and a path to turn right at the intersection  410  is set for the autonomous vehicle  401  on the basis of path information which is set from a navigation system or the like. It is assumed that a vehicle can turn right in any of the right lane and the left lane. In a manner similar to the first embodiment, (the vehicle control device  1  of) the autonomous vehicle  401  is planned so that an error of the estimation position information of the autonomous vehicle  401  is decreased by using peripheral objects (a median strip  1501 , a solid object  1502   a , a solid object  1502   b , and the like). On the other hand, since the vehicle  402  ahead exists, when the vehicle travels rearward of the vehicle  402  ahead, there is the possibility that feature points of the peripheral objects (the median strip  1501 , the solid object  1502   a , and the solid object  1502   b ) cannot be detected by the sensors, and an error cannot be decreased. 
     Consequently, a position error corrected movement candidate is generated as described by using the step S 804  in  FIG. 9  and the flowchart of  FIG. 10 . That is, whether front-rear direction avoidance is possible or not is determined in step S 1801 . In the repeating process by the error condition determination of S 803 , movement in the front-rear direction avoidance is rejected. When it is determined that the error condition cannot be satisfied only by the avoiding movement in the front-rear direction for the obstructive factor object (for example, the vehicle  402  ahead), the right-left direction avoiding movement generation in S 1803  is performed. 
     Consequently, a movement of avoiding the vehicle  402  ahead in the right-left directions (driving movement candidate) is generated. 
     In  FIG. 15, 1001   a  expresses a lane center point sequence of the left lane (current travel lane), and  1001   b  expresses a lane center point sequence of the right lane (the lane to which the vehicle avoids in the right-left direction (concretely, the right side)). 
     Next, the movement of the autonomous vehicle and the peripheral environment in the case where a driving movement candidate in the right-left direction is generated will be described with reference to  FIG. 16 . 
     At time T=Ta, the autonomous vehicle  401  predicts the state of the autonomous vehicle  401  at time T=Tb and determines a movement (a driving movement candidate and a target track). In the case where the autonomous vehicle  401  is positioned rearward of the vehicle  402  ahead at time T=Tb, it is predicted that the peripheral objects (the median strip  1501 , the solid object  1502   a , the solid object  1502   b , and the like) are hardly detected (the sensor detection range  412   a ). On the other hand, in the case where the lane is changed to the right lane as the avoiding movement in the right-left direction, it is predicted that the peripheral objects (the median strip  1501 , the solid object  1502   a , the solid object  1502   b , and the like) can be detected by the sensors at time T=Tb (the sensor detection range  412   b ) and, as a result, it can be predicted that the position error of the autonomous vehicle  401  becomes equal to or less than a predetermined value. 
     Subsequently, with respect to the movement of the vehicle movement control unit  203  of the vehicle control device  1  of the embodiment, the effect in the situation of  FIG. 15  will be described hereinafter with reference to  FIG. 17 . In  FIG. 17 , the case of turning right at the intersection while traveling on the lane  1  (left lane) is indicated by the dotted line, and the case of changing the lane from the lane  1  (left lane) to the lane  2  (right lane) halfway and turning right at the intersection is indicated by the solid line. 
     As illustrated in  FIG. 17 , in the case of changing the lane halfway, it is understood that the front-rear position estimation error decreases at the time of turning right at the intersection as compared with the case where the lane is not changed. Since the front-rear position estimation error decreases, the control of tracking the lane center line becomes stable in a scene of turning right at an intersection, so that it is expected that the ride comfort improves. 
     As described above, also in the second embodiment, in a manner similar to the foregoing first embodiment, a position error of an autonomous vehicle (particularly, a position error at an intersection having no white line) decreases, so that deterioration in ride comfort caused by decreasing the acceleration/deceleration frequency and the vehicle steering amount of the autonomous vehicle can be prevented. 
     The present invention is not limited to the foregoing embodiments but includes various modifications. For example, the forgoing embodiments have been described to make the present invention easily understood and are not necessarily limited to a device having all of the configurations described. A part of the configuration of a certain embodiment can be replaced with a configuration of another embodiment, or a configuration of an embodiment can be added to a configuration of another embodiment. With respect to a part of the configuration of each embodiment, addition, deletion, or replacement of another configuration can be performed. 
     A part or all of the configurations, functions, processing units, processing means, and the like may be realized by hardware by, for example, designing by an integration circuit. The configurations, functions, and the like may be realized by software in a manner such that a processor interprets a program realizing each function and executes it. Information of a program, a table, a file, and the like realizing each function can be stored in a storing device such as a memory, a hard disk, an SSD (Solid State Drive) or the like or a recording medium such as an IC card, an SD card, a DVD, or the like. 
     The control lines and information lines which are considered to be necessary for the description are illustrated, and all of control lines and information lines necessary for a product are not always illustrated. It may be considered that almost all of the components are mutually connected in practice. 
     LIST OF REFERENCE SIGNS 
     
         
           1  . . . vehicle control device 
           2  to  5  . . . external sensor 
           8  . . . steering control device 
           10  . . . steering control mechanism 
           13  . . . brake control mechanism 
           15  . . . braking control device 
           19  . . . acceleration control device 
           20  . . . throttle control mechanism 
           23  . . . communication device 
           24  . . . display device 
           201  autonomous driving planning unit 
           202  . . . autonomous parking planning unit 
           203  . . . vehicle motion control unit 
           204  . . . actuator control unit 
           205  . . . vehicle network 
           301  . . . radar 
           302  . . . stereo camera 
           303  . . . vehicle sensor 
           304  . . . sensor information processing unit 
           305  . . . map information processing unit 
           306  . . . self-location estimation processing unit 
           307  . . . solid object movement predicting unit 
           308  . . . storage unit 
           309  . . . driving movement candidate generating unit (driving movement planning unit) 
           401  . . . autonomous driving vehicle (autonomous vehicle) 
           402  . . . vehicle ahead (forward vehicle) 
           404  . . . prediction track information of forward vehicle 
           405  . . . fence 
           406  . . . road center line 
           407  . . . road end point sequence 
           408  . . . stop line 
           409  . . . traffic light 
           410  . . . intersection 
           411   a ,  411   b  . . . solid object 
           412 ,  412   a ,  4121   b  . . . sensor detection range 
           501   a ,  501   b  . . . feature points generated by solid objects 
           502   a ,  502   b  . . . feature points generated by fence 
           601   a ,  601   b ,  601   c  . . . matching candidate map 
           602  . . . matching center point (including error information) 
           701   a ,  701   b  . . . feature point map obtained from image 
           901  . . . road situation presuming unit 
           902  . . . feature point acquisition result predicting unit 
           903  . . . driving movement candidate generation computing unit 
           1001 ,  1001   a ,  1001   b  lane center point sequence (autonomous vehicle lane) 
           1002  . . . lane center point sequence (oncoming lane) 
           1003  . . . pedestrian crossing information 
           1004  . . . traffic light information 
           1005  . . . stop line position information 
           1101  . . . lane center line generating unit 
           1102  . . . track candidate generating unit 
           1103  . . . track evaluating unit 
           1104  . . . target track determining unit 
           1105  . . . track trace control command value computing unit 
           1201  . . . path candidate generating unit 
           1202  . . . speed candidate generating unit 
           1501  . . . median strip 
           1502   a ,  1502   b  solid object