JP7321034B2 - Driving support method and driving support device - Google Patents

Description

The present invention relates to a driving assistance method and a driving assistance device.

Techniques have been proposed for detecting the position of the own vehicle and assisting the running of the own vehicle based on the position of the own vehicle. For example, in the technology proposed in Patent Document 1, a set speed is set for each section on a travel route based on traffic information, and based on which section the position of the own vehicle (self-position) is, the speed is determined. Control the speed of the car at the set speed for the current section.

International Publication No. 2013/069130 pamphlet

Since there is an error in the detection of the self-position, it is necessary to estimate the detection error of the self-position and provide a margin corresponding to the detection error when supporting the running of the own vehicle based on the self-position.
However, if the estimated value of the detection error is larger than the actual detection error, the margin becomes unnecessarily large, resulting in driving assistance that is excessively biased toward safety. Margin may not be secured.

For example, in the example of Patent Document 1, when the set speed for the next section is lower than the set speed for the current section, if the estimated value of the detection error is larger than the actual detection error, the vehicle will decelerate unnecessarily early before the vehicle is running. There is a risk that the distance traveled at a low vehicle speed will become longer. Conversely, if the estimated value of the detection error is smaller than the actual detection error, there is a risk that the vehicle will enter the next section at a speed exceeding the set speed.
SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to suppress the detection error of the self-position when the self-vehicle reaches an arbitrary target point to a threshold value or less.

In a driving support method according to an aspect of the present invention, a self-position, which is the current position of the self-vehicle on a map, is detected, a target point to be reached by the self-vehicle is set, and the self-vehicle is driven from the self-position to the target point. Generating a plurality of trajectory candidates, estimating the detection error of the self-position that will be detected when the target point is reached in the future, for each trajectory candidate, and driving such that the detection error is less than the threshold among the plurality of trajectory candidates. Any one of the trajectory candidates is selected as the target travel trajectory, and the vehicle is supported so as to travel along the selected target travel trajectory.

According to one aspect of the present invention, it is possible to suppress the detection error of the self-position when the host vehicle reaches an arbitrary target point to a threshold value or less.

BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows an example of schematic structure of the vehicle which mounts the driving assistance device of embodiment. 1 is a schematic explanatory diagram of a driving support method according to an embodiment; FIG. 1 is a schematic explanatory diagram of a driving support method according to an embodiment; FIG. 2 is a block diagram showing an example of a functional configuration of a controller shown in FIG. 1; FIG. 4 is a block diagram showing an example of a functional configuration of an error detection unit shown in FIG. 3; FIG. 4 is a block diagram showing an example of a functional configuration of a first interval error calculator shown in FIG. 3; FIG. FIG. 4 is an explanatory diagram of a first example of errors calculated by a first interval error calculator; FIG. 9 is an explanatory diagram of a second example of errors calculated by the first section error calculator; FIG. 11 is an explanatory diagram of a third example of errors calculated by the first interval error calculator; FIG. 4 is an explanatory diagram of a first example of an error calculated by a second interval error calculator shown in FIG. 3; FIG. 10 is an explanatory diagram of a second example of errors calculated by the second interval error calculator; 4 is an explanatory diagram of a first example of an error calculated by a track error calculator shown in FIG. 3; FIG. FIG. 9 is an explanatory diagram of a second example of errors calculated by the track error calculator; 4 is a flowchart of an example of a driving support method according to the embodiment; FIG. 11 is an explanatory diagram of an example of a driving support method according to the third embodiment;

(First embodiment)
(composition)
BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that each drawing is schematic and may differ from the actual one. Further, the embodiments of the present invention shown below are examples of apparatuses and methods for embodying the technical idea of the present invention. are not specific to the following: Various modifications can be made to the technical idea of the present invention within the technical scope defined by the claims.

The host vehicle 1 includes a travel support device 10 that assists the travel of the host vehicle 1 . The driving support device 10 detects the current position of the vehicle 1 based on detection results of targets such as road shapes, features, and landmarks around the vehicle 1, odometry, a satellite positioning system, and the like. and assists the running of the own vehicle 1 based on the detected self position.
The self-position detection method using odometry is one of known methods for estimating the self-position of a mobile object by obtaining the moving distance and direction of the mobile object according to the rotation angles and rotational angular velocities of the left and right wheels of the mobile object. is one.

For example, the driving support device 10 supports driving by performing autonomous driving control that automatically drives the own vehicle 1 without the involvement of the driver, based on the detected self-position and the surrounding driving environment. It is also possible to partially support the driving motion related to the running of the own vehicle 1, such as by controlling only the steering angle or only the acceleration/deceleration based on the estimated self-position and the surrounding running environment. In the following, as driving support, an example of performing autonomous driving control for automatically driving the own vehicle 1 without involvement of the driver to perform driving support will be described.

The driving support device 10 includes a satellite positioning device 11, a map database 12, an ambient environment sensor 13, a vehicle sensor 14, a known area database 15, a controller 16, a navigation system 17, an actuator 18, and a user interface device. 19 and.
In the drawings, the map database, the known area database and the user interface device will be referred to as "map DB", "known area DB" and "user I/F device", respectively.

The satellite positioning device 11 measures the current position of the own vehicle 1 by a satellite positioning system. The satellite positioning device 11 may comprise, for example, a Global Positioning System (GNSS) receiver. The GNSS receiver is, for example, a global positioning system (GPS) receiver or the like, and measures the current position of the vehicle 1 by receiving radio waves from a plurality of navigation satellites.
The map database 12 is stored in a storage device such as a flash memory, and stores map information such as the shape of roads necessary for estimating the self-position of the vehicle 1 and the positions and types of targets such as landmarks. there is

As the map database 12, for example, high-precision map data suitable as a map for autonomous driving (hereinafter simply referred to as "high-precision map") may be stored. A high-precision map is map data with higher precision than map data for navigation (hereinafter simply referred to as "navigation map"), and includes more detailed lane-by-lane information than road-by-road information.
For example, a high-definition map includes lane node information indicating a reference point on a lane reference line (for example, a central line within a lane) and lane link information indicating a section of a lane between lane nodes as information for each lane. including.

The lane node information includes the identification number of the lane node, the position coordinates, the number of connected lane links, and the identification number of the connected lane link. The lane link information includes the lane link identification number, lane type, lane width, lane boundary line type, lane shape, lane marking line shape, and lane reference line shape. The high-precision map further includes the types and position coordinates of targets such as traffic lights, stop lines, signs, buildings, utility poles, curbs, crosswalks, and landmarks existing on or near lanes, and their position coordinates. including target information such as the lane node identification number and the lane link identification number corresponding to the .

Also, the map database 12 may store a navigation map. A navigation map contains information on a road-by-road basis. For example, a navigation map includes, as information for each road, road node information that indicates a reference point on a road reference line (for example, a line in the center of a road) and road link information that indicates a section of a road between road nodes. .
Note that the map database 12 may acquire map information from the outside via a communication system such as wireless communication (road-to-vehicle communication or vehicle-to-vehicle communication is also possible). In this case, the map database 12 may periodically acquire the latest map information and update the map information it holds. Further, the map database 12 may store the track on which the vehicle 1 has actually traveled as map information.

The surrounding environment sensor 13 detects various information (surrounding environment information) about the surrounding environment of the own vehicle 1 , for example, targets around the own vehicle 1 . The ambient environment sensor 13 detects the presence of a target existing around the vehicle 1, the relative position between the vehicle 1 and the target, the distance between the vehicle 1 and the target, the direction in which the target exists, and the like. Detect the surrounding environment of the vehicle.
For example, the ambient environment sensor 13 detects the relative positions of targets around the vehicle 1 with respect to the vehicle 1 . Here, the target is, for example, a line on the road surface on which the vehicle 1 travels (lane marking line, etc.), a curb on the road shoulder, a guardrail, or an immovable stationary object such as a building. The ambient environment sensor 13 is an example of the target detection sensor described in the claims.

The ambient environment sensor 13 may include a rangefinder such as a laser range finder (LRF) or radar, or a camera. The camera may be, for example, a stereo camera. The camera may be a monocular camera, and the same target is photographed from a plurality of viewpoints by the monocular camera, and the distance and angle to the target (that is, the relative position of the target with respect to the own vehicle 1) are calculated. good too. Also, the distance to the target may be calculated based on the grounding position of the target detected from the captured image. Various known sensors can be applied to the ambient environment sensor 13 as long as they can detect at least the relative position of the target with respect to the own vehicle.

The vehicle sensor 14 detects various information (vehicle information) obtained from the own vehicle 1 . The vehicle sensor 14 includes, for example, a vehicle speed sensor that detects the running speed (vehicle speed) of the vehicle 1, a wheel speed sensor that detects the rotation speed of each tire of the vehicle 1, acceleration in three axial directions of the vehicle 1 ( 3-axis acceleration sensor (G sensor) that detects deceleration), steering angle sensor that detects steering angle (including turning angle), gyro sensor that detects angular velocity generated in own vehicle 1, yaw rate that detects yaw rate Sensors include an accelerator sensor that detects the accelerator opening of the vehicle 1 and a brake sensor that detects the amount of brake operation by the driver.

The navigation system 17 recognizes the current position of the vehicle 1 using the satellite positioning device 11 and acquires map information for the current position from the map database 12 . The navigation system 17 sets the planned travel route to the destination input by the passenger, and provides route guidance to the passenger according to the planned travel route.
The navigation system 17 also outputs information on the set travel route to the controller 16 . During autonomous travel control, the controller 16 automatically drives (controls driving behavior) the first vehicle so as to autonomously travel along the planned travel route set by the navigation system 17 .

The user interface device 19 is a human machine interface (HMI) for exchanging information with a passenger, and the user interface device 19 is an information terminal separate from the driving support device 10 (for example, smartphone or tablet device). In addition to the driver, the occupant includes an occupant and a fellow passenger who have operation instruction authority related to the autonomous travel control of the own vehicle 1 .

User interface device 19 may include, for example, a speaker and a microphone for transmitting and receiving audio information. The user interface device 19 may also comprise a display device for providing display information. The user interface device 19 may include a keyboard, buttons, dials, sliders, mice, touch panels, levers, joysticks, touch pads, etc. that are physically operated by the passenger.

The controller 16 is an electronic control unit (ECU) that performs driving support control of the own vehicle 1 . Controller 16 includes a processor 20 and peripheral components such as storage device 21 . The processor 20 may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit).
The storage device 21 may include a semiconductor storage device, a magnetic storage device, an optical storage device, or the like. The storage device 21 may include memories such as a register, a cache memory, a ROM (Read Only Memory) used as a main memory, and a RAM (Random Access Memory).
The functions of the controller 16 to be described below are implemented, for example, by the processor 20 executing a computer program stored in the storage device 21 .

Note that the controller 16 may be formed of dedicated hardware for executing each information processing described below.
For example, controller 16 may comprise functional logic circuitry implemented in a general purpose semiconductor integrated circuit. For example, controller 16 may comprise a programmable logic device (PLD), such as a Field-Programmable Gate Array (FPGA), or the like.

The controller 16 detects the current position of the vehicle 1, which is the current position of the vehicle 1. The controller 16 detects the current position of the vehicle 1, the map information of the roads in the map database 12, the route information output from the navigation system 17, the surrounding environment, and the vehicle 1. A target point to be reached by the own vehicle 1 is set based on the running state of (1), and a target travel trajectory is set for the own vehicle 1 to travel from the current position to the target point. The controller 16 performs autonomous travel control of the own vehicle 1 based on the target travel trajectory, and drives the actuator 18 to control travel of the own vehicle 1 .

The actuator 18 operates the steering wheel, the accelerator opening and the braking device of the own vehicle 1 according to the control signal from the controller 16 to generate the vehicle behavior of the own vehicle 1 . The actuator 18 includes a steering actuator, an accelerator opening actuator, and a brake control actuator. The steering actuator controls the steering direction and amount of steering of the vehicle 1 . The accelerator opening actuator controls the accelerator opening of the vehicle 1 . The brake control actuator controls the braking operation of the brake system of the host vehicle 1 .

The driving support control by the controller 16 will be described in more detail below. Please refer to FIGS. 2A and 2B.
First, the controller 16 sets a target point to be reached by the vehicle 1 on the planned travel route.

The target point may be, for example, a stop point at an intersection or crosswalk stop line, or a turning start point at a curved road or intersection. Alternatively, it may be a change point of the set speed as in Patent Document 1.
Note that the controller 16 may set the target point as a point on a one-dimensional line along the planned travel route. For example, one point on the lane link of the map information may be set as the target point.

In this case, the position in the road width direction orthogonal to the planned travel route is not specified. Therefore, on a two-dimensional plane, the target point is set as a two-dot chain line extending in the road width direction as shown in FIG. 2A.
Alternatively, the controller 16 may set the target point by two-dimensional coordinates indicating one point on a two-dimensional plane.

Next, the controller 16 sets a target travel trajectory for causing the vehicle 1 to travel from the current self-position to the target point. At that time, the controller 16 generates a plurality of candidates for the target travel trajectory.
In the example of FIGS. 2A and 2B, it is assumed that a plurality of travel trajectory candidates T1, T2, and T3 for causing the own vehicle 1 to travel on the travel path 25 are generated.

Next, the controller 16 calculates the detection error when the self-position is detected when the controller 16 reaches the target point when the host vehicle 1 travels on these traveling trajectory candidates T1 to T3. Estimate for each of T3.
Here, it is assumed that the detection accuracy of the self-position by the controller 16 in the partial regions R1 to R3 on the travel path 25 is known. Note that the accuracy of self-position detection by the controller 16 is known when the accuracy of self-position detection is specified when the controller 16 has traveled in the past, or when the self-position detection accuracy is calculated by a simulation or the like in advance. It means that the detection accuracy of the self-position is specified by some means before traveling this time.

The running trajectory candidate T1 passes through the partial region R2 and other regions. The running trajectory candidate T2 passes through the partial regions R1 and R3 and other regions. The running trajectory candidate T3 passes through the partial region R1 and other regions.
Hereinafter, a partial area in which the detection accuracy of the self-position by the controller 16 is known may be referred to as a "known area". Also, a region other than the known region, that is, a partial region where the accuracy of self-position detection by the controller 16 is unknown may be referred to as an “unknown region”.

When the controller 16 detects the self-position based on the detection results of the targets around the vehicle 1, the detection accuracy of the self-position depends on the number of targets that can be detected by the surrounding environment sensor 13 and the detection direction. Therefore, the known area may be an area in which the number of targets that can be detected by the ambient environment sensor 13 and their detection directions are known.
That is, a target around the own vehicle 1 is imaged, the relative position of the target with respect to the own vehicle 1 is calculated based on the imaged image, and the relative position of the target with respect to the own vehicle 1 and the position of the target on the map are calculated. When the position of the own vehicle is detected based on , the detection accuracy of the relative position of the target to the own vehicle 1 affects the detection accuracy of the own position. More specifically, it is known to calculate the relative position of a target with respect to the own vehicle 1 based on the distances (the number of pixels) and the positions of a plurality of targets on a captured image. The greater the distance (the number of pixels) of the plurality of targets on the image and the greater the number of targets, the higher the detection accuracy of the relative position of the target with respect to the own vehicle 1 . Therefore, if the number of targets that can be detected by the surrounding environment sensor 13 and the detection direction are known, it is possible to estimate the detection accuracy of the relative position of the target with respect to the own vehicle 1. An area in which the number of possible targets and detection directions are known can be defined as a known area.

For example, an area where the distance between two targets can be observed may be set as the known area. Also, for example, an area where three or more target positions can be observed may be set as the known area. Also, for example, an area in which three or more target positions that are not aligned on a straight line can be observed may be set as the known area. Also, for example, an area in which three or more target positions that are not aligned on the quadratic curve can be observed may be set as the known area.
Further, for example, an area in which the target can be photographed with a resolution equal to or higher than a predetermined threshold may be set as the known area. For example, an area in which the target can be photographed with a resolution that allows the controller 16 to detect its own position within a predetermined error range based on the shape of the photographed target may be set as the known area.

When the controller 16 detects the self-position by odometry, the detection accuracy of the self-position depends on the road surface condition of the traveling road. For example, the detection accuracy depends on the road surface μ (friction coefficient of the road surface) and the road surface gradient. Therefore, the known area may be an area in which road surface conditions such as road surface μ and road surface gradient are known.
In other words, when the self-position is detected by odometry, usually, the moving distance per unit time calculated based on the wheel speed and the moving direction calculated based on the steering angle of the wheels are accumulated, for example, the previously detected position. The current self-position is detected by calculating the movement distance and movement direction of the vehicle 1 from a predetermined reference position such as the self-position to the present. The direction of movement based on this is affected by the road surface conditions such as the road surface μ (friction coefficient of the road surface) and the road surface gradient. Therefore, if the road surface conditions such as road surface μ and road surface gradient are known, it is possible to estimate the accuracy of calculation of the moving distance based on the wheel speed and the moving direction based on the turning angle. can be defined as a known area.
Further, for example, an area with a high road surface μ, an area with a small road surface gradient, and an area where puddles are unlikely to occur may be set as known areas.

When the controller 16 detects the self-position by the satellite positioning device 11, the known region may be a region where the accuracy of self-position detection by the satellite positioning system is known. For example, the known region R2 may be a region in which signals from positioning satellites can be easily received because there are no tall buildings, elevated wires, or magnetic bodies in the surrounding area, and the self-position can be detected with the detection accuracy guaranteed by the satellite positioning system. . Hereinafter, tall buildings, elevated lines, and magnetic bodies may be collectively referred to as "tall buildings, etc."

Please refer to FIG. The known area database 15 is stored in a storage device such as a flash memory, and stores known area information related to known areas.
The known area information includes position information representing the range of each known area, and information capable of specifying detection accuracy of the self-position in each known area, for example, detection error occurring in each known area.

For example, the known area information may include information on the positions, numbers, and detection directions of targets that can be detected in the known area as information that can specify the detection accuracy of the self-position based on the target detection results.
Further, for example, the known area information may include road surface condition information such as the road surface μ and the road surface gradient of the known area as information capable of specifying the detection accuracy of the self-position by odometry.
Also, for example, the known area information may include absence information such as tall buildings as information that can specify the detection accuracy of the satellite positioning system.

These known area information are, for example, information on the position, number, and direction of detection of target objects detected when the own vehicle 1 actually travels in the area and detects its own position, information on the road surface condition, information on the surrounding area, and so on. may be obtained by recording the absence of tall buildings, etc. In addition, known area information collected by other vehicles having the same self-position detection performance actually traveling in the area is transmitted via a communication system such as wireless communication (road-to-vehicle communication or vehicle-to-vehicle communication is also possible). It may be obtained from outside.

Please refer to FIGS. 2A and 2B. The controller 16 estimates self-position detection errors occurring in each of the known regions R1 to R3 through which the traveling trajectory candidates T1 to T3 pass, according to the information that can specify the detection accuracy included in the known region information. .
For example, the controller 16 estimates the detection error according to the positions, numbers, and detection directions of targets that are known to be detectable in the known area.

Further, for example, the controller 16 estimates the self-position detection error accumulated by odometry in the known area according to the known road surface condition of the known area.
Also, for example, the controller 16 estimates the detection error of a known area without tall buildings or the like according to the detection accuracy guaranteed by the satellite positioning system.

Further, the controller 16 calculates detection errors occurring in unknown regions other than the known region. For example, the controller 16 may assume a predetermined road surface condition and calculate a detection error accumulated by odometry in an unknown region.
The controller 16 estimates detection errors occurring in the travel trajectory candidates T1 to T3 based on the calculated detection errors in the known regions R1 to R3 and the detection errors in the unknown regions.

The controller 16 compares each of the detection errors of the travel trajectory candidates T1 to T3 with a predetermined threshold value, and sets any travel trajectory candidate whose detection error is equal to or less than the predetermined threshold value as a target travel trajectory.
As a result, the detection error of the self-position of the vehicle 1 at the future target point can be suppressed below the threshold. Also, by estimating the detection error based on the known area information, the detection error at the target point can be improved.

The operation of the controller 16 will be described in detail below with reference to FIG. The controller 16 includes a first self-position estimation unit 30, a second self-position estimation unit 31, a self-position detection unit 32, an error detection unit 33, a trajectory candidate generation unit 34, a trajectory candidate selection unit 35, and a It includes a one-section error calculator 36 , a second section error calculator 37 , a track error calculator 38 , a travel track setter 39 , and a travel controller 40 .

The first self-position estimator 30 estimates the position of the vehicle based on the relative position between the vehicle 1 and the target detected by the surrounding environment sensor 13 and the position of the target on the map stored in the map database 12. Estimate the self-position of 1.
For example, the first self-position estimator 30 calculates the movement amount and the movement direction of the vehicle 1 from the time when the self-position was last estimated, and updates the previously detected self-position based on the movement amount and the movement direction. estimates a temporary self-position on the map.

The first self-position estimation unit 30 converts the coordinates of the relative position of the target detected by the ambient environment sensor 13 into coordinates on the map based on the temporary self-position on the map. The first self-position estimator 30 compares the position of the target after the coordinate conversion with the position of the target stored in the map database 12, and determines the position and attitude of the vehicle 1 that minimizes the error. position and estimate.
Alternatively, the first self-position estimation unit 30 estimates the self-position of the own vehicle 1 according to the positioning result obtained by the satellite positioning device 11 .

The second self-position estimator 31 estimates the self-position of the vehicle 1 by odometry based on the detection result of the vehicle sensor 14 . That is, based on the detection result of the vehicle sensor 14, the amount of movement and direction of movement of the vehicle 1 from the previous estimation of the self-position are calculated, and the previously detected self-position is updated based on the amount of movement and direction of movement. The self-position of the own vehicle 1 is estimated by the following.

The self-position detector 32 detects the self-position of the vehicle 1 based on the estimation results from the first self-position estimator 30 and the second self-position estimator 31 .
For example, when self-position detection based on the detection results of targets around the own vehicle 1 is possible, the self-position detection unit 32 detects the self-position estimated by the first self-position estimation unit 30 based on the targets. , is detected as the self-position of the own vehicle 1 .

Further, when the self-position cannot be detected based on the target detection result and the positioning by the satellite positioning device 11 is possible, the self-position detection unit 32 detects the positioning result by the satellite positioning device 11 as the self-position. Detect as
If neither self-position detection based on a target nor positioning by the satellite positioning device 11 is possible, the self-position detection unit 32 detects the estimation result by the odometry of the second self-position estimation unit 31 as the self-position.

The error detection unit 33 detects a detection error of the self-position by the self-position detection unit 32 .
Please refer to FIG. The error detection section 33 includes a first error detection section 50 , a second error detection section 51 , a third error detection section 52 and an error correction section 53 .
The first error detection unit 50 inputs the self-position information estimated by the second self-position estimation unit 31 and the map information stored in the map database 12, and calculates the accumulated self-position in the odometry of the second self-position estimation unit 31. Estimate the detection error of

For example, the first error detection unit 50 may estimate a detection error that occurs in odometry according to the vehicle speed and turning curvature of the own vehicle 1 .
Further, the first error detection unit 50 may calculate the slip ratio based on the vehicle speed and wheel speed of the own vehicle 1, and estimate the road surface μ of the traveling road of the own vehicle 1 based on the slip ratio and the longitudinal acceleration. Also, the first error detection unit 50 may detect the road gradient of the road on which the vehicle 1 is traveling based on the differential value of the vehicle speed of the vehicle 1 and the detection signal of the three-axis acceleration sensor.
The first error detection unit 50 may estimate the detection error generated in the odometry based on the road surface condition such as the road surface μ and the road surface gradient.

The first error detection unit 50 adds the current estimated detection error to the previous self-position detection error output from the error correction unit 53, thereby calculating the accumulated detection error up to the current self-position. 53.
In addition, the first error detection unit 50 stores the position information of the point where the detection error is estimated, and the road surface condition information such as the estimated road surface μ and the detected road surface gradient as known area information in the known area database 15 .

The second error detection unit 51 detects a detection error when the first self-position estimation unit 30 detects the self-position based on the target detection result, and outputs the detection error to the error correction unit 53 . For example, the second error detection unit 51 detects the detection error by the first self-position estimation unit 30 based on the detected positions, numbers, and detection directions of targets around the own vehicle 1 .
At that time, the second error detection section 51 may detect the detection error by the first self-position estimation section 30 according to the covariance matrix when applying the detection error of the ambient environment sensor 13 to the normal distribution.

The second error detection section 51 may detect the detection error by the first self-position estimation section 30 according to the likelihood of the detection result of the ambient environment sensor 13 . The second error detection unit 51 detects the likelihood of the detection result of the surrounding environment sensor 13 according to the distribution of errors when comparing target positions such as white lines detected by the surrounding environment sensor 13 with target positions in the map database 12 . You can ask for degrees.
The second error detection unit 51 stores the position information of the point where the detection error is estimated and the information of the position, number and direction of detection of targets detected at this point as known area information in the known area database 15 .

The third error detection unit 52 detects an error in the detection of the self-position by the satellite positioning device 11 according to the reception state of satellite signals in the satellite positioning device 11 and outputs it to the error correction unit 53 .
Also, the third error detection unit 52 determines whether there is a tall building around the vehicle 1 based on the surrounding environment information detected by the surrounding environment sensor 13 . If there is no tall building around the vehicle 1, the third error detection unit 52 stores the position information of the point where the detection error was estimated in the known area database 15 as known area information.

Also, the third error detection unit 52 determines whether there is an overhead wire or magnetic material around the vehicle 1 based on the signal strength of the satellite signal. If there is no overhead wire or magnetic material around the vehicle 1, the third error detection unit 52 stores the position information of the point where the detection error was estimated as known area information in the known area database 15. FIG.

When the first self-position estimating unit 30 detects the self-position based on targets around the own vehicle 1, the error correcting unit 53 converts the detection error accumulated by the first error detecting unit 50 into a second Correct (reset) to the detection error detected by the error detection unit 51, and output to the first error detection unit 50, the first interval error calculation unit 36, and the second interval error calculation unit 37 as the detection error up to the current self-position. do.

When the satellite positioning device 11 detects its own position, the error correction unit 53 corrects (resets) the detection error accumulated by the first error detection unit 50 to the detection error detected by the third error detection unit 52 . ) and output to the first error detection section 50, the first section error calculation section 36, and the second section error calculation section 37 as the detection error up to the current self-position.
When the first self-position estimation unit 30 and the satellite positioning device 11 do not detect the self-position, the detection error accumulated by the first error detection unit 50 is regarded as the detection error up to the current self-position. 50 , the first interval error calculator 36 and the second interval error calculator 37 .

Please refer to FIG. The trajectory candidate generation unit 34 generates a plurality of travel trajectory candidates for causing the vehicle 1 to travel from the self-position detected by the self-position detection unit 32 .
The trajectory candidate generation unit 34 generates a driving action plan for automatically causing the vehicle 1 to travel on the planned travel route based on the planned travel route set by the navigation system 17, the route space map, and the risk map. .
A driving action plan is a driving action plan at a lane level (lane level) in a range of medium and long distances, which defines lanes (lanes) in which the own vehicle 1 is to travel and driving actions required to travel in these lanes. It's a plan.

Such driving actions include stopping at a stop line, turning right or left at an intersection, going straight, driving on a curved road with a curvature greater than a predetermined curvature, and changing lanes when driving in a merging section or in multiple lanes.
The trajectory candidate generation unit 34 sets a target point to which the vehicle 1 should reach based on the driving action plan, the motion characteristics of the vehicle 1, and the route space map, and makes the vehicle 1 travel from the vehicle 1 to the target point. Generate candidates for the trajectory of

The candidates for the traveling trajectory generated by the trajectory candidate generating unit 34 are referred to as "traveling trajectory candidates". The travel trajectory candidate may include a speed profile from the self position to the target point.
The trajectory candidate selection unit 35 sequentially selects a plurality of traveling trajectory candidates generated by the trajectory candidate generation unit 34, and outputs them to the first section error calculation unit 36, the second section error calculation unit 37, and the trajectory error calculation unit 38. Output.

The first section error calculator 36 refers to the known region database 15 and determines whether or not there is a known region ahead of the self-position on the traveling trajectory candidate selected by the trajectory candidate selector 35. do.
If there is a known region, the first section error calculator 36 estimates the detection error of self-position detection by the self-position detector 32 in the known region.

See FIG. 5A. The first interval error calculator 36 includes a first error estimator 60 , a second error estimator 61 , and an error corrector 62 .
The first section error calculation unit 36 calculates known area information about road surface conditions and known area information about targets detectable at each point through which the own vehicle 1 traveling in the known area along the running trajectory candidate passes. , and which of the known area information indicating the absence of a tall building or the like is stored.

At the point where the known area information on the road surface state is stored, the first error estimator 60 estimates the self-position detection error when the second self-position estimator 31 detects the self-position by odometry.
The first error estimator 60 reads road surface condition information such as road surface μ and road surface gradient at the point from the known area database 15 as known area information. The first error estimator 60 estimates a detection error generated by odometry based on the vehicle speed, turning curvature, and road surface condition of the vehicle 1 traveling at the point.

The first error estimating section 60 sequentially calculates detection errors generated by odometry at each point along the running trajectory candidate, accumulates them, and outputs them to the error correcting section 62 .
For example, the first error estimating unit 60 calculates the detection error that occurs at each point along the running trajectory candidate, and calculates the accumulated detection error up to the point immediately before that point from the error correcting unit 62. By inputting and adding, the detection error accumulated up to the position may be calculated.
At that time, the first error estimator 60 determines whether the vehicle 1 has not yet entered the known area ahead as shown in FIG. 5B or has already entered as shown in FIG. 5C.

When the own vehicle 1 has not yet entered the known area ahead (FIG. 5B), the first error estimating section 60 estimates the amount of increase in detection error accumulated from the entry point of the known area.
On the other hand, when the vehicle 1 has already entered the known area ahead (FIG. 5C), the first error estimator 60 adds the current self-position detection error detected by the error detector 33 to the self-position detection error. A detection error is estimated by adding a detection error accumulated in a known area in front of the vehicle 1 .

On the other hand, at the point where the known area information about the target is stored, the second error estimator 61 detects the detection error when the first self-position estimator 30 detects the self-position of the vehicle 1 based on the target. to estimate
The second error estimator 61 reads from the known area database 15 information on the positions, numbers, and detection directions of targets that can be detected in the known area as known area information.
The second error estimator 61 estimates the detection error by the first self-position estimator 30 based on the known region information. At that time, the detection error may be estimated according to the covariance matrix of the detection error of the ambient environment sensor 13 .

At a location where known area information indicating the absence of a tall building or the like is stored, the second error estimator 61 replaces the detection error guaranteed by the satellite positioning system with the detection error of the self-position by the first self-position estimator 30. Calculated as an estimate of
The second error estimator 61 outputs the estimated value of the self-position detection error by the first self-position estimator 30 to the error corrector 62 .

The error correction unit 62 detects the accumulated detection error of the first error estimation unit 60 at the point where the second error detection unit 51 estimates the detection error (that is, the point where the known area information such as targets and tall buildings is stored). The estimated error value is corrected (reset) to the detection error detected by the second error detection unit 51 and output.
On the other hand, at points where the second error detection section 51 does not estimate the detection error, the error correction section 62 outputs the estimated value of the detection error output from the first error estimation section 60 as it is.
When the calculation of the detection error is completed up to the advance point of the known area as described above, the error correction unit 62 outputs one of the following values as the estimated value of the detection error in the known area.

(FIG. 5B) If the host vehicle 1 has not yet entered the known area in front of it and there is no point in the known area for which the known area information such as a target or a tall building is stored, the entry into the known area The amount of increase in detection error accumulated by odometry from the point to the departure point (that is, the amount of increase in detection error accumulated in the known area) is output.
(FIG. 5C) If the host vehicle 1 has already entered a known area in front of the vehicle, and the point where the known area information such as a target or a tall building is stored does not exist in the known area, the error detection unit 33 A value obtained by adding the detected error of the current self-position to the detection error accumulated by odometry from the current self-position to the advance point is output as the detection error of the advance position.

(FIG. 5D) If the point where the known area information about the target is stored (denoted as "target detection point" in the figure) exists within the known area, the self-position by the target at the target detection point A value obtained by adding the detection error estimated to occur when detection is performed and the detection error accumulated by odometry from the target detection point to the advance point is output as the detection error at the advance position.
Similarly, if a point where known area information indicating the absence of a tall building or the like is stored within the known area, the satellite positioning system guarantees the detection error accumulated by odometry from this point to the advance point. By adding to the detection error, the detection error at the advance position is output.
Please refer to FIG. The estimated detection error output from the error correction unit 62 is output to the trajectory error calculation unit 38 .

The second section error calculation unit 37 determines whether or not there is an unknown area on the running track candidate selected by the track candidate selection unit 35 and ahead of the self position.
If there is an unknown region, the second section error calculator 37 estimates the detection error of the self-position detection by the self-position detector 32 in the unknown region and outputs it to the trajectory error calculator 38 .
The second section error calculation unit 37 calculates the following based on the vehicle speed and turning curvature of the own vehicle 1 when traveling in the unknown region along the running trajectory candidate, and on a predetermined road surface condition set in advance using a statistical model or the like. Estimate the detection error that occurs in odometry.

At this time, the second interval error calculator 37 determines whether the vehicle 1 has not yet entered the unknown region ahead as shown in FIG. 6A, or has already entered as shown in FIG. 6B.
When the host vehicle 1 has not yet entered the unknown area ahead (FIG. 6A), the second interval error calculator 37 calculates the amount of increase in the detection error accumulated by odometry from the entry point to the exit point of the unknown area. (that is, the amount of increase in the detection error accumulated in the unknown region) is output as the estimated value of the detection error in the unknown region.

On the other hand, when the vehicle 1 has already entered the unknown region ahead (FIG. 6B), the second interval error calculator 37 adds the current self-position detection error detected by the error detector 33 to A value obtained by adding the detection error accumulated by odometry from the current self-position to the advance point is output as the detection error at the advance position in the unknown region.
The trajectory error calculator 38 calculates the trajectory based on the estimated value of the detection error in the known region calculated by the first section error calculator 36 and the estimated value of the detection error in the unknown region calculated by the second section error calculator 37. The detection error of the self-position detection by the self-position detection unit 32 at the time when the target point is reached along the traveling trajectory candidate selected by the candidate selection unit 35 is estimated.

At that time, the trajectory error calculator 38 determines whether or not there is a point stored with known area information such as a target or a tall building in any of the known areas in front of the vehicle 1 .
FIG. 7A shows a case where the known area in front of the host vehicle 1 does not include a point for which known area information such as a target or a tall building is stored. A reference symbol P0 indicates an exit point of the area in which the vehicle 1 is traveling.

The estimated value e0 of the detection error at the exit point P0 is calculated by the second interval error calculator 37 as described with reference to FIG. 6B. If the region in which the host vehicle 1 is traveling is a known region, it is calculated by the first section error calculator 36 as described with reference to FIG. 5C.
Further, the increments Δe1, Δe2, Δe3, and Δe4 of the detection error accumulated in each region ahead of the exit point P0 are calculated by the first section error calculator 36 and the It is calculated by the 2-section error calculator 37 .
The trajectory error calculator 38 calculates the sum of these (e0+Δe1+Δe2+Δe3+Δe4) as an estimated value of the detection error when the target point is reached.

FIG. 7B shows a case where there is a point where known area information such as a target or a tall building is stored (denoted as "target detection point" in the drawing). The trajectory error calculator 38 selects the known area closest to the target point from among the known areas where the target detection point exists. A reference sign P1 indicates an exit point of the selected known area.
The estimated value e1 of the detection error at the exit point P1 is calculated by the first section error calculator 36 as described with reference to FIG. 5D.

The trajectory error calculator 38 calculates the sum (e1+Δe5+Δe6+Δe7) obtained by adding the detection error increments Δe5, Δe6, and Δe7 accumulated in each region ahead of the advance point P1 to the estimated value e1 of the detection error at the advance point P1. It is calculated as an estimated value of the detection error when the target point is reached.
The trajectory error calculator 38 outputs the calculated estimated value to the travel trajectory setter 39 .

The traveling trajectory setting unit 39 selects one of the plurality of traveling trajectory candidates based on the estimated value of the detection error calculated by the trajectory error calculating unit 38 for each of the plurality of traveling trajectory candidates generated by the trajectory candidate generating unit 34. is set as the target running trajectory.
Specifically, any one of the plurality of traveling trajectory candidates for which the estimated value of the detection error is equal to or less than a predetermined threshold value is set as the target traveling trajectory. If there are a plurality of traveling trajectory candidates for which the estimated value of the detection error is equal to or less than a predetermined threshold value, for example, the candidate traveling trajectory with the smallest estimated value of the detection error may be selected.

The travel control unit 40 drives the actuator 18 so that the vehicle 1 travels on the target travel trajectory at a speed that follows the speed profile included in the target travel trajectory set by the travel trajectory setting unit 39, thereby causing the vehicle 1 to The driving behavior of the own vehicle 1 is controlled so that it autonomously travels along the planned travel route.

(motion)
Next, an example of a driving support method according to the embodiment will be described with reference to FIG.
In step S<b>1 , the self-position detection unit 32 detects the self-position, which is the current position of the vehicle 1 .
In step S<b>2 , the error detection unit 33 detects an error in detection of the self-position by the self-position detection unit 32 .
In step S<b>3 , the trajectory candidate generation unit 34 generates a plurality of travel trajectory candidates for causing the vehicle 1 to travel from the self-position detected by the self-position detection unit 32 .

In step S<b>4 , the trajectory candidate selection unit 35 sequentially selects a plurality of traveling trajectory candidates generated by the trajectory candidate generation unit 34 .
In step S5, the first section error calculator 36 estimates the detection error of the self-position by the self-position detector 32 in the known region ahead of the self-position on the traveling trajectory candidate selected by the trajectory candidate selector 35. do.

In step S6, the second section error calculator 37 estimates the detection error of the self-position by the self-position detector 32 in the unknown region ahead of the self-position on the running track candidate selected by the track candidate selector 35. do.
In step S7, the trajectory error calculation unit 38 calculates the error detected by the self-position detection unit 32 at the time when the target point is reached along the traveling trajectory candidate selected by the trajectory candidate selection unit 35, based on the detection errors estimated in steps S5 and S6. Estimate the detection error of self-localization.

In step S<b>8 , the traveling trajectory setting unit 39 determines whether or not all the traveling trajectory candidates generated by the trajectory candidate generating unit 34 have been selected by the trajectory candidate selecting unit 35 . If unselected running track candidates remain (step S8: N), the process returns to step S4, selects one of the unselected running track candidates, and repeats steps S5 to S8.
If all the traveling trajectory candidates have been selected (step S8: Y), the process proceeds to step S9.
In step S9, the traveling trajectory setting unit 39 selects a traveling trajectory candidate for which the estimated value of the detection error calculated by the trajectory error calculating unit 38 is equal to or less than a predetermined threshold among the plurality of traveling trajectory candidates generated by the trajectory candidate generating unit 34. Either one is set as the target travel trajectory.
In step S10, the travel control unit 40 drives the actuator 18 so that the vehicle 1 travels on the target travel trajectory at a speed according to the speed profile included in the target travel trajectory set by the travel trajectory setting unit 39. The driving behavior of the own vehicle 1 is controlled so that the vehicle 1 autonomously travels along the scheduled travel route.

(Effect of the first embodiment)
(1) The self-position detector 32 detects the self-position, which is the current position of the vehicle 1 . The trajectory candidate generation unit 34 sets a target point to which the vehicle 1 should reach, and generates a plurality of travel trajectory candidates for driving the vehicle 1 from the vehicle's own position to the target point. The trajectory error calculator 38 estimates, for each travel trajectory candidate, the detection error of the self-position that will be detected when the vehicle reaches the target point in the future. The traveling trajectory setting unit 39 selects, as a target traveling trajectory, one of the plurality of traveling trajectory candidates whose detection error is equal to or less than a threshold value. The travel control unit 40 assists the travel of the own vehicle 1 so that it travels along the selected target travel trajectory.
As a result, the detection error of the self-position of the vehicle 1 at the target point can be suppressed below the threshold.

(2) If the target detection sensor that detects the target around the own vehicle 1 detects the target, the error detection unit 33 detects the target based on the target detection result and the target map information. When the self-position is detected and the target detection sensor does not detect the target, the self-position is detected based on the odometry of the own vehicle 1 or the satellite positioning system.
As a result, when the target detection sensor detects the target, it is possible to detect the self-position with high accuracy, and even when the target is not detected, the detection of the self-position can be continued. .

(3) The trajectory candidate generation unit 34 generates, as a trajectory candidate, a trajectory that passes through a known region in which the self-position detection accuracy is known among the regions on the trajectory of the vehicle 1 .
As a result, it is possible to know in advance the detection error that occurs on the traveling trajectory candidate, so that the detection error at the target point can be improved.
(4) The first section error calculator 36 estimates the detection error of the self-position in the known region according to the known detection accuracy.
As a result, it is possible to know in advance the detection error that will occur on the traveling trajectory candidate in the known area.

(5) The known area may be an area in which targets that can be detected in the area are known. Also, the known area may be an area in which the road surface condition is known. Also, the known area may be an area in which the accuracy of self-location detection by a satellite positioning system is known. This makes it possible to more accurately estimate the detection error of the self-position in the known area.

(6) The error detection unit 33 calculates the detection error of the self-position according to the variance of the detection error of the target detection sensor or the likelihood of the target detection result.
By calculating the detection error according to the variance value of the detection error of the target detection sensor, the error can be calculated at high speed with a small amount of processing by analytical calculation. The robustness of error estimation can be improved by calculating the detection error according to the likelihood of the detection result of a target that is easy to detect, such as a white line.

(Second embodiment)
Next, a second embodiment will be described. The driving assistance device 10 of the second embodiment receives requests from the occupant regarding the driving method of the own vehicle 1 by the driving assistance device 10 .
For example, the driving support device 10 may accept a driver's request regarding the driving method, such as driving with a faster estimated arrival time to the target point, or safer driving even if the estimated arrival time to the target point is later. . Further, the driving support device 10 may receive a request for sporty driving or quiet and smooth driving.
The driving support device 10 receives these requests via the user interface device 19, for example.

The travel trajectory setting unit 39 selects, as a target travel trajectory, one of a plurality of travel trajectory candidates for which the detection error calculated by the trajectory error calculator 38 is equal to or less than a threshold, according to the request of the passenger. .
For example, when the occupant desires driving with a faster estimated arrival time, the travel trajectory setting unit 39 selects a travel trajectory candidate that has a shorter trajectory length to the target point, a faster vehicle speed, and less smooth acceleration and deceleration.

In addition, for example, if safer driving is desired even if the expected arrival time to the target point is delayed, the travel trajectory setting unit 39 reduces the speed and allows the travel to pass through the section to the target point over a long period of time. Select a trajectory candidate.
Further, for example, when a sporty driving is desired, the running trajectory setting unit 39 selects a running trajectory candidate for cornering at a sharp angle, running at a higher vehicle speed, or low acceleration/deceleration smoothness.
Further, for example, when quiet and smooth driving is desired, the travel trajectory setting unit 39 selects a travel trajectory candidate with obtuse cornering or high smoothness of acceleration and deceleration. In addition, a running track candidate is selected that avoids areas such as gravel roads where running noise is loud, over manholes, and areas where vertical vibration occurs.

(Effect of Second Embodiment)
The driving support device 10 accepts requests from the occupant regarding the driving method of the own vehicle 1 . The traveling trajectory setting unit 39 selects, as the target traveling trajectory, one of a plurality of traveling trajectory candidates for which the detection error calculated by the trajectory error calculating unit 38 is equal to or less than a threshold, according to the passenger's request. do.
As a result, the detection error at the target point becomes equal to or less than the threshold value, and driving that matches the preference of the passenger can be realized.

(Third embodiment)
See FIG. It is assumed that targets L1 and L2 are present near the travel path 25 of the own vehicle 1 and the self position is detected based on the detection results of the targets L1 and L2.
In this case, when the vehicle 1 is caused to travel along the candidate traveling trajectory T2, the distance between the directions for detecting the targets L1 and L2 becomes narrower than when traveling along the candidate trajectory T1, resulting in an error in detecting the self-position. becomes larger.
Therefore, in order to suppress the detection error of the self-position of the vehicle 1 traveling along the traveling trajectory candidate T2, it is necessary to reduce the vehicle speed and increase the number of observations of the targets L1 and L2. As a result, the distance traveled at unnecessarily low speed is increased.

Therefore, the trajectory candidate generator 34 in the third embodiment generates travel trajectory candidates according to the positions of a plurality of targets that can be detected between the current self-position of the vehicle 1 and the target point. For example, the trajectory candidate generating unit 34 may generate a traveling trajectory candidate so as to pass through a point where the interval between directions for detecting a plurality of targets is wider than a predetermined threshold value.
The trajectory candidate generating unit 34 generates a traveling trajectory candidate so that the vehicle 1 passes through a point where the interval between directions for detecting a plurality of targets detected at the current self-position of the vehicle 1 is wider than a predetermined threshold value. Based on the known area information, the positions of multiple targets that can be detected in the known area in front of the self-position are determined, and the point where the interval of the directions for detecting these targets becomes wider than a predetermined threshold. A running trajectory candidate may be generated so as to pass through .

(Effect of the third embodiment)
The trajectory candidate generator 34 of the third embodiment generates travel trajectory candidates according to the positions of a plurality of targets that can be detected from the current self-position of the vehicle 1 to the target point.
Therefore, it is possible to generate a travel trajectory candidate that suppresses the detection error of the self-position, and it is possible to shorten the distance traveled at unnecessarily low speed.

DESCRIPTION OF SYMBOLS 1... Own vehicle 10... Driving assistance apparatus 11... Satellite positioning apparatus 12... Map database 13... Surrounding environment sensor 14... Vehicle sensor 15... Known area database 16... Controller 17... Navigation system 18... Actuator 19 User interface device 20 Processor 21 Storage device 25 Traveling path 30 First self-position estimation unit 31 Second self-position estimation unit 32 Self-position detection unit 33 Error Detector 34 Track candidate generator 35 Track candidate selector 36 First section error calculator 37 Second section error calculator 38 Track error calculator 39 Running track setter 40 ... traveling control section 50 ... first error detection section 51 ... second error detection section 52 ... third error detection section 53 ... error correction section 60 ... first error estimation section 61 ... second error estimation section , 62 ... error correction unit

Claims (10)

Detecting the self-position by self-position detection means for detecting the self-position, which is the current position of the vehicle;
setting a target point for the vehicle to reach and generating a plurality of travel trajectory candidates for causing the vehicle to travel from the vehicle's own position to the target point;
estimating a detection error of the self-position detected by the self-position detecting means when the target point is reached in the future for each of the running trajectory candidates;
selecting any one of the plurality of running trajectory candidates for which the detection error is equal to or less than a threshold as a target running trajectory;
assisting the own vehicle to travel on the selected target travel trajectory;
A driving support method characterized by: When a target detection sensor that detects the relative position of a target around the own vehicle with respect to the own vehicle detects the target, the detection result of the target and the position of the target on the map are combined. and detecting the self-position based on the odometry of the vehicle or the satellite positioning system when the target detection sensor does not detect the target. The driving support method according to claim 1. a range of a partial area for which the detection accuracy of the self-position is known in the area on the travel road of the vehicle; and information on the detection accuracy of the self-position detected by the self-position detecting means in the partial area. determining whether or not the partial area exists on the running trajectory candidate by referring to a database storing known area information;
2. When it is determined that said partial area exists on said traveling trajectory candidate, said detection error occurring in said traveling trajectory candidate is estimated according to known area information stored in said database. 2. The driving support method according to 2. 4. The driving support method according to claim 3, wherein the detection error of the self-position in the partial area is estimated according to the known detection accuracy. 5. The driving support method according to claim 3 , wherein the partial area is an area in which road surface conditions are known. The driving support method according to any one of claims 3 to 5 , wherein the partial area is an area in which the accuracy of self-location detection by a satellite positioning system is known. 3. The driving support method according to claim 2 , wherein the detection error of the self-position is calculated according to a variance of the detection error of the target detection sensor or a likelihood of the detection result of the target. Receiving a passenger's request regarding the driving method of the own vehicle,
Selecting, as the target travel trajectory, one of the plurality of travel trajectory candidates whose detection error is equal to or less than a threshold, according to the request of the occupant;
The driving support method according to any one of claims 1 to 7, characterized in that: Self-position detection means for detecting the self-position, which is the current position of the vehicle;
trajectory candidate generation means for setting a target point to which the vehicle is to be reached and generating a plurality of travel trajectory candidates for driving the vehicle from the vehicle's own position to the target point;
error detection means for estimating the detection error of the self-position detected by the self-position detection means at the time of arrival at the target point for each of the running trajectory candidates;
a traveling trajectory selecting means for selecting, as a target traveling trajectory, one of the plurality of traveling trajectory candidates for which the detection error is equal to or less than a threshold;
a travel support means for supporting travel of the own vehicle so as to travel along the selected target travel trajectory;
A driving support device comprising: The error detection means detects a range of partial areas in which the detection accuracy of the self-position is known in the area on the travel road of the vehicle, and the detection accuracy of the self-position detected by the self-position detection means in the partial areas. and determining whether or not the partial area exists on the candidate traveling trajectory by referring to a database storing known area information including the information of . 10. The driving support device according to claim 9, wherein when it is determined that the detection error occurs in each of the driving trajectory candidates according to the known area information stored in the database.

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