Patent Description:
There is proposed a method of performing an autonomous driving by accurately estimating the own vehicle position based on the detection result of features in surroundings of the vehicle by use of a radar or a camera. Patent Reference <NUM> discloses method of determining the degree of deterioration of a compartment line provided on the current road based on the output of external sensor(s) and controlling the vehicle to move on a lane, if any, on which a compartment line can be more accurately detected than on the current lane.

Patent Reference1: <CIT>
Patent Reference2: <CIT> discloses an automatic driving support system that comprises: road information acquiring means for acquiring road information for specifying the line type of a lane marking and the type of connection of a lane; a case where the vehicle carries out driving by automatic driving support; a travel planning route specifying means for specifying a planned travel route on which the vehicle will travel in the future on the basis of the road information in accordance with the planned travel route identified by the travel planned route identifying means; and vehicle control means for performing the vehicle control. The document <CIT> also represents background art with respect to the subject matter claimed for the present invention.

According to such a mode that the state of each compartment line is measured by external sensor(s) and monitored, there are cases that the state of compartment lines on other lanes cannot be correctly determined under such a condition that there are other vehicles in the vicinity. Furthermore, according to the above mode, it is also impossible to recognize the state of compartment lines existing out of the measurement range by the external sensor(s).

The above is an example of issues to be solved by the present invention. It is an object of the present invention to provide an output device capable of suitably controlling the vehicle based on information relating to road markings.

The object is achieved by the present invention in the aspects of an output device and a control method having the features of the independent claims. Additional features for advantageous embodiments of the present invention are provided in the dependent claims.

A preferred embodiment of the present invention will be described below with reference to the attached drawings.

<FIG> is a schematic configuration diagram of a driving support system according to the embodiments. The driving support system illustrated in <FIG> roughly includes an onboard device <NUM> loaded on a vehicle, a lidar (Light Detection and Ranging or Laser Illuminated Detection and Ranging) <NUM>, a gyro sensor <NUM>, a vehicle speed sensor <NUM> and a GPS receiver <NUM>.

The onboard device <NUM> is electrically connected with the lidar <NUM>, the gyro sensor <NUM>, the vehicle speed sensor <NUM> and the GPS receiver <NUM>, and estimates the position of the vehicle (referred to as "own vehicle position") on which the onboard device <NUM> is loaded based on the outputs of those elements. Then, on the basis of the estimation result of the own vehicle position, the onboard device <NUM> performs autonomous driving control of the vehicle to guide the vehicle along the route to the set destination. The onboard device <NUM> includes a map DB (DB: Database) <NUM> which stores road data and feature information which is information related to the features serving as marks and provided near roads. The features serving as marks may be not only a three-dimensional object such as a kilometer post and a street sign which are periodically provided along the side of the road but also a road marking (road marking paint) such as a compartment line and a sign painted on a road surface. Then, the onboard device <NUM> estimates the own vehicle position by checking (collating) the output of the lidar <NUM> with information registered in the map DB <NUM>.

The lidar <NUM> emits pulse lasers within a predetermined angle range in a horizontal direction and a vertical direction to discretely measure the distance to an object existing in an external field, and generates three-dimensional point cloud information indicating the position of the object. In this case, the lidar <NUM> includes an irradiation unit which irradiates the laser light while changing the irradiation direction, a light receiving unit which receives the reflected light (scattering light) of the irradiated laser light, and an output unit which outputs scan data based on the light receiving signal outputted by the light receiving unit. The scan data is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the response delay time of the laser light specified based on the light receiving signal. Generally, the shorter the distance to the target object is, the higher the accuracy of the distance measurement value outputted by the lidar becomes, whereas the longer the distance to the target object is, the lower the above accuracy becomes. It is noted that the reflection rate of road markings is different from the reflection rate of areas other than the road markings on a road surface. Thus, it is possible to discriminate the point cloud data of the road marking based on the level of the light receiving signal generated in accordance with the light amount of the reflection light. According to the embodiment, the lidar <NUM> is provided so that the lidar <NUM> can scan at least the surface of the road where the vehicle is travelling. The lidar <NUM>, the gyro sensor <NUM>, the vehicle speed sensor <NUM> and the GPS receiver <NUM> supply the output data to the onboard device <NUM>, respectively. The onboard device <NUM> is an example of the "output device" according to the present invention and the lidar <NUM> is an example of the "detection device" according to the present invention.

<FIG> is a block diagram illustrating a functional configuration of the onboard device <NUM>. The onboard device <NUM> mainly includes an interface <NUM>, a storage unit <NUM>, an input unit <NUM>, a control unit <NUM> and an information output unit <NUM>. These elements are connected with each other by a bus line.

The interface <NUM> acquires the output data from the sensors such as the lidar <NUM>, the gyro sensor <NUM>, the vehicle speed sensor <NUM> and the GPS receiver <NUM>, and supplies them to the control unit <NUM>. Also, the interface <NUM> supplies the signals associated with the driving control of the vehicle, which is generated by the control unit <NUM>, to an ECU (Electronic Control Unit) of the vehicle. A signal which the control unit <NUM> sends through the interface <NUM> to the ECU (electronic control device) of the vehicle is an example of the "control information" according to the present invention.

The storage unit <NUM> stores programs executed by the control unit and information necessary for the control unit <NUM> to execute predetermined processing. In the embodiments, the storage unit <NUM> stores the map DB <NUM> including road marking information.

<FIG> illustrates an example of the data structure of the road marking information. For example, the road marking information is information associated with road data of a road where the road marking is provided. According to the example illustrated in <FIG>, the road marking information includes identification information for identifying each individual road marking, position information indicative of the position of each road marking and information (referred to as "detection accuracy information Idet") relating to the detection accuracy at the time of detecting each road marking by external sensor such as the lidar <NUM>. The road marking information also includes information (referred to as "suitable direction information Sdi") indicative of the degree of suitability of each road marking as a reference of the own vehicle position estimation in the travelling direction of the vehicle and in the direction (referred to as "lateral direction") perpendicular to the travelling direction of the vehicle.

The detection accuracy of the road marking indicated by the detection accuracy information Idet indicates the accuracy of collating (checking) the position of the road marking identified based on the output of the lidar <NUM> with the position of the road marking identified based on the map DB <NUM>. Examples of road markings, whose detection accuracies indicated by the detection accuracy information Idet are low, include not only a road marking with a low detectability in a state where the paint thereof is faded (deteriorated) but also a road marking with an anomalistic shape expressed by complex lines. As described later, the latter road marking is a road marking which tends to generate the difference between the position of the road marking identified based on the output of the lidar <NUM> and the position of the road marking identified based on the map DB <NUM>. The detection accuracy information Idet may be frag information which indicates whether or not the detection accuracy of the road marking is low or may be numerical information which indicates the staged degree of the detection accuracy. In the former case, the detection accuracy information Idet may be included only in the road marking information corresponding to road markings whose detection accuracy is low. The detection accuracy information Idet is not limited to information directly indicating the detection accuracy but may be information indirectly indicating the detection accuracy. In the latter case, the detection accuracy information Idet may be information relating to the type of the road marking such as information indicating whether or not the road marking has a complex shape.

The suitable direction information Sdi is information indicative of the degree of the suitability of the road marking at the time when the road marking is used as a reference of the own vehicle position estimation in the travelling direction and the lateral direction of the vehicle, in cases where the onboard device <NUM> estimates the own vehicle position by checking (collating) the position of the road marking detected by the lidar <NUM> with the position of the road marking registered in the map DB <NUM>. The suitable direction information Sdi is predetermined information generated based on the shape of the road marking in the extending direction on the road surface. Specifically, for example, a stop line extends in the lateral direction of the vehicle. Thus, it is suitable as a reference of the own vehicle position estimation in the travelling direction of the vehicle whereas it is not suitable as a reference of the own vehicle position estimation in the lateral direction of the vehicle. In anther example, a solid compartment line continuously extends in the travelling direction of the vehicle. Thus, it is suitable as a reference of the own vehicle position estimation in the lateral direction of the vehicle whereas it is not suitable as a reference of the own vehicle position estimation in the travelling direction of the vehicle. In still another example, a dashed compartment line intermittently extends in the travelling direction, so it is preferable as a reference of the own vehicle position estimation in the lateral direction of the vehicle though it has no advantage over a solid compartment line. In addition, since the edges of the dashed lines can be used as a reference of the own vehicle position estimation in the travelling direction of the vehicle, it is also preferable as a reference of the own vehicle position estimation in the travelling direction of the vehicle. As indicated by the above examples, with respect to each road marking, information indicating the degree of the suitability as a reference of the own vehicle position estimation in the travelling direction and the lateral direction of the vehicle is stored as the suitable direction information Sdi. The suitable direction information Sdi may be frag information which indicates whether or not the degree of the suitability of the road marking is low or may be numerical information which indicates the staged degree of the suitability. In the former case, the suitable direction information Sdi may be included only in the road marking information corresponding to road markings whose degree of the suitability is low. The detection accuracy information Idet is not limited to information directly indicating the degree of the suitability but may be information indirectly indicating the degree of the suitability. In the latter case, the detection accuracy information Idet may be information indicative of the type of the road marking.

It is noted that the map DB <NUM> may be regularly updated. In this case, for example, through a communication unit not shown, the control unit <NUM> receives partial map information regarding the area to which the own vehicle position belongs from a server device which manages map information and control unit <NUM> updates the map DB <NUM> by using it.

A description will be given of the configuration of the onboard device <NUM> with reference to <FIG> again. The input unit <NUM> may be buttons, a touch panel, a remote controller or a voice input device to be operated by a user, and receives an input to designate a destination for the route search or an input designating ON or OFF of the autonomous driving. The information output unit <NUM> may be a display or a speaker for outputting information based on the control by the control unit <NUM>. The information output unit <NUM> is an example of the "display unit" according to the present invention.

The control unit <NUM> includes a CPU for executing programs, and controls the entire onboard device <NUM>. In the embodiments, the control unit <NUM> includes an own vehicle position estimation unit <NUM>, and an autonomous driving control unit <NUM>. The control unit <NUM> is an example of the "collation unit", "first acquisition unit", "second acquisition unit", "position estimation unit", "output unit", "detection unit" and "computer" which executes a program according to the present invention.

The own vehicle position estimation unit <NUM> corrects the own vehicle position estimated from the output data of the gyro sensor <NUM>, the vehicle speed sensor <NUM> and/or the GPS receiver <NUM> based on the measurement values of the distance and the angle with respect to the feature measured by the lidar <NUM> and the position information of the feature extracted from the map DB <NUM>. In the embodiments, as an example, the own vehicle position estimation unit <NUM> alternately executes a prediction step that is a process to estimate the own vehicle position from the output data of the gyro sensor <NUM> and the vehicle speed sensor <NUM>, etc., by a state estimation method based on Bayesian inference, and a measurement updating step that is a process to correct the estimated value of the own vehicle position calculated in the preceding prediction step.

The autonomous driving control unit <NUM> refers to the map DB <NUM>, and transmits signals necessary for the autonomous driving control to the vehicle based on the set route and the own vehicle position estimated by the own vehicle position estimation unit <NUM>. The autonomous driving control unit <NUM> sets a target track based on the set route, and controls the position of the vehicle by transmitting the guide signal to the vehicle such that the own vehicle position estimated by the own vehicle position estimation unit <NUM> stays within a deviation width smaller than a predetermined width from the target track.

A supplementary explanation will be given of the process of estimating the own vehicle position by the own vehicle position estimation unit <NUM>. The own vehicle position estimation unit <NUM> successively repeats the prediction step and the measurement updating step to perform the own vehicle position estimation. The state estimation filter used in those steps may be various filters developed to perform the Bayesian inference, for example, an extended Kalman filter, an unscented Kalman filter and a particle filter. Thus, as the position estimation based on the Bayesian inference, various methods are proposed. In the following, as an example, the own vehicle position estimation using the extended Kalman filter will be briefly described.

<FIG> is a diagram expressing a state variable vector X on two-dimensional rectangular coordinates. In the first embodiment, the z-coordinate is projected on the xy-two-dimensional rectangular coordinates. As shown in <FIG>, the own vehicle position on the plane defined on the xy-two-dimensional rectangular coordinates is expressed by the coordinates " (x, y) " and the azimuth "θ" of the own vehicle. Here, the azimuth θ is defined as an angle formed by the traveling direction of the vehicle and the x-axis. The coordinates (x, y) indicate an absolute position corresponding to the combination of the latitude and the longitude, for example.

<FIG> is a diagram illustrating a schematic relation of the prediction step and the measurement updating step. As shown in <FIG>, by repeating the prediction step and the measurement updating step, calculation and updating of the estimated value of the state variable vector X are successively performed. Here, the state variable vector of the reference time (i.e., current time) "t" subjected to the calculation is expressed as "X-t" or "X^t". Hereinafter, the state variable vector is expressed as "Xt = (xt, yt, θt)T )". It is noted that a subscript "-" is put to the provisional prediction value predicted by the prediction step, and a subscript "^" is put to the higher-accuracy estimation value updated by the measurement updating step.

At the prediction step, by applying the moving speed "v" of the vehicle and the angular rate "ω" (which are collectively expressed hereinafter as "control value ut = (vt, ωt) T") to the state variable vector X^t-<NUM> at the time t-<NUM> calculated at the last measurement updating step, the own position estimator <NUM> calculates an estimated value (referred to as "prior estimated value") X-t of the own vehicle position at the time t. At the same time, the own position estimator <NUM> calculates, from a covariance matrix "Σ-t-<NUM>" calculated at the time t-<NUM> of the last measurement updating step, a covariance matrix "Σ-t" corresponding to the error distribution of the prior estimated value X-t.

At the measurement updating step, the own vehicle position estimation unit <NUM> associates the position vector of a feature registered in the map DB <NUM> and the scan data of the lidar <NUM>. Then, when they are associated, the own vehicle position estimation unit <NUM> acquires the measurement value (hereinafter referred to as "measurement value") "Zt" of the associated feature by the lidar <NUM> and the estimated measurement value "Z^t" of the feature acquired by modelling the measurement processing by the lidar <NUM> using the prior estimated value X-t and the position vector of the feature registered in the map DB <NUM>, respectively. The measurement value Zt is a two-dimensional vector indicating the distance and the scan angle of the feature measured at the time t by the lidar <NUM>. Then, the own vehicle position estimation unit <NUM> multiples the difference between the measurement value Zt and the estimated measurement value Z^t by a Kalman gain "Kt" and add it to the prior estimated value X-t. Thereby, as indicated by the following equation (<NUM>), the own vehicle position estimation unit <NUM> calculates the updated state variable vector (referred to as "post estimated value") X^t.

At the measurement updating step, in the same way as the prediction step, the own vehicle position estimation unit <NUM> calculates, from the prior covariance matrix Σ-t, a covariance matrix Σ^t corresponding to the error distribution of the post estimated value X^t. The parameters such as Kalman gain Kt can be calculated in the same way as a known own-position estimation method using an extended Kalman filter.

When the post estimated value X^t is calculated through the equation (<NUM>) with reference to the road marking whose detection accuracy indicated by the detection accuracy information Idet is low, the difference between the measurement value Zt calculated based on the output of the lidar <NUM> and the estimated measurement value Z^t calculated by use of the position vector of the feature registered in the map DB <NUM> becomes large. Namely, in this case, the difference "Zt - Z^t" multiplied by the Kalman gain Kt becomes large. In this case, the estimation accuracy of the post estimated value X^t calculated through the equation (<NUM>) becomes low.

Above things considered, the autonomous driving control unit <NUM> controls the vehicle so as to avoid estimating the position with reference to such a road marking whose detection accuracy indicated by the detection accuracy information Idet is low. Thereby, the autonomous driving control unit <NUM> suitably suppress the deterioration of the accuracy of the estimated position. The specific control method thereof will be described in the following sections "First Vehicle Control Based on Road Marking Information" and "Second Vehicle Control Based on Road Marking Information".

The first vehicle control based on the road marking information is to correct the target track so that the vehicle changes lanes in cases where the vehicle is travelling on a lane with a road marking whose detection accuracy is low.

<FIG> illustrates a flowchart indicating the first vehicle control executed by the autonomous driving control unit <NUM> based on the road marking information which does not fall within the scope of the claims. According to the flowchart in <FIG>, the autonomous driving control unit <NUM> corrects the predetermined target track of the vehicle if, near the target track, there is a road marking whose detection accuracy is determined to be low according to the detection accuracy information Idet of the road marking information. It is noted that the autonomous driving control unit <NUM> has already set the target track of the vehicle along the route to the set destination by the time of the beginning of the execution of the flowchart in <FIG>.

First, the autonomous driving control unit <NUM> determines whether or not the accuracy of the estimated current position is worse (lower) than a predetermined value (step S100). For example, the autonomous driving control unit <NUM> determines that the accuracy of the estimated current position is worse than the predetermined value if the length of the major axis of the error ellipse identified based on the covariance matrix of the error acquired in the calculation process of the position estimation based on the extended Kalman filter is longer than a predetermined length. Then, when the accuracy of the estimated current position is worse than the predetermined value (step S100; Yes), the autonomous driving control unit <NUM> proceeds with step S101. In contrast, when the accuracy of estimated current position is not worse than the predetermined value (step S100; No), the autonomous driving control unit <NUM> ends the process of the flowchart.

When the accuracy of the estimated current position is worse than the predetermined value, the autonomous driving control unit <NUM> acquires, from the map DB <NUM>, the road marking information associated with the road data of the road(s) which constitutes the route to the destination (step S101). In this case, for example, the autonomous driving control unit <NUM> acquires from the map DB <NUM> the road marking information corresponding to the road (s) on the route existing within a predetermined distance from the current position.

Then, the autonomous driving control unit <NUM> determines whether or not, near the target track (e.g., on the same lane as the target track), there is such a road marking track that the detection accuracy ("referred to as "low detection accuracy") thereof indicated by the detection accuracy information Idet is lower than a threshold (step S102). For example, the above threshold is determined in advance through experimental trials in consideration of the presence/absence of the deterioration of the accuracy of the position estimated by the own vehicle position estimation unit <NUM> and is stored on the storage unit <NUM> in advance.

Then, if the autonomous driving control unit <NUM> determines that there is a road marking near the target track whose detection accuracy indicated by the detection accuracy information Idet is a low detection accuracy (step S102; Yes), the autonomous driving control unit <NUM> reduces the weight on the collation result with the map DB <NUM> to be used to the own vehicle position estimation (step S103). For example, for such a travelling section where the road marking thereof corresponding to the low detection accuracy is possibly within a measurement range by the lidar <NUM>, the autonomous driving control unit <NUM> multiplies "Kt (Zt - Z^t)" in the equation (<NUM>) by a coefficient smaller than <NUM>. Thereby, according to step S103, the autonomous driving control unit <NUM> can suitably reduce the deterioration of the accuracy of the estimated position even when the road marking corresponding to the low detection accuracy is within the measurement range by the lidar <NUM>.

The autonomous driving control unit <NUM> corrects the target track of the vehicle to avoid (get away from) the road marking corresponding to the low detection accuracy (step S104). Specifically, the autonomous driving control unit <NUM> corrects the target track to move on a lane other than the lane on which the road marking of the low detection accuracy is provided. In another example, in such a case that a compartment line of the low detection accuracy is provided on one side of a single lane road where the vehicle is travelling, the autonomous driving control unit <NUM> corrects the target track within the lane to make the travelling position approximate to the comportment line on the other side of the comportment line of the low detection accuracy. In these ways, the autonomous driving control unit <NUM> controls the vehicle to get away from the road marking corresponding to the low detection accuracy, thereby suitably suppressing the position estimation with reference to the road marking corresponding to the low detection accuracy.

<FIG> illustrates a plane view of the vehicle travelling on a two-lane road <NUM> where a complex road marking corresponding to the low detection accuracy is on the right. According to <FIG>, there are a single compartment line <NUM> and a complex compartment line <NUM> for separating the road <NUM> and the opposite road <NUM>. The single compartment line <NUM> is a compartment line consisting of a white line and the complex compartment line <NUM> is mixed compartment lines including white lines and an orange line. The complex compartment line <NUM> includes the comb-shaped white lines on both sides of the orange line for emphasizing the orange line. The dashed line "Lt" illustrates the target track of the vehicle set by the autonomous driving control unit <NUM>.

Since the complex compartment line <NUM> includes the comb-shaped white lines on both sides of the orange line, not only the point cloud data of the orange line but also the point cloud data of the comb-shaped white lines on both sides thereof are obtained at the time of detection of the complex compartment line <NUM> by the lidar <NUM>. Generally, at the time of determining the measurement position (measurement value Z in <FIG>) of the feature by the lidar <NUM> in the own vehicle position estimation, the own vehicle position estimation unit <NUM> calculates the two-dimensional centroid coordinates of the feature with respect to the vehicle based on the point cloud data of the reference feature with respect to the vehicle. In this case, according to the position estimation with reference to the complex compartment line <NUM>, the point cloud data having a large variability in the road width direction is obtained. Thus, in this case, the measurement position (measurement value Z in <FIG>) is determined based on the point cloud data having a large variability in the road width direction and therefore the accuracy of the measurement value Z indicating the coordinates of the position of the complex compartment line <NUM> measured by the lidar <NUM> is relatively low. Thus, in the case of the example illustrated in <FIG>, the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the complex compartment line <NUM> is determined as the low detection accuracy that is lower than the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the other compartment lines <NUM> and <NUM>.

In this case, by referring to the road marking information in the map DB <NUM> associated with the road <NUM> where the vehicle is travelling, the autonomous driving control unit <NUM> determines that the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the complex compartment line <NUM> is a low detection accuracy that is lower than a threshold. Thus, in this case, in order to avoid estimating the position with reference to the complex compartment line <NUM>, the autonomous driving control unit <NUM> determines the target track (see the dashed line Lt) to change lanes to move on the left lane in the road <NUM> that is not adjacent to the complex compartment line <NUM>. When the vehicle travels along the target track indicated the dashed line Lt, the compartment lines <NUM> and <NUM> are the nearest compartment lines at the time of going past the complex compartment line <NUM>. Thus, in this case, the own vehicle position estimation unit <NUM> estimates the position with reference to the compartment line <NUM> and/or the compartment line <NUM>, therefore keeping the positional accuracy in the lateral direction of the vehicle in a high level.

<FIG> illustrates a plane view of the vehicle travelling on a road having a single lane each way on which a road marking of the low detection accuracy is provided. According to <FIG>, between the single lane road <NUM> and the opposite road <NUM>, there are provided a single compartment line <NUM> and a complex compartment line <NUM>. The single compartment line <NUM> has a dashed white line and the complex compartment line <NUM> has a dashed white line and an orange line.

In the case of the example illustrated in <FIG>, not only the point cloud data of the dashed white line but also the point cloud data of the orange line are obtained at the time of detection of the complex compartment line <NUM> by the lidar <NUM>. In this case, as the point cloud data of the complex compartment line <NUM>, the point cloud data having a large variability in the road width direction is obtained from the lidar <NUM> and therefore the accuracy of the measurement value Z corresponding to the complex compartment line <NUM> is relatively low. Thus, in the case of the example illustrated in <FIG>, the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the complex compartment line <NUM> is determined as the low detection accuracy.

In this case, by referring to the road marking information in the map DB <NUM> associated with the road <NUM> where the vehicle is travelling, the autonomous driving control unit <NUM> determines that the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the complex compartment line <NUM> is a low detection accuracy that is lower than a threshold. Thus, in this case, in order to avoid estimating the position with reference to the complex compartment line <NUM>, the autonomous driving control unit <NUM> determines the target track (see the dashed line Lt) to travel on the position nearer to the compartment line <NUM> than the complex compartment line <NUM> on the road <NUM>. When the vehicle travels along the target track indicated the dashed line Lt, the compartment line <NUM> is the nearest compartment lines at the time of going past the complex compartment line <NUM>. Thus, in this case, the own vehicle position estimation unit <NUM> estimates the position with reference to the compartment line <NUM>, therefore keeping the positional accuracy in the lateral direction of the vehicle in a high level.

<FIG> illustrates another example of the road marking whose detection accuracy indicated by the detection accuracy information Idet is the low detection accuracy. As illustrated in <FIG>, the complex compartment line <NUM> is provided between a road <NUM> and the opposite road <NUM>. The width of the complex compartment line <NUM> gradually changes along the road <NUM>. For such a complex compartment line <NUM>, the detection accuracy indicated by the detection accuracy information Idet of the road marking information is also set to the low detection accuracy.

<FIG> illustrates a road <NUM> with two lanes on which the traffic markings <NUM> to <NUM> are provided. In the example illustrated in <FIG>, the traffic markings <NUM> and <NUM> provided on the left lane in the road <NUM> are not faded whereas the traffic markings <NUM> and <NUM> provided on the right lane in the road <NUM> are faded. In the road marking information corresponding to the traffic markings <NUM> and <NUM>, the detection accuracy information Idet indicative of the low detection accuracy is registered. Thus, in the example illustrated in <FIG>, when the vehicle passes through the road <NUM>, the autonomous driving control unit <NUM> determines, on the basis of the detection accuracy information Idet of the road marking information corresponding to the traffic markings <NUM> to <NUM>, that the vehicle should avoid travelling on the lane where the traffic markings <NUM> and <NUM> are provided. Then, the autonomous driving control unit <NUM> determines the target track so as for the vehicle to travel on the left lane having the traffic markings <NUM> and <NUM> thereon whose detection accuracy indicated by the detection accuracy information Idet is equal to or larger than a threshold.

At the time of searching for a route to a destination, by referring to the detection accuracy information Idet of the road marking information, the autonomous driving control unit <NUM> may search for the route which avoids a road marking corresponding to the low detection accuracy.

In this case, for example, on the basis of Dijkstra's algorithm, the autonomous driving control unit <NUM> searches for such a route that the sum of link costs is the smallest wherein each of the link costs is calculated per road in consideration of the necessary time and the distance. In this case, in addition to the cost based on the necessary time and the distance, the autonomous driving control unit <NUM> adds the cost based on the detection accuracy indicated by the detection accuracy information Idet to the link cost. In this case, for example, the cost added based on the detection accuracy information Idet increases with decreasing detection accuracy indicated by the detection accuracy information Idet. Accordingly, the autonomous driving control unit <NUM> can suitably search for a route composed of roads whose detection accuracy indicated by the detection accuracy information Idet is high. In addition, by adding the cost based on the detection accuracy information Idet, that is much larger than the cost based on the necessary time and the distance, to the link cost corresponding to such a road having the road marking corresponding to the low detection accuracy, the autonomous driving control unit <NUM> can search for a route substantially avoiding the road having the road marking corresponding to the low detection accuracy.

<FIG> schematically illustrates a route selection screen displayed by the information output unit <NUM>. On the basis of the destination specified through the user input, the autonomous driving control unit <NUM> searches for a recommended route (referred to as "determined without detection accuracy route") <NUM> based on a normal route search without considering the detection accuracy information Idet and a recommended route (referred to as "determined with detection accuracy route") <NUM> based on a route search in consideration of the detection accuracy information Idet and displays them on the route selection screen. According to <FIG>, the autonomous driving control unit <NUM> displays, by a dashed line, a low detection accuracy road section that is a road section of the determined without detection accuracy route <NUM> including road marking(s) corresponding to the low detection accuracy. In <FIG>, the mark <NUM> indicates the destination and the mark <NUM> indicates the current position.

According to the example illustrated in <FIG>, the determined without detection accuracy route <NUM> includes the low detection accuracy road section that is a road section including road marking(s) corresponding to the low detection accuracy and therefore the accuracy of the estimated position could be decreased in the road section. In contrast, the determined with detection accuracy route <NUM> does not have any low detection accuracy road section. Thus, if the determined with detection accuracy route <NUM> is selected as the target route, the autonomous driving control unit <NUM> can suitably perform the position estimation with reference to a road marking. In this way, by using the road marking information for route search, the autonomous driving control unit <NUM> can let the user select, as the target route, a route avoiding any road section where a road marking corresponding to the low detection accuracy is provided.

The second vehicle control based on the road marking information is: to search, on the basis of the detection accuracy information Idet and the suitable direction information Sdi included in the road marking information, for a road marking suitable as a reference of the own vehicle position estimation in the traveling direction or the lateral direction whichever error is larger, in a case that the error of the estimated position in the travelling direction or the lateral direction is larger than a threshold; and to correct the target track so as for the vehicle to approach the above road marking suitable as the reference of the own vehicle position estimation.

<FIG> is a flowchart indicating the second vehicle control based on the road marking information executed by the autonomous driving control unit <NUM>. According to the flowchart in <FIG>, the autonomous driving control unit <NUM> searches, on the basis of the detection accuracy information Idet and the suitable direction information Sdi included in the road marking information, for a road marking suitable as a reference of the own vehicle position estimation in the traveling direction or the lateral direction whichever error is larger and corrects the target track of the vehicle if the suitable road marking exists near the target track. It is noted that the autonomous driving control unit <NUM> has already set the target track of the vehicle along the route to the set destination by the time of the beginning of the execution of the flowchart in <FIG>.

First, the autonomous driving control unit <NUM> identifies each error of the estimated position in the travelling direction and in the lateral direction of the vehicle (step S201). For example, the autonomous driving control unit <NUM> identifies each error of estimated position in the travelling direction and in the lateral direction of the vehicle, respectively, by converting a covariance matrix of the errors, which is acquired in the calculation process of the estimated position based on the extended Kalman filter, by using a rotation matrix in which the azimuth θ of the own vehicle is used.

Next, the autonomous driving control unit <NUM> monitors each accuracy of the position estimated by the own vehicle position estimation unit <NUM> in the travelling direction and the accuracy of the estimated position in the lateral direction. Then, the autonomous driving control unit <NUM> determines whether or not there exists such a direction (referred to as "low positional accuracy direction Dtag") in which the accuracy of the estimated position is low (step S202). For example, the autonomous driving control unit <NUM> compares each error of the estimated position, identified at step S201, in the travelling direction and in the lateral direction with a threshold and thereby detects the direction in which the error of the estimated position is larger than the threshold as the low positional accuracy direction Dtag. Then, the autonomous driving control unit <NUM> determines whether or not the low positional accuracy direction Dtag is detected. The travelling direction and the lateral direction of the vehicle are examples of the "first direction" and the "second direction" according to the present invention, respectively.

When determining that the low positional accuracy direction Dtag does not exist (step S202; No), the autonomous driving control unit <NUM> determines that there is no need to correct the target track of the vehicle and ends the process of the flowchart. In contrast, when determining that the low positional accuracy direction Dtag exists (step S202; Yes), the autonomous driving control unit <NUM> acquires from the map DB <NUM> the road marking information associated with the road data corresponding to roads constituting the route to the destination (step S203). In this case, for example, the autonomous driving control unit <NUM> acquires from the map DB <NUM> the road marking information corresponding to roads on the route situated within a predetermined distance from the current position.

Then, the autonomous driving control unit <NUM> determines whether or not a road marking, which is suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag, is situated near the target track (step S204). In this case, on the basis of the detection accuracy information Idet and the suitable direction information Sdi included in the road marking information, among road markings near the target track, the autonomous driving control unit <NUM> searches for a road marking that is suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag, wherein the autonomous driving control unit <NUM> omits, from the above road markings near the target track, such road marking(s) determined that the detection accuracy is lower than a threshold and such road marking(s) determined to be unsuitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag. Then, on the basis of the search result, the autonomous driving control unit <NUM> determines whether or not there is a road marking suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag.

Then, when the autonomous driving control unit <NUM> determines that a road marking, which is suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag, is situated near the target track (step S204; Yes), the autonomous driving control unit <NUM> corrects the target track of the vehicle so as for the vehicle to approach the above road marking (step S205). Specifically, the autonomous driving control unit <NUM> corrects the target track so that the vehicle moves on the lane where the road marking suitable as the reference is provided. If there are multiple road markings suitable as the reference of the own vehicle position estimation, the autonomous driving control unit <NUM> may correct the target track so as for the vehicle to move on the lane where the most suitable road marking as the reference is provided or to move on the lane where the road marking nearest to the target track among the multiple road makings is provided. In another example, if a compartment line suitable as the reference is provided on one side of a single lane road where the vehicle is travelling, the autonomous driving control unit <NUM> corrects the target track within the range of the lane so as to shift the travelling position to the side of the compartment line. In contrast, when the autonomous driving control unit <NUM> determines that a road marking, which is suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag, is not situated near the target track (step S204; No), the autonomous driving control unit <NUM> determines that there is no need to correct the target track of the vehicle. Then, the autonomous driving control unit <NUM> ends the process of the flowchart.

As explained above, if there exists near the target track such a road marking that is suitable as a reference for the own vehicle position estimation in the low positional accuracy direction Dtag, the autonomous driving control unit <NUM> controls the vehicle so as to approach the above road marking. Thereby, the autonomous driving control unit <NUM> can suitably perform the position estimation using the above road marking as the reference.

For the second vehicle control based on the road marking information, in the same way as the first vehicle control, at the time of determining that there exists near the target track such a road marking whose detection accuracy indicated by the detection accuracy information Idet is the low detection accuracy, the autonomous driving control unit <NUM> may reduce the weight on the collation result with the map DB <NUM> to be used to the own vehicle position estimation. Thereby, the autonomous driving control unit <NUM> suitably reduces the deterioration of the accuracy of the estimated position even when there exists within the measurement range by the lidar <NUM> such a road marking corresponding to the low detection accuracy.

<FIG> illustrates a plane view of the vehicle travelling on a two-lane road <NUM> where a road marking corresponding to the low detection accuracy is on the left. In the example illustrated in <FIG>, there exists a solid line <NUM> that is a compartment line for separating the road <NUM>, the road <NUM> opposite to the road <NUM>, a dashed line <NUM> that is a compartment line for separating lanes in the road <NUM> and a solid line <NUM> that is a compartment line on the left side of the road <NUM>. It is assumed that the solid line <NUM> is faded. The dashed line "Lt" indicates the target track of the vehicle which the autonomous driving control unit <NUM> determines.

Since the solid line <NUM> is faded, the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the solid line <NUM> is set to a low detection accuracy lower than the detection accuracy indicated by the detection accuracy information Idet of the road marking information corresponding to the dashed line <NUM> and the solid line <NUM>. Since the solid line <NUM> and the solid line <NUM> continuously extend in the travelling direction of the vehicle, the suitable direction information Sdi of the road marking information corresponding to the solid line <NUM> and the solid line <NUM> is determined to be information indicating that they are the most suitable road markings as a reference for the own vehicle position estimation in the lateral direction and that they are unsuitable as a reference for the own vehicle position estimation in the travelling direction. Since the dashed line <NUM> intermittently extends in the travelling direction, the suitable direction information Sdi of the road marking information corresponding to the dashed line <NUM> is determined to be information indicating that it is suitable as a reference for the own vehicle position estimation in the lateral direction and that it is also suitable as a reference for the own vehicle position estimation in the travelling direction.

It is herein assumed that the autonomous driving control unit <NUM> determines that the error of the own vehicle position estimation in the lateral direction of the vehicle is larger than a threshold, i.e., the autonomous driving control unit <NUM> determines that the lateral direction of the vehicle is the low positional accuracy direction Dtag. In this case, the autonomous driving control unit <NUM> refers to the road marking information in the map DB <NUM> corresponding to the deashed line <NUM>, the solid line <NUM> and solid line <NUM>. Then, the autonomous driving control unit <NUM> firstly omits the solid line <NUM> from candidates of the reference to be used in the position estimation since the detection accuracy information Idet corresponding to the solid line <NUM> indicates the low detection accuracy. Next, by referring to the suitable direction information Sdi corresponding to the dashed line <NUM> and the solid line <NUM> that are the remaining candidates of the reference, the autonomous driving control unit <NUM> compares the degrees of the suitability thereof for the own vehicle position estimation in the lateral direction of the vehicle that is the low positional accuracy direction Dtag. Since the degree of the suitability of the solid line <NUM> is higher, the autonomous driving control unit <NUM> determines the solid line <NUM> as the reference of the position estimation. Then, the autonomous driving control unit <NUM> corrects the target track so as for the vehicle to approach the solid line <NUM>. When controlling the vehicle to travel in accordance with the target track indicated by the dashed line Lt, the own vehicle position estimation unit <NUM> estimates the position with reference to the solid line <NUM>. Thereby, it is possible to the maintain the positional accuracy in the lateral direction of the vehicle in a high level. Thereafter, if the travelling direction of the vehicle is also determined to be the low positional accuracy direction Dtag, the autonomous driving control unit <NUM> slightly corrects the target track to approach the dashed line <NUM> within the current lane. Thereby, the autonomous driving control unit <NUM> can also maintain the positional accuracy in the travelling direction of the vehicle in a high level.

Instead of the above approach, in some embodiments, regarding each of the dashed line <NUM>, the solid line <NUM> and the solid line <NUM>, the autonomous driving control unit <NUM> converts the detection accuracy indicated by the detection accuracy information Idet and the degree of the suitability indicated by the suitable direction information Sdi into scores through predetermined algorithms, respectively. Then, on the basis of the score of the detection accuracy and the score of the degree of the suitability, the autonomous driving control unit <NUM> may comprehensively determine the road marking suitable as the reference to be used in the own vehicle position estimation in the low positional accuracy direction Dtag.

It is noted that such a case where the error of the own vehicle position estimation in the lateral direction of the vehicle is larger than a threshold is explained with the above example. In contrast, in cases that the error of the own vehicle position estimation in the travelling direction of the vehicle is larger than the threshold, it is necessary to determine, as the reference of the own vehicle position estimation, such a road marking whose degree of the suitability for the own vehicle position estimation in the travelling direction of the vehicle according to the suitable direction information Sdi is high. If both of the error of the own vehicle position estimation in the travelling direction of the vehicle and the error of the own vehicle position estimation in the lateral direction are larger than the threshold, it is necessary to determine, as the reference of the own vehicle position estimation, such a road marking whose degrees of the suitability for the own vehicle position estimation in the travelling direction and in the lateral direction of the vehicle according to the suitable direction information Sdi are both larger than a threshold.

As described above, the onboard device <NUM> according to the embodiment is provided with the own vehicle position estimation unit <NUM> and the autonomous driving control unit <NUM>. The own vehicle position estimation unit <NUM> estimates the own vehicle position by collating a detection result of a road marking by the lidar <NUM> with the map DB <NUM>. The autonomous driving control unit <NUM> acquires, from the map DB <NUM>, the road marking information including the detection accuracy information Idet indicative of an accuracy of the collation with respect to each road marking. On the basis of the detection accuracy information Idet, the autonomous driving control unit <NUM> outputs, to an electronic control device or the information output unit <NUM> of the vehicle, information for controlling the vehicle so that the accuracy of the collation is equal to or larger than a predetermined value. According to this mode, the onboard device <NUM> can suitably increase the accuracy of the own vehicle position estimation.

Modifications suitable for the above embodiment will be described below. The following modifications may be applied to the embodiments in combination.

Instead of the onboard device <NUM> storing the map DB <NUM> on the storage unit <NUM>, a server device not shown may include the map DB <NUM>. In this case, the onboard device <NUM> acquires necessary road marking information and the like by communicating with the server device through a communication unit not shown.

Claim 1:
An output device comprising:
a collation unit (<NUM>, <NUM>) configured to collate a position (Zt) of a road marking based on a detection result by a detection device (<NUM>) with the position (Z^t) of the road marking according to map information, the detection device (<NUM>) detecting an object by emitting a light and receiving the light reflected at the object;
a position estimation unit (<NUM>, <NUM>) configured to estimate a position of a moving body based on a result of the collation;
an autonomous driving control unit (<NUM>) configured to determine whether or not the accuracy of the estimated current position is lower than a predetermined value,
wherein, only when the accuracy of the estimated current position is lower than the predetermined value, the autonomous driving control unit (<NUM>) is configured to
acquire, from a map database (<NUM>), road marking information including detection accuracy information (Idet) indicating an accuracy of the collation with respect to each road marking associated with road data of one or a plurality of roads which constitute the route to a destination,
determine whether the detection accuracy information indicates a road marking whose detection accuracy is lower than a threshold on the same lane as a target track of the moving body, and
output control information for modifying the target track to get away from the road marking,
wherein the autonomous driving control unit (<NUM>) is adapted to search, on the basis of the detection accuracy information (Idet) and suitable direction information (Sdi) included in the road marking information, for a second road marking suitable as a reference of the own vehicle position estimation in the traveling direction or the lateral direction whichever error is larger, in a case that the error of the estimated position in the travelling direction or the lateral direction is larger than a threshold, and to correct the target track of the vehicle to approach the second road marking.