Patent Description:
A method of continuing to track an object in a case where the object overlaps and is hidden behind another object has been known (Patent Literature <NUM>). According to Patent Literature <NUM>, it is determined whether the hidden object reappears based on estimated information about the object, and an identical identification number is assigned to the object when the object reappears.

The article "<NPL>) discloses a technique for tracking objects under natural conditions, including occlusions and complex object motion.

Patent Literature <NUM> discloses a technique for object detection and tracking, which can handle static and dynamic occlusions. According to this technique, a spatial relationship among a group of objects or objct parts is infered or predicted in order to resolve relationships between different object parts and generate assignments of object parts to objects.

Techniques for object detection and tracking are also disclosed in the articles "<NPL>) and "<NPL>), as well as in Patent Literature <NUM>.

In a case, however, where multiple objects are simultaneously hidden behind a different object and conditions of the multiple objects change while the multiple objects are being hidden behind the different object, the technique of the Patent Literature <NUM> is likely to make the estimated information deviate from the actual state and accordingly assign wrong identification numbers to the objects which reappear.

The present invention has been made with the above problem taken into consideration. An object of the present invention is to provide an object tracking method and an object tracking apparatus which, even in a case where multiple objects enter a hidden area and temporarily disappear, are capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden.

Preferred embodiments are the subject matter of the dependent claims.

Referring to the drawings, descriptions will be hereinbelow provided for embodiments of the present invention. The same components will be denoted by the same reference signs throughout the drawings, and descriptions will be omitted.

Referring to <FIG>, descriptions will be provided for an object tracking apparatus <NUM> according to a first embodiment. As illustrated in <FIG>, the object tracking apparatus <NUM> includes an object detector <NUM>, a map information acquirer <NUM>, and a controller <NUM>.

The object detector <NUM> is a sensor for detecting objects around a moving body (vehicle) periodically, and is, for example, a laser range finder. The laser range finder detects objects (pedestrians, bicycles, motorbikes, other vehicles) which are around (for example, within <NUM> from) a host vehicle as the moving body. To put it specifically, the laser range finder scans a laser beam within a certain angle range, and receives a reflected laser beam to detect a time difference between the time of emitting the laser beam and the time of receiving the reflected laser beam. Thereby, the laser range finder detects the distance and speed of each object relative to the host vehicle, as well as a direction in which the object is moving. The object detector <NUM> outputs information about the detected objects to the controller <NUM>. Incidentally, an infrared sensor, an ultrasonic sensor, a camera or the like may be used as the object detector <NUM>. The object detector <NUM> outputs the detection result to the controller <NUM>.

The map information acquirer <NUM> is a device for acquiring map information, and is, for example, a navigation system. The map information is about, for example, the number of lanes in a roadway, junction points, traffic rules, and the like. The map information acquirer <NUM> outputs the acquired map information to the controller <NUM>. Incidentally, the map information acquirer <NUM> may be configured to acquire the map information from a recording medium, or from a server.

The controller <NUM> is a circuit for processing the data acquired from the object detector <NUM> and the map information acquirer <NUM>, and is formed from, for example, an IC, an LSI, or the like. From a functional viewpoint, the controller <NUM> may be divided into an object tracker <NUM>, a hidden area calculator <NUM>, a positional relationship estimator <NUM>, a route calculator <NUM>, and an identification number assigner <NUM>.

The object tracker <NUM> tracks an object by associating parameters of the object as currently detected and parameters of the object as previously detected. The parameters of an object represents the position of the object, the speed of the object, the size of the object, the direction in which the object is moving, the color of the object, and the like. In a case where the parameters of an object as previously detected cannot be associated with the parameters of any object as currently detected, the object tracker <NUM> determines that the previously-detected object has disappeared. Furthermore, in a case where an object has disappeared from the detection range of the sensor, the object tracker <NUM> determines that the object has entered a hidden area.

The hidden area calculator <NUM> calculates a hidden area. The hidden area is an area where an object is hidden behind an obstacle and accordingly cannot be detected by the object detector <NUM>. Detailed descriptions will be provided for the hidden area later.

In a case where the object tracker <NUM> determines that multiple objects have disappeared, the positional relationship estimator <NUM> estimates a positional relationship between the hidden multiple objects by use of the parameters of the multiple objects which are obtained before the disappearance of the multiple objects.

Based on the map information acquired from the map information acquirer <NUM>, the route calculator <NUM> calculates lanes and routes into which the hidden objects may change their courses.

The identification number assigner <NUM> assigns an identification number to each object detected by the object detector <NUM>. Furthermore, the identification number assigner <NUM> continuously assigns the same identification number to each object whose current and previous parameters are associated with each other by the object tracker <NUM>, and assigns a new identification number to each newly-detected object. Moreover, based the positional relationship between the multiple objects which are acquired from the positional relationship estimator <NUM>, as well as the routes and the like into which the multiple objects may change their courses and which are acquired from the route calculator <NUM>, the identification number assigner <NUM> assigns an identification number to each object which comes out of the hidden area.

Next, referring to <FIG>, descriptions will be provided for how an object tracking method is performed, and what process is performed how in a case where an object disappears. As illustrated in <FIG>, in a case where the object detector <NUM> detects objects <NUM> to <NUM> at time t, the object tracker <NUM> sets trackers T1 to T3 to the objects <NUM> to <NUM>. A tracker is information about the positon and speed of the corresponding object. Furthermore, the identification number assigner <NUM> assigns identification numbers ID1 to ID3 to the objects <NUM> to <NUM>. Incidentally, in the first embodiment, a tracker and an identification number are included in identification information to be used to track the corresponding object.

Thereafter, as illustrated in <FIG>, based on the position information and speed information about each of the objects <NUM> to <NUM>, the object tracker <NUM> estimates positions at which the respective objects <NUM> to <NUM> are expected to be at time t+<NUM>. Subsequently, as illustrated in <FIG>, based on the estimated positions, the object tracker <NUM> moves the trackers T1 to T3. After that, the object tracker <NUM> matches the information about the object <NUM> detected at time t+<NUM>, the information about the object <NUM> detected at time t+<NUM>, and the information about the object <NUM> detected at time t+<NUM> with the trackers T1 to T3 which are moved at time t. The object tracker <NUM> sets a new tracker T4 to an object <NUM> whose tracker cannot be found within a certain distance (for example, <NUM>) from the object <NUM>. In contrast, the object tracker <NUM> changes the flag of the tracker T3 from <NUM> to <NUM>, because the object corresponding to the tracker T3 currently does not exist within the certain distance from the tracker T3 although existing more than a certain number of times in the past time. The object tracker <NUM> continues estimating the position of the object corresponding to the tracker T3 using the position information and speed information which the tracker T3 has. In the first embodiment, there are two types of flag which is set for the tracker: <NUM> and <NUM>. The flag <NUM> means that no corresponding object exists within the certain distance from the tracker. The flag <NUM> means that a corresponding object exists within the certain distance from the tracker. In the example illustrated in <FIG>, the flags of the respective trackers T1, T2 are set at <NUM>.

Next, referring to <FIG>, descriptions will be provided for the hidden area. In the first embodiment, as illustrated in <FIG>, the detection range of the object detector <NUM> is <NUM>° in front of the host vehicle. Incidentally, this is one example of the detection range, and the detection range is widened when the sensor is mounted on a side of the vehicle, or the rear of the vehicle. In the detection range illustrated in <FIG>, the hidden area calculator <NUM> calculates a hidden area R. To put it specifically, in a case where the object detector <NUM> detects a different vehicle M1 in the left front of the host vehicle M0, the hidden area calculator <NUM> calculates the hidden area R which is an area hidden behind the different vehicle M1 inside an area formed by straight lines extended from the host vehicle M0, and passing through corner portions P1, P2 of the different vehicle M1. Incidentally, the corner portions P1, P2 of the different vehicle M1 are two points whose are the farthest from the host vehicle M0 among the portions of the different vehicle M1.

Next, referring to <FIG> and <FIG>, descriptions will be provided for how to determine identification information about each of multiple objects in a case where the multiple objects enter a hidden area R. An arrow in <FIG> represents a direction in which the vehicles are travelling.

As illustrated in <FIG>, at time t, the object tracker <NUM> sets trackers T1 to T3 to different vehicles M1 to M3 existing around the host vehicle M0. Meanwhile, the identification number assigner <NUM> assigns identification numbers ID1 to ID3 to the different vehicles M1 to M3. Furthermore, the hidden area calculator <NUM> calculates a hidden area R which is hidden behind the different vehicle M3. Incidentally, illustrations of the trackers T1 to T3 are omitted from <FIG>, and also from <FIG>.

Thereafter, as illustrated in <FIG>, at time t+<NUM>, the different vehicles M1, M2 enter the hidden area R, and the object tracker <NUM> becomes unable to track the different vehicles M1, M2. In this case, the object tracker <NUM> sets the flags of the tracker T1, T2 set for the different vehicles M1, M2 at <NUM>. Subsequently, based on the map information, the route calculator <NUM> calculates zones into which the different vehicles M1, M2 may come out of the hidden area R. In the example illustrated in <FIG>, the zones into which the different vehicles M1, M2 may come out of the hidden area R are zones X, Y.

After that, the positional relationship estimator <NUM> estimates a positional relationship between the different vehicles M1, M2 in the hidden area R. In the first embodiment, the positional relationship between multiple objects in a hidden area R include not only the positional relationship in the hidden area R, but also orders in which the multiple objects come out of the hidden area R. In the example illustrated in <FIG>, the positional relationship between the different vehicles M1, M2 indicates that the different vehicle M2 exists in front of the different vehicle M1. The positional relationship estimator <NUM> is capable of estimating a positional relationship between the different vehicles M1, M2 illustrated in <FIG> from the positional relationship between the different vehicles M1, M2 at time t. Thereafter, the positional relationship estimator <NUM> estimates orders in which the different vehicles M1, M2 may come out of the hidden area R into the zones X, Y. The order in which the vehicles M1, M2 may come out of the hidden area R into the zone X is estimated to be the vehicle M1 before the vehicle M2. In contrast, the order in which the vehicles M1, M2 may come out of the hidden area R into the zone Y is estimated to be the vehicle M2 before the vehicle M1. The positional relationship estimator <NUM> creates a table illustrated in <FIG> based on the estimated orders. In the table illustrated in <FIG>, First Zone Y indicates that a first tracker which may come out of the zone Y is the tracker T2. In addition, Second Zone Y (First Zone Y) indicates that a second tracker which may come out of the zone Y after the detection of the first object having coming out of the zone Y is the tracker T1.

Next, the object detector <NUM> detects an object which comes out of the hidden area R. As illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicle M2 comes out of the hidden area R into the zone Y first, the object tracker <NUM> refers to the table illustrated in <FIG>, and continues associating information about the tracker T2 with the different vehicle M2. In addition, the object tracker <NUM> sets the flag of the tracker T2 at <NUM>. Furthermore, the identification number assigner <NUM> continues assigning the identification number ID2 to the different vehicle M2.

Thereafter, the as illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicle M1 comes out of the hidden area R into the zone Y second, the object tracker <NUM> refers to the table illustrated in <FIG>, and continues associating information about the first tracker T1 with the different vehicle M1. In addition, the object tracker <NUM> sets the flag of the tracker T1 at <NUM>. Furthermore, the identification number assigner <NUM> continues assigning the identification number ID1 to the different vehicle M1.

Next, referring to a flowchart illustrated in <FIG>, descriptions will be provided for an example of how the object tracking apparatus <NUM> according to the first embodiment works. This flowchart starts when an ignition switch is turned on.

In step S101, the object detector <NUM> detects objects around the host vehicle.

In step S103, the object tracker <NUM> sets trackers to the respective objects, and starts to track the objects. Meanwhile, the identification number assigner <NUM> assigns identification numbers to the respective objects.

In step S105, the object tracker <NUM> determines whether multiple objects disappear. In a case where the multiple objects disappear (if Yes in step S105), the process proceeds to step S107. On the other hand, in a case where no multiple objects disappear (if No in step S105), the process returns to step S103. Furthermore, in the case where the object tracker <NUM> determines that the multiple objects disappear, the object tracker <NUM> sets the flags of the multiple objects at <NUM>.

In step S107, the hidden area calculator <NUM> calculates a hidden area.

In step S109, the map information acquirer <NUM> acquires map information.

In step S111, based on the map information, the route calculator <NUM> calculates zones into which the objects may come out of the hidden area.

In step S113, the positional relationship estimator <NUM> estimates a positional relationship between the multiple objects in the hidden area. Moreover, based on the estimated positional relationship, the positional relationship estimator <NUM> estimates orders in which the multiple objects may come out of the hidden area into the zones.

In step S115, in a case where the object detector <NUM> detects objects which come out of the hidden area (if Yes in step S115), the process proceeds to step S117. On the other hand, in a case where the object detector <NUM> detects no such objects (if No in step S115), the process waits for the object detector <NUM> to detect objects which comes out of the hidden area.

In step S117, the object tracker <NUM> refers to the table created by the positional relationship estimator <NUM>.

In step S119, based in a result of referring to the table, the object tracker <NUM> continues associating information about each tracker with the corresponding object.

In step S121, the object tracker <NUM> sets the flags of the continued trackers at <NUM>.

In step S123, the identification number assigner <NUM> determines identification numbers to be assigned to the objects.

In step S125, the object tracking apparatus <NUM> determines whether the ignition switch is off. In a case where the ignition switch is on (if No in step S125), the process returns to step S101. In a case where the ignition switch is off (if Yes in step S125), the object tracking apparatus <NUM> terminates the series of the process.

As discussed above, the following working and effects can be obtained from the object tracking apparatus <NUM> according to the first embodiment.

In the case where the multiple objects enter the hidden area, the object tracking apparatus <NUM> estimates the positional relationship between the multiple objects in the hidden area. When the objects come out of the hidden area, the object tracking apparatus <NUM> determines identification information about each object based on the estimated positional relationship. Thereby, even in the case where the multiple objects enter the hidden area and temporarily disappear, the object tracking apparatus <NUM> is capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden, and can enhances the object tracking performance.

Moreover, in the case where the object tracking apparatus <NUM> can estimating the positional relationship between the multiple objects in the hidden area, the object tracking apparatus <NUM> continues associating the identification information assigned to each object with the object when the object comes out of the hidden area. Thereby, even in the case where the multiple objects enter the hidden area and temporarily disappear, the object tracking apparatus <NUM> is capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden, and can enhances the object tracking performance.

Next, descriptions will be provided for a second embodiment of the present invention. An object tracking apparatus <NUM> according to the second embodiment has the same configuration as the object tracking apparatus <NUM> according to the first embodiment. The second embodiment is different from the first embodiment in terms of the positional relationship between the multiple objects. The first embodiment has been discussed in which the positional relationship estimator <NUM> can estimate the positional relationship between the multiple objects in the hidden area. On the other hand, the second embodiment will be discussed in which the positional relationship estimator <NUM> cannot estimate the positional relationship between the multiple objects in the hidden area.

As illustrated in <FIG>, at time t, the objector tracker <NUM> sets trackers T1 to T3 for other vehicles M1 to M3 existing around the host vehicle M0. Meanwhile, the identification number assigner <NUM> assigns identification numbers ID1 to ID3 to the other vehicles M1 to M3.

Thereafter, as illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicles M1, M2 enter the hidden area R and the object tracker <NUM> becomes unable to track the different vehicles M1, M2, the object tracker <NUM> sets the flags of the trackers T1, T2 set for the different vehicles M1, M2 at <NUM>. Thereafter, the route calculator <NUM> calculates zones X, Y, Z into which the different vehicles M1, M2 may come out of the hidden area R based on the map information.

Subsequently, the positional relationship estimator <NUM> estimates the positional relationship between the different vehicles M1, M2 in the hidden area R. The positional relationship estimator <NUM> estimates which different vehicle may come out of the hidden area R into the zones X, Y, Z, and an order in which the different vehicles M1, M2 may come out of the hidden area R into the zone Z. A vehicle which may come out of the hidden area R into the zone X is the different vehicle M1. Meanwhile, a vehicle which may come out of the hidden area R into the zone Y is the different vehicle M2. In the case illustrated in <FIG>, however, the positional relationship estimator <NUM> cannot estimate the order in which the different vehicles M1, M2 may come out of the hidden area R into the zone Z. The reason for this is that the different vehicle M1 or the different vehicle M2 may come out of the hidden area R into the zone Z after changing lanes. The positional relationship estimator <NUM> creates a table illustrated in <FIG> based on the estimated positional relationship.

After that, the object detector <NUM> detects an object which comes out the hidden area R. As illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicle M1 comes out of the hidden area R into the zone X first, the object tracker <NUM> refers to the table illustrated in <FIG>, continues associating the information about the tracker T1 with the different vehicle M1, and sets the flag of the tracker T1 at <NUM>. Meanwhile, the identification number assigner <NUM> continues assigning the identification number ID1 to the different vehicle M1.

Thereafter, as illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicle M2 comes out of the hidden area R into the zone Z second, the object tracker <NUM> refers to the table illustrated in <FIG>, continues associating the information about the tracker T2 with the different vehicle M2, and sets the flag of the tracker T2 at <NUM>. Meanwhile, the identification number assigner <NUM> continues assigning the identification number ID2 to the different vehicle M2.

On the other hand, as illustrated in <FIG>, in a case where at time t+<NUM>, the object detector <NUM> detects the object <NUM> which comes out of the hidden area R into the zone Z first, the object tracker <NUM> cannot determine whether the object <NUM> is the different vehicle M1 or the different vehicle M2. For this reason, the object tracker <NUM> refers to the table illustrated in <FIG>, and sets a new tracker T4 to the object <NUM>. Meanwhile, the identification number assigner <NUM> assigns a new identification number ID4 to the object <NUM>. In this event, the object tracker <NUM> retains, but does not delete, the information about the tracker T1 and the information about the tracker T2.

After that, as illustrated in <FIG>, in a case where at time t+<NUM>, the object detector <NUM> detects the object <NUM> which comes out of the hidden area R into the zone Z second, the object tracker <NUM> cannot determine whether the object <NUM> is the different vehicle M1 or the different vehicle M2. For this reason, the object tracker <NUM> refers to the table illustrated in <FIG>, and sets a new tracker T5 to the object <NUM>. Meanwhile, the identification number assigner <NUM> assigns a new identification number ID5 to the object <NUM>. In this event, since the number of assigned new identification numbers is just equal to the number (two) of objects which have existed in the hidden area R, the object tracker <NUM> determines that no object exists in the hidden area R, and deletes the information about each of the trackers T1, T2 which the object tracker <NUM> has retained until now. In other words, in the case where the number of assigned new identification numbers is just equal to the number of objects having entered the hidden area R, the object tracker <NUM> deletes the information which the object tracker <NUM> has retained.

On the other hand, as illustrated in <FIG> in a case where at time t+<NUM>, the object detector <NUM> detects the object <NUM> which comes out of the hidden area R into the zone Y second, the object tracker <NUM> can determine that the object <NUM> is the different vehicle M2 by referring to the table illustrated in <FIG>. Thereby, the object tracker <NUM> can determine that the object <NUM> is the different vehicle M1. Accordingly, the object tracker <NUM> continues associating the information about the tracker T1 and the information about the tracker T2 with the respective different vehicles M1, M2, and sets the flags of the trackers T1, T2 at <NUM>. Meanwhile, the identification number assigner <NUM> deletes the identification number ID4 which is newly assigned to the different vehicle M1 at time t+<NUM>, and continuously assigns the identification number ID1 to the different vehicle M1. In addition, the he identification number assigner <NUM> continuously assigns the identification number ID2 to the different vehicle M2.

Next, referring to a flowchart illustrated in <FIG>, descriptions will be provided for an example of how the object tracking apparatus <NUM> according to the second embodiment works. This flowchart starts when the ignition switch is turned on. Incidentally, operations in steps S201 to S217, steps S221 to S223 and step S233 are the same as those in steps S101 to S117, steps S121 to S123 and step S125 in <FIG>, and detailed descriptions will be omitted. Descriptions will be provided for only what makes the flowchart in <FIG> different from the flowchart in <FIG>.

In step S219, the object tracker <NUM> determines whether to continue the information about each tracker. In a case where the object tracker <NUM> can continue the information about each tracker (if Yes in step S219), the process proceeds to step S221. On the other hand, in a case where the object tracker <NUM> cannot continue the information about each tracker (if No in step S219), the process proceeds to step S225.

In step S225, the identification number assigner <NUM> assigns a new identification number to an object which comes out of the hidden area R.

In a case where all of the objects having entered the hidden area R are detected in step S227 (if Yes in step S227), the process proceeds to step S229. In a case where some of the objects having entered the hidden area R are not detected (if No in step S227), the process returns to step S225.

In step S229, the object tracker <NUM> again determines whether to continue the information about each tracker. Depending on an order in which the objects come out of the hidden area R, the object tracker <NUM> can continue the information about each tracker. In a case where the object tracker <NUM> can continue the information about each tracker (if Yes in step S229), the processor proceeds to step S221. On the other hand, in a case where the object tracker <NUM> cannot continue the information about each tracker (if No in step S229), the processor proceeds to step S231.

In step S231, the object tracker <NUM> deletes the information about each tracker which the object tracker <NUM> has retained until now.

As discussed above, the following working and effects can be obtained from the object tracking apparatus <NUM> according to the second embodiment.

In the case where the object tracking apparatus <NUM> cannot estimate the positional relationship between the multiple objects in the hidden area, the object tracking apparatus <NUM> assigns new identification information to each object when the object comes out of the hidden area. Thereby, even in the case where the multiple objects enter the hidden area and temporarily disappear, the object tracking apparatus <NUM> is capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden. As far as Patent Literature <NUM> is concerned, however, in the case illustrated in <FIG>, the object which comes out of the hidden area R first is the different vehicle M1, but there is likelihood that the object which comes out of the hidden area R first is determined as being the different vehicle M2. In this case, it is determined that the host vehicle M0 will be able to overtake the different vehicle M3 and enter the left lane from the current lane. This is because it is determined that the different vehicle M2 has come out of the hidden area R. There is likelihood, however, that if the different vehicle M2 actually exists in the hidden area R and is going to enter the right lane from the current lane, the course of the different vehicle M2 may cross the course of the host vehicle M0. In contrast, the object tracking apparatus <NUM> according to the second embodiment assigns new identification information to the object <NUM> which comes out of the hidden area R, and does not delete the information about the different vehicle M1 or the information about the different vehicle M2. This makes it possible for the object tracking apparatus <NUM> to perform a reliable driving action.

In addition, the object tracking apparatus <NUM> continues determining that an object(s) exists in the hidden area, until the object tracking apparatus <NUM> finishes assigning new identification information to each of the objects which have existed in the hidden area. Once finishing assigning the new identification information to each object which have existed in the hidden area, the object tracking apparatus <NUM> deletes all the old identification information. Thereby, even in the case where the multiple objects enter the hidden area and temporarily disappear, the object tracking apparatus <NUM> is capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden, and can enhances the object tracking performance.

Next, descriptions will be provided for a third embodiment of the present invention. The configuration of the object tracking apparatus <NUM> according to the third embodiment is the same as that of the object tracking apparatus <NUM> according to the first embodiment. The third embodiment is different from the first embodiment in terms of the driving scene. The driving scene discussed in the first embodiment is a straight roadway which has no bifurcation. The driving scene to be discussed in the third embodiment will be a roadway which has a collision point.

Referring to <FIG>, descriptions will be provided for how the object tracking is performed on a roadway having a collision point like in a junction. Incidentally, in the third embodiment, the collision point means a point where the tracks of the respective different vehicles M1, M2 may collide with each other.

As illustrated in <FIG>, at time t, the object tracker <NUM> sets trackers T1 to T3 to different vehicles M1 to M3 existing around the host vehicle M0. Meanwhile, the identification number assigner <NUM> assigns identification numbers ID1 to ID3 to the different vehicles M1 to M3.

Thereafter, as illustrated in <FIG>, in a case where at time t+<NUM>, the different vehicles M1, M2 enter a hidden area R and the object tracker <NUM> becomes unable to track the vehicles M1, M2, the object tracker <NUM> sets the flags of the trackers T1, T2 set for the vehicles M1, M2 at <NUM>. Subsequently, based on the map information, the route calculator <NUM> calculates a collision point P where the different vehicles M1, M2 may collide with each other. After that, the positional relationship estimator <NUM> estimates amounts of time T it takes for the different vehicles M1, M2 to reach the collision point P. Each amount of time T is expressed with Equation (<NUM>). <MAT> where L is a distance to the collision point P, and V is a speed at which an object runs before entering the hidden area R. Incidentally, in the third embodiment, the positional relationship between the multiple objects includes not only the positional relationship in the hidden area R, but also the amounts of time it takes for the multiple objects to reach the collision point P.

Using the amounts of time T, the object tracker <NUM> determines whether to continue the trackers. When the amount of time it takes for the different vehicle M1 to reach the collision point P is denoted by TM1 and the amount of time it takes for the different vehicle M2 to reach the collision point P is denoted by TM2, the time difference between the amounts of time TM1, TM2 it takes for the different vehicles M1, M2 to reach the collision point P is calculated as the amount of time TM2 minus the amount of time TM1. The object tracker <NUM> determines whether this time difference is not less than a predetermined amount of time (for example, <NUM> seconds). In a case where the time difference is equal to or greater than the predetermined amount of time, the object tracker <NUM> determines that the different vehicle M2 in a priority lane will reach the collision point P earlier than the different vehicle M1. In other words, as illustrated in <FIG>, at time t+<NUM>, the object tracker <NUM> determines that the object which comes out of the hidden area R is the different vehicle M2. The object tracker <NUM> continues information about the tracker T2 associated with the different vehicle M2, and sets the flag of the tracker T2 at <NUM>. Meanwhile, the identification number assigner <NUM> continues assigning the identification number ID2 to the different vehicle M2.

On the other hand, in a case where the time difference is less than the predetermined amount of time, when as illustrated in <FIG>, the object <NUM> which comes out of the hidden area R first is detected, the object tracker <NUM> cannot determine whether the object <NUM> is the different vehicle M1 or the different vehicle M2. The reason for this is that the object tracker <NUM> cannot determine which one of the different vehicles M1, M2 yields to the other. With this taken into consideration, the object tracker <NUM> sets a new tracker T4 to the object <NUM>. Meanwhile, the identification number assigner <NUM> assigns a new identification number ID4 to the object <NUM>. In this event, the object tracker <NUM> retains, but does not delete, the information about the tracker T1 and the information about the tracker T2.

After that, when as illustrated in <FIG>, the object detector <NUM> detects the object <NUM> which comes out of the hidden area R second at time t+<NUM>, the object tracker <NUM> cannot determine whether the object <NUM> is the different vehicle M1 or the different vehicle M2. With this taken into consideration, the object tracker <NUM> sets a new tracker T5 to the object <NUM>. Meanwhile, the identification number assigner <NUM> assigns a new identification number ID5 to the object <NUM>. In this event, since the number of assigned new identification numbers is just equal to the number (two) of objects which have existed in the hidden area R, the object tracker <NUM> determines that no object exists in the hidden area R, and deletes the information about each of the trackers T1, T2 which the object tracker <NUM> has retained until now.

Next, referring to a flowchart illustrated in <FIG>, descriptions will be provided for an example of how the object tracking apparatus <NUM> according to the third embodiment works. This flowchart starts when the ignition switch is turned on. Incidentally, operations in steps S301 to S309 and steps S321 to S327 are the same as those in steps S101 to S109 and steps S119 to S125 in <FIG>, and detailed descriptions will be omitted. Descriptions will be provided for only what makes the flowchart in <FIG> different from the flowchart in <FIG>.

In step S311, the positional relationship estimator <NUM> estimates the amounts of time it takes for the multiple objects to reach the collision point P.

In step S313, the object tracker <NUM> determines whether the time difference between the amounts of time it takes for the multiple objects to reach the collision point P is not less than the predetermined amount of time. In a case where the time difference is equal to or greater than the predetermined amount of time (if Yes in step S313), the process proceeds to step S321. On the other hand, in a case where the time difference is less than the predetermined amount of time (if No in steps S313), the process proceeds to step S315.

In step S315, the identification number assigner <NUM> assigns new identification numbers to the objects which come out of the hidden area.

In step S317, in a case where all of the objects which have entered the hidden area R are detected (if Yes in step S317), the process proceeds to steps S319. On the other hand, in a case where some of the objects which have entered the hidden area R are not detected (if No in step S317), the process returns to step S315.

In step S319, the object tracker <NUM> deletes the information about each tracker which the object tracker <NUM> has retained until now.

As discussed above, the following working and effects can be obtained from the object tracking apparatus <NUM> according to the third embodiment.

The object tracking apparatus <NUM> estimates the time difference between the amounts of time it takes for the objects to reach the collision point P. In the case where the time difference is less than the predetermined amount of time, the object tracking apparatus <NUM> assigns new identification information to each object when the object comes out of the hidden area. Thereby, even in the case where the multiple objects enter the hidden area and temporarily disappear, the object tracking apparatus <NUM> is capable of preventing each object from being assigned wrong identification information different from that which is assigned to the object before the object is hidden. As far as Patent Literature <NUM> is concerned, however, in the case illustrated in <FIG>, the object which comes out of the hidden area R first is the different vehicle M1, but there is likelihood that the object which comes out of the hidden area R first is determined as being the different vehicle M2. In this case, it is determined that the host vehicle M0 will be able to overtake the different vehicle M3 and enter the left lane from the current lane. This is because it is determined that the different vehicle M2 has come out of the hidden area R. There is likelihood, however, that if the different vehicle M2 actually exists in the hidden area R and is going to enter the right lane, the course of the different vehicle M2 may cross the course of the host vehicle M0. In contrast, the object tracking apparatus <NUM> according to the third embodiment assigns new identification information to the object <NUM> which comes out of the hidden area R, and does not delete the information about the different vehicle M1 or the information about the different vehicle M2. This makes it possible for the object tracking apparatus <NUM> to perform a reliable driving action.

The foregoing descriptions have been provided for the embodiments of the present invention. Descriptions or drawings which are parts of this disclosure shall not be construed as limiting the present invention.

It should be noted that each function in the above-discussed embodiments can be implemented with one or more processing circuits. The processing circuits include programmed processors such as processing devices including electric circuits. The processing circuits further include an application-specific integration circuit (ASIC) arranged to perform the functions discussed for the embodiments, and a device such as a conventional circuit component.

Claim 1:
An object tracking method for
an object tracking apparatus which includes a sensor (<NUM>) for detecting one or more objects around a moving body, and a controller (<NUM>) for tracking the detected one or more objects,
the object tracking method comprising:
in a case where a plurality of the objects enter a hidden area within a detection range of the sensor (<NUM>), estimating a positional relationship between the plurality of the objects in the hidden area;
said method being characterized by
determining whether the order in which the plurality of objects may come out of the hidden area can be estimated;
if the order can be estimated:
estimating, based on the positional relationship and before the plurality of objects come out from the hidden area, an order in which the plurality of objects may come out of the hidden area, and
assigning an object that comes out of the hidden area with identification information that was already assigned to one of the plurality of the objects that entered the hidden area; and
if the order cannot be estimated:
assigning an object that comes out of the hidden area with new identification information that was not already assigned to one of the plurality of the objects that entered the hidden area.