JP2019151261A - Operation characteristic estimation method and operation characteristic estimation device - Google Patents

Abstract

To provide an operation characteristic estimation method that can properly estimate movements of other vehicles around an own vehicle.SOLUTION: An operation characteristic estimation method includes: detecting other vehicles around an own vehicle and estimating an approach area that requires other vehicles to operate their direction indicators from surrounding environments of other vehicles (S31 to S33); and estimating operation characteristics of other vehicles based on lighting states of the direction indicators of other vehicles with respect to the estimated approach areas (S35 to S42).SELECTED DRAWING: Figure 8

Description

  The present invention relates to a driving characteristic estimation method and a driving characteristic estimation apparatus.

  Patent Document 1 discloses a vehicle travel control device that controls the host vehicle in accordance with the operation of another vehicle. When the adjacent vehicle changes its lane to the traveling lane of the own vehicle, the vehicle traveling control device is a relative speed at which the adjacent vehicle moves away from the own vehicle even if the inter-vehicle distance between the adjacent vehicle and the own vehicle is shorter than the set inter-vehicle distance. And a limiting means for limiting the control for decelerating the host vehicle.

Japanese Patent Laid-Open No. 2005-199930

  Here, the driving characteristics of the other vehicle, that is, the tendency of the driving operation by the driver of the other vehicle are individually different, and the operation selected by the other vehicle is different according to the driving characteristic of the other vehicle. However, in Patent Document 1, since the driving characteristics of the other vehicle are not taken into consideration, the operation of the other vehicle may not be appropriately predicted.

  An object of the present invention is to provide a driving characteristic estimation method and a driving characteristic estimation apparatus that can appropriately predict the operation of another vehicle around the host vehicle.

  According to one aspect of the present invention, another vehicle around the host vehicle is detected, an approach area where the other vehicle requires a turn signal operation is estimated from the surrounding environment of the other vehicle, and the turn signal of the other vehicle with respect to the estimated approach area is estimated. The driving characteristics of other vehicles are estimated based on the lighting state.

  ADVANTAGE OF THE INVENTION According to this invention, the driving characteristic estimation method and driving characteristic estimation apparatus which can predict appropriately the operation | movement of the other vehicle around the own vehicle can be provided.

It is the schematic which shows an example of the driving scene to which the driving assistance apparatus which concerns on embodiment of this invention is applied. It is a block diagram which shows an example of a structure of the driving assistance apparatus which concerns on embodiment of this invention. It is the schematic for demonstrating the driving characteristic estimation process which concerns on embodiment of this invention. It is the schematic for demonstrating an example of the difference of the basic track and effective track | orbit which concerns on embodiment of this invention. It is the schematic for demonstrating another example of the difference of the basic track | orbit and effective track | orbit which concerns on embodiment of this invention. It is a flowchart which shows an example of the driving assistance method which concerns on embodiment of this invention. It is a flowchart which shows an example of the operation | movement prediction method which concerns on embodiment of this invention. It is a flowchart which shows an example of the driving | running characteristic estimation method which concerns on embodiment of this invention. It is the schematic which shows an example of the driving scene to which the driving assistance apparatus which concerns on other embodiment is applied. It is the schematic which shows another example of the driving scene to which the driving assistance device which concerns on other embodiment is applied. It is the schematic which shows another example of the driving scene to which the driving assistance device which concerns on other embodiment is applied.

  Embodiments of the present invention will be described below with reference to the drawings. Each drawing is schematic and may be different from the actual one. The following embodiments of the present invention exemplify apparatuses and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is the structure, arrangement, etc. of components. Is not specified as follows. The technical idea of the present invention can be variously modified within the technical scope defined by the claims described in the claims.

  The driving support apparatus according to the embodiment of the present invention can be mounted on a vehicle (hereinafter, a vehicle including the driving support apparatus according to the embodiment of the present invention is referred to as “own vehicle”). The driving support apparatus according to the embodiment of the present invention predicts the driving characteristics of the other vehicle of the own vehicle, predicts the operation of the other vehicle based on the predicted driving characteristic of the other vehicle, and determines the own driving based on the predicted operation of the other vehicle. Provide vehicle driving support. In this specification, “running support” includes driving support that supports driving of the host vehicle by intervening in driving operation by the driver, in addition to automatic driving control without driving operation by the occupant (driver). Good.

  In the present specification, the “driving characteristics of another vehicle” means a tendency of a driving operation by the driver of the other vehicle when the other vehicle is manually operated by the driver. “Driving characteristics of other vehicle” means a tendency of automatic driving control when the other vehicle is in automatic driving control without driving operation by the driver. “Driving characteristics of other vehicle” means the tendency of comprehensive control by driving operation by the driver of the other vehicle and driving support control when the other vehicle includes driving support control that intervenes in the driving operation by the driver. To do. The driving characteristics of other vehicles include, for example, a tendency to drive suddenly in a self-centered manner, a tendency to carefully drive in consideration of vehicles around other vehicles, and a cognitive function with respect to the surrounding environment of other vehicles. A tendency to be low and a tendency to have a relatively high cognitive function surrounding other vehicles can be cited.

  First, an example of a driving scene to which the driving support device according to the embodiment of the present invention is applied will be described. As shown in FIG. 1, the host vehicle 1 travels in the right lane of a one-way two-lane road, and the other vehicle 2 travels in parallel at the left front position of the host vehicle 1 in the left lane. A parked vehicle 3 is present in front of the other vehicle 2 on the left lane, and the other vehicle 2 is approaching the parked vehicle 3. In such a driving scene, as the other vehicle 2 overtakes the parked vehicle 3, the vehicle 1 gives way ahead and continues traveling in the left lane while decelerating, and the own vehicle 1 overtakes the other vehicle 2. A track t1 for changing the lane to the right lane and a track t2 for changing the lane to the right lane ahead of the host vehicle 1 are conceivable.

  Whether the operation of the other vehicle 2 is on the track t1 or t2 depends on the driving characteristics of the other vehicle 2. For example, when the driver is a driving beginner or the like and has a tendency to perform cautious driving, it is estimated that there is a high possibility that the trajectory t1 to be transferred to the host vehicle 1 is selected. Moreover, when the recognition function with respect to the surrounding environment of the other vehicle 2 has a relatively high tendency, it is highly likely that the track t <b> 1 to be transferred to the own vehicle 1 is selected by early recognition of the own vehicle 1. It is estimated to be. On the other hand, when the driver is self-centered and has a tendency to perform a relatively steep driving operation, it is estimated that there is a high possibility that the track t2 for changing the lane ahead of the host vehicle 1 is selected. . In addition, when the recognition function for the surrounding environment of the other vehicle 2 has a relatively low tendency, since the recognition of the own vehicle 1 is slow, the track t2 for changing the lane ahead of the own vehicle 1 can be selected. Estimated to be high. The driving support apparatus according to the embodiment of the present invention makes it possible to appropriately predict the operation of the other vehicle 2 by estimating such driving characteristics of the other vehicle 2.

  As illustrated in FIG. 2, the travel support device 10 according to the embodiment of the present invention includes an object detection device 11, a host vehicle position estimation device 12, a map acquisition device 13, and a controller 14. The object detection device 11 includes a plurality of different types of object detection sensors that are mounted on the host vehicle 1 and detect objects around the host vehicle 1 such as a laser radar, a millimeter wave radar, and a camera. The object detection device 11 detects an object around the host vehicle 1 as a surrounding environment using a plurality of object detection sensors.

  The object detection device 11 detects a moving object including another vehicle, a bicycle, and a pedestrian, and a stationary object including a parked vehicle and a falling object as the surrounding environment. Other vehicles include two-wheeled vehicles and four-wheeled vehicles. The object detection device 11 detects, for example, the position, posture (yaw angle), size, speed, acceleration, deceleration, and yaw rate of a moving object and a stationary object with respect to the host vehicle 1. In the following description, the position, orientation, size, speed, acceleration, deceleration, and yaw rate of an object are collectively referred to as “behavior” of the object. For example, the object detection device 11 outputs the behavior of a two-dimensional object in a zenith view (also referred to as a plan view) viewed from the air above the host vehicle 1 as a detection result. The object detection device 11 further detects the lighting state (turned on or off) of the direction indicator (blinker) of the other vehicle. For example, the object detection device 11 can extract the turn signal area of the other vehicle from the captured image including the other vehicle by pattern matching, and determine the lighting state of the turn signal based on the brightness of the extracted turn signal area.

  The own vehicle position estimation device 12 includes a global positioning system (GPS) receiver mounted on the own vehicle 1 and a position detection sensor that measures the absolute position of the own vehicle 1 such as odometry. The host vehicle position estimation device 12 uses a position detection sensor to measure the absolute position of the host vehicle 1, that is, the position, posture, and speed of the host vehicle 1 with respect to a predetermined reference point.

  The map acquisition device 13 acquires map information indicating the structure of the road on which the host vehicle 1 travels. The map information indicating the structure of the road corresponds to, for example, static information updated at a frequency of 1 hour or less. The map acquisition device 13 may own a map database storing map information, and may acquire map information from an external map data server by cloud computing. The map information acquired by the map acquisition device 13 includes road structure information such as absolute lane positions, lane connection relationships, and relative position relationships. The map acquisition device 13 further acquires map information with a high update frequency (for example, information embedded in the dynamic map). For example, the map acquisition device 13 automatically stores dynamic information updated at a frequency of 1 second or less, quasi-dynamic information updated at a frequency of 1 minute or less, and quasi-static information updated at a frequency of 1 hour or less. Obtained from outside the vehicle 1 by wireless communication. The dynamic information includes information on surrounding vehicles, pedestrians, and traffic lights. The quasi-dynamic information includes accident information, traffic jam information, and narrow area weather information. The quasi-static information includes traffic regulation information, road construction information, and wide area weather information.

  The controller 14 can be configured by a processing circuit such as an electronic control unit (ECU) including a processor and peripheral components such as a storage device. The processor may be, for example, a central processing unit (CPU) or a microprocessor (MPU). The storage device may include any one of a semiconductor storage device, a magnetic storage device, and an optical storage device. The storage device may include a register, a cache memory, and a memory such as a ROM (Read Only Memory) and a RAM (Random Access Memory) used as a main storage device. The controller 14 may be formed by dedicated hardware for executing the following information processing. For example, the controller 14 may be realized by a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, the controller 14 may include a programmable logic device (PLD) such as a field programmable gate array (FPGA).

  The controller 14 drives the other vehicle 2 based on the detection result of the object by the object detection device 11, the estimation result of the position of the own vehicle 1 by the own vehicle position estimation device 12, and the acquisition result of the map information by the map acquisition device 13. Estimate the characteristics. The controller 14 further predicts the operation of the other vehicle 2 based on the driving characteristics of the other vehicle 2, generates a route of the own vehicle 1 from the operation of the other vehicle 2, and controls the own vehicle 1 according to the generated route. In addition, in embodiment, although the controller 14 is illustrated as a driving assistance device which controls the own vehicle 1, it is not limited to this. For example, the controller 14 may be a driving characteristic estimation device that estimates the driving characteristics of the other vehicle 2, or may be a motion prediction device that predicts the operation of the other vehicle 2. That is, the controller 14 does not perform the route generation of the host vehicle 1 and the travel support along the route, but finally determines the estimation result of the driving characteristics of the other vehicle 2 or the prediction result of the operation of the other vehicle 2 based on the estimation result. It is good also as an output.

  The controller 14 includes a detection integration unit 20, an object tracking unit 21, an in-map position calculation unit 22, an operation prediction unit 23, a host vehicle route generation unit 24, and a vehicle control unit 25. The controller 14 executes a computer program stored in a predetermined storage device by a processor, whereby a detection integration unit 20, an object tracking unit 21, an in-map position calculation unit 22, an operation prediction unit 23, and a host vehicle route generation unit 24. And the function of the vehicle control part 25 may be implement | achieved.

  The detection integration unit 20 integrates a plurality of detection results obtained from each of the plurality of object detection sensors included in the object detection device 11, and outputs one detection result for each object. For example, the detection integration unit 20 calculates the most reasonable behavior of an object with the smallest error from the behavior of the object obtained from each of the object detection sensors in consideration of error characteristics of each object detection sensor. . The detection integration unit 20 may obtain a more accurate detection result by comprehensively evaluating the detection results obtained by a plurality of types of sensors by using a known sensor fusion technique.

  The object tracking unit 21 tracks the object detected by the object detection device 11. For example, the object tracking unit 21 verifies (associates) the identity of objects at different times from the behaviors of the objects output at different times as the detection results integrated by the detection integration unit 20, and Predict the behavior of the object based on the attachment. Note that the behaviors of the objects output at different times are stored in a memory in the controller 14 and are used for trajectory prediction described later.

  The in-map position calculation unit 22 estimates the position and orientation of the host vehicle 1 on the map from the absolute position of the host vehicle 1 obtained by the host vehicle position estimation device 12 and the map information acquired by the map acquisition device 13. To do. The intra-map position calculation unit 22 identifies the road on which the host vehicle 1 is traveling, and further the lane on which the host vehicle 1 is traveling.

  The motion prediction unit 23 predicts the motion of the moving object around the host vehicle 1 based on the detection result obtained by the detection integration unit 20 and the position of the host vehicle 1 specified by the in-map position calculation unit 22. The motion prediction unit 23 includes a behavior determination unit 30, a motion candidate prediction unit 31, a motion candidate correction unit 32, a trajectory prediction unit 33, a driving characteristic estimation unit 34, and a likelihood estimation unit 35.

  The behavior determination unit 30 specifies the position and behavior of the object on the map from the position of the host vehicle 1 on the map and the behavior of the object obtained by the detection integration unit 20. Further, when the position of the object on the map changes with time, the behavior determination unit 30 determines that the object is a “moving object” and determines the attribute (traveling) of the moving object from the size and speed of the moving object. Other vehicle inside, pedestrian). When it is determined that the moving object is the “other vehicle” that is traveling, the behavior determination unit 30 determines the road and lane on which the other vehicle travels. On the other hand, when the position of the object on the map does not change with time, the behavior determination unit 30 determines that the object is a stationary object, and determines the attribute of the stationary object (from the position, orientation, and size of the stationary object on the map). Other vehicles that are stopped, parked vehicles, pedestrians, etc.) are determined.

  The motion candidate prediction unit 31 predicts motion candidates for other vehicles based on the map information acquired by the map acquisition device 13. The motion candidate prediction unit 31 predicts the motion intention of how the other vehicle will travel next from the road structure included in the map information and the lane information to which the other vehicle belongs, and the basics of the other vehicle based on the motion intention. The trajectory is calculated based on the road structure. Here, the “motion candidate” is a superordinate concept including a motion intention and a basic trajectory. The basic track shows not only the profile of the position of the other vehicle at different times but also the profile of the speed of the other vehicle at each position. In the calculation of the basic trajectory, the road structure is considered, but the moving object and the stationary object integrated by the detection integration unit 20 are not considered.

  For example, when another vehicle travels on a single lane and a curved road, the motion candidate prediction unit 31 predicts the motion intention (straight forward) traveling along the shape of the lane, and uses the basic trajectory on the map. Calculate the track along the lane. In addition, when another vehicle travels on a single road and a curved road with a plurality of lanes, the motion candidate prediction unit 31 predicts the motion intention (straight forward) and the motion intention (lane change) to change the lane to the right side or the left side. The basic trajectory of the other vehicle in the operation intention (lane change) is a trajectory for changing the lane based on the road structure and a predetermined lane change time. In addition, when another vehicle travels the intersection, the motion candidate prediction unit 31 predicts the intention of moving straight, right turn, and left turn, and uses the straight track, right turn track, and left turn track based on the road structure at the intersection on the map as the basic track. Calculate as

  For example, the travel scene shown in FIG. 3 is the same as the travel scene shown in FIG. 1, but the motion candidate predicting unit 31 operates as a motion candidate of the other vehicle 2 and intends to travel along the left lane (straight forward) and The basic trajectory t3 is calculated. Note that the motion candidate prediction unit 31 does not calculate the motion intention (lane change) caused by the parked vehicle 3 that is a stationary object around the host vehicle 1 and the basic tracks t1 and t2 of the lane change. In addition, when there is a moving object around the host vehicle 1, the motion candidate prediction unit 31 does not calculate the motion intention (lane change) caused by the moving object and the basic track of the lane change.

  The motion candidate correction unit 32 corrects the motion candidate predicted by the motion candidate prediction unit 31 in consideration of the stationary object and the moving object detected by the object detection device 11. For example, the motion candidate correction unit 32 determines whether the basic trajectory of the other vehicle interferes with the position of the stationary object or the moving object. When it is determined that the basic trajectory of the other vehicle interferes with the position of the stationary object or the moving object, the motion candidate correction unit 32 newly sets the motion intention and the basic trajectory of the other vehicle 2 for avoiding the stationary object or the moving object. Generate.

  For example, as illustrated in FIG. 3, when the parked vehicle 3 that is a stationary object is detected by the object detection device 11, the motion candidate correction unit 32 has interference between the parked vehicle 3 and the basic track t <b> 3 of the other vehicle 2. Whether or not is determined over time. When it is determined that there is interference between the parked vehicle 3 and the basic trajectory t3 of the other vehicle 2, the motion candidate correction unit 32 intends to operate the lane 2 (lane change) of the other vehicle 2 that avoids interference with the parked vehicle 3. And basic trajectories t1 and t2 are newly generated. The basic track t1 is a track that gives way to the host vehicle 1, and the basic track t2 is a track that changes the lane ahead of the host vehicle 1. For example, the basic trajectory t2 may be generated using a clothoid curve based on the center of the traveling lane of the other vehicle 2 before the lane change indicated by the map information and the center of the traveling lane of the other vehicle 2 after the lane change. However, the present invention is not limited to this.

  The trajectory prediction unit 33 predicts a trajectory (effective trajectory) on which the other vehicle 2 actually travels based on the actual behavior of the other vehicle 2 detected by the behavior determination unit 30. For example, the trajectory prediction unit 33 calculates the effective trajectory of the other vehicle 2 when operating according to the motion intention predicted by the motion candidate prediction unit 31 using a known state estimation technique such as a Kalman filter. Like the basic track, the effective track indicates not only the position of the other vehicle 2 at different times but also the speed profile of the other vehicle 2 at each position. The effective trajectory and the basic trajectory are common in that the other vehicle 2 travels, but the effective trajectory is calculated in consideration of the behavior of the other vehicle 2, whereas the basic trajectory represents the behavior of the other vehicle. The difference is that it is calculated without consideration.

  4 and 5 show a state in which the other vehicle 2 is traveling on a curve. The basic tracks t21 and t22 of the other vehicle 2 shown in FIGS. 4 and 5 are examples of the track of the other vehicle 2 derived based on the operation intention and the road structure, and the behavior of the other vehicle 2 is not considered. Therefore, for example, since the current posture (yaw angle) of the other vehicle 2 is not taken into consideration, a plurality of basic tracks t21 and t22 extend from the current position of the other vehicle 2 in different directions. On the other hand, the trajectory prediction unit 33 calculates trajectories (effective trajectories) t31 and t32 along the motion intention predicted by the motion candidate prediction unit 31 in consideration of the behavior of the other vehicle 2. In other words, the effective trajectories t31 and t32 of the other vehicle 2 when the motion in accordance with the motion intention predicted by the motion candidate prediction unit 31 is taken are calculated.

  In FIG. 4, the posture (yaw angle) of the other vehicle 2 is tilted to the left of the basic trajectory t21 of travel along the shape of the road, and the speed of the other vehicle 2 is composed only of the speed component in the traveling direction. The velocity component in the direction is zero. That is, the other vehicle 2 is in a straight traveling state. Therefore, when the other vehicle 2 travels according to the motion intention of traveling along the shape of the road with this posture and speed as a starting point, the effective trajectory t31 that approaches the basic trajectory t21 and coincides with it after moving away from the basic trajectory t21 to the left side. Become. In other words, it is predicted that a correction trajectory (overshoot trajectory) that corrects the deviation from the travel lane is drawn. The trajectory predicting unit 33 predicts an effective trajectory t31 that travels according to the motion intention (straight forward) along the road shape, starting from the attitude (yaw angle) and speed of the other vehicle 2.

  Also, as shown in FIG. 5, when the other vehicle 2 travels according to the lane change operation intention starting from the same posture and speed as in FIG. 4, the vehicle starts to turn left and moves to the left lane. The effective trajectory t32 is corrected by turning to the trajectory along the left lane. In other words, an effective trajectory t32 is drawn which includes a left-turning clothoid curve and a right-turning clothoid curve starting from a state where the steering angle is in the neutral position. Therefore, the effective trajectory t32 is a trajectory in which the lane change is completed over substantially the same time as the predetermined lane change time when calculating the lane change trajectory t22. The curve for drawing the effective trajectory does not necessarily need to be a clothoid curve, and may be drawn using other curves. In the example of FIG. 5, the effective track t32 is substantially the same track as the basic track t22 in the lane change.

  Similar to FIGS. 4 and 5, for the basic trajectories t <b> 1 and t <b> 2 shown in FIG. 3, the trajectory predicting unit 33 calculates the trajectory (effective trajectory) according to the motion intention in consideration of the behavior of the other vehicle 2. . For example, the trajectory prediction unit 33 determines the effective trajectory of the other vehicle 2 that travels according to the operation intention of changing the lane in front of the own vehicle 1 based on the position, posture (yaw angle), speed, and acceleration of the other vehicle 2, The effective trajectory of the other vehicle 2 that travels according to the operation intention of continuing to travel in the left lane while decelerating without changing the lane to the right lane until the vehicle 1 passes is calculated.

  The driving characteristic estimation unit 34 estimates an approach area where the other vehicle 2 requires a winker operation from the surrounding environment of the other vehicle 2 obtained from the object detection device 11 or the like. An approach area that requires a winker operation is an area that requires a winker operation when the other vehicle 2 enters, such as a section that requires a lane change for avoiding an obstacle or an intersection at the time of turning left or right. The driving characteristic estimation unit 34 further estimates the driving characteristic of the other vehicle 2 based on the lighting state of the turn signal of the other vehicle 2 with respect to the estimated approach area. The blinker lighting state with respect to the entry area is, for example, an entry time from the start of blinking of the blinker of the other vehicle 2 to the entry of the other vehicle 2 into the entry area. The approach time can be estimated based on the distance, speed, acceleration, and the like to the approach area of the other vehicle 2.

  For example, in the driving scene shown in FIG. 3, the driving characteristic estimation unit 34 is based on the position of the host vehicle 1 estimated by the host vehicle position estimation device 12, the vehicle speed and acceleration of the host vehicle 1 detected by the vehicle speed sensor, and the like. The estimated route t0 of the host vehicle 1 is estimated. The driving characteristic estimation unit 34 further estimates an approach area A1 where the other vehicle 2 requires a turn signal operation based on the predicted route t0 of the host vehicle 1 and the predicted track t2 of the other vehicle 2. The approach area A1 is an area that requires a turn signal operation when the other vehicle 2 changes lanes to avoid the parked vehicle 3. The range of the approach area A1 can be appropriately set in consideration of the width of the road, the vehicle width of the other vehicle 2, and the like. The approach area A1 is an intersection area where the predicted route t0 of the host vehicle 1 and the predicted track t2 of the other vehicle 2 intersect. The driving characteristic estimation unit 34, instead of estimating the approach area A1 where the other vehicle 2 requires a turn signal operation, is based on the predicted route t0 of the own vehicle 1 and the predicted trajectory t2 of the other vehicle 2, and The intersection area where the other vehicles 2 intersect may be estimated, and the estimated intersection area may be set as the entry area.

  Based on the distance, speed, acceleration, and the like to the entry area A1 of the host vehicle 1, the driving characteristic estimation unit 34 determines the time until the host vehicle 1 enters the entry area A1, for example, from the current time (hereinafter referred to as "own vehicle entry time" "). The driving characteristic estimation unit 34 determines whether the difference between the entry time when the other vehicle 2 enters the entry area A1 with the own vehicle 1 and the own vehicle entry time when the own vehicle 1 enters the entry area A1 is less than a predetermined threshold. Determine whether or not. The predetermined threshold can be set as appropriate. When it is determined that the difference between the entry time and the own vehicle entry time is less than the predetermined threshold value, the driving characteristic estimation unit 34 performs a process of estimating the driving characteristic of the other vehicle 2. On the other hand, when it is determined that the difference between the entry time and the own vehicle entry time is greater than or equal to a predetermined threshold, the process of estimating the driving characteristics of the other vehicle 2 is not performed. The driving characteristic estimation unit 34 determines that the difference between the time when the other vehicle 2 is estimated to enter the entry area A1 with the own vehicle 1 and the time when the own vehicle 1 is estimated to enter the entry area A1 is predetermined. It may be determined whether it is less than the threshold value. When the difference in this case is determined to be less than the predetermined threshold, processing for estimating the driving characteristics of the other vehicle 2 is performed, and when the difference is determined to be greater than or equal to the predetermined threshold, the driving characteristics of the other vehicle 2 are estimated. There is no need to perform the process.

  The driving characteristic estimation unit 34 may calculate a predicted collision time (TTC) or an inter-vehicle time (THW) for the other vehicle 2 of the host vehicle 1. TTC is a value obtained by dividing the inter-vehicle distance by the relative speed with the other vehicle 2. THW is a value obtained by dividing the inter-vehicle distance by the speed of the other vehicle 2. For example, when THW and TTC are equal to or greater than a predetermined threshold, the controller 1 does not perform the process of estimating the driving characteristics of the other vehicle 2, and when THW or TTC is less than the predetermined threshold, You may perform the process which estimates a characteristic.

  The driving characteristic estimation unit 34 estimates the entry time from the start of turn-on of the blinker of the other vehicle 2 to the entry into the entry area A1. The timing for starting the turn-on of the turn signal of the other vehicle 2 can be determined from the turn-on state of the turn signal detected by the object detection device 11. The timing at which the other vehicle 2 enters the entry area A1 can be estimated based on the distance, speed, acceleration, and the like of the other vehicle 2 to the entry area A1. The timing at which the other vehicle 2 enters the entry area A1 may be, for example, a timing at which a part of the other vehicle 2 enters the entry area A1, and an operation (for example, a lane) for the other vehicle 2 to enter the entry area A1. (Change) may be started. Note that the driving characteristic estimation unit 34 calculates the entry time using the timing at which the other vehicle 2 actually enters as obtained from the detection result by the object detection device 11 as the timing at which the other vehicle 2 enters the entry area A1. May be.

  The driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 based on the estimated or calculated approach time. For example, the driving characteristic estimation unit 34 determines whether the estimated or calculated approach time is equal to or greater than a predetermined threshold (predetermined). If the approach time is equal to or greater than the predetermined threshold, the blinker is turned on at a relatively earlier timing than the lane change timing, and thus the driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 as “Cautious”. On the other hand, if the approach time is less than the predetermined threshold, since the turn-on of the blinker starts relatively immediately before the lane change timing, the driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 as “Aggressive”. To do.

  “Aggressive” is a driving characteristic having a tendency to perform self-centered driving, and has a tendency to perform a relatively abrupt driving operation. In other words, the tendency of safe driving is relatively weak and the predictability of operation is relatively low. On the other hand, “Cautious” is a driving characteristic that tends to drive cautiously, such as driving relatively slowly or driving at a speed lower than the average surrounding vehicle or legal speed. Have a tendency to In other words, the tendency of safe driving is relatively strong and the predictability of operation is relatively high. The driving characteristic estimation unit 34 may use “Unknown” as a state indicating that the driving characteristic is not estimated when the driving characteristic of the other vehicle 2 is not estimated. The driving characteristics used for predicting the operation of the other vehicle 2 are not limited to the exemplified driving characteristics, and other driving characteristics useful for predicting the operation of the other vehicle 2 may be employed.

  The driving characteristic estimation unit 34 is based on the turn-on state of the turn signal of the other vehicle 2 at a time (second time) before a predetermined threshold (predetermined time) before the time (first time) estimated to enter the entry area A1. The driving characteristics of the other vehicle 2 may be estimated. For example, the driving characteristic estimation unit 34 turns on the blinker of the other vehicle 2 at a time (second time) before a predetermined threshold (predetermined time) before the time (first time) estimated to enter the entry area A1. It is determined whether or not. The predetermined threshold is about 3 seconds, for example, and can be set as appropriate. When it is determined that the turn signal of the other vehicle 2 is turned on at the second time, the turn signal is turned on at a relatively earlier timing than the timing of the lane change. The driving characteristic of 2 is estimated as “Cautious”. On the other hand, if it is determined that the turn signal of the other vehicle 2 is not turned on (turned off) at the second time, the turn signal turn on starts relatively immediately before the lane change timing. The unit 34 estimates the driving characteristic of the other vehicle 2 as “Aggressive”.

  The driving characteristic estimation unit 34 may estimate that the driving characteristic of the other vehicle 2 is “Aggressive” instead of estimating the driving characteristic of the other vehicle 2 based on the lighting state of the turn signal of the other vehicle 2 with respect to the approach area A1 (likely And the possibility of being “Cautious” (likelihood) may be estimated respectively. Hereinafter, the possibility that the driving characteristic of the other vehicle 2 is “Aggressive” is expressed as ““ Aggressive ”likelihood”, and the possibility that it is “Cautious” is expressed as ““ Cautious ”likelihood”. For example, the driving characteristic estimation unit 34 estimates the “Cautious” likelihood relatively high and the “Aggressive” likelihood relative to the other vehicle 2 when the entry time for the other vehicle 2 to enter the entry area A1 is equal to or greater than a predetermined threshold. Estimated low. On the other hand, when the entry time during which the other vehicle 2 enters the entry area A1 is less than a predetermined threshold, the “Cautious” likelihood is estimated to be relatively low, and the “Aggressive” likelihood is estimated to be relatively high.

  The driving characteristic estimator 34 determines the “Aggressive” likelihood and the “Cautious” likelihood, and the respective likelihoods that the plurality of motion candidates predicted by the motion prediction unit 23 and the motion candidate correction unit 32 become the motion of the other vehicle 2 ( That is, it is stored in the storage device as the likelihood selected as the operation of the other vehicle 2. For example, the “Aggressive” likelihood corresponds to the likelihood that the other vehicle 2 selects the basic trajectory t2, and the “Cautious” likelihood corresponds to the likelihood of selecting the basic trajectory t1. The likelihood that each motion candidate predicted based on the estimation of the driving characteristics of the other vehicle 2 becomes the motion of the other vehicle 2 is denoted as “first likelihood α1”.

  The driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 based on the lighting state of the turn signal of the other vehicle 2 at the second time before the first time estimated to enter the entry area A1, and then the other vehicle 2 The estimated driving characteristics of the other vehicle 2 may be corrected based on the entry time from the start of lighting of the turn signal No. 2 to the entry into the entry area A1. For example, when it is determined that the approach time is equal to or greater than a predetermined threshold, the driving characteristic estimation unit 34 increases the estimated “Cautious” likelihood that is the driving characteristic of the other vehicle 2 and increases the “Aggressive” likelihood. Decrease. On the other hand, when it is determined that the approach time is less than the predetermined threshold, the driving characteristic estimation unit 34 increases the “Cautious” likelihood that is the estimated driving characteristic of the other vehicle 2 and also increases the “Aggressive” likelihood. Decrease. On the contrary, the driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 based on the entry time from the start of lighting of the turn signal of the other vehicle 2 to the entry to the entry area A1, and then enters the entry area. The estimated driving characteristic of the other vehicle 2 may be corrected based on the lighting state of the turn signal of the other vehicle 2 at the second time before the first time estimated to enter A1. Further, the driving characteristic estimation unit 34 estimates the driving characteristics of the other vehicle 2 and then estimates the other vehicle 2 based on the approach time from the turn-on start time of the turn signal of the other vehicle 2 to the actual entry into the entry area A1. The driving characteristics may be corrected.

  The likelihood estimator 35 includes the driving characteristics of the other vehicle 2 estimated by the driving characteristics estimator 34, the motion candidates predicted by the motion candidate predictor 31 and the motion candidate corrector 32, and the effective trajectory predicted by the trajectory predictor 33. Based on the comparison result, the operation of the other vehicle 2 is predicted. The operation of the other vehicle includes a track and speed profile of the other vehicle. The track of the other vehicle 2 shows a profile of the position of the other vehicle 2 at different times.

  For example, the likelihood estimation unit 35 compares the basic trajectory and the effective trajectory for each of the motion candidates predicted by the motion candidate prediction unit 31 and the motion candidate correction unit 32. Then, the likelihood that each motion candidate is selected as the motion of the other vehicle 2 is obtained from the difference between the basic track and the effective track. The difference between the basic trajectory and the effective trajectory is calculated based on, for example, the sum of differences in position and speed profiles between the two trajectories. The areas S1 and S2 shown in FIG. 4 and FIG. 5 are an example of a sum total obtained by integrating the difference in position between the basic trajectory and the effective trajectory. Since it can be determined that the difference in position is smaller as the areas S1 and S2 are smaller, a higher likelihood is calculated. Alternatively, even if the position difference is small, a low likelihood may be calculated if the speed profiles differ greatly. The likelihood that each motion candidate becomes the motion of the other vehicle 2 predicted based on the difference between the basic trajectory and the effective trajectory is denoted as “second likelihood α2”. The likelihood estimating unit 35 calculates the second likelihood α2 so that the second likelihood α2 is higher as the difference between the basic trajectory and the effective trajectory is smaller.

  Further, the likelihood estimating unit 35 predicts the operation of the other vehicle 2 based on the driving characteristic of the other vehicle 2 estimated by the driving characteristic estimating unit 34 and the second likelihood α2. For example, the likelihood estimating unit 35 associates the driving characteristics of the other vehicle 2 estimated by the driving characteristic estimating unit 34 with the motion candidates predicted by the motion candidate predicting unit 31 and the motion candidate correcting unit 32. At this time, the “Cautious” or “Cautious” likelihood, which is the driving characteristic of the other vehicle 2 estimated by the driving characteristic estimation unit 34, enters the approach area A1 before the own vehicle 1 among the respective motion candidates. Can be associated with motion candidates. Further, the “Aggressive” or “Aggressive” likelihood, which is the driving characteristic of the other vehicle 2 estimated by the driving characteristic estimation unit 34, can be associated with the motion candidate that enters the approach area A1 more than the own vehicle 1. . For example, in the driving scene shown in FIG. 3, the “Cautious” or “Cautious” likelihood is associated with the track t1 that is given off to the host vehicle 1, and the “Aggressive” or “Aggressive” likelihood precedes the host vehicle 1. Corresponding to the trajectory t2.

  Furthermore, when the first likelihood α1 (for example, “Aggressive” likelihood or “Cautious” likelihood) is estimated as the driving characteristic of the other vehicle 2, the likelihood estimating unit 35 sets the first likelihood α1. Based on this, the second likelihood α2 of each motion candidate is weighted. For example, for each motion candidate, the final likelihood (final likelihood α) is calculated by multiplying the second likelihood α2 by using the first likelihood α1 as a coefficient. Alternatively, the first likelihood α1 and the second likelihood α2 are weighted for each motion candidate, and the final likelihood α is calculated by adding the weighted first likelihood α1 and second likelihood α2. Thereby, the final likelihood α combined with the first likelihood α1 predicted by the driving characteristic estimation unit 34 and the second likelihood α2 estimated by the likelihood estimation unit 35 can be obtained.

  For example, in the driving scene shown in FIG. 3, when the driving characteristic of the other vehicle 2 is “Aggressive”, the second likelihood α2 of the track t1 is larger than the second likelihood α2 of the track t2 (first likelihood). Multiply degree α1). The motion candidate for which the maximum likelihood is calculated can be determined as the most likely motion candidate in consideration of the driving characteristics and behavior of the other vehicle 2. Therefore, the likelihood estimating unit 35 may determine the motion candidate having the highest final likelihood α as the motion of the other vehicle 2. The first likelihood α1, the second likelihood α2, and the final likelihood α are an example of an index representing the possibility that the motion candidate actually occurs, and may be expressed in a manner other than the likelihood. . In this way, the motion prediction unit 23 predicts the motion of the other vehicle 2 based on the likelihood of each motion candidate assumed by the likelihood estimation unit 35.

  The own vehicle route generation unit 24 generates a route of the own vehicle 1 based on the operation of the other vehicle 2 predicted by the operation prediction unit 23. The “route of the host vehicle 1” indicates not only the profile of the position of the host vehicle 1 at different times but also the profile of the speed of the host vehicle 1 at each position. Here, based on the behavior of the other vehicle 2 on the map, the operation of the other vehicle including the track of the other vehicle 2 is predicted. For this reason, generating the route of the host vehicle 1 based on the track of the other vehicle 2 generates the route of the host vehicle 1 based on the change in the relative distance from the other vehicle 2, the acceleration / deceleration, or the difference in the attitude angle. Will be.

  For example, in the traveling scene shown in FIG. 3, when the trajectory t2 is predicted by the motion prediction unit 23, or when the final likelihood α of the trajectory 2 calculated by the motion prediction unit 23 is equal to or greater than a predetermined threshold, The route of the host vehicle 1 can be generated after predicting the lane departure of the vehicle 2. The route of the host vehicle 1 is a route that does not interfere with the lane change, and is a route that decelerates the other vehicle 2 to change the lane before the host vehicle 1. Alternatively, as long as the lane width (lane width) is sufficiently wide, a route that travels to the right side in the right lane may be used. Furthermore, when there is an adjacent lane on the right side, a route for performing a lane change in advance may be used. Therefore, it is possible to generate a smooth route of the host vehicle 1 that does not come into contact with the other vehicle 2 and that does not cause the host vehicle 1 to suddenly decelerate or suddenly handle due to the behavior of the other vehicle 2.

  In the travel scene shown in FIG. 3, when the other vehicle 2 shows a behavior of decelerating and continuing to travel in the left lane, the behavior of the other vehicle 2 causes the own vehicle 1 to go first, and then the other vehicle 2 then proceeds to the right lane. This can be interpreted as indicating an intention to change the lane. In this case, the route of the own vehicle 1 is formed in consideration of the operation intention of the other vehicle 2 or the own vehicle 1 is not decelerated or accelerated by controlling the own vehicle 1 so that the side of the parked vehicle 3 is ahead. Can pass through. As a result, it is possible to avoid a situation in which both the other vehicle 2 and the host vehicle 1 yield, so a smooth traffic flow can be realized. In the scene where the other vehicle 2 is traveling around the host vehicle 1 even after passing through the travel scene shown in FIG. 3, the host vehicle route generation unit 24 of the other vehicle 2 predicted by the motion prediction unit 23. Based on the operation, the route of the host vehicle 1 may be generated.

  In the vehicle control unit 25, a steering actuator, an accelerator pedal actuator, and the like based on the own position calculated by the in-map position calculation unit 22 so that the own vehicle 1 travels according to the route generated by the own vehicle route generation unit 24. Drive at least one of the brake pedal actuators. In addition, although the case where it controls according to the path | route of the own vehicle 1 is shown in embodiment, you may control the own vehicle 1 without producing | generating the path | route of the own vehicle 1. FIG. In this case, it is also possible to perform control based on a relative distance from the other vehicle 2 or a difference in posture angle between the other vehicle 2 and the host vehicle 1.

(Driving support method)
Next, an example of a driving support method by the driving support device 10 according to the embodiment will be described with reference to the flowchart of FIG.

  In step S <b> 1, the object detection device 11 detects the behavior of an object such as another vehicle around the host vehicle 1 using a plurality of object detection sensors. The object detection device 11 further detects the lighting state of the turn signal of the other vehicle. In step S2, the detection integration unit 20 integrates a plurality of detection results obtained from each of the plurality of object detection sensors, and outputs one detection result for each object. The object tracking unit 21 tracks each object detected and integrated by the detection integration unit 20.

  In step S3, the host vehicle position estimation device 12 measures the position, posture, and speed of the host vehicle 1 with respect to a predetermined reference point using a position detection sensor. In step S4, the map acquisition device 13 acquires map information indicating the structure of the road on which the host vehicle 1 travels. In step S5, the in-map position calculation unit 22 estimates the position and orientation of the host vehicle 1 on the map from the position of the host vehicle 1 measured in step S3 and the map information acquired in step S4.

  In step S6, the motion predicting unit 23 determines other vehicles around the own vehicle 1 based on the behavior of the other vehicle 2 included in the detection result obtained in step S2 and the position of the own vehicle 1 estimated in step S5. The motion prediction process for predicting the motion 2 is performed. Here, the motion prediction unit 23 estimates the driving characteristics of the other vehicle 2, and predicts the movement of the other vehicle 2 based on the estimated driving characteristics of the other vehicle 2 (details of step S6 will be described later). In step S7, the own vehicle route generation unit 24 generates a route of the own vehicle 1 based on the operation of the other vehicle predicted in step S6. In step S8, the vehicle control unit 25 controls the host vehicle 1 to travel along the route of the host vehicle 1 generated in step S7.

(Operation prediction process)
Next, an example of the motion prediction process (motion prediction method) in step S6 in FIG. 6 will be described with reference to the flowchart in FIG.

  In step S10, the behavior determination unit 30 determines the road and lane on which the other vehicle 2 travels based on the position of the host vehicle 1 on the map and the behavior of the object obtained in step S2. In step S11, the motion candidate prediction unit 31 predicts motion candidates of the other vehicle 2 based on the map. For example, the motion candidate prediction unit 31 predicts the motion intention from the road structure included in the map information. The motion candidate prediction unit 31 selects any of various motion intentions predicted for the other vehicle 2 (for example, going straight or changing lanes, or going straight or turning left or right at an intersection). The motion candidate prediction unit 31 calculates the basic trajectory of the other vehicle 2 in the selected motion intention, and generates the selected motion intention and the corresponding basic trajectory as motion candidates.

  In step S12, the controller 14 determines whether or not the processes in steps S10 and S11 have been performed for all other vehicles detected in step S1. If it is determined that all other vehicles 2 have not been processed, the process returns to step S10. On the other hand, when it determines with the process having been performed about all the other vehicles 2, it transfers to step S13.

  In step S13, the motion candidate correction unit 32 corrects the motion candidate predicted in step S11 in consideration of the stationary object detected simultaneously with the other vehicle 2 in step S1, and generates a corrected motion candidate. In step S14, the motion candidate correcting unit 32 corrects the motion candidate predicted in step S11 in consideration of the moving object other than the other vehicle 2 detected in step S1, and generates a corrected motion candidate.

  In step S <b> 15, the driving characteristic estimation unit 34 performs a driving characteristic estimation process for estimating the driving characteristic of the other vehicle 2 based on the vehicle behavior of the other vehicle 2. For example, the driving characteristic estimation unit 34 estimates “Aggressive” likelihood and “Cautious” likelihood as the driving characteristics of the other vehicle 2 (details of step S15 will be described later). In step S <b> 16, the motion predictor 23 predicts the “Cautious” likelihood and the “Aggressive” likelihood estimated by the driving characteristic estimator 34 by the motion candidate predictor 31 and the motion candidate corrector 32. Is determined as the first likelihood α1 corresponding to.

  In step S17, the controller 14 determines whether or not the processing in steps S13 to S16 has been performed for all other vehicles 2 detected in step S1. If it is determined that all other vehicles 2 have not been processed, the process returns to step S13. On the other hand, when it determines with the process having been performed about all the other vehicles 2, it transfers to step S18.

  In step S18, the trajectory prediction unit 33 uses a known state estimation technique, such as a Kalman filter, for the effective trajectory of the other vehicle 2 when the other vehicle 2 maintains its behavior and operates according to the predicted motion intention. Use to calculate. In step S <b> 19, the likelihood estimation unit 35 compares the basic trajectory of the motion candidate predicted by the motion candidate prediction unit 31 and the motion candidate correction unit 32 with the effective trajectory. The likelihood estimation unit 35 obtains each second likelihood α2 at which the motion candidate predicted by the motion candidate prediction unit 31 and the motion candidate correction unit 32 becomes the motion of the other vehicle 2 based on the difference between the basic trajectory and the effective trajectory. presume. In step S20, the likelihood estimating unit 35 weights the second likelihood α2 of each motion candidate based on the first likelihood α1, and finally determines the likelihood that the motion candidate can be operated with the other vehicle 2 for each motion candidate. Each likelihood (final likelihood α) is determined.

  In step S21, the controller 14 determines whether or not the processing in steps S18 to S20 has been performed for all of the motion candidates predicted by the motion candidate prediction unit 31 and the motion candidate correction unit 32. If it is determined that any operation candidate has not been processed, the process returns to step S18. On the other hand, if it is determined that processing has been performed for all motion candidates, the process proceeds to step S22. When the final likelihood α is determined for all the motion candidates, the likelihood estimation unit 35 may determine the motion candidate having the highest final likelihood α as the motion of the other vehicle 2.

  In step S22, the controller 14 determines whether or not the processing in steps S18 to S21 has been performed for all other vehicles 2 detected in step S1. If it is determined that all other vehicles 2 have not been processed, the process returns to step S18. On the other hand, when it is determined that the processing has been performed for all the other vehicles 2, the processing is completed.

(Driving characteristics estimation process)
Next, an example of the driving characteristic estimation process (driving characteristic estimation method) in step S15 of FIG. 7 will be described with reference to the flowchart of FIG.

  In step S30, the driving characteristic estimation unit 34 selects one of the motion candidates of the other vehicle 2 predicted in steps S11, S13, and S14 of FIG. In step S31, the driving characteristic estimation unit 34 acquires the predicted route of the host vehicle 1. In step S <b> 32, the driving characteristic estimation unit 34 estimates an approach area A <b> 1 that requires a turn signal operation when the other vehicle 2 enters based on the selected basic trajectory t <b> 2 of the other vehicle 2. The approach area A1 is, for example, an intersection area where the basic trajectory t2 of the selected motion candidate of the other vehicle 2 and the predicted route of the host vehicle 1 intersect. If the basic track of the other vehicle 2 and the predicted route of the host vehicle 1 do not intersect, the process may move to step S43. If none of the basic tracks of the other vehicle 2 intersects the predicted route of the host vehicle 1, the process of estimating the driving characteristic of the other vehicle 2 is not performed, and the driving characteristic of the other vehicle 2 is unknown (unestimated). It is good.

  In step S33, the driving characteristic estimation unit 34 estimates the own vehicle entry time until the own vehicle 1 enters the entry area A1. The driving characteristic estimation unit 34 further estimates an entry time until the other vehicle 2 enters the entry area A1. In step S34, the driving characteristic estimation unit 34 determines whether or not the time difference between the time when the own vehicle 1 is estimated to enter the entry area A1 and the time when the other vehicle 2 is estimated to enter the entry area A1 is less than a threshold value. It is determined whether or not the own vehicle 1 and the other vehicle 2 are approaching. If the time difference is equal to or greater than the threshold, the host vehicle 1 and the other vehicle 2 are not approaching, and the process proceeds to step S43. On the other hand, if the time difference is less than the threshold value, the host vehicle 1 and the other vehicle 2 are close to each other, and the process proceeds to step S35.

  In steps S35 to S42, the driving characteristic estimation unit 34 estimates the driving characteristic of the other vehicle 2 based on the lighting state of the turn signal of the other vehicle 2 with respect to the approach area A1. In step S <b> 35, the driving characteristic estimation unit 34 detects the lighting state of the blinker of the other vehicle 2. In step S36, the driving characteristic estimator 34 determines whether or not the turn signal of the other vehicle 2 is turned on at a second time before a predetermined threshold value from the first time when the other vehicle 2 is estimated to enter the entry area A1. It is determined whether or not the turn signal of the other vehicle 2 is lit at two times. When it determines with the turn signal of the other vehicle 2 having lighted at 2nd time, it transfers to step S37. In step S <b> 37, the driving characteristic estimation unit 34 sets the driving characteristic of the other vehicle 2 to “Cautious”. For example, the “Aggressive” likelihood of the driving characteristics of the other vehicle 2 is set relatively low, and the “Cautious” likelihood of the driving characteristics of the other vehicle 2 is set relatively high. On the other hand, when it is determined in step S36 that the turn signal of the other vehicle 2 is not lit at the second time, the process proceeds to step S38. In step S <b> 38, the driving characteristic estimation unit 34 sets the driving characteristic of the other vehicle 2 to “Aggressive”. For example, the “Aggressive” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively high, and the “Cautious” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively low.

  In step S36, the driving characteristic estimation unit 34 estimates the entry time from the start of turn-on of the turn signal of the other vehicle 2 until the other vehicle 2 enters the entry area A1, and the estimated entry time is equal to or greater than a predetermined threshold value. It may be determined whether or not. If it is determined that the approach time is equal to or greater than the predetermined threshold, the process proceeds to step S37. In step S <b> 37, the driving characteristic estimation unit 34 sets the driving characteristic of the other vehicle 2 to “Cautious”. For example, the “Aggressive” likelihood of the driving characteristics of the other vehicle 2 is set relatively low, and the “Cautious” likelihood of the driving characteristics of the other vehicle 2 is set relatively high. On the other hand, if it is determined in step S36 that the approach time is less than the predetermined threshold, the process proceeds to step S38. In step S <b> 38, the driving characteristic estimation unit 34 sets the driving characteristic of the other vehicle 2 to “Aggressive”. For example, the “Aggressive” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively high, and the “Cautious” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively low.

  In step S39, the driving characteristic estimation unit 34 detects the operation of the other vehicle 2 such as a lane change for entering the approach area A1. The driving characteristic estimation unit 34 calculates the time when the other vehicle 2 enters the entry area A1. The time when the other vehicle 2 enters the entry area A1 may be, for example, the time when the other vehicle 2 enters the entry area A1. Alternatively, the time when the other vehicle 2 enters the entry area A1 is, for example, the start of the operation (lane change) for the other vehicle 2 to enter the entry area A1 before the time when the other vehicle 2 enters the entry area A1. It may be a time.

  In step S40, the driving characteristic estimation unit 34 calculates the entry time (lighting time) from the turn-on start time of the turn signal of the other vehicle 2 to the time when the other vehicle 2 enters the entry area A1. The driving characteristic estimation unit 34 determines whether or not the calculated approach time is less than a predetermined threshold (for example, about 3 seconds). When it is determined that the approach time is less than the predetermined threshold, the process proceeds to step S41. In step S <b> 41, the driving characteristic estimation unit 34 increases the “Cautious” likelihood of the driving characteristic of the other vehicle 2 and decreases the “Aggressive” likelihood of the driving characteristic of the other vehicle 2. Correct the estimated driving characteristics. On the other hand, if it is determined in step S40 that the entry time is equal to or greater than the predetermined threshold, the process proceeds to step S42. In step S <b> 42, the driving characteristic estimation unit 34 increases the “Aggressive” likelihood of the driving characteristics of the other vehicle 2 and decreases the “Cautious” likelihood of the driving characteristics of the other vehicle 2. Correct the estimated driving characteristics.

  In step S43, the driving characteristic estimation unit 34 determines whether all predicted motion candidates for the other vehicle 2 have been selected in step S30. If it is determined that all motion candidates have been selected in step S30, the process is completed. On the other hand, if it is determined in step S30 that all motion candidates have not been selected, the process returns to step S30, and steps S30 to S42 are repeated for unselected motion candidates.

  Note that the steps S36 to S38 may be omitted. In this case, instead of correcting the driving characteristics of the other vehicle 2 estimated in steps S36 to S38 in steps S40 to S42, the driving characteristics of the other vehicle 2 may be estimated in steps S40 to S42. That is, in step S40, the driving characteristic estimation unit 34 calculates the entry time (lighting time) from the turn-on start time of the turn signal of the other vehicle 2 to the time when the other vehicle 2 enters the entry area A1. The driving characteristic estimation unit 34 determines whether or not the calculated approach time is less than a predetermined threshold (for example, about 3 seconds). When it is determined that the approach time is less than the predetermined threshold, the process proceeds to step S41. In step S <b> 41, the driving characteristic estimation unit 34 sets the driving characteristic of the other vehicle 2 to “Cautious”. For example, the “Aggressive” likelihood of the driving characteristics of the other vehicle 2 is set relatively low, and the “Cautious” likelihood of the driving characteristics of the other vehicle 2 is set relatively high. On the other hand, if it is determined in step S40 that the entry time is equal to or greater than the predetermined threshold, the process proceeds to step S42. In step S42, the driving characteristic of the other vehicle 2 is set to “Aggressive”. For example, the “Aggressive” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively high, and the “Cautious” likelihood of the driving characteristic of the other vehicle 2 is set to be relatively low.

  As described above, according to the embodiment, the driving characteristic estimation unit 34 estimates the approach area A1 where the other vehicle 2 requires a turn signal operation from the surrounding environment of the other vehicle 2, and the other vehicle with respect to the estimated approach area A1. Based on the lighting state of the turn signal No. 2, the driving characteristics of the other vehicle 2 are estimated. Thereby, in consideration of the driving characteristics of the other vehicle 2, the operation of the other vehicle 2 such as a sudden lane change or a left / right turn can be appropriately predicted in advance. As a result, the host vehicle 1 can take a safer behavior and can reduce the uncomfortable feeling given to the occupant of the host vehicle 1.

  Furthermore, the turn signal lighting state of the other vehicle 2 with respect to the entry area A1 is an entry time from the start of turn-on of the turn signal of the other vehicle 2 to the entry to the entry area A1 of the other vehicle 2, and the driving characteristic estimation unit 34 Based on this approach time, the driving characteristics of the other vehicle 2 are estimated. Thereby, the driving characteristics of the other vehicle 2 can be appropriately estimated based on the entry time for entering the entry area A1 of the other vehicle 2.

  Further, based on the distance, speed, and acceleration to the entry area A1 of the other vehicle 2, the entry time from the start of lighting of the blinker of the other vehicle 2 to the entry of the entry area A1 of the other vehicle 2 is estimated. Thereby, based on the distance, speed, and acceleration to approach area A1 of other vehicle 2, the driving characteristic of other vehicle 2 can be estimated appropriately.

  Furthermore, the own vehicle entry time for the own vehicle 1 to enter the entry area A1 is estimated, and it is determined whether or not the difference between the entry time for the other vehicle 2 to enter the entry area A1 and the own vehicle entry time is less than a threshold value. When the difference between the entry time and the own vehicle entry time is determined to be less than the threshold, the driving characteristics of the other vehicle 2 are estimated, and when the difference between the entry time and the own vehicle entry time is determined to be equal to or greater than the threshold, the other vehicle The process which estimates the driving | operation characteristic of 2 is not performed. Thereby, processing time can be reduced and the driving characteristic of the other vehicle 2 can be estimated at an earlier timing.

  Further, by calculating the blinker lighting time from the turn-on start time of the turn signal of the other vehicle 2 to the operation start time of the other vehicle 2, and correcting the estimated driving characteristics of the other vehicle 2 based on the calculated turn signal turn-on time. The driving characteristics of the other vehicle 2 can be accurately estimated.

  Further, the first predicted trajectory based on the map of the other vehicle 2 is estimated, the second predicted trajectory based on the behavior of the other vehicle 2 is estimated, the comparison result between the first predicted trajectory and the second predicted trajectory, and the other vehicle 2 Based on the driving characteristics, the operation of the other vehicle 2 is predicted. Thereby, a sudden lane change or a left / right turn of the other vehicle 2 can be appropriately predicted, and the host vehicle 1 can take a safer behavior.

  Furthermore, the traveling of the host vehicle 1 is controlled in accordance with the predicted operation of the other vehicle 2. For example, as shown in FIG. 3, when it is predicted that the other vehicle 2 selects the track t2 preceding the own vehicle 1, or the final likelihood α for selecting the track t2 preceding the own vehicle 1 by the other vehicle 2 is predetermined. The vehicle 1 decelerates, stops, changes lanes, and the like in advance when the vehicle is equal to or greater than the threshold value. Thus, the own vehicle 1 can take a safe behavior in advance according to the operation of the other vehicle 2 appropriately predicted.

(Other embodiments)
As mentioned above, although this invention was described by embodiment, it should not be understood that the statement and drawing which form a part of this indication limit this invention. From this disclosure, various alternative embodiments, examples and operational techniques will be apparent to those skilled in the art.

  For example, the driving support device 10 according to the embodiment of the present invention can be applied to the driving scene shown in FIG. In FIG. 9, the host vehicle 1 travels in the left lane of one lane on one side, and the other vehicle 2 that is an oncoming vehicle travels in the right lane. A parked vehicle 3 is present in front of the other vehicle 2. In this case, the trajectory prediction unit 33 waits by decelerating or stopping behind the parked vehicle 3 until the own vehicle 1 passes by the side of the parked vehicle 3 as an operation in which the other vehicle 2 passes the parked vehicle 3. The trajectory t41 where the vehicle 1 enters the left lane after passing and the other vehicle 2 enters the left lane and passes the side of the parked vehicle 3 before the own vehicle 1 passes the side of the parked vehicle 3. The trajectory t42 of is predicted.

  The driving characteristic estimator 34 estimates an approach area A2 that requires a winker operation (turns the blinker on the right side of the other vehicle 2) for the other vehicle 2 to avoid the parked vehicle 3 to change lanes, and the estimated approach area A2 The driving characteristics of the other vehicle 2 are estimated on the basis of the turn-on state of the blinker of the other vehicle 2. The likelihood estimating unit 35 calculates the likelihood of selecting the tracks t41 and t42 based on the driving characteristics of the other vehicle 2 estimated by the driving characteristic estimating unit 34, and predicts the operation of the other vehicle 2. At this time, the “Cautious” or “Cautious” likelihood of the driving characteristic of the other vehicle 2 is associated with the track t41, and the “Aggressive” or “Aggressive” likelihood of the driving characteristic of the other vehicle 2 is associated with the track t42. .

  Moreover, the driving assistance apparatus 10 which concerns on embodiment of this invention is applicable also to the driving | running | working scene in case the own vehicle 1 approachs an intersection as shown in FIG. As shown in FIG. 10, when the own vehicle 1 enters the intersection, the other vehicle 2 that is an oncoming vehicle is scheduled to make a right turn. In this case, the trajectory prediction unit 33 waits until the own vehicle 1 passes the intersection as the operation of the other vehicle 2 turning right, and the own vehicle 1 passes the intersection and the trajectory t51 where the own vehicle 1 turns right after passing. Before, the track t52 on which the other vehicle 2 makes a right turn is predicted. The driving characteristic estimation unit 34 estimates an approach area A3 that requires a winker operation (turns the right turn signal blinker of the other vehicle 2) for the other vehicle 2 to turn right, and turns on the turn signal of the other vehicle 2 with respect to the estimated approach area A3. Based on the state, the driving characteristics of the other vehicle 2 are estimated. As the predetermined threshold, for example, a value obtained by dividing 10 m by the estimated speed of the other vehicle 2 can be used, and can be set as appropriate.

  In addition, the driving characteristic estimation unit 34 determines the driving characteristic of the other vehicle 2 when it is determined that the entry time from when the turn-on of the blinker of the other vehicle 2 starts until the other vehicle 2 enters the estimation area A3 is equal to or greater than a predetermined threshold. Increase "Cautious" likelihood and decrease "Aggressive" likelihood. On the other hand, if it is determined that the entry time from the start of turn-on of the turn signal of the other vehicle 2 until the other vehicle 2 enters the estimated area A3 is less than a predetermined threshold, the “Cautious” likelihood is increased and the “Aggressive” likelihood is increased. The degree may be decreased. As the predetermined threshold, for example, a value obtained by dividing 10 m by the estimated speed of the other vehicle 2 can be used, and can be set as appropriate. The likelihood estimating unit 35 calculates the likelihood of selecting the tracks t51 and t52 based on the driving characteristics of the other vehicle 2 estimated by the driving characteristic estimating unit 34, and predicts the operation of the other vehicle 2. At this time, the “Cautious” or “Cautious” likelihood of the driving characteristic of the other vehicle 2 is associated with the track t51, and the “Aggressive” or “Aggressive” likelihood of the driving characteristic of the other vehicle 2 is associated with the track t52. .

  Moreover, the driving assistance apparatus 10 which concerns on embodiment of this invention is applicable also to the driving | running | working scene shown in FIG. In FIG. 11, the host vehicle 1 travels in the left lane of two lanes on one side, and the other vehicle 2 is scheduled to join the lane in which the host vehicle 1 is traveling. In this case, the trajectory predicting unit 33, as the operation of the other vehicle 2 joining, the trajectory t61 that joins the host vehicle 1 so as to follow the host vehicle 1 after passing, and the host vehicle 1 before the host vehicle 1 passes. A trajectory t62 that merges ahead of is predicted.

  The driving characteristic estimation unit 34 estimates an approach area A4 that requires a winker operation (turns the right turn signal blinker of the other vehicle 2) for the other vehicle 2 to join, and lights the turn signal of the other vehicle 2 with respect to the estimated approach area A4. Based on the state, the driving characteristics of the other vehicle 2 are estimated. The likelihood estimation unit 35 calculates the likelihood of selecting the tracks t61 and t62 based on the driving characteristics of the other vehicle 2 estimated by the driving characteristic estimation unit 34, and predicts the operation of the other vehicle 2. At this time, the driving characteristic “Cautious” or “Cautious” likelihood of the other vehicle 2 is associated with the track t61, and the driving characteristic “Aggressive” or “Aggressive” of the other vehicle 2 is associated with the track t62. .

  In addition, the driving support device 10 according to the embodiment of the present invention is not illustrated, but when an other vehicle enters a roundabout, when an other vehicle enters a road from a parking lot, an oncoming vehicle is parked on a narrow road. The present invention can be applied to various traveling scenes in which there are entry areas where other vehicles require a winker operation, such as when overtaking a vehicle.

  As described above, the present invention naturally includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specifying matters according to the scope of claims reasonable from the above description.

DESCRIPTION OF SYMBOLS 1 ... Own vehicle 2 ... Other vehicle 3 ... Parked vehicle 10 ... Driving assistance apparatus 11 ... Object detection apparatus 12 ... Own vehicle position estimation apparatus 13 ... Map acquisition apparatus 14 ... Controller 20 ... Detection integration part 21 ... Object tracking part 22 ... Map Inner position calculation unit 23 ... motion prediction unit 24 ... own vehicle route generation unit 25 ... vehicle control unit 30 ... behavior determination unit 31 ... motion candidate prediction unit 32 ... motion candidate correction unit 33 ... trajectory prediction unit 34 ... driving characteristic estimation unit 35 ... likelihood estimation unit

Claims (5)

Detect other vehicles around your vehicle,
Estimating the approach area where the other vehicle requires a turn signal operation from the surrounding environment of the other vehicle,
The driving characteristic estimation method, wherein the driving characteristic of the other vehicle is estimated based on a lighting state of the turn signal of the other vehicle with respect to the estimated approach area. The lighting state of the turn signal is an entry time from the start of turning on the turn signal of the other vehicle to the entry into the entry area of the other vehicle,
The driving characteristic estimation method according to claim 1, wherein the driving characteristic of the other vehicle is estimated based on the approach time.   The driving characteristic estimation method according to claim 2, wherein the approach time is estimated based on a distance, speed, and acceleration to the approach area of the other vehicle. Estimating the vehicle entry time for the vehicle to enter the entry area,
Determining whether the difference between the entry time and the own vehicle entry time is less than a threshold;
The driving characteristic estimation method according to claim 3, wherein the driving characteristic is estimated when the difference is determined to be less than the threshold. A sensor for detecting other vehicles around the host vehicle;
A controller for estimating an approach area where the other vehicle requires a turn signal operation from an ambient environment of the other vehicle, and estimating a driving characteristic of the other vehicle based on a lighting state of the turn signal of the other vehicle with respect to the estimated approach area; ,
A driving characteristic estimation apparatus comprising:

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