JP2020160899A - Vehicle behavior prediction method, vehicle behavior prediction system, and vehicle controller - Google Patents

Abstract

To provide a vehicle behavior prediction method, a vehicle behavior prediction system, and a vehicle controller capable of predicting lane change of another vehicle on an early stage.SOLUTION: A vehicle behavior prediction method, a vehicle behavior prediction system, and a vehicle controller identify a first another vehicle whose possibility of changing lanes is predicted, and predict a start prediction time that is the time required for a second another vehicle, which is at a halt ahead of the first another vehicle, to start. The longer the start prediction time is, the higher the possibility that the first another vehicle may change lanes is predicted to be.SELECTED DRAWING: Figure 1

Description

The present invention relates to a vehicle behavior prediction method, a vehicle behavior prediction device, and a vehicle control device.

Conventionally, a technique for determining a lane change of a vehicle has been known (for example, Patent Document 1). The technique described in Patent Document 1 detects another vehicle traveling in an adjacent traveling lane, obtains the angle of the movement vector of the other vehicle, and when the angle of the moving vector satisfies a predetermined condition, from the adjacent traveling lane. Judge that another vehicle changes lanes. In addition, this technology predicts the inter-vehicle distance between the other vehicle and the own vehicle when it is determined that there is an interruption of another vehicle from the adjacent traveling lane, and the predicted inter-vehicle distance and the target when the other vehicle changes lanes. The vehicle speed is controlled so that it matches the inter-vehicle distance.

Japanese Unexamined Patent Publication No. 2001-199260

However, in the technique described in Patent Document 1, the lane change cannot be determined unless the other vehicle actually starts the lane change and the movement vector does not meet a certain condition. Therefore, there is a problem that it is not possible to predict the lane change of another vehicle at an early stage.

The present invention has been made in view of such a problem, and an object of the present invention is to provide a vehicle behavior prediction method, a vehicle behavior prediction device, and a vehicle control device capable of predicting a lane change of another vehicle at an early stage. is there.

The vehicle behavior prediction method according to one aspect of the present invention identifies a first other vehicle that predicts the possibility of changing lanes, and a second other vehicle that stops in front of the first other vehicle. Predict the estimated start time, which is the time until the vehicle starts. Then, the vehicle behavior prediction method predicts that the longer the start prediction time, the higher the possibility that the first other vehicle will change lanes.

According to the present invention, it is possible to predict a lane change of another vehicle at an early stage.

FIG. 1 is a block diagram showing a configuration of a vehicle behavior prediction device according to the present embodiment. FIG. 2 is a flowchart showing a processing procedure for vehicle behavior prediction according to the present embodiment. FIG. 3A is an explanatory diagram schematically showing an intersection which is an example of an intersection position. FIG. 3B is an explanatory diagram showing a position where side roads intersect, which is an example of an intersection position. FIG. 3C is an explanatory view showing a position where private land, which is an example of an intersection position, is adjacent to each other. FIG. 4 is a flowchart showing a processing procedure for predicting the estimated start time. FIG. 5 is a diagram schematically showing a traffic condition at an intersection. FIG. 6 is a flowchart showing a processing procedure for predicting the possibility of changing lanes. FIG. 7A is an explanatory diagram showing the relationship between the predicted start time and the possibility of changing lanes. FIG. 7B is an explanatory diagram showing the relationship between the stop time and the possibility of changing lanes. FIG. 7C is an explanatory diagram showing the relationship between the added value of the predicted start time and the stop time and the possibility of changing lanes. FIG. 8 is a flowchart showing a processing procedure for calculating the effect of changing lanes. FIG. 9A is an explanatory diagram showing a reference position set with respect to the intersection. FIG. 9B is an explanatory diagram showing a reference position set with respect to the side road. FIG. 10A is a diagram schematically showing an example of a situation in which the difficulty of changing lanes is high. FIG. 10B is a diagram schematically showing an example of a situation in which the difficulty of changing lanes is high. FIG. 10C is a diagram schematically showing an example of a situation in which the difficulty of changing lanes is high. FIG. 11A is a diagram schematically showing a state of a vehicle entering a side road. FIG. 11B is a diagram schematically showing a state of a vehicle entering private land beside a road. FIG. 11C is a diagram schematically showing a state in which a traffic jam is occurring.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

The configuration of the vehicle behavior prediction device according to the present embodiment will be described with reference to FIG. The vehicle behavior prediction device includes an object detection device 1, a vehicle position estimation device 3, a map acquisition device 4, and a microcomputer 50.

The vehicle behavior prediction device may be applied to a vehicle having an automatic driving function, or may be applied to a vehicle not having an automatic driving function. Further, the vehicle behavior prediction device may be applied to a vehicle capable of switching between automatic driving and manual driving. Hereinafter, the vehicle to which the vehicle behavior prediction device is applied is referred to as a own vehicle.

Autonomous driving refers to a state in which at least one actuator among actuators such as a brake, an accelerator, and a steering wheel is controlled without the operation of an occupant. Therefore, other actuators may be operated by the operation of the occupant. Further, the automatic operation may be a state in which any control such as acceleration / deceleration control or lateral position control is executed. Further, the manual operation in the present embodiment refers to a state in which the occupant is operating the brake, the accelerator, and the steering, for example.

The object detection device 1 includes a plurality of object detection sensors such as a laser radar, a millimeter wave radar, and a camera mounted on the own vehicle. The object detection device 1 detects an object around the own vehicle by using a plurality of object detection sensors. The object detection device 1 detects moving objects including other vehicles, motorcycles, bicycles, and pedestrians, and stationary objects including parked vehicles and buildings. For example, the object detection device 1 detects the position, posture (yaw angle), size, speed, acceleration, deceleration, and yaw rate of a moving object and a stationary object with respect to the own vehicle.

The own vehicle position estimation device 3 measures the absolute position of the own vehicle by using a position estimation technique such as GPS (Global Positioning System) and odometry. The own vehicle position estimation device 3 measures the absolute position of the own vehicle, that is, the position, vehicle speed, acceleration, steering angle, and posture of the own vehicle with respect to a predetermined reference point by using the position detection sensor. The own vehicle position estimation device 3 includes a GPS receiver, an inertial navigation system, sensors provided on a brake pedal and an accelerator pedal, sensors for acquiring vehicle behavior such as a wheel speed sensor and a yaw rate sensor, a laser radar, and a camera. It has been.

The map acquisition device 4 acquires map information indicating the structure of the road on which the own vehicle travels. The map information acquired by the map acquisition device 4 includes road structure information such as absolute lane positions, lane connection relationships, and relative positional relationships, traffic rules, road signs, and the like. In addition, the map information acquired by the map acquisition device 4 also includes facility information such as a parking lot and a gas station, which are privately owned land on the side of the road. In addition, the map information includes traffic light information such as the position of the traffic light and the type of the traffic light. The map acquisition device 4 may own a map database that stores map information, or may acquire map information from an external map data server by cloud computing. Further, the map acquisition device 4 may acquire map information by using vehicle-to-vehicle communication and road-to-vehicle communication.

The microcomputer 50 predicts the behavior of other vehicles existing around the own vehicle based on the detection result of the object detection device 1, the estimation result by the own vehicle position estimation device 3, and the acquisition result of the map acquisition device 4. Further, the microcomputer 50 controls the running state of the own vehicle based on the predicted behavior of the other vehicle.

In the behavior prediction of the other vehicle according to the present embodiment, the other vehicle traveling in the lane (adjacent traveling lane) adjacent to the lane in which the own vehicle is traveling (own vehicle traveling lane) moves from the adjacent traveling lane to the own vehicle traveling lane. It predicts that the lane will change to. In particular, in the behavior prediction of the present embodiment, there is a possibility that the other vehicle changes lanes from the adjacent traveling lane to the own vehicle traveling lane before the other vehicle actually starts the lane change (hereinafter, simply "lane change". Predict the possibility). In the following description, when indicating a lane, a lane (own vehicle traveling lane, adjacent traveling lane) is defined with reference to the own vehicle.

The microcomputer 50 is a general-purpose microcomputer including a CPU (central processing unit), a memory, and an input / output unit. A computer program (vehicle behavior prediction program) for functioning as a vehicle behavior prediction device is installed in the microcomputer. By executing the computer program, the microcomputer functions as a plurality of information processing circuits included in the vehicle behavior prediction device. In this embodiment, an example of realizing a plurality of information processing circuits included in the vehicle behavior prediction device by software is shown, but of course, dedicated hardware for executing each of the following information processing is prepared. It is also possible to configure an information processing circuit. Further, a plurality of information processing circuits may be configured by individual hardware.

The microcomputer 50 includes a detection integration unit 2a, an object tracking unit 2b, a map position estimation unit 5, a behavior prediction unit 10, and a vehicle control unit 20 as a plurality of information processing circuits.

The detection integration unit 2a integrates a plurality of detection results obtained from each of the plurality of object detection sensors included in the object detection device 1 and outputs one detection result for each object. Specifically, from the behavior of the object obtained from each of the object detection sensors, the rational behavior of the object with the smallest error is calculated in consideration of the error characteristics of each object detection sensor. Specifically, by using a known sensor fusion technique, the detection results acquired by a plurality of object detection sensors are comprehensively evaluated to obtain more accurate detection results.

The object tracking unit 2b tracks the object detected by the detection integration unit 2a. Specifically, the object tracking unit 2b verifies (associates) the identity of the object between different times from the behavior of the objects output at different times, and tracks the object based on the association. ..

The position estimation unit 5 in the map estimates the position of the own vehicle on the map from the absolute position of the own vehicle obtained by the own vehicle position estimation device 3 and the map information acquired by the map acquisition device 4. Specifically, the position estimation unit 5 in the map estimates which lane the own vehicle is traveling in.

The behavior prediction unit 10 predicts the possibility of changing lanes. The behavior prediction unit 10 includes a lane determination unit 11, a vehicle identification unit 12, an intersection position acquisition unit 13, a factor acquisition unit 14, a timing prediction unit 15, a stop time acquisition unit 16, and an effect calculation unit 17. It has a difficulty calculation unit 18.

The lane determination unit 11 is based on the tracking result of the object obtained by the detection integration unit 2a and the object tracking unit 2b and the position (traveling lane) of the own vehicle on the map obtained by the position estimation unit 5 in the map. Then, it is determined in which lane on the map other vehicles around the own vehicle are located.

The vehicle identification unit 12 is in the adjacent traveling lane (an example of the first traveling lane) based on the tracking result of the object obtained by the detection integration unit 2a and the object tracking unit 2b and the determination result of the lane determination unit 11. Identify the stopped vehicle (second other vehicle) that is stopped at. The stopped state does not mean only a completely stopped state, but also includes a low-speed state that can be regarded as stopped.

In addition, the vehicle identification unit 12 identifies another vehicle (first other vehicle) that predicts the possibility of changing lanes from among other vehicles traveling in the adjacent traveling lane (hereinafter referred to as "prediction target vehicle"). The vehicle identification unit 12 identifies the vehicle to be predicted based on the tracking result of the object obtained by the detection integration unit 2a and the object tracking unit 2b, the determination result of the lane determination unit 11, and the identification result of the stopped vehicle. Do.

The intersection position acquisition unit 13 acquires information on the intersection position where the routes of moving objects intersect with respect to the adjacent traveling lane (road including the adjacent traveling lane) based on map information or the like. The intersection position acquisition unit 13 determines whether or not an intersection position exists in front of the stopped vehicle based on the acquired information. Since the prediction target vehicle is located behind the stopped vehicle, the existence of the intersection position in front of the stopped vehicle is synonymous with the existence of the intersection position in front of the prediction target vehicle.

The factor acquisition unit 14 acquires a stop factor, that is, a factor that the stopped vehicle specified by the vehicle identification unit 12 is stopped, based on map information, traffic conditions, and the like. Factors that stop the vehicle include the passage of oncoming vehicles that have a course that intersects the course of the stopped vehicle, the presence of pedestrians in the course of the stopped vehicle, and the occurrence of traffic congestion in the course of the stopped vehicle. Can be mentioned.

The timing prediction unit 15 predicts the timing at which the stopped vehicle can start based on the stop factor acquired by the factor acquisition unit 14. The timing prediction unit 15 predicts the timing at which the stop factor is eliminated, and predicts the start prediction time, which is the time until the stopped vehicle starts, based on the predicted timing.

The stop time acquisition unit 16 acquires the stop time. The stop time refers to the time from the identification of a stopped vehicle to stop in the adjacent driving lane to the identification of the prediction target vehicle in the adjacent driving lane.

The effect calculation unit 17 calculates the effect of changing lanes. The effect of changing lanes is that it is possible to drive in a shorter time by changing lanes to the own vehicle's driving lane (an example of the second driving lane) than to keep the predicted vehicle running in the adjacent driving lane. It is an index to show.

The difficulty calculation unit 18 calculates the difficulty of changing lanes. The difficulty level of changing lanes is an index showing the degree of difficulty of changing lanes when the vehicle to be predicted changes lanes from the adjacent traveling lane to the own vehicle traveling lane.

The lane change calculation unit 19 predicts the possibility of lane change based on the calculation results of the timing prediction unit 15, the stop time acquisition unit 16, the effect calculation unit 17, and the difficulty calculation unit 18.

The vehicle control unit 20 controls the vehicle of its own vehicle based on the possibility of lane change predicted by the behavior prediction unit 10. The vehicle control unit 20 controls various actuators (steering actuator, accelerator pedal actuator, brake actuator, etc.) of the own vehicle to execute automatic driving control or driving support control (for example, deceleration control).

The vehicle behavior prediction device may include a communication device (not shown). In this case, the behavior prediction unit 10 uses vehicle-to-vehicle communication or road-to-vehicle communication to provide information in front of the own vehicle (traffic condition information, traffic light). Information on roads including) may be obtained. Further, the behavior prediction unit 10 may acquire information in front of the own vehicle by using information obtained from a camera or the like. In addition, the behavior prediction unit 10 holds vehicle behavior data representing the average behavior of the vehicle in various driving scenes as a database.

In the present embodiment, the microcomputer 50 is configured to have the function of the vehicle control unit 20, and the vehicle behavior prediction device can be applied as a vehicle control device. However, the vehicle behavior prediction device may not have the function of the vehicle control unit 20, but may have only the function of predicting the behavior of another vehicle.

Next, with reference to FIGS. 2 to 10C, a processing procedure for vehicle behavior prediction according to the present embodiment will be described. This processing procedure is called by the on of the ignition switch (IGN) as a trigger and executed by the microcomputer 50. Hereinafter, in explaining the processing procedure, the reference numerals L1, L2, Va, Vb, and Vc shown in the drawings correspond to the own vehicle traveling lane L1, the adjacent traveling lane L2, the own vehicle Va, the prediction target vehicle Vb, and the stopped vehicle Vc. are doing. When identifying a plurality of vehicles, they are described as Vc1, Vc2, and the like.

First, in step S10, the detection integration unit 2a acquires object information, that is, information on an object around the own vehicle from the object detection device 1. When the object information is acquired, the detection integration unit 2a calculates the behavior of the object based on the object information. In addition, the object tracking unit 2b tracks the object detected by the detection integration unit 2a.

In step S11, the in-map position estimation unit 5 acquires map information from the map acquisition device 4. The position estimation unit 5 in the map acquires the position information of the own vehicle Va from the own vehicle position estimation device 3. When this information is acquired, the position estimation unit 5 in the map estimates the position (traveling lane) of the own vehicle Va on the map.

In step S12, the lane determination unit 11 acquires the position information of the own vehicle Va estimated by the position estimation unit 5 in the map. The lane determination unit 11 determines which traveling lane on the map an object around the own vehicle, particularly another vehicle, belongs to based on the tracking result of the object by the object tracking unit 2b and the position information of the own vehicle Va. To do.

In step S13, the vehicle identification unit 12 identifies the stopped vehicle Vc from other vehicles around the own vehicle Va. Specifically, the vehicle identification unit 12 determines another vehicle having a speed equal to or lower than the speed determination value (for example, 1 km / h or the like, which is such that the vehicle can be determined to be in a substantially stopped state). It is specified as a stopped vehicle Vc. The speed determination value is determined in consideration of the type of sensor included in the object detection device 1 and the detection error. The identification of the stopped vehicle Vc performed by the vehicle identification unit 12 is performed for another vehicle on the adjacent traveling lane L2.

Further, when the stopped vehicle Vc is specified, the vehicle identification unit 12 performs a process of specifying the prediction target vehicle Vb. The vehicle identification unit 12 identifies other vehicles in the adjacent traveling lane L2 as the prediction target vehicle Vb, which are expected to affect the behavior of the own vehicle Va when the lane is changed to the own vehicle traveling lane L1. Will be done. For example, the vehicle identification unit 12 identifies the other vehicle that satisfies the following three conditions as the prediction target vehicle Vb among the other vehicles traveling in the adjacent traveling lane L2. Condition 1 is that the vehicle is traveling ahead of the own vehicle Va. Condition 2 is that the stopped vehicle Vc is present in front of the vehicle. Condition 3 is that the inter-vehicle distance to the stopped vehicle Vc is shorter than the assumed distance at which the driver will be willing to change lanes.

In step S14, the intersection position acquisition unit 13 acquires the intersection position based on map information or the like. Then, the intersection position acquisition unit 13 determines whether or not the intersection position exists in front of the stopped vehicle Vc.

The intersection position will be described with reference to FIGS. 3A to 3C. As shown in FIG. 3A, an example of the intersection position is the position of the intersection where the intersection road R2 intersects with the adjacent traveling lane L2. In this case, the crossing road R2 corresponds to the route of a moving object intersecting the adjacent traveling lane L2 (the road R1 including the road R1). Further, as shown in FIG. 3B, another example of the intersection position is a position where the side road R4, which can be entered from the adjacent traveling lane L2, intersects with the adjacent traveling lane L2. In this case, the side road R4 corresponds to the route of a moving object intersecting the adjacent traveling lane L2 (the road R1 including the side road R1). As shown in FIG. 3C, another example of the intersection position is a position where the private land Pa accessible from the adjacent traveling lane L2 (the road R1 including the road R1) is adjacent. The private land Pa is a facility Pa1 and a parking lot Pa2 thereof, and includes a path (not shown) of a moving object moving in the private land Pa. In this case, the private land Pa corresponds to the route of a moving object intersecting the adjacent traveling lane L2 (road R1 including the road R1). In addition to this, an example of the intersection position is a position where a pedestrian crossing crossing an adjacent traveling lane intersects. In this case, the pedestrian crossing corresponds to the path of a moving object intersecting the adjacent traveling lane.

In step S15, the factor acquisition unit 14 determines whether or not it is difficult to acquire the traffic condition in front of the stopped vehicle Vc. If the traffic condition can be obtained, a negative determination is made in step S15, and the process proceeds to step S16. On the other hand, if it is difficult to obtain the traffic condition, an affirmative determination is made in step S15, and the process proceeds to step S17.

In step S16, the timing prediction unit 15 predicts the start prediction time based on the traffic condition acquired by the factor acquisition unit 14. A processing procedure for predicting the estimated start time will be described with reference to FIGS. 4 and 5. For example, with reference to FIG. 5, consider a scene in which the own vehicle Va passes through an intersection. In FIG. 5, there are three stopped vehicles Vc1, Vc2, and Vc3 in front of the prediction target vehicle Vb.

In step S30, the factor acquisition unit 14 determines whether or not the front of the stopped vehicle Vc is an intersection with a traffic light based on the map information. If the front of the stopped vehicle is an intersection with a traffic light, an affirmative determination is made in step S30, and the process proceeds to step S31. On the other hand, if the front of the stopped vehicle Vc is not an intersection with a traffic light, a negative determination is made in step S30, and the process proceeds to step S32.

In step S32, the factor acquisition unit 14 acquires the traffic light state (an example of traffic conditions) at the intersection. The factor acquisition unit 14 acquires the current state of the traffic light, the switching time of the lighting state of the traffic light, and the like.

In step S32, the factor acquisition unit 14 determines whether or not the stopped vehicle Vc intends to turn. Specifically, the factor acquisition unit 14 determines the intention to turn based on the state of the blinker of the stopped vehicle Vc and the presence or absence of the width adjustment on the adjacent traveling lane L2. The blinker state of the stopped vehicle Vc can be detected by using a camera that images the surroundings of the own vehicle Va, vehicle-to-vehicle communication, or road-to-vehicle communication. The presence or absence of width adjustment can be determined by the position of the stopped vehicle Vc on the adjacent traveling lane L2. If there is an intention to turn, a positive determination is made in step S32, and the process proceeds to step S33. On the other hand, if there is no intention of turning, a negative determination is made in step S32, and the process proceeds to step S36.

In step S33, the factor acquisition unit 14 acquires the traffic jam condition (an example of the traffic condition) at which the stopped vehicle Vc turns. The traffic jam situation is information such as, for example, the number of traffic jam vehicles stopped at the turning point, the vehicle speed of the traffic jam vehicle, and the like. The factor acquisition unit 14 may acquire the traffic congestion situation by using vehicle-to-vehicle communication or road-to-vehicle communication, or may acquire it from the information obtained by the object detection.

In step S34, the factor acquisition unit 14 acquires pedestrian information (an example of traffic conditions) existing at the point where the stopped vehicle Vc turns. The pedestrian information is information indicating the pedestrian status such as the position, speed, and number of pedestrians. The factor acquisition unit 14 may acquire pedestrian information using vehicle-to-vehicle communication or road-to-vehicle communication, or may acquire pedestrian information from the information obtained by object detection.

In step S35, the factor acquisition unit 14 acquires oncoming vehicle information (an example of traffic conditions). The oncoming vehicle information is information indicating the status of the oncoming vehicle such as the position, vehicle speed, and number of oncoming vehicles traveling in the oncoming lane. The factor acquisition unit 14 may acquire oncoming vehicle information using the information obtained by object detection, or may acquire oncoming vehicle information from vehicle-to-vehicle communication or road-to-vehicle communication.

When acquiring oncoming vehicle information using an image, it may be difficult to detect oncoming vehicles Vd1 and Vd2 due to the influence of occlusion. In this case, the factor acquisition unit 14 acquires the traffic light state at the intersection one stop ahead of the intersection where the stopped vehicles Vc1, Vc2, and Vc3 stop. The acquired traffic light state is the state of the traffic light that displays the traffic in the straight direction when viewed from the stopped vehicles Vc1, Vc2, and Vc3. Then, the factor acquisition unit 14 acquires oncoming vehicle information based on the signal state of the next intersection ahead. For example, the factor acquisition unit 14 determines that if the traffic light one ahead is a green light, there is a high possibility that an oncoming vehicle that has passed the intersection one ahead will approach. On the other hand, if the traffic light is a red light, the factor acquisition unit 14 determines that the oncoming vehicle is unlikely to approach because the oncoming vehicle is stopped at the next intersection.

In step S36, the factor acquisition unit 14 acquires the traffic condition (an example of the traffic condition) ahead of the adjacent traveling lane L2.

In step S37, the timing prediction unit 15 calculates the start prediction time based on the information acquired by the factor acquisition unit 14. Specifically, the timing prediction unit 15 calculates the start prediction time based on the traffic light state, the traffic jam situation, the pedestrian information, and the oncoming vehicle information.

The method of calculating the estimated start time based on the traffic light state is as follows, for example. If the current traffic light is a red light, the timing prediction unit 15 predicts the time until the red light is switched to the green light, and predicts that the stopped vehicles Vc1, Vc2, and Vc3 will start at the timing when the signal is switched to the green light. The timing prediction unit 15 calculates the start prediction time based on the start timing predicted for the stopped vehicles Vc1, Vc2, and Vc3. The time until the red light is switched to the green light can be calculated based on the information from the infrastructure, for example, when the information on the time until the switch is transmitted from the infrastructure. Alternatively, it can be calculated by receiving the elapsed time from the timing when the red light changes to the present from another stopped vehicle by inter-vehicle communication, and subtracting the received duration from the average red light lighting duration. Alternatively, the vehicle is equipped with a traffic light detection device that detects the lighting color of the signal, and the elapsed time from the time when it detects that it changed to a red light while approaching the traffic light is calculated to the present, and the average red light It can be calculated by subtracting the elapsed time from the lighting duration of. The method for calculating the time from switching from the red light to the green light is not limited to the above, and can be calculated or estimated by applying various known methods.

The method of calculating the estimated start time based on the traffic jam information is as follows, for example. The timing prediction unit 15 predicts the timing at which the vehicle at the end of the traffic jam starts to move based on the number of traffic jam vehicles and the vehicle speed of each traffic jam vehicle. Then, the timing prediction unit 15 predicts that the stopped vehicles Vc1, Vc2, and Vc3 will start at the timing when the vehicle at the end of the traffic jam starts to move, and calculates the start prediction time based on the predicted start timing.

The method of calculating the estimated start time based on the pedestrian information is as follows, for example. The timing prediction unit 15 predicts the time until the pedestrian crosses the road based on the position, speed and number of pedestrians and the width of the road crossed by the pedestrian. The timing prediction unit 15 predicts that the stopped vehicles Vc1, Vc2, and Vc3 will start at the timing when the pedestrian has crossed the road, and calculates the start prediction time based on the predicted start timing.

The method of calculating the estimated start time based on the oncoming vehicle information is as follows, for example. The timing prediction unit 15 predicts the time until the oncoming vehicles Vd1 and Vd2 pass through the intersection based on the positions, speeds and number of oncoming vehicles Vd1 and Vd2. The timing prediction unit 15 predicts that the stopped vehicles Vc1, Vc2, and Vc3 will start at the passing timing of the oncoming vehicles Vd1 and Vd2, and calculates the start prediction time based on the predicted start timing.

When calculating the start time in consideration of a plurality of situations, the timing prediction unit 15 calculates the start prediction time for each situation, and then determines the longest time as the final start prediction time. May be good. Alternatively, the timing prediction unit 15 may determine the final start prediction time in consideration of the weight coefficient for the start prediction time calculated for each situation.

In step S17, the stop time acquisition unit 16 acquires the stop time. The vehicle identification unit 12 identifies the stopped vehicles Vc1, Vc2, and Vc3 in step S13, and then identifies the prediction target vehicle Vb for the stopped vehicles Vc1, Vc2, and Vc3 when the above conditions are satisfied. The stop time acquisition unit 16 acquires, as a stop time, the time from when the stopped vehicles Vc1, Vc2, and Vc3 are specified in the adjacent traveling lane L2 until the prediction target vehicle Vb is specified in the adjacent traveling lane L2.

In step S18, the lane change calculation unit 19 predicts the possibility of lane change. A processing procedure for predicting the possibility of changing lanes will be described with reference to FIG.

In step S40, the lane change calculation unit 19 executes the process of step S16 and determines whether or not the estimated start time can be predicted. If the predicted start time can be predicted, that is, if the traffic conditions ahead of the stopped vehicles Vc1, Vc2, and Vc3 can be obtained, a positive determination is made in step S40, and the process proceeds to step S41. On the other hand, if the predicted start time cannot be predicted, that is, if the traffic conditions in front of the stopped vehicles Vc1, Vc2, and Vc3 cannot be obtained, a negative determination is made in step S40, and the process proceeds to step S46.

In step S41, the lane change calculation unit 19 determines whether or not the predicted start time is equal to or greater than the first predetermined value. This first predetermined value determines that the predicted vehicle Vb is within the time when it is considered that the lane change will be postponed even in the situation where the stopped vehicles Vc1, Vc2, and Vc3 do not start. The first predetermined value is set to a time that can be tolerated without changing lanes (for example, 3 seconds) by statistically processing the human psychological situation.

If the predicted start time is equal to or longer than the first predetermined value, a positive determination is made in step S41, and the process proceeds to step S42. On the other hand, if the start prediction time is shorter than the first predetermined value, a negative determination is made in step S41, and the process proceeds to step S43.

In step S42, the lane change calculation unit 19 calculates the possibility of lane change. The lane change calculation unit 19 holds correlation data between the possibility of lane change and the predicted start time, and calculates the possibility of lane change from the predicted start time based on the correlation data.

FIG. 7A illustrates correlation data between the possibility of changing lanes and the predicted start time. In this correlation data, when the predicted start time is lower than the first predetermined value, the possibility that the predicted vehicle Vb changes lanes and the possibility that the predicted vehicle Vb goes straight without changing lanes are the same (0). It becomes .5 (50%)). On the other hand, when the predicted start time is equal to or longer than the first predetermined value, it may take time for the stopped vehicles Vc1, Vc2, and Vc3 to start. Therefore, the longer the predicted start time, the higher the possibility that the predicted vehicle Vb will change lanes. According to such a correlation data relationship, the lane change calculation unit 19 predicts that the longer the start prediction time, the higher the possibility of lane change of the prediction target vehicle Vb.

In step S43, the lane change calculation unit 19 acquires the stop time acquired in step S17.

In step S44, the lane change calculation unit 19 calculates the sum of the stop time and the start prediction time (hereinafter referred to as “addition time”).

In step S45, the lane change calculation unit 19 calculates the possibility of lane change. The lane change calculation unit 19 has correlation data between the possibility of lane change and the addition time, and calculates the possibility of lane change from the addition time based on the correlation data.

FIG. 7B illustrates correlation data between the possibility of changing lanes and the addition time. In this correlation data, when the addition time is lower than the second predetermined value (for example, 5 seconds), the prediction target vehicle Vb may change lanes and the prediction target vehicle Vb may go straight without changing lanes. Each will be the same (0.5 (50%)). On the other hand, when the addition time is equal to or greater than the second predetermined value, it may take time for the stopped vehicles Vc1, Vc2, and Vc3 to start. Therefore, the longer the addition time, the higher the possibility that the forecast target vehicle Vb will change lanes. According to such a relationship of correlation data, the lane change calculation unit 19 predicts that when the addition time is equal to or longer than the second predetermined value, the longer the addition time, the higher the possibility of lane change of the prediction target vehicle Vb. ..

The reasons for considering the additional time when predicting the possibility of lane change are as follows. Even in the case where the predicted start time is shorter than the first predetermined value, if the driver recognizes the stopped vehicles Vc1, Vc2, and Vc3 for a long time, it is considered that the intention to change lanes works. Therefore, the time during which the driver recognizes the stopped vehicles Vc1, Vc2, and Vc3 is predicted based on the stop time. Then, by obtaining the sum of the stop time and the start prediction time, the possibility of changing lanes can be predicted in consideration of the time when the driver recognizes the stopped vehicles Vc1, Vc2, and Vc3.

In step S46, the lane change calculation unit 19 acquires the stop time acquired in step S17.

In step S47, the lane change calculation unit 19 calculates the possibility of lane change. The lane change calculation unit 19 has correlation data between the possibility of lane change and the stop time, and calculates the possibility of lane change from the stop time based on the correlation data.

FIG. 7C illustrates correlation data between the possibility of changing lanes and the stop time. In this correlation data, when the stop time is lower than the third predetermined value (for example, 6 seconds), the prediction target vehicle Vb may change lanes and the prediction target vehicle Vb may go straight without changing lanes. Each will be the same (0.5 (50%)). On the other hand, when the stop time is equal to or greater than the third predetermined value, it may take time for the stopped vehicles Vc1, Vc2, and Vc3 to start. Therefore, the longer the stop time is, the higher the possibility that the forecast target vehicle Vb will change lanes is set. According to such a correlation data relationship, the lane change calculation unit 19 predicts that when the stop time is equal to or longer than the third predetermined value, the longer the stop time, the higher the possibility of changing the lane of the prediction target vehicle Vb.

In step S19, the effect calculation unit 17 calculates the effect of changing lanes. Hereinafter, the details of the calculation process regarding the effect of the lane change will be described with reference to FIG.

In step S50, the effect calculation unit 17 sets the reference position. The reference position is a position for calculating the effect of changing lanes, and is set ahead of the predicted vehicle Vb. For example, as shown in FIG. 9A, when there is an intersection in front of the prediction target vehicle Vb, the effect calculation unit 17 sets the intersection position with the intersection road R2 as the reference position PS. Further, as shown in FIG. 9B, when the side road is connected in front of the prediction target vehicle Vb, the effect calculation unit 17 sets the intersection position with the side road R4 as the reference position PS.

In step S51, the effect calculation unit 17 acquires the traffic light state when there is a traffic light in front of the prediction target vehicle Vb. If the traffic light in front is a red light, you cannot move forward even if you change lanes, and the driver feels that you will not change lanes. For example, the effect calculation unit 17 acquires information such as the current lighting color of the traffic light and the signal switching timing. Then, the lane change calculation unit 19 uses this information to calculate the time required for the prediction target vehicle Vb to pass the reference position PS from the current position in consideration of the state of the traffic light ahead.

In step S52, the effect calculation unit 17 acquires the traffic congestion situation in front of the prediction target vehicle Vb. When the driving lane to which the lane is changed is congested, the driver cannot move forward even if the lane is changed, and the driver feels that he / she will not change lanes. For example, the lane change calculation unit 19 acquires information such as the length of the traffic jam ahead, the average speed of the traffic jam, and the severity of the traffic jam. Then, by using this information, the effect calculation unit 17 calculates the time required for the prediction target vehicle Vb to pass the reference position PS from the current position in consideration of the traffic jam situation ahead.

In step S53, the effect calculation unit 17 acquires pedestrian information crossing the pedestrian crossing in front of the prediction target vehicle Vb. When a pedestrian is crossing the driving lane to which the lane is changed, the driver cannot move forward until the pedestrian has crossed the lane, and the driver has the psychology of trying not to change lanes. For example, the effect calculation unit 17 acquires information such as the crossing speed of pedestrians and the number of pedestrians. Then, by using this information, the effect calculation unit 17 calculates the time required for the vehicle to be predicted to pass from the current position to the reference position in consideration of the pedestrian in front.

In step S54, the effect calculation unit 17 is required to pass the reference position PS when the prediction target vehicle Vb changes lanes to the own vehicle traveling lane L1 based on the time calculated from steps S51 to S53. The transit time T1 is calculated. For example, the effect calculation unit 17 determines the longest time among the times calculated from steps S51 to S53 as the transit time T1. Alternatively, the effect calculation unit 17 determines the transit time T1 in consideration of the weighting coefficient for each of the times calculated from steps S51 to S53.

In step S55, the effect calculation unit 17 acquires the start prediction time predicted in step S16.

In step S56, the effect calculation unit 17 calculates the passing time T2 required to pass the reference position PS when the prediction target vehicle Vb goes straight on the adjacent traveling lane L2 without changing lanes. For example, the effect calculation unit 17 reaches the reference position PS after the stopped vehicles Vc1, Vc2, and Vc3 have started, passing through the positions where the stopped vehicles Vc1, Vc2, and Vc3 existed, based on the predicted start time. The transit time T2 up to is calculated.

In step S57, the effect calculation unit 17 calculates the difference ΔT of the passing time, specifically, the difference ΔT obtained by subtracting the passing time T2 calculated in step S56 from the passing time T1 calculated in step S54. If it is possible to pass the reference position PS in a shorter time by maintaining the current driving lane, the difference ΔT becomes a positive value. On the other hand, if the person who changes lanes to the own vehicle traveling lane L1 can pass the reference position PS in a short time, the difference ΔT becomes a negative value. That is, it can be said that the larger the difference ΔT is in the negative direction, the higher the effect of changing lanes.

In step S20, the difficulty calculation unit 18 calculates the difficulty of changing lanes. In the situation shown in FIG. 10A, there is another vehicle traveling in the own vehicle traveling lane L1 diagonally behind the prediction target vehicle Vb traveling in the adjacent traveling lane L2. In this case, even if the vehicle Vb to be predicted changes lanes, it is difficult to secure a sufficient inter-vehicle distance from the vehicle diagonally behind. Therefore, it can be said that the difficulty of changing lanes is high. Further, in the situation shown in FIG. 10B, the inter-vehicle distance between the last stopped vehicle Vc1 and the prediction target vehicle Vb in the adjacent traveling lane L2 is small. In this case, it is difficult for the forecast target vehicle Vb to change lanes. Therefore, it can be said that the difficulty of changing lanes is high. Further, in the situation shown in FIG. 10C, another vehicle Ve traveling in the own vehicle traveling lane L1 exists diagonally in front of the prediction target vehicle Vb traveling in the adjacent traveling lane L2. In this case, even if the vehicle Vb to be predicted changes lanes, it is difficult to secure a sufficient inter-vehicle distance from the vehicle Ve in front. Therefore, it can be said that the difficulty of changing lanes is high. Therefore, the difficulty calculation unit 18 determines the distance between another vehicle traveling in the own vehicle traveling lane L1 and the prediction target vehicle Vb traveling in the adjacent traveling lane L2, the stopped vehicle Vc1 in the adjacent traveling lane L2, and the prediction target vehicle Vb. Calculate the distance between vehicles. Then, the difficulty calculation unit 18 calculates that the shorter the distance between vehicles, the higher the difficulty of changing lanes.

In step S21, the lane change calculation unit 19 corrects the possibility of lane change predicted in step S18 based on the calculation results of steps S19 and S20. Specifically, the lane change calculation unit 19 corrects the possibility of changing lanes so that the lower the effect of changing lanes, the lower the possibility of changing lanes. Further, the lane change calculation unit 19 corrects the possibility of changing lanes so that the higher the difficulty of changing lanes, the lower the possibility of changing lanes.

In step S22, the vehicle control unit 20 controls the own vehicle based on the predicted possibility of lane change. For example, the vehicle control unit 20 performs deceleration control when the possibility of changing lanes reaches a certain value, for example, 0.8 or more.

In step S23, the lane change calculation unit 19 determines whether or not the ignition switch has been turned off. When the ignition switch is turned off, a positive determination is made in step S23, and a series of processes is terminated (END). On the other hand, when the ignition switch (IGN) is on, a negative determination is made in step S23, and the process returns to step S10.

In the above description, the embodiment has been described by taking as an example a situation where an intersection exists in front of the stopped vehicles Vc1, Vc2, and Vc3. However, as shown in FIG. 11A, the same applies even when a side road R4 exists in the adjacent lane L2 in front of the stopped vehicle Vc. Further, as shown in FIG. 11B, the same applies even when the private land Pa including the facility Pa1 and the parking lot Pa2 is adjacent to the adjacent lane L2 in front of the stopped vehicle Vc. Further, there may be a case where there is no such intersection position. For example, as shown in FIG. 11C, there may be a case where the stopped vehicles Vc1, Vc2, and Vc3 are present due to a traffic jam in the adjacent traveling lane L2.

As described above, the vehicle behavior prediction method and the vehicle behavior prediction device according to the present embodiment specify the prediction target vehicle Vb that predicts the possibility of changing lanes. Further, the vehicle behavior prediction method and the vehicle behavior prediction device predict the start prediction time, which is the time until the stopped vehicle Vc starts, for the stopped vehicle Vc that stops in front of the prediction target vehicle Vb on the adjacent traveling lane L2. .. Then, the vehicle behavior prediction method and the vehicle behavior prediction device predict that the longer the start prediction time, the higher the possibility that the prediction target vehicle Vb will change lanes to the adjacent traveling lane L2.

When the time until the stopped vehicle Vc starts is long, the driver of the prediction target vehicle Vb initially has a psychology of trying to change lanes, and then actually starts the lane change operation. Therefore, by predicting the start prediction time, it is possible to predict the possibility of lane change before the lane change operation is actually started. As a result, it is possible to predict the lane change of the prediction target vehicle Vb at an early stage.

Further, in the method of the present embodiment, when the prediction target vehicle Vb changes lanes to the own vehicle traveling lane L1, the deceleration control of the own vehicle is performed based on the possibility of the predicted lane change for the prediction target vehicle Vb. Is going.

According to this method, the lane change of the vehicle Vb to be predicted can be predicted at an early stage, so that the vehicle can be smoothly decelerated without a large change in behavior.

Further, the method of the present embodiment acquires an intersection position where the paths of moving objects intersect with respect to the adjacent traveling lane L2, and predicts the start when the intersection position exists in front of the stopped vehicle Vc (prediction target vehicle Vb). You may predict that the longer the time, the more likely you are to change lanes.

In this case, the intersection position is the position of the intersection where the intersection road R2 intersects, the position where the side road R4 accessible from the adjacent traveling lane L2 intersects, the position where the private land Pa accessible from the adjacent traveling lane L2 is adjacent, or the adjacent traveling. This is the position where the pedestrian crossing that crosses the lane L2 intersects.

At the intersection position, the stopped vehicle Vc is likely to occur, and the prediction target vehicle Vb tends to change lanes. By predicting the start prediction time at such an intersection position, it is possible to predict the lane change of the prediction target vehicle Vb at an early stage.

Further, the method of the present embodiment may predict the expected start time based on the traffic condition in front of the stopped vehicle Vc.

Since it is considered that the stopped vehicle Vc is stopped due to the influence of the traffic ahead, the start prediction time can be accurately predicted from the traffic condition. As a result, the possibility of changing the lane of the vehicle Vb to be predicted can be appropriately predicted.

Further, in the present embodiment, the traffic conditions in front of the stopped vehicle Vc include the situation of pedestrians crossing the pedestrian crossing existing in the path of the stopped vehicle Vc, the situation of the oncoming vehicle Vd traveling in the oncoming lane, and the situation in front of the stopped vehicle Vc. It is preferable to include at least one of the state of the traffic light existing in the vehicle and the condition of the traffic jam occurring in front of the stopped vehicle Vc.

According to this method, the start prediction time can be predicted accurately, so that the possibility of changing lanes can be predicted accurately.

Further, the method of the present embodiment determines whether or not the traffic condition in front of the stopped vehicle Vc can be acquired. Then, if the traffic condition can be obtained, the estimated start time is predicted based on the traffic condition. When the traffic condition cannot be obtained, the stop time from the identification of the stopped vehicle Vc to be stopped in the adjacent lane L2 to the identification of the prediction target vehicle Vb in the adjacent lane L2 is calculated, and the stop time is calculated based on the stop time. You may predict the possibility of changing lanes.

If the driver is aware of the stopped vehicle Vc for a long time, it is predicted that the psychology of trying to change lanes will work on the driver. Therefore, by estimating the psychology of trying to change lanes according to the stop time, it is possible to determine the possibility of changing lanes before the lane change operation actually starts. As a result, it is possible to predict the lane change of the prediction target vehicle Vb at an early stage.

Further, the method of the present embodiment may predict that the longer the stop time, the higher the possibility of changing lanes.

When the driver of the prediction target vehicle Vb recognizes the state in which the stopped vehicle Vc is stopped for a long time, the driver tends to have a psychology of trying to change lanes. Therefore, by estimating the psychology of trying to change lanes according to the stop time, it is possible to determine the possibility of changing lanes before the lane change operation actually starts. As a result, the possibility of changing lanes can be predicted at an early stage.

Further, the method of the present embodiment determines whether or not the predicted start time is equal to or longer than a predetermined value, and if the predicted start time is longer than the predetermined value, the longer the predicted start time, the more the lane can be changed. Predict that the sex is high. On the other hand, in the method of the present embodiment, when the start prediction time is shorter than the determination time, the possibility of changing lanes may be predicted based on the sum of the stop time and the start prediction time.

Even when the predicted start time is short, if the time for recognizing the state in which the second other vehicle is stopped becomes long, the psychology of trying to change lanes works. Therefore, by estimating the psychology of trying to change lanes according to the stop time, it is possible to determine the possibility of changing lanes before the lane change operation actually starts. As a result, it is possible to predict the lane change of the first other vehicle at an early stage.

Further, in the method of the present embodiment, when the prediction target vehicle Vb changes lanes to the own vehicle traveling lane L1, the time until passing the intersection position is predicted as the first passing time T1. In addition, the time until the vehicle Vb to be predicted passes the intersection position when the vehicle does not change lanes is predicted as the second passing time T2. In this case, the possibility of changing lanes may be modified according to the difference ΔT between the first passing time T1 and the second passing time T2.

When the first passing time T1 is longer than the second passing time T2, it is conceivable that the driver will continue to drive in the adjacent traveling lane L2 without changing lanes. Therefore, by correcting the possibility of changing lanes according to the time difference, it is possible to make an accurate prediction.

Further, in the method of the present embodiment, the first passing time T1 may be predicted based on one or both of the traffic jam condition in the own vehicle traveling lane L1 and the traffic light condition in front of the stopped vehicle Vc.

The passing time T1 passing through the own vehicle traveling lane L1 depends on the traffic congestion condition of the own vehicle traveling lane L1 and the state of the traffic light ahead. By considering these situations, the first transit time T1 can be appropriately predicted. As a result, the possibility of changing lanes can be corrected with high accuracy.

Further, in the method of the present embodiment, the difficulty level indicating the degree of difficulty when changing lanes to the own vehicle traveling lane L1 may be calculated, and the possibility of changing lanes may be modified according to this difficulty level.

In a situation where it is difficult to change lanes to the own vehicle lane L1, it is conceivable that the driver will continue to drive in the adjacent lane L2 without changing lanes. Therefore, by modifying the possibility of changing lanes according to the degree of difficulty, it is possible to make a more accurate prediction.

Further, in the method of the present embodiment, the distance between another vehicle and the prediction target vehicle Vb diagonally behind the prediction target vehicle Vb in the own vehicle traveling lane L1 and diagonally forward of the prediction target vehicle Vb in the own vehicle traveling lane L1. The difficulty level may be calculated based on the distance between the other vehicle and the vehicle Vb to be predicted, or the distance between the stopped vehicle Vc and the vehicle Vb to be predicted.

When the prediction target vehicle Vb and another vehicle in the own vehicle traveling lane L1 are close to each other, it is difficult to change lanes because the space for changing the course of the vehicle is also limited. Therefore, the difficulty of changing lanes can be quantitatively evaluated by considering the inter-vehicle distance. The possibility of changing lanes can be corrected accurately. Further, when the prediction target vehicle Vb is close to the stopped vehicle Vc, it is difficult to change lanes because the space for changing the course of the vehicle is also limited. Therefore, the difficulty of changing lanes can be quantitatively evaluated by considering the inter-vehicle distance. As a result, the possibility of changing lanes can be appropriately corrected. At this time, the difficulty level may be calculated in consideration of the number of other vehicles in the own vehicle traveling lane L1 traveling diagonally behind or diagonally forward of the prediction target vehicle Vb.

In the above-described embodiment, it is assumed that the prediction target vehicle Vb traveling in the adjacent traveling lane L2 adjacent to the own vehicle traveling lane L1 changes lanes to the own vehicle traveling lane L1, and the possibility of the lane change is predicted. However, the vehicle behavior prediction method and the vehicle behavior prediction device according to the present embodiment do not need to exist in the traveling lanes in which the prediction target vehicle and the own vehicle are adjacent to each other, and the prediction target vehicle is in its own driving lane (first). It suffices as long as it predicts the possibility of changing lanes from the traveling lane) to the adjacent traveling lane (second traveling lane).

Although embodiments of the invention have been described above, the discourses and drawings that form part of this disclosure should not be understood to limit the invention. Various alternative embodiments, examples and operational techniques will be apparent to those skilled in the art from this disclosure.

1 Object detection device 2a Detection integration unit 2b Object tracking unit 3 Own vehicle position estimation device 4 Map acquisition device 5 In-map position estimation unit 10 Behavior prediction unit 11 Lane determination unit 12 Vehicle identification unit 13 Crossing position acquisition unit 14 Factor acquisition unit 15 Timing prediction unit 16 Stop time acquisition unit 17 Effect calculation unit 18 Difficulty calculation unit 19 Lane change calculation unit 20 Vehicle control unit 50 Microcomputer

Claims (14)

Detects other vehicles around your vehicle and
From the other vehicles, identify the first other vehicle that predicts the possibility of changing lanes.
Predicted start time, which is the time until the second other vehicle starts for the second other vehicle that stops in front of the first other vehicle on the first traveling lane in which the first other vehicle travels. Predict and
It is predicted that the longer the predicted start time, the more likely the first other vehicle will change lanes from the first traveling lane to the second traveling lane adjacent to the first traveling lane. Prediction method. Acquire the intersection position where the path of the moving object intersects with the first traveling lane, and obtain the intersection position.
The vehicle behavior prediction method according to claim 1, wherein when the intersection position exists in front of the first other vehicle, the longer the start prediction time is, the higher the possibility of changing lanes is. The intersection position is the position of the intersection where the intersection roads intersect, the position where the side roads accessible from the first traveling lane intersect, the position where the private land accessible from the first traveling lane is adjacent, or the first The vehicle behavior prediction method according to claim 2, wherein the pedestrian crossing crossing the traveling lane of the vehicle intersects. The vehicle behavior prediction method according to any one of claims 1 to 3, wherein the start prediction time is predicted based on the traffic condition in front of the second other vehicle. The traffic conditions in front of the second other vehicle include the situation of a pedestrian crossing a pedestrian crossing existing in the path of the second other vehicle, the situation of an oncoming vehicle traveling in the oncoming lane, and the front of the second other vehicle. The vehicle behavior prediction method according to claim 4, which includes at least one of the state of the traffic light existing in the vehicle and the state of traffic congestion occurring in front of the second other vehicle. It is determined whether or not the traffic condition in front of the second other vehicle can be obtained, and
If the traffic conditions can be obtained,
Based on the traffic conditions, the estimated start time is predicted,
If the traffic conditions cannot be obtained,
The stop time, which is the time from the identification of the second other vehicle to stop in the first traveling lane to the identification of the first other vehicle in the first traveling lane, is acquired.
The vehicle behavior prediction method according to claim 4 or 5, which predicts the possibility of changing lanes based on the stop time. The vehicle behavior prediction method according to claim 6, wherein it is predicted that the longer the stop time is, the higher the possibility of changing lanes is. It is determined whether or not the predicted start time is equal to or longer than a predetermined value.
When the predicted start time is equal to or longer than the predetermined value,
It is predicted that the longer the predicted start time, the higher the possibility of changing lanes.
When the predicted start time is shorter than the predetermined value,
A claim for predicting the possibility of changing lanes based on the sum of the stop time, which is the time from the detection of the second other vehicle to the determination of the first other vehicle, and the predicted start time. Item 6. The vehicle behavior prediction method according to any one of Items 1 to 5. When the first other vehicle changes lanes to the second traveling lane, the time until the first other vehicle passes the intersection position is predicted as the first passing time.
The time until the first other vehicle passes the intersection position when the other vehicle does not change lanes is predicted as the second passing time.
The vehicle behavior prediction method according to claim 2, wherein the possibility of changing lanes is corrected according to the time difference between the first passing time and the second passing time. The vehicle behavior prediction method according to claim 9, wherein the first passing time is predicted based on one or both of the traffic congestion situation in the second traveling lane and the state of the traffic light in front of the first other vehicle. The difficulty level indicating the difficulty level when changing lanes from the first driving lane to the second driving lane is calculated.
The vehicle behavior prediction method according to any one of claims 1 to 10, wherein the possibility of changing lanes is modified according to the difficulty level. The distance between another vehicle in the second traveling lane and the first other vehicle in the first traveling lane, which travels diagonally behind the first other vehicle, and diagonally in front of the first other vehicle. The distance between the other vehicle in the second traveling lane and the first other vehicle in the first traveling lane, or the first other vehicle and the second other in the first traveling lane. The vehicle behavior prediction method according to claim 11, wherein the difficulty level is calculated based on the distance between the vehicle and the vehicle. Sensors that detect other vehicles around your vehicle and
It has a control unit that predicts the behavior of the other vehicle, and
The control unit
From the other vehicles, identify the first other vehicle that predicts the possibility of changing lanes.
Predicted start time, which is the time until the second other vehicle starts for the second other vehicle that stops in front of the first other vehicle on the first traveling lane in which the first other vehicle travels. Predict and
The longer the predicted start time, the more likely it is that the first other vehicle will change lanes from the first traveling lane to the second traveling lane adjacent to the first traveling lane. Predictor. Sensors that detect other vehicles around your vehicle and
It has a control unit that predicts the behavior of the other vehicle and controls the vehicle based on the behavior of the other vehicle.
The control unit
From the other vehicles, identify the first other vehicle that predicts the possibility of changing lanes.
Predicted start time, which is the time until the second other vehicle starts for the second other vehicle that stops in front of the first other vehicle on the first traveling lane in which the first other vehicle travels. Predict and
It is predicted that the longer the predicted start time, the higher the possibility that the first other vehicle will change lanes from the first traveling lane to the second traveling lane adjacent to the first traveling lane.
A vehicle control device that controls deceleration of the own vehicle based on the predicted possibility of changing the lane when the traveling lane in which the own vehicle is traveling is the second traveling lane.

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