WO2018139294A1 - Dispositif de prédiction d'objet mobile - Google Patents

Dispositif de prédiction d'objet mobile Download PDF

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Publication number
WO2018139294A1
WO2018139294A1 PCT/JP2018/001136 JP2018001136W WO2018139294A1 WO 2018139294 A1 WO2018139294 A1 WO 2018139294A1 JP 2018001136 W JP2018001136 W JP 2018001136W WO 2018139294 A1 WO2018139294 A1 WO 2018139294A1
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Prior art keywords
prediction
moving object
unit
vehicle
prediction unit
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PCT/JP2018/001136
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English (en)
Japanese (ja)
Inventor
龍 稲葉
茂規 早瀬
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日立オートモティブシステムズ株式会社
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Publication of WO2018139294A1 publication Critical patent/WO2018139294A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems

Definitions

  • the present invention relates to a moving object prediction apparatus for predicting the behavior of a moving object existing around a vehicle such as an automobile, in particular, a self-driving vehicle.
  • Patent Document 1 Conventionally, a system has been developed that acquires object information around the course of the host vehicle and predicts the path of each moving object from the acquired object information. (For example, see Patent Document 1)
  • an object of the present invention is to enable reduction of calculation load while ensuring the prediction accuracy of a moving object that affects the host vehicle.
  • the present invention is a moving object prediction apparatus for predicting a future position of a moving object around a host vehicle, and includes a first prediction unit that simply predicts a future position of the moving object, and a first prediction unit A second prediction unit that predicts the future position of the moving object with high accuracy, and an allocation unit that allocates the moving object that predicts the future position by the second prediction unit according to the result of the simple prediction;
  • a moving object prediction apparatus comprising:
  • 1 is an explanatory diagram of a traveling drive system and sensors of a host vehicle according to a first embodiment.
  • 1 is a block diagram of a vehicle travel control apparatus according to a first embodiment.
  • the block diagram of the moving object action prediction part which concerns on Example 1 is shown.
  • the block diagram of the prediction object precision judgment part which concerns on Example 1 is shown.
  • the relationship of the own vehicle influence determination result and precision information which concern on Example 1 is shown.
  • An example of the own vehicle which concerns on Example 1, and the surrounding moving object is shown.
  • 3 shows a control flow according to the first embodiment.
  • the relationship of the influence degree to the pedestrian and the own vehicle which concerns on Example 1 is shown.
  • An example of the own vehicle which concerns on Example 1, and its surrounding object is shown.
  • the block diagram of the prediction object precision determination part which concerns on Example 2 is shown.
  • Example 2 An example of the moving object with interaction which concerns on Example 2 is shown. The relationship with the future time and interaction probability which concern on Example 2 is shown. The relationship of the own vehicle influence determination result which concerns on Example 2, and an interaction determination result is shown. An example of the own vehicle which concerns on Example 2, and the surrounding moving object is shown. The control flow which concerns on Example 2 is shown. 10 shows a control flow according to a third embodiment. The block diagram of the moving object action prediction part which concerns on Example 4 is shown.
  • FIG. 1 is an explanatory diagram showing the overall configuration of the host vehicle 100 equipped with the vehicle travel control device 1 according to the first embodiment.
  • the FL wheel, the FR wheel, the RL wheel, and the RR wheel mean a left front wheel, a right front wheel, a left rear wheel, and a right rear wheel, respectively.
  • the own vehicle 100 includes a camera 2 at the front, laser radars 3 and 4 at the left and right sides, and a millimeter wave radar 5 at the rear as sensors for recognizing the outside world. Distance and relative speed can be detected.
  • a combination of the above-described sensors is shown as an example of the configuration of the sensor.
  • the present invention is not limited to this, and a combination with an ultrasonic sensor, a stereo camera, an infrared camera, or the like may be used.
  • the sensor signal is input to the vehicle travel control device 1.
  • the host vehicle 100 includes a vehicle travel control device 1, a steering control device 8, a braking control device 15, an acceleration control device 19, a communication device 23, and a display device 24.
  • the vehicle travel control device 1 includes, for example, a CPU, a ROM, a RAM, and an input / output device.
  • the ROM stores a flow of vehicle travel control.
  • the vehicle travel control device 1 calculates command values to the steering control mechanism 10, the brake control mechanism 13, and the throttle control mechanism 20 for controlling the traveling direction based on information from the various sensors 2, 3, 4, and 5. .
  • the steering control device 8 controls the steering control mechanism 10 based on the command value from the vehicle travel control device 1.
  • the brake control device 15 controls the brake control mechanism 13 based on the command value from the vehicle travel control device 1 to adjust the brake force distribution of each wheel.
  • the acceleration control device 19 controls the throttle control mechanism 20 based on the command value from the vehicle travel control device 1 to adjust the torque output of the engine.
  • the communication device 23 performs communication between road vehicles or between vehicles.
  • the display device 24 displays a travel plan of the host vehicle 100 and a behavior prediction of a moving object existing in the vicinity.
  • the vehicle travel control device 1 configured as described above calculates command values of the actuators 10, 13, and 20 for controlling the vehicle travel according to the generated travel plan, which will be described in detail later.
  • the control devices 8, 15, and 19 of the actuators 10, 13, and 20 receive the command value of the vehicle travel control device 1 through communication, and control the actuators 10, 13, and 20 based on the command value.
  • the pedaling force of the driver stepping on the brake pedal 12 is boosted by a brake booster (not shown), and a hydraulic pressure corresponding to the force is generated by a master cylinder (not shown).
  • the generated hydraulic pressure is supplied to the wheel cylinder 16 via the brake control mechanism 13.
  • Each of the wheel cylinders 16FL to 16RR includes a cylinder (not shown), a piston, a pad, and the like.
  • the piston is propelled by the hydraulic fluid supplied from the master cylinder 9, and the pad connected to the piston is a disk rotor. Pressed.
  • the disc rotor rotates with the wheels. Therefore, the brake torque acting on the disc rotor becomes a braking force acting between the wheel and the road surface.
  • a braking force can be generated in each wheel in accordance with the driver's brake pedal operation.
  • the braking control device 15 includes, for example, a CPU, a ROM, a RAM, and an input / output device, like the vehicle travel control device 1.
  • the brake controller 15 includes a combine sensor 14, wheel speed sensors 11FL to 11RR, a brake force command from the brake controller 15 described above, and a sensor from the steering wheel angle detector 21 via a steering controller 8 described later. Signal.
  • the combine sensor 14 detects longitudinal acceleration, lateral acceleration, and yaw rate. Wheel speed sensors 11FL to 11RR are installed on each wheel.
  • the braking control device 15 is connected to a pump (not shown) on the output side and a brake control mechanism 13 having a control valve, and applies an arbitrary braking force to each wheel independently of the driver's brake pedal operation. Can be generated.
  • the braking control device 15 estimates the spin, drift-out, and wheel lock of the host vehicle 100 based on the various information described above, and generates the braking force of the corresponding wheel so as to suppress them, thereby stabilizing the steering of the driver. It plays a role to improve sex. Furthermore, the vehicle travel control device 1 can generate an arbitrary braking force on the host vehicle 100 by communicating a brake command to the braking control device 15. It plays the role of braking. However, in the present embodiment, the present invention is not limited to the braking control device 15, and other actuators such as a brake-by-wire may be used.
  • the steering control device 8 controls the motor 9 based on the information and generates assist torque.
  • the steering control device 8 also has, for example, a CPU, a ROM, a RAM, and an input / output device, like the vehicle travel control device 1.
  • the steering control device 8 can control the steering control mechanism 10 by generating torque by the motor 9 independently of the driver's steering operation. Therefore, the vehicle travel control device 1 can control the front wheels to an arbitrary turning angle by communicating a steering force command to the steering control device 8, and automatically steers in automatic driving where no driver operation occurs. I have a role to do.
  • the present invention is not limited to the steering control device 8, and other actuators such as steer-by-wire may be used.
  • the acceleration control device 19 also has, for example, a CPU, a ROM, a RAM, and an input / output device, like the vehicle travel control device 1.
  • the acceleration control device 19 controls the engine by adjusting the throttle opening in accordance with the depression amount of the accelerator pedal 17.
  • the host vehicle 100 can be accelerated in accordance with the driver's operation of the accelerator pedal 17.
  • the acceleration control device 19 can control the throttle opening independently of the driver's accelerator operation. Therefore, the vehicle travel control device 1 can generate an arbitrary acceleration in the host vehicle 100 by communicating an acceleration command to the acceleration control device 19, and automatically in the case of an automatic driving in which no driver operation occurs. Has a role to accelerate.
  • FIG. 2 is a block diagram of the vehicle travel control apparatus 1 according to the present embodiment.
  • the sensor input processing unit 201 When information on the surrounding environment obtained based on information from various sensors 2, 3, 4, and 5 that recognize the outside world is input to the sensor input processing unit 201, a moving object that exists around the host vehicle 100 Converted to information. As specific object information, for example, attribute information of pedestrians, bicycles, and vehicles, and their current position and current speed vector are extracted.
  • the moving object includes a parked vehicle that may move in the future even if the speed obtained at the current time is zero.
  • the moving object behavior prediction unit 202 calculates future position and velocity information (object prediction information) of each moving object based on input information, which will be described in detail later.
  • the trajectory / motion planning unit 203 plans the travel trajectory of the host vehicle 100 and the speed at that time based on the object prediction information and map information that are input information. Specifically, for example, the trajectory / motion planning unit 203 generates a trajectory that avoids collision with these moving objects from the object prediction information of the future position of the traveling vehicle and the parked vehicle in front of the host vehicle 100. . At the same time, the track / motion planning unit 203 plans to drive automatically with acceleration / deceleration within a range in which the driver riding in the vehicle 100 does not feel as uncomfortable as possible.
  • the actuator command value calculation unit 204 calculates, for example, brake, steering, and accelerator operation amounts based on the trajectory / speed plan information that is input information. Specifically, since the track / speed plan information is target information in the future of the host vehicle 100, the actuator command value calculation unit 204 is based on the physical model of the host vehicle 100 when the target position and speed are input. Thus, the control amount of each actuator 10, 13, 20 is output.
  • FIG. 3 is a block diagram of the moving object behavior prediction unit 202 implemented in the vehicle travel control device 1 of the present embodiment.
  • the predicted object accuracy determination unit 301 calculates accuracy information of each object based on object information and trajectory / speed plan information at the time of the previous calculation, which will be described later in detail.
  • the object allocation unit 302 as “allocation unit” extracts the individual object information from the object information based on the accuracy information, and transmits it to the plurality of individual prediction units 303 and 304.
  • the plurality of individual prediction units 303 and 304 includes two sets of a plurality of individual prediction units 303 with low prediction accuracy and a set of individual prediction units 304 as “second prediction units” with high prediction accuracy.
  • the groups are divided into sets, and the individual prediction units 303 and 304 of both sets have different prediction models.
  • the object allocation unit 302 allocates individual object information to appropriate individual prediction units 303 and 304 based on accuracy information in which necessary prediction accuracy is considered.
  • the individual prediction results predicted by the individual prediction units 303 and 304 are made into a format in which the prediction calculation cycle and the coordinate system have the same format and are aggregated as object prediction information.
  • the individual prediction units 303 and 304 since the calculation cycle may be different for each prediction model, it is necessary to perform an interpolation calculation so that the calculation results predicted in the short cycle and the long cycle are the same prediction cycle.
  • FIG. 4 is a block diagram of the predicted object accuracy determination unit 301 installed in the vehicle travel control device 1 of the present embodiment.
  • the simple prediction unit 401 as the “first prediction unit” simply predicts the position R (X (T), Y (T)) of each object at the future time T based on the object information. Specifically, the simple prediction unit 401 sets the current position of the object OB_N to Rn0 (Xn (0), Yn (0)) and the current speed to Vn. In the case of (Vxn, Vyn), the prediction calculation is performed based on the following linear prediction equation (1).
  • Rn (Xn (T), Yn (T)) Vn (Vxn, Vyn) ⁇ T + Rn0 (Xn (0), Yn (0)) (1)
  • the calculation method assumes constant velocity linear motion in which each object moves while maintaining the current speed even in the future time. Thereby, many objects can be predicted in a short time.
  • the own vehicle influence determination unit 402 determines the influence on the own vehicle 100. Specifically, the own vehicle influence determination unit 402 calculates whether or not the probability that the own vehicle 100 and the predicted moving object collide at a future time is equal to or greater than a predetermined value. 100 is determined to have an influence, and if it is equal to or less than a predetermined value, it is determined that there is no influence on the host vehicle 100.
  • a method for calculating the collision probability an estimated collision time TTC (Time To Collation) between each object and the vehicle 100 at a future position is used.
  • TTC [s] relative distance [m] ⁇ relative speed [m / s].
  • the collision probability is 50% when it is less than or equal to the first predetermined value, and the collision probability is 70% when it is less than or equal to the second predetermined value ( ⁇ first predetermined value).
  • the accuracy information calculation unit 403 calculates accuracy information based on the own vehicle influence determination result.
  • FIG. 5 shows the relationship between the vehicle effect determination result and accuracy information.
  • the accuracy information calculation unit 403 indicates “low” as accuracy information when the vehicle influence determination result is “small”, and as accuracy information when the vehicle influence determination result is “medium” If the result of the vehicle influence determination is “large”, the accuracy information is classified into one of three levels of “high”.
  • the reason why the accuracy information is set to three levels is to make it equal to the classification level of the prediction model used in the individual prediction units 303 and 304.
  • FIG. 6 shows an example of the own vehicle 601 and the surrounding pedestrians 603 and 604 according to the present embodiment.
  • the own vehicle 601 travels automatically along a travel planned track 605 on a road standardized by road ends 602 on both sides.
  • Two pedestrians 603 and 604 as “moving objects” are walking around the host vehicle 601.
  • the pedestrian 603 is walking in the direction opposite to the host vehicle 601 on the right rear side of the host vehicle 601.
  • the pedestrian 604 is walking in a direction that intersects the travel plan trajectory of the host vehicle 601.
  • FIG. 7 shows a control flow in the situation of FIG. 6 of the present embodiment.
  • step S700 the control unit (CPU) of the vehicle travel control device 1 inputs sensor information about the environment around the host vehicle 601 obtained based on information from various sensors 2, 3, 4, and 5 that recognize the outside world.
  • the information is input to the processing unit 201 and converted into object information of a moving object existing around the host vehicle 601. Thereby, the information of the position and speed vector of both pedestrians 603 and 604 can be obtained.
  • step S701 the control unit (CPU) of the vehicle travel control device 1 performs simple prediction for each moving object individually based on the object information of the moving object obtained in step S700.
  • the positions of both pedestrians 603 and 604 up to 5 seconds later are predicted at a prediction interval of 1 second.
  • the positions 1 to 5 seconds after both pedestrians 603 and 604 are represented by reference numerals 608 and 609.
  • step S702 the control unit (CPU) of the vehicle travel control device 1 determines the influence on the host vehicle 601.
  • a pedestrian 603 is walking in a direction opposite to the travel planned track 605 of the host vehicle 601.
  • FIG. 8 shows the relationship of the degree of influence on the pedestrians 603 and 604 and the own vehicle 601.
  • the prediction information is determined to be “low” in the prediction time of 5 seconds.
  • the pedestrian 604 may cross the travel plan trajectory 605 of the host vehicle 601 between 2 seconds and 3 seconds in the future, and the collision probability (the degree of influence on the host vehicle 601) is high.
  • simple prediction requires a more detailed prediction because the prediction interval becomes longer. Therefore, “high” is selected as the prediction information.
  • step S708 if the prediction information is determined to be “high”, the process proceeds to step S703, and if it is determined to be “low”, the process proceeds to step S704.
  • step S703 the control unit (CPU) of the vehicle travel control device 1 performs the main prediction of the action using the prediction accuracy (high) model.
  • a model that can be predicted with higher accuracy than the prediction model used in step S701 is used.
  • a model using a parameter calculated by statistically processing motion as a correction term is used.
  • FIG. 9 shows an example of a prediction result when the pedestrian 902 moves to avoid the parked vehicle 901 in the potential field where the parked vehicle 901 and the road edge 903 are generated.
  • the potential method for example, assuming that a virtual dynamic potential is generated from an obstacle at the road end 903 and the parked vehicle 901, the pedestrian 902 moves to pass through the lowest potential and walks. This is a method for predicting the position of the person 902.
  • a dynamic potential to be generated for example, a Gaussian function is used or approximated by a power function.
  • the potential generated from the parked vehicle 901 is expressed by the following equation.
  • U (x, y) W ⁇ exp ( ⁇ (x ⁇ x0) / ⁇ x ⁇ 2 ⁇ (y ⁇ y0) / ⁇ y ⁇ 2) (2)
  • W is the potential weight
  • (x0, y0) is the position of the parked vehicle 901
  • ( ⁇ x, ⁇ y) is the variance in the x and y directions.
  • step S704 the control unit (CPU) of the vehicle travel control device 1 simply predicts the behavior using a prediction accuracy (low) model.
  • a model having the same accuracy as the constant velocity linear motion model used in step 701 is used.
  • the result calculated in step 701 may be stored in the ROM, and the result of the calculation may be reused.
  • the calculation may be omitted (for example, a fixed value) in order to reduce memory and communication traffic.
  • step S705 the control unit (CPU) of the vehicle travel control apparatus 1 collects the prediction results calculated in step S703 and step S704.
  • the result of prediction with the prediction accuracy (high) model has a shorter calculation cycle interval than the result of prediction with the prediction accuracy (low) model. To do.
  • the moving object behavior prediction unit 202 simply predicts the future position of the pedestrians 603 and 604 and the future position of the pedestrian 604 with higher accuracy than the simple prediction unit 401. Since the individual prediction unit 304 that performs the main prediction and the object allocation unit 302 that allocates the pedestrian 604 that performs the main prediction of the future position by the individual prediction unit 304 according to the result of the simple prediction, the prediction accuracy for the pedestrians 603 and 604 is provided.
  • the calculation load can be reduced while maintaining the above. Accordingly, the frequency of sudden acceleration / deceleration of the host vehicle 601 can be reduced, and the CPU mounted on the ECU can be replaced with a low-speed CPU, so that the cost of the ECU can be reduced.
  • the object allocation unit 302 allocates the pedestrian 604 that is highly likely to interfere with the host vehicle 601 to the individual prediction unit 304, the object allocation unit 302 predicts the pedestrian 604 that has a high influence on the host vehicle 601 with high accuracy.
  • the pedestrian 603 having a small influence can reduce the load on the prediction calculation, and can reduce the interference between the host vehicle 601 and both pedestrians 603 and 604 and the calculation load.
  • the prediction accuracy of the main prediction can be increased.
  • the prediction model differs between the preliminary prediction by the simple prediction unit 401 and the main prediction by the individual prediction unit 304, the degree of freedom to select a prediction model for each pedestrian 603, 604 is improved.
  • the simple prediction unit 401 simply predicts the future position of the moving object by the linear prediction method, the calculation load of the moving object behavior prediction unit 202 can be reduced.
  • the individual prediction unit 304 predicts the future position of the pedestrian 604 by the potential method using the potential map, the pedestrian 604 can be predicted with high accuracy.
  • the simple prediction unit 401 and the individual prediction unit 304 individually predict the future positions of the plurality of pedestrians 603 and 604, the prediction accuracy for each pedestrian 603 and 604 can be increased.
  • the calculation load on the pedestrian 603 can be reduced because the result of the simple prediction is used for the pedestrian 603 determined not to perform the main prediction according to the result of the simple prediction.
  • FIG. 10 is a block diagram of the predicted object accuracy determination unit 301 implemented in the vehicle travel control device 1 of the second embodiment.
  • an interaction determination unit 1004 is added to the predicted object accuracy determination unit 301 according to the first embodiment.
  • the interaction determination unit 1004 performs an operation for determining whether or not each moving object causes an interaction based on the simple prediction result of each predicted moving object. Specifically, when it is determined that there is a case where trajectories of the respective prediction results intersect with each other in the simple prediction calculation result, or TTC may become a predetermined value or less in the future time, there is an interaction between these objects. Suppose there is.
  • 11 and 12 show an example in which two pedestrians are walking along the x-axis direction and the y-axis direction. Hereinafter, the interaction between pedestrians will be described.
  • FIG. 11 the predicted positions from 1 second to 5 seconds after each pedestrian are represented by white circles.
  • the predicted trajectory of each pedestrian crosses in 3-4 seconds.
  • FIG. 12 shows the interaction probability at a future time. The calculation of the interaction probability standardizes, for example, the reciprocal of TTC. When the interaction probability is equal to or higher than a predetermined value, it is determined that the two pedestrians interact.
  • the accuracy information calculation unit 1005 calculates accuracy information based on the own vehicle influence determination result and the interaction determination result.
  • FIG. 13 shows the relationship between the vehicle effect determination result and the interaction determination result.
  • the accuracy information is “low”, “medium”, “ Toggle high.
  • the accuracy information is set to “low” when the own vehicle influence determination result is “small”.
  • the accuracy information is “interaction”.
  • the accuracy information is “interaction”
  • a prediction model capable of considering the interaction of moving objects is used.
  • An example in which the interaction can be considered is a prediction method using the potential method described above.
  • FIG. 14 shows an example of the own vehicle 601 of the present embodiment and the surrounding pedestrians 603 and 604 and the parked vehicle 1411.
  • the host vehicle 601 travels automatically along a travel planned track 605 on a road defined by road ends 602 on both sides.
  • Two pedestrians 603 and 604 are walking around the host vehicle 601.
  • the pedestrian 603 is walking in the direction opposite to the host vehicle 601 on the right rear side of the host vehicle 601.
  • a pedestrian 604 is walking in a direction parallel to the host vehicle 601.
  • a parked vehicle 1411 is parked in front of the host vehicle 601.
  • FIG. 15 shows a control flow in the situation of FIG. 14 of the present embodiment.
  • step S700, step S701, and step S702 the control unit (CPU) of the vehicle travel control device 1 processes the pedestrian 603, the pedestrian 604, and the parked vehicle 1411 in the same manner as in the first embodiment.
  • the vehicle influence determination is “low”.
  • the pedestrian 604 is walking in the direction parallel to the host vehicle 601 at the current time, and since the distance from the host vehicle 601 is short, the collision probability is not small. Therefore, the influence degree is determined to be “medium” here. Since the parked vehicle 1411 is stopped, the degree of influence on the own vehicle is “low”.
  • step S1506 the control unit (CPU) of the vehicle travel control device 1 determines the interaction.
  • the interaction between the moving objects is calculated.
  • the parked vehicle 1411 exists in the walking direction of the pedestrian 604. Therefore, it is determined that there is an interaction between the pedestrian 604 and the parked vehicle 1411.
  • the interaction with the pedestrian 604 and the parked vehicle 1411 is determined as “none”.
  • step S1509 the control unit (CPU) of the vehicle travel control apparatus 1 calculates prediction information based on the vehicle influence determination and the interaction determination result, and allocates the prediction information to each prediction unit.
  • the moving object behavior prediction unit 202 according to the second embodiment includes an individual prediction unit 304 that can calculate an interaction in the moving object behavior prediction unit 202.
  • step S704 behavior prediction using the prediction accuracy (low) model of step S704 is performed for the pedestrian 603, and the prediction accuracy (interaction) model of step S1507 is performed for the pedestrian 604 and the parked vehicle 1411. Predict behavior using.
  • step S705 the control unit (CPU) of the vehicle travel control device 1 collects the prediction results calculated in steps S1507 and S704.
  • the pedestrian 604 can predict a track 1414 that avoids the parked vehicle 1411.
  • the pedestrian 604 whose influence on the own vehicle 601 is in the middle is predicted to intersect the travel planned track 605 of the own vehicle 601.
  • the own vehicle 601 can change the travel plan trajectory 605 and calculate a new travel plan trajectory 1411 (indicated by a broken line in the figure) that avoids a collision with the pedestrian 604.
  • the interaction between the pedestrian 604 other than the host vehicle 701 and the parked vehicle 1411 is predicted, even if the pedestrian 604 and the parked vehicle 1411 interfere with each other, the pedestrian 604. And the prediction accuracy with the parked vehicle 1411 can be increased.
  • the future position of each of the plurality of pedestrians 603 and 604 may be predicted repeatedly and sequentially. Thereby, the future position of the pedestrians 603 and 604 can be predicted with high accuracy.
  • FIG. 16 shows a control flow of the third embodiment.
  • the control step (804) of the control by the moving object behavior prediction unit 202 according to the first embodiment is performed by the control unit (CPU) of the vehicle travel control device 1 with a prediction accuracy (medium) model.
  • Step S1604 is used to predict the action.
  • the moving object behavior prediction unit 202 of this embodiment includes an individual prediction unit 304 as a “third prediction unit”. The individual prediction unit 304 predicts the future positions of the pedestrians 603 and 604 with accuracy intermediate between the simple prediction and the main prediction.
  • the individual predicting unit 304 that predicts the future position of the pedestrian 604 with intermediate accuracy between the simple predicting unit 401 and the individual predicting unit 304 is provided, and the object allocating unit 302 depends on the result of the simple prediction.
  • Pedestrians 603 and 604 that predict future positions are allocated to the first prediction unit and the third prediction unit.
  • FIG. 17 is a block diagram of the moving object behavior prediction unit 202 according to the fourth embodiment.
  • Each individual prediction unit 303, 304 of the moving object behavior prediction unit 202 of this embodiment has a model accuracy switching unit 1704.
  • the model accuracy switching unit 1704 selects “interaction model”. A plurality of pieces of individual object information can be input to this interaction model.
  • the plurality of individual prediction units 303 and 304 need to be prepared in advance for each prediction model in the memory, there may be a case where an object larger than the prepared prediction model cannot be calculated with the prediction accuracy.
  • N individual prediction units 304 having a high-precision prediction model are prepared in the memory, it is impossible to predict N + 1 or more pieces of object information with high accuracy.
  • a model accuracy switching unit 1704 that switches prediction models used for prediction in the individual prediction units 303 and 304 based on accuracy information is provided. In this configuration, it is not necessary to secure a memory area unnecessarily, so that the memory can be used effectively.
  • the model accuracy switching unit 1704 described as “low” indicates that a low-precision prediction model is used.

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Abstract

L'invention permet de réduire les charges de calcul tout en garantissant la précision de prédiction des positions futures d'objets mobiles. L'invention comprend une unité de prédiction de comportement d'objet mobile (202) qui prédit les positions futures de piétons (603, 604) à proximité d'un véhicule hôte (601). L'unité de prédiction de comportement d'objet mobile (202) comprend : une unité de prédiction simple (401) qui mesure simplement les positions futures des piétons (603, 604) ; une unité de prédiction individuelle (304) qui prédit les positions futures du piéton (604) de façon plus précise que l'unité de prédiction simple (401) ; et une unité d'attribution d'objet (302) qui attribue le piéton (604) dont les positions futures doivent être prédites par l'unité de prédiction individuelle (304) en réponse aux résultats de la prédiction simple.
PCT/JP2018/001136 2017-01-30 2018-01-17 Dispositif de prédiction d'objet mobile WO2018139294A1 (fr)

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JP7232166B2 (ja) 2019-10-18 2023-03-02 本田技研工業株式会社 予測装置、車両システム、予測方法、およびプログラム
JP7470584B2 (ja) 2020-07-03 2024-04-18 日産自動車株式会社 走行支援方法及び走行支援装置
CN113393669A (zh) * 2021-06-11 2021-09-14 阿波罗智联(北京)科技有限公司 交通工具的控制方法、装置、设备、介质及程序产品
WO2023209942A1 (fr) * 2022-04-28 2023-11-02 株式会社Subaru Système d'aide à la conduite, véhicule et support d'enregistrement doté d'un programme informatique enregistré dans celui-ci

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