JP2020042599A - Automatic drive controller and automatic drive control method - Google Patents

Abstract

To provide an automatic drive controller enabling an automatic drive control, for which a travel state of another vehicle is taken into consideration.SOLUTION: An automatic drive controller 100 acquires self-vehicle information as vehicle information on a self-vehicle, peripheral environment information of the self-vehicle, object vehicle information as vehicle information on an object vehicle that is present around the self-vehicle, and a travel history of past vehicles that traveled in the past around a position where the object vehicle travels. An object vehicle traveling estimation calculation unit 106 calculates object vehicle traveling estimation as a traveling estimation result of the object vehicle by taking into account a correlation between the object vehicle information and the travel history of the past vehicles, on the basis of preset correlation determination parameters 105. A travel plan calculation unit 108 calculates a travel plan of automatically driving the self-vehicle by taking into account preset travel plan parameters 107, on the basis of the self-vehicle information, the peripheral environment information, the object vehicle information and the object vehicle traveling estimation.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an automatic driving control device and an automatic driving control method for a vehicle.

  In recent years, computerization of vehicles has been progressing in response to demands for improved fuel economy, safety, and convenience of vehicles, and the number of electronic control units (ECUs) mounted on vehicles has increased. Tend to. For example, there is known a vehicle control device that calculates a control command value to be transmitted to a control target such as an engine, a transmission, a traction motor, a brake device, and a steering device based on output data of a vehicle-mounted sensor such as a camera or a millimeter-wave radar. ing.

  Further, development of a driving support technology for supporting a driving operation of a vehicle user (including a driver) and an automatic driving technology for automatically controlling the driving of the vehicle by a system regardless of the driving operation of the user are progressing. In an automatic driving control device equipped with a driving support function and an automatic driving function, a part or all of the driving operation is performed by the system on behalf of the user, and therefore, a trust relationship between the system and the user is important. Therefore, improvement of the trust relationship between the system and the user is a major issue.

  For example, Patent Document 1 listed below proposes an automatic operation control device that realizes automatic operation in which operation control of a system and operation operation of a user are less likely to conflict with each other. The automatic driving control device of Patent Literature 1 generates a driving history in which a past driving environment of a vehicle is associated with an action of a driver for each driver, and generates a driving history similar to the current driving history of the vehicle. A driver model is generated based on the driver model, and the automatic driving of the vehicle is controlled based on the driver's next action predicted from the driver model and the current driving environment of the vehicle.

International Publication No. 2016/170763

  In the technology of Patent Document 1, by predicting the next action of the driver from the past running history of the vehicle, it is possible to suppress the conflict between the driving control of the system and the driving operation of the user, and to improve the trust between the system and the user. Improving relationships. However, the technique of Patent Document 1 does not consider the influence of other vehicles existing around the vehicle, and considers the running state of other vehicles such as ACC (Adaptive Cruise Control) and TJA (Traffic Jam Assist). It is difficult to realize comfortable automatic driving in an automatic driving control device equipped with a driving support function and an automatic driving function.

  The present invention has been made to solve the above-described problems, and has as its object to provide an automatic driving control device capable of performing automatic driving control in consideration of the traveling state of another vehicle.

  An automatic driving control unit according to the present invention includes a host vehicle information obtaining unit that obtains host vehicle information that is vehicle information of the host vehicle, a surrounding environment information obtaining unit that obtains surrounding environment information of the host vehicle, and the host vehicle. A target vehicle information acquisition unit that acquires target vehicle information that is vehicle information of one or more target vehicles existing around the vehicle, and a single or multiple past vehicles that have traveled in the past near the position where the target vehicle is traveling A travel history acquisition unit that acquires the travel history of the vehicle, and based on a correlation determination parameter set in advance, considering the correlation between the target vehicle information and the travel history of the past vehicle, and predicting the travel of the target vehicle. A target vehicle travel prediction calculation unit for calculating the target vehicle travel prediction, and a preset travel plan parameter, taking into account the own vehicle information, the surrounding environment information, the target vehicle information and A travel plan calculation unit that calculates a travel plan of automatic driving of the host vehicle based on the target vehicle travel prediction; and an automatic drive control unit that calculates a control command value for a control target based on the travel plan. Things.

  According to the automatic driving control device according to the present invention, the traveling of the target vehicle is predicted based on the traveling history of the past vehicle, and the prediction accuracy of the next action of the own vehicle is improved by using the prediction result. It is possible to perform automatic driving control in consideration of the traveling state of another vehicle. Therefore, even when another vehicle exists around the own vehicle, it is possible to suppress the occurrence of a conflict between the driving control of the system and the driving operation of the user.

It is a block diagram showing composition of an automatic operation control device concerning an embodiment of the invention. 4 is a flowchart illustrating an operation of the automatic driving control device according to the embodiment of the present invention. It is a figure for explaining the example of calculation of target vehicle running prediction. It is a figure for explaining the example of calculation of a run plan. It is a figure showing the example of hardware constitutions of an automatic operation control device. It is a figure showing the example of hardware constitutions of an automatic operation control device.

  FIG. 1 is a block diagram illustrating a configuration of an automatic operation control device 100 according to an embodiment of the present invention. The automatic driving control device 100 is mounted on a vehicle and various types of information acquired from an environment 121 outside the vehicle (hereinafter, “external environment 121”) and an infrastructure 122 existing outside the vehicle (hereinafter, “external infrastructure 122”). By controlling the control target 123 of the vehicle based on the information, automatic driving control of the vehicle is performed. The control target 123 is, for example, an engine, a transmission, a traveling motor, a brake device, a steering device, and the like. Hereinafter, a vehicle on which the automatic driving control device 100 is mounted is referred to as “own vehicle”, and vehicles other than the own vehicle are referred to as “other vehicles”. In particular, another vehicle that is detected by the automatic driving control device 100 to be present around the own vehicle is referred to as a “target vehicle”.

  The automatic driving performed by the automatic driving control device 100 does not require a user's driving operation, and is not limited to automatic driving in which all driving operations are automatically controlled (fully automatic driving), for example, ACC (Adaptive Cruise Control). , TJA (Traffic Jam Assist), LKS (Lane Keep System), and the like, driving assistance (semi-automatic driving) for assisting the user's driving operation by automatically controlling a part of the driving operation is also included.

  In addition, the automatic driving control device 100 has a function of determining the end of the automatic driving control. For example, the automatic driving control device 100 may automatically perform the automatic driving when the driving operation is intervened (overrided) by the user during the execution of the automatic driving, or when the own vehicle is stopped at the destination and the automatic driving is completed. It is determined to end the operation control.

  As shown in FIG. 1, the automatic driving control device 100 includes a host vehicle information acquisition unit 101, a surrounding environment information acquisition unit 102, a target vehicle information acquisition unit 103, a travel history acquisition unit 104, a target vehicle travel prediction calculation unit 106, a travel plan. A calculation unit 108 and an automatic operation control unit 109 are provided. In the automatic driving control device 100, a correlation determination parameter 105 and a travel planning parameter 107 are set in advance.

  The own-vehicle information acquisition unit 101 acquires vehicle information of the own vehicle (hereinafter, “own-vehicle information”) from in-vehicle devices that monitor the state of the own vehicle, such as an in-vehicle sensor and an in-vehicle ECU. The own-vehicle information is information, such as the running state of the own-vehicle, necessary for calculating the travel plan of the own-vehicle. The host vehicle information includes, for example, the current speed, acceleration / deceleration, and steering angle of the host vehicle. The own vehicle information acquired by the own vehicle information acquisition unit 101 is input to the travel plan calculation unit 108.

  The peripheral environment information acquisition unit 102 acquires peripheral environment information as a part of the external environment 121 from an in-vehicle device that senses the surrounding environment of the vehicle, such as an in-vehicle sensor or a communication device. Examples of in-vehicle devices that sense the surrounding environment include autonomous in-vehicle sensors such as cameras, millimeter-wave radars, LiDARs (Light Detection and Ranging), and ultrasonic sonars, and communications with vehicles outside such as V2X (Vehicle to Everything) and smartphones. There are in-vehicle communication devices and the like that can be used. The surrounding environment information is information on the surrounding environment of the own vehicle, which is necessary for calculating the travel plan of the own vehicle. The surrounding environment information includes at least one of information on a stationary object and information on a moving object. Examples of stationary objects include road shapes, signs, and traffic lights, and examples of moving objects include other vehicles, pedestrians, and bicycles. The surrounding environment information acquired by the surrounding environment information acquiring unit 102 is input to the travel plan calculating unit 108.

  The target vehicle information acquisition unit 103 performs automatic driving control of the own vehicle as partial information of the external environment 121 from an in-vehicle device that senses another vehicle (target vehicle) existing around the own vehicle, such as an in-vehicle sensor or a communication device. , Vehicle information of one or more target vehicles (hereinafter referred to as “target vehicle information”) required. Examples of the in-vehicle device for sensing the target vehicle include an in-vehicle autonomous sensor such as a camera, a millimeter wave radar, a LiDAR, and an ultrasonic sonar, and an in-vehicle communication device such as a V2X and a smartphone capable of communicating with the outside of the vehicle. The target vehicle information is information, such as the running state of the target vehicle, which is required for the target vehicle running prediction and the calculation of the running plan. The target vehicle information includes, for example, vehicle information such as the speed, acceleration / deceleration, and steering angle of the target vehicle. The target vehicle information acquired by the target vehicle information acquisition unit 103 is input to the target vehicle travel prediction calculation unit 106 and the travel plan calculation unit 108.

  The surrounding environment information acquisition unit 102 and the target vehicle information acquisition unit 103 may be configured as individual devices, respectively, or may be configured as one device including both. Further, the surrounding environment information obtained by the surrounding environment information obtaining unit 102 and the target vehicle information obtained by the target vehicle information obtaining unit 103 may include the same information in some cases. May be shared with each other.

  The travel history acquisition unit 104 uses an in-vehicle communication device that communicates with an external infrastructure 122 such as a data server center to perform one or more arbitrary vehicles (hereinafter, referred to as “past vehicles”) that have traveled in the vicinity of the position where the target vehicle is currently traveling. The traveling history of the “vehicle” is acquired from the external infrastructure 122. Examples of in-vehicle communication devices that can communicate with the external infrastructure 122 include V2X and smartphones. The running history of the past vehicle is information for determining the correlation between the running of the target vehicle and the running of the past vehicle.For example, information indicating the running state of the past vehicle such as a running route history, a speed history, an acceleration / deceleration history, etc. included. These pieces of information may be information on a single or a plurality of past vehicles, or statistical information obtained by statistically processing information on a plurality of past vehicles. The travel history of the past vehicle acquired by the travel history acquisition unit 104 is input to the target vehicle travel prediction calculation unit 106.

  In the present embodiment, the traveling history acquisition unit 104 acquires the traveling history of the past vehicle from the external infrastructure 122. For example, the automatic driving control device 100 stores the traveling history of the past vehicle in the data storage unit. You may have. In this case, the traveling history acquisition unit 104 may acquire the traveling history of the past vehicle from the data storage unit in the automatic driving control device 100 without using the in-vehicle communication device.

  The target vehicle travel prediction calculation unit 106 predicts the travel of the target vehicle based on the correlation determination parameter 105 set in advance and in consideration of the correlation between the target vehicle information and the past vehicle travel history. The prediction result of the running of the target vehicle is referred to as “target vehicle running prediction”. The correlation determination parameter 105 is a parameter such as a speed, an acceleration / deceleration, a traveling route, and a frequency. When calculating the target vehicle travel prediction, a constraint or a weight may be set for each parameter of the correlation determination parameter 105. The correlation determination parameter 105 may include a parameter for setting the number of target vehicle travel predictions to be calculated. Further, the target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106 may be single or plural, or a weight may be set for each of the plurality of target vehicle travel predictions according to the correlation determination parameter 105. Is also good. The target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106 is input to the travel plan calculation unit 108.

  The travel plan calculation unit 108 considers the travel plan parameters 107 set in advance, the own vehicle information acquired by the own vehicle information acquisition unit 101, the surrounding environment information acquired by the surrounding environment information acquisition unit 102, and the target vehicle information. Based on the target vehicle information acquired by the acquisition unit 103 and the target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106, a travel plan for automatic driving of the own vehicle is calculated. The travel plan parameters 107 are, for example, parameters such as speed, acceleration / deceleration, travel route, and inter-vehicle distance. When calculating the travel plan, constraints or weights may be set for each parameter of the travel plan parameters 107. The travel plan parameter 107 may include a parameter for setting the number of travel plans of the host vehicle to be calculated. The travel plan calculated by the travel plan calculation unit 108 is input to the automatic driving control unit 109.

  The automatic driving control unit 109 controls a control target 123 (for example, an engine, a transmission, a driving motor, a brake device, a steering device, and the like) for executing the travel plan based on the travel plan calculated by the travel plan calculation unit 108. Calculate the control command value. The control command value calculated by the automatic driving control unit 109 is input to the control target 123, and the control target 123 operates according to the control input value, so that the host vehicle travels according to the travel plan.

  FIG. 2 is a flowchart showing the operation of the automatic driving control device 100. Hereinafter, the operation of the automatic driving control device 100 will be described based on the flowchart of FIG.

  When the user (driver) performs an operation for starting automatic driving after the host vehicle or the vehicle-mounted system of the host vehicle starts, the automatic driving control device 100 starts, and in step S100, the automatic driving control device 100 Application to in-vehicle systems is started.

  In step S101, the own vehicle information acquisition unit 101 acquires vehicle information (own vehicle information) of the own vehicle from an in-vehicle device that monitors a vehicle state, such as an in-vehicle sensor or an in-vehicle ECU.

  In step S102, the surrounding environment information obtaining unit 102 obtains the surrounding environment information of the vehicle as a part of the external environment 121 from the on-board device that senses the surrounding environment of the vehicle such as the on-board sensor and the communication device.

  In step S103, the target vehicle information acquisition unit 103 transmits vehicle information of a single vehicle or a plurality of target vehicles as a part of the external environment 121 from a vehicle-mounted device that senses the target vehicle, such as a vehicle-mounted sensor or a communication device (target vehicle method). To get.

  In step S104, the travel history acquisition unit 104 travels, using an in-vehicle communication device capable of communicating with the external infrastructure 122, the travel of one or a plurality of arbitrary vehicles (past vehicles) that have traveled in the past around the target vehicle. The history is acquired from the external infrastructure 122. As described above, when the automatic driving control device 100 includes the data storage unit in which the traveling history of the past vehicle is stored in advance, the traveling history acquisition unit 104 does not use the in-vehicle communication device. The travel history may be obtained from the data storage unit.

  Since the processes in steps S101 to S104 are independent processes, the order of the processes may be changed or the processes may be executed simultaneously.

  In step S105, the target vehicle travel prediction calculation unit 106 calculates the target vehicle travel prediction of the target vehicle based on the correlation determination parameter 105 set in advance and considering the correlation between the target vehicle information and the travel history of the past vehicle. calculate.

  The calculation of the target vehicle travel prediction performed in step S105 will be specifically described with reference to FIG. FIG. 3 is an example of the traveling history of the past vehicle acquired by the traveling history acquisition unit 104, and shows the speed history of the past vehicle traveling near the position where the target vehicle is traveling. In FIG. 3, the speed of the past vehicle is shown for each position, and the horizontal axis corresponds to the traveling direction. FIG. 3 shows three speed histories R1, R2, and R3, which may be the speed histories of three past vehicles or the results of statistical processing of the speed histories of four or more past vehicles. What extracted three representative speed histories may be used. In FIG. 3, point A is the current position of the vehicle, point B is the current position of the target vehicle, and point C is a point where a difference occurs between the speed history R2 and the speed history R3 ( The speed history R2 and the speed history R3 match before the point C).

  Here, for simplicity of explanation, the case where the speed of the target vehicle from the point A to the point B included in the target vehicle information matches the speed history R1, the case where the speed history R2 and the speed history R3 match, and Consider two ways. It is not necessary that the speed of the target vehicle and the speed history of the past vehicle match, and the case where both are similar can also be considered.

  First, when the speed of the target vehicle from the point A to the point B matches the speed history R1, the target vehicle information of the target vehicle has a high correlation with the speed history R1, and the speed history R2 and the speed history R3. And the correlation becomes lower. Therefore, the target vehicle travel prediction calculation unit 106 calculates the target vehicle travel prediction for the point B and onward, with emphasis on the speed history R1.

  On the other hand, when the speed of the target vehicle from the point A to the point B matches the speed history R2 and the speed history R3, the target vehicle information of the target vehicle has a high correlation with the speed history R2 and the speed history R3. , The correlation with the speed history R1 decreases. Therefore, the target vehicle travel prediction calculation unit 106 calculates the target vehicle travel prediction for the point B and onward, with emphasis on the speed history R2 and the speed history R3.

  As described above, the correlation determination parameter 105 (for example, parameters such as speed, acceleration / deceleration, travel route, and frequency) may be set with restrictions and weights, or set with the number of target vehicle travel predictions to be calculated. Parameters may be included.

  When weighting is set for the travel history based on the frequency, for example, if the frequency at which the speed history R2 has occurred in the past is higher than the frequency at which the speed history R3 has occurred, the target vehicle travel prediction calculation unit 106 sets the speed history R2 to The target vehicle travel prediction is calculated with emphasis. When weighting is set for the traveling history based on the acceleration / deceleration, for example, if the absolute value of the acceleration / deceleration of the speed history R3 is larger than the absolute value of the acceleration / deceleration of the speed history R2, the speed history R3 is regarded as important. Calculate the vehicle running prediction. The target vehicle travel prediction calculation unit 106 may calculate a plurality of target vehicle travel predictions. The target vehicle travel prediction that emphasizes the speed history R2 and the target vehicle travel prediction that emphasizes the speed history R3 are calculated. Alternatively, a weight may be set for each target vehicle travel prediction.

  In addition, the speed of the target vehicle from the point A to the point B may be measured using an autonomous vehicle-mounted sensor such as a camera, a millimeter-wave radar, a LiDAR, or an ultrasonic sonar mounted on the own vehicle, or V2X. Alternatively, the information may be obtained using an in-vehicle communication device such as a smartphone capable of communication outside the vehicle.

  In step S <b> 106, the travel plan calculation unit 108 performs automatic travel of the vehicle based on the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction in consideration of the preset travel plan parameters 107. Calculate the plan.

  The calculation of the travel plan performed in step S106 will be specifically described with reference to FIG. 3 described above and FIG. 4 illustrating an example of the travel plan. Here, an example in which the travel plan in FIG. 4 is calculated based on the speed history of the past vehicle in FIG. 3 under the condition that the own vehicle uses ACC.

  When the own vehicle recognizes a preceding vehicle (another vehicle ahead) and uses the ACC, the own vehicle keeps a distance between the own vehicle and the preceding vehicle at a specified inter-vehicle distance according to the speed. The vehicle follows the vehicle ahead. In this case, the preceding vehicle is the target vehicle.

  For example, when the target vehicle travel prediction that matches the speed history R1 in FIG. 3 is calculated in step S105, the travel plan calculation unit 108 assumes that the target vehicle travels from the point B on according to the speed history R1. Calculate the travel plan of the own vehicle. That is, the travel plan calculation unit 108 sets the speed of the own vehicle after the point B to be approximately the same as the speed history R1 within a range satisfying the condition that the distance between the own vehicle and the preceding vehicle is maintained at the specified inter-vehicle distance. A travel plan for controlling the own vehicle is calculated as follows. Note that the target vehicle traveling prediction does not need to match the speed history R1, and they may have a similar relationship. In that case, however, it is necessary to consider the difference between the two in the calculation of the traveling plan.

  When constraints and weights such as speed, acceleration / deceleration, travel route, and inter-vehicle distance are set in the travel plan parameters 107, the travel plan calculation unit 108 calculates a travel plan emphasizing them. For example, when a constraint is set for acceleration / deceleration and rapid acceleration / deceleration of the own vehicle is prohibited, the traveling plan calculation unit 108 sets a condition that the distance between the own vehicle and the preceding vehicle is maintained at a specified inter-vehicle distance. A travel plan that satisfies the acceleration / deceleration constraints is calculated by making a correction such as slightly sacrificing the user's comfort in the inter-vehicle distance within a range that satisfies the requirements.

  Also, in step S105, when the target vehicle travel prediction that matches either the speed history R2 or the speed history R3 shown in FIG. 3 is calculated, the travel plan calculation unit 108 performs the same operation as in the case of the speed history R1 described above. Assuming that the target vehicle travels according to the speed history R2 or the speed history R3, the travel plan of the own vehicle is calculated.

  Also, in step S105, when the target vehicle travel prediction matching the speed history R2 in FIG. 3 and the target vehicle travel prediction matching the speed history R3 are calculated, the travel plan calculation unit 108 The travel plan of the own vehicle is calculated in consideration of both the possibility that the vehicle travels according to the speed history R2 and the possibility that the vehicle travels according to the speed history R3.

  For example, when emphasizing the speed history R2 of the speed history R2 and the speed history R3, the travel plan calculation unit 108 assumes that the target vehicle travels from the point B on according to the speed history R2, and Is calculated. That is, the travel plan calculation unit 108 calculates a travel plan that controls the own vehicle such that the speed of the own vehicle after the point B is approximately equal to the speed history R2. As a result, the travel plan of the own vehicle becomes like the travel plan P1 shown in FIG.

  In this case, when the target vehicle actually travels according to the speed history R2, the own vehicle is speed-controlled as in the travel plan P1, and the distance between the own vehicle and the preceding vehicle is maintained at the specified inter-vehicle distance. Within the range that satisfies, the control is performed while maintaining the user's comfort in the speed and the inter-vehicle distance to some extent.

  However, when the target vehicle travels according to the speed history R3 instead of the speed history R2, the target vehicle starts decelerating at the point C. When the target vehicle decelerates, it is necessary to decelerate the own vehicle in order to maintain the specified inter-vehicle distance, and the own vehicle travels at a speed such as own vehicle travel S shown in FIG. When the target vehicle travel prediction deviates in this way, it may be necessary for the automatic driving control device 100 to suddenly decelerate the own vehicle while changing the target vehicle travel prediction and the travel plan. Control may be impaired.

  In order to prevent such a problem from occurring, when a plurality of target vehicle travel predictions are calculated, the travel plan calculation unit 108 calculates a travel plan in consideration of the plurality of travel predictions. That is, restrictions and weights to be considered are set in advance for the travel plan parameters 107, and the travel plan calculation unit 108 calculates a travel plan in consideration of the restrictions and weights of each parameter. For example, acceleration / deceleration constraints are set in advance, and even if the target vehicle traveling prediction is incorrect, a traveling plan in which acceleration / deceleration that does not satisfy the constraints does not occur, that is, the vehicle travels within a range that satisfies the acceleration / deceleration constraints. Calculate possible travel plans.

  For example, in step S105, when the target vehicle travel prediction matching the speed history R2 of FIG. 3 and the target vehicle travel prediction matching the speed history R3 are calculated, the travel plan calculation unit 108 first determines whether the target vehicle A travel plan P1 calculated assuming that the vehicle travels according to the speed history R2 and a travel plan P2 (same as the own vehicle travel S) calculated assuming that the target vehicle travels according to the speed history R3 are calculated. Then, travel plan calculation section 108 checks whether travel plan P2 and travel plan P3 satisfy preset acceleration / deceleration constraints, and if any of them does not satisfy the constraints, the travel illustrated in FIG. A plan P3 and a travel plan P4 are calculated.

  The travel plan P3 and the travel plan P4 are obtained by modifying the travel plan P1 and the travel plan P2 such that the acceleration / deceleration constraint is satisfied regardless of whether the target vehicle travels in the speed history R2 or the speed history R3. It is. The travel plan P3 and the travel plan P4 in FIG. 4 decelerate the host vehicle to the vicinity of the point C where the speed history R2 and the speed history R3 differ from the travel plan P1 and the travel plan P2, respectively. It is obtained by modifying the distance between vehicles to be larger.

  For example, the travel plan P3 sets the inter-vehicle distance to the target vehicle to be large until it is determined whether the target vehicle runs in the speed history R2 or the speed history R3 (to the vicinity of the point C). When it is determined that the vehicle will run as described in the above, it is planned to accelerate the own vehicle to return the inter-vehicle distance to the specified inter-vehicle distance.

  The travel plan P4 is based on a case where the inter-vehicle distance with the target vehicle is set to be large until the target vehicle travels in the speed history R2 or the speed history R3, and the target vehicle travels according to the speed history R3. In addition, it is planned that the inter-vehicle distance can be maintained at a specified inter-vehicle distance without suddenly decelerating the own vehicle. In the travel plan P4, abrupt deceleration of the own vehicle is not required as compared with the travel plan P2, so that it is possible to prevent the user's comfort from being impaired.

  As described above, the travel plan calculation unit 108 sacrifice the allowable parameters and keep the unacceptable parameters within the allowable range according to the constraints and weights set for the travel plan parameters 107, thereby making the plurality of target vehicle travel possible. It is possible to calculate a travel plan that can respond to the prediction.

  Returning to FIG. 2, in step S107, the automatic driving control unit 109 calculates and calculates a control command value of the control target 123 for executing the travel plan based on the travel plan calculated by the travel plan calculation unit 108. The control command value is input to the control target 123. In addition, since the automatic driving control unit 109 needs to uniquely output the command value to the control target, at least the travel plan of the current position of the vehicle (including the immediately succeeding position) is unique in step S106. Must be specified in

  In step S108, the automatic driving control device 100 determines the end of the automatic driving control. For example, the automatic driving control apparatus 100 performs the automatic driving when the driving operation is intervened (overrided) by the user during the automatic driving, or when the own vehicle is stopped at the destination and the automatic driving is completed. It is determined to end the control.

  When the automatic driving control device 100 determines that the automatic driving control is to be continued, the process returns to step S101, and the processing of steps S101 to S107 is repeated. If the automatic driving control device 100 determines to end the automatic driving control, the process proceeds to step S109, and the application of the automatic driving control device 100 to the on-vehicle system ends.

  Note that the flow of FIG. 2 is usually repeatedly and sequentially executed, and the automatic operation control device 100 continuously outputs a control command value to the control target 123.

  In the present embodiment, for the sake of simplicity, it is assumed that the correlation between the target vehicle information and the travel history of the past vehicle is 1, and the target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106 is the past vehicle travel prediction. An example in which the running history matches the running history is shown. However, in practice, the correlation between the target vehicle information and the running history of the past vehicle is likely to be less than 1, so the target vehicle running prediction calculation unit 106 considers that the correlation is less than 1 and After the traveling history of the vehicle is corrected based on the target vehicle information, the target vehicle traveling prediction is calculated.

  As described above, according to the automatic driving control device 100 according to the present embodiment, the traveling of the target vehicle is predicted based on the traveling history of the past vehicle, and the next action of the own vehicle is performed using the prediction result. By improving the prediction accuracy, it is possible to perform automatic driving control in consideration of the traveling state of another vehicle. Therefore, even when another vehicle (target vehicle) exists around the own vehicle, it is possible to suppress the occurrence of a conflict between the driving control of the system and the driving operation of the user.

  5 and 6 are diagrams illustrating examples of the hardware configuration of the automatic operation control device 100, respectively. Each function of the components of the automatic driving control device 100 shown in FIG. 1 is realized by, for example, a processing circuit 50 shown in FIG. That is, the automatic driving control device 100 acquires own vehicle information which is vehicle information of the own vehicle, acquires surrounding environment information of the own vehicle, and obtains vehicle information of one or more target vehicles existing around the own vehicle. Acquire certain target vehicle information, acquire the traveling history of one or more past vehicles that have traveled in the past near the position where the target vehicle is traveling, and based on preset correlation determination parameters, target vehicle information and Considering the correlation with the running history of the past vehicle, calculating the target vehicle travel prediction which is the predicted result of the travel of the target vehicle, taking into account the preset travel plan parameters, own vehicle information, surrounding environment information, A processing circuit is provided for calculating a travel plan for automatic driving of the own vehicle based on the target vehicle information and the target vehicle travel prediction, and calculating a control command value for the control target based on the travel plan. The processing circuit 50 may be dedicated hardware, or a processor that executes a program stored in a memory (a central processing unit (CPU), a processing device, an arithmetic device, a microprocessor, a microcomputer, And may be configured using a DSP (Digital Signal Processor).

  When the processing circuit 50 is dedicated hardware, the processing circuit 50 includes, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable). Gate Array) or a combination of these. Each function of the components of the automatic operation control device 100 may be realized by an individual processing circuit, or the functions may be realized by a single processing circuit.

  FIG. 6 illustrates an example of a hardware configuration of the automatic operation control device 100 when the processing circuit 50 is configured using a processor 51 that executes a program. In this case, the functions of the components of the automatic driving control device 100 are realized by software or the like (software, firmware, or a combination of software and firmware). Software and the like are described as programs and stored in the memory 52. The processor 51 realizes the function of each unit by reading and executing the program stored in the memory 52. That is, when the automatic driving control device 100 is executed by the processor 51, the automatic driving control device 100 acquires the own vehicle information that is the vehicle information of the own vehicle, the process of acquiring the surrounding environment information of the own vehicle, and the vicinity of the own vehicle. A process of acquiring target vehicle information that is vehicle information of one or more target vehicles existing in the vehicle, and a process of acquiring traveling history of one or more past vehicles that have traveled in the vicinity of the position where the target vehicle is traveling in the past A process of calculating a target vehicle travel prediction, which is a prediction result of travel of the target vehicle, in consideration of the correlation between the target vehicle information and the travel history of the past vehicle based on a preset correlation determination parameter; A travel plan for automatic driving of the own vehicle is calculated based on the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction in consideration of the parameters for the travel plan. Comprising a sense, the process of calculating the control command value to the controlled object based on a travel plan, the memory 52 for storing a program that will but executed consequently. In other words, it can be said that this program causes a computer to execute the procedure and method of the operation of the components of the automatic driving control device 100.

  Here, the memory 52 is, for example, a nonvolatile memory such as a random access memory (RAM), a read only memory (ROM), a flash memory, an erasable programmable read only memory (EPROM), and an electrically erasable programmable read only memory (EEPROM). Volatile semiconductor memory, HDD (Hard Disk Drive), magnetic disk, flexible disk, optical disk, compact disk, mini disk, DVD (Digital Versatile Disc) and its drive device, or any storage medium used in the future. You may.

  The configuration in which the functions of the components of the automatic driving control device 100 are realized by one of hardware and software has been described. However, the present invention is not limited to this, and a configuration may be adopted in which some components of the automatic driving control device 100 are realized by dedicated hardware, and other components are realized by software or the like. For example, for some components, the function is realized by the processing circuit 50 as dedicated hardware, and for other components, the processing circuit 50 as the processor 51 executes a program stored in the memory 52. The function can be realized by reading and executing.

  As described above, the automatic driving control device 100 can realize the above-described functions by hardware, software, or the like, or a combination thereof.

  In the present invention, the embodiments can be appropriately modified and omitted within the scope of the invention.

  REFERENCE SIGNS LIST 100 automatic driving control device, 101 own vehicle information acquisition unit, 102 surrounding environment information acquisition unit, 103 target vehicle information acquisition unit, 104 travel history acquisition unit, 105 correlation determination parameter, 106 target vehicle travel prediction calculation unit, 107 travel plan Parameters, 108 travel plan calculation unit, 109 automatic operation control unit, 121 external environment, 122 external infrastructure, 123 control target, 50 processing circuit, 51 processor, 52 memory.

An automatic driving control unit according to the present invention includes a host vehicle information obtaining unit that obtains host vehicle information that is vehicle information of the host vehicle, a surrounding environment information obtaining unit that obtains surrounding environment information of the host vehicle, and the host vehicle. A target vehicle information acquisition unit that acquires target vehicle information that is vehicle information of one or more target vehicles existing around the vehicle, and a single or multiple past vehicles that have traveled in the past near the position where the target vehicle is traveling A travel history acquisition unit that acquires the travel history of the vehicle, and based on a correlation determination parameter set in advance, considering the correlation between the target vehicle information and the travel history of the past vehicle, and predicting the travel of the target vehicle. A target vehicle travel prediction calculation unit for calculating the target vehicle travel prediction, and a preset travel plan parameter, taking into account the own vehicle information, the surrounding environment information, the target vehicle information and Based on the target vehicle traveling prediction, comprising the a travel plan calculating unit that calculates a travel plan for automatic operation of the vehicle, and an automatic driving control section for calculating a control command value to the controlled object based on the travel plan , the subject vehicle, wherein a front vehicle of the vehicle, the travel plan calculating unit is a shall be calculated travel plans to travel to follow the subject vehicle to the preceding vehicle.

An automatic driving control unit according to the present invention includes a host vehicle information obtaining unit that obtains host vehicle information that is vehicle information of the host vehicle, a surrounding environment information obtaining unit that obtains surrounding environment information of the host vehicle, and the host vehicle. A target vehicle information acquisition unit that acquires target vehicle information that is vehicle information of one or more target vehicles existing around the vehicle, and a single or multiple past vehicles that have traveled in the past near the position where the target vehicle is traveling A travel history acquisition unit that acquires the travel history of the vehicle, and based on a correlation determination parameter set in advance, considering the correlation between the target vehicle information and the travel history of the past vehicle, and predicting the travel of the target vehicle. A target vehicle travel prediction calculation unit for calculating the target vehicle travel prediction, and a preset travel plan parameter, taking into account the own vehicle information, the surrounding environment information, the target vehicle information and A travel plan calculation unit that calculates a travel plan for automatic driving of the host vehicle based on the target vehicle travel prediction, and an automatic drive control unit that calculates a control command value for a control target based on the travel plan. The target vehicle is a vehicle ahead of the host vehicle, the travel plan calculation unit calculates a travel plan that causes the host vehicle to travel following the preceding vehicle, and the travel plan calculation unit includes the travel plan When the target vehicle travel prediction calculation unit calculates a plurality of the target vehicle travel predictions by setting a constraint on each parameter included in the target parameter, the travel plan calculation unit determines whether the forward vehicle has a plurality of the target vehicle travel predictions. as can be run within said vehicle be traveling in either the street meets the constraints, to calculate the travel plan to take inter-vehicle distance larger than normal and the said front vehicle and the subject vehicle Than it is.

Claims (17)

Own-vehicle information acquisition unit for acquiring own-vehicle information, which is vehicle information of the own vehicle,
A surrounding environment information acquisition unit that acquires surrounding environment information of the vehicle;
A target vehicle information acquisition unit that acquires target vehicle information that is vehicle information of a single or a plurality of target vehicles existing around the own vehicle,
A traveling history acquisition unit that acquires the traveling history of a single vehicle or a plurality of past vehicles that have traveled in the past around the position where the target vehicle is traveling,
A target vehicle travel that calculates a target vehicle travel prediction, which is a prediction result of travel of the target vehicle, in consideration of a correlation between the target vehicle information and the travel history of the past vehicle based on a preset correlation determination parameter. A prediction calculation unit,
A travel plan that calculates a travel plan for automatic driving of the own vehicle based on the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction, in consideration of a preset travel plan parameter. A calculating unit;
An automatic operation control unit that calculates a control command value to the control target based on the travel plan,
An automatic operation control device comprising: The travel history acquisition unit acquires the travel history of the past vehicle by communication from an external infrastructure,
The automatic operation control device according to claim 1. The automatic driving control device further includes a data storage unit in which the traveling history of the past vehicle is stored,
The automatic driving control device according to claim 1, wherein the traveling history acquisition unit acquires the traveling history of the past vehicle from the data storage unit. The peripheral environment information acquisition unit and the target vehicle information acquisition unit are configured to use one or both of an autonomous in-vehicle sensor mounted on the own vehicle and an in-vehicle communication device capable of out-of-vehicle communication, and the peripheral environment information and the target The automatic driving control device according to any one of claims 1 to 3, which acquires vehicle information. The surrounding environment information includes at least one of information on a stationary object and information on a moving object,
The automatic operation control device according to any one of claims 1 to 4. The target vehicle information includes one or more of the speed, acceleration / deceleration, and steering angle of the target vehicle,
The automatic operation control device according to any one of claims 1 to 5. The travel history obtaining unit obtains the travel history of the past vehicle using an in-vehicle communication device capable of out-of-vehicle communication,
The automatic operation control device according to any one of claims 1 to 6. The travel history of the past vehicle includes one or more of a travel route history, a speed history, and an acceleration / deceleration history of the past vehicle,
The automatic operation control device according to any one of claims 1 to 7. The travel history of the past vehicle is information obtained by statistically processing information of a single or a plurality of past vehicles, or information of a plurality of past vehicles.
The automatic operation control device according to any one of claims 1 to 8. The correlation determination parameter is one or more parameters of speed, acceleration / deceleration, travel route, and frequency.
The automatic operation control device according to any one of claims 1 to 9. The target vehicle travel prediction calculation unit sets a constraint or weight on each parameter included in the correlation determination parameter,
The automatic operation control device according to any one of claims 1 to 10. The correlation determination parameter includes a parameter for setting the number of target vehicle travel predictions to be calculated,
The automatic operation control device according to any one of claims 1 to 11. The automatic driving according to any one of claims 1 to 12, wherein the target vehicle travel prediction calculation unit sets a weight for each of the plurality of target vehicle travel predictions when the plurality of target vehicle travel predictions are calculated. Control device. The travel plan parameter is one or more of speed, acceleration / deceleration, travel route, and inter-vehicle distance.
The automatic operation control device according to any one of claims 1 to 13. The travel plan calculation unit sets a constraint or weight on each parameter included in the travel plan parameters,
The automatic operation control device according to any one of claims 1 to 14. The travel plan calculation unit calculates a travel plan in consideration of the plurality of target vehicle travel predictions,
The automatic operation control device according to any one of claims 1 to 15. The own vehicle information acquisition unit of the automatic driving control device acquires own vehicle information that is vehicle information of the own vehicle,
A surrounding environment information acquiring unit of the automatic driving control device acquires surrounding environment information of the own vehicle,
The target vehicle information acquisition unit of the automatic driving control device acquires target vehicle information that is vehicle information of a single or a plurality of target vehicles existing around the own vehicle,
A traveling history acquisition unit of the automatic driving control device acquires a traveling history of a single vehicle or a plurality of past vehicles that have traveled in the past around the position where the target vehicle is traveling,
The target vehicle travel prediction calculation unit of the automatic driving control device, based on a correlation determination parameter set in advance, considering the correlation between the target vehicle information and the travel history of the past vehicle, the travel of the target vehicle Calculate the target vehicle travel prediction that is the prediction result,
The travel plan calculation section of the automatic driving control device considers the travel plan parameters set in advance, and based on the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction, Calculate the driving plan for automatic driving of the vehicle,
An automatic operation control unit of the automatic operation control device calculates a control command value to a control target based on the travel plan,
Automatic operation control method.

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