JP6689337B2 - Automatic operation control device and automatic operation control method - Google Patents

Description

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

  In recent years, in order to meet the demands for fuel efficiency, safety, and convenience of vehicles, the computerization of vehicles has progressed, and the number of electronic control units (ECU: Electric Control Units) mounted on vehicles has increased. Tend to do. For example, a vehicle control device is known that calculates a control command value to be transmitted to a control target such as an engine, a transmission, a traveling motor, a brake device, and a steering device based on output data of an in-vehicle sensor such as a camera or a millimeter wave radar. ing.

  Further, the development of driving assistance technology for assisting the driving operation of the user (including the driver) of the vehicle and automatic driving technology in which the system automatically controls the driving of the vehicle regardless of the driving operation of the user are in progress. In an automatic driving control device equipped with a driving support function or an automatic driving function, the system performs a part or all of the driving operation on behalf of the user, and therefore the trust relationship between the system and the user is important. Therefore, the improvement of the trust relationship between the system and the user is a major issue.

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

International publication 2016/170763

  In the technique of Patent Document 1, by predicting the next action of the driver from the past travel 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 trust the system and the user. We are trying to improve relationships. However, the technique of Patent Document 1 does not consider the influence of other vehicles existing around the vehicle, and considers the traveling 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 or an automatic driving function.

  The present invention has been made to solve the above problems, and an object of the present invention is to provide an automatic driving control device capable of 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 acquisition unit that acquires host vehicle information that is vehicle information of the host vehicle, a surrounding environment information acquisition unit that acquires 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 a single target vehicle or multiple target vehicles existing in the vicinity of the target vehicle, and 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 preset correlation determination parameter, considering the correlation between the target vehicle information and the travel history of the past vehicle, a prediction result of the travel of the target vehicle In consideration of the target vehicle travel prediction calculation unit that calculates the target vehicle travel prediction and the preset travel planning parameters, the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle information and A travel plan calculation unit that calculates a travel plan for automatic driving of the own vehicle based on the target vehicle travel prediction, and an automatic driving control unit that calculates a control command value to a control target based on the travel plan. The target vehicle is a vehicle in front of the own vehicle, the travel plan calculation unit calculates a travel plan that causes the own vehicle to travel following the front vehicle, and the travel plan calculation unit includes the travel plan. When a constraint is set for each parameter included in the target parameter, and the target vehicle travel prediction calculation unit calculates a plurality of the target vehicle travel predictions, the travel plan calculation unit includes the target vehicle travel predictions for a plurality of the preceding vehicles. 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.

  According to the automatic driving control device according to the present invention, by predicting the travel of the target vehicle based on the travel history of the past vehicle, by using the prediction result to improve the prediction accuracy of the next behavior of the own vehicle, 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 conflict between the driving control of the system and the driving operation of the user.

It is a block diagram which shows the structure of the automatic driving control apparatus which concerns on embodiment of this invention. It is a flowchart which shows operation | movement of the automatic driving control apparatus which concerns on embodiment of this invention. It is a figure for explaining an example of calculation of target vehicle running prediction. It is a figure for explaining an example of calculation of a run plan. It is a figure which shows the hardware structural example of an automatic driving control apparatus. It is a figure which shows the hardware structural example of an automatic driving control apparatus.

  FIG. 1 is a block diagram showing a configuration of an automatic driving 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 environments 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, the automatic driving control of the vehicle is performed. The controlled object 123 is, for example, an engine, a transmission, a traveling motor, a brake device, a steering device, or the like. Hereinafter, a vehicle equipped with the automatic driving control device 100 will be referred to as “own vehicle”, and a vehicle other than the own vehicle will be referred to as “other vehicle”. In particular, another vehicle that is detected by the automatic driving control device 100 to be present around the host vehicle is referred to as a “target vehicle”.

  It should be noted that the automatic driving performed by the automatic driving control device 100 does not require the driving operation of the user, and not only the automatic driving (fully automatic driving) in which all the driving operations are automatically controlled but also, for example, ACC (Adaptive Cruise Control) , TJA (Traffic Jam Assist), LKS (Lane Keep System), and the like, which also includes driving assistance (semi-automatic driving) that assists the user's driving operation by automatically controlling part of the driving operation.

  Further, the automatic driving control device 100 has a function of performing a termination determination of the automatic driving control. For example, the automatic driving control device 100 automatically performs automatic operation when a user intervenes (overrides) a driving operation during execution of automatic driving, or when the own vehicle is stopped at a destination and automatic driving is completed. It is determined to end the operation control.

  As shown in FIG. 1, the automatic driving control device 100 includes an own 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. The calculator 108 and the automatic operation controller 109 are provided. Further, the automatic operation control device 100 is preset with a correlation determination parameter 105 and a travel plan parameter 107.

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

  The surrounding environment information acquisition unit 102 acquires surrounding environment information as a part of the external environment 121 from an in-vehicle device such as an in-vehicle sensor or a communication device that senses the surrounding environment of the vehicle. In-vehicle devices that sense the surrounding environment include, for example, cameras, millimeter-wave radar, LiDAR (Light Detection and Ranging), autonomous in-vehicle sensors such as ultrasonic sonar, and V2X (Vehicle to Everything), communication with the outside of the vehicle such as smartphones. There is an in-vehicle communication device etc. The surrounding environment information is information about 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 stationary object information and moving object information. 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 acquisition unit 102 is input to the travel plan calculation unit 108.

  The target vehicle information acquisition unit 103 controls the automatic driving of the own vehicle as a part of the external environment 121 from an on-vehicle device such as an on-vehicle sensor or a communication device that senses another vehicle (target vehicle) existing around the own vehicle. The vehicle information (hereinafter referred to as “target vehicle information”) of a single target vehicle or a plurality of target vehicles necessary for the above is acquired. Examples of in-vehicle devices that sense the target vehicle include autonomous in-vehicle sensors such as cameras, millimeter-wave radars, LiDARs, and ultrasonic sonars, and in-vehicle communication devices that can communicate with outside the vehicle such as V2X and smartphones. The target vehicle information is information such as the running state of the target vehicle, which is necessary for predicting the target vehicle travel and calculating the travel plan. The target vehicle information includes 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 separate devices, or may be configured as a single device including both. In addition, the surrounding environment information acquired by the surrounding environment information acquisition unit 102 and the target vehicle information acquired by the target vehicle information acquisition unit 103 may include the same information, and in that case, the information acquired by both parties. 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 singly or plural arbitrary vehicles that have traveled in the past near the position where the target vehicle is currently traveling (hereinafter referred to as “past The travel history of “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 past vehicle traveling history is information for determining the correlation between the traveling of the target vehicle and the traveling of the past vehicle, and information indicating the traveling state of the past vehicle, such as traveling route history, speed history, and acceleration / deceleration history, is provided. included. Each of these pieces of information may be information on a single or a plurality of past vehicles, or may be 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. However, for example, the automatic driving control device 100 may include a data storage unit that stores the traveling history of the past vehicle in advance. You may have it. In this case, the travel history acquisition unit 104 may acquire the travel 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 in consideration of the correlation between the target vehicle information and the travel history of the past vehicle based on the preset correlation determination parameter 105. The prediction result of traveling of the target vehicle is referred to as "target vehicle traveling prediction". The correlation determination parameter 105 is a parameter such as speed, acceleration / deceleration, travel route, and frequency. When calculating the target vehicle travel prediction, constraints or weighting may be set for each parameter of the correlation determination parameter 105. Further, the correlation determination parameter 105 may include a parameter for setting the number of target vehicle traveling predictions to be calculated. Further, the target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106 may be independent or plural depending on the correlation determination parameter 105, and weighting is set for each of the plurality of target vehicle travel predictions. 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 preset travel planning parameter 107, 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 parameters such as speed, acceleration / deceleration, travel route, and inter-vehicle distance. When calculating the travel plan, constraints or weights may be set for the respective parameters of the travel plan parameter 107. Further, the travel plan parameter 107 may include a parameter for setting the number of travel plans of the own 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, based on the travel plan calculated by the travel plan calculation unit 108, controls a control target 123 (for example, an engine, a transmission, a traveling motor, a brake device, a steering device, etc.) for executing the travel plan. Calculate the control command value. The control command value calculated by the automatic driving control unit 109 is input to the controlled object 123, and the controlled object 123 operates according to the control input value, whereby the host vehicle travels according to the travel plan.

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

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

  In step S101, the own vehicle information acquisition unit 101 acquires vehicle information (own vehicle information) of the own vehicle from on-vehicle devices such as an on-vehicle sensor and an on-vehicle ECU that monitor the vehicle state.

  In step S102, the surrounding environment information acquisition unit 102 acquires the surrounding environment information of the vehicle as a part of the external environment 121 from an in-vehicle device such as an in-vehicle sensor or a communication device that senses the surrounding environment of the vehicle.

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

  In step S104, the traveling history acquisition unit 104 uses an in-vehicle communication device capable of communicating with the external infrastructure 122 to travel a single vehicle or a plurality of arbitrary vehicles (past vehicles) that have traveled in the vicinity of the target vehicle in the past. 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 travel history of the past vehicle is stored in advance, the travel history acquisition unit 104 does not use the in-vehicle communication device, The travel history may be acquired from the data storage unit.

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

  In step S105, the target vehicle traveling prediction calculation unit 106 predicts the target vehicle traveling of the target vehicle based on the preset correlation determination parameter 105 in consideration of the correlation between the target vehicle information and the traveling 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 represents 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. Although three speed histories R1, R2, and R3 are shown in FIG. 3, these may be speed histories of three past vehicles, or from the result of statistically processing the speed histories of four or more past vehicles, It is also possible to extract three representative speed histories. In addition, in FIG. 3, point A is the current position of the own vehicle, point B is the current position of the target vehicle, and point C is the 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 the sake of simplicity of description, the speed of the target vehicle between the points A and B included in the target vehicle information matches the speed history R1 and the speed history R2 and the speed history R3. Consider two ways. The speed of the target vehicle and the speed history of the past vehicle do not have to match, and the same can be considered when the two are similar to each other.

  First, when the speed of the target vehicle between the point A and 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. Correlation with. Therefore, the target vehicle traveling prediction calculation unit 106 calculates the target vehicle traveling prediction regarding the point B and thereafter, with emphasis on the speed history R1.

  On the other hand, when the speed of the target vehicle between the point A and 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. , And the correlation with the speed history R1 becomes low. Therefore, the target vehicle traveling prediction calculating unit 106 calculates the target vehicle traveling prediction regarding the point B and subsequent points 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 or weighting, or the number of target vehicle travel predictions to be calculated is set. Parameters may be included.

  When weighting the travel history based on the frequency, for example, if the frequency of the speed history R2 in the past is higher than the frequency of the speed history R3 in the past, the target vehicle travel prediction calculation unit 106 sets the speed history R2 to the speed history R2. The target vehicle traveling prediction is calculated with emphasis. Further, when weighting is set for the travel 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 emphasized. Calculate the vehicle travel prediction. Further, the target vehicle travel prediction calculated by the target vehicle travel prediction calculation unit 106 may be plural, and two of 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. A weight may be set for each target vehicle travel prediction.

  The speed of the target vehicle between point A and point B may be measured using an on-vehicle sensor such as a camera mounted on the vehicle, millimeter wave radar, LiDAR, or ultrasonic sonar, or V2X. Alternatively, it may be acquired using an in-vehicle communication device capable of communication outside the vehicle such as a smartphone.

  In step S106, the travel plan calculation unit 108 considers the preset travel planning parameter 107, and based on the own vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction, the vehicle travels automatically. 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 showing an example of the travel plan. Here, an example in which the travel plan of FIG. 4 is calculated based on the speed history of the past vehicle of FIG. 3 under the condition that the own vehicle uses ACC.

  When the host vehicle recognizes the preceding vehicle (another vehicle ahead) and uses the ACC, the host vehicle maintains the distance between the host vehicle and the preceding vehicle at a specified inter-vehicle distance according to the speed, Drive following 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 after the point B as the speed history R1. , Calculate the travel plan of your vehicle. That is, the travel plan calculation unit 108 sets the speed of the host vehicle after the point B to be approximately the same as the speed history R1 within a range in which the distance between the host vehicle and the preceding vehicle is maintained at the specified inter-vehicle distance. A travel plan for controlling the own vehicle is calculated so that Note that the target vehicle travel prediction need not match the speed history R1 and may have a similar relationship, but in that case, it is necessary to consider the difference between the two when calculating the travel plan.

  When constraints or weights such as speed, acceleration / deceleration, travel route, and inter-vehicle distance are set in the travel plan parameter 107, the travel plan calculation unit 108 calculates a travel plan with emphasis on these. For example, when a constraint is set on the acceleration / deceleration and the rapid acceleration / deceleration of the host vehicle is prohibited, the travel plan calculation unit 108 sets a condition that the distance between the host vehicle and the preceding vehicle is maintained at the specified inter-vehicle distance. A travel plan that satisfies the acceleration / deceleration constraint is calculated by making a correction such as sacrificing the user's comfort in the inter-vehicle distance to a certain extent.

  In addition, in step S105, when the target vehicle travel prediction that matches either the speed history R2 or the speed history R3 illustrated in FIG. 3 is calculated, the travel plan calculation unit 108, similar to the case of the speed history R1 described above, The travel plan of the host vehicle is calculated on the assumption that the target vehicle travels according to the speed history R2 or the speed history R3.

  Further, in step S105, when the target vehicle travel prediction that matches the speed history R2 and the target vehicle travel prediction that matches the speed history R3 in FIG. 3 are calculated, the travel plan calculation unit 108 targets the points B and beyond. The travel plan of the host vehicle is calculated in consideration of both the possibility of the vehicle traveling according to the speed history R2 and the possibility of traveling according to the speed history R3.

  For example, if importance is attached to 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 according to the speed history R2 after the point B, and the travel plan of the host vehicle. To calculate. That is, the travel plan calculation unit 108 calculates a travel plan that controls the host vehicle so that the speed of the host vehicle after the point B is approximately the same as 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 speed of the own vehicle is 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 a range that satisfies the above condition, control is performed while maintaining a certain level of user comfort in speed and inter-vehicle distance.

  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, the host vehicle also needs to decelerate in order to maintain the specified inter-vehicle distance, and the host vehicle travels at a speed like the host vehicle travel S shown in FIG. When the target vehicle traveling prediction is deviated in this way, there is a possibility that the automatic driving control device 100 may be required to drastically decelerate the own vehicle while changing the target vehicle traveling prediction or the traveling plan, which may improve user comfort. There is a risk that the control will be damaged.

  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, the travel plan parameters 107 are set in advance with constraints and weights to be considered, and the travel plan calculation unit 108 calculates a travel plan in consideration of the constraints and weights of the parameters. For example, if the acceleration / deceleration constraint is set in advance and even if the target vehicle travel prediction fails, a travel plan in which the acceleration / deceleration that does not satisfy the constraint does not occur, that is, the vehicle travels within the range that satisfies the acceleration / deceleration constraint. Calculate possible travel plans.

  For example, when the target vehicle travel prediction that matches the speed history R2 and the target vehicle travel prediction that matches the speed history R3 in FIG. 3 are calculated in step S105, the travel plan calculation unit 108 first determines that the target vehicle travels. A travel plan P1 obtained on the assumption that the vehicle travels according to the speed history R2 and a travel plan P2 (same as the own vehicle travel S) obtained on the assumption that the target vehicle travels according to the speed history R3 are calculated. Then, the travel plan calculation unit 108 confirms whether or not the travel plans P2 and P3 satisfy the constraint of the preset acceleration / deceleration, and when either one does not satisfy the constraint, the travel plan shown in FIG. The plan P3 and the travel plan P4 are calculated.

  The travel plan P3 and the travel plan P4 are modifications of the travel plan P1 and the travel plan P2 so that the acceleration / deceleration constraint is satisfied regardless of which of the speed history R2 and the speed history R3 the target vehicle travels. 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, and are compared with the target vehicle. It is obtained by correcting the inter-vehicle distance so that it is larger.

  For example, the travel plan P3 has a large vehicle-to-vehicle distance with the target vehicle until it is determined which of the speed history R2 and the speed history R3 the target vehicle travels (up to near the point C), and the target vehicle has the speed history R2. It is planned to accelerate the host vehicle to return the inter-vehicle distance to the specified inter-vehicle distance when it is decided that the vehicle will travel on the street.

  In addition, the travel plan P4 is set such that the distance between the target vehicle and the target vehicle is set to be larger until it is determined which of the speed history R2 and the speed history R3 the target vehicle travels. In addition, it is planned that the vehicle-to-vehicle distance can be maintained at the prescribed vehicle-to-vehicle distance without suddenly decelerating the own vehicle. Compared to the travel plan P2, the travel plan P4 does not require abrupt deceleration of the host vehicle, and thus the comfort of the user can be prevented from being impaired.

  In this way, the travel plan calculation unit 108 sacrifices the allowable parameters and keeps the unacceptable parameters within the allowable range according to the constraints and weights set in the travel plan parameters 107, thereby driving the plurality of target vehicles. It is possible to calculate a travel plan that can correspond to the prediction.

  Returning to FIG. 2, in step S107, the automatic driving control unit 109 calculates and calculates the 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 controlled object 123. Since the automatic driving control unit 109 needs to uniquely output the command value to the controlled object, at least the travel plan of the point where the host vehicle is currently located (including the point immediately after) is unique in step S106. Must be specified in.

  In step S108, the automatic driving control device 100 determines whether to end the automatic driving control. For example, when an intervention (override) of a driving operation is performed by a user during execution of automatic driving, or when the own vehicle is stopped at a destination and the automatic driving is completed, the automatic driving control device 100 automatically performs the automatic driving. Determine to end control.

  When the automatic driving control device 100 determines to continue the automatic driving control, the process returns to step S101 and the processes of steps S101 to S107 are repeated. When 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 in-vehicle system ends.

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

  In the present embodiment, for simplicity of explanation, it is assumed that the correlation between the target vehicle information and the traveling history of the past vehicle is 1, and the target vehicle traveling prediction calculated by the target vehicle traveling prediction calculating unit 106 is the past vehicle. An example is shown that matches the running history of. However, in reality, since the correlation between the target vehicle information and the traveling history of the past vehicle is likely to be less than 1, the target vehicle traveling prediction calculation unit 106 considers that the correlation is less than 1, and The target vehicle travel prediction is calculated after correcting the travel history of the vehicle based on the target vehicle information.

  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 behavior of the own vehicle is predicted using the prediction result. By improving the prediction accuracy, it is possible to perform automatic driving control in consideration of the traveling states of other vehicles. Therefore, even when another vehicle (target vehicle) is present around the host vehicle, it is possible to prevent the conflict between the driving control of the system and the driving operation of the user.

  5 and 6 are diagrams each showing an example of the hardware configuration of the automatic driving control device 100. Each function of the components of the automatic driving control device 100 shown in FIG. 1 is realized by the processing circuit 50 shown in FIG. 5, for example. That is, the automatic driving control device 100 acquires the own vehicle information which is the vehicle information of the own vehicle, acquires the surrounding environment information of the own vehicle, and uses the vehicle information of the single or multiple target vehicles existing around the own vehicle. Acquires certain target vehicle information, acquires the travel history of a single or multiple past vehicles that have traveled in the vicinity of the position where the target vehicle is traveling, and based on the preset correlation determination parameters, the target vehicle information and In consideration of the correlation with the travel history of the past vehicle, calculate the target vehicle travel prediction is a prediction result of the travel of the target vehicle, considering the preset travel planning parameters, own vehicle information, surrounding environment information, A processing circuit 50 is provided for calculating a travel plan for automatic driving of the host vehicle based on the target vehicle information and target vehicle travel prediction, and for 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 (Central Processing Unit (CPU), processing device, arithmetic device, microprocessor, microcomputer, It may be configured by using a DSP (also referred to as a digital signal processor).

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

  FIG. 6 shows an example of the hardware configuration of the automatic driving control device 100 when the processing circuit 50 is configured using the processor 51 that executes a program. In this case, the functions of the constituent elements of the automatic driving control device 100 are realized by software or the like (software, firmware, or a combination of software and firmware). The software and the like are described as a program 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, the automatic driving control device 100, when executed by the processor 51, acquires the own vehicle information that is the vehicle information of the own vehicle, the process that acquires the surrounding environment information of the own vehicle, and the surroundings of the own vehicle. Processing for obtaining target vehicle information, which is vehicle information of single or multiple target vehicles existing in the vehicle, and processing for obtaining travel history of single or multiple past vehicles that have traveled in the past near the position where the target vehicle is traveling. And a process of calculating a target vehicle traveling prediction that is a prediction result of traveling of the target vehicle, considering the correlation between the target vehicle information and the traveling history of the past vehicle, based on the preset correlation determination parameter, and preset The travel plan for automatic driving of the host vehicle is calculated based on the host vehicle information, the surrounding environment information, the target vehicle information, and the target vehicle travel prediction in consideration of the travel plan parameters that are set. 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 operation of the components of the automatic driving control device 100.

  Here, the memory 52 is a nonvolatile memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (Electrically Erasable Programmable Read Only Memory). It is a volatile semiconductor memory, a hard disk drive (HDD), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc) and its drive device, or any storage medium used in the future. May be.

  Heretofore, a configuration has been described in which the functions of the constituent elements of the automatic driving control device 100 are realized by either hardware or software. However, the configuration is not limited to this, and a configuration in which some of the components of the automatic driving control device 100 are implemented by dedicated hardware and another of the components is implemented by software or the like may be used. For example, for some of the constituent elements, the function is realized by the processing circuit 50 as dedicated hardware, and for some of the other constituent elements, the processing circuit 50 as the processor 51 executes the 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 each function described above by hardware, software, etc., or a combination thereof.

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

  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.

Claims (17)

A vehicle information acquisition unit that acquires vehicle information that is vehicle information of the 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 target vehicle or a plurality of target vehicles existing around the own vehicle;
A travel history acquisition unit that acquires a travel history of a single vehicle or a plurality of past vehicles that have traveled in the vicinity of the position where the target vehicle is traveling,
Based on a preset correlation determination parameter, the target vehicle traveling for calculating the target vehicle traveling prediction that is the prediction result of the traveling of the target vehicle in consideration of the correlation between the target vehicle information and the traveling history of the past vehicle A prediction calculator,
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 preset travel planning parameters. A calculator,
An automatic driving control unit that calculates a control command value to a controlled object based on the travel plan,
Equipped with
The target vehicle is a vehicle ahead of the own vehicle,
The trip plan calculator calculates a travel plan to travel to follow the subject vehicle to the preceding vehicle,
The travel plan calculation unit sets a constraint on each parameter included in the travel plan parameter,
When the target vehicle travel prediction calculation unit calculates a plurality of the target vehicle travel predictions, the travel plan calculation unit determines that the own vehicle is running regardless of which of the plurality of target vehicle travel predictions the preceding vehicle travels. Calculate a travel plan in which the inter-vehicle distance between the host vehicle and the preceding vehicle is set to be larger than usual so that the vehicle can travel within a range that satisfies the constraint.
Automatic operation control device. The travel history acquisition unit acquires the travel history of the past vehicle from an external infrastructure through communication,
The automatic driving 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 surrounding environment information acquisition unit and the target vehicle information acquisition unit use one or both of an autonomous vehicle-mounted sensor mounted on the vehicle and an on-vehicle communication device capable of communication outside the vehicle, and use the surrounding 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 stationary object information and mobile object information,
The automatic driving control device according to any one of claims 1 to 4. The target vehicle information includes any one or more of the speed, acceleration / deceleration, and steering angle of the target vehicle,
The automatic driving control device according to any one of claims 1 to 5. The traveling history acquisition unit acquires the traveling history of the past vehicle using an in-vehicle communication device capable of communication outside the vehicle,
The automatic driving control device according to any one of claims 1 to 6. The travel history of the past vehicle includes any one or more of a travel route history, a speed history, and an acceleration / deceleration history of the past vehicle,
The automatic driving control device according to any one of claims 1 to 7. The traveling history of the past vehicle is information obtained by statistically processing information of each of a plurality of past vehicles or information of a plurality of past vehicles,
The automatic driving control device according to any one of claims 1 to 8. The correlation determination parameter is any one or more of speed, acceleration / deceleration, travel route, and frequency,
The automatic driving control device according to any one of claims 1 to 9. The target vehicle travel prediction calculation unit sets a constraint or a weight on each parameter included in the correlation determination parameter,
The automatic driving 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 traveling predictions to be calculated,
The automatic driving control device according to any one of claims 1 to 11. The said target vehicle driving | running | working prediction calculation part sets weighting with respect to each of the said target vehicle driving | running | working prediction, when several said target vehicle driving | running | working prediction is calculated, The automatic according to any one of claims 1 to 12. Operation control device. The travel planning parameter is any one or more of speed, acceleration / deceleration, travel route, and inter-vehicle distance,
The automatic driving 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 driving control device according to any one of claims 1 to 14. The travel plan calculation unit calculates a travel plan in consideration of a plurality of target vehicle travel predictions,
The automatic driving 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 which is the vehicle information of the own vehicle,
The surrounding environment information acquisition unit of the automatic driving control device acquires the surrounding environment information of the own vehicle,
The target vehicle information acquisition unit of the automatic driving control device acquires target vehicle information which is vehicle information of a single or a plurality of target vehicles existing around the own vehicle,
The travel history acquisition unit of the automatic driving control device acquires a travel history of a single vehicle or a plurality of past vehicles that have traveled in the vicinity of the position where the target vehicle is traveling,
The target vehicle traveling prediction calculation unit of the automatic driving control device, based on the preset correlation determination parameter, considering the correlation between the target vehicle information and the traveling history of the past vehicle, the traveling of the target vehicle Calculate the target vehicle traveling prediction that is the prediction result,
The travel plan calculation unit of the automatic driving control device considers preset travel planning parameters, 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,
The automatic driving control unit of the automatic driving control device calculates a control command value to the control target based on the travel plan,
The target vehicle is a vehicle ahead of the own vehicle,
The trip plan calculator calculates a travel plan to travel to follow the subject vehicle to the preceding vehicle,
The travel plan calculation unit sets a constraint on each parameter included in the travel plan parameter,
When the target vehicle travel prediction calculation unit calculates a plurality of the target vehicle travel predictions, the travel plan calculation unit determines that the host vehicle is running regardless of which of the plurality of target vehicle travel predictions the preceding vehicle travels. Calculate a travel plan in which the inter-vehicle distance between the host vehicle and the preceding vehicle is set to be larger than usual so that the vehicle can travel within a range that satisfies the constraint.
Automatic driving control method.

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