Travel control system, travel control device, travel control method, and travel control program

Abstract

To provide a travel control system and the like capable of realizing a platooning according to an acceleration performance for each vehicle.SOLUTION: A travel control system includes a processor and controls travel of a plurality of autonomous vehicles capable of autonomous travel. The processor is configured to perform monitoring for an establishment of a platoon condition in which the plurality of autonomous vehicles form a platoon. The processor is configured to, in a state in which the platoon condition is satisfied, acquire a scene-specific acceleration performance index in which a basic acceleration performance index correlated with a vehicle configuration of each autonomous vehicle is corrected in a time series manner. The scene-specific acceleration performance index is an index that matches a travel environment for each travel scene with a longitudinal correlation between the autonomous vehicles forming the platoon. The processor is configured to perform acquisition of the scene-specific acceleration performance index. The processor is configured to perform output of control parameters of the respective autonomous vehicles correlated with the scene-specific acceleration performance index.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a driving control technology for controlling the driving of multiple autonomous vehicles. [Background technology]

[0002] Patent Document 1 discloses a vehicle control system that controls multiple autonomous vehicles to travel in a convoy. This vehicle control system executes convoy driving control that controls the multiple vehicles to travel at the same target acceleration. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-2911 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the acceleration performance of vehicles may differ among the vehicles in the platoon. Meanwhile, the technology disclosed in Patent Document 1 does not take into account the acceleration performance of each vehicle, but simply sets the same target acceleration for all vehicles. In this case, it may be difficult to adjust the inter-vehicle distance while taking into account the variations in actual acceleration due to differences in the acceleration performance of each vehicle. Therefore, the technology disclosed in Patent Document 1 may make it difficult to realize platooning that is suited to the acceleration performance of each vehicle.

[0005] An object of the present disclosure is to provide a driving control system capable of realizing platooning according to the acceleration performance of each vehicle. Another object of the present disclosure is to provide a driving control device capable of realizing platooning according to the acceleration performance of each vehicle. Yet another object of the present disclosure is to provide a driving control method capable of realizing platooning according to the acceleration performance of each vehicle. Yet another object of the present disclosure is to provide a driving control program capable of realizing platooning according to the acceleration performance of each vehicle. [Means for solving the problem]

[0006] The technical means of the present disclosure for solving the problems will be described below. Note that the claims and the reference symbols in parentheses in this section indicate the correspondence with the specific means described in the embodiments described later in detail, and do not limit the technical scope of the present disclosure.

[0007] A first aspect of the present disclosure is a driving control system having a processor (12) and controlling driving of a plurality of autonomous vehicles (2) capable of autonomous driving, The processor Monitoring whether a platooning condition is met for forming a platoon of a plurality of autonomous vehicles; When the platooning conditions are met, a basic acceleration performance index correlated with the vehicle configuration of each autonomous vehicle is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles in the platoon; and outputting a control parameter for each autonomous vehicle correlated with the scene-specific acceleration performance index; is configured to execute

[0008] A second aspect of the present disclosure is a driving control device having a processor (12), configured to be mountable on an autonomous vehicle (2) capable of autonomous driving, and controlling driving of a plurality of autonomous vehicles, The processor Monitoring whether a platooning condition is met for forming a platoon of a plurality of autonomous vehicles; When the platooning conditions are met, a basic acceleration performance index correlated with the vehicle configuration of each autonomous vehicle is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles in the platoon; and outputting a control parameter for each autonomous vehicle correlated with the scene-specific acceleration performance index; is configured to execute

[0009] A third aspect of the present disclosure is a driving control method executed by a processor (12) to control driving of a plurality of autonomous vehicles (2) capable of autonomous driving, the method comprising: Monitoring whether a platooning condition is met for forming a platoon of a plurality of autonomous vehicles; When the platooning conditions are met, a basic acceleration performance index correlated with the vehicle configuration of each autonomous vehicle is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles in the platoon; and outputting a control parameter for each autonomous vehicle correlated with the scene-specific acceleration performance index; Includes.

[0010] A fourth aspect of the present disclosure is a travel control program stored in a storage medium (11) for controlling travel of a plurality of autonomous vehicles (2) capable of autonomous travel, the program including instructions to be executed by a processor (12), The command is, Monitoring whether a platooning condition for forming a platoon by a plurality of autonomous vehicles is met; When the platooning conditions are met, a basic acceleration performance index correlated with the vehicle configuration of each autonomous vehicle is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles in the platoon; and outputting a control parameter for each autonomous vehicle correlated with the scene-specific acceleration performance index; Includes.

[0011] According to these first to fourth aspects, when the platooning conditions are met, a scene-specific acceleration performance index is obtained by time-series correcting the basic acceleration performance index correlated with the vehicle configuration of each vehicle, and control parameters for each vehicle correlated with the scene-specific acceleration performance index are output. Therefore, for each vehicle in the platoon, control correlated with acceleration performance matching the driving environment for each driving scene and the front-to-rear correlation between the vehicles in the platoon may be possible. Therefore, it may be possible to realize platooning that is suited to the acceleration performance of each vehicle. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing the overall configuration of a first embodiment. [Figure 2] 1 is a block diagram showing a configuration of a vehicle to which a first embodiment is applied; [Figure 3] FIG. 2 is a block diagram showing the functional configuration of the management system according to the first embodiment. [Figure 4] 10 is a flowchart showing a part of a management flow according to the first embodiment. [Figure 5] 10 is a flowchart showing a part of a management flow according to the first embodiment. [Figure 6] FIG. 2 is a schematic diagram showing an example of an inter-vehicle distance in a platoon. [Figure 7] FIG. 2 is a schematic diagram showing an example of an inter-vehicle distance in a platoon. [Figure 8] FIG. 2 is a schematic diagram showing an example of an inter-vehicle distance in a platoon. [Figure 9] FIG. 10 is a schematic diagram illustrating an example of a time-series scene-specific acceleration performance index. [Figure 10] FIG. 10 is a schematic diagram illustrating an example of a time-series scene-specific acceleration performance index. [Figure 11] FIG. 10 is a schematic diagram illustrating an example of a time-series scene-specific acceleration performance index. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, several embodiments of the present disclosure will be described with reference to the drawings.

[0014] (First embodiment) The management system 10 of the first embodiment shown in FIG. 1 is constructed in a management center 1, which is an external facility for an autonomously driven vehicle 2. The management center 1 is a facility that manages a plurality of vehicles 2. For example, the management center 1 is a center that manages vehicles 2 that travel in a specific management area that is assigned to the vehicle 2. The management center 1 may be a traffic control center that is operated by a road management company, the police, or the like, and that adjusts the traffic flow in the management area. Alternatively, the management center 1 may be a center in a facility separate from the traffic control center that manages the vehicle 2 in cooperation with the traffic control center. Alternatively, the management center 1 may be an integrated management center that manages a plurality of vehicles 2 in an integrated manner based on information from a plurality of centers.

[0015] The vehicle 2 managed by the management system 10 is an autonomous vehicle, such as an automobile, capable of traveling on a roadway with an occupant on board. The vehicle 2 is provided with an autonomous driving mode, which is classified into levels according to the degree of manual intervention by the occupant in the dynamic driving task (DDT). The autonomous driving mode may be realized by autonomous driving control, such as conditional driving automation, high driving automation, or full driving automation, in which a system performs all dynamic driving tasks when activated. The autonomous driving mode may also be realized by advanced driving assistance control, such as driving assistance or partial driving automation, in which the occupant performs some dynamic driving tasks. The autonomous driving mode may be realized by either the autonomous driving control or the advanced driving assistance control, or by a combination or switching between them. The vehicle 2 may also be referred to as an autonomous device (autonomous robot), an autonomous vehicle, or the like.

[0016] As shown in FIG. 2, the vehicle 2 includes a communication system 21, a sensor system 22, a map database 25, an information presentation system 26, and an on-board control unit 27.

[0017] The communication system 21 acquires communication information available to the on-board control unit 27 via wireless communication. The communication system 21 may be a V2X type that transmits and receives communication signals to and from a V2X system existing outside the vehicle 2. The V2X type communication system 21 is, for example, at least one of a DSRC (Dedicated Short Range Communications) communication device and a cellular V2X (C-V2X) communication device. The V2X type communication system 21 enables the vehicle 2 to communicate wirelessly with the management system 10.

[0018] The sensor system 22 acquires sensor information, which can be used by the on-board control unit 27, about the external and internal worlds of the vehicle 2. To this end, the sensor system 22 includes an external sensor 23 and an internal sensor 24.

[0019] The external sensor 23 acquires external information as sensor information from the external environment that is the surrounding environment of the vehicle 2. The external sensor 23 is a target detection type that detects targets that exist in the external world of the vehicle 2. The target detection type external sensor 23 includes, for example, a camera that captures images of the external world. The camera outputs the captured images to the information processing unit 19 at each capture cycle. The external sensor 23 may include a LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging) that can detect reflected light of irradiated laser light as point cloud data. Note that the external sensor 23 may include at least one of radar, sonar, etc.

[0020] Alternatively, the external sensor 23 may be a positioning type that receives positioning signals from artificial satellites of the Global Navigation Satellite System (GNSS) that exist in the external world of the vehicle 2. The positioning type external sensor 23 is, for example, a GNSS receiver.

[0021] The internal sensor 24 acquires internal information as sensor information from the internal world, which is the internal environment of the vehicle 2. The internal sensor 24 may be a vehicle state detection type that detects the state of the vehicle 2. The vehicle state detection type internal sensor 24 is at least one of, for example, a driving speed sensor, an acceleration sensor, a gyro sensor, an actuator sensor, a remaining amount sensor, a weight sensor, etc. The actuator sensor detects at least one of, for example, the operation state of an accelerator pedal, the operation state of a brake pedal, the steering state of a steering wheel, the on / off state of a starter switch, and the shift state of a shift lever, as the control state of a driving actuator of the vehicle 2. The remaining amount sensor detects the remaining amount of an energy source that drives the vehicle 2, such as fuel or a battery. The weight sensor detects the weight of luggage loaded on the vehicle 2.

[0022] The internal sensor 24 may be an occupant detection type that detects a specific state of an occupant inside the vehicle 2. The occupant detection type internal sensor 24 is, for example, at least one of a Driver Status Monitor (registered trademark), a biometric sensor, a seating sensor, an actuator sensor, and an in-vehicle equipment sensor. Here, the driver status monitor detects at least one of the following as the state of the driver who manually operates the vehicle 2: eye state including saccades, facial direction, and posture. The biometric sensor detects at least one of the following as vital signs of the occupant: pulse (i.e., heart rate), electrocardiogram, respiration, body temperature, and fingerprints. The seating sensor detects the seating state of the occupant in the vehicle seat. The in-vehicle equipment sensor detects at least one of the following as instructions given by the occupant to in-vehicle equipment: operation state of an on / off switch, operation state of a touch panel, and gestures that can be recognized without contact.

[0023] The map database 25 stores map information that can be used by the on-board control unit 27. The map database 25 includes at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium. The map database 25 may be a database of a locator that estimates the vehicle's own state quantities, including the vehicle's own position. The map database 25 may be a database of a navigation unit that navigates the vehicle's travel route. The map database 25 may be configured by combining multiple types of these databases.

[0024] The map database 25 acquires and stores the latest map information, for example, by communicating with an external center via a V2X type communication system 21. Here, the map information is information representing the driving environment of the vehicle 2, and is converted into two-dimensional or three-dimensional data. In particular, digital data of a high-precision map is preferably used as the three-dimensional map data. The map information may include road information representing at least one of the following: the position, shape, and road surface condition of the road itself. The map information may also include marking information representing at least one of the following: the position and shape of signs and lane markings attached to the road. The map information may also include structure information representing at least one of the following: the position and shape of buildings and traffic lights facing the road.

[0025] The information presentation system 26 presents alarm information to the occupants of the vehicle 2. The information presentation system 26 may be a visual stimulation type that stimulates the occupants' vision through a display. The visual stimulation type information presentation system 26 is, for example, at least one of a head-up display (HUD), a multi-function display (MFD), a combination meter, a navigation unit, etc. The information presentation system 26 may be an auditory stimulation type that stimulates the occupants' hearing through sound. The auditory stimulation type information presentation system 26 is, for example, at least one of a speaker, a buzzer, a vibration unit, etc.

[0026] The vehicle control unit 27 is connected to the communication system 21, the sensor system 22, the map database 25, and the information presentation system 26 via at least one of, for example, a LAN line, a wire harness, an internal bus, or a wireless communication line. The vehicle control unit 27 is configured to include at least one dedicated computer.

[0027] The dedicated computer constituting the in-vehicle control unit 27 may be an integrated ECU (Electronic Control Unit) that integrates the driving control of the vehicle 2. The dedicated computer constituting the in-vehicle control unit 27 may be a determination ECU that determines a driving task in the driving control of the vehicle 2. The dedicated computer constituting the in-vehicle control unit 27 may be a monitoring ECU that monitors the driving control of the vehicle 2. The dedicated computer constituting the in-vehicle control unit 27 may be an evaluation ECU that evaluates the driving control of the vehicle 2.

[0028] The dedicated computer constituting the on-vehicle control unit 27 may be a navigation ECU that navigates the driving route of the vehicle 2. The dedicated computer constituting the on-vehicle control unit 27 may be a locator ECU that estimates the self-state quantity of the vehicle 2. The dedicated computer constituting the on-vehicle control unit 27 may be an actuator ECU that controls the driving actuator of the vehicle 2. The dedicated computer constituting the on-vehicle control unit 27 may be an HCU (Human Machine Interface Control Unit (HMI)) that controls the presentation of information in the vehicle 2.

[0029] The dedicated computer constituting the on-board control unit 27 has at least one memory 28 and one processor 29. The memory 28 is at least one type of non-transient tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs, data, and the like. Here, "storage" may refer to accumulation in which data is retained even when the vehicle 2 is turned off, or may refer to temporary storage in which data is erased when the vehicle 2 is turned off. The processor 29 includes, as a core, at least one type of processor selected from the group consisting of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a CISC (Complex Instruction Set Computer)-CPU, a DFP (Data Flow Processor), and a GSP (Graph Streaming Processor).

[0030] Furthermore, in the on-vehicle control unit 27, the processor 29 executes a plurality of instructions included in a control program stored in the memory 28 in order to perform driving control of the vehicle 2. In this way, the on-vehicle control unit 27 constructs a plurality of function blocks for driving control of the vehicle 2. The plurality of function blocks constructed in the on-vehicle control unit 27 include a communication control block 270, a recognition block 271, a planning block 272, and a driving control block 273, as shown in FIG.

[0031] The communication control block 270 receives driving instructions for the vehicle 2 from the vehicle management center 3 via the communication system 21. The driving instructions are, for example, a medium- to long-term driving plan such as a driving route. The communication control block 270 may also obtain driving instructions from other vehicles 2. For example, the communication control block 270 may obtain driving instructions from a representative vehicle in group control. In this case, the driving instructions may include speed adjustment, steering, braking, etc. The communication control block 270 also transmits driving information of the vehicle 2 to the management system 10 via the communication system 21. In this embodiment, the communication control block 270 transmits the driving information to the vehicle management center 3. For example, the driving information includes at least one of the following: the position of the vehicle 2, the driving route, the destination, intermediate points, the estimated arrival time at each point on the driving route, the speed, the accelerator opening, the brake opening, the steering angle, diagnostic information, and the remaining amount of the energy source.

[0032] The recognition block 271 acquires sensor information from the external sensors 23 and internal sensors 24 of the sensor system 22. The recognition block 271 acquires communication information from the communication system 21. The recognition block 271 acquires map information from the map database 25. The recognition block 271 fuses this acquired information as input to determine the internal and external environments of the vehicle 2 and recognize the driving scene.

[0033] Specifically, the recognition block 271 recognizes objects including other road users, obstacles, and structures in the external world of the vehicle 2. At this time, the recognition block 271 may further recognize at least one of, for example, distance, relative speed, relative acceleration, and attributes of the recognized objects. The recognition block 271 recognizes the current and future travel paths of the vehicle 2. At this time, the recognition block 271 may further recognize at least one of, for example, road surface, lane, road edge, free space, traffic light, and markings of the recognized travel path.

[0034] The recognition block 271 estimates, by localization, the self-state quantities including the self-position of the vehicle 2. The recognition block 271 recognizes the time of each traveling scene of the vehicle 2. At this time, the recognition block 271 may output time information relating to the recognized time in association with other recognition results.

[0035] The planning block 272 acquires recognition information representing the recognition results from the recognition block 271. The planning block 272 acquires control parameters for the vehicle 2 from the communication control block 270. The planning block 272 determines and chronologically plans the vehicle 2's future route, future trajectory, dynamic driving tasks for future actions, and future transitions of interactions with other road users for each driving scene based on the acquired recognition information and control parameters. In this case, the planning block 272 may perform the determination and planning using a vehicle driving model, such as a simulation model or a machine learning model. Here, the DDT, which is one of the planning targets, may be defined as real-time operational and tactical functions for operating the vehicle 2 in traffic. Alternatively, the DDT may be defined as all real-time operational and tactical functions required to operate the vehicle 2 in road traffic.

[0036] The planning block 272, which has acquired the control parameters, determines whether or not the vehicle 2 can transition to the DDT that represents the control parameters. If the result is a positive determination of the transition, the planning block 272 executes the plan to realize the transition to the DDT that the driving instruction represents. On the other hand, if the result is a negative determination of the transition, the planning block 272 executes the plan to realize the transition to the DDT that is determined by its own judgment. The planning block 272 may provide the communication control block 270 with information regarding whether or not the transition to the DDT that represents the control parameters can be made.

[0037] The cruise control block 273 obtains a control command for the vehicle 2 from the planning block 272. The cruise control block 273 controls the driving behavior of the vehicle 2 in accordance with the control command from the planning block 272.

[0038] Next, we will explain the details of the management system 10 that manages the above-mentioned vehicles 2. The management system 10 is connected to a communication system 13, a road database 14, a vehicle database 15, a traffic light database 16, and an inter-vehicle distance database 17 via at least one of, for example, a LAN line, a wire harness, an internal bus, or a wireless communication line. The communication system 13 acquires communication information transmitted from the vehicles 2 via wireless communication.

[0039] The road database 14, vehicle database 15, and traffic light database 16 are configured to include at least one type of non-transient physical storage medium, such as a semiconductor memory, a magnetic medium, an optical medium, etc. The road database 14 stores gradient information for each predetermined section of each travel lane of a road, for example.

[0040] The vehicle database 15 stores vehicle information about each vehicle 2 to be managed. The vehicle information includes, for example, at least one of the following: the vehicle's identification ID, vehicle model, basic weight when unladen, estimated weight when laden, basic acceleration performance index, distance to the vehicle ahead, and current position. The vehicle database 15 acquires and stores the latest vehicle information by, for example, communicating with the vehicle 2 via the communication system 13.

[0041] The traffic light database 16 stores traffic light information about traffic lights installed on roads. The traffic light information includes, for example, the identification ID of the intersection where the traffic light is installed, and the latest scheduled start and end times for each traffic light color.

[0042] The inter-vehicle distance database 17 stores set distance ranges for the inter-vehicle distance from the preceding vehicle, which are set for each of a plurality of vehicle types. Specifically, the inter-vehicle distance database 17 stores set distance ranges for each predetermined speed for each vehicle type. The set distance range is a distance range that exceeds or is equal to or greater than a lower threshold and is less than or equal to an upper threshold. The lower threshold of the set distance range is, for example, the lower limit of the allowable inter-vehicle distance. The upper threshold of the set distance range is, for example, the lower limit of the inter-vehicle distance at which acceleration is allowed to further reduce the inter-vehicle distance.

[0043] The management system 10 is configured to include at least one dedicated computer. The dedicated computer that constitutes the management system 10 may be a monitoring server that monitors the autonomous driving of the vehicles 2. The dedicated computer that constitutes the management system 10 may be a management server that comprehensively manages the operation of multiple vehicles 2. The dedicated computer that constitutes the management system 10 may be configured from multiple servers, and its functions may be distributed.

[0044] The dedicated computer constituting the management system 10 has at least one memory 11 and one processor 12. The memory 11 is at least one type of non-transient tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs and data. Here, "storage" may refer to accumulation in which data is retained even when the management system 10 is turned off, or may refer to temporary storage in which data is erased when the management system 10 is turned off. The processor 12 includes at least one type of core, such as a CPU, a GPU, a RISC-CPU, a CISC-CPU, a DFP, or a GSP.

[0045] In the management system 10, the processor 12 executes a plurality of instructions included in a remote assistance program stored in the memory 11 to remotely assist the driving of the vehicle 2. In this way, the management system 10 constructs a plurality of function blocks for remotely assisting the driving of the vehicle 2. The plurality of function blocks constructed in the management system 10 include a monitoring block 110, an index acquisition block 120, and an output block 130, as shown in FIG. 3 .

[0046] The management method for managing the vehicle 2 is executed by the cooperation of blocks 110, 120, and 130 according to the multiple management flows shown in Figures 4 and 5. Each of these management flows is repeatedly executed while the management system 10 is running. Note that each "S" in the management flow represents a multiple step executed by a multiple command included in the remote assistance program.

[0047] FIG. 4 is a flowchart for monitoring the current estimated weight of a managed vehicle 2 according to its load. In FIG. 4, first, in S1, the monitoring block 110 monitors the departure of the vehicle 2. Next, in S2, the monitoring block 110 determines whether a load change condition is met. The load change condition is, for example, that the vehicle is parked for a long period of time that falls on the long-term side of the allowable time range between the previous departure and the current departure. Note that the load change condition may also be that an increase in load is detected by a weight sensor or the like mounted on the vehicle 2.

[0048] If it is determined that the load change condition is met, the flow proceeds to S3, where the monitoring block 110 clears the history of estimated weights in the vehicle database 15. If a negative determination is made in S2, or if the processing of S3 is executed, the flow proceeds to S4. In S4, the monitoring block 110 executes a weight estimation process for the vehicle 2 in the current departure scene. The weight can be estimated, for example, from the relationship between the road gradient in the departure scene, and the torque and actual acceleration of the vehicle 2.

[0049] Thereafter, in S5, the monitoring block 110 updates the estimated weight of the current vehicle 2 in the vehicle database 15. For example, the monitoring block 110 sets the estimated weight to the average value of the current weight estimated in the current departure scene and the past weight in the past history.

[0050] The estimated weight updated by the above process is used when acquiring an environment-specific acceleration performance index of the vehicle 2, which will be described later.

[0051] Next, Fig. 5 is a flowchart for actually executing platooning. First, in S10 of Fig. 5, the monitoring block 110 determines whether or not the platooning conditions for forming a platoon of multiple vehicles 2 are met. The platooning conditions are determined based on at least one of the positions, speeds, and accelerations of the multiple vehicles 2. For example, the platooning conditions are met for multiple vehicles 2 that are lined up along a common driving lane and are not in a steady driving state. Here, "not in a steady driving state" means that the vehicles are stopped or in an unsteady driving state.

[0052] Such a formation condition is met, for example, for multiple vehicles 2 waiting at a traffic light at an intersection, etc. Also, such a formation condition is met for multiple vehicles 2 that have temporarily stopped due to the detection of an obstacle that obstructs travel, such as an object fallen on the road, on their path. The formation condition is resolved when the multiple vehicles 2 transition to a steady travel state.

[0053] Next, in S20, the index acquisition block 120 acquires scene-specific acceleration performance indexes in chronological order when the platooning conditions are met. The scene-specific acceleration performance index is an index correlated with the acceleration performance of the vehicle 2 according to the driving scene. The acceleration performance is, for example, the upper limit acceleration that can be output when accelerating from a stopped state or a steady driving state. The index acquisition block 120 acquires the scene-specific acceleration performance index by correcting the basic acceleration performance index, which is an acceleration performance index correlated with the vehicle configuration, to match the driving environment for each driving scene and the front-rear correlation between the vehicles 2. Here, the vehicle configuration with which the basic acceleration performance index correlates is at least one type, for example, a driving drive source such as an engine and a motor, and a vehicle body. In the following, it is assumed that the higher the acceleration performance index of the vehicle 2, the greater the acceleration that it can output.

[0054] In more detail, the index acquisition block 120 first acquires a basic acceleration performance index from information such as the vehicle database 15. The basic acceleration performance index is defined as an index that correlates with at least one of the performance of the driving source and the vehicle weight, for example. The basic acceleration performance index is an acceleration performance index when the vehicle is traveling on a level road surface without any cargo. The index acquisition block 120 acquires the basic acceleration performance index of the corresponding vehicle 2, for example, by reading it from the vehicle database 15. Alternatively, the index acquisition block 120 may acquire the basic acceleration performance index by estimating it from the vehicle type and vehicle weight read from the vehicle database 15.

[0055] The index acquisition block 120 first corrects the basic acceleration performance index by matching it with the driving environment, thereby acquiring an environment-specific acceleration performance index. Here, the driving environment is an environmental element that affects the acceleration performance of the vehicle 2 of interest when the vehicle 2 is driving alone. In other words, the driving environment is an environmental element that occurs when there are no other vehicles 2 around. The driving environment is, for example, at least one of the following: the payload (estimated weight) of the vehicle 2, the current and future road gradients, and the maximum target acceleration of the driving lane.

[0056] For example, the index acquisition block 120 sets the environment-specific acceleration performance index lower as the estimated weight of the vehicle 2 increases. Furthermore, the index acquisition block 120 sets the environment-specific acceleration performance index lower as the gradient of the current or future uphill road increases. Here, a future uphill road is an uphill road within a predetermined distance range from the current position of the vehicle 2, or an uphill road that is predicted to be reached within a predetermined driving time. Note that the index acquisition block 120 may set the environment-specific acceleration performance index higher as the gradient of the current or future downhill slope increases. Furthermore, the index acquisition block 120 sets the environment-specific acceleration performance index higher as the maximum target acceleration of the driving lane increases.

[0057] Here, the maximum target acceleration of a driving lane is the upper limit of acceleration allowed in the driving lane, for example, in correlation with the driving direction specified for each driving lane. For example, in an area where driving on the left side of the road is legal, a road exists that includes a pedestrian-vehicle separated left-turn lane, a straight lane, and a right-turn lane as driving lanes. In this case, the maximum target acceleration is set highest for the straight lane, next for the right-turn lane, and lowest for the left-turn lane.

[0058] The index acquisition block 120 then acquires a scene-specific acceleration performance index by further correcting the environment-specific acceleration performance index by matching it with the front-rear correlation between the vehicles 2 in the platoon. Specifically, when focusing on a specific front-rear pair of vehicles 2 in the platoon, the index acquisition block 120 acquires a scene-specific acceleration performance index that matches the inter-vehicle distance between the pair.

[0059] Specifically, when the inter-vehicle distance is within a set distance range, the index acquisition block 120 acquires the scene-specific acceleration performance index of the rear vehicle by correcting it to be common with the scene-specific acceleration performance index of the leading vehicle. Therefore, even if the environment-specific acceleration performance index of the rear vehicle is higher than the environment-specific acceleration performance index of the leading vehicle, the scene-specific acceleration performance index of the rear vehicle is common to the scene-specific acceleration performance index of the leading vehicle. Note that the index acquisition block 120 may limit the scene-specific acceleration performance index of the rear vehicle so that it does not become larger than the environment-specific acceleration performance index of the rear vehicle as a result of the commonalization. For example, when the inter-vehicle distance from the leading vehicle is within a set distance range and the scene-specific acceleration performance index of the leading vehicle is higher than the environment-specific acceleration performance index of the rear vehicle, the index acquisition block 120 may adopt the environment-specific acceleration performance index of the rear vehicle as the scene-specific acceleration performance index. In other words, when the inter-vehicle distance to the vehicle ahead is within a set distance range, the index acquisition block 120 may adopt the lower of the scene-specific acceleration performance index of the vehicle ahead and the environment-specific acceleration performance index of the vehicle behind as the scene-specific acceleration performance index of the vehicle ahead.

[0060] Here, the set distance range is a range of distances that are less than or equal to the upper threshold value and greater than or equal to the lower threshold value. The set distance range may be, for example, a fixed range. In this case, as shown in FIG. 6, platooning is performed such that a substantially constant inter-vehicle distance is maintained regardless of time. Alternatively, the set distance range may be a range that correlates with the traveling speed. More specifically, the set distance range may be a range that correlates with the free running distance and braking distance, which change depending on the traveling speed. In this case, the higher the traveling speed, the smaller the set distance range becomes. In other words, the higher the traveling speed, the shorter the allowable inter-vehicle distance.

[0061] The set distance range for the inter-vehicle distance in platooning may vary depending on the traveling position. For example, the set distance range may be set to a relatively small value near an intersection, and may be set to a larger value as time passes as the vehicle moves away from the intersection. That is, as shown in FIG. 7, a set distance range may be set such that the inter-vehicle distance increases as the vehicle moves away from the intersection. Alternatively, the set distance range may be set to a smaller value as time passes, such as when approaching an intersection. That is, as shown in FIG. 8, a set distance range may be set such that the inter-vehicle distance decreases as time passes.

[0062] Furthermore, when the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle and the inter-vehicle distance is outside the set distance range, the index acquisition block 120 corrects the environment-specific acceleration performance index of the rear vehicle to be larger than the scene-specific acceleration performance index of the front vehicle. As a result, the index acquisition block 120 acquires the scene-specific acceleration performance index of the rear vehicle. That is, when the inter-vehicle distance is relatively large, the index acquisition block 120 increases the scene-specific acceleration performance index of the rear vehicle. For example, the index acquisition block 120 multiplies the scene-specific acceleration performance index of the front vehicle by a predetermined factor of 1 or more, or adds a predetermined additive value to the scene-specific acceleration performance index of the rear vehicle. The index acquisition block 120 may impose a restriction on the scene-specific acceleration performance index of the rear vehicle so that the corrected value does not exceed the environment-specific acceleration performance index of the rear vehicle. Alternatively, the index acquisition block 120 may remove the restriction that prevents the scene-specific acceleration performance index of the rear vehicle from being larger than the environment-specific acceleration performance index of the rear vehicle when the inter-vehicle distance is outside the set distance range.

[0063] Furthermore, when the inter-vehicle distance is outside the set distance range, the index acquisition block 120 acquires the scene-specific acceleration performance index of the rear vehicle by correcting it to be smaller than the scene-specific acceleration performance index of the leading vehicle. That is, when the inter-vehicle distance is relatively small, the index acquisition block 120 relatively reduces the scene-specific acceleration performance index of the rear vehicle. For example, the index acquisition block 120 multiplies the scene-specific acceleration performance index of the leading vehicle by a predetermined factor less than 1, or subtracts a predetermined subtraction value from it, to obtain the scene-specific acceleration performance index of the rear vehicle. Note that the index acquisition block 120 may limit the scene-specific acceleration performance index of the rear vehicle so that it does not become larger than the environment-specific acceleration performance index of the rear vehicle.

[0064] The index acquisition block 120 acquires the above-described scene-specific acceleration performance indexes in order, starting with the front vehicle 2 in the platoon. Furthermore, the index acquisition block 120 acquires the scene-specific acceleration performance indexes for each vehicle 2 in the platoon in chronological order. For example, the index acquisition block 120 first estimates the position of each vehicle 2 at a future time relative to a specific time, based on the scene-specific acceleration performance index and traveling speed at that time. Then, based on the estimated position, the index acquisition block 120 acquires a scene-specific acceleration performance index for each vehicle 2 that matches the traveling environment and front-rear correlation at the future time.

[0065] The index acquisition block 120 further repeats the above process with the future time set as the specific time, thereby acquiring, for each vehicle 2, a time-series scene-specific acceleration performance index up to the upper limit time, in which the basic acceleration performance index is corrected in time series. The upper limit time may be a time up to a predetermined time in the future. Alternatively, the upper limit time may be a time up to a time in the future when all vehicles 2 making up the platoon are estimated to be traveling at a steady state.

[0066] When a splitting condition for splitting the platoon is met, the index acquisition block 120 acquires a scene-specific acceleration performance index for the leading vehicle in the rear platoon after splitting, the scene-specific acceleration performance index being corrected by matching with the splitting factor. The splitting condition may be, for example, that a traffic light is expected to turn red while the platoon is passing through an intersection. In this case, the index acquisition block 120 acquires a scene-specific acceleration performance index for the leading vehicle in the rear platoon, i.e., a negative acceleration performance index, that is correlated with the red light, which is the splitting factor, and that is used to decelerate and stop vehicle 2.

[0067] After the above processing of S20, in S30, the output block 130 outputs a control parameter correlated with the scene-specific acceleration performance index to each vehicle 2 in the platoon. The output block 130 outputs at least the target acceleration as the control parameter. For example, the output block 130 may output the scene-specific acceleration performance index itself as the target acceleration. Alternatively, the output block 130 may output a parameter that is weighted by a predetermined amount with respect to the scene-specific acceleration performance index as the target acceleration.

[0068] The output block 130 may output the time-series control parameters all at once. Alternatively, the output block 130 may output the time-series control parameters multiple times. The output block 130 may output the corresponding control parameters to each vehicle 2. Alternatively, the output block 130 may output the control parameters of each vehicle 2 collectively to a specific vehicle 2 in the platoon (for example, the lead vehicle). In this case, the specific vehicle 2 that has acquired the control parameters distributes the corresponding control parameters to the other vehicles 2 in the platoon.

[0069] Next, FIGS. 9 to 11 will be referred to for a specific example of controlling each vehicle 2 in the platoon by the above processing. In this example, the vehicles are traveling on a road that has an uphill section. In each example, the platoon conditions are met for five vehicles 2. That is, five vehicles 2 form a platoon. In the following, the vehicles 2 constituting the platoon may be distinguished by assigning the symbols 2_1, 2_2, 2_3, 2_4, and 2_5 starting from the front. In this example, vehicles 2_1, 2_2, 2_3, and 2_5 are general vehicles, and vehicle 2_4 is a large vehicle, i.e., heavier than a general vehicle. Furthermore, in this example, the vehicles are controlled to maintain equal inter-vehicle distances.

[0070] In this case, the basic acceleration performance index of vehicles 2_1, 2_2, 2_3, and 2_5 is 4 m / s 2 The basic acceleration performance index of vehicle 2_4 is 3m / s, which is lower than the others. 2 The index acquisition block 120 first acquires environment-specific acceleration performance indexes from these basic acceleration performance indexes.

[0071] In the example shown in FIG. 9, which will be described first, it is assumed that the inter-vehicle distance between the vehicles 2 is always maintained within a set distance range while the vehicles are traveling in a convoy.

[0072] 9, it is assumed that at time T=0, the vehicles 2_1 and 2_2 are traveling on a flat road after an upslope, the vehicle 2_3 is traveling on an upslope road, and the vehicles 2_3 and 2_4 are traveling on a flat road before an upslope. In this case, there are no driving environment factors that would reduce the acceleration performance indexes of the vehicles 2_1 and 2_2. Therefore, the index acquisition block 120 acquires the basic acceleration performance indexes as the environment-specific acceleration performance indexes for the vehicles 2_1 and 2_2.

[0073] For the vehicle 2_3, the current uphill road is present as a driving environment element that reduces the acceleration performance index. Therefore, the index acquisition block 120 obtains an environment-specific acceleration performance index of 3 m / s 2 is obtained as an acceleration performance index for each environment.

[0074] Further, for the vehicles 2_4 and 2_5, there is a future uphill slope road as a driving environment element that lowers the acceleration performance index. Therefore, the index acquisition block 120 obtains an environment-specific acceleration performance index of 2 m / s 2 ,3m / s2 is obtained as the acceleration performance index for each environment.

[0075] The index acquisition block 120 acquires the scene-specific acceleration performance index of each vehicle 2 at time T=0 by matching and correcting these environment-specific acceleration performance indexes to the front-rear correlation. Here, for vehicle 2_1, since it is the leading vehicle, the environment-specific acceleration performance index is acquired as it is as the scene-specific acceleration performance index. Also, for vehicle 2_2, the index acquisition block 120 acquires the environment-specific acceleration performance index of vehicle 2_2 as it is as the scene-specific acceleration performance index common to vehicle 2_1.

[0076] The environment-specific acceleration performance index of the vehicle 2_3 is lower than the environment-specific acceleration performance index of the vehicle 2_2, which is the preceding vehicle. In this case, the index acquisition block 120 acquires the scene-specific acceleration performance index of the preceding vehicle, 4 m / s 2 and 3m / s, which is the environmental acceleration performance index of vehicle 2_3 itself. 2 The latter, which is lower, is taken as the scene-specific acceleration performance index.

[0077] The vehicle 2_4 has an environment-specific acceleration performance index lower than the environment-specific acceleration performance index of the vehicle 2_3, which is the preceding vehicle. In this case, the index acquisition block 120 acquires the 3 m / s 2 and 2m / s, which is the environmental acceleration performance index of vehicle 2_4 itself. 2 The latter, which is lower, is taken as the scene-specific acceleration performance index.

[0078] The vehicle 2_5 has an environment-specific acceleration performance index higher than the environment-specific acceleration performance index of the vehicle 2_4, which is the preceding vehicle. In this case, the index acquisition block 120 acquires the 2 m / s2 and 3m / s, which is the environmental acceleration performance index of vehicle 2_5 itself. 2 The lower of the two is taken as the scene-specific acceleration performance index.

[0079] When the scene-specific acceleration performance index at time T=0 is acquired, the index acquisition block 120 acquires the scene-specific acceleration performance index for the next time T=1. For the vehicles 2_1 and 2_2, the environment-specific acceleration performance index is substantially the same as that at T=0. Here, the vehicles 2_1 and 2_2 continue to travel with the inter-vehicle distance within the set distance range. Therefore, the index acquisition block 120 acquires the scene-specific acceleration performance index of 4 m / s 2 is obtained as the scene-specific acceleration performance index at time T=1.

[0080] It is assumed that the vehicle 2_3 is estimated to have passed an uphill road and is traveling on a flat road at time T=1. In other words, it is assumed that at time T=1, the factors of the traveling environment that reduce the acceleration performance of the vehicle 2_3 are expected to disappear. In this case, the environment-specific acceleration performance index is set to 4 m / s, the same as the vehicles 2_1 and 2_2. 2 Therefore, the index acquisition block 120 calculates the velocity of 4 m / s 2 is obtained as the scene-specific acceleration performance index.

[0081] It is assumed that the vehicle 2_4 is traveling on an uphill road at time T=1. In this case, the environment-specific acceleration performance index is 2 m / s 2 Therefore, the index acquisition block 120 obtains the environment-specific acceleration performance index of the vehicle 2_4 of 2 m / s 2 is obtained as the scene-specific acceleration performance index.

[0082] It is assumed that the vehicle 2_5 is traveling on a flat road just before an uphill road at time T=1. In this case, the environment-specific acceleration performance index is 3 m / s 2This is higher than the environment-specific acceleration performance index of the preceding vehicle. Therefore, the index acquisition block 120 uses the 2 m / s 2 is obtained as the scene-specific acceleration performance index.

[0083] When the scene-specific acceleration performance index at time T=1 is acquired, the index acquisition block 120 acquires a scene-specific acceleration performance index for the next time T=2. It is estimated that the vehicles 2_1, 2_2, and 2_3 are continuing to travel on a flat road without any driving environment factors that reduce acceleration performance. In this case, the index acquisition block 120 acquires a common scene-specific acceleration performance index of 4 m / s 2 In other words, the index acquisition block 120 continues to acquire the acceleration performance index for each scene at the previous time T=1 as the acceleration performance index for each scene at time T=2.

[0084] It is assumed that the vehicle 2_4 has passed the uphill road and is running on a flat road at time T=2. In this case, the environment-specific acceleration performance index is 3 m / s 2 Therefore, the index acquisition block 120 uses the 4 m / s 2 Instead, the acceleration performance index for vehicle 2_4 is 3m / s 2 is obtained as the scene-specific acceleration performance index.

[0085] It is assumed that the vehicle 2_5 is traveling on a flat road just before an uphill road at time T=2. In this case, the environment-specific acceleration performance index is 3 m / s 2 This is the same value as the environment-specific acceleration performance index of the vehicle ahead. Therefore, the index acquisition block 120 acquires this value as the scene-specific acceleration performance index.

[0086] 10, which will be described next, it is assumed that the inter-vehicle distance between the pair of vehicles 2_1 and 2_2 deviates from the small side of the set distance range, i.e., the inter-vehicle distance becomes excessively short, at time T=0. Similarly, it is assumed that the inter-vehicle distance between the pair of vehicles 2_4 and 2_5 also deviates from the small side of the set distance range.

[0087] In this case, for the vehicle 2_2, the index acquisition block 120 sets the environment-specific acceleration performance index to 4 m / s 2 to 3.8 m / s 2 The acceleration performance index for each scene is obtained by correcting the decrease.

[0088] Also, for the vehicle 2_5, the index acquisition block 120 acquires a common acceleration performance index of 2 m / s 2 Further reduced and corrected to 1.8m / s 2 As for the vehicle 2_5, if the inter-vehicle distance remains excessively short even at time T=1, as shown in FIG. 10, the index acquisition block 120 acquires 1.8 m / s 2 is continuously obtained as an acceleration performance index for each scene.

[0089] Furthermore, for vehicle 2_5, if the inter-vehicle distance remains excessively short even at time T=2, the index acquisition block 120 acquires the same scene-specific acceleration performance index as the preceding vehicle, 3 m / s 2 The speed is corrected to 2.8m / s 2 is obtained as the scene-specific acceleration performance index.

[0090] In the example shown in FIG. 11 which will be described next, it is assumed that at time T=0, the inter-vehicle distance between the pair of vehicles 2_4 and 2_5 deviates from the larger side of the set distance range, that is, there is a margin in the inter-vehicle distance.

[0091] In this case, the indicator acquisition block 120 acquires a scene acceleration performance indicator of 2 m / s 2 Further increased and corrected to 2.1m / s 2is acquired as the scene-specific acceleration performance index of the vehicle 2_5. If the vehicle distance is still sufficient at time T=1, the index acquisition block 120 acquires 2.1 m / s 2 is continuously obtained as the scene-specific acceleration performance index for vehicle 2_5.

[0092] Furthermore, it is assumed that the vehicle 2_5 is estimated to maintain a sufficient inter-vehicle distance even at time T=2. In this case, the index acquisition block 120 further increases and corrects the scene-specific acceleration performance index shared with the preceding vehicle to 3.1 m / s 2 is obtained as the scene-specific acceleration performance index for vehicle 2_5.

[0093] In the above embodiment, the management system 10 corresponds to the "driving control system," the management method corresponds to the "driving control method," and the management program corresponds to the "driving control program."

[0094] According to the first embodiment described above, when the platooning conditions are met, scene-specific acceleration performance indexes obtained by correcting the basic acceleration performance index correlated with the vehicle configuration of each vehicle 2 are acquired in time series, and control parameters for each vehicle 2 correlated with the scene-specific acceleration performance index are output. Therefore, for each vehicle 2 constituting the platoon, control correlated with acceleration performance matching the driving environment for each driving scene and the front-to-rear correlation between the vehicles 2 in the platoon may be possible. Therefore, platooning according to the acceleration performance of each vehicle 2 may be realized. This also makes it possible to adjust traffic volume according to acceleration performance, which may also improve road utilization efficiency, especially when autonomous vehicles become widespread.

[0095] Furthermore, according to the first embodiment, the scene-specific acceleration performance index is an index obtained by correcting the environment-specific acceleration performance index, which is the basic acceleration performance index corrected by matching with the driving environment, and then correcting it by matching with the front-rear correlation. Therefore, the acceleration performance index is first corrected according to the relationship between the vehicle 2 alone and the driving environment, and then the acceleration performance index can be corrected according to the front-rear correlation between the vehicles 2. Therefore, the acceleration performance index can be corrected efficiently.

[0096] Furthermore, according to the first embodiment, when focusing on a pair of front and rear vehicles 2 in a platoon, if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, a common scene-specific acceleration performance index is acquired for the rear vehicle. Therefore, even if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, it may be possible to regulate the control parameters of the rear vehicle according to the control parameters of the front vehicle by using a common scene-specific acceleration performance index. Therefore, it may be possible to reliably ensure a sufficient inter-vehicle distance.

[0097] In addition, according to the first embodiment, when focusing on a pair of front and rear vehicles 2 in a platoon, if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, a scene-specific acceleration performance index corrected to be lower for the rear vehicle than for the front vehicle is acquired. Therefore, if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, it may be possible to deal with the increase in braking distance that occurs in response to an increase in speed due to acceleration over time.

[0098] Furthermore, according to the first embodiment, when focusing on a pair of front and rear vehicles 2 in a platoon, if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, a corrected scene-specific acceleration performance index is acquired for the rear vehicle that is higher than that of the front vehicle. Therefore, if the environment-specific acceleration performance index of the rear vehicle is higher than that of the front vehicle, it may be possible to at least temporarily increase the inter-vehicle distance. This may make it possible to further improve road use efficiency.

[0099] Furthermore, according to the first embodiment, when a division condition for dividing a platoon is met, a scene-specific acceleration performance index corrected by matching with the division factor is acquired for the leading vehicle in the rear platoon after the division. Therefore, when the platoon is divided, the rear platoon after the division can be controlled separately from the front platoon in accordance with the division factor.

[0100] Furthermore, according to the first embodiment, the fulfillment of the platooning conditions is monitored for multiple vehicles in each driving lane. Therefore, platooning control according to the situation-specific acceleration performance index can be executed for each driving lane. Therefore, individual platooning can be realized for each driving lane depending on the situation of each driving lane.

[0101] (Other embodiments) Although one embodiment has been described above, the present disclosure should not be construed as being limited to this embodiment, and can be applied to various embodiments and combinations within the scope that does not deviate from the gist of the present disclosure.

[0102] In a modified example, the monitoring block 110, the index acquisition block 120, and the output block 130 may be configured in the on-board control unit 27. For example, when the on-board control unit 27 of a specific vehicle 2 determines that the platooning conditions for the platoon with itself at the front are met, it may acquire a time-series scene-specific acceleration performance index for each vehicle 2 including itself, and output the control parameters to each subsequent vehicle 2. In this case, the on-board control unit 27 is an example of a "cruising control system" and a "cruising control device."

[0103] In a modified example, the platooning condition may be that the vehicles are traveling in a specific driving area, such as a highway, or the platooning condition may simply be that the vehicles are traveling autonomously.

[0104] In a modified example, the control parameter correlated with the scene-specific acceleration performance index may be a parameter other than the target acceleration. For example, the control parameter may be a target speed. Alternatively, the control parameter may be a jerk.

[0105] In a modified example, at least one of the databases 14, 15, 16, and 17 of the management center 1 may be provided in an external facility. For example, the traffic light database 16 may be provided in a traffic control center separate from the management center 1. Furthermore, the vehicle database 15 may be provided for each vehicle manufacturer that produces the vehicle 2, and may be provided in a center that manages the vehicles 2 manufactured by the corresponding vehicle manufacturer.

[0106] In a modified example, the dedicated computer constituting the management system 10 may have at least one of a digital circuit and an analog circuit as the processor 12. Here, the digital circuit refers to at least one of an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), an SOC (System on a Chip), a PGA (Programmable Gate Array), and a CPLD (Complex Programmable Logic Device). Such a digital circuit may also have a memory 11 that stores a program.

[0107] In a modified example, the vehicle 2 may be, for example, an autonomous robot capable of transporting luggage or collecting information by autonomous or remote driving. In addition to the forms described so far, the above-described embodiments and modified examples may be implemented as a control device that is configured to be mountable on the vehicle 2 and has at least one processor and one memory. Specifically, the above-described embodiments and modified examples may be implemented in the form of a processing circuit (e.g., a processing ECU, etc.) or a semiconductor device (e.g., a semiconductor chip, etc.). [Explanation of symbols]

[0108] 2: Vehicle (autonomous vehicle), 10: Management system (driving control system), 11: Memory (storage medium), 12: Processor, 27: In-vehicle control unit (driving control device)

Claims

1. A driving control system having a processor (12) and controlling driving of a plurality of autonomous vehicles (2) capable of autonomous driving, The processor: monitoring whether a platooning condition for forming a platoon by the plurality of autonomous vehicles is met; When the platooning condition is met, a basic acceleration performance index correlated with the vehicle configuration of each of the autonomous vehicles is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles in the platoon; and outputting a control parameter for each of the autonomous vehicles that correlates with the scene-specific acceleration performance index; a cruise control system configured to perform the above steps.

2. The acquiring of the scene-specific acceleration performance index includes:

2. The cruise control system according to claim 1, further comprising: obtaining an environment-specific acceleration performance index obtained by correcting the basic acceleration performance index by matching it with the driving environment, and further obtaining the scene-specific acceleration performance index by matching it with the front-rear correlation.

3. The acquiring of the scene-specific acceleration performance index includes:

3. The cruise control system of claim 2, further comprising: when focusing on a front-rear pair of the autonomous vehicles in the platoon, if the environment-specific acceleration performance index is higher for the rear vehicle than for the front vehicle, acquiring the scene-specific acceleration performance index for the rear vehicle that is common to the front vehicle.

4. The acquiring of the scene-specific acceleration performance index includes:

3. The cruise control system of claim 2, further comprising: when focusing on a pair of front and rear autonomous vehicles in the platoon, if the environment-specific acceleration performance index is higher for the rear vehicle than for the front vehicle, acquiring the scene-specific acceleration performance index for the rear vehicle that is corrected to be lower than for the front vehicle.

5. The acquiring of the scene-specific acceleration performance index includes:

3. The cruise control system of claim 2, further comprising: when focusing on a pair of front and rear autonomous vehicles in the platoon, if the environment-specific acceleration performance index is higher for the rear vehicle than for the front vehicle, acquiring the scene-specific acceleration performance index for the rear vehicle whose inter-vehicle distance to the front vehicle is outside a set distance range, corrected to be higher than that of the front vehicle.

6. The acquiring of the scene-specific acceleration performance index includes: The driving control system of claim 1 further includes, when a splitting condition for splitting the convoy is met, obtaining the scene-specific acceleration performance index for the leading vehicle in the rear convoy after the split, corrected by matching with the splitting factor.

7. Monitoring the establishment of the formation condition includes: The cruise control system according to claim 1 , further comprising monitoring whether the formation condition is met for a plurality of the autonomous vehicles in each driving lane.

8. A driving control device having a processor (12), configured to be mountable on an autonomous vehicle (2) capable of autonomous driving, and controlling driving of a plurality of the autonomous vehicles, The processor: monitoring whether a platooning condition for forming a platoon by the plurality of autonomous vehicles is met; When the platooning condition is met, a basic acceleration performance index correlated with the vehicle configuration of each of the autonomous vehicles is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles forming the platoon; and outputting a control parameter for each of the autonomous vehicles that correlates with the scene-specific acceleration performance index; A cruise control device configured to perform the above.

9. A driving control method executed by a processor (12) for controlling driving of a plurality of autonomous vehicles (2) capable of autonomous driving, comprising: monitoring whether a platooning condition for forming a platoon by the plurality of autonomous vehicles is met; When the platooning condition is met, a basic acceleration performance index correlated with the vehicle configuration of each of the autonomous vehicles is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles forming the platoon; and outputting a control parameter for each of the autonomous vehicles that correlates with the scene-specific acceleration performance index; A driving control method including:

10. A travel control program stored in a storage medium (11) for controlling travel of a plurality of autonomous vehicles (2) capable of autonomous travel, the program including instructions to be executed by a processor (12), The instruction: monitoring whether a platooning condition for forming a platoon by the plurality of autonomous vehicles is met; When the platooning condition is met, a basic acceleration performance index correlated with the vehicle configuration of each of the autonomous vehicles is corrected in time series to obtain a scene-specific acceleration performance index that matches the driving environment for each driving scene and the front-rear correlation between the autonomous vehicles forming the platoon; and outputting a control parameter for each of the autonomous vehicles that correlates with the scene-specific acceleration performance index; A driving control program including:

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