Vehicle control system, vehicle control device, vehicle control method, and vehicle control program

Abstract

This vehicle control system has a processor and controls a host vehicle. The processor of the vehicle control system is configured to establish a future action plan that includes the position and motion state of the host vehicle. The processor is configured to execute acquisition of a predicted control amount at a future predicted time based on a delay time of a response relating to traveling control in the host vehicle from the action plan. The processor is configured to execute determination of a current output control amount corresponding to the predicted control amount.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on Patent Application No. 2021-194047 filed in Japan on November 30, 2021, and the contents of the original application are incorporated by reference in their entirety. [Technical Field]

[0002] The present disclosure relates to a vehicle control technique for controlling a host vehicle. [Background technology]

[0003] Patent Document 1 discloses a technology for compensating for time delays in controlling the movement of a vehicle that automatically travels while following a target trajectory. This technology predicts vehicle behavior when a movement command is given to reduce the deviation between the vehicle's position and a target position on the target trajectory, and predicts the vehicle's position at a future time. Then, vehicle behavior at a time further in the future is predicted from the predicted vehicle position in the same way, and the vehicle's position at that time further in the future is predicted. In Patent Document 1, this process is repeated to predict a command value for vehicle movement a predetermined time in the future, and the command value is output. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 152977 Summary of the Invention

[0005] In the technique of Patent Document 1, since a command value for reducing the deviation from the target position on the target trajectory is predicted sequentially at each sampling time, prediction errors may accumulate, resulting in a deterioration in controllability.

[0006] An object of the present disclosure is to provide a vehicle control system capable of suppressing deterioration of controllability and improving responsiveness. Another object of the present disclosure is to provide a vehicle control device capable of suppressing deterioration of controllability and improving responsiveness. Yet another object of the present disclosure is to provide a vehicle control method capable of suppressing deterioration of controllability and improving responsiveness. Yet another object of the present disclosure is to provide a vehicle control program capable of suppressing deterioration of controllability and improving responsiveness.

[0007] 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.

[0008] A first aspect of the present disclosure is a vehicle control system having a processor and controlling a host vehicle, The processor Future time series information including a position and a motion state including at least an acceleration of a host vehicle, Based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road, at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road is optimized over a time series. Developing an action plan; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; is configured to execute A second aspect of the present disclosure is a vehicle control system having a processor (102) for controlling a host vehicle (A), The processor Developing a future action plan including the location and motion state of the host vehicle; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; configured to run The predicted time is determined according to the braking mode.

[0009] A third aspect of the present disclosure is a vehicle control device having a processor and controlling a host vehicle, The processor Future time series information including a position and a motion state including at least an acceleration of a host vehicle, Based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road, at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road is optimized over a time series. Developing an action plan; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; is configured to execute A fourth aspect of the present disclosure is a vehicle control device having a processor (102) and controlling a host vehicle (A), The processor Developing a future action plan including the location and motion state of the host vehicle; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; configured to run The predicted time is determined according to the braking mode.

[0010] A fifth aspect of the present disclosure is a vehicle control method executed by a processor to control a host vehicle, the method comprising: Future time series information including a position and a motion state including at least an acceleration of a host vehicle, Based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road, at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and driving along a road is optimized over a time series. Developing an action plan; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Includes: A sixth aspect of the present disclosure is a vehicle control method executed by a processor (102) to control a host vehicle (A), the method comprising: Developing a future action plan including the location and motion state of the host vehicle; Obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Including, The predicted time is determined according to the braking mode.

[0011] A seventh aspect of the present disclosure is a vehicle control program stored in a storage medium for controlling a host vehicle, the vehicle control program including instructions to be executed by a processor, the vehicle control program including: The command is, Formulating an action plan optimized over time for at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, which is future time-series information including the position and motion state including at least acceleration of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Includes: An eighth aspect of the present disclosure is a vehicle control program stored in a storage medium (101) for controlling a host vehicle (A), the vehicle control program including instructions to be executed by a processor (102), The command is, Future including the host vehicle's position and motion state row To develop an action plan, obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Including, The predicted time is determined according to the braking mode.

[0012] These first to second eight According to this embodiment, the current output control amount is determined based on the predicted control amount obtained from the action plan including the host vehicle's position and motion state. Therefore, the prediction error is reduced and the response delay can be compensated. Therefore, it is possible to suppress deterioration of controllability and improve responsiveness. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a block diagram showing the overall configuration of a first embodiment. [Figure 2] FIG. 2 is a schematic diagram showing a traveling environment of a host vehicle to which the first embodiment is applied. [Figure 3] 1 is a block diagram showing a functional configuration of a vehicle control system according to a first embodiment. [Figure 4] FIG. 10 is a diagram for explaining a response delay in a host vehicle. [Figure 5] 10 is a graph for explaining a change in response depending on whether or not a tire is deformed. [Figure 6] 3 is a flowchart illustrating a vehicle control method according to the first embodiment. [Figure 7] 10 is a graph showing the vehicle behavior in adaptive cruise control when it is assumed that there is no response delay. [Figure 8] 10 is a graph showing the vehicle behavior in the adaptive cruise control without compensation for response delay. [Figure 9] 10 is a graph showing the vehicle behavior in the preceding vehicle following control when compensation for response delay is performed. [Figure 10] FIG. 10 is a diagram illustrating a vehicle control according to a second embodiment. [Figure 11] FIG. 10 is a diagram schematically illustrating vehicle control in a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, multiple embodiments of the present disclosure will be described with reference to the drawings. Note that corresponding components in each embodiment are designated by the same reference numerals, and redundant description may be omitted. Furthermore, when only a portion of the configuration is described in each embodiment, the configuration of another previously described embodiment may be applied to the remaining portions of the configuration. Furthermore, in addition to the combinations of configurations explicitly stated in the description of each embodiment, configurations of multiple embodiments may be partially combined together even if not explicitly stated, provided that there is no particular problem with the combination.

[0015] (First embodiment) A vehicle control system 100 of the first embodiment shown in Fig. 1 controls the traveling of a host vehicle A shown in Fig. 2. From a viewpoint centered on the host vehicle A, the host vehicle A can also be said to be an ego-vehicle. From a viewpoint centered on the host vehicle A, the target vehicle B can also be said to be an other road user.

[0016] The host vehicle A is provided with an autonomous driving mode that is classified into levels according to the degree of manual intervention by the occupant in the driving task. 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 the system performs all 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 or all driving tasks. The autonomous driving mode may be realized by either autonomous driving control or advanced driving assistance control, or by a combination of these, or by switching between them.

[0017] The host vehicle A is equipped with a sensor system 10, a communication system 20, a map database (hereinafter referred to as "DB") 30, and a driving system 40, all of which are shown in Fig. 3. The sensor system 10 acquires sensor information that can be used by the vehicle control system 100 by detecting the external and internal environments of the host vehicle A. To this end, the sensor system 10 is configured to include an external sensor 11 and an internal sensor 12.

[0018] The external sensor 11 acquires external information usable by the vehicle control system 100 from the external environment surrounding the host vehicle A. The external sensor 11 may acquire the external information by detecting targets present in the external world of the host vehicle A. The target detection type external sensor 11 is at least one of a camera, a LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging), a radar, a sonar, and the like.

[0019] The external sensor 11 may acquire external information by receiving positioning signals from artificial satellites of a Global Navigation Satellite System (GNSS) that exist in the external world of the host vehicle A. The positioning type external sensor 11 is, for example, a GNSS receiver. The external sensor 11 may acquire external information by transmitting and receiving communication signals to and from a V2X system that exists in the external world of the host vehicle A. The communication type external sensor 11 is, for example, at least one of a Dedicated Short Range Communications (DSRC) communication device, a cellular V2X (C-V2X) communication device, a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, and an infrared communication device.

[0020] The internal sensor 12 acquires internal information that can be used by the vehicle control system 100 from the internal world, which is the internal environment of the host vehicle A. The internal sensor 12 may acquire the internal information by detecting a specific physical quantity of motion in the internal world of the host vehicle A. The internal sensor 12 of the physical quantity detection type is at least one type of sensor, such as a driving speed sensor, an acceleration sensor, or a gyro sensor.

[0021] The communication system 20 acquires communication information usable by the vehicle control system 100 via wireless communication. The communication system 20 may receive positioning signals from artificial satellites of a Global Navigation Satellite System (GNSS) present in the external world of the host vehicle A. The positioning type communication system 20 is, for example, a GNSS receiver. The communication system 20 may transmit and receive communication signals to and from a V2X system present in the external world of the host vehicle A. The V2X type communication system 20 is, for example, at least one of a Dedicated Short Range Communications (DSRC) communication device and a Cellular V2X (C-V2X) communication device. The communication system 20 may transmit and receive communication signals to and from a terminal present in the internal world of the host vehicle A. The terminal communication type communication system 20 is, for example, at least one of a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, an infrared communication device, etc.

[0022] The map DB 30 stores map information that can be used by the vehicle control system 100. The map DB 30 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 DB 30 may be a database of a locator that estimates the host vehicle A's own state quantities, including its own position. The map DB 30 may be a database of a navigation unit that navigates the host vehicle A's travel route. The map DB 30 may be configured by combining multiple types of these databases.

[0023] The map DB 30 acquires and stores the latest map information, for example, by communicating with an external center via a V2X type communication system 20. Here, the map information is converted into two-dimensional or three-dimensional data as information representing the driving environment of the host vehicle A. 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 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 include structure information representing at least one of the following: the position and shape of buildings and traffic lights facing the road.

[0024] The driving system 40 is configured to drive the body of the host vehicle A based on commands from the vehicle control system 100. The driving system 40 includes a drive unit that drives the host vehicle A, a braking unit that brakes the host vehicle A, and a steering unit that steers the host vehicle A.

[0025] The vehicle control system 100 is connected to a sensor system 10, a communication system 20, a map DB 30, and a driving system 40 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line. The vehicle control system 100 is configured to include at least one dedicated computer.

[0026] The dedicated computer constituting the vehicle control system 100 may be a driving control ECU (Electronic Control Unit) that controls driving of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be a navigation ECU that navigates the driving route of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be a locator ECU that estimates the self-state quantity of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be an actuator ECU that controls the driving actuator of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be an HCU (Human Machine Interface Control Unit (HMI)) that controls information presentation in the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be a computer other than the host vehicle A that constitutes, for example, an external center or mobile terminal that can communicate via a V2X type communication system 20.

[0027] The dedicated computer constituting the vehicle control system 100 may be an integrated ECU (Electronic Control Unit) that integrates the driving control of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be a determination ECU that determines a driving task in the driving control of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be a monitoring ECU that monitors the driving control of the host vehicle A. The dedicated computer constituting the vehicle control system 100 may be an evaluation ECU that evaluates the driving control of the host vehicle A.

[0028] The dedicated computer constituting the vehicle control system 100 has at least one memory 101 and one processor 102. The memory 101 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs, data, etc. The processor 102 includes at least one type of core, such as a central processing unit (CPU), a graphics processing unit (GPU), a reduced instruction set computer (RISC)-CPU, a data flow processor (DFP), or a graph streaming processor (GSP).

[0029] In the vehicle control system 100, the processor 102 executes a plurality of instructions included in a vehicle control program stored in the memory 101 to control the host vehicle A. In this way, the vehicle control system 100 constructs a plurality of function blocks for controlling the host vehicle A. The plurality of function blocks constructed in the vehicle control system 100 include a recognition block 110, an action planning block 120, and a control variable determination block 130, as shown in FIG.

[0030] The recognition block 110 executes a recognition process to recognize the environment around the host vehicle A and the state of the host vehicle A based on external information from the external sensor 11 and internal information from the internal sensor 12. The environment around the host vehicle A includes at least one of, for example, position and speed information about surrounding moving objects, and position information about features such as road markings, roadside structures, and road edges. The state of the host vehicle A includes at least one of, for example, the host vehicle A's own position, speed, acceleration, steering angle, yaw rate, etc. The recognition block 110 executes the recognition process using information from each sensor 11, 12 acquired immediately before the recognition process.

[0031] The recognition block 110 may correct the difference between the detection time of information from each sensor 11, 12 and the execution time of the recognition process based on a movement model of the recognition object and the host vehicle A. Alternatively, the recognition block 110 may provide the movement amount of the recognition object or the host vehicle A corresponding to the time difference as an error to the behavior planning block 120. Note that even when the recognition block 110 corrects the difference, it provides the prediction error to the behavior planning block 120.

[0032] The behavior planning block 120 formulates a future behavior plan for the host vehicle A. The behavior plan includes the future position and motion state of the host vehicle A as control target values. The behavior plan is time-series information that defines this information for each predetermined time in the future. Here, the motion state of the host vehicle A includes at least one of speed, acceleration, yaw angle, yaw rate, etc. The motion state may further include pitch angle, pitch rate, pitch acceleration, etc. The behavior plan including such information can also be expressed as the driving trajectory of the host vehicle A.

[0033] The behavior planning block 120 formulates a behavior plan under constraint conditions that restrict the behavior of the host vehicle A. The constraint conditions include area conditions that the host vehicle A must fit within a drivable area in which it can travel. The behavior planning block 120 estimates the drivable area of the host vehicle A based on surrounding information and vehicle information. The behavior planning block 120 generates a behavior plan for the host vehicle A within this drivable area. In addition, the constraint conditions include a vehicle model and an obstacle model. Specifically, the constraint conditions include that the control amount of the host vehicle A must fit within an allowable range, that it must not come into contact with an obstacle, and so on. The behavior planning block 120 formulates a behavior plan under these constraint conditions.

[0034] More specifically, the behavior planning block 120 optimizes the behavior plan under constraint conditions. For example, if the state variables and control variables are z, the cost function is L(z, k), and the prediction point is N, the evaluation function f(z) of the behavior plan is given by the following mathematical formula (1). The evaluation function is a weighted linear combination of parameters that are mathematically formulated for, for example, maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration and deceleration, and staying within the road.

number

[0035] The behavior planning block 120 sets, as a parameter defining the behavior plan, a variable z that satisfies, for example, the following equation (2) showing the constraints on vehicle motion and the following equation (3) showing the constraints on the driving environment, and that minimizes f(z) over the prediction horizon. Here, the prediction horizon is a time interval, as shown in Fig. 2, for which the behavior plan is defined from the current time to a future time.

number

number

[0036] The control amount determination block 130 determines the output control amount of the host vehicle A based on the control target value defined in the action plan. The output control amount includes at least one of the following: running speed, acceleration, yaw rate (steering angle), jerk, and yaw acceleration. The control amount determination block 130 has a feedback control block 131, a prediction control block 132, and an output determination block 133 as blocks with more specific functions.

[0037] The FB control block 131 determines a feedback (FB) control amount according to the running state of the host vehicle A. The FB control block 131 compensates for disturbances, modeling errors, etc. by feedback. The FB control block 131 determines the FB control amount based on the internal information of the host vehicle A detected by the internal sensor 12, etc., and a control target value.

[0038] The predictive control block 132 determines a predicted control amount, which is a future control amount, from the action plan. The predictive control block 132 sets the predicted control amount so as to compensate for a response delay in the host vehicle A. The response delay here is the delay time from the sensing timing by the sensor system 10 to the control response (see FIG. 4). The delay time can also be expressed as dead time.

[0039] The predictive control block 132 obtains a predicted control amount for the future time by adding a margin to the dead time from the action plan. The margin is a value for dealing with fluctuations in the dead time. The margin is set as a value that complements the phase lead compensation of the feedback control. In the example shown in FIG. 2, the control amount in the action plan for the future time x(k+5) from the current time is obtained as the predicted control amount.

[0040] The prediction control block 132 sets the prediction time in accordance with at least one of the following: whether or not downshift control occurs during acceleration, the braking mode, and the degree of tire deformation during a curve.

[0041] Consider a case where the vehicle is controlled by a target acceleration. When the target acceleration is high, the amount of throttle control increases. In this case, the delay time differs depending on whether or not downshift control of the transmission occurs. If downshift control does not occur, the delay time is short. On the other hand, if downshift control occurs, the delay time is longer due to the influence of shift control. For example, if there is no downshift, a response delay of approximately 150 msec occurs regardless of the current vehicle speed. In this host vehicle A, if downshifting occurs, a response delay of approximately 300 msec occurs when downshifting to first gear, and approximately 400 msec when downshifting to second gear.

[0042] Therefore, when downshift control is performed, the prediction control block 132 sets the prediction time to be longer than when downshift control is not performed. Furthermore, the prediction control block 132 sets the prediction time to be longer as the number of shifts in downshift control increases.

[0043] Consider a case where the vehicle is controlled at a target deceleration. The effectiveness of engine braking when the throttle is released varies slightly depending on the current vehicle speed. When the vehicle speed is high, the delay is small, and when the vehicle speed is low, the delay is large. For example, in the high-speed range (e.g., 80 km / h or above), there is a delay of 200 msec, in the medium-speed range (e.g., 40-80 km / h), there is a delay of approximately 250 msec, and in the low-speed range (e.g., up to 40 km / h), there is a delay of approximately 300 msec. Deceleration using the foot brake is mainly due to the delay in the response of the brake hydraulic pressure, so the delay time does not change significantly depending on the vehicle speed. Since the response characteristics of the actuator are almost constant regardless of the amount of pressure applied, the responsiveness is not dependent on the target deceleration amount either, and is constant at approximately 300 msec.

[0044] Therefore, when the braking mode is engine braking, the prediction control block 132 sets a longer prediction time as the deceleration start speed decreases. On the other hand, when the braking mode is foot braking, the prediction control block 132 sets a constant prediction time regardless of the deceleration start speed. When the response characteristics are different from this, the prediction control block 132 sets the prediction time according to the response characteristics.

[0045] Consider the case of vehicle control using a target yaw rate. The response delay due to steering is affected by tire deformation in addition to actuator delay. Therefore, the response characteristics to the target yaw rate vary significantly depending on the vehicle speed. For example, in the low-speed range (e.g., up to 40 km / h), tire deformation is small, resulting in a response delay of approximately 100 to 200 msec. In the medium-speed range (e.g., 40 to 70 km / h), tire deformation is small, resulting in a response delay of approximately 200 to 300 msec. Furthermore, in the high-speed range (e.g., 70 km / h or faster), the response delay is approximately 100 to 200 msec. When tire deformation is small, the yaw rate of host vehicle A responds so as to track the target, but when tire deformation is present, the yaw rate becomes oscillatory. Because the vehicle characteristics are changing, the response appears to be improving (see Figure 5).

[0046] Therefore, when the traveling speed is in the high speed range, the prediction control block 132 sets the prediction time to be shorter than when the traveling speed is in the medium speed range. On the other hand, when the traveling speed is in the low speed range, the prediction control block 132 sets the prediction time to be shorter than when the traveling speed is in the medium speed range. The prediction time may be set according to tire characteristics.

[0047] The output determination block 133 determines the current output control amount based on the feedback control amount and the predicted control amount. Specifically, the output determination block 133 calculates the output control amount by multiplying the sum of the feedback control amount and the predicted control amount by a gain (for example, 0.5). The output determination block 133 commands the driving system 40 to control the host vehicle A according to the calculated output control amount.

[0048] The vehicle control flow, which is the flow of the vehicle control method in which the vehicle control system 100 controls the running of the host vehicle A through cooperation of the blocks 110, 120, and 130 described above, will be described below with reference to Figure 6. This vehicle control flow is executed repeatedly while the host vehicle A is running. Note that each "S" in this vehicle control flow represents a plurality of steps executed by a plurality of instructions included in the vehicle control program.

[0049] First, in S10, the recognition block 110 acquires data necessary for the action plan from the sensor system 10, etc., and executes recognition processing on the data. Next, in S20, the action plan block 120 formulates a future action plan including the position and motion state of the host vehicle A. Next, in S30, the prediction control block 132 acquires a predicted control amount from the action plan. Next, in S40, the feedback control block 131 calculates the feedback control amount. Furthermore, in S50, the control amount calculation block calculates the output control amount and outputs it to the driving system 40.

[0050] Next, an example of the operation of following the preceding vehicle when it is assumed that the host vehicle A has no dead time will be described (see FIG. 7). In this example, the host vehicle A follows a preceding vehicle that is accelerating and decelerating in a sinusoidal manner. In the preceding vehicle following control, the vehicle control system 100 controls the acceleration / deceleration so that the host vehicle follows the preceding vehicle without exceeding the ideal inter-vehicle time (e.g., 1.4 seconds) and the set vehicle speed (e.g., 30 m / s).

[0051] As shown in Fig. 8, if there is a one-second dead time, the response to the movement of the preceding vehicle is delayed, resulting in hunting of the target acceleration. Thus, if a predicted control amount for the response delay is not calculated, and control is performed to maintain the set vehicle speed and ideal inter-vehicle time, the speed and inter-vehicle distance will become oscillatory. On the other hand, if the response delay is compensated for by calculating a predicted control amount as in this embodiment, the controllability is improved to the same level as that of a vehicle with no dead time, as shown in Fig. 9.

[0052] According to the first embodiment described above, a future action plan including the position and motion state of the host vehicle A is created, future predicted control amounts are obtained from the action plan, and current control amounts are determined based on the predicted control amounts. Therefore, since the motion state of the host vehicle A is included in the action plan, errors are less likely to occur in the predicted control amounts, and further, response delays can be compensated for by the predicted control amounts. Therefore, it may be possible to improve responsiveness while suppressing deterioration in controllability.

[0053] In vehicle control, a response delay may occur due to noise filtering during recognition processing and communication processing of the recognition results. Furthermore, a delay may also occur when filtering is performed to prevent malfunctions in decision-making processes such as action planning. In this embodiment, the delay from recognition to a control request can be compensated for together with the delay from a control request to a vehicle response.

[0054] Second Embodiment As shown in FIG. 10, the second embodiment is a modification of the first embodiment.

[0055] In the second embodiment, the action planning block 120 in S20 creates multiple types of candidate plans as candidates for the action plan P and selects an action plan from among the candidate plans. The action planning block 120 creates multiple types of candidate plans according to, for example, the travel route of the host vehicle A. The action planning block 120 creates multiple candidate plans using high-order polynomials in, for example, a Freinet coordinate system. As an example, as shown in FIG. 10 , the action planning block 120 creates a candidate plan P3 for a straight route, candidate plans PC1 and PC2 for a type of lane change to the left from the straight route, and candidate plans PC4 and PC5 for a type of lane change to the right from the straight route.

[0056] The action planning block 120 evaluates each of the multiple types of candidate plans based on an evaluation function or the like. The action planning block 120 selects the candidate plan with the highest evaluation as the action plan P to be actually executed. The left side of FIG. 10 shows an example in which a candidate plan PC3 of a straight route is selected as the action plan P in a certain cycle. The right side of FIG. 10 shows an example in which the action plan P is switched to a candidate plan PC5 that changes lanes to the right in the next cycle.

[0057] In the second embodiment, when the candidate plan selected as the action plan P has been changed from the previous time, the prediction control block 132 in S30 calculates predicted control amounts according to both the pre-change action plan (pre-change plan) Pp and the changed action plan P. More specifically, the prediction control block 132 calculates predicted control amounts based on the pre-change plan Pp (pre-change predicted control amounts) and predicted control amounts based on the changed action plan P (post-change predicted control amounts). Then, the prediction control block 132 calculates intermediate predicted control amounts for each target value as predicted control amounts according to the pre-change predicted control amounts and the post-change predicted control amounts.

[0058] For example, the predictive control block 132 may calculate the average value of the pre-change predictive control amount and the post-change predictive control amount as the intermediate predictive control amount. The predictive control block 132 may also calculate the average value by weighting at least one of the pre-change predictive control amount and the post-change predictive control amount. The predictive control block 132 provides this intermediate predictive control amount to the output determination block 133 as a parameter for determining the output control amount. In the example shown in Fig. 10, the pre-change predictive control amount is schematically shown by a hollow circle on the pre-change plan Pp, and the post-change predictive control amount is schematically shown by a hollow circle on the post-change action plan P. The intermediate predictive control amount is then schematically shown by a filled circle.

[0059] According to the second embodiment described above, the output control amount is determined based on the predicted control amount from the changed action plan P and the pre-change predicted control amount from the pre-change action plan P. This makes it possible to suppress large changes in the predicted control amount in response to changes in the selected action plan P. This makes it possible to suppress the vehicle behavior from becoming unstable.

[0060] (Third embodiment) As shown in FIG. 11, the third embodiment is a modification of the first embodiment.

[0061] In the third embodiment, similarly to the second embodiment, the action planning block 120 in S20 formulates multiple candidate plans as candidates for the action plan P and selects the candidate plan with the highest evaluation as the action plan P to be actually executed. In addition, the action planning block 120 predicts the candidate plan to be selected in the future according to the evaluation results of each candidate plan.

[0062] For example, the behavior planning block 120 calculates the gradient of the change in the evaluation value over time for each candidate plan. The behavior planning block 120 calculates a predicted evaluation value for a future period according to this gradient for each candidate plan. The behavior planning block 120 only needs to calculate the predicted evaluation value up to a specified number of periods ahead. The behavior planning block 120 predicts the behavior plan to be selected in the future according to this predicted evaluation value.

[0063] Then, when the type of the action plan to be selected in the future is different from the current action plan, that is, when the action plan will be switched within a specified period, the action plan block 120 generates a composite plan Ps as an action plan that combines the current action plan P and the future action plan (future plan) Pn. In the example shown in Fig. 11, the composite plan Ps is generated as the action plan P by combining the current action plan P in which the candidate plan PC3 is selected with the future plan Pn resulting from the selection of the candidate plan PC5.

[0064] In this case, the predictive control block 132 in S30 obtains the predicted control amount from this composite plan Ps. In the example shown in Fig. 11, the predicted control amount is schematically shown as a black circle.

[0065] According to the third embodiment described above, when a change in the type of a candidate plan selected as an action plan in the future is predicted, the candidate plan of the predicted type is combined with the current action plan. Therefore, since the candidate plan selected as a future action plan is combined with the current action plan, a control amount corresponding to the change in the action plan can be obtained as a predicted control amount. Therefore, it is possible to prevent the vehicle behavior from becoming unstable.

[0066] (Other embodiments) Although multiple embodiments have been described above, the present disclosure should not be construed as being limited to the described embodiments, and can be applied to various embodiments within the scope that does not deviate from the gist of the present disclosure.

[0067] In a modified example, the behavior planning block 120 may use a Markov decision process (MDP) or a dynamic programming (DP) as a method for estimating a behavior plan.

[0068] In a modified example, the control amount determination block 130 may be a target value filter type two-degree-of-freedom control system instead of the two-degree-of-freedom control system of feedforward control and feedback control.

[0069] In a modified example, the dedicated computer constituting the vehicle control system 100 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is at least one of an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip (SOC), a programmable gate array (PGA), and a complex programmable logic device (CPLD). Furthermore, such a digital circuit may have a memory that stores a program.

[0070] In addition to the embodiments described above, the vehicle control system 100 according to the above-described embodiments and modifications may be implemented as a vehicle control device that is a processing device (e.g., a processing ECU) mounted on the host vehicle A. Furthermore, the above-described embodiments and modifications may be implemented as a semiconductor device (e.g., a semiconductor chip) having at least one processor 102 and one memory 101 of the vehicle control system 100.

Claims

1. A vehicle control system having a processor (102) for controlling a host vehicle (A), The processor: Formulating an action plan optimized over time for at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, which is future time-series information including the position and motion state including at least acceleration of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; A vehicle control system configured to perform the above.

2. The vehicle control system according to claim 1 , wherein the predicted time is determined depending on whether or not a downshift control is performed.

3. The vehicle control system according to claim 2 , wherein the predicted time is determined according to the number of shifts in a downshift control.

4. The vehicle control system according to claim 1 , wherein the predicted time is determined in accordance with a braking mode.

5. The vehicle control system according to claim 1 , wherein the predicted time is determined according to a degree of deformation of a tire.

6. The vehicle control system according to claim 1 , wherein the predicted time is determined according to a traveling speed.

7. A vehicle control system having a processor (102) and controlling a host vehicle (A), The processor: Developing a future action plan including the location and motion state of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; configured to run The vehicle control system includes determining the predicted time in accordance with a braking mode.

8. Developing the action plan includes: selecting the action plan from a plurality of candidate plans; Obtaining the predicted control variable when the type of the candidate plan selected as the action plan is changed, further acquiring a pre-change predicted control amount which is the predicted control amount from the action plan before the change; Determining the output control amount The vehicle control system according to claim 1 , further comprising determining the output control amount in accordance with the predicted control amount from the changed action plan and the pre-change predicted control amount.

9. Developing the action plan includes: selecting the action plan from a plurality of candidate plans; When a change in type of the candidate plan selected as the action plan is predicted in the future, combining the candidate plan of the predicted type with the current action plan; The vehicle control system according to any one of claims 1 to 7, comprising:

10. A vehicle control device having a processor (102) and controlling a host vehicle (A), The processor: Formulating an action plan optimized over time for at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, which is future time-series information including the position and motion state including at least acceleration of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; A vehicle control device configured to execute the above.

11. A vehicle control device having a processor (102) and controlling a host vehicle (A), The processor: Developing a future action plan including the location and motion state of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; configured to run The vehicle control device includes determining the predicted time in accordance with a braking mode.

12. A vehicle control method executed by a processor (102) for controlling a host vehicle (A), comprising: Formulating an action plan optimized over time for at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, which is future time-series information including the position and motion state including at least acceleration of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; A vehicle control method comprising:

13. A vehicle control method executed by a processor (102) to control a host vehicle (A), comprising: Developing a future action plan including the location and motion state of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Including, The vehicle control method includes determining the predicted time in accordance with a braking mode.

14. A vehicle control program stored in a storage medium (101) for controlling a host vehicle (A) and including instructions to be executed by a processor (102), The instruction: Formulating an action plan optimized over time for at least one of maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, based on an evaluation function that is a weighted linear combination of mathematically formulated parameters for maintaining a specified inter-vehicle distance, maintaining a target vehicle speed, avoiding sudden acceleration / deceleration, and running along a road, which is future time-series information including the position and motion state including at least acceleration of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; A vehicle control program including:

15. A vehicle control program stored in a storage medium (101) for controlling a host vehicle (A) and including instructions to be executed by a processor (102), The instruction: generating a future action plan including the position and motion state of the host vehicle; obtaining a predicted control amount for a predicted time ahead based on a delay time of a response related to driving control in the host vehicle from the action plan; determining a current output control amount according to the predicted control amount; Including, The vehicle control program includes determining the predicted time in accordance with a braking mode.

Images