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

Abstract

To provide a travel support system that ensures travel support accuracy for a vehicle.SOLUTION: A processor of a travel support system that performs travel support processing for supporting the travel of a host vehicle is configured to: acquire, as specific physical quantities related to the travel of the host vehicle, a speed V, a yaw rate ω, and a lateral acceleration G; monitor, as a compensation error Δε, a difference between a first lateral slip angular velocity ε1 which is correlated with a lateral slip angle βm estimated by a first estimation model that is an equivalent two-wheel model using the speed V and the yaw rate ω as inputs, and a second lateral slip angular velocity ε2 estimated by a second estimation model that is a rigid body model using the speed V, the yaw rate ω, and the lateral acceleration G as inputs; and perform the travel support processing on the basis of the lateral slip angle βm whose yaw rate ω is compensated by the compensation error Δε fed back to the estimation by the first estimation model.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present disclosure relates to a driving assistance technique for assisting the driving of a vehicle.

Background Art

[0002] The driving assistance technique disclosed in Patent Document 1 corrects the planned driving route of the host vehicle by estimating the sideslip angle of the host vehicle.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the driving assistance technique disclosed in Patent Document 1, the sideslip angle is estimated based on an equivalent two-wheeled model, which is a motion model in which the host vehicle is simulated as a two-wheeled vehicle, in response to the input of the speed and yaw rate of the host vehicle. In such an equivalent two-wheeled model, the slip characteristics of the wheels in the vehicle are not sufficiently considered, so an error occurs in the estimated sideslip angle. As a result, it becomes difficult to ensure the driving assistance accuracy of the host vehicle.

[0005] An object of the present disclosure is to provide a driving assistance system that ensures the driving assistance accuracy of a vehicle. Another object of the present disclosure is to provide a driving assistance device that ensures the driving assistance accuracy of a vehicle. Still another object of the present disclosure is to provide a driving assistance method that ensures the driving assistance accuracy of a vehicle. Yet another object of the present disclosure is to provide a driving assistance program that ensures the driving assistance accuracy of a vehicle.

Means for Solving the Problems

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

[0007] The first aspect of the present disclosure is a driving support system having a processor (12) and performing a driving support process for supporting the driving of a host vehicle (2), wherein the processor acquires the speed (V), yaw rate (ω), and lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle, monitors, as a compensation error (Δε), the difference between a first lateral slip angular velocity (ε1) correlated with a lateral slip angle (βm) estimated by a first estimation model (M1) which is an equivalent two-wheel model taking the speed and yaw rate as inputs, and a second lateral slip angular velocity (ε2) estimated by a second estimation model (M2) which is a rigid body model taking the speed, yaw rate, and lateral acceleration as inputs, and is configured to execute performing the driving support process based on the lateral slip angle with the yaw rate compensated by the compensation error fed back to the estimation by the first estimation model.

[0008] The second aspect of the present disclosure is a driving support device having a processor (12), configured to be mountable on a host vehicle (2), and performing a driving support process for supporting the driving of the host vehicle, wherein the processor acquires the speed (V), yaw rate (ω), and lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle, monitors, as a compensation error (Δε), the difference between a first lateral slip angular velocity (ε1) correlated with a lateral slip angle (βm) estimated by a first estimation model (M1) which is an equivalent two-wheel model taking the speed and yaw rate as inputs, and a second lateral slip angular velocity (ε2) estimated by a second estimation model (M2) which is a rigid body model taking the speed, yaw rate, and lateral acceleration as inputs, It is configured to execute performing a driving assistance process based on a yaw-rate-compensated sideslip angle with a compensation error fed back to the estimation by the first estimation model.

[0009] A third aspect of the present disclosure is A driving assistance method executed by a processor (12) for performing a driving assistance process for assisting the driving of a host vehicle (2), the method comprising: acquiring a speed (V), a yaw rate (ω), and a lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle; monitoring a difference between a first sideslip angular velocity (ε1) correlated with a sideslip angle (βm) estimated by a first estimation model (M1) which is an equivalent two-wheeled model taking the speed and the yaw rate as inputs, and a second sideslip angular velocity (ε2) estimated by a second estimation model (M2) which is a rigid body model taking the lateral acceleration as an input together with the speed and the yaw rate, as a compensation error (Δε); including performing a driving assistance process based on a yaw-rate-compensated sideslip angle with a compensation error fed back to the estimation by the first estimation model.

[0010] A fourth aspect of the present disclosure is A driving assistance program stored in a storage medium (10) and including instructions for causing a processor (12) to execute a driving assistance process for assisting the driving of a host vehicle (2), the program comprising: acquiring a speed (V), a yaw rate (ω), and a lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle; monitoring a difference between a first sideslip angular velocity (ε1) correlated with a sideslip angle (βm) estimated by a first estimation model (M1) which is an equivalent two-wheeled model taking the speed and the yaw rate as inputs, and a second sideslip angular velocity (ε2) estimated by a second estimation model (M2) which is a rigid body model taking the lateral acceleration as an input together with the speed and the yaw rate, as a compensation error (Δε); instructions for causing the driving assistance process to be performed based on a yaw-rate-compensated sideslip angle with a compensation error fed back to the estimation by the first estimation model.

[0011] As described above, in the first to fourth aspects, as specific physical quantities related to the running of the host vehicle, speed, yaw rate, and lateral acceleration are acquired. Therefore, the first to fourth aspects use in combination a first estimation model, which is an equivalent two-wheel model that takes speed and yaw rate as inputs, and a second estimation model, which is a rigid body model that takes lateral acceleration as an input together with speed and yaw rate.

[0012] Specifically, according to the first to fourth aspects, a first lateral slip angular velocity correlated with the lateral slip angle estimated by the first estimation model is monitored as a compensation error, which is the difference from a second lateral slip angular velocity estimated by a second estimation model that can reflect the slip characteristics of the wheels in the host vehicle. According to this, by assuming a compensation error for compensating the yaw rate as an error caused by the slip characteristics of the wheels in the estimation of the lateral slip angle by the first estimation model, the estimation of the lateral slip angle becomes accurate. Therefore, by performing the running support process based on the lateral slip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model, it becomes possible to perform the running support process that ensures the running support accuracy of the host vehicle.

Brief Description of the Drawings

[0013]

Figure 1

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Figure 12

Figure 13

Mode for Carrying Out the Invention

[0014] Hereinafter, a plurality of embodiments of the present disclosure will be described with reference to the drawings. In each embodiment, the same reference numerals may be assigned to corresponding components, and redundant explanations may be omitted. Further, when only a part of the configuration is described in each embodiment, the configuration of other embodiments described previously can be applied to the other parts of the said configuration. Furthermore, not only the combinations of configurations explicitly shown in the description of each embodiment, but also the configurations of a plurality of embodiments can be partially combined with each other as long as there is no problem with the combination.

[0015] (First Embodiment) The driving support system 1 of the first embodiment shown in FIG. 1 performs driving support processing for supporting the driving of the host vehicle 2. From the perspective centered on the host vehicle 2, the host vehicle 2 can also be said to be an ego-vehicle. The host vehicle 2 is a moving body such as an automobile that can travel on a road in the state where a passenger is on board.

[0016] In the host vehicle 2, an automated driving mode is provided that is classified into levels according to the degree of manual intervention of the occupant in the dynamic driving task. The automated driving mode may be realized by autonomous driving control in which the system during operation executes all dynamic driving tasks, such as conditional driving automation, highly automated driving, or fully automated driving. The automated driving mode may be realized by advanced driving assistance control in which the occupant executes some or all of the dynamic driving tasks, such as driving assistance or partial driving automation. The automated driving mode may be realized by either one, combination, or switching of the autonomous driving control and the advanced driving assistance control.

[0017] The host vehicle 2 is equipped with a sensor system 5, a communication system 6, and a map database 7. The sensor system 5 acquires sensor information available to the driving assistance system 1 for the external and internal environments of the host vehicle 2. For this purpose, the sensor system 5 is configured to include an external sensor 50 and an internal sensor 52 shown in FIGS. 1 and 2. The external sensor 50 acquires external information as sensor information from the external environment that is the surrounding environment of the host vehicle 2. The external sensor 50 may include a target detection type that detects a target existing in the external environment of the host vehicle 2. The external sensor 50 of the target detection type is at least one of, for example, a camera, LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging), radar, and sonar.

[0018] The internal sensor 52 acquires internal information as sensor information from the internal environment that is the internal environment of the host vehicle 2. The internal sensor 52 includes a physical quantity detection type that detects a specific motion physical quantity in the internal environment of the host vehicle 2. The internal sensor 52 of the physical quantity detection type is a plurality of types including at least the sensors 52a, 52b, and 52c among a speed sensor 52a, a gyro sensor 52b, an acceleration sensor 52c, a steering angle sensor, a steering angle sensor, an accelerator sensor, and a brake sensor. Here, in particular, the gyro sensor 52b and the acceleration sensor 52c may be integrated as an inertial sensor (IMU: Inertial Measurement Unit).

[0019] The communication system 6 acquires communication information available to the driving support system 1 by wireless communication. The communication system 6 may include a positioning type that receives a positioning signal from an artificial satellite of GNSS (Global Navigation Satellite System) existing outside the host vehicle 2. The communication system 6 of the positioning type is, for example, a GNSS receiver or the like. The communication system 6 may include a V2X type that transmits and receives communication signals to and from a V2X system existing outside the host vehicle 2. The communication system 6 of the V2X type is at least one of, for example, a DSRC (Dedicated Short Range Communications) communicator and a cellular V2X (C-V2X) communicator. The communication system 6 may include a terminal communication type that transmits and receives communication signals to and from a terminal existing inside the host vehicle 2. The communication system 6 of the terminal communication type is at least one of, for example, a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, and an infrared communication device.

[0020] The map database 7 acquires and stores map information available to the driving support system 1, for example, by communication with an external center via the communication system 6 of the V2X type. The map database 7 is configured to include at least one type of non-transitory tangible storage medium such as, for example, a semiconductor memory, a magnetic medium, and an optical medium. The map database 7 may be a database of a locator that estimates the self-state quantity including the self-position of the host vehicle 2. The map database 7 may be a database of a navigation unit that navigates the driving route of the host vehicle 2. The map database 7 may be configured by a combination of a plurality of types of these databases or the like.

[0021] The driving support system 1 shown in Fig. 1 is configured to include at least one dedicated computer. The driving support system 1 is connected to the sensor system 5, the communication system 6, and the map database 7 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line. When there are a plurality of dedicated computers constituting the driving support system 1, the connection between these dedicated computers is the same.

[0022] The dedicated computer constituting the driving support system 1 may be a driving control ECU (Electronic Control Unit) that controls the driving of the host vehicle 2. The dedicated computer constituting the driving support system 1 may be a navigation ECU that navigates the driving route of the host vehicle 2. The dedicated computer constituting the driving support system 1 may be a locator ECU that estimates the self-state quantity of the host vehicle 2. The dedicated computer constituting the driving support system 1 may be an actuator ECU that controls the driving actuator of the host vehicle 2. The dedicated computer constituting the driving support system 1 may be an HCU (HMI (Human Machine Interface) Control Unit) that controls the information presentation in the host vehicle 2. The dedicated computer constituting the driving support system 1 may be a computer other than the host vehicle 2 that constructs, for example, an external center or a mobile terminal capable of communicating via a V2X type communication system 6.

[0023] The dedicated computer that constitutes the travel support system 1 has at least one memory 10 and one processor 12. The memory 10 is a non-transitory tangible storage medium, such as at least one of a semiconductor memory, a magnetic medium, and an optical medium, that stores a program readable by the computer and data, etc., non-temporarily. Here, storage may be an accumulation in which data is retained even when the host vehicle 2 is powered off, or it may be a temporary storage in which data is erased when the host vehicle 2 is powered off. The processor 12 includes at least one of, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), and a GSP (Graph Streaming Processor) as a core.

[0024] In the travel support system 1, the processor 12 executes a plurality of instructions included in the travel support program stored in the memory 10 in order to perform the travel support process of the host vehicle 2 in each repeated processing cycle. As a result, the travel support system 1 constructs a plurality of functional blocks for performing the travel support process of the host vehicle 2. The plurality of functional blocks constructed in the travel support system 1 include a physical quantity acquisition block 100, a skid estimation block 110, and a support process block 120 as shown in FIG. 2. Among these functional blocks, the skid estimation block 110 further includes a first estimation block 111, a second estimation block 112, a first monitoring block 113, and a second monitoring block 114 as sub-functional blocks as shown in FIG. 3.

[0025] The physical quantity acquisition block 100 shown in FIG. 2 acquires specific physical quantities shown in FIGS. 4 and 5 regarding the running of the host vehicle 2. The specific physical quantities acquired at this time include the speed V of the host vehicle 2 detected by the speed sensor 52a. At the same time, the specific physical quantities include the yaw rate ω of the host vehicle 2 detected by the gyro sensor 52b. Furthermore, the specific physical quantities include the lateral acceleration G of the host vehicle 2 detected by the acceleration sensor 52c.

[0026] The sideslip estimation block 110 shown in FIG. 2 estimates the sideslip angle β of the host vehicle 2 shown in FIGS. 4 and 5 regarding the running of the host vehicle 2. Specifically, among the sideslip estimation block 110, the first estimation block 111 shown in FIG. 3 outputs the sideslip angle β estimated by the first estimation model M1 as the sideslip angle βm in the current processing cycle. At this time, the first estimation model M1 is an equivalent two-wheel model that takes the speed V and yaw rate ω acquired by the physical quantity acquisition block 100 in the current processing cycle as inputs and outputs the sideslip angle βm.

[0027] Therefore, the first estimation block 111 estimates and calculates the sideslip angle βm according to the following formula 1, which represents the first estimation model M1 by a functional formula. Here, lr in formula 1 means the distance from the center of gravity of the host vehicle 2 to the center of the rear wheel axle as shown in FIG. 4 among the wheelbase information constituting the equivalent two-wheel model that simulates the four-wheel host vehicle 2 as a two-wheel vehicle and describes the motion state. At the same time, Δε in formula 1 means the compensation error fed back from the second monitoring block 114 to the first estimation block 111 as will be described in detail later.

Formula

[0028] Of the lateral slip estimation block 110, the second estimation block 112 shown in FIG. 3 estimates a second lateral slip angular velocity ε2 that is compared with a first lateral slip angular velocity ε1 by a first monitoring block 113 as will be described in detail later, using a second estimation model M2. At this time, the second estimation model M2 is a rigid body model that takes as input the lateral acceleration G acquired by the physical quantity acquisition block 100 together with the velocity V and the yaw rate ω in the current processing cycle, and outputs the second lateral slip angular velocity ε2.

[0029] Therefore, the second estimation block 112 estimates and calculates a second lateral slip angular velocity ε2 according to the following Equation 2, which represents the second estimation model M2 by a functional expression. Here, the differential notation in Equation 2 represents the differential operation of the lateral slip angle β with respect to time t, which means the second lateral slip angular velocity ε2.

Equation

[0030] Of the lateral slip estimation block 110, the first monitoring block 113 shown in FIG. 3 monitors a first lateral slip angular velocity ε1 as a physical quantity correlated with the lateral slip angle βm output from the first estimation block 111 in the past processing cycle. At this time, the first lateral slip angular velocity ε1 is monitored by the time gradient between the lateral slip angles βm in the previous cycle and the cycle before the previous cycle in the past, as the differential operation of the lateral slip angle βm with respect to time t according to the following Equation 3.

Equation

[0031] Of the lateral slip estimation block 110, the second monitoring block 114 shown in FIG. 3 monitors a compensation error Δε for compensating the yaw rate ω in the estimation of the lateral slip angle βm by the first estimation block 111 in the current processing cycle. At this time, the compensation error Δε is defined by the following Equation 4, which represents the difference operation between the first lateral slip angular velocity ε1 monitored by the first monitoring block 113 and the second lateral slip angular velocity ε2 estimated by the second estimation block 112.

Equation

[0032] Therefore, the second monitoring block 114 feeds back to the first estimation block 111, as a compensation error Δε, the difference between the first lateral sliding angular velocity ε1 corresponding to the past processing cycle and the second lateral sliding angular velocity ε2 corresponding to the current processing cycle. In response to this, in the first estimation block 111, a lateral sliding angle βm in which the yaw rate ω in the current processing cycle is compensated according to Equation 1 based on the received compensation error Δε is estimated and output.

[0033] Now, the support processing block 120 shown in FIG. 2 performs, with respect to the host vehicle 2, a driving support process based on the lateral sliding angle βm (see also FIG. 3) in the current processing cycle, which is compensated by the compensation error Δε and output from the first estimation block 111. The driving support process performed at this time may be a motion control process that controls the motion of the host vehicle 2 based on the lateral sliding angle βm together with sensor information, communication information, and map information. One of such motion control processes is a lateral sliding control process that controls the lateral sliding angle βm so as to suppress the lateral sliding motion of the host vehicle 2. The driving support process may be a self-position estimation process that estimates the self-position of the host vehicle 2 based on the lateral sliding angle βm together with sensor information, communication information, and map information.

[0034] By the cooperation of the respective blocks 100, 110, and 120 described so far, the driving support method by which the driving support system 1 performs the driving support process for the host vehicle 2 is executed according to the driving support flow shown in FIG. 6. Note that each "S" in the driving support flow means each step executed by a plurality of instructions included in the driving support program.

[0035] The driving support flow is repeatedly executed for each processing cycle during the startup of the host vehicle 2. First, in S100, the first monitoring block 113 of the lateral sliding estimation block 110 monitors the first lateral sliding angular velocity ε1 correlated with the lateral sliding angle βm estimated by the first estimation block 111 in the past processing cycle.

[0036] In S101, the physical quantity acquisition block 100 acquires the speed V, the yaw rate ω, and the lateral acceleration G as specific physical quantities in the current processing cycle regarding the running of the host vehicle 2. In S102, the second estimation block 112 of the lateral slip estimation block 110 estimates the second lateral slip angular velocity ε2 by using a second estimation model M2 that takes as inputs the speed V, the yaw rate ω, and the lateral acceleration G acquired in the current processing cycle in S101.

[0037] In S103, the second monitoring block 114 of the lateral slip estimation block 110 monitors, as the compensation error Δη in the current processing cycle, the difference between the second lateral slip angular velocity ε2 estimated in S102 corresponding to the current processing cycle and the first lateral slip angular velocity ε1 monitored in S100 corresponding to the past processing cycle.

[0038] In S104, the first estimation block 111 of the lateral slip estimation block 110 estimates the lateral slip angle βm to be output by using a first estimation model M1 that takes as inputs the speed V and the yaw rate ω acquired in the current processing cycle in S101. At this time, the first estimation block 111 receives the feedback of the compensation error Δη, which is the difference monitored in the current processing cycle in S103. Therefore, in the current processing cycle, the first estimation block 111 estimates and outputs the lateral slip angle βm so as to compensate the yaw rate ω by the feedback compensation error Δη.

[0039] In S105, the assistance processing block 120 performs a running assistance process for the host vehicle 2 based on the lateral slip angle βm in the current processing cycle output in S104. When S105 is completed, the execution of the running assistance flow in the current processing cycle ends.

[0040] (Function and effect) Hereinafter, the function and effect of the first embodiment will be described.

[0041] According to the first embodiment, as specific physical quantities related to the running of the host vehicle 2, the speed V, the yaw rate ω, and the lateral acceleration G are acquired. Therefore, the first embodiment uses in combination a first estimation model M1, which is an equivalent two-wheel model that takes the speed V and the yaw rate ω as inputs, and a second estimation model M2, which is a rigid body model that takes the lateral acceleration G as an input together with the speed V and the yaw rate ω.

[0042] Specifically, in the first embodiment, a first lateral slip angular velocity ε1 correlated with the lateral slip angle βm estimated by the first estimation model M1 is monitored as a compensation error Δη, which is the difference from a second lateral slip angular velocity ε2 estimated by the second estimation model M2 that can reflect the slip characteristics of the wheels in the host vehicle 2. According to this, by assuming the compensation error Δη for compensating the yaw rate ω as an error resulting from the slip characteristics of the wheels in the estimation of the lateral slip angle βm by the first estimation model M1, the estimation of the lateral slip angle βm becomes accurate. Therefore, by performing the driving support process based on the lateral slip angle βm compensated for the yaw rate ω by the compensation error Δη fed back to the estimation by the first estimation model M1, it becomes possible to perform the driving support process while ensuring the driving support accuracy of the host vehicle 2.

[0043] According to the first embodiment, in each repeated processing cycle, specific physical quantities are acquired. Therefore, in the first embodiment, the difference between a first lateral slip angular velocity ε1 correlated with the lateral slip angle βm estimated corresponding to a past processing cycle and a second lateral slip angular velocity ε2 estimated corresponding to the current processing cycle is monitored as the compensation error Δη. According to this, since the compensation error Δη that can appropriately reflect the lateral slip angle βm in the past processing cycle can be used, the current processing cycle can be based on the lateral slip angle βm compensated for the yaw rate ω, so it becomes possible to increase the reliability in ensuring the accuracy of the driving support process.

[0044] (Second Embodiment) The second embodiment is a modification of the first embodiment.

[0045] As shown in FIG. 7, a filtering block 2115 is added to the sideslip estimation block 2110 of the second embodiment. This filtering block 2115 outputs the compensation error Δε fed back from the second monitoring block 114 to the first estimation block 111 via a low-pass filter F.

[0046] Specifically, for the low-pass filter F, the difference between the first sideslip angular velocity ε1 and the second sideslip angular velocity ε2 monitored by the second monitoring block 114 is passed through, and a value with the noise frequency component cut from the difference is passed through as the compensation error Δε. Here, the noise frequency component means the frequency component cut by the low-pass filter F in response to the fact that the frequency of the time change of the difference between the first sideslip angular velocity ε1 and the second sideslip angular velocity ε2 is higher than the threshold value by monitoring the time change of the difference from the past cycle to the current processing cycle due to noise such as disturbance. In the low-pass filter F, further, the proportional gain of the transfer function is further adjusted according to the difference between the first sideslip angular velocity ε1 and the second sideslip angular velocity ε2, so that the cut accuracy of the noise frequency component may be improved.

[0047] In the driving support flow shown in FIG. 8 in such a second embodiment, S2106 is substantially added between S103 and S104. Specifically, in S2106, the filtering block 2115 filters the difference monitored in the current processing cycle by S103, and feeds back the compensation error Δε with the noise frequency component cut from the difference to the estimation of the sideslip angle βm in the subsequent S104.

[0048] According to the second embodiment described so far, the difference between the first lateral slip angular velocity ε1 correlated with the lateral slip angle βm estimated corresponding to the past processing cycle and the second lateral slip angular velocity ε2 estimated corresponding to the current processing cycle is passed as an input to the low-pass filter F. According to this, the compensation error Δε with the noise frequency component cut through the low-pass filter F can be based on the lateral slip angle βm by which the yaw rate ω can be compensated with high precision in the current processing cycle, so it becomes possible to ensure the reliability in ensuring the accuracy of the driving assistance process.

[0049] (Third Embodiment) The third embodiment is a modification of the first embodiment.

[0050] As shown in FIGS. 9 and 10, the inner world sensor 3052 of the third embodiment includes a steering angle sensor 3052d. Therefore, as shown in FIG. 10, the physical quantity acquisition block 3100 of the third embodiment also acquires the steering angle θ in the current processing cycle from the steering angle sensor 3052d. However, in the host vehicle 2, the steering angle sensor 3052d that detects the steering angle θ of the wheels may be replaced by a steering angle sensor that detects the steering angle of the steering wheel corresponding to the steering angle θ.

[0051] Furthermore, as shown in FIG. 11, a third estimation block 3116 and an output determination block 3117 are added to the lateral slip estimation block 3110 of the second embodiment shown in FIG. 10. Specifically, the third estimation block 3116 derives, as a sub-lateral slip angle βs in the current processing cycle, the lateral slip angle β estimated by the third estimation model M3, separately from the main lateral slip angle βm estimated as the lateral slip angle β by the first estimation model M1. At this time, the third estimation model M3 is an equivalent two-wheel model that outputs the sub-lateral slip angle βs with the steering angle θ acquired by the physical quantity acquisition block 3100 in the current processing cycle as an input.

[0052] Therefore, the third estimation block 3116 estimates the sub-lateral slip angle βs according to the following equation 5, which represents the third estimation model M3 by a functional expression. Here, lf in Equation 5 means the distance from the center of gravity of the host vehicle 2 to the center of the front wheel axle as shown in FIG. 12, among the wheelbase information constituting the equivalent two-wheel model that describes the motion state by simulating the four-wheel host vehicle 2 as a two-wheel vehicle. Incidentally, lr in Equation 5 means the distance from the center of gravity of the host vehicle 2 to the center of the rear wheel axle, among the wheelbase information constituting the equivalent two-wheel model, in the same manner as in the case of Equation 1 described in the first embodiment.

Equation

[0053] Now, the output determination block 3117 shown in FIG. 11 determines, according to conditions, the output to the assist processing block 120 of either the main lateral slip angle βm or the sub-lateral slip angle βs estimated by the first estimation block 111 and the third estimation block 3116, respectively, in the current processing cycle. At this time, the output condition of the main lateral slip angle βm is satisfied when all of the specific physical quantities, that is, the acquisition by the physical quantity acquisition block 3100 of the physical quantities of the speed V, the yaw rate ω, and the lateral acceleration G, are all determined to be normal. On the other hand, the output condition of the sub-lateral slip angle βs is satisfied when the acquisition by the physical quantity acquisition block 3100 of at least one of the physical quantities of the speed V, the yaw rate ω, and the lateral acceleration G as the specific physical quantity is determined to be abnormal. At this time, the abnormality of the acquisition may be a state in which the detection signal from the corresponding sensor is interrupted with respect to the corresponding specific physical quantity. The abnormality of the acquisition may be a state in which the time variation from the past processing cycle exceeds the allowable range with respect to the corresponding specific physical quantity.

[0054] In the driving support flow shown in FIG. 13 in such a third embodiment, S3101 replacing S101 is executed, and accordingly, S3107 and S3108 are added. Specifically, in S3101, the physical quantity acquisition block 3100 acquires the steering angle θ in the current processing cycle, together with the speed V, yaw rate ω, and lateral acceleration G, which are inputs of at least one of S102 and S104, as the specific physical quantity in the current processing cycle. Therefore, in S3107, the third estimation block 3116 estimates a sub-slip angle βs different from the main slip angle βm by the third estimation model M3 that takes the steering angle θ acquired in the current processing cycle by S3101 as an input.

[0055] Furthermore, in S3108, the output determination block 3117 determines which one of the slip angles βm and βs estimated by S104 and S3107 in the current processing cycle is to be output for the driving support process in S105. At this time, when it is determined that all acquisitions of the specific physical quantities by S3101 are normal, the main slip angle βm estimated by S104 is output, and in S105, the driving support process based on the main slip angle βm is performed. On the other hand, when it is determined that the acquisition of at least one type of specific physical quantity by S3101 is abnormal, the sub-slip angle βs estimated by S3107 is output, and in S105, the driving support process based on the sub-slip angle βs is performed instead of the main slip angle βm.

[0056] According to the third embodiment described so far, when the acquisition of the specific physical quantity is normal, based on the main sideslip angle βm as the accurate sideslip angle β compensated by the feedback compensation error Δε for the yaw rate ω, it is possible to ensure the accuracy of the driving support process. However, in the third embodiment when the acquisition of the specific physical quantity is abnormal, instead of the main sideslip angle βm, the driving support process is performed based on the sub-sideslip angle βs estimated by the third estimation model M3 which is an equivalent two-wheel model that takes the steering angle θ of the host vehicle 2 as an input. According to this, the steering angle θ other than the specific physical quantity can be effectively utilized for the fail-safe regarding the estimation of the sub-sideslip angle βs and the driving support process so that the accuracy degradation due to the abnormally acquired specific physical quantity can be suppressed with respect to the estimation of the main sideslip angle βm and the driving support process. Note that the above third embodiment may be applied to the second embodiment described above.

[0057] (Other embodiments) Although a plurality of embodiments have been described above, the present disclosure is not to be construed as being limited to those embodiments, and can be applied to various embodiments and combinations without departing from the gist of the present disclosure.

[0058] In the modification examples of the first to third embodiments, the dedicated computer constituting the driving support system 1 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is, for example, at least one type among ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), and CPLD (Complex Programmable Logic Device). Further, such a digital circuit may have a memory storing a program.

[0059] In addition to the description forms up to this point, the above-described embodiments and modification examples may be implemented in the form of a processing circuit (e.g., a processing ECU or the like) or a semiconductor device (e.g., a semiconductor chip or the like) as a driving support device configured to be mounted on a host moving body and having at least one of a processor 12 and a memory 10 each.

Explanation of Signs

[0060] 1: Driving support system, 2: Host vehicle, 10: Memory, 12: Processor, F: Low-pass filter, G: Lateral acceleration, M1: First estimation model, M2: Second estimation model, M3: Third estimation model, V: Speed, Δε: Compensation error, βm: Side slip angle · Main side slip angle, βs: Sub side slip angle, ε1: First side slip angular velocity, ε2: Second side slip angular velocity, θ: Steering angle, ω: Yaw rate

Claims

1. A driving support system having a processor (12) and performing a driving support process for assisting the driving of a host vehicle (2), wherein the processor acquires a speed (V), a yaw rate (ω), and a lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle, monitors a difference between a first lateral slip angular velocity (ε1) correlated with a lateral slip angle (βm) estimated by a first estimation model (M1), which is an equivalent two-wheeled model taking the speed and the yaw rate as inputs, and a second lateral slip angular velocity (ε2) estimated by a second estimation model (M2), which is a rigid body model taking the speed, the yaw rate, and the lateral acceleration as inputs, as a compensation error (Δε), and is configured to execute the driving support process based on the lateral slip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model.

2. The acquiring of the specific physical quantity includes acquiring the specific physical quantity in each repeated processing cycle, the monitoring of the compensation error includes monitoring the difference between the first lateral slip angular velocity correlated with the lateral slip angle estimated corresponding to a past processing cycle and the second lateral slip angular velocity estimated corresponding to the current processing cycle as the compensation error, and the performing of the driving support process includes performing the driving support process based on the lateral slip angle compensated for the yaw rate by the compensation error in the current processing cycle. The driving support system according to claim 1.

3. The performing of the driving support process includes performing the driving support process based on the lateral slip angle compensated for the yaw rate in the current processing cycle by the compensation error having noise frequency components cut through a low-pass filter (F) taking the difference as an input in the estimation by the first estimation model. The driving support system according to claim 2.

4. The performing of the driving support process includes, when the acquisition of the specific physical quantity is normal, performing the driving support process based on a main lateral slip angle (βm) as the lateral slip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model. When the acquisition of the specific physical quantity becomes abnormal, instead of the main side slip angle, the running assistance process is performed based on a sub side slip angle (βs) estimated by a third estimation model (M3) which is an equivalent two-wheel model using the steering angle (θ) of the host vehicle as an input. The running assistance device according to claim 1 or 2, including this.

5. Performing the running assistance process The running assistance device according to claim 1 or 2, including performing, as the running assistance process, a motion control process for controlling the motion of the host vehicle.

6. Performing the running assistance process The running assistance device according to claim 5, including performing, as the running assistance process, a side slip control process for controlling the side slip motion of the host vehicle.

7. Performing the running assistance process The running assistance device according to claim 1 or 2, including performing, as the running assistance process, a self-position estimation process for estimating the self-position of the host vehicle.

8. A running assistance device having a processor (12), configured to be mounted on a host vehicle (2), and performing a running assistance process for assisting the running of the host vehicle, The processor Acquiring speed (V), yaw rate (ω), and lateral acceleration (G) as specific physical quantities related to the running of the host vehicle, Monitoring, as a compensation error (Δε), the difference between a first side slip angular velocity (ε1) correlated with a side slip angle (βm) estimated by a first estimation model (M1) which is an equivalent two-wheel model using the speed and the yaw rate as inputs, and a second side slip angular velocity (ε2) estimated by a second estimation model (M2) which is a rigid body model using the speed, the yaw rate, and the lateral acceleration as inputs, The running assistance device is configured to execute performing the running assistance process based on the side slip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model.

9. A running assistance method executed by a processor (12) to perform a running assistance process for assisting the running of a host vehicle (2), Acquiring speed (V), yaw rate (ω), and lateral acceleration (G) as specific physical quantities related to the running of the host vehicle, A first yaw rate of sideslip (ε1) correlated with a sideslip angle (βm) estimated by a first estimation model (M1), which is an equivalent two-wheel model taking the speed and the yaw rate as inputs, and a second yaw rate of sideslip (ε2) estimated by a second estimation model (M2), which is a rigid body model taking the speed, the yaw rate, and the lateral acceleration as inputs, are monitored with the difference therebetween being a compensation error (Δε). A driving assistance method including performing the driving assistance process based on the sideslip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model.

10. A driving assistance program stored in a storage medium (10) and including instructions to be executed by a processor (12) to perform a driving assistance process for assisting the driving of a host vehicle (2), acquiring a speed (V), a yaw rate (ω), and a lateral acceleration (G) as specific physical quantities related to the driving of the host vehicle, monitoring a difference between a first yaw rate of sideslip (ε1) correlated with a sideslip angle (βm) estimated by a first estimation model (M1), which is an equivalent two-wheel model taking the speed and the yaw rate as inputs, and a second yaw rate of sideslip (ε2) estimated by a second estimation model (M2), which is a rigid body model taking the speed, the yaw rate, and the lateral acceleration as inputs, with the difference therebetween being a compensation error (Δε), The driving assistance program including the instructions to execute performing the driving assistance process based on the sideslip angle compensated for the yaw rate by the compensation error fed back to the estimation by the first estimation model.

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