KR20210090313A - Method for Following Real Time Target Vehicle Speed and Vehicle Acceleration/Deceleration Learning System Thereof - Google Patents

Abstract

A method for learning real-time following of a target vehicle speed is implemented by a vehicle acceleration/deceleration learning system (1). A test driving point setting UI is set using a training control system (10). Accelerator acceleration learning and brake deceleration/acceleration deceleration learning is sequentially performed on a training vehicle (100) by using a test driving model (11-1) having, as a test map (11-1A), an acceleration map and a deceleration map, and thus an acceleration/deceleration learning result is secured. Learning of following of a target vehicle speed using the current vehicle speed [km/h] and an accelerator and brake voltage for each vehicle speed [km/h] is performed using a driver model (11-2) having an acceleration map and a deceleration map matching the acceleration/deceleration learning result, and thus a vehicle can follow the set target vehicle speed until reaching the final target vehicle speed. As a result, the limitations of an existing target vehicle speed following algorithm that simply follows the final target vehicle speed are overcome.

Description

{Method for Following Real Time Target Vehicle Speed and Vehicle Acceleration/Deceleration Learning System Thereof}

The present invention relates to a method for following a target vehicle speed, and more particularly, to a vehicle acceleration/deceleration learning system in which a target vehicle speed can be tracked in real time by matching an acceleration/deceleration map with vehicle acceleration/deceleration learning.

The increasing demand for driving safety in vehicles in recent years is increasing the application of driving safety systems such as Highway Driving Assist (HDA) and Cruise Control (CC) to vehicles.

For example, the HDA program controls the vehicle speed of the own vehicle so as to secure an inter-vehicle distance from the surrounding vehicle without driver intervention from detection information of the surrounding vehicle while driving the vehicle, and the CC program provides the own vehicle from the acquired information of the driving vehicle and the surrounding vehicle. It sets the target vehicle speed and controls the current vehicle speed with the target vehicle speed without driver intervention.

As described above, the HDA program and the HDA program follow the target vehicle speed, thereby satisfying the driver's vehicle driving safety requirements and improving vehicle marketability.

Domestic Patent Publication 2018-0084744 A (2018.07.25)

However, the target vehicle speed tracking algorithm applied to the current driving safety system is inevitably a method that cannot follow the set target vehicle speed until the final target vehicle speed is reached by simply following the final target vehicle speed.

Therefore, in consideration of the above points, the present invention creates a learning map of acceleration/deceleration as a result of learning the accelerator force and learning the brake deceleration force/accel deceleration force, and uses the current vehicle speed and vehicle speed differential accelerator and brake voltage to master the target vehicle speed. A vehicle target vehicle speed real-time tracking learning method and vehicle acceleration/deceleration learning system that can overcome the limitations of the existing target vehicle speed tracking algorithm, which used to simply follow the final target vehicle speed, with target vehicle speed tracking set until the final target vehicle speed is reached. is intended to provide

In the vehicle target vehicle speed real-time tracking learning method of the present invention for achieving the above object, the learning control system starts the acceleration/deceleration learning for the learning vehicle in connection with the dyno system as a test driving driving point setting UI, and learning the acceleration/deceleration speed Acceleration map and deceleration map built in the test map of the test driving model are matched as the learning result by sequentially executing the Excel acceleration force learning, the brake deceleration force learning, and the Excel force learning to which the test driving driving point setting UI is applied. a test driving model acceleration/deceleration learning, matching the vehicle speed curve of the test map to the acceleration/deceleration learning result to generate a learning map of the driver model, setting a vehicle speed difference of the learning vehicle based on the learning map, and the vehicle speed difference It is characterized in that the driver model acceleration/deceleration learning is included to verify the learning map by sequentially executing the learning of the accelerator acceleration force, the learning of the brake deceleration force, and the learning of the accelerator deceleration force to follow the .

As a preferred embodiment, an accelerator acceleration point, a brake deceleration point, an accelerator deceleration point, and a deceleration start vehicle speed are applied to the test driving driving point setting UI, the accelerator acceleration point, the brake deceleration point, the accelerator deceleration point, and the deceleration start Each of the vehicle speeds is given a holding time as a point length.

As a preferred embodiment, the driver model acceleration/deceleration learning includes the loading step of the acceleration/deceleration learning data in which the test driving driving point setting UI is downloaded to the test driving model, and the output of the accelerator pedal amount and the brake amount provided by the learning control system is external. The controller outputs the accelerator pedal amount voltage signal and the brake amount voltage signal to the dyno system, respectively, and the learning vehicle performs the learning of the accelerator acceleration force, the learning of the brake deceleration force, and the learning of the accelerator deceleration force in sequence.

In a preferred embodiment, the learning control system is connected to the external controller through CAN communication to transmit a CAN message.

As a preferred embodiment, the loading of the acceleration/deceleration force learning data includes the accelerator pedal_accel_length of the excel acceleration point applied as the test driving driving point setting UI, the brake_deceleration_length of the brake deceleration point, and the accelerator_deceleration_ of the accelerator deceleration point. length, it is performed by reading the vehicle speed_length during deceleration of the deceleration start vehicle speed.

As a preferred embodiment, the Excel acceleration force learning is performed in the Excel acceleration interval giving step in which CAN message initialization, acceleration map matching, and timer setting are performed in the driving state of the learning vehicle, Excel of the Excel acceleration point among the parameters of the acceleration/deceleration learning data loading Acceleration by applying the pedal_acceleration_length to implement the accelerator pedal acceleration for one acceleration point, stopping the driving of the learning vehicle after the accelerator pedal acceleration for each acceleration point, and performing all of the accelerator pedal accelerations for each acceleration point Completing the setting UI and generating an acceleration map of the learning map by matching the Excel acceleration force learning result with the acceleration map of the test map.

As a preferred embodiment, the timer applies 90 seconds to the learning of the accelerator force, and the accelerator pedal accelerates the learning vehicle to 125 [Km/h].

As a preferred embodiment, the stopping of the operation is deceleration by manipulating the brake pedal of the dyno system or the pedal robot, and a standby condition is applied for a predetermined time after vehicle speed 0 [km/h] to the repetition of the accelerator pedal acceleration.

As a preferred embodiment, the learning of the brake deceleration force is a system target deceleration driving step in which the brake amount set by the external controller is applied as a deceleration value of the learning vehicle, CAN message initialization in the driving state of the learning vehicle, and the test driving driving point Target vehicle speed setting for brake operation as deceleration start vehicle speed_length of deceleration start vehicle speed in the setting UI, deceleration map matching, timer setting, and brake_deceleration_length condition of the brake deceleration point among the parameters of the acceleration/deceleration learning data loading are applied. In the step of applying the brake deceleration interval, the brake pedal deceleration for one brake deceleration point is performed by applying the brake_deceleration_length, the brake pedal operation is performed through the brake voltage signal set by the external controller, and the learning vehicle is decelerated. The deceleration point brake pedal deceleration step in which the vehicle speed is confirmed to be 0 [Km/h], and a step in which the brake deceleration setting UI is completed by performing all manipulations of the brake pedal for each brake deceleration point.

As a preferred embodiment, the set brake amount applies a 30 [%] brake control amount, and the timer applies a waiting time of 20 seconds to learning the brake deceleration force.

As a preferred embodiment, in the provision of the brake deceleration interval, if the brake_deceleration_length condition is not satisfied, the vehicle speed is reduced to set the brake count to 0, and then the CAN message is initialized to start the provision of the brake deceleration interval again.

As a preferred embodiment, the deceleration point brake pedal deceleration uses the target vehicle speed as the target brake and outputs a forced brake signal when a forced brake is determined so that the vehicle speed becomes 0 [Km/h] by operating the brake pedal of the dyno system. slowing the vehicle.

As a preferred embodiment, completion of the brake deceleration setting UI initializes the CAN message and the brake deceleration point, and is converted to learning the accelerator deceleration force.

As a preferred embodiment, the learning of the accelerator deceleration force is a system target deceleration driving step in which the accelerator pedal control amount set by the external controller is applied as a deceleration value of the learning vehicle, CAN message initialization in the driving state of the learning vehicle, and the test driving operation Set the target vehicle speed for Excel operation as the deceleration start vehicle speed_length of the deceleration start vehicle speed in the point setting UI, match the deceleration map, set the timer, and apply the Excel_deceleration_length condition of the Excel deceleration point among the parameters of the acceleration/deceleration learning data loading In this step of providing the accelerator deceleration interval, the accelerator pedal deceleration for one accelerator deceleration point is performed by applying the accelerator_deceleration_length, and the accelerator pedal operation is performed through the brake voltage signal set by the external controller, and the learning vehicle decelerates. The deceleration point accelerator pedal deceleration step at which the vehicle speed is confirmed to be 0 [Km/h], the accelerator deceleration setting UI is completed by performing all operations of the accelerator pedal for each accelerator deceleration point, and the CAN message and the accelerator deceleration point are initialized. Step, the deceleration map of the learning map is generated by matching the brake deceleration force learning result and the accelerator deceleration force learning result to the deceleration map of the test map.

As a preferred embodiment, the set accelerator pedal control amount is 30 [%] the accelerator pedal control amount is applied, and the timer applies a waiting time of 120 seconds to learning the accelerator deceleration force.

As a preferred embodiment, the deceleration point accelerator pedal deceleration uses the target vehicle speed as the target brake and outputs a forced brake signal when a forced brake is determined, so that the vehicle speed becomes 0 [Km/h] by operating the brake pedal of the dyno system. slowing the vehicle.

As a preferred embodiment, in the driver model acceleration/deceleration learning, an acceleration map is generated among the learning maps of the driver model by matching the accelerator learning result to the acceleration map of the test map, and learning the brake deceleration force in the deceleration map of the test map A step of generating a deceleration map among the learning map of the driver model by matching the result and the accelerator learning result, accelerating the learning vehicle with the accelerator pedal voltage for each current vehicle speed [km/h] and vehicle speed difference [km/h] A step of confirming target vehicle speed tracking performance by the acceleration map of the learning map, decelerating the learning vehicle with the current vehicle speed [km/h] and the brake control robot voltage for each vehicle speed difference [km/h] deceleration map of the learning map This step is performed in which the target vehicle speed tracking performance by

In a preferred embodiment, the vehicle speed difference is calculated by subtracting the current vehicle speed from the target vehicle speed after 1 second.

And the vehicle acceleration/deceleration learning system of the present invention for achieving the above object is a test driving model in which a test driving driving point setting UI is set, and an acceleration map and a deceleration map as a test map. Acceleration/deceleration learning results are secured by sequentially executing brake deceleration force learning and accelerator deceleration force learning, and target vehicle speed master-slave learning is performed with a driver model equipped with an acceleration map and a deceleration map that match the acceleration and deceleration learning results as a learning map. learning control system; an external controller that exchanges a CAN message with the learning control system and outputs an accelerator pedal amount control voltage signal and a brake amount control voltage signal, respectively; and a dyno system for accelerating the learning vehicle by ETC control according to the accelerator pedal amount control voltage signal and decelerating the learning vehicle by controlling the brake pedal and accelerator pedal according to the brake amount control voltage signal.

In a preferred embodiment, the learning control system includes an automation program device for constructing the test driving model and the driver model, and a target vehicle speed program device for setting a target vehicle speed for learning the target vehicle speed, master/slave.

As a preferred embodiment, a pedal robot is applied to control the brake pedal.

The real-time vehicle target vehicle speed tracking learning method using the vehicle acceleration/deceleration learning system of the present invention implements the following actions and effects.

First, the target vehicle speed tracking algorithm can be built with acceleration/deceleration learning to control the accelerator pedal amount [%] and the brake amount [%] at a set point. Second, by inputting the current vehicle speed [km/h] and the vehicle speed difference [km/h] as inputs, the accelerator pedal amount [%] and the brake amount [%] are output in real time until the target vehicle speed reaches the final target vehicle speed. This may be performed according to the set target vehicle speed. Third, the vehicle acceleration/deceleration learning algorithm-based map that inputs the current vehicle speed [km/h] and the vehicle speed difference [km/h] is used to build a driver model for following the target vehicle speed, thereby tracking the target vehicle speed and cruise control. Applicability is easily achieved. Fourth, by being modularized into a drive model, the performance of highway driving assistance and cruise control that tracks the target vehicle speed can be improved, and vehicle marketability can be improved.

1 is a flowchart of a vehicle target vehicle speed real-time tracking learning method using acceleration/deceleration learning according to the present invention, and FIG. 2 is an example of a vehicle acceleration/deceleration learning system in which acceleration/deceleration learning for following a target vehicle speed according to the present invention is performed. , FIG. 3 is an example of application of the test driving driving point setting UI according to the present invention, FIG. 4 is a flowchart of loading acceleration/deceleration learning data according to the present invention, and FIGS. 5 and 6 are flowcharts of learning the acceleration force of Excel according to the present invention 7 is an example of an acceleration driving result diagram (A) and an acceleration map (B) through learning of the accelerator force according to the present invention, and FIGS. 8 and 9 are flowcharts of learning the brake deceleration force according to the present invention, FIG. 10 and 11 are flowcharts of learning the accelerator deceleration force according to the present invention, and FIG. 12 is an example of a deceleration driving result diagram (C) and a deceleration map (D) through brake deceleration force learning/excel deceleration force learning according to the present invention , and FIG. 13 is a test example of a driver model based on acceleration/deceleration learning according to the present invention, and FIG. 14 is an example of an FTP (Federal Test Procedure) mode driving result of the driver model according to the present invention. 15 is an example of a mode driving result of WLTP (Worldwide Harmonized Light-duty vehicle Test Procedure) of the driver model according to the present invention.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying illustrative drawings, and since such an embodiment may be implemented in various different forms by those of ordinary skill in the art to which the present invention pertains as an example, it will be described herein It is not limited to the embodiment.

Referring to FIG. 1 , the vehicle target vehicle speed real-time tracking learning method is performed by preparing for vehicle acceleration/deceleration learning (S10 to S20), followed by test driving model learning (S30 to S70), followed by driver model learning (S80 to S84).

In particular, the test driving model learning (S30 to S70) is an algorithm for learning the acceleration/deceleration force of the current vehicle by performing the accelerator acceleration force learning (S50), the brake deceleration force learning (S60), and the accelerator deceleration force learning (S70). .

In this way, the learning of the accelerator deceleration force (S70) together with the learning of the brake deceleration force (S60) in the test driving model learning (S30 to S70) may be performed by a brake operation in which the vehicle decelerates by pressing the brake pedal at a constant vehicle speed, It is based on the fact that it can be achieved by not pressing the accelerator pedal in the vehicle or by stepping on the accelerator a little.

In addition, the driver model learning (S80 to S84) is implemented as an algorithm for controlling the accelerator and brake to follow a predetermined target vehicle speed, and is applied to highway driving assistance and cruise control.

Therefore, the vehicle target vehicle speed real-time tracking learning method learns the acceleration/deceleration by controlling the accelerator pedal amount [%] and the brake amount [%] at a point determined by the acceleration/deceleration learning algorithm, and the learning result is the current vehicle speed [km/h] and It is possible to output the accelerator pedal amount [%] and brake amount [%] in real time by following the target vehicle speed [km/h] with the driver model reflected in the acceleration/deceleration map with the vehicle speed difference [km/h] as input.

As a result, the vehicle target vehicle speed real-time tracking learning method is characterized as a vehicle target vehicle speed real-time tracking learning method through acceleration/deceleration learning, and these characteristics are the current vehicle speed [km/h] and the accelerator and brake voltage for each vehicle speed difference [km/h] By using target vehicle speed master/slave learning, it is possible to follow the target vehicle speed set until the final target vehicle speed is reached, thereby overcoming the limitations of the existing target vehicle speed master/slave algorithm that simply tracks the final target vehicle speed.

In addition, the vehicle target vehicle speed real-time tracking learning method through the acceleration/deceleration learning is applied to highway driving assistance and cruise control that follow the target vehicle speed by being built as a driver model, thereby improving system performance and enabling vehicle marketability.

Hereinafter, the vehicle target vehicle speed real-time tracking learning method of FIG. 1 will be described in detail with reference to FIGS. 2 to 15 . In this case, the control subject is the automation program device 11 and the external controller 20 or the automation program device 11, the target vehicle speed programming device 13 and the external controller 20, and the control target is the pedal robot 30 and the dyno. A learning vehicle 100 associated with the system 40 .

First, the preparation for learning the vehicle acceleration/deceleration (S10 to S20) is performed by the vehicle acceleration/deceleration learning system setting step of S10 and the test driving driving point setting UI establishment step of S20.

Referring to FIG. 2 , the vehicle acceleration/deceleration learning system 1 includes a learning control system 10 , an external controller 20 , a pedal robot 30 , and a dyno system 40 .

For example, the learning control system 10 is an automated program device 11 in which an algorithm for controlling the accelerator and brake while performing acceleration/deceleration learning of the learning vehicle 100 is built as a test driving model 11-1, learning An algorithm for the vehicle 100 to follow a predetermined target vehicle speed is divided into a target vehicle speed program device 13 constructed as a target vehicle speed driving model. In this case, the test driving model 11-1 includes a test map 11-1A divided into an acceleration map and a deceleration map.

In particular, the automation program device 11 further includes a driver model 11-2 (refer to FIG. 13), and the driver model 11-2 updates the test map 11-1A with the vehicle acceleration/deceleration learning results. Alternatively, the mapped learning map 11-2A is included.

For example, the external controller 20 is connected to the automation program device 11 and CAN (Controller Area Network) communication to exchange CAN messages, and control the dyno system 40 . To this end, the external controller 20 generates a voltage signal output for controlling the accelerator pedal amount [%] and a voltage signal output for controlling the brake amount [%].

As an example, the pedal robot 30 operates the brake pedal and the accelerator pedal of the learning vehicle 100 with the output values of the external controller 20 for the accelerator pedal amount [%] and the brake amount [%] to operate the learning vehicle 100 ) to brake deceleration and accelerator deceleration. The dyno system 40 is a system for automatically following a target vehicle speed, and includes an Electronic Control Unit (ECU), an Electronic Throttle Control Apparatus (ETC), an external Digital to Analog Converter (DAC) module, and a brake control device as system components. and, in particular, may include the pedal robot 30 as a brake control device. Therefore, the pedal robot 30 and the dyno system 40 are typical driving test equipment.

For example, the learning vehicle 100 is a general vehicle.

Therefore, the setting of the vehicle acceleration/deceleration learning system ( S10 ) means the connection and system activation of the vehicle acceleration/deceleration learning system 1 and the learning vehicle 100 .

Referring to FIG. 3 , an example of a user interface (UI) applied to the automation program device 11 for establishing the test driving driving point setting UI ( S20 ) is shown.

Therefore, the test driving driving point setting UI ( S20 ) means setting of the vehicle acceleration/deceleration learning variable downloaded to the automation program device 11 . In this case, in establishing the test driving driving point setting UI, the accelerator acceleration point, brake deceleration point, and accelerator deceleration point are applied while dividing the accelerator acceleration [%], brake deceleration [%], and accelerator deceleration [%] into setting points, and the target The deceleration start vehicle speed with the deceleration vehicle speed [Km/h] as the set point for following the vehicle speed is applied.

In particular, the accelerator acceleration point sets the accelerator pedal_acceleration_length to 7, the brake deceleration point sets the brake_deceleration_length to 6, and the accelerator deceleration point sets the accelerator_deceleration_length to 6, and starts the deceleration. When decelerating the vehicle speed, set the vehicle speed_length to 1.

Then, the automation program device 11 performs the test driving model learning (S30 to S70) in the test driving model execution step of S30, the acceleration/deceleration learning data loading step of S40, the accelerator acceleration force learning step of S50, and the brake deceleration force learning step of S60. , performed as the learning step of the accelerator deceleration force of S70.

Referring to FIG. 2 , the automation program device 11 transmits a control value to the external controller 20 through CAN, and the external controller 20 provides a voltage signal and/or brake amount for controlling the accelerator pedal amount [%] [%] A voltage signal for control is output to the pedal robot 30 and the dyno system 40 .

Then, the dyno system 40 operates the system components to control the driving state of the learning vehicle 100 . For example, the ETC of the dyno system 40 controls the ETC by the amount of the accelerator pedal [%] to accelerate the learning vehicle 100, and the brake control device of the pedal robot 30 or the dyno system 40 controls the brake amount [ %] or by operating the accelerator pedal by the amount of the accelerator pedal [%], thereby decelerating the learning vehicle 100 .

For example, the learning control system 10 is an automated program device 11 in which an algorithm for controlling the accelerator and brake while performing acceleration/deceleration learning of the learning vehicle 100 is built as a test driving model 11-1, learning An algorithm for the vehicle 100 to follow a predetermined target vehicle speed is divided into a target vehicle speed program device 13 constructed as a target vehicle speed driving model. In this case, the test driving model 11-1 includes a test map 11-1A divided into an acceleration map and a deceleration map.

For example, the test driving model execution (S30) is performed by starting the algorithm of the test driving model 11-1 in the automation program device 11, and the acceleration/deceleration learning data loading (S40) is performed by the automation program device 11 In the test driving driving point setting UI (S20), the accelerator acceleration point, the brake deceleration point, the accelerator deceleration point, and the deceleration start vehicle speed are read.

Referring to FIG. 4 , the loading of the acceleration/deceleration learning data ( S40 ) consists of a variable initialization step of S41 and a test map matching step of S42 .

For example, in the activation of the test driving model ( S41 ), the activation of the test driving model ( 11 - 1 ) releases the program monitoring limit and initialization of the monitoring variable values of the algorithm, and the test map matching ( S42 ) is the test map ( 11-) The test driving driving point setting UI matching for the acceleration map and deceleration map of 1A) is made, and through this, the accelerator pedal_accel_length and brake_deceleration for the accelerator acceleration point, brake deceleration point, accelerator deceleration point, and deceleration start vehicle speed are made. _length, Excel_deceleration_length, and vehicle speed_length when decelerating are applied.

Specifically, the learning of the accelerator force (S50) is the vehicle driving step of S51, the step of providing the accelerator acceleration interval of S52, the acceleration point (%) of the accelerator pedal acceleration step of S53, the vehicle driving stop step of S54, and the completion of the acceleration setting UI of S55. , the acceleration map generation step of S55 is performed.

For example, the vehicle driving ( S51 ) means that the learning vehicle 100 is driven in a test lab, and means a warm-up setting state of the automation program device 11 for applying the Excel acceleration point in the test driving driving point setting UI. .

Referring to FIG. 5 , it is exemplified that the detailed procedure for granting the accelerator acceleration interval ( S52 ) is performed in steps S52-1 to S52-7. However, steps S52-1 to S52-7 may be sequentially applied, or appropriate step integration/omission/order change may be made according to system conditions or test items.

For example, S52-1 is a measurement start step, S52-2 is a CAN message initialization step, S52-3 is a system standby step with about 5 seconds applied, S52-4 is an accelerator pedal_acceleration_length determination step, and S52-5 is a target An acceleration map mapping step of the vehicle speed, S52-6 may be divided into a timer setting step applying about 90 seconds, and S52-7 may be divided into a starting step of learning the accelerator force by timer counting.

On the other hand, the acceleration point (%) accelerator pedal acceleration S53 is matched with an acceleration map in the test map 11-1A of the test driving model 11-1 to apply the accelerator pedal_acceleration_length of the accelerator acceleration point, and , refers to an actual acceleration force learning state in which acceleration force learning is performed by controlling the ETC of the dyno system 40 by the accelerator pedal amount [%] according to the step of the accelerator acceleration point setting value. In this case, the acceleration point (%) accelerator pedal acceleration (S53) sets the 90 [sec] operation time for the corresponding accelerator pedal amount [%] as the end condition or sets the vehicle speed exceeding 125 [km/h] as the end condition can

For example, stopping the vehicle operation ( S54 ) is to operate the brake pedal with the pedal robot 30 to decelerate the learning vehicle 100 , and to set the vehicle speed 0 [km/h] to the first accelerator point among the accelerator points. After waiting for a certain period of time at vehicle speed 0 [km/h], it moves to the next point, the second accelerator acceleration point.

As an example, when the acceleration setting UI is completed (S55), the process of acceleration point (%) accelerator pedal acceleration (S53) and vehicle driving stop (S54) applies all of the final set points from the initial set points of the accelerator points to learn the accelerator force This means that it has ended after it has been repeated.

Referring to FIG. 6 , the detailed procedure for the acceleration point (%) accelerator pedal acceleration (S53) is performed in steps S53-1 to S53-3, and the detailed procedure for stopping the vehicle operation (S54) is performed at S54-1 to S54-4, and exemplifies that the detailed procedure for completing the acceleration setting UI (S55) is performed in steps S55-1 to S55-3. However, each of steps S53-1 to S53-3, steps S54-1 to S54-4, and steps S55-1 to S55-3 are sequentially applied, or appropriate steps are integrated/omitted/according to system conditions or test items, etc. A change of order may be made.

For example, S53-1 is the step of applying the accelerator pedal amount [%] acceleration condition according to the start of learning the accelerator force, the step of outputting the ETC control voltage by the CAN message between the automation program device 11 and the external controller 20 of S53-2, S53-3 is divided into 125 [Km/h] acceleration steps of the learning vehicle 100 by the dyno system 40 .

For example, S54-1 is about 50% brake voltage signal output step for controlling the brake amount [%] by CAN message between the automation program device 11 and the external controller 20, S54-2 is the pedal robot 30 The vehicle speed deceleration step of the learning vehicle 100 up to 0 [Km/h] by the S54-3 is a waiting step of about 5 seconds, and S54-4 proceeds to the second Excel acceleration point by increasing the count according to the completion of the first Excel acceleration point step, S55-1 is the step of initializing the CAN message of the automation program device 11 by performing all the set Excel acceleration points, S55-2 is the Excel acceleration force learning end step, S55-3 is the automation program device 11 It is divided into steps to initialize the Excel acceleration learning variable items.

Meanwhile, in generating the acceleration map ( S56 ), the target vehicle speed for the acceleration of the accelerator pedal and the accelerator point are applied, and the accelerator pedal_acceleration_length is applied to the test driving model 11-1 of the automation program device 11 . It means a state in which the test map 11-1A is generated in the same format as the acceleration map, and through this, the Excel acceleration force learning result is reflected.

Referring to FIG. 7 , the acceleration map (B) is obtained from the acceleration driving result diagram (A) showing the relationship between the vehicle speed [Km/h] to which the accelerator pedal_accel_length of the accelerator point is applied and the accelerator pedal amount [%] This indicates the rate of change of vehicle speed according to the accelerator pedal_acceleration_length of the accelerator acceleration point. Therefore, the result of the acceleration map B is applied to update or map the acceleration map among the test maps 11-1A of the test driving model 11-1 and convert it into the learning map 11-2A (refer to FIG. 13). do.

Specifically, the learning of the brake deceleration force (S60) is performed in the system target deceleration driving step of S61, the step of providing the brake deceleration interval of S62, the deceleration point (%) brake pedal deceleration step of S63, and the completion of the brake deceleration setting UI step of S64. .

For example, the system target deceleration operation (S61) converts the automation program device 11 to the brake deceleration force learning measurement state, and the brake amount [%] by the CAN message between the automation program device 11 and the external controller 20 About 30% brake voltage signal for control is generated.

For example, in the application of the brake deceleration interval ( S62 ), a target vehicle speed and a brake deceleration point for brake operation are applied, and brake_deceleration_length is decelerated among the test maps 11-1A applied to the test driving model 11-1. After matching with the map, it means the ready state to learn the brake deceleration force that applies a waiting time of about 20 seconds.

Referring to FIG. 8 , the detailed procedure for the system target deceleration operation (S61) is S61-1 to S61-7, and the detailed procedure for the brake deceleration interval application (S62) is S62-1 to S62-5. example to be performed. However, steps S61-1 to S62-5 may be sequentially applied or appropriate step integration/omission/order change may be made according to system conditions or test items.

For example, S61-1 is a measurement start step, S61-2 is a CAN message initialization step, S61-3 is a system standby step applying about 5 seconds, S61-4 is a brake_deceleration_length determination step, S61-5 is a target vehicle speed of the deceleration map mapping step, S61-6 is a step of setting the brake pedal control amount [%] of reverse 30%, and S61-7 is a step in which the CAN message is transmitted from the automation program device 11 to the external controller 20. have.

For example, S62-1 is a step in which acceleration or brake braking of the learning vehicle 100 is determined according to a target vehicle speed, S62-2 is a step in which it is confirmed that the condition of brake_deceleration_length is satisfied, and S62-3A is brake_decel_ Deceleration map matching step according to length condition satisfaction, S62-4A is a timer setting step applying about 20 seconds, S62-3B is a vehicle speed reduction counting step of the learning vehicle 100 according to non-fulfillment of brake_deceleration_length condition, S62- 4B can be divided into a step of returning to the start of measurement after setting the brake count 0, and S62-5 can be divided into a step of starting learning the brake deceleration force by timer counting.

On the other hand, the deceleration point (%) brake pedal deceleration (S63) is the actual deceleration force learning by controlling the operation of the pedal robot 30 by the amount of the brake pedal amount [%] according to the sequential application procedure of the brake deceleration point setting value. It means the state of learning brake deceleration force. In particular, the deceleration point (%) brake pedal deceleration (S63) is based on the vehicle deceleration through the set value of about 30% by the brake pedal deceleration (S63). carry out

For example, when the brake deceleration setting UI is completed (S64), the process of system target deceleration operation (S61), brake deceleration interval application (S62) and deceleration point (%) brake pedal deceleration (S63) is performed at the initial set point of the brake deceleration point. It means that the brake deceleration force learning is completed after applying all the final set points.

Referring to FIG. 9 , the detailed procedure for the deceleration point (%) brake pedal deceleration (S63) is S63-1 to S63-7, and the detailed procedure for the completion of the brake deceleration setting UI (S64) is S64-1 to S64-1 to It is exemplified that step S64-3 is performed. However, steps S63-1 to S64-3 may be sequentially applied or appropriate step integration/omission/order change may be made according to system conditions or test items.

For example, S63-1 is a step in which the pedal robot 30 operates the brake pedal to decelerate the learning vehicle 100 with about 30% brake voltage signal from the external controller 20, S63-2 is about 30% brake voltage A step in which the vehicle speed of the learning vehicle 100 is confirmed as 0 [Km/h] with a signal, S63-3 is a forced brake determination step for a target brake (target brake) during braking of the learning vehicle 100, and S63-4 is Step in which the pedal robot 30 decelerates the learning vehicle 100 with the forced brake signal, S63-5 is a step in which the vehicle speed of the learning vehicle 100 is confirmed as 0 [Km/h] with the forced brake signal, S63- 6 is a waiting step of about 5 seconds, and S63-7 is divided into a step of proceeding with the second brake deceleration point by increasing the count according to the completion of the first brake deceleration point.

For example, S64-1 is a step in which all set brake deceleration points are performed and the brake deceleration force learning is finished, S64-2 is a step in which the CAN message of the automation program device 11 is initialized by the end of the brake deceleration force learning, S64-3 is divided into steps of initializing the brake deceleration force learning variable item of the automation program device 11 .

Specifically, the learning of the accelerator deceleration force (S70) includes the system target deceleration operation step of S71, the accelerator deceleration interval provision step of S72, the deceleration point (%) accelerator deceleration step of S73, the accelerator deceleration setting UI completion step of S74, and the step of S75 This is performed as a deceleration map generation step.

As an example, the system target deceleration operation (S71) converts the automation program device 11 to the Excel deceleration force learning measurement state to which the Excel pedal is applied, and the Excel by the CAN message between the automation program device 11 and the external controller 20 About 30% of the accelerator pedal amount voltage signal for controlling the pedal amount [%] is generated.

For example, in the application of the accelerator deceleration interval ( S72 ), the target vehicle speed and the accelerator deceleration point for brake operation are applied, and the accelerator_deceleration_length is decelerated among the test maps 11-1A applied to the test driving model 11-1. After matching with the map, it means the state of preparation for learning the acceleration deceleration force that applies a waiting time of about 120 seconds.

Referring to FIG. 10 , the detailed procedure for the system target deceleration operation (S71) is S71-1 to S71-7, and the detailed procedure for the accelerator deceleration interval granting (S72) is S72-1 to S72-6 steps. example to be performed. However, steps S71-1 to S72-6 may be sequentially applied, or appropriate step integration/omission/order change may be made according to system conditions or test items.

For example, S71-1 is a measurement start step, S71-2 is a CAN message initialization step, S71-3 is a system standby step with about 5 seconds applied, S71-4 is an Excel_deceleration_length determination step, S71-5 is a target vehicle speed of deceleration map mapping step, S71-6 is a step of setting the accelerator pedal control amount [%] of about 30%, and S71-7 is a step in which the CAN message is transmitted from the automation program device 11 to the external controller 20. have.

For example, S72-1 is a step in which acceleration or deceleration of the learning vehicle 100 is determined according to a target vehicle speed, S72-2 is a step in which it is confirmed that a condition of Excel_deceleration_length is satisfied, and S72-3A is Excel_deceleration_ Deceleration map matching step according to the satisfaction of the length condition, S72-4A is a step of determining the accelerator pedal control amount [%] of about 30%, S72-3B is the dyno system 40 according to the non-fulfillment of the condition of the accelerator_deceleration_length Step in which the vehicle speed reduction of the learning vehicle 100 is counted by operating the brake pedal, S72-4B is a step of returning to measurement start after setting brake count 0, S72-5 is a timer setting step applying about 120 seconds, S72-6 is It can be divided into a start stage of learning the brake deceleration force by timer counting.

On the other hand, the deceleration point (%) accelerator pedal deceleration (S73) is the actual deceleration force learning by controlling the operation of the pedal robot 30 by the accelerator pedal amount [%] according to the sequential application procedure of the accelerator deceleration point setting value. It means the state of learning brake deceleration force. In particular, the deceleration point (%) accelerator pedal deceleration (S73) is based on the vehicle deceleration through the set value of about 30% by the accelerator pedal, and when necessary, the vehicle decelerates by operating the brake pedal instead of the accelerator pedal with a forced brake. .

For example, completion of the accelerator deceleration setting UI (S74) indicates that the process of system target deceleration operation (S71), accelerator deceleration interval provision (S72) and deceleration point (%) accelerator pedal deceleration (S73) is performed at the initial set point of the accelerator deceleration point. It means that the Excel deceleration force learning has been repeated by applying all the final set points and then ended.

Referring to FIG. 11 , the detailed procedure for decelerating the deceleration point (%) accelerator pedal deceleration (S73) is S63-1 to S63-7, and the detailed procedure for completing the accelerator deceleration setting UI (S74) is S74-1 to It is exemplified that step S74-3 is performed. However, steps S73-1 to S74-3 may be sequentially applied or appropriate step integration/omission/order change may be made according to system conditions or test items.

For example, S73-1 is about 30% of the accelerator pedal amount voltage signal of the external controller 20, and the pedal robot 30 operates the accelerator pedal to decelerate the learning vehicle 100, S73-2 is about 30% A step in which the vehicle speed of the learning vehicle 100 is confirmed as 0 [Km/h] by the accelerator pedal amount voltage signal, S73-3 is a forced brake determination step for the target brake (target brake) during braking of the learning vehicle 100; S73-4 is a step in which the pedal robot 30 operates the brake pedal to decelerate the learning vehicle 100 with a forced brake signal, and S73-5 is a forced brake signal when the vehicle speed of the learning vehicle 100 is 0 [Km/ h], S73-6 is a waiting step for about 5 seconds, and S73-7 is a step of proceeding with the second Excel deceleration point by increasing the count according to the completion of the first Excel deceleration point.

For example, S74-1 is a step in which all the set Excel deceleration points are performed and the Excel deceleration force learning is finished, S74-2 is a step of initializing the CAN message of the automation program device 11 with the end of the Excel deceleration force learning, S74-3 is divided into steps of initializing the Excel deceleration force learning variable item of the automation program device 11 .

Meanwhile, in generating the deceleration map ( S75 ), the target vehicle speed and the brake/excel deceleration point for the brake operation of the brake pedal/excel pedal are respectively applied, and each of the brake_deceleration_length/excel_deceleration_length is used as a test driving model. It means a state in which the brake/accel deceleration force learning is reflected through the generation of the deceleration map in the same format as the deceleration map among the test maps 11-1A applied to (11-1).

12 , the deceleration map D is obtained from the deceleration driving result diagram C indicating the vehicle speed when the brake is 50 [%], so that the result is obtained from the test map 11-1A of the test driving model 11-1 ) is applied to update or map the acceleration map to transform it into the learning map 11-2A (refer to FIG. 13).

Then, the automation program device 11 performs the driver model learning (S80 to S84) in the driver model creation step of S80, the target vehicle speed driving model execution step of S81, the vehicle driving step of S82, the target vehicle speed tracking test execution step of S83, S84 Complete the driver model tracking performance evaluation and carry out the vehicle application phase.

For example, in generating the driver model ( S80 ), an acceleration map of the acceleration model and a deceleration map of the deceleration model are generated as a driver model. In this case, the acceleration map is the acceleration map (B) of FIG. 7 that is a result of generating the acceleration map (S56), and the deceleration map means the deceleration map (D) of FIG. 12 that is the result of generating the deceleration map (S75), The driver model refers to the driver model 11-2 of FIG. 13 .

In particular, the acceleration model of the acceleration map generates an accelerator pedal voltage [mV] output for each current vehicle speed [km/h] and a vehicle speed difference [km/h], and performs acceleration control through the accelerator pedal voltage output. The deceleration model of the deceleration map generates a brake control robot voltage [V] output for each current vehicle speed [km/h] and a vehicle speed difference [km/h], and performs deceleration control through the brake control robot voltage output. In this case, the vehicle speed difference (vehicle speed diff) is calculated by subtracting the current vehicle speed from the target vehicle speed after 1 second.

Vehicle speed difference (vehicle speed diff) = target vehicle speed after 1 second - current vehicle speed

For example, the execution of the target vehicle speed driving model ( S81 ) means that the driver model 11 - 2 is activated based on the learning map 11 - 2A in the automation program device 11 . The vehicle driving ( S82 ) means that the learning vehicle 100 is switched to the driving state.

The target vehicle speed tracking test execution S83 means a vehicle control state in which the learning vehicle 100 tracks the target vehicle speed according to the acceleration/deceleration learning procedure based on the learning map 11-2A.

Referring to FIG. 13 , in the driver model learning (S80 to S84), the driver model 11-2 replaces the test driving model 11-1 in the learning map 11-2A reflecting the vehicle acceleration/deceleration learning results. It is implemented in the automation program device 11 . In this case, the procedure of learning the driver model (S80 to S84) is the same or similar to the flow of the vehicle acceleration/deceleration learning preparation (S10 to S20)/test driving model learning (S30 to S70). However, in the driver model learning ( S80 to S84 ), the target vehicle speed program device 13 controls the target vehicle speed that the learning vehicle 100 follows, and the automation program device 11 uses the target vehicle speed program device 13 to control the vehicle speed. The difference in transmitting the accelerator pedal voltage [mV] and the brake control voltage [V] to the external controller 20 as a CAN message while receiving the target vehicle speed set value and matching it with the learning map 11-2A of the driver model 11-2 there is

Therefore, in the driver model 11-2 of the automation program device 11, the vehicle target vehicle speed tracking control of the learning vehicle 100 is the current vehicle speed [km/h]/the accelerator pedal voltage [mV] for each vehicle speed difference [km/h] ] is implemented as acceleration control through output and deceleration control through brake control voltage [V] [pedal robot 30] for each current vehicle speed [km/h]/vehicle speed difference [km/h], and through this, up to the final target vehicle speed By following the target vehicle speed set until it is reached, the limitation of the algorithm applied to the existing method of simply tracking only the final target vehicle speed can be overcome.

As an example, in the completion of the driver model tracking performance evaluation and vehicle application ( S84 ), the target vehicle speed tracking performance of the learning map 11 - 2A is evaluated after the driving of the learning vehicle 100 is finished, and the performance of the learning map 11 - 2A is evaluated. When satisfied, it means a state in which the learning map 11-2A is applied to the vehicle. In this case, the application of the learning map 11-2A is highway driving assistance (HDA). Alternatively, it may be linked with a target vehicle speed tracking algorithm or program of the cruise control system SC.

Meanwhile, FIGS. 14 and 15 exemplify results of driver model learning ( S80 to S84 ) applied to the learning vehicle 100 , respectively. As an example, FIG. 14 is a Federal Test Procedure (FTP) mode driving result of the driver model 11-2, and FIG. 15 is a Worldwide harmonized light-duty vehicle test procedure (WLTP) mode driving result of the driver model 11-2. , it is experimentally proven that the acceleration/deceleration learning is suitable for real-time tracking of the target vehicle speed from the FTP mode driving result and the WLTP mode driving result.

As described above, in the vehicle target vehicle speed real-time tracking learning method implemented by the vehicle acceleration/deceleration learning system 1 according to the present embodiment, the test driving driving point setting UI is set using the learning control system 10, and the acceleration map Acceleration/deceleration learning result by sequentially executing the learning of the accelerator force for the learning vehicle 100 and the learning of the brake deceleration/excel deceleration force with the test driving model 11-1 equipped with the overspeed and deceleration map as the test map 11-1A The current vehicle speed [km/h] and the vehicle speed difference [km/h] with the driver model 11-2 having the acceleration map and the deceleration map matched with the acceleration and deceleration learning results as the learning map 11-2A. By performing target vehicle speed master-slave learning using each accelerator and brake voltage, it is possible to follow the target vehicle speed set until the final target vehicle speed is reached, thereby overcoming the limitations of the existing target vehicle speed master-slave algorithm that simply tracks the final target vehicle speed.

1: Vehicle acceleration/deceleration learning system\
10: learning control system 11: automation program device
11-1: Test driving model 11-1A: Test map
11-2: Driver model 11-2A: Learning map
13: target vehicle speed program device
20: external controller 30: pedal robot
40: Dino System
100: learning vehicle

Claims (23)

The learning control system starts the acceleration/deceleration learning phase for the learning vehicle in connection with the Dino system with the test driving driving point setting UI,
In the acceleration/deceleration learning step, the acceleration map and deceleration built in the test map of the test driving model as a result of learning by executing any one of an accelerator learning, a brake deceleration force learning, and an excel force learning to which the test driving driving point setting UI is applied A test driving model acceleration/deceleration learning step of confirming the matching result of the map, and
matching the vehicle speed curve of the test map with the acceleration/deceleration learning result to generate the learning map 11-2A of the driver model, setting the vehicle speed difference of the learning vehicle based on the learning map, and following the vehicle speed difference A driver model acceleration/deceleration learning step of verifying the learning map by executing any one of Excel acceleration force learning, brake deceleration force learning, and Excel deceleration force learning
A vehicle target vehicle speed real-time tracking learning method comprising a.
The method according to claim 1, wherein an accelerator acceleration point, a brake deceleration point, an accelerator deceleration point, and a deceleration start vehicle speed are applied to the test driving driving point setting UI.
The method according to claim 2, wherein a holding time is given as a point length to each of the accelerator acceleration point, the brake deceleration point, the accelerator deceleration point, and the deceleration start vehicle speed.
The method according to claim 1, The driver model acceleration/deceleration learning is the step of loading the acceleration/deceleration learning data in which the test driving driving point setting UI is downloaded to the test driving model, and the output of the accelerator pedal amount and the brake amount provided by the learning control system is external. After the controller outputs the accelerator pedal amount voltage signal and the brake amount voltage signal to the dyno system, the learning vehicle learns the accelerator acceleration force, learns the brake deceleration force, and learns the accelerator deceleration force
A vehicle target vehicle speed real-time tracking learning method, characterized in that performed as
The method according to claim 4, wherein the learning control system is connected to the external controller through CAN (Controller Area Network) communication to transmit a CAN message.
The method according to claim 4, wherein the loading of the acceleration/deceleration force learning data includes the accelerator pedal_accel_length of the excel acceleration point applied as the test driving driving point setting UI, the brake_decel_length of the brake deceleration point, and the accelerator_deceleration_ of the accelerator deceleration point. length, a vehicle target vehicle speed real-time tracking learning method, characterized in that it is performed by reading the deceleration time _length of the deceleration start vehicle speed.
The method according to claim 4, wherein the learning of the Excel acceleration force comprises the steps of initializing a CAN message, matching an acceleration map, and setting a timer in the driving state of the learning vehicle, and providing an Excel acceleration interval among parameters of the acceleration/deceleration learning data loading. Acceleration by applying the pedal_acceleration_length to implement the accelerator pedal acceleration for one acceleration point, stopping the driving of the learning vehicle after the accelerator pedal acceleration for each acceleration point, and performing all of the accelerator pedal accelerations for each acceleration point Completing the setting UI, generating an acceleration map of the learning map by matching the Excel acceleration force learning result with the acceleration map of the test map
A vehicle target vehicle speed real-time tracking learning method, characterized in that performed as
The method according to claim 7, wherein the timer applies 90 seconds to learning the accelerator force, and the accelerator pedal accelerates the learning vehicle up to 125 [Km/h].
The method according to claim 7, wherein the stopping of driving is deceleration by manipulating the brake pedal of the dyno system or the pedal robot, and applying a standby condition for a certain period of time after vehicle speed 0 [km/h] to the repetition of the accelerator pedal acceleration A vehicle target vehicle speed real-time tracking learning method.
The method according to claim 4, wherein the learning of the brake deceleration force is a system target deceleration driving step in which the brake amount set by the external controller is applied as a deceleration value of the learning vehicle, CAN message initialization in the driving state of the learning vehicle, and the test driving driving point Target vehicle speed setting for brake operation as deceleration start vehicle speed_length of deceleration start vehicle speed in the setting UI, deceleration map matching, timer setting, and brake_deceleration_length condition of the brake deceleration point among the parameters of the acceleration/deceleration learning data loading are applied. In the step of applying the brake deceleration interval, the brake pedal deceleration for one brake deceleration point is performed by applying the brake_deceleration_length, the brake pedal operation is performed through the brake voltage signal set by the external controller, and the learning vehicle is decelerated. Step of decelerating the brake pedal at the deceleration point where the vehicle speed is confirmed to be 0 [Km/h], the step of completing the brake deceleration setting UI by performing all the manipulation of the brake pedal for each brake deceleration point
A vehicle target vehicle speed real-time tracking learning method, characterized in that performed as
The method of claim 10 , wherein the set brake amount applies 30 [%] a brake control amount, and the timer applies a waiting time of 20 seconds to learning the brake deceleration force.
The method according to claim 10, wherein the granting of the brake deceleration interval comprises reducing the vehicle speed when the brake_deceleration_length condition is not met to set the brake count to 0, and then initialize the CAN message to start the granting of the brake deceleration interval again. A vehicle target vehicle speed real-time tracking learning method.
The method according to claim 10, wherein the deceleration point brake pedal deceleration uses the target vehicle speed as the target brake and outputs a forced brake signal when a forced brake is determined, so that the vehicle speed becomes 0 [Km/h] by operating the brake pedal of the dyno system. slowing down the vehicle
A vehicle target vehicle speed real-time tracking learning method comprising a.
The method of claim 10 , wherein the completion of the brake deceleration setting UI initializes a CAN message and the brake deceleration point, and converts to learning the accelerator deceleration force.
The method according to claim 4, wherein the learning of the acceleration deceleration force comprises a system target deceleration driving step in which the accelerator pedal control amount set by the external controller is applied as a deceleration value of the learning vehicle, CAN message initialization in the driving state of the learning vehicle, and the test driving driving Set the target vehicle speed for Excel operation as the deceleration start vehicle speed_length of the deceleration start vehicle speed in the point setting UI, match the deceleration map, set the timer, and apply the Excel_deceleration_length condition of the Excel deceleration point among the parameters of the acceleration/deceleration learning data loading In this step of providing the accelerator deceleration interval, the accelerator pedal deceleration for one accelerator deceleration point is performed by applying the accelerator_deceleration_length, and the accelerator pedal operation is performed through the brake voltage signal set by the external controller, and the learning vehicle decelerates. The deceleration point accelerator pedal deceleration step at which the vehicle speed is confirmed to be 0 [Km/h], the accelerator deceleration setting UI is completed by performing all operations of the accelerator pedal for each accelerator deceleration point, and the CAN message and the accelerator deceleration point are initialized. Step, generating a deceleration map of the learning map by matching the brake deceleration force learning result and the accelerator deceleration force learning result to the deceleration map of the test map
A vehicle target vehicle speed real-time tracking learning method, characterized in that performed as
The method according to claim 15, wherein the set accelerator pedal control amount is 30 [%] and the accelerator pedal control amount is applied, and the timer applies a waiting time of 120 seconds to learning the accelerator deceleration force. .
The method according to claim 15, wherein the deceleration point accelerator pedal deceleration uses the target vehicle speed as the target brake and outputs a forced brake signal when a forced brake is determined so that the vehicle speed becomes 0 [Km/h] by operating the brake pedal of the dyno system. slowing down the vehicle
A vehicle target vehicle speed real-time tracking learning method comprising a.
The method according to claim 1, The driver model acceleration/deceleration learning is by matching the learning result of the acceleration force to the acceleration map of the test map to generate an acceleration map of the learning map of the driver model, and learning the brake deceleration force in the deceleration map of the test map A step of generating a deceleration map from the learning map of the driver model by matching the result and the accelerator learning result, accelerating the learning vehicle with an accelerator pedal voltage that is different from the current vehicle speed and the vehicle speed, and the target vehicle speed according to the acceleration map of the learning map Checking the tracking performance, decelerating the learning vehicle with the current vehicle speed and the vehicle speed differential brake control robot voltage to check the target vehicle speed tracking performance by the deceleration map of the learning map
A vehicle target vehicle speed real-time tracking learning method, characterized in that performed as
The method of claim 18 , wherein the vehicle speed difference is calculated by subtracting the current vehicle speed from the target vehicle speed after 1 second.
The method according to claim 1, wherein the learning of the accelerator force, the learning of the brake deceleration force, and the learning of the accelerator force are sequentially executed.
Test driving driving point setting UI is set, and acceleration and deceleration learning results are obtained by sequentially executing accelerator learning, brake deceleration force learning, and accelerator deceleration force learning for the learning vehicle as a test driving model equipped with an acceleration map and a deceleration map as a test map. a learning control system that secures and performs target vehicle speed master/slave learning with a driver model equipped with an acceleration map and a deceleration map matched with the acceleration/deceleration learning result as a learning map;
an external controller that exchanges a CAN message with the learning control system and outputs an accelerator pedal amount control voltage signal and a brake amount control voltage signal, respectively; and
Dino system for accelerating the learning vehicle by ETC (Electronic Throttle Control Apparatus) control by the accelerator pedal amount control voltage signal and decelerating the learning vehicle by controlling the brake pedal and accelerator pedal according to the brake amount control voltage signal
Vehicle acceleration/deceleration learning system, characterized in that it is included.
22. The method of claim 21, wherein the learning control system comprises an automation program device for building the test driving model and the driver model, and a target vehicle speed program device for setting a target vehicle speed for learning the target vehicle speed and master/slave speed.
Vehicle acceleration/deceleration learning system, characterized in that consisting of.
The system according to claim 21, wherein a pedal robot is applied to control the brake pedal.

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