KR102468158B1 - Method and apparatus for controlling acceleration of autonomous vehicle according to slope of road - Google Patents

Abstract

According to the present invention, in a method for controlling acceleration of an autonomous vehicle according to a slope of a road, (a) self-vehicle sensing information for detecting an operating state of the autonomous vehicle and environment sensing information for detecting the surrounding environment of the autonomous vehicle are provided. Once obtained, the self-driving vehicle's computing device refers to the self-vehicle sensing information and environment sensing information to generate a target acceleration for driving the autonomous vehicle, and the autonomous vehicle's driving through the acceleration/deceleration control device of the autonomous vehicle controlling the self-driving vehicle to correspond to the target acceleration; and (b) the computing device measures (i) the wheel acceleration measured from the wheel speed sensor of the autonomous vehicle and (ii) the acceleration of the autonomous vehicle from the IMU (Inertial Measurement Unit) sensor of the autonomous vehicle. Inclination weights are generated by referring to the measured IMU acceleration, and the actual acceleration of the autonomous vehicle according to the slope of the road on which the autonomous vehicle is traveling is generated by weighting the wheel acceleration and the IMU acceleration using the gradient weighting, and the actual acceleration and controlling the self-driving vehicle through an acceleration/deceleration control device to minimize an error of the target acceleration.

Description

Method and apparatus for controlling acceleration of autonomous vehicle according to road slope

The present invention relates to a method and apparatus for controlling the acceleration of an autonomous vehicle according to a slope of a road, and more particularly, to a method and apparatus for controlling the acceleration of an autonomous vehicle by detecting the actual acceleration of the autonomous vehicle according to the slope of a road on which the autonomous vehicle is running. It relates to a control method and device.

As the self-driving vehicle market has grown rapidly in recent years, research on driving stability and comfortable riding comfort of autonomous vehicles has also become active.

In the case of an IMU (Inertial Measurement Unit) sensor currently used to measure vehicle acceleration, acceleration is measured according to the vehicle's inclination. At this time, on a road with a slope such as a hill, a long uphill, and a long downhill, the inclination of the vehicle is affected by the slope. Therefore, it is difficult for the IMU sensor to accurately measure acceleration on a sloped road, which adversely affects driving stability and riding comfort of passengers as the autonomous vehicle is controlled differently from the target acceleration on the slope.

For this reason, there is a need for a method and apparatus for controlling the acceleration of an autonomous vehicle according to the slope of the road.

The object of the present invention is to solve all of the above problems.

In addition, the present invention refers to the wheel acceleration measured from the wheel speed sensor and the IMU acceleration measured from the IMU sensor in a state of controlling the self-driving vehicle to correspond to the target acceleration of the self-driving vehicle, and generates gradient weights, and uses them. Another object is to generate actual acceleration and control the self-driving vehicle to correspond to an error between the actual acceleration and the target acceleration.

In addition, another object of the present invention is to improve riding comfort of a passenger when driving an autonomous vehicle on a slope by controlling the autonomous vehicle to correspond to a target acceleration even on a slope.

In order to achieve the object of the present invention as described above and realize the characteristic effects of the present invention described later, the characteristic configuration of the present invention is as follows.

According to one aspect of the present invention, in a method for controlling acceleration of an autonomous vehicle according to a slope of a road, (a) detecting self-vehicle sensing information for detecting an operating state of the autonomous vehicle and a surrounding environment of the autonomous vehicle When environmental sensing information is obtained, the self-driving vehicle's computing device generates a target acceleration for driving of the autonomous vehicle by referring to the host vehicle sensing information and the environmental sensing information, and acceleration and deceleration of the autonomous vehicle controlling the self-driving vehicle so that driving of the self-driving vehicle corresponds to the target acceleration through a control device; and (b) the computing device determines (i) wheel acceleration obtained by measuring the acceleration of the autonomous vehicle from a wheel speed sensor of the autonomous vehicle and (ii) measuring the acceleration of the autonomous vehicle from an Inertial Measurement Unit (IMU) sensor of the autonomous vehicle. The self-driving vehicle according to the slope of the road on which the self-driving vehicle travels by generating a gradient weight by referring to the IMU acceleration measured for the acceleration of the self-driving vehicle, and weighting the wheel acceleration and the IMU acceleration using the gradient weight. Generating an actual acceleration of the vehicle, and controlling the autonomous vehicle through the acceleration/deceleration control device to minimize an error between the actual acceleration and the target acceleration.

As an example, the computing device discloses a method for applying a weight proportional to a magnitude of an inclination of the road to the wheel acceleration by the weighted sum.

As an example, the computing device applies the gradient weight to the wheel acceleration by the weighted sum, and applies (1-the gradient weight) to the IMU acceleration to generate the actual acceleration.

As an example, the gradient weight is an absolute value of an average error, which is a difference between an average wheel acceleration of the wheel acceleration measured every first unit time and an average IMU acceleration of the IMU acceleration measured every second unit time for a predetermined time. A method for creating a reference is disclosed.

As an example, a method of generating the gradient weight by applying a sigmoid function to the absolute value of the average error is disclosed.

As an example, the self-driving vehicle includes at least a brake motor controller and an accelerator pedal controller, and the computing device outputs a voltage required for a target brake pedal position corresponding to the target acceleration to the brake motor controller, or the target acceleration. A method of outputting a voltage required for a target accelerator pedal position corresponding to an acceleration to the accelerator pedal controller is disclosed.

According to another aspect of the present invention, an apparatus for controlling acceleration of an autonomous vehicle according to a slope of a road includes at least one memory for storing instructions; and at least one processor configured to execute the instructions, wherein the processor includes (I) self-vehicle sensing information for detecting an operating state of the autonomous vehicle and environment sensing information for detecting a surrounding environment of the autonomous vehicle. is obtained, a target acceleration for driving of the autonomous vehicle is generated with reference to the vehicle sensing information and the environment sensing information, and the driving of the autonomous vehicle is controlled by the acceleration/deceleration control device of the autonomous vehicle. a process of controlling the self-driving vehicle to correspond to a target acceleration; and (II) (i) wheel acceleration obtained by measuring the acceleration of the autonomous vehicle from a wheel speed sensor of the autonomous vehicle, and (ii) acceleration of the autonomous vehicle from an Inertial Measurement Unit (IMU) sensor of the autonomous vehicle. A gradient weight is generated with reference to the measured IMU acceleration, and the wheel acceleration and the IMU acceleration are weighted together using the gradient weight to determine the actual acceleration of the self-driving vehicle according to the slope of the road on which the self-driving vehicle is traveling. A process for generating and controlling the self-driving vehicle through the acceleration/deceleration control device to minimize an error between the actual acceleration and the target acceleration.

As an example, the processor is provided with a device for applying a weight proportional to an inclination of the road to the wheel acceleration by the weighted sum.

As an example, the processor applies the gradient weight to the wheel acceleration by the weighted sum, and applies (1-the gradient weight) to the IMU acceleration to generate the actual acceleration.

As an example, the gradient weight is an absolute value of an average error, which is a difference between an average wheel acceleration of the wheel acceleration measured every first unit time and an average IMU acceleration of the IMU acceleration measured every second unit time for a predetermined time. A device for creating a reference is provided.

As an example, an apparatus for generating the gradient weight by applying a sigmoid function to an absolute value of the mean error is provided.

As an example, the self-driving vehicle includes at least a brake motor controller and an accelerator pedal controller, and the processor outputs a voltage required for a target brake pedal position corresponding to the target acceleration to the brake motor controller, or the target acceleration. A device outputting a voltage required for a target accelerator pedal position corresponding to an acceleration to the accelerator pedal controller is provided.

According to the present invention, there are the following effects.

The present invention refers to the wheel acceleration measured from the wheel speed sensor and the IMU acceleration measured from the IMU sensor in a state of controlling the self-driving vehicle to correspond to the target acceleration of the self-driving vehicle to generate gradient weights, and using them to create actual It has an effect of generating acceleration and controlling the self-driving vehicle to correspond to an error between the actual acceleration and the target acceleration.

In addition, the present invention controls the autonomous vehicle to correspond to the target acceleration even on the slope, and has an effect of improving the riding comfort of the occupant when the autonomous vehicle drives on the slope.

1 schematically illustrates an apparatus for controlling acceleration of an autonomous vehicle according to an inclination of a road according to an embodiment of the present invention;
2 is a schematic flowchart of a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention;
3 schematically illustrates obtaining gradient weights and applying them to IMU acceleration and wheel acceleration in a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention;
4 schematically illustrates PI control of a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention;
5 schematically illustrates a graph of an absolute value of an average error and a gradient weight in a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention which follows refers to the accompanying drawings which illustrate, by way of illustration, specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable one skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other but are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in one embodiment in another embodiment without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description set forth below is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all equivalents as claimed by those claims. Like reference numbers in the drawings indicate the same or similar function throughout the various aspects.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily practice the present invention.

1 schematically illustrates an apparatus 100 for controlling acceleration of an autonomous vehicle according to an inclination of a road according to an embodiment of the present invention.

As shown in FIG. 1, the apparatus 100 for controlling the acceleration of an autonomous vehicle according to the slope of a road according to the present invention includes a memory 110 storing instructions for controlling the acceleration of an autonomous vehicle according to the slope of the road, and It may include a processor 120 that performs acceleration control of the self-driving vehicle according to the slope of the road according to instructions stored in the memory 110 .

Specifically, the device 100 for controlling the acceleration of an autonomous vehicle according to the slope of a road may include a computer processor, a memory, a storage, an input device and an output device, and other components of a conventional computing device; electronic communication devices such as routers, switches, and the like; The desired system performance includes electronic information storage systems, such as network attached storage (NAS) and storage area networks (SAN), in combination with computer software (ie, instructions that cause a computing device to function in a particular way). may be to achieve

In addition, the processor of the computing device may include hardware components such as a micro processing unit (MPU) or a central processing unit (CPU), a cache memory, and a data bus. In addition, the computing device may further include an operating system and a software component of an application performing a specific purpose.

However, it is not excluded that the computing device includes an integrated processor in which a medium for implementing the present invention, a processor, and a memory are integrated.

Referring to FIG. The explanation is as follows.

First, when host vehicle sensing information for detecting an operating state of the autonomous vehicle and environment sensing information for detecting the surrounding environment of the autonomous vehicle are obtained, the self-driving vehicle computing device detects the host vehicle sensing information and the environment sensing information. A target acceleration for driving of the autonomous vehicle may be generated with reference to the self-driving vehicle, and the autonomous vehicle may be controlled to correspond to the target acceleration through the acceleration/deceleration control device of the autonomous vehicle (S210).

Here, the self-vehicle sensing information may include the speed, acceleration, position, posture, engine speed, brake pedal operating state, accelerator pedal operating state, steering angle, turn signal information, etc. of the autonomous vehicle related to the operation of the autonomous vehicle. . In this case, the vehicle sensing information may be obtained using sensing signals of various sensors installed in the vehicle, such as a wheel speed sensor, an inertial measurement unit (IMU) sensor, a GPS sensor, a steering angle sensor, a turn signal sensor, a brake sensor, and an accelerator sensor. In addition, the environmental sensing information may include the speed, acceleration, location of surrounding vehicles, object information such as other vehicles or pedestrians around the host vehicle, lane information, weather information, road conditions and weather conditions on which autonomous vehicles are driving. will be. In this case, the environmental sensing information may be obtained through one or a combination of a lidar sensor, a radar sensor, and an image sensor, but is not limited thereto.

As an example, the self-driving vehicle is traveling at a constant speed while maintaining a horizontal attitude at a speed of 60 km/h based on the self-vehicle sensing information, the width of the road around the self-driving vehicle is wide and the road surface condition is good, and , when there is no surrounding vehicle, the autonomous vehicle's computing device sets the target speed to 80 km/h, and sets the target acceleration of the autonomous vehicle accordingly (e.g., the target acceleration to achieve 80 km/h in 20 seconds). can be set), and the self-driving vehicle can be controlled to correspond to the target acceleration.

Thereafter, the computing device of the autonomous vehicle, (i) the wheel acceleration measured from the wheel speed sensor of the autonomous vehicle and (ii) the acceleration of the autonomous vehicle measured from the IMU sensor of the autonomous vehicle Inclination weights are generated by referring to the IMU acceleration, and wheel acceleration and IMU acceleration are weighted together using the inclination weights to generate the actual acceleration of the autonomous vehicle according to the slope of the road on which the autonomous vehicle is traveling, and the actual acceleration and the target acceleration The self-driving vehicle may be controlled through an acceleration/deceleration control device to minimize an error of (S220).

Below, a process of generating gradient weights will be described in detail with reference to FIG. 3 .

3 schematically illustrates obtaining gradient weights and applying them to IMU acceleration and wheel acceleration in a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention.

First, the computing device obtains the absolute value of the average error, which is the difference between the average wheel acceleration of the wheel acceleration measured every first unit time and the average IMU acceleration of the IMU acceleration measured every second unit time for a predetermined time. Expressing this as a mathematical expression, it is as follows.

Here, Err may be the absolute value of the average error, n may be the number of times wheel accelerations are measured during a preset time period, and m may be the number of times IMU accelerations are measured during a preset time period.

For example, if the preset time is 200 ms, the wheel acceleration is measured twice assuming that the first unit time is 100 ms, so all wheel accelerations measured during the two times are added and n (where n is 2) is obtained. The divided value is used, and since the IMU acceleration is measured 20 times assuming that it has a second unit time of 10 ms, a value obtained by adding all the IMU accelerations measured for 20 times and dividing by m (where m is 20) can be used. will be.

After that, the gradient weight may be generated using the following equation.

는 경사 가중치, a와 b는 동일한 임의의 정수, c는 임의의 음수인 정수, d는 1 이상의 임의의 정수일 수인 경우일 수 있다. 이때, 상기 경사 가중치는 0과 1사이의 값을 가지며, 상기 평균오차의 절댓값이 커질수록 그 값이 커지게 된다. 이는 이후 도 5를 사용하여 상세히 설명하도록 한다.The gradient weight may be generated by applying a sigmoid function to the absolute value of the average error, which is

may be a gradient weight, a and b are the same arbitrary integer, c is an arbitrary negative integer, and d is an arbitrary integer greater than or equal to 1. At this time, the gradient weight has a value between 0 and 1, and the value increases as the absolute value of the average error increases. This will be described in detail later using FIG. 5 .

Here, an increase in the absolute value of the average error means that the difference between the IMU acceleration and the wheel acceleration is large. This is equivalent to the fact that the larger the absolute value of the average error, the steeper the slope on which the self-driving vehicle is running. In addition, the steeper the slope, the more inaccurate the IMU acceleration and the more accurate the wheel acceleration. Therefore, it is more efficient to increase the weight of the wheel acceleration as the slope steepens.

Accordingly, the computing device may apply the gradient weight to the wheel speed so that a weight proportional to the gradient of the road is applied to the wheel acceleration by the weighted sum.

Conversely, the computing device may generate the actual acceleration by applying the gradient weight to the wheel acceleration by the weighted sum and applying (1-the gradient weight) to the IMU acceleration.

Expressing this as a mathematical expression, it is as follows.

Actual Acceleration = IMU Acceleration*(1 - α) + Wheel Acceleration*α

Next, referring to FIG. 4 , the PI control of the autonomous vehicle through the acceleration/deceleration control device performed to minimize the error between the actual acceleration and the target acceleration will be described in detail.

4 schematically illustrates PI control of a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention.

In the state in which the target acceleration is obtained, if the computing device outputs a control signal and controls the autonomous vehicle through step S210 described above, (i) the wheel of which the acceleration of the autonomous vehicle is measured from the wheel speed sensor of the autonomous vehicle Acceleration and (ii) IMU acceleration obtained by measuring the acceleration of the autonomous vehicle from the IMU sensor of the autonomous vehicle may be respectively measured. In this case, the computing device may generate the gradient weight using an error of the wheel acceleration and the IMU acceleration. In this case, the larger the error between the wheel acceleration and the IMU acceleration, the larger the gradient, and a gradient weight with a larger value may be generated.

Thereafter, the gradient weight may be applied to the wheel acceleration, and (1 - the gradient weight) may be applied to the IMU weight so that the proportion of the wheel acceleration in the actual acceleration increases as the gradient becomes steeper. Then, PI control of the self-driving vehicle may be performed by referring to the error between the actual acceleration and the target acceleration.

5 schematically illustrates a graph of an absolute value of an average error and a gradient weight in a method for controlling acceleration of an autonomous vehicle according to a slope of a road according to an embodiment of the present invention.

Referring to FIG. 5 , a graph of the sigmoid function with a and b set to 1, c set to -1, and d set to 4 can be seen. At this time, it can be confirmed that the gradient weight increases as the absolute value of the average error increases. Here, since it is a sigmoid function, if the average error becomes infinitely small, the gradient weight converges to 0. However, since the absolute value of the average error is used in the present invention, the portion with the average error below 0 may not be used. Also, when the mean error becomes infinitely large, the gradient weight converges to 1. Therefore, regardless of the absolute value of the average error, the gradient weight may exist between 0 and 1.

Also, the values of a, b, c, and d used in FIG. 5 are merely examples, and any combination that can obtain similar results can be used.

The autonomous vehicle may include at least a brake motor controller and an accelerator pedal controller, and the computing device outputs a voltage required for a target brake pedal position corresponding to the target acceleration to the brake motor controller, or A voltage required for a target accelerator pedal position corresponding to a target acceleration may be output to the accelerator pedal controller. In addition, it may be possible to control a fuel injection amount of an autonomous vehicle engine to correspond to the target acceleration. Meanwhile, when the self-driving vehicle is an electric vehicle, the output of the driving motor may be controlled so that the self-driving vehicle operates at a target acceleration.

Although the present invention has been described above with specific details such as specific components and limited embodiments and drawings, these are only provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments. No, and those skilled in the art to which the present invention pertains may seek various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and not only the claims described later, but also all modifications equivalent or equivalent to these claims belong to the scope of the spirit of the present invention. will do it

Claims (12)

In the acceleration control method of an autonomous vehicle according to the slope of the road,
(a) When host vehicle sensing information for detecting an operating state of the autonomous vehicle and environment sensing information for detecting the surrounding environment of the autonomous vehicle are obtained, the self-driving vehicle computing device performs the self-vehicle sensing information and the autonomous vehicle sensing information. Creating a target acceleration for driving of the autonomous vehicle by referring to environmental sensing information, and controlling the autonomous vehicle so that the driving of the autonomous vehicle corresponds to the target acceleration through an acceleration/deceleration control device of the autonomous vehicle doing; and
(b) the computing device determines (i) wheel acceleration obtained by measuring the acceleration of the autonomous vehicle from a wheel speed sensor of the autonomous vehicle and (ii) the autonomous vehicle from an Inertial Measurement Unit (IMU) sensor of the autonomous vehicle The self-driving vehicle according to the slope of the road on which the autonomous vehicle travels by generating a gradient weight by referring to the IMU acceleration measured for the acceleration of the driving vehicle, and weighting the wheel acceleration and the IMU acceleration using the gradient weight. generating an actual acceleration of and controlling the self-driving vehicle through the acceleration/deceleration control device to minimize an error between the actual acceleration and the target acceleration;
Including,
The gradient weight is generated by referring to the absolute value of the average error, which is the difference between the average wheel acceleration of the wheel acceleration measured every first unit time and the average IMU acceleration of the IMU acceleration measured every second unit time for a predetermined time period. How to. According to claim 1,
The method of claim 1 , wherein, by the weighted sum, a weight proportional to the magnitude of the slope of the road is applied to the wheel acceleration. According to claim 2,
The computing device applies the gradient weight to the wheel acceleration by the weighted sum, and applies (1-the gradient weight) to the IMU acceleration to generate the actual acceleration. delete According to claim 1,
The gradient weight is generated by applying a sigmoid function to the absolute value of the average error. According to claim 1,
The autonomous vehicle includes at least a brake motor controller and an accelerator pedal controller,
The computing device outputs a voltage required for a target brake pedal position corresponding to the target acceleration to the brake motor controller or outputs a voltage required for a target accelerator pedal position corresponding to the target acceleration to the accelerator pedal controller. Way. In the acceleration control device of an autonomous vehicle according to the slope of the road,
at least one memory for storing instructions; and
comprising at least one processor configured to execute the instructions;
the processor,
(I) When host vehicle sensing information for detecting an operating state of the autonomous vehicle and environment sensing information for detecting the surrounding environment of the autonomous vehicle are acquired, the self-driving vehicle is performed by referring to the host vehicle sensing information and the environment sensing information. A process of generating a target acceleration for driving of the vehicle and controlling the autonomous vehicle so that driving of the autonomous vehicle corresponds to the target acceleration through an acceleration/deceleration control device of the autonomous vehicle; and (II) (i) wheel acceleration obtained by measuring the acceleration of the autonomous vehicle from a wheel speed sensor of the autonomous vehicle, and (ii) acceleration of the autonomous vehicle from an Inertial Measurement Unit (IMU) sensor of the autonomous vehicle. A gradient weight is generated with reference to the measured IMU acceleration, and the wheel acceleration and the IMU acceleration are weighted together using the gradient weight to determine the actual acceleration of the self-driving vehicle according to the slope of the road on which the self-driving vehicle is traveling. and a process of controlling the self-driving vehicle through the acceleration/deceleration control device to minimize an error between the actual acceleration and the target acceleration;
The gradient weight is generated by referring to the absolute value of the average error, which is the difference between the average wheel acceleration of the wheel acceleration measured every first unit time and the average IMU acceleration of the IMU acceleration measured every second unit time for a predetermined time period. device to do. According to claim 7,
wherein the processor applies a weight proportional to the gradient of the road to the wheel acceleration by the weighted sum. According to claim 8,
wherein the processor applies the gradient weight to the wheel acceleration by the weighted sum, and applies (1-the gradient weight) to the IMU acceleration to generate the actual acceleration. delete According to claim 7,
The gradient weight is generated by applying a sigmoid function to the absolute value of the average error. According to claim 7,
The autonomous vehicle includes at least a brake motor controller and an accelerator pedal controller,
The processor outputs a voltage required for a target brake pedal position corresponding to the target acceleration to the brake motor controller or outputs a voltage required for a target accelerator pedal position corresponding to the target acceleration to the accelerator pedal controller. .

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