KR20240018048A - Method and apparatus for optimizing vehicle trajectory - Google Patents

Abstract

The present disclosure relates to a method and apparatus for optimizing the trajectory of a vehicle. One embodiment of the present disclosure includes obtaining a reference trajectory; Calculating a plurality of expected trajectories according to one or more control inputs; calculating a cost function based on at least one of the reference trajectory, the plurality of expected trajectories, and the one or more control inputs; and selecting an optimal control input that minimizes the cost function among the one or more control inputs.

Description

Method and apparatus for optimizing the trajectory of a vehicle {METHOD AND APPARATUS FOR OPTIMIZING VEHICLE TRAJECTORY}

The present disclosure provides a method and apparatus for optimizing the trajectory of a vehicle.

Smartization of vehicles is rapidly progressing due to the convergence of information and communication technology and the vehicle industry. Due to smartization, vehicles are evolving from simple mechanical devices to smart cars, and in particular, autonomous driving is attracting attention as a core technology for smart cars. Autonomous driving is a technology that allows a vehicle to reach its destination on its own without the driver having to operate the steering wheel, accelerator pedal, or brakes.

Various additional functions related to autonomous driving are continuously being developed, and ways to provide a safe autonomous driving experience to both passengers and pedestrians by controlling the car by recognizing and judging the driving environment using various data and self-driving vehicles Research on how to control a vehicle when other vehicles are recognized is continuously required.

The above-mentioned background technology is technical information that the inventor possessed for deriving the present invention or acquired in the process of deriving the present invention, and cannot necessarily be said to be known art disclosed to the general public before filing the application for the present invention.

The purpose of the present disclosure is to provide a method and device for optimizing the trajectory of a vehicle. The problem that the present disclosure aims to solve is not limited to the problems mentioned above, and other problems and advantages of the present disclosure that are not mentioned can be understood through the following description and can be understood more clearly by the examples of the present disclosure. It will be. In addition, it will be appreciated that the problems and advantages to be solved by the present disclosure can be realized by the means and combinations thereof indicated in the patent claims.

A first aspect of the present disclosure includes obtaining a reference trajectory; Calculating a plurality of expected trajectories according to one or more control inputs; calculating a cost function based on at least one of the reference trajectory, the plurality of expected trajectories, and the one or more control inputs; and selecting an optimal control input that minimizes the cost function among the one or more control inputs.

A second aspect of the present disclosure includes a memory storing at least one program; and a processor operating by executing the at least one program, wherein the processor acquires a reference trajectory, calculates a plurality of expected trajectories according to one or more control inputs, and determines the reference trajectory and the plurality of expected trajectories. and calculating a cost function based on at least one of the one or more control inputs, and selecting an optimal control input that minimizes the cost function among the one or more control inputs.

A third aspect of the present disclosure may provide a computer-readable recording medium recording a program for executing the method of the first aspect on a computer.

In addition to this, another method for implementing the present invention, another device, and a computer-readable recording medium recording a program for executing the method may be further provided.

Other aspects, features and advantages in addition to those described above will become apparent from the following drawings, claims and detailed description of the invention.

According to the problem-solving means of the present disclosure described above, driving safety can be guaranteed even in various situations by planning a future driving route.

In addition, according to the problem solving means of the present disclosure, the trajectory of the vehicle can be optimized by selecting a steering control input that takes riding comfort into consideration.

1 to 3 are diagrams for explaining an autonomous driving method according to an embodiment.
Figure 4 is an example diagram schematically showing a process for optimizing the trajectory of a vehicle according to an embodiment.
FIG. 5 is a diagram illustrating a process in which a vehicle trajectory optimization device acquires a reference trajectory according to an embodiment.
6A and 6B are diagrams for explaining a prediction model for selecting an optimal control input by a vehicle trajectory optimization device according to an embodiment.
FIG. 7 is a diagram illustrating a process in which a vehicle trajectory optimization apparatus according to an embodiment sets a control cycle non-uniformly.
Figure 8 is a flowchart of a method for optimizing the trajectory of a vehicle according to one embodiment.
Figure 9 is a block diagram of an apparatus for optimizing the trajectory of a vehicle according to an embodiment.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments presented below, but can be implemented in various different forms, and should be understood to include all transformations, equivalents, and substitutes included in the spirit and technical scope of the present invention. do. The examples presented below are provided to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention. In describing the present invention, if it is determined that a detailed description of related known technologies may obscure the gist of the present invention, the detailed description will be omitted.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Some embodiments of the present disclosure may be represented by functional block configurations and various processing steps. Some or all of these functional blocks may be implemented in various numbers of hardware and/or software configurations that perform specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors, or may be implemented by circuit configurations for certain functions. Additionally, for example, functional blocks of the present disclosure may be implemented in various programming or scripting languages. Functional blocks may be implemented as algorithms running on one or more processors. Additionally, the present disclosure may employ conventional technologies for electronic environment setup, signal processing, and/or data processing. Terms such as “mechanism,” “element,” “means,” and “configuration” may be used broadly and are not limited to mechanical and physical configurations.

Additionally, connection lines or connection members between components shown in the drawings merely exemplify functional connections and/or physical or circuit connections. In an actual device, connections between components may be represented by various replaceable or additional functional connections, physical connections, or circuit connections.

Hereinafter, 'vehicle' may refer to any type of transportation used to move people or objects with engine, such as a car, bus, motorcycle, kickboard, or truck.

Hereinafter, the present disclosure will be described in detail with reference to the attached drawings.

Referring to FIG. 1, the autonomous driving device according to an embodiment of the present invention can be mounted on a vehicle to implement an autonomous vehicle 10. The autonomous driving device mounted on the autonomous vehicle 10 may include various sensors (including cameras) to collect surrounding situation information. For example, the autonomous driving device may detect the movement of the preceding vehicle 20 running in front through an image sensor and/or an event sensor mounted on the front of the autonomous vehicle 10. The self-driving device may further include sensors for detecting not only the front of the self-driving vehicle 10, but also other driving vehicles 30 running in the lane next to the self-driving vehicle 10 and pedestrians around the self-driving vehicle 10.

At least one of the sensors for collecting situational information around the autonomous vehicle may have a predetermined field of view (FoV), as shown in FIG. 1 . For example, when a sensor mounted on the front of the autonomous vehicle 10 has a field of view (FoV) as shown in FIG. 1, information detected at the center of the sensor may have relatively high importance. This may be because the information detected at the center of the sensor includes most of the information corresponding to the movement of the preceding vehicle 20.

The self-driving device processes information collected by the sensors of the self-driving vehicle 10 in real time to control the movement of the self-driving vehicle 10, while storing at least some of the information collected by the sensors in a memory device. .

Referring to FIG. 2, the autonomous driving device 40 may include a sensor unit 41, a processor 46, a memory system 47, and a vehicle body control module 48. The sensor unit 41 includes a plurality of sensors (including cameras) 42-45, and the plurality of sensors 42-45 include an image sensor, an event sensor, an illumination sensor, a GPS device, an acceleration sensor, etc. It can be included.

Data collected by sensors 42-45 may be transmitted to processor 46. The processor 46 stores the data collected by the sensors 42-45 in the memory system 47 and controls the vehicle body control module 48 based on the data collected by the sensors 42-45 to control the vehicle body control module 48. movement can be determined. The memory system 47 may include two or more memory devices and a system controller for controlling the memory devices. Each of the memory devices may be provided as one semiconductor chip.

In addition to the system controller of the memory system 47, each of the memory devices included in the memory system 47 may include a memory controller, and the memory controller may include an artificial intelligence (AI) operation circuit such as a neural network. The memory controller may generate calculation data by assigning a predetermined weight to data received from the sensors 42 - 45 or the processor 46 and store the calculation data in a memory chip.

Figure 3 is a diagram showing an example of image data acquired by sensors (including cameras) of an autonomous vehicle equipped with an autonomous driving device. Referring to FIG. 3, image data 50 may be data acquired by a sensor mounted on the front of an autonomous vehicle. Therefore, the image data 50 may include the front part 51 of the autonomous vehicle, the preceding vehicle 52 in the same lane as the autonomous vehicle, the vehicles 53 and the background 54 around the autonomous vehicle. .

In the image data 50 according to the embodiment shown in FIG. 3, the data in the area where the front part 51 and the background 54 of the autonomous vehicle appear are data that are unlikely to affect the operation of the autonomous vehicle. It can be. In other words, the front 51 and background 54 of the autonomous vehicle can be considered data with relatively low importance.

On the other hand, the distance to the preceding vehicle 52 and the lane change movement of the driving vehicle 53 may be very important factors in the safe operation of an autonomous vehicle. Accordingly, in the image data 50, data in an area including the preceding vehicle 52 and the driving vehicle 53 may have relatively high importance in the operation of the autonomous vehicle.

The memory device of the autonomous driving device may store the image data 50 received from the sensor by assigning different weights to each region. For example, a high weight is given to data in an area that includes the preceding vehicle 52 and the driving vehicle 53, and a low weight is given to data in an area where the front 51 and background 54 of an autonomous vehicle appear. can be given.

Hereinafter, operations according to various embodiments may be understood as being performed by the autonomous driving device or a processor included in the autonomous driving device.

Figure 4 is an example diagram schematically showing a process for optimizing the trajectory of a vehicle according to an embodiment.

Referring to FIG. 4, the environment for optimizing the trajectory of the vehicle according to the present invention includes a reference trajectory 410, driving information 420, MPC (Model Predictive Control) module 430, control input 440, and vehicle 450. ) may include.

The reference trajectory 410 may mean the path along which the vehicle 450 should travel. In one embodiment, the reference trajectory 410 is a global path corresponding to the path from the origin to the destination of the vehicle 450 and a local path corresponding to a predetermined forward path to avoid unexpected non-fixed obstacles. may include.

In another embodiment, the reference trajectory 410 may be generated by interpolating a local path among the obtained global path and local path with a velocity profile.

The optimal control input 440 or optimal trajectory, which will be described later, may mean a control input or trajectory for the vehicle 450 to drive without deviating from the reference trajectory 410.

In one embodiment, the reference trajectory 410 may mean a local path.

Driving information 420 may include all information related to the driving state of the vehicle, such as the current location of the vehicle 450, current control status, current speed, and received control input 440. Driving information 420 may mean the current state variable of the vehicle 450.

Control input 440 refers to an input that controls the steering angle. When the control input 440 is input to the vehicle 450, a DC motor for rotating the steering wheel and an encoder for reading the steering angle may be connected through gears.

The MPC module 430 refers to a module for determining the optimal control input 440 for the vehicle 450. MPC is a method of optimal control that generates optimized control commands by inputting the movement of an object and surrounding environmental conditions into a cost function. The MPC module 430 can select the optimal control input 440 by calculating a plurality of expected trajectories according to one or more control inputs and calculating a cost function. The MPC module 430 can use the iLQR algorithm (iterative Linear Quadratic Regulator) to select the optimal control input 440.

The iLQR algorithm is an algorithm that approximates non-linear functions as linear functions and non-quadratic functions as quadratic functions through Taylor approximation. It can calculate the optimal trajectory from the initial state to the target state by efficiently optimizing the cost function. there is.

In the present invention, a device for optimizing the trajectory of a vehicle (hereinafter referred to as 'device') obtains the reference trajectory 410 and driving information 420 of the vehicle 450 and uses a control input 440 determined through the MPC module 430. can be input into the vehicle 450. Hereinafter, the method by which the device optimizes the trajectory of the vehicle will be described in detail in FIGS. 5 to 7.

FIG. 5 is a diagram illustrating a process by which a vehicle trajectory optimization device calculates an expected trajectory according to an embodiment.

Referring to FIG. 5, the device can calculate a plurality of expected trajectories (501, 502, 503, 504) according to one or more control inputs (510) and select the optimal control input among one or more control inputs (510).

In one embodiment, the device may determine a plurality of model prediction time points including the current time point using the prediction horizon 520 and the control cycle 530.

The prediction reference horizon 520 refers to the number of future outputs to be predicted and is the future period that the model can predict. It corresponds to the number of future control inputs (Control horizon) to be predicted.

In one embodiment, the device may determine the 0th time point corresponding to the current time point to the Nth time point corresponding to the last time point of the prediction reference horizon 520 based on the control cycle 530. N is a natural number greater than or equal to 2, and the control cycle 530 refers to the interval between each time point. That is, the device can determine the 0th time point corresponding to the current time point to the Nth time point corresponding to the last time point of the prediction reference horizon 520 at intervals of the control period 530. At this time, N may correspond to a natural number divided by the prediction reference horizon 520 by the control period 530.

The device can calculate a plurality of expected trajectories (501, 502, 503, 504) based on a plurality of model prediction points.

Since the expected trajectory varies depending on the control input at a plurality of model prediction time points, the device generates one or more control inputs 510 at each of the plurality of model prediction time points, and generates a plurality of predictions according to one or more control inputs 510. Trajectories (501, 502, 503, 504) can be calculated.

In one embodiment, the device may generate a plurality of predicted trajectories (501, 502, 503, and 504) by inputting one or more control inputs (510) into a prediction model. In one embodiment, the iLQR-based MPC module can select the optimal control input that minimizes the cost function through iterative optimization calculations, and at this time, the future movement of the vehicle can be identified using a prediction model. Accordingly, the prediction model according to one embodiment will be described in detail below with reference to FIGS. 6A and 6B.

6A and 6B are diagrams for explaining a prediction model for selecting an optimal control input by a vehicle trajectory optimization device according to an embodiment.

Figure 6a is a Kinematic Bicycle Model with control points on the rear wheel axle, and Figure 6b is a Kinematic Bicycle Model with control points on the front wheel axle. A bicycle model can be used because at most vehicle speeds the turning radius is much larger than that of the vehicle's wheels.

Referring to FIG. 6A, when the control point is at the rear axle, the device can apply the Instantaneous Center of Rotation (ICR) and calculate the state change rate with respect to the control point. Specifically, the device can derive the steering angle through the rotation rate, rotation radius, length, etc. of the model bicycle, and calculate the state change rate for the control point through this.

Likewise, referring to Figure 6B, the device can calculate the rate of change of state for the control point, even if the control point is on the front axle. At this time, unlike in FIG. 6A, the state change rate may vary due to differences in control points.

In addition, the prediction model may correspond to a kinematic bicycle model when the control point is at the center of gravity, and the prediction model is not limited to the kinematic bicycle model.

FIG. 7 is a diagram illustrating a process in which a vehicle trajectory optimization apparatus according to an embodiment sets the control cycles 710 and 720 non-uniformly.

In one embodiment, the device may determine a plurality of model prediction points by setting the control cycles 710 and 720 to be non-uniform. For example, the device may determine the reference time 750 and set the control cycles 710 and 720 non-uniformly based on the reference time 750.

In general, higher control performance is required for driving stability for the front, which is close to the predicted reference horizon of the local route 730. However, as the control period (710, 720) becomes shorter, the amount of calculation increases, so setting the control period (710, 720) to be short may not be efficient for relatively long distances. Therefore, according to this embodiment, reasonable trajectory optimization can be achieved by setting a short control cycle (710) for the very close ahead and a long control cycle (720) for the future prediction reference horizon.

The reference time 750 refers to the time when the control cycles 710 and 720 are changed. The reference time 750 can be set based on the required control performance and efficiency according to the amount of association.

In one embodiment, the device needs to regenerate the reference trajectory 730 using the velocity profile in the non-uniform control cycles 710 and 720, so the reference trajectory 730 interpolates the local path with the velocity profile. It may be a trajectory created by doing so. For example, the device Calculate time information about a point in time, and The distance to be moved can be calculated according to the time information calculated by the vehicle at the starting point, and a reference trajectory can be created using the distance. Therefore, the reference trajectory may be a trajectory that reflects the vehicle's speed profile.

In one embodiment, the device may adjust the reference time 750 in response to determining that it has entered an environment where control performance is required above a threshold. For example, if the device determines that the vehicle has many unexpected obstacles, such as a dirt road, or has entered rough terrain, it can adjust the reference time 750 to be longer. As another example, if the device determines that the device has entered a very congested road with very heavy traffic, it may adjust the reference time 750 to be longer. Accordingly, unexpected accidents can be prevented by applying high control performance to a more distant point on the forecast reference horizon.

In another embodiment, the device may analyze the global path and adjust the reference time 750 in response to determining that the device is driving a global path requiring control performance above a threshold value. For example, when analyzing the global path corresponding to the route from the departure point to the destination, if the device determines that high control performance is required due to unpaved roads or rough terrain on the route, it adds an additional reference time (750) before driving. It can be adjusted for a long time.

The device can be set to a short control cycle (710) from the 0th time point to the reference time (750), and a long control cycle (720) from the reference time (750) to the Nth time point. For example, referring to FIG. 7, reference time 750 is a point in the prediction reference horizon. Based on the control cycle (710, 720) and You can see that it is set unevenly. Therefore, from point 0, which is the current point, Up to (750), the short control cycle (710) is A plurality of expected trajectories (740) according to the control input of the interval, From (750) to the Nth time, there is a long control cycle (720). A plurality of expected trajectories 740 can be calculated according to the control input of the interval.

In one embodiment, the device may adjust the control period 710, 720 in response to entering an environment where control performance is required above a threshold or determining that the device is traveling on a global path where control performance is required above a threshold. there is. For example, as described above, if the device determines that the global path corresponds to a lot of obstacles, rough terrain, or a congested road, or has entered the corresponding environment, the control period 710, 720 can be adjusted to be shorter. there is.

Returning to Figure 5, the device may calculate the cost function based on at least one of the reference trajectory 503, a plurality of expected trajectories 501, 502, 503, and 504, and one or more control inputs 510.

Among one or more control inputs 510, the control input when the cost function has the minimum value is determined as the optimal control input, so it is very important how the cost function is set in the process of optimizing the vehicle trajectory. Therefore, there is a need to set a cost function that can guarantee both riding comfort and safety.

In one embodiment, the cost function may include error cost, input cost, and change cost.

Specifically, the device may calculate the error cost based on the reference trajectory 503. The error cost may correspond to a cost depending on the difference between the actual trajectory to be driven compared to the reference trajectory 503 along which the vehicle should drive. If there is a large difference between the reference trajectory 503 and the driving trajectory, driver and pedestrian safety is affected, so for safety, it is desirable to minimize the difference with the reference trajectory 503. For example, the device may calculate the error cost based on the difference between the reference trajectory 503 and the expected trajectory.

Additionally, the device can calculate the input cost based on the control input. The input cost may correspond to the cost of inputting a control input. Since controlling the steering angle of the steering wheel while driving affects ride comfort, it is desirable to minimize control input for ride comfort. For example, the device may calculate the input cost based on the control input itself.

Additionally, the device can calculate the change cost based on the change amount of the control input. The change cost may correspond to a cost depending on the steering angle of the steering wheel. Since the larger the steering angle of the steering wheel, the greater the impact on ride comfort, it is desirable to minimize the amount of change in control input for ride comfort.

In one embodiment, the device may calculate the cost function by considering the weights of each of the error cost, input cost, and change cost. The weight can be set by considering which of driving safety and ride comfort to give greater weight to.

In one embodiment, the weights of the error cost, input cost, and variation cost may adaptively change depending on the vehicle's behavior. For example, the expected trajectory (501, 502, 503, 504) according to the control input 510, safety, and ride comfort are different when the vehicle speed is slow and high, so the weight is not a constant but the vehicle speed, etc. It may be a function that can change in real time and/or adaptively depending on the vehicle's behavior.

In one embodiment, the cost function may further include constraints regarding the control input and/or the amount of change in the control input.

In one embodiment, the device may select an optimal control input from one or more control inputs 510. For example, the device can calculate a plurality of expected trajectories through a prediction model, calculate a cost function for each of the plurality of expected trajectories, and select the expected trajectory with the minimum cost function as the optimal expected trajectory 501. . At this time, the control input from which the optimal expected trajectory 501 is calculated may correspond to the optimal control input.

Meanwhile, as described in FIG. 4, the device can use the iLQR algorithm to select the optimal control input through the prediction model and cost function.

In one embodiment, the device may repeat the steps described above every control cycle 530 to optimize the vehicle's trajectory. For example, in response to selecting the optimal control input at the Mth time point (M is a natural number between 1 and N-1), the device may delete the control input at the (M+1)th to Nth time points. . Accordingly, the device can select a new optimal control input at every time, thereby demonstrating excellent control performance.

Figure 8 is a flowchart of a method for optimizing the trajectory of a vehicle according to one embodiment.

The method for generating a virtual stop line shown in FIG. 8 is related to the previously described embodiments, so even if the content is omitted below, the content described above can also be applied to the method of FIG. 8.

The operations shown in FIG. 8 can be executed by the above-described autonomous driving device. Specifically, the operations shown in FIG. 8 may be executed by a processor included in the above-described autonomous driving device.

At step 810, the device may obtain a reference trajectory.

In step 820, the device may calculate a plurality of expected trajectories according to one or more control inputs.

In one embodiment, the device may determine a plurality of model prediction time points, including the current time point, using a prediction reference horizon and a control cycle.

In one embodiment, the device may determine the 0th time point corresponding to the current time point of the prediction reference horizon to the Nth time point corresponding to the last time point of the prediction reference horizon (N is a natural number divided by the prediction reference horizon divided by the control period).

The control input may be one or more control inputs at each of the first to Nth time points, and a plurality of predicted trajectories may be generated by inputting one or more control inputs into the prediction model.

In one embodiment, the device may calculate a plurality of expected trajectories based on a plurality of model prediction time points.

In step 830, the device may calculate a cost function based on at least one of a reference trajectory, a plurality of expected trajectories, and one or more control inputs.

In one embodiment, the cost function may include error cost, input cost, and change cost.

In one embodiment, the device calculates an error cost based on a reference trajectory, calculates an input cost and a change cost based on a control input, and calculates a cost function based on at least one of the error cost, the input cost, and the change cost. You can.

In one embodiment, the error cost may be calculated based on the difference between the reference trajectory and the expected trajectory, and the change cost may be calculated based on the change amount of the control input.

In one embodiment, the cost function may be calculated by giving weights to each of the error cost, input cost, and change cost.

In step 840, the device may select the optimal control input with the minimum cost function among one or more control inputs.

In one embodiment, the device may repeat steps 820 to 840 every control cycle.

In one embodiment, in response to selecting the optimal control input at the Mth time point (M is a natural number between 1 and N-1), the control input at the (M+1)th to Nth time points may be deleted. there is.

Figure 9 is a block diagram of an apparatus for optimizing the trajectory of a vehicle according to an embodiment.

Referring to FIG. 9, the device 900 may include a communication unit 910, a processor 920, and a DB 1030. In the device 900 of FIG. 9, only components related to the embodiment are shown. Accordingly, those skilled in the art can understand that other general-purpose components may be included in addition to the components shown in FIG. 9.

The communication unit 910 may include one or more components that enable wired/wireless communication with an external server or external device. For example, the communication unit 910 may include at least one of a short-range communication unit (not shown), a mobile communication unit (not shown), and a broadcast receiver (not shown). In one embodiment, the communication unit 910 may receive traffic information and use it to recognize crosswalks on the driving route.

The DB 1030 is hardware that stores various data processed within the device 900, and can store programs for processing and control of the processor 920.

The DB 1030 is a random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD- It may include ROM, Blu-ray or other optical disk storage, a hard disk drive (HDD), a solid state drive (SSD), or flash memory.

The processor 920 controls the overall operation of the device 900. For example, the processor 920 can generally control the input unit (not shown), display (not shown), communication unit 910, DB 1030, etc. by executing programs stored in the DB 1030. The processor 920 can control the operation of the device 900 by executing programs stored in the DB 1030.

The processor 920 may control at least some of the operations of the autonomous driving device described above with reference to FIGS. 1 to 8 .

The processor 920 includes application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. It may be implemented using at least one of micro-controllers, microprocessors, and other electrical units for performing functions.

In one embodiment, device 900 may be a mobile electronic device. For example, the device 900 may be implemented as a smartphone, tablet PC, PC, smart TV, personal digital assistant (PDA), laptop, media player, navigation, device equipped with a camera, and other mobile electronic devices. Additionally, the device 900 may be implemented as a wearable device such as a watch, glasses, hair band, or ring equipped with communication functions and data processing functions.

In another embodiment, device 900 may be an electronic device embedded in a vehicle. For example, the device 900 may be an electronic device inserted into a vehicle through tuning after the production process. In this case, the locations of the device 900 and the above-described vehicle (or autonomous vehicle) may be the same.

In another embodiment, the device 900 may be a server located outside the vehicle. A server may be implemented as a computer device or a plurality of computer devices that communicate over a network to provide commands, codes, files, content, services, etc. The server may receive data necessary to optimize the trajectory of the vehicle from devices mounted on the vehicle, and optimize the trajectory of the vehicle based on the received data.

In another embodiment, the process performed in the device 900 may be performed by at least some of a mobile electronic device, an electronic device embedded in the vehicle, and a server located outside the vehicle.

Embodiments according to the present invention may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded on a computer-readable medium. At this time, the media includes magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROM. , RAM, flash memory, etc., may include hardware devices specifically configured to store and execute program instructions.

Meanwhile, the computer program may be designed and configured specifically for the present invention, or may be known and available to those skilled in the art of computer software. Examples of computer programs may include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

According to one embodiment, methods according to various embodiments of the present disclosure may be included and provided in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or through an application store (e.g. Play StoreTM) or between two user devices. It may be distributed in person or online (e.g., downloaded or uploaded). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

Unless there is an explicit order or statement to the contrary regarding the steps constituting the method according to the invention, the steps may be performed in any suitable order. The present invention is not necessarily limited by the order of description of the above steps. The use of any examples or illustrative terms (e.g., etc.) in the present invention is merely to describe the present invention in detail, and unless limited by the claims, the scope of the present invention is limited by the examples or illustrative terms. It doesn't work. Additionally, those skilled in the art will recognize that various modifications, combinations and changes may be made depending on design conditions and factors within the scope of the appended claims or their equivalents.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and the scope of the patent claims described below as well as all scopes equivalent to or equivalently changed from the scope of the claims are within the scope of the spirit of the present invention. It will be said to belong to

Claims (11)

In a method of optimizing the trajectory of a vehicle,
(a) acquiring a reference trajectory;
(b) calculating a plurality of expected trajectories according to one or more control inputs;
(c) calculating a cost function based on at least one of the reference trajectory, the plurality of expected trajectories, and the one or more control inputs; and
(d) selecting an optimal control input that minimizes the cost function among the one or more control inputs. According to claim 1,
In step (b),
Determining a plurality of model prediction time points including the current time point using a prediction reference horizon and a control cycle; and
The method further comprising calculating the plurality of expected trajectories based on the plurality of model prediction time points. According to claim 2,
The step of determining the plurality of model prediction points is,
determining a 0th time point corresponding to the current time point of the prediction reference horizon to an Nth time point corresponding to the last time point of the prediction reference horizon - where N corresponds to a natural number greater than 2 obtained by dividing the prediction reference horizon by the control period; Including,
The one or more control inputs correspond to one or more control inputs at each of the 0th time point to the Nth time point,
The method wherein the plurality of predicted trajectories are generated by inputting the one or more control inputs into a prediction model. According to claim 1,
The step of calculating the cost function is,
calculating an error cost based on the reference trajectory;
calculating an input cost and a change cost based on the control input; and
Comprising the cost function based on at least one of the error cost, the input cost, and the change cost. According to claim 4,
The error cost is,
Calculated based on the difference between the reference trajectory and the expected trajectory,
The change cost is,
A method calculated based on the amount of change in the control input. According to claim 4,
The cost function is,
A method that is calculated by giving weights to each of the error cost, the input cost, and the change cost. According to claim 1,
The step of calculating the plurality of expected trajectories is,
The method further comprising calculating the plurality of expected trajectories by inputting the one or more control inputs into the prediction model. According to claim 3,
The above method is,
(e) repeating steps (b) to (d) at every control cycle. According to claim 8,
In step (d),
In response to selecting the optimal control input at the M-th time point, deleting the control input at the (M+1)-th time point to the N-th time point,
The method wherein M corresponds to a natural number greater than or equal to 1 and less than or equal to N-1. In a device for optimizing the trajectory of a vehicle,
a memory in which at least one program is stored; and
A processor that operates by executing the at least one program;
The processor,
Obtain a reference trajectory,
Calculate multiple expected trajectories according to one or more control inputs,
Calculating a cost function based on at least one of the reference trajectory, the plurality of expected trajectories, and the one or more control inputs,
A device for selecting an optimal control input that minimizes the cost function among the one or more control inputs. A computer-readable recording medium recording a program for executing the method of claim 1 on a computer.

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