Vehicle control method, vehicle control device, and vehicle control system including the vehicle control device

Abstract

The present disclosure relates to a vehicle control method, a vehicle control device, and a vehicle control system including the vehicle control device. Specifically, a vehicle control device according to the present disclosure includes a vehicle travel route estimation unit that estimates a first travel route of a vehicle based on a turning radius and a travel distance of the vehicle when a speed of the vehicle is equal to or less than a predetermined speed, a target travel route prediction unit that confirms a target from peripheral detection information regarding a periphery of the vehicle and predicts a second travel route of the target, a collision determination unit that determines whether there is a possibility of a collision between the vehicle and the target, and a vehicle control unit that controls at least one of a warning control, a braking control, and an evasive control to be executed when there is the possibility of the collision between the vehicle and the target.

Description

Technical Field

[0001] This disclosure relates to a vehicle control device, a method for controlling a vehicle using the vehicle control device, and a system including the device and capable of implementing the method. Background Technology

[0002] To enhance driving stability and comfort, vehicles are increasingly equipped with various convenient mechanisms to provide operators with information about driving status or conditions. Beyond the need for these convenient mechanisms, the demand for vehicle safety devices is also growing. Vehicle safety devices include active safety systems for proactively preventing vehicle accidents and hazards, such as anti-lock braking systems (ABS) and electronically controlled suspension (ECS), as well as passive safety devices for recording information related to vehicle accidents, such as vehicle black boxes.

[0003] Recently, autonomous vehicles, including unmanned ground vehicles (UGVs), have been widely used in military and commercial applications. These applications require such autonomous vehicles to move freely in unknown environments with dynamic and physical constraints, rather than simply following pre-planned routes and algorithms in offline driving environments.

[0004] Research on autonomous vehicles includes active driving or steering in complex environments. A model prediction method based on continuous online optimization control is employed to generate dynamic trajectories related to obstacle avoidance.

[0005] Therefore, in typical model prediction methods, the probability of a potential collision between the vehicle and the target is uniformly determined by using driving information detection sensors such as yaw rate sensors to calculate the vehicle's turning radius and predict the vehicle's travel path.

[0006] However, a significant drawback of typical model prediction methods is that in environments with low skidding probability or low vehicle speeds, the turning radius calculated using yaw rate sensors can lead to inaccurate path predictions. Furthermore, typical model prediction methods have an additional drawback: the probability of a potential collision is uniformly determined without considering the characteristics of obstacles, thus also causing errors. Summary of the Invention

[0007] [Technical Issues]

[0008] To address these issues, embodiments of this disclosure provide a vehicle control method and a vehicle control device for accurately predicting travel paths, as well as a system including the device and capable of implementing the method.

[0009] [Technical Solution]

[0010] According to one aspect of this disclosure, a vehicle control device is provided, comprising: a vehicle travel path predictor capable of calculating a turning radius based on a steering wheel angle and a vehicle steering ratio, calculating a travel distance based on at least one of a pulse signal from at least one wheel and a vehicle speed, and predicting a first travel path of the vehicle based on the turning radius and the travel distance; a target travel path predictor capable of identifying a target based on information obtained by detecting objects near the vehicle, and predicting a second travel path of the target; a collision probability determiner capable of setting a collision determination boundary region corresponding to the size of the vehicle, and determining the probability of a potential collision between the vehicle and the target based on the collision determination boundary region, the first travel path, the target, and the second travel path; and a vehicle controller capable of controlling the execution of at least one of warning control, braking control, and avoidance control when there is a probability of a potential collision between the vehicle and the target.

[0011] According to another aspect of this disclosure, a vehicle control method is provided, comprising: a vehicle travel path prediction step: calculating a turning radius based on the steering angle of the steering wheel and the steering ratio of the vehicle, calculating a travel distance based on the vehicle speed, and predicting a first travel path of the vehicle based on the turning radius and the travel distance; a target travel path prediction step: identifying a target based on information obtained by detecting objects near the vehicle, and predicting a second travel path of the target; a collision probability determination step: setting a collision determination boundary region corresponding to the size of the vehicle, and determining the probability of a potential collision between the vehicle and the target based on the collision determination boundary region, the first travel path, the target, and the second travel path; and a vehicle control step: when there is a probability of a potential collision between the vehicle and the target, controlling the execution of at least one of warning control, braking control, and avoidance control.

[0012] According to another aspect of this disclosure, a vehicle control system is provided, comprising: a steering angle sensor for detecting the steering angle of a steering wheel; a vehicle speed sensor for detecting the speed of a vehicle; an object detection sensor for detecting objects near the vehicle; a vehicle control device capable of determining the probability of a potential collision between a target and the vehicle in the vicinity of the vehicle, and controlling the vehicle when the probability of a potential collision exists; and a drive device configured to operate under the control of the vehicle control device, the vehicle control device being capable of: receiving steering angle information from the steering angle sensor, receiving vehicle speed information from the vehicle speed sensor, and receiving object detection information from the object detection sensor; calculating a turning radius based on the steering angle of the steering wheel and the steering ratio of the vehicle; calculating a travel distance based on the vehicle speed; predicting a first travel path of the vehicle based on the turning radius and the travel distance; predicting a second travel path of a target by identifying the target based on the object detection information; setting a collision determination boundary region corresponding to the size of the vehicle; determining the probability of a potential collision between the vehicle and the target based on the collision determination boundary region, the first travel path, the target, and the second travel path; and controlling the drive device to perform at least one of warning control, braking control, and avoidance control when the probability of a potential collision between the vehicle and the target exists.

[0013] [Technical Effects]

[0014] According to embodiments of the present disclosure, a vehicle control method and a vehicle control device for accurately predicting travel paths, as well as a system including the device and capable of implementing the method, can be provided. Attached Figure Description

[0015] Figure 1 This is a schematic block diagram illustrating a vehicle control system according to aspects of this disclosure.

[0016] Figure 2 This is a schematic block diagram illustrating a vehicle control device according to aspects of this disclosure.

[0017] Figure 3 An example of calculating the turning radius in a vehicle control device and / or system according to aspects of this disclosure is shown.

[0018] Figure 4 Another example of calculating the turning radius in a vehicle control device and / or system according to aspects of this disclosure is shown.

[0019] Figure 5 Examples of predicting the travel path of a vehicle in a vehicle control device and / or system according to aspects of this disclosure are shown.

[0020] Figure 6 Examples are shown of predicting the travel path of a target based on the type of the target in a vehicle control device and / or system, according to aspects of this disclosure.

[0021] Figure 7 This illustrates another example of predicting the travel path of a target based on the type of the target in a vehicle control device and / or system, according to aspects of this disclosure.

[0022] Figure 8 An example is shown of modifying the size of a collision determination boundary region based on the target type in a vehicle control device and / or system, according to aspects of this disclosure.

[0023] Figure 9 This illustrates another example of modifying the size of a collision determination boundary region based on the target type in a vehicle control device and / or system, according to aspects of this disclosure.

[0024] Figure 10 An example is shown of modifying the size of a collision determination boundary region based on the shape of a target in a vehicle control device and / or system, according to aspects of this disclosure.

[0025] Figure 11 An example is shown of modifying the size of a collision determination boundary region based on the vehicle speed in a vehicle control device and / or system, according to aspects of this disclosure.

[0026] Figure 12 and Figure 13 Examples are shown of determining the likelihood of a potential collision between a vehicle and a target in a vehicle control device and / or system according to aspects of this disclosure.

[0027] Figure 14 This is a flowchart illustrating a vehicle control method according to aspects of this disclosure.

[0028] Figure 15 It is a flowchart for specifically illustrating a vehicle control method according to aspects of this disclosure. Detailed Implementation

[0029] In the following description of examples or embodiments of this disclosure, reference will be made to the accompanying drawings, which illustrate specific examples or embodiments that may be implemented, and the same reference numerals and symbols may be used to denote the same or similar components, even if they are shown in different drawings. Furthermore, in the following description of examples or embodiments of this disclosure, detailed descriptions of well-known functions and components incorporated herein are omitted where it is determined that the description might obscure the subject matter of some embodiments of this disclosure. Terms such as “comprising,” “having,” “including,” “constituting,” “forming,” and “formed from” as used herein are generally intended to allow for the addition of additional components, unless these terms are used in conjunction with the term “only.” As used herein, singular forms are intended to include plural forms unless the context clearly indicates otherwise.

[0030] Terms such as “first,” “second,” “A,” “B,” “(A),” or “(B)” may be used herein to describe elements of this disclosure. Each of these terms is not used to define the nature, order, sequence, or number of elements, but only to distinguish the corresponding element from other elements.

[0031] When referring to a first element as "connected to or coupled to," "in contact with," or "overlapping" with a second element, it should be interpreted that not only can the first element be "directly connected to or coupled to" or "directly in contact with or overlapping" the second element, but a third element can also be "inserted" between the first and second elements, or the first and second elements can be "connected to or coupled to," "in contact with," or "overlapping" with each other through a fourth element. Here, a second element can be included in at least one of two or more elements that are "connected to or coupled to," "in contact with," or "overlapping" with each other.

[0032] When time-related terms such as “after,” “following,” “next,” or “before” are used to describe the process or operation of an element or structure, or the flow or steps in an operation, processing, or manufacturing method, these terms may be used to describe a discontinuous or non-sequential process or operation, unless used with the terms “directly” or “immediately.”

[0033] Additionally, when referring to any size, relative dimensions, etc., the numerical values ​​or corresponding information of the component or feature (e.g., grade, range, etc.) should be considered, including tolerances or error ranges that may also be caused by various factors (e.g., process factors, internal or external influences, noise, etc.), even if no relevant description is specified. Furthermore, the term "may" fully encompasses all the meanings of the term "can".

[0034] Figure 1 This is a schematic block diagram illustrating a vehicle control system according to aspects of this disclosure.

[0035] Reference Figure 1 According to aspects of this disclosure, the vehicle control system 10 can refer to a system for controlling a vehicle to avoid collisions between the vehicle and obstacles.

[0036] The vehicle control system 10 may include a steering angle sensor 111, a vehicle speed sensor 112, an object detection sensor 113, a vehicle control device 200, a drive device 300, etc.

[0037] The steering angle sensor 111 can detect the steering angle of the steering wheel. Specifically, when the driver turns the steering wheel, the steering angle sensor 111 can detect the steering angle caused by the steering wheel rotation and output information related to the detected steering angle to the vehicle control unit 200.

[0038] The vehicle speed sensor 112 can detect the vehicle speed. Specifically, the vehicle speed sensor 112 can detect the rotational speed of at least one wheel, convert the detected rotational speed into the corresponding vehicle speed, and output the vehicle speed information to the vehicle control device 200.

[0039] The object detection sensor 113 can detect situations or objects near the vehicle, such as one or more nearby vehicles, one or more obstacles, etc. That is, the object detection sensor 113 can detect situations or objects near the vehicle and output the object detection information to the vehicle control device 200.

[0040] In some embodiments, the object detection sensor 113 may include a camera, LiDAR, radar, ultrasonic sensor, etc. However, the embodiments of this disclosure are not limited thereto.

[0041] The object detection sensor 113 can be located on the exterior of the vehicle and includes multiple object detection sensors 113 of different or the same type.

[0042] The vehicle control unit 200 can determine the likelihood of a potential collision between the vehicle and a target located near the vehicle, and control the vehicle when such a potential collision is detected. In one embodiment, when a potential collision is detected, the vehicle control unit 200 can output a control signal to the drive unit 300 to avoid a collision between the vehicle and the target.

[0043] The vehicle control unit 200 can receive steering angle information, vehicle speed information and object detection information, predict the vehicle's travel path, predict the target's travel path, determine the probability of a potential collision between the vehicle and the target, and output a control signal to the drive unit 300 when there is a probability of a potential collision.

[0044] The vehicle control device 200 can be implemented using electronic components and software such as an electronic control unit (ECU) and a domain control unit (DCU). However, embodiments of this disclosure are not limited thereto.

[0045] The following will refer to Figure 2 The vehicle control device 200 is described in more detail.

[0046] The drive unit 300 can be driven by the vehicle control unit 200. Specifically, when the vehicle control unit 200 outputs a control signal, the drive unit 300 can receive the control signal and execute the control operation indicated by the control signal.

[0047] The drive unit 300 may be, for example, a braking device for braking a vehicle, a steering actuator for performing evasive steering of the vehicle, a display for visually displaying a warning message to the driver, a warning device for outputting a warning sound, a tactile actuator for issuing a tactile signal to the driver, etc. However, the embodiments disclosed herein are not limited thereto.

[0048] Although not shown, the vehicle control system 10 according to aspects of this disclosure may further include a yaw rate sensor, a torque sensor, a heading angle detection sensor, a wheel pulse sensor for detecting pulse signals of at least one wheel, etc.

[0049] The vehicle control device 200 according to aspects of this disclosure will be described in more detail below.

[0050] Figure 2 This is a schematic block diagram of a vehicle control device 200 according to aspects of the present disclosure.

[0051] Reference Figure 2 The vehicle control device 200 according to aspects of this disclosure may include a vehicle travel path predictor 210, a target travel path predictor 220, a collision probability determiner 230, a vehicle controller 240, etc.

[0052] The vehicle travel path predictor 210 can calculate the turning radius based on the steering angle of the steering wheel and the steering ratio of the vehicle, calculate the travel distance based on at least one of the pulse signal of at least one wheel and the vehicle speed, and predict the first travel path of the vehicle based on the turning radius and the travel distance.

[0053] For example, when the vehicle speed is less than or equal to a preset speed, the vehicle path predictor 210 can predict a first travel path. For example, when the vehicle enters a parking mode or driving mode and travels at a speed less than or equal to the preset speed, the vehicle control device 200 can determine the probability of a potential collision by performing operations according to embodiments of this disclosure. This is because the accuracy of the collision probability determination technique according to the embodiments described herein can be further improved when the vehicle is traveling at a preset speed or lower. In this case, when the vehicle speed is less than or equal to the preset speed, and when at least one wheel slips relative to the road surface, one or more additional collision probability determination logic can be used alone or in combination with the above-described collision probability determination technique. Hereinafter, for ease of explanation, the case of a vehicle speed less than or equal to the preset speed will be discussed. However, it should be understood that the collision probability determination method and collision probability prediction according to embodiments of this disclosure can be applied to cases where the vehicle speed is greater than or equal to the preset speed.

[0054] Here, the steering ratio can refer to the ratio between the steering angle of the steering wheel and the steering angle of at least one front wheel. That is, the steering ratio can be obtained by dividing the steering angle of the steering wheel by the steering angle of at least one front wheel. For example, when the steering angles of the steering wheel and the front wheels are 480 degrees and 30 degrees, respectively, the steering ratio can be 480 / 30 = 16. The steering ratio can typically be between 12 and 20; however, the embodiments of this disclosure are not limited thereto.

[0055] Furthermore, in the dynamic modeling of a vehicle, the steering ratios of the left and right front wheels can have different values. For example, in the case of vehicle modeling, the steering ratios of the left and right front wheels can be different from each other. This is because the Ackerman-Jantoud type is applied, in which concentric circles are drawn at all positions along the path the vehicle turns. That is, according to the Ackerman-Jantoud type, this is because the wheels drawn on the inner circle of a turning vehicle (e.g., the right front wheel of a right-turning vehicle) rotate more than the wheels drawn on the outer circle (e.g., the left front wheel of a right-turning vehicle).

[0056] Furthermore, the steering ratio can have different values ​​depending on any dynamic modeling applied. For example, in the case of bicycle modeling, the steering ratio can be obtained by dividing the steering angle of the steering wheel by the average of the steering angles of the left and right front wheels in vehicle modeling.

[0057] Such steering ratios can be pre-stored based on measurement data. In some embodiments, the steering ratio can be determined by data designed by the designer, or calculated based on a computational algorithm after measuring the turning radius plotted using a differential global positioning system (DGPS) while the steering wheel is turned, or the steering ratio can be a value processed in data form based on measurements of the rotation angle of at least one wheel when the steering wheel is turned, which are generated by physically measuring the rotation angle of at least one wheel when the steering wheel is turned. However, embodiments of this disclosure are not limited thereto.

[0058] In another embodiment, the steering ratio can be determined in real time while the vehicle is in motion, and the determined steering ratio can be stored in memory. In this case, the pre-stored steering ratio can be updated based on the determined steering ratio.

[0059] The following will refer to Figure 3 and Figure 4 This section describes in more detail the method for calculating the turning radius based on the vehicle's steering angle and steering ratio. The following will refer to... Figure 5 The method for calculating the distance traveled using vehicle speed, pulse signals from at least one wheel, etc., and the method for predicting the vehicle's first travel path are described in more detail.

[0060] The target travel path predictor 220 can identify at least one target and predict a second travel path for the target based on object detection information obtained by detecting conditions or objects near the vehicle. The object detection information can refer to the aforementioned reference... Figure 1 Information output from at least one object detection sensor 113 (e.g., a camera, etc.) as described.

[0061] Various methods can be used to identify targets from object detection information. In one embodiment, the target travel path predictor 220 can calculate the pixel values ​​of a still or moving image acquired by a camera, divide the calculated pixel values ​​into one or more groups, each group containing regions with similar color values, and extract one of the groups as a target for each group or a target for each group.

[0062] In another embodiment, the target travel path predictor 220 may utilize edge detection algorithms, such as the Canny edge detection algorithm, line edge detection algorithm, Laplacian edge detection algorithm, etc., to detect boundary lines in still or moving images generated by the camera, and then extract the object. However, embodiments of this disclosure are not limited thereto.

[0063] The following will refer to Figure 6 and Figure 7 The method for predicting the second travel path is described in more detail.

[0064] The collision probability determiner 230 can set a collision determination boundary area corresponding to the vehicle size, and determine the potential collision probability of the vehicle based on the collision determination boundary area, the first travel path, the target, and the second travel path.

[0065] A collision determination boundary region can refer to a contour set within a vehicle to determine a potential collision. The size of the collision determination boundary region can be set to correspond to the dimensions of the vehicle. In one embodiment, the vehicle dimensions can be obtained from pre-stored vehicle specification information and include the vehicle's lateral width (or total width) and longitudinal length (or total length). In another embodiment, the size of the collision determination boundary region can be set for each segment divided based on the vehicle's dimensions.

[0066] For example, when the collision determination boundary region overlaps with the target at a specific time, the collision probability determiner 230 can determine the probability of a potential collision. (See below for further details.) Figure 12 and Figure 13 The method for determining the probability of a potential collision is described in more detail.

[0067] When there is a potential collision possibility between the vehicle and the target, the vehicle controller 240 may perform at least one of warning control, braking control, and avoidance control. In one embodiment, if there is a potential collision possibility, the vehicle controller 240 may output a warning control signal to a warning device, a braking control signal to a braking device, and / or an avoidance control signal to a steering actuator.

[0068] Although not shown, the vehicle control device 200 according to aspects of this disclosure may further include a memory for storing steering ratio, vehicle specification information, etc., and a processor for processing input information.

[0069] The following section will describe in more detail an embodiment of calculating the turning radius.

[0070] Figure 3 An example of calculating the turning radius in a vehicle control device and / or system according to aspects of this disclosure is shown.

[0071] The turning radius can be calculated based on the vehicle's dynamic modeling. Such dynamic modeling can include, for example, vehicle modeling, bicycle modeling, etc. However, the embodiments of this disclosure are not limited thereto.

[0072] For example, the vehicle path predictor 210 can calculate the steering angle of at least one wheel of the vehicle based on the steering angle and steering ratio, and use the steering angle of the wheel and the distance between pre-stored wheel axles, such as the distance between the front wheel axle connecting the front wheels and the rear wheel axle connecting the rear wheels, to calculate the turning radius.

[0073] As mentioned above, the distance between the wheel axles can be included in the pre-stored vehicle specification information.

[0074] In this case, the turning radius can be the distance between the center of the circle and the center of at least one front wheel of the vehicle, or the distance between the center of the circle and the center of at least one rear wheel of the vehicle.

[0075] In one embodiment, based on at least one front wheel, the vehicle path predictor 210 can calculate the steering angle of the front wheels by dividing the steering wheel angle by the steering ratio. Furthermore, the vehicle path predictor 210 can calculate the turning radius R by substituting the steering angle of the front wheels and a pre-stored first distance between the wheel axles into Equation 1 below. f .

[0076] [Equation 1]

[0077]

[0078] Here, L is the first distance between the wheel and axle, θ w It is the steering angle of the front wheels, θ swIt is the steering angle of the steering wheel, and r is the steering ratio.

[0079] Furthermore, vehicle modeling typically involves multiple front wheels, and as mentioned above, the individual steering ratios of each front wheel may differ. Therefore, due to the multiple front wheels, multiple radii can be calculated, and these radii can have different values.

[0080] In this case, the vehicle path predictor 210 can ultimately calculate a turning radius that can be used as a reference based on multiple radii.

[0081] That is, based on vehicle modeling, the vehicle travel path predictor 210 can pre-store multiple steering ratios corresponding to multiple front wheels included in the vehicle modeling, calculate the steering angle of each of the multiple front wheels based on the steering angle of the steering wheel and the multiple steering ratios, calculate multiple radii using the steering angle of each of the multiple front wheels and the first distance between the wheel axles, and calculate the turning radius using the calculated multiple radii.

[0082] More specifically, refer to Figure 3 The vehicle travel path predictor 210 can pre-store a first steering ratio of the first front wheel (left front wheel, A) and a second steering ratio of the second front wheel (right front wheel, B) in the vehicle model.

[0083] Subsequently, the vehicle path predictor 210 can calculate the first steering angle α of the first front wheel by dividing the steering angle of the steering wheel by the first steering ratio, and calculate the second steering angle β of the second front wheel by dividing the steering angle of the steering wheel by the second steering ratio.

[0084] Then, the vehicle path predictor 210 can use Equation 1 above to calculate the first radius R of the first front wheel (left front wheel, A). fl The second radius R of the second front wheel (right front wheel, B) fr And based on the first radius R input into the mathematical algorithm fl Second radius R fr The turning radius R is calculated using mathematical algorithms. f .

[0085] Using the first radius R fl Second radius R fr The final calculated turning radius can be the radius around the center position (not shown) of the front axle connecting the two front wheels A and B.

[0086] In this case, due to the first radius R fl Second radius R frThe relationship between them is not as linear as the relationship between multiple radii calculated based on the rear wheel, which will be described in more detail below. Therefore, relatively complex mathematical algorithms (such as similarity ratios) can be used to calculate the radius around the center position of the front wheel axle.

[0087] Furthermore, when a vehicle is turning at low speed, the turning radius calculated based on the front wheels may lead to some errors in determining the likelihood of a potential collision.

[0088] In this case, the vehicle path predictor 210 can calculate the turning radius of at least one rear wheel of the vehicle.

[0089] In one embodiment, the vehicle path predictor 210 can calculate the steering angle of at least one front wheel by dividing the steering wheel angle by the steering ratio. Subsequently, the vehicle path predictor 210 can calculate the turning radius R by substituting the steering angle of the front wheels and a pre-stored first distance between the wheel axles into Equation 2 below. r .

[0090] [Equation 2]

[0091]

[0092] Here, L is the first distance between the wheel and axle, θw is the steering angle of the front wheel, θsw is the steering angle of the steering wheel, and r is the steering ratio.

[0093] As described above, since the vehicle modeling includes multiple front wheels, the vehicle travel path predictor 210 can ultimately calculate a turning radius that can be used as a reference based on multiple turning radii calculated based on the rear wheels.

[0094] That is, based on vehicle modeling, the vehicle travel path predictor 210 can calculate the individual steering angles of multiple rear wheels based on multiple pre-stored steering ratios and steering wheel steering angles, calculate the individual radii of multiple rear wheels included in the vehicle modeling using the individual steering angles of multiple rear wheels and the first distance between the wheel axles, and calculate the turning radius by averaging the calculated radii.

[0095] More specifically, refer to Figure 3 In the same manner as described above, the vehicle path predictor 210 can pre-store the first steering ratio and the second steering ratio, and calculate the first steering angle α and the second steering angle β.

[0096] Subsequently, the vehicle path predictor 210 can use Equation 2 above to calculate the first radius R of the first rear wheel (left rear wheel, D). rl The second radius R of the second rear wheel (right rear wheel, C) fr And by using the first radius R rl Second radius Rrr Take the average value ((R) rl -R rr ) / 2) to calculate the turning radius R r .

[0097] Using the first radius R rl Second radius R rr The final calculated turning radius can be referred to as the center position (P) around the rear axle connecting the two rear wheels C and D. center The radius of the rear wheel axle. Due to the center position of the rear wheel axle (P) center The distance between () and the center (O) is the first radius R rl Second radius R rr The intermediate value is used, therefore, the calculation speed of the turning radius based on the rear wheels is faster than that based on the front wheels.

[0098] As described above, the vehicle control device according to aspects of this disclosure can provide a more accurate prediction of the travel path by calculating the turning radius relative to at least one wheel or tire in contact with the road surface in an environment with a low probability of slippage.

[0099] Furthermore, the vehicle control device according to this disclosure can maximize calculation speed and minimize power consumption by calculating the turning radius of a low-speed vehicle based on the rear wheels.

[0100] Meanwhile, as another example of vehicle dynamics modeling, bicycle modeling, which is simpler than vehicle modeling, is provided. An example of using bicycle modeling to calculate the turning radius will be described in detail below.

[0101] Figure 4 Another example of calculating the turning radius in a vehicle control device and / or system according to aspects of this disclosure is shown.

[0102] Reference Figure 4 Bicycle modeling according to embodiments of this disclosure may include a front wheel A, a rear wheel B, and a second distance between the axles. Here, the second distance between the wheel and axle It can correspond to Figure 3 The first distance L between the wheel and axle is shown.

[0103] As shown above (refer to the reference) Figure 3 As described, the vehicle travel path predictor 210 can calculate the steering angle δ of the front wheel A using the steering wheel angle and steering ratio, and utilize the steering angle δ of the front wheel A and the second distance between the wheel axle. To calculate the turning radius.

[0104] Here, the turning radius can be calculated based on either the front or rear wheels in the same manner as described above.

[0105] In one embodiment, based on the front wheel, the vehicle path predictor 210 can pre-store the steering ratio r corresponding to the front wheel A included in the bicycle-based modeling. Here, it can be based on... Figure 3 The vehicle model shown calculates the steering ratio r by dividing the steering wheel's steering angle θsw by the average of the first steering angle α of the first front wheel and the second steering angle β of the second front wheel ((α-β) / 2).

[0106] Furthermore, the vehicle travel path predictor 210 can calculate the steering angle δ of the front wheel A by dividing the steering angle θsw of the steering wheel by the steering ratio r, and by using the steering angle δ and the second distance between the wheel axle... Substitute into Equation 1 above to calculate the turning radius R. f .

[0107] In another embodiment, based on the rear wheels, as described above, the vehicle path predictor 210 can predict the path by using the steering angle δ of the front wheels A and the second distance between the wheel axles. Substitute into Equation 2 as described above to calculate the turning radius Rr.

[0108] based on Figure 4 The turning radius R calculated for the front wheel A of the bicycle model shown. f Can be based on Figure 3 The turning radius is calculated for the center position of the front axle (not shown) in the vehicle model shown.

[0109] Furthermore, based on Figure 4 The turning radius R calculated for the rear wheel B of the bicycle model shown. r Can be based on Figure 3 The center position of the rear axle in the vehicle model shown (P) center The calculated turning radius is the same.

[0110] Preferably, when traveling at low speeds, the vehicle path predictor 210 can utilize the turning radius R calculated based on at least one rear wheel. r To predict the vehicle's travel path, and in non-low-speed driving conditions, the vehicle travel path predictor 210 can utilize the turning radius R calculated based on at least one front wheel. f To predict the vehicle's travel path.

[0111] Therefore, as described above, the vehicle control device 200 according to aspects of this disclosure can provide the effect of accurately predicting the travel path by utilizing a turning radius calculated using a method that minimizes potential errors based on vehicle speed.

[0112] The following describes an implementation of predicting a travel path using calculated turning radii and travel distances. For ease of description, it will be based on... Figure 4 The bicycle model shown is discussed, along with the turning radius calculated based on the rear wheel.

[0113] Figure 5 Examples are shown of predicting the travel path of a vehicle in a vehicle control device and / or system according to aspects of this disclosure.

[0114] Reference Figure 5 The vehicle path predictor 210 can calculate the travel distance S based on at least one of the pulse signals of at least one wheel and the vehicle speed.

[0115] Here, the distance traveled, S, can refer to the actual distance the vehicle travels while turning. In this case, the distance traveled, S, can be calculated based on a constant periodic signal or data.

[0116] In one embodiment, when the vehicle speed sensor 112 outputs vehicle speed information to the vehicle control device 200 at a period of 20ms, and the vehicle speed indicated by the vehicle speed information is 10kph, it can be determined that the vehicle has traveled 55mm in 20ms as the distance S. However, the embodiments of this disclosure are not limited thereto.

[0117] As another embodiment, when the wheel pulse sensor outputs one or more pulses to the vehicle control device 200, each pulse representing a travel distance of 2 cm, and a pulse has been generated for 20 ms, the travel distance S in 20 ms can be determined to be 2 cm. However, the embodiments of this disclosure are not limited thereto.

[0118] Once the turning radius R and the travel distance S are calculated, the vehicle travel path predictor 210 can use the turning radius R and the travel distance S to calculate the vehicle's heading angle Δθ.

[0119] Specifically, the vehicle path predictor 210 can calculate the vehicle's heading angle by substituting the turning radius R and the travel distance S into Equation 3.

[0120] [Equation 3]

[0121]

[0122] This is because the travel distance S has the property of being an arc formed by the radius R and the angle Δθ.

[0123] Once the vehicle's heading angle Δθ is calculated, the vehicle path predictor 210 can calculate the vehicle's displacement based on the turning radius R and the heading angle Δθ.

[0124] Displacement It can represent the straight-line distance between the starting point and another point reached by the vehicle traveling a distance S from the starting point, and it can also be the length of the chord of a sector formed by the turning radius R, the heading angle Δθ, and the arc travel distance S.

[0125] Specifically, the vehicle path predictor 210 can calculate the travel displacement by substituting the turning radius R and the heading angle Δθ into Equation 4.

[0126] [Equation 4]

[0127]

[0128] When calculating the travel displacement The vehicle path predictor 210 can be based on the heading angle Δθ and the travel displacement. To predict the vehicle's first travel path.

[0129] Here, the vehicle's first travel path can be represented as the trajectory of coordinates P(Δx, Δy) measured during the vehicle's movement.

[0130] In this case, the coordinates P(Δx, Δy) can be determined by the following equation 5.

[0131] [Equation 5]

[0132]

[0133]

[0134] Here, θ0 represents the vehicle's initial heading angle, or the heading angle before it starts moving.

[0135] The vehicle path predictor 210 can predict the first travel path by predicting the trajectory of the determined coordinates P(Δx, Δy).

[0136] In the case of vehicle modeling, since the travel distance S can be calculated, the travel distances of the two rear wheels C and D can be calculated using the aforementioned pulse or vehicle speed information (for example, the distance S1 of the left rear wheel is A). r *θ; The distance S2 traveled by the right rear wheel is B r *θ;A r B is the turning radius of the left rear wheel; r The turning radius of the right rear wheel is θ; the heading angle is θ. The average of the calculated travel distances is used to calculate the center position (P) relative to the rear axle. center The vehicle path predictor 210 can use the distance traveled relative to the rear axle center position (P) to predict the vehicle's path. center The turning radius, travel distance, and heading angle are used to predict the first travel path.

[0137] Furthermore, in order for the vehicle control device 200 according to aspects of this disclosure to determine the likelihood of a potential collision between the vehicle and a target, it is necessary to identify the target and predict its travel path. Embodiments for predicting the target's travel path will be described in detail below.

[0138] Figure 6 Examples are shown of predicting the travel path of a target based on the type of the target in a vehicle control device and / or system, according to aspects of this disclosure. Figure 7 This illustrates another example of predicting the travel path of a target based on the type of the target in a vehicle control device and / or system, according to aspects of this disclosure.

[0139] The target path predictor 220 according to embodiments of the present disclosure can identify the type of a target and predict a second path based on the identified type. That is, the target path predictor 220 can determine whether to predict a second path by identifying the type of the target.

[0140] This is to prevent unnecessary computation when the target is an immovable object such as a fence or utility pole, where it is not necessary to predict the target's second path.

[0141] Various methods can be employed here to identify the type of target. In one embodiment, the target travel path predictor 220 can extract the target from image data captured by a camera and use the extracted target as input to identify the type of target through a machine learning algorithm. However, embodiments of this disclosure are not limited thereto.

[0142] That is, when the target is an immovable object, the target path predictor 220 can determine that there is no need to predict a second path.

[0143] Reference Figure 6 For example, when the target path predictor 220 determines that the target is the street tree 511, the target path predictor 220 can determine that there is no need to predict a second path.

[0144] When the target is a movable object, the target travel path predictor 220 can predict a second travel path by detecting the target's travel speed based on object detection information from one or more object detection sensors 113, etc.

[0145] Even if the target is a moving object, it may be necessary to detect the target's speed, considering that the target may be stationary.

[0146] Various methods can be employed to detect the speed of travel. In one embodiment, differences between images of the same object captured by the camera can be determined, and then the speed of travel of the object can be detected based on these differences. In another embodiment, since radar can directly detect the speed of a target (using the Doppler effect, etc.), after measuring the speed of the target, the radar can provide object detection information containing the speed of travel to the vehicle control device 200, so that the target travel path predictor 220 can extract the speed of travel of the target from the object detection information.

[0147] In this case, the target path predictor 220 can predict a second path by identifying the target's direction of travel based on the target's travel speed.

[0148] Reference Figure 7 For example, when the target path predictor 220 determines that the target is a cyclist 512, the target path predictor 220 can predict a second path based on the cyclist 512's travel speed.

[0149] As described above, the vehicle control device 200 according to aspects of this disclosure can provide the effect of rapid information processing by preventing unnecessary calculations and simplifying calculation processes.

[0150] The collision probability determiner 230 can determine the probability of a potential collision based on whether the collision determination boundary region 410 overlaps with the target at a specific time. In this respect, even if the target is a movable target, the criteria or conditions used to determine the probability of a potential collision can be set differently depending on the specific type of the target.

[0151] The following will describe in detail an embodiment of modifying the collision determination boundary region 410 to set different collision probability determination criteria.

[0152] Figure 8 An example is shown of modifying the size of a collision determination boundary region based on the target type in a vehicle control device and / or system, according to aspects of this disclosure. Figure 9 This illustrates another example of modifying the size of a collision determination boundary region based on the target type in a vehicle control device and / or system, according to aspects of this disclosure.

[0153] The target path predictor 220 can provide the collision probability determiner 230 with type information indicating the target type based on the identified target type, so that the collision probability determiner 230 can correct or modify the size of the collision determination boundary region 410 based on the target type.

[0154] Reference Figure 8For example, when the target is another vehicle 513, the target travel path predictor 220 can provide the collision probability determiner 230 with type information indicating that the target is another vehicle 513. When the target is determined to be another vehicle 513, the collision probability determiner 230 can set the collision determination boundary region 410 to have a size that is close to or approximately the same as that of the corresponding vehicle 513.

[0155] Because the other vehicle 513 typically includes avoidance systems, warning systems, etc., the likelihood of a potential collision can be relatively lower than the likelihood of a pedestrian 514 as described below.

[0156] Reference Figure 9 When the target is pedestrian 514, the degree of potential collision probability with pedestrian 514 may be relatively high, which is different from the degree of potential collision probability with another vehicle 513. That is, if the target is pedestrian 514, the collision probability determiner 230 may increase the size of the collision determination boundary region 410a by a predetermined size as shown by reference numeral 410b in the figure.

[0157] That is, the collision probability determiner 230 can change the collision determination boundary region according to the type of the target. For example, the size of the collision determination boundary region can be adjusted according to the size of the detected target. In this case, the size of the collision determination boundary region can be adjusted proportionally or inversely to the size of the detected target. It is understood that the size of the collision determination boundary region cannot be reduced below the minimum collision determination boundary region, where a certain tolerance is applied to the vehicle size, nor can it be increased above the maximum collision determination boundary region.

[0158] As described above, the vehicle control device 200 according to aspects of this disclosure can protect other vehicles or pedestrians outside and the occupants of the vehicle 400 by setting collision probability standards differently according to the type of target.

[0159] At the same time, since targets may deform due to various reasons, such as aging or damage, and there are various types of targets, errors may occur when identifying the target type if the data pre-stored in the vehicle control device 200 cannot be updated.

[0160] In this case, even without distinguishing the type of target, the size of the collision determination boundary region 410 needs to be adjusted.

[0161] The following will describe in detail an embodiment of determining the size of the boundary region 410 based on the target's shape-modified collision.

[0162] Figure 10An example is shown of modifying the size of a collision determination boundary region based on the shape of a target in a vehicle control device and / or system, according to aspects of this disclosure.

[0163] Reference Figure 10 The target path predictor 220 can determine the shape of the target based on object detection information from one or more object detection sensors, and output shape information indicating the shape of the determined target to the collision probability determiner 230. In this case, the collision probability determiner 230 can determine the size of the boundary region 410a based on the shape-modified collision.

[0164] Various methods can be employed to specify the shape of the target. In one embodiment, the target path predictor 220 can determine the shape of the target by setting a region of interest (ROI). As another embodiment, the target path predictor 220 can determine the shape of the target present in the image acquired from the camera by utilizing feature point extraction algorithms, such as Scale Invariant Feature Transform (SIFT), Speed-Up Robust Feature Transform (SURF), etc. However, the embodiments of this disclosure are not limited thereto.

[0165] Meanwhile, as the size of the target determined based on its shape increases, the size of the corresponding collision determination boundary region 410a can also increase.

[0166] refer to Figure 10 As shown by reference numeral 1000 in the attached figures, for example, when the target is a small car 515, the size of the small car 515 is smaller than the size of the ordinary vehicle 400. Therefore, the collision probability determiner 230 can set the size of the corresponding collision determination boundary region 410a to be equal to or close to or approximately equal to the size of the vehicle 400.

[0167] refer to Figure 10 As shown by reference numeral 1010 in the attached figure, for example, when the target is a truck 516, the size of the truck 516 is larger than the size of the vehicle 400. Therefore, the collision probability determiner 230 can set the size of the corresponding collision determination boundary region 410a to be larger than the size of the vehicle 400.

[0168] Although not shown, the size of the collision-determined boundary region 410a can be adjusted based on the target's travel speed.

[0169] As described above, the vehicle control device 200 according to aspects of this disclosure can provide an effective way to prevent vehicle accidents by setting different collision probability determination criteria or conditions even without considering the type of target.

[0170] At the same time, when vehicle 400 is traveling at high speed, the probability of a collision between vehicle 400 and the target may be high. Therefore, it may be necessary to set different collision probability determination criteria or conditions based on the vehicle speed. For example, the size of the collision determination boundary area can be adjusted based on or proportional to the vehicle speed.

[0171] Figure 11 An example is shown of modifying the size of a collision-determined boundary region based on the vehicle speed in a vehicle control device and / or system, according to aspects of this disclosure.

[0172] Reference Figure 11 The collision probability determiner 230 can compare the vehicle speed with a pre-set reference vehicle speed and modify the size of the corresponding collision determination boundary region 410a according to the comparison result.

[0173] In this scenario, if the vehicle speed is equal to or greater than the reference speed, the collision probability determiner 230 can increase the size of the collision determination boundary region 410a. Here, the reference speed can refer to a reference used to determine whether the vehicle 400 is traveling at high speed.

[0174] refer to Figure 11 As shown by reference mark 1100, for example, when the vehicle speed is less than the reference vehicle speed, the collision probability determiner 230 can set the size of the collision determination boundary region 410a to be equal to, close to, or approximately equal to the size of the vehicle 400. That is, when the vehicle speed is less than the reference vehicle speed, the size of the collision determination boundary region 410a may remain unchanged.

[0175] Conversely, refer to Figure 11 As shown by reference numeral 1110 in the attached figure, when the vehicle speed is equal to or greater than the reference vehicle speed, the collision probability determiner 230 can uniformly change the size of the collision determination boundary region 410a according to a preset modification level or level, or it can be set to increase according to the vehicle speed as shown by reference numeral 410b. That is, the collision probability determiner 230 can increase the size of the collision determination boundary region 410a based on the level or value of the vehicle speed.

[0176] For example, the size of the collision determination boundary region 410a can increase according to the level or rate of increase in vehicle speed or proportionally to the level or rate of increase in vehicle speed. However, the embodiments of this disclosure are not limited thereto.

[0177] As described above, the vehicle control device 200 according to aspects of this disclosure can provide an effective way to prevent vehicle accidents by setting different collision probabilities based on the vehicle's speed to determine standards or conditions.

[0178] When the collision determination boundary region 410 is finally determined, the collision probability determiner 230 can determine the probability of a potential collision between the vehicle 400 and the target based on the collision determination boundary region 410.

[0179] Figure 12 and Figure 13 Examples are shown of determining the likelihood of a potential collision between a vehicle and a target in a vehicle control device and / or system according to aspects of this disclosure.

[0180] Based on the time of target identification, the collision probability determiner 230 can determine the probability of a potential collision based on whether the boundary region 410 overlaps with the target traveling on the second path.

[0181] That is, by taking the time when the target is identified as a reference time and measuring the length of time from the reference time, the probability of a potential collision can be determined based on whether the boundary region 410 overlaps with the target at a specific time, based on the collision that moves with the vehicle 400.

[0182] Specifically, the collision probability determiner 230 can measure each of the first predicted position of the vehicle 400 existing on the first travel path and the second predicted position of the target existing on the second travel path at preset unit time intervals, and can determine the probability of a potential collision when the collision determination boundary region 410 at the first predicted position and the target at the second predicted position overlap with each other at a specific time.

[0183] Reference Figure 12 For example, by measuring the length of time from when the target (e.g., pedestrian 514) is identified (t0), the collision probability determiner 230 can measure the first and second predicted positions every preset unit time (t1, t2, t3). Furthermore, when it is determined that the collision determination boundary region 410 overlaps with the target at a specific time t3, the collision probability determiner 230 can determine the probability of a potential collision.

[0184] In another example, refer to Figure 13 By measuring the length of time from the time t0 when the target (e.g., bicycle 512) is identified, the collision probability determiner 230 can predict a first predicted position and a second predicted position, and when it is determined that the collision determination boundary region 410 does not overlap with the target, it can determine the probability that there is no potential collision.

[0185] As described above, the vehicle control device 200 according to aspects of this disclosure provides the effect of more accurately determining the likelihood of a potential collision by utilizing a collision determination boundary region 410 that can be adjusted according to the size of the vehicle 400.

[0186] Furthermore, the vehicle control device 200 according to this disclosure provides the effect of accurately determining the likelihood of a potential collision by predicting a travel path that is more closely matched to the actual travel direction of the vehicle 400 compared to the situation using yaw rate sensors, etc.

[0187] In the following, a vehicle control method capable of performing all or part of the embodiments described herein will be described.

[0188] Figure 14 This is a flowchart illustrating a vehicle control method according to aspects of this disclosure.

[0189] Reference Figure 14 The vehicle control device 200 according to aspects of this disclosure may include a vehicle travel path prediction step S110, a target travel path prediction step S120, a collision probability determination step S130, a vehicle control step S140, etc.

[0190] In the vehicle travel path prediction step S110, when the vehicle speed is less than or equal to the preset speed, the turning radius can be calculated based on the steering angle of the steering wheel and the steering ratio of the vehicle 400; the travel distance can be calculated based on the vehicle speed; and the first travel path of the vehicle 400 can be predicted based on the turning radius and the travel distance.

[0191] In the target travel path prediction step S120, the target can be identified based on object detection information obtained by detecting situations or objects present near the vehicle 400, and the second travel path of the target can be predicted.

[0192] In the collision probability determination step S130, a collision determination boundary region 410 corresponding to the size of the vehicle 400 can be set, and the probability of a potential collision of the vehicle 400 can be determined based on the collision determination boundary region 410, the first travel path, the target, and the second travel path.

[0193] In vehicle control step S140, when there is a possibility of a potential collision between the vehicle and the target, at least one of warning control, braking control and avoidance control can be controlled to be executed.

[0194] Figure 15 It is a flowchart for specifically illustrating a vehicle control method according to aspects of this disclosure.

[0195] Reference Figure 15In step S210, the vehicle control device 200 of this disclosure can predict the first travel path of the vehicle 400. For example, the vehicle travel path predictor 210 can predict the first travel path of the vehicle by measuring the trajectory of coordinates P (Δx, Δy) using the turning radius (R), travel distance (S), and heading angle (Δθ) of the rear wheel (B) based on bicycle modeling.

[0196] Next, the vehicle control unit 200 can acquire information about at least one target in step S220, set a collision determination boundary region 410 based on the target information in step S231, and predict a second travel path for the target. For example, the target travel path predictor 220 can identify the target and predict the second travel path based on object detection information from one or more object detection sensors. Additionally, the target travel path predictor 220 can detect or extract the number of targets, the type of targets, the shape of targets, the travel speed of targets, etc., and provide the detected or extracted information to the collision probability determiner 230. Thereafter, the collision probability determiner 230 can set the collision determination boundary region 410 based on the information received from the target travel path predictor 220.

[0197] Next, in step S240, the vehicle control device 200 can measure (estimate) the predicted position of the vehicle 400. For example, the collision probability determiner 230 can measure the first predicted position of the vehicle 400 at preset unit time intervals.

[0198] Next, in step S250, the vehicle control device 200 can measure the respective predicted positions of one or more targets. For example, in the case of three targets being detected, the collision probability determiner 230 can measure the second predicted position of the first target at preset unit time intervals. However, embodiments of this disclosure are not limited thereto.

[0199] Next, in step S260, the vehicle control device 200 can determine whether there is a possibility of a potential collision between the vehicle 400 and one or more targets. For example, if three targets are detected, the collision probability determiner 230 can determine the probability of a potential collision based on whether the collision determination boundary region 410 overlaps with the first target at a specific time.

[0200] If there is no possibility of a potential collision, in step S271, the vehicle control device 200 can check whether the possibility of a potential collision has been determined for all targets. If the possibility of a potential collision has not been determined for all targets, in step S272, the vehicle control device 200 can change from the first target to another target, measure the second predicted position of the changed target, and in step S260, determine whether there is a possibility of a potential collision between the vehicle 400 and the target.

[0201] For example, if three targets are detected and only the probability of a potential collision between vehicle 400 and the first target O1 is determined, the collision probability determiner 230 can change the target whose second predicted position is to be measured from the first target O1 to the second target O2, measure the second predicted position of the second target O2, and determine whether there is a probability of a potential collision between vehicle 400 and the second target O2. Thereafter, the above operations can be performed for the third target O3 in the same manner. However, embodiments of this disclosure are not limited thereto.

[0202] On the other hand, if the probability of a potential collision has been determined for all targets, then in step S273, the vehicle control device 200 can determine whether a specific time is equal to or later than the warning time ttw, which serves as a warning reference time. If the specific time is earlier than the warning time ttw, the vehicle control device 200 can measure the first predicted position of the vehicle 400 in subsequent unit times in step S274, up to step S240, and in step S250, measure the respective second predicted position of each target again.

[0203] Here, the warning time can refer to a preset time period used to repeatedly determine the probability of a potential collision, or a time used as a reference to terminate the probability of a potential collision.

[0204] On the other hand, if there is a potential collision possibility between vehicle 400 and at least one target, in step S280, vehicle control device 200 can determine whether a specific time is earlier than the collision time ttc, and if the specific time is later than the collision time ttc, in step S291, vehicle control device 200 can perform level 1 control. On the other hand, if the specific time is earlier than the collision time, in step S292, vehicle control device 200 can perform level 2 control.

[0205] In one embodiment, at Level 1 control, warning control operations can be performed, but movement control operations for controlling the movement of the vehicle 400, such as braking control and avoidance control, may not be performed. At Level 2, both movement control operations and warning control operations can be performed. However, embodiments of this disclosure are not limited thereto.

[0206] As described above, according to the embodiments described herein, a vehicle control method and apparatus can be provided that can more accurately predict the travel path of a vehicle and / or at least one target by calculating the turning radius of at least one wheel in contact with the ground in an environment with low slippage probability, as well as a vehicle control system that includes a vehicle control apparatus and is capable of performing the vehicle control method.

[0207] Additionally, according to the embodiments described herein, a vehicle control method and apparatus can be provided that maximizes calculation speed and minimizes power consumption by calculating the turning radius of a low-speed vehicle based on at least one rear wheel, as well as a vehicle control system that includes a vehicle control apparatus and is capable of executing the vehicle control method.

[0208] Additionally, according to the embodiments described herein, a vehicle control method and apparatus can be provided that can protect other vehicles or pedestrians and vehicle occupants present outside by setting different criteria or conditions for determining the likelihood of a potential collision based on information about the target, such as target type and target shape, as well as a vehicle control system that includes a vehicle control apparatus and is capable of executing the vehicle control method.

[0209] The above description and accompanying drawings are provided to enable those skilled in the art to make and use the technical ideas of this disclosure, and are provided in the context of a particular application and its requirements. Various modifications, additions, and substitutions to the described embodiments will be apparent to those skilled in the art without departing from the spirit and scope of this disclosure, and the general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of the invention. The above description and accompanying drawings are provided for illustrative purposes only, illustrating examples of the technical ideas of this disclosure. That is, the disclosed embodiments are intended to illustrate the scope of the technical ideas of this disclosure. Therefore, the scope of this disclosure is not limited to the illustrated embodiments, but is consistent with the widest scope consistent with the claims. The scope of protection of this disclosure should be interpreted based on the appended claims, and all technical ideas within their equivalent scope should be interpreted as included within the scope of this disclosure.

[0210] Cross-references to related applications

[0211] Where applicable, this application claims priority to Korean patent application No. 10-2019-0067524, filed on June 7, 2019, the entire contents of which are incorporated herein by reference. Additionally, this non-provisional application claims priority in countries outside the United States based on the Korean patent application for the same reasons, the entire contents of which are incorporated herein by reference.

Claims

1. A vehicle control device, comprising: A vehicle travel path predictor calculates the turning radius of the rear wheels (not the front wheels) of a vehicle based on the steering angle of the steering wheel and the steering ratio of the vehicle when the vehicle speed is equal to or less than a preset speed. It calculates the travel distance based on at least one of the pulse signals of at least one wheel and the vehicle speed, and predicts the first travel path of the vehicle based on the turning radius and the travel distance. A target travel path predictor identifies a target based on information obtained by detecting objects near the vehicle and predicts a second travel path for the target. A collision probability determiner sets a collision determination boundary region corresponding to the size of the vehicle, and determines the probability of a potential collision between the vehicle and the target based on the collision determination boundary region, the first travel path, the target, and the second travel path; as well as The vehicle controller, when there is a possibility of a potential collision between the vehicle and the target, controls the execution of at least one of warning control, braking control, and avoidance control. The collision probability determiner is configured as follows: The first predicted position of the vehicle existing on the first travel path and the second predicted position of the target existing on the second travel path are measured at pre-set unit time intervals. The likelihood of a potential collision is determined based on whether the boundary region of the collision determination on the first travel path overlaps with the target traveling on the second travel path, and When a collision boundary region at the first predicted location and the target at the second predicted location are predicted to overlap at a specific time, the probability of a potential collision is determined. The collision probability determiner is modified based on the shape of the target to set the collision determination boundary region size within the vehicle to determine the contour of a potential collision, thereby preventing vehicle accidents by setting different collision probability determination criteria or conditions without considering the target type. Specifically, based on vehicle modeling, the vehicle travel path predictor calculates the radii of multiple rear wheels included in the vehicle modeling information, and calculates the turning radius of the vehicle's rear wheels by averaging the radii of the multiple rear wheels.

2. The vehicle control device according to claim 1, wherein, The vehicle path predictor calculates the steering angle of at least one wheel based on the steering wheel angle and the steering ratio, and calculates the turning radius using the steering angle of at least one wheel and the pre-stored distance between the wheel axles.

3. The vehicle control device according to claim 2, wherein, Based on vehicle modeling, the vehicle travel path predictor pre-stores multiple steering ratios corresponding to multiple front wheels included in the vehicle modeling related information. Based on the steering angle of the steering wheel and the multiple steering ratios, it calculates the steering angle of each of the multiple front wheels, calculates multiple radii using the steering angles of each of the multiple front wheels and a pre-set first distance between the wheel axles, and calculates the turning radius of the vehicle using the calculated multiple radii.

4. The vehicle control device according to claim 2, wherein, Based on bicycle modeling, the vehicle path predictor pre-stores the steering ratio of the front wheel, which corresponds to the bicycle modeling information. It calculates the steering angle of the front wheel based on the steering angle of the steering wheel and the steering ratio, and calculates the turning radius using the steering angle of the front wheel and a pre-set second distance between the wheel axle.

5. The vehicle control device according to claim 1, wherein, The vehicle path predictor calculates the vehicle's heading angle using the turning radius and the travel distance, calculates the vehicle's travel displacement based on the turning radius and the heading angle, and predicts the first travel path based on the heading angle and the travel displacement.

6. The vehicle control device according to claim 1, wherein, The target path predictor identifies the type of the target, and when the target is an immovable target, determines that it is not necessary to predict the second path, and when the target is a movable object, predicts the second path by detecting the target's speed based on the target detection information.

7. The vehicle control device according to claim 1, wherein, The collision probability determiner modifies the size of the collision determination boundary region based on the type of the target.

8. The vehicle control device according to claim 7, wherein, When the target is a pedestrian, the collision probability determiner increases the size of the collision determination boundary region.

9. The vehicle control device according to claim 1, wherein, The collision probability determiner compares the vehicle's speed with a pre-set reference speed and modifies the size of the collision determination boundary region based on the comparison result.

10. The vehicle control device according to claim 9, wherein, When the vehicle speed is equal to or greater than the reference vehicle speed, the probability determiner increases the size of the collision determination boundary region.

11. The vehicle control device according to claim 1, wherein, The collision probability determiner increases the size of the collision determination boundary region based on or proportional to the vehicle's speed.

12. The vehicle control device according to claim 1, wherein, The collision determination boundary region is adjusted to either increase based on the size of the target determined based on its shape, or increase proportionally to the size of the target determined based on its shape.

13. A vehicle control method, comprising: Vehicle travel path prediction steps: When the vehicle speed is equal to or less than a preset speed, calculate the turning radius of the rear wheels of the vehicle instead of the front wheels based on the steering angle of the steering wheel and the steering ratio of the vehicle, calculate the travel distance based on the vehicle speed, and predict the first travel path of the vehicle based on the turning radius and the travel distance. Target travel path prediction step: Identify the target based on information obtained by detecting objects near the vehicle, and predict a second travel path for the target; Collision probability determination steps: Set a collision determination boundary area corresponding to the size of the vehicle, and determine the probability of a potential collision between the vehicle and the target based on the collision determination boundary area, the first travel path, the target, and the second travel path; as well as Vehicle control steps: When there is a possibility of a potential collision between the vehicle and the target, control executes at least one of warning control, braking control, and avoidance control. Determining the probability of a potential collision includes: The first predicted position of the vehicle existing on the first travel path and the second predicted position of the target existing on the second travel path are measured at pre-set unit time intervals. The likelihood of a potential collision is determined based on whether the boundary region of the collision determination on the first travel path overlaps with the target traveling on the second travel path, and When a collision boundary region at the first predicted location and the target at the second predicted location are predicted to overlap at a specific time, the probability of a potential collision is determined. Specifically, the collision determination boundary region size, which is set in the vehicle to determine the contour of a potential collision based on the shape of the target, allows vehicle accidents to be prevented by setting different collision probability determination criteria or conditions without considering the target type. Specifically, based on vehicle modeling, the vehicle travel path predictor calculates the radii of multiple rear wheels included in the vehicle modeling information, and calculates the turning radius of the vehicle's rear wheels by averaging the radii of the multiple rear wheels.

14. A vehicle control system, comprising: Steering angle sensor, used to detect the steering angle of the steering wheel; Vehicle speed sensor, used to detect the speed of a vehicle; An object detection sensor is used to detect objects near the vehicle; A vehicle control device capable of determining the likelihood of a potential collision between a target near the vehicle and the vehicle, and controlling the vehicle when the likelihood of such a potential collision exists; as well as The drive unit is configured to operate under the control of the vehicle control unit. The vehicle control device is configured to receive steering angle information from the steering angle sensor, vehicle speed information from the vehicle speed sensor, and object detection information from the object detection sensor; calculate the turning radius of the rear wheels (not the front wheels) based on the steering wheel angle and the vehicle's steering ratio when the vehicle speed is equal to or less than a preset speed; calculate the travel distance based on the vehicle speed; predict a first travel path of the vehicle based on the turning radius and the travel distance; predict a second travel path of the target by identifying the target based on the object detection information; set a collision determination boundary region corresponding to the size of the vehicle; determine the probability of a potential collision between the vehicle and the target based on the collision determination boundary region, the first travel path, the target, and the second travel path; and, when the probability of a potential collision between the vehicle and the target exists, control the drive device to perform at least one of warning control, braking control, and avoidance control. The first predicted position of the vehicle existing on the first travel path and the second predicted position of the target existing on the second travel path are measured at pre-set unit time intervals. The likelihood of a potential collision is determined based on whether the boundary region of the collision determination on the first travel path overlaps with the target traveling on the second travel path, and When a collision boundary region at the first predicted location and the target at the second predicted location are predicted to overlap at a specific time, the probability of a potential collision is determined. Specifically, the collision determination boundary region size, which is set in the vehicle to determine the contour of a potential collision based on the shape of the target, allows vehicle accidents to be prevented by setting different collision probability determination criteria or conditions without considering the target type. Specifically, based on vehicle modeling, the vehicle travel path predictor calculates the radii of multiple rear wheels included in the vehicle modeling information, and calculates the turning radius of the vehicle's rear wheels by averaging the radii of the multiple rear wheels.

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