Method and Apparatus for Predicting Vehicle States Based on Dynamic Dataset

Abstract

A method and apparatus for predicting vehicle status based on dynamic data are disclosed. According to one aspect of the present disclosure, a method for predicting the state of a vehicle based on dynamic data is provided, comprising: a process of collecting driving data of the vehicle; a process of detecting a change in the driving environment of the vehicle based on the driving data; a process of generating dynamic data based on the driving data; a process of predicting the current state of the vehicle based on the dynamic data; and a process of generating a final prediction of the current state of the vehicle based on the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle.

Description

Technology Field

[0001] The present disclosure relates to a method and apparatus for predicting vehicle state based on dynamic data. Background Technology

[0002] The following description merely provides background information related to the present embodiment and does not constitute prior art.

[0003] In the context of vehicle control systems, the importance of technology that predicts vehicle conditions during driving and performs real-time adaptive control based on this is steadily increasing. In particular, dynamic variables such as vehicle speed need to be accurately predicted to maintain vehicle stability and efficiency.

[0004] Existing vehicle state prediction technologies are designed to operate by predicting the vehicle's state under certain conditions based on linear models. These technologies have limitations in that their prediction accuracy is reduced because they fail to incorporate non-linear external factors, such as changes in road gradient, weather conditions, and vehicle load. Furthermore, existing technologies suffer from reduced prediction accuracy because they do not modify their data structures even when the vehicle's driving environment changes.

[0005] Therefore, technology is needed to generate dynamic data that reflects the changing driving environment of the vehicle and to predict the state of the vehicle based on this data. The problem to be solved

[0006] The primary purpose of the present disclosure is to provide a method and apparatus capable of more accurately predicting the state of a vehicle. Specifically, the primary purpose is to provide a method and apparatus that generates dynamic data reflecting a changing driving environment of a vehicle, and combines the result of predicting the vehicle state based on basic data generated based on the initial driving environment of the vehicle with the result of predicting the vehicle state based on the dynamic data to generate a final prediction of the vehicle state.

[0007] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0008] According to one aspect of the present disclosure, a method for predicting the state of a vehicle based on dynamic data is provided, comprising: a process of collecting driving data of the vehicle; a process of detecting a change in the driving environment of the vehicle based on the driving data; a process of generating dynamic data based on the driving data; a process of predicting the current state of the vehicle based on the dynamic data; and a process of generating a final prediction of the current state of the vehicle based on the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle.

[0009] According to another aspect of the present disclosure, an apparatus for predicting the state of a vehicle based on dynamic data comprises: at least one memory for storing instructions; and at least one processor, wherein the at least one processor performs the following steps by executing the instructions: collecting driving data of the vehicle; detecting a change in the driving environment of the vehicle based on the driving data; generating dynamic data based on the driving data; predicting the current state of the vehicle based on the dynamic data; and generating a final prediction of the current state of the vehicle based on the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle. Effects of the invention

[0010] According to one embodiment of the present disclosure, by predicting the initial state of a vehicle using an extended Kalman filter, a prediction considering a non-linear driving pattern can be performed, and the accuracy of the prediction can be improved accordingly.

[0011] According to one embodiment of the present disclosure, the accuracy of the prediction can be improved by generating dynamic data that includes characteristics capable of representing the changed driving environment whenever the driving environment of the vehicle changes.

[0012] According to one embodiment of the present disclosure, by generating dynamic data by adding characteristics representing the driving environment of a vehicle that has changed based on basic data, a prediction considering non-linear driving patterns can be performed, and the accuracy of the prediction can be improved accordingly.

[0013] According to one embodiment of the present disclosure, by generating dynamic data and reflecting various environmental changes occurring during driving in real time, it is possible to adapt to the changing driving environment in real time.

[0014] According to one embodiment of the present disclosure, by predicting the initial state of a vehicle using a machine learning model, a prediction considering non-linear driving patterns can be performed, and the accuracy of the prediction can be improved accordingly.

[0015] According to one embodiment of the present disclosure, the accuracy of the prediction can be improved by predicting the vehicle state by reflecting the latest driving conditions of the vehicle based on dynamic data.

[0016] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0017] FIG. 1 is a block diagram schematically showing the components of a vehicle state prediction device according to one embodiment of the present disclosure. FIG. 2 is a diagram illustrating the process of a vehicle state prediction device according to one embodiment of the present disclosure predicting the initial state of a vehicle. FIG. 3 is a diagram illustrating the process of a vehicle state prediction device according to one embodiment of the present disclosure finally predicting the current state of a vehicle. FIG. 4 is a diagram illustrating the accuracy of dynamic data-based vehicle state prediction compared to basic data-based vehicle state prediction according to one embodiment of the present disclosure. FIG. 5 is a flowchart schematically illustrating a dynamic data-based vehicle state prediction method according to one embodiment of the present disclosure. FIG. 6 is a schematic block diagram of an exemplary computing device that can be used to implement the devices and methods described in the present disclosure. Specific details for implementing the invention

[0018] Some embodiments of the present disclosure are described in detail below with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the present disclosure, if it is determined that a detailed description of related known components or functions could obscure the essence of the present disclosure, such detailed description is omitted.

[0019] In describing the components of the embodiments according to the present disclosure, symbols such as first, second, i), ii), a), b), etc., may be used. These symbols are intended only to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the symbols. When a part in the specification is described as 'comprising' or 'having' a component, this means that, unless explicitly stated otherwise, it does not exclude other components but may include additional components.

[0020] The detailed description set forth below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present disclosure and is not intended to represent the only embodiment in which the present disclosure can be practiced.

[0021] FIG. 1 is a block diagram schematically showing the components of a vehicle state prediction device according to one embodiment of the present disclosure.

[0022] Referring to FIG. 1, the vehicle state prediction device may include a driving data collection unit (101), a situation change detection unit (103), a data characteristic extraction unit (105), a first vehicle state prediction unit (107), a second vehicle state prediction unit (109), and a final vehicle state prediction unit (111).

[0023] The driving data collection unit (101) can collect driving data of the vehicle. The driving data may include vehicle speed, vehicle acceleration, and vehicle angular velocity. The driving data collection unit (101) can collect driving data obtained based on one or more sensors attached to the vehicle. One or more sensors attached to the vehicle may include one or more of an accelerometer, a gyroscope, and an IMU (Inertial Measurement Unit) sensor.

[0024] The situation change detection unit (103) can detect changes in the driving environment of the vehicle based on driving data. The situation change detection unit (103) can detect changes in the driving environment of the vehicle by determining whether at least one of the first condition, the second condition, and the third condition is satisfied based on driving data. The situation change detection unit (103) can be implemented using part or all of one or more computing devices (60).

[0025] The data characteristic extraction unit (105) can generate basic data or dynamic data based on driving data. Each of the basic data and dynamic data may include a plurality of characteristics. The data characteristic extraction unit (105) can extract one or more characteristics based on driving data and can generate basic data and dynamic data based on the extracted characteristics. The data characteristic extraction unit (105) may be implemented using part or all of one or more computing devices (60).

[0026] The data feature extraction unit (105) can extract one or more features based on initial driving data and generate basic data based on the extracted features. The basic data can take the form of a dataset.

[0027] characteristic Description of characteristics speed Vehicle speed value acceleration Vehicle acceleration value Angular velocity Vehicle's rotational motion state Wheel speed rotational speed of each wheel GPS coordinates Vehicle location information Tire condition Tire pressure and wear condition Fuel status Amount of fuel and consumption

[0028] Table 1 is a table to explain examples of characteristics that may be included in basic data. Basic data may include one or more of speed, acceleration, angular velocity, wheel speed, GPS coordinates, tire condition, and fuel condition as characteristics.

[0029] When a change in the vehicle's driving environment is detected, the data characteristic extraction unit (105) can extract one or more characteristics based on driving data and generate dynamic data based on the extracted characteristics. The initial driving data and the driving data may differ in the time at which they were measured and collected. In this regard, a detailed explanation will be provided below with reference to FIGS. 2 and FIGS. 3. The process of the data characteristic extraction unit (105) generating dynamic data may include adding one or more characteristics that are different from the characteristics included in the basic data based on the change in the vehicle's driving environment. That is, in order to update the basic data, which is an existing dataset, to the changed environment, the data characteristic extraction unit (105) can generate dynamic data that reflects the changed driving environment by adding one or more characteristics that are not included in the basic data among the characteristics extracted based on driving data to the basic data. The dynamic data may take the form of a dataset.

[0030] situation Additional attributes Description of additional features Access to the ramp Inclination, coefficient of friction To reflect the angle of the slope The road is wet Friction coefficient, tire condition To reflect changes in friction between the tire and the road due to road humidity Add passenger Vehicle mass, acceleration pattern To reflect changes in vehicle mass and acceleration patterns due to the addition of passengers or cargo. sudden braking Brake input, braking distance To reflect the vehicle's braking distance and brake input data during sudden braking. Highway driving Torque, fuel consumption To reflect changes in vehicle torque and fuel consumption as vehicle speed increases on highways. lane change Lane position, vehicle position change To reflect changes in lane position and vehicle position due to lane movement

[0031] Table 2 is a table to explain examples of features that may be additionally included in dynamic data compared to basic data. Depending on the situation, dynamic data may additionally include one or more features that differ from those included in the basic data. For example, if it is determined that a lane change is occurring, 'lane position' and 'vehicle position change' may be added.

[0032] Basic data may be based on the assumption that the vehicle's driving environment is flat. Dynamic data may be based on the assumption that the vehicle's driving environment has changed, that is, that it is not flat. Situations not flat may include entering a slope, the road being wet, adding passengers, sudden braking, driving on a highway, and changing lanes, as exemplified in Table 2.

[0033] The first vehicle state prediction unit (107) can predict the initial state of the vehicle based on basic data. For example, the first vehicle state prediction unit (107) can predict the initial mass of the vehicle based on basic data such as Table 1, which is generated based on driving data collected while the vehicle is driving on flat ground.

[0034] In the present disclosure, 'state' is a term included in the initial state of a vehicle, the current state of a vehicle, and the final prediction of the current state of a vehicle, and may be any one of the vehicle's speed, mass, and torque. Accordingly, the process of predicting the state of a vehicle may be a process of predicting one or more of the vehicle's speed, mass, and torque.

[0035] The initial state of the vehicle, the current state of the vehicle, and the final prediction of the current state of the vehicle may represent values ​​for the same fundamental characteristics, differing only in the time at which they are predicted. For example, if one wishes to predict the speed as the state of the vehicle, the first vehicle state prediction unit (107) can predict the initial speed of the vehicle, the second vehicle state prediction unit (109) can predict the current speed of the vehicle, and the vehicle state final prediction unit (111) can generate a final prediction for the current speed of the vehicle. That is, the initial state of the vehicle, the current state of the vehicle, and the final prediction for the current state of the vehicle can all be values ​​for the speed of the vehicle.

[0036] The first vehicle state prediction unit (107) can predict the initial state of the vehicle based on basic data. For example, the first vehicle state prediction unit (107) can predict the initial mass of the vehicle based on basic data such as Table 1, which is generated based on driving data collected while the vehicle is driving on flat ground. The first vehicle state prediction unit (107) can be implemented using part or all of one or more computing devices (60).

[0037] The second vehicle state prediction unit (109) can predict the current state of the vehicle based on dynamic data. For example, the second vehicle state prediction unit (109) can predict the mass of the vehicle based on dynamic data generated based on driving data collected before and after the vehicle enters an incline. The dynamic data may additionally include characteristics such as incline and friction coefficient when compared with basic data. The second vehicle state prediction unit (109) may be implemented using part or all of one or more computing devices (60).

[0038] The vehicle state final prediction unit (111) can generate a final prediction of the current state of the vehicle based on a prediction of the initial state of the vehicle and a prediction of the current state of the vehicle. For example, the vehicle state final prediction unit (111) can generate a final prediction of the mass of the vehicle based on the initial mass of the vehicle predicted by the first vehicle state prediction unit (107) and the mass of the vehicle predicted by the second vehicle state prediction unit (109). As an example, the vehicle state final prediction unit (111) can determine the value obtained by arithmetically averaging the initial mass of the vehicle predicted by the first vehicle state prediction unit (107) and the mass of the vehicle predicted by the second vehicle state prediction unit (109) as the final prediction of the mass of the vehicle. The vehicle state final prediction unit (111) can be implemented using part or all of one or more computing devices (60).

[0039] FIG. 2 is a diagram illustrating the process of a vehicle state prediction device according to one embodiment of the present disclosure predicting the initial state of a vehicle.

[0040] FIG. 3 is a diagram illustrating the process of a vehicle state prediction device according to one embodiment of the present disclosure finally predicting the current state of a vehicle.

[0041] Figures 2 and 3 may be processes performed at different times. For example, the process of Figure 3 may be performed after the process of Figure 2 has been performed. In other words, the process of Figure 3 may be based on the premise that the process of Figure 2 has been performed.

[0042] FIG. 2 may be based on an initial driving situation. Referring to FIG. 2, the driving data collection unit (101) may transmit driving data to the data characteristic extraction unit (S210). The data characteristic extraction unit (105) may extract one or more characteristics based on the driving data and generate basic data based on the extracted characteristics (S220). The driving data may be initial driving data. In the present disclosure, the initial driving situation and the initial driving data may refer to a situation presupposed to generate basic data that serves as a standard for dynamic data generation, and driving data collected in that situation. The situation presupposed to generate basic data may be a situation where the vehicle is driving on flat ground. That is, the vehicle state prediction device may consider a situation where the vehicle's state is approximately constant and can serve as a standard for state change as a situation where the vehicle is driving on flat ground.

[0043] The data feature extraction unit (105) can transmit basic data to the first vehicle state prediction unit (107). The first vehicle state prediction unit (107) can predict the initial state of the vehicle based on the basic data (S230). The first vehicle state prediction unit (107) can predict the initial state of the vehicle based on the basic data using an extended Kalman filter. The extended Kalman filter can be described using Equations 1 through 5. The result of predicting the initial state of the vehicle can be summarized based on the estimated value and the error covariance. The estimated value is of Equation 4. and the error covariance is of Equation 5 It could be.

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] Mathematical Equation 1 is an equation for calculating the predicted value for the estimated value. is the predicted value for the k-th estimate. is the predicted value for the k-1th estimate.

[0050] Mathematical Equation 2 is an equation for calculating the predicted value for the error covariance. is the predicted value for the k-th error covariance. A is the system matrix. is the predicted value for the k-1th error covariance. Q is the system noise.

[0051] Mathematical formula 3 is a formula for calculating Kalman gain. The knife is the only advantage. is the predicted value for the k-th error covariance. H is the system matrix. R is the sensor noise.

[0052] Mathematical Equation 4 is an equation for calculating the estimated value. is the k-th estimate. is the predicted value for the k-th estimate. The knife is the only advantage. is the k-th sensor data.

[0053] Mathematical Equation 5 is an equation for calculating the error covariance. is the k-th error covariance. is the predicted value for the k-th error covariance. is the Kalman gain. H is the system matrix.

[0054] Thus, a vehicle state prediction device according to one embodiment of the present disclosure can perform a prediction that considers non-linear driving patterns by predicting the initial state of a vehicle using an extended Kalman filter, and thereby improve the accuracy of the prediction.

[0055] FIG. 3 may be based on an arbitrary driving situation following an initial driving situation. Referring to FIG. 3, the driving data collection unit (101) may transmit driving data to the situation change detection unit (103) (S310). The driving data in FIG. 3 may be arbitrary driving data rather than initial driving data. Each of the arbitrary driving situation and arbitrary driving data in FIG. 3 may mean a driving situation other than the initial driving situation and a driving situation other than the initial driving data. Additionally, the driving situation and driving data in FIG. 3 may mean the current driving situation of the vehicle and the driving data collected in that situation, based on the premise that basic data is generated based on the initial driving data obtained in the initial driving situation of FIG. 2.

[0056] The situation change detection unit (103) can detect changes in the driving environment of the vehicle based on driving data. The process of detecting changes in the driving environment of the vehicle may include a process of determining whether at least one of a first condition, a second condition, and a third condition is satisfied based on the driving data of the vehicle. Since the situation change detection unit (103) can detect that the driving environment of the vehicle has changed by determining whether at least one of the first condition, a second condition, and a third condition is satisfied, the process of the situation change detection unit (103) determining whether each condition is satisfied can be understood as a process in which the situation change detection unit (103) performs a detection algorithm corresponding to each condition.

[0057] The first condition may be a condition set based on the rate of change of driving data. For example, the situation change detection unit (103) may collect a plurality of driving data during a predetermined time interval, calculate the rate of change of driving data based on the driving data, and determine that the first condition is satisfied if the magnitude (e.g., absolute value) of the calculated rate of change is greater than or equal to a threshold value. Furthermore, if the situation change detection unit (103) determines that the first condition is satisfied, it may determine that there is a change in the driving environment of the vehicle.

[0058]

[0059]

[0060] Equation 6 is an equation for calculating the rate of change. ΔA(t) is the rate of change. A(t) is driving data for a specific time period. A(t-1) is driving data for the previous time period. The driving data can be, for example, acceleration.

[0061] Equation 7 is used to determine whether the first condition is satisfied. |ΔA(t)| is the magnitude of the rate of change. Threshold is the critical value.

[0062] The second condition may be a condition set based on the size of the driving data. For example, the situation change detection unit (103) may determine that the second condition is satisfied if the size of the driving data is greater than or equal to a threshold value. Furthermore, if the situation change detection unit (103) determines that the second condition is satisfied, it may determine that there is a change in the driving environment of the vehicle. The driving data may be, for example, acceleration or speed.

[0063] The third condition may be a condition set based on the pattern of driving data. The situation change detection unit (103) may determine that the third condition is satisfied if the driving data of a specific time period is not included within the normal variation range of driving data of a plurality of previous time periods. For example, the situation change detection unit (103) may calculate the moving average and moving standard deviation of driving data of a plurality of previous time periods, and determine that the third condition is satisfied if the absolute value of the difference between the specific time period and the moving average is greater than or equal to a certain multiple of the moving standard deviation. Furthermore, if the situation change detection unit (103) determines that the third condition is satisfied, it may determine that there is a change in the driving environment of the vehicle.

[0064]

[0065]

[0066]

[0067] Mathematical Equation 8 is an equation for calculating the moving average of driving data from multiple previous time cycles. MA(t) is the moving average.

[0068] Equation 9 is used to calculate the moving standard deviation of driving data from multiple previous time cycles. σ(t) is the moving standard deviation.

[0069] Mathematical Equation 10 is an equation used to determine whether the third condition is satisfied. α is an arbitrary non-zero constant.

[0070] When a situation change detection unit (103) detects a driving change, it can transmit driving data to the data characteristic extraction unit (105) (S320).

[0071] The data characteristic extraction unit (105) can generate dynamic data based on driving data (S330). The data characteristic extraction unit (105) can acquire basic data. The basic data is generated as a result of performing the process included in FIG. 2 and may be stored in any memory (not shown) included in the vehicle state prediction device. The data characteristic extraction unit (105) can add one or more characteristics different from the characteristics included in the basic data based on changes in the vehicle's driving environment. Specifically, the data characteristic extraction unit (105) can generate dynamic data by extracting one or more characteristics based on driving data and updating the basic data by adding characteristics that are not included in the basic data among the extracted characteristics to the basic data. When extracting one or more characteristics based on driving data, the data characteristic extraction unit (105) can consider changes in the vehicle's driving environment.

[0072] Thus, a vehicle state prediction device according to one embodiment of the present disclosure can improve the accuracy of the prediction by generating dynamic data that includes characteristics capable of representing the changed driving environment whenever the driving environment of the vehicle changes.

[0073] Thus, a vehicle state prediction device according to one embodiment of the present disclosure can perform a prediction considering a non-linear driving pattern by generating dynamic data by adding a characteristic representing the driving environment of a vehicle that has changed based on basic data, and thereby improve the accuracy of the prediction.

[0074] Thus, a vehicle state prediction device according to one embodiment of the present disclosure can adapt to a changing driving environment in real time by generating dynamic data and reflecting various environmental changes occurring during driving in real time.

[0075] The data feature extraction unit (105) can transmit dynamic data to the second vehicle state prediction unit (109). The second vehicle state prediction unit (109) can predict the current state of the vehicle based on the dynamic data (S340). The second vehicle state prediction unit (109) can predict the current state of the vehicle based on the dynamic data using a machine learning model. The machine learning model may be an XGBoost regression model. The machine learning model may be a pre-trained model. For the pre-training of the machine learning model, a training module (not shown) separate from the machine learning model may be used. The training module can fit the machine learning model to a training dataset. The training module can train the machine learning model so that the machine learning model corrects errors in predicting the state of the vehicle. For example, the training module can input input data into the machine learning model and calculate a loss based on the output data produced by the machine learning model and the Ground Truth (GT) data mapped to the input data. The Mean Square Error (MSE) may be used as the loss function, but it is not limited to this. The training module can update the parameters of the machine learning model in a direction that minimizes the loss. For example, the training module can update the weights of the layers (or nodes) included in the machine learning model based on the backpropagation algorithm.

[0076]

[0077]

[0078] Mathematical Equation 11 is an equation to represent one example of the XGBoost regression model. is the predicted value. f m is the prediction function of each tree. M is the number of trees. x i- is input data.

[0079] Equation 12 is an equation used to represent an example of the loss function of the XGBoost regression model. L(θ) is the loss function. l is the loss. y i is GT data. Ω is the normalization term.

[0080] As such, a vehicle state prediction device according to one embodiment of the present disclosure can perform a prediction that considers non-linear driving patterns by predicting the initial state of a vehicle using a machine learning model, and thereby improve the accuracy of the prediction.

[0081] The second vehicle state prediction unit (109) can transmit a prediction of the current state of the vehicle to the vehicle state final prediction unit (111). The vehicle state final prediction unit (111) can generate a final prediction of the current state of the vehicle based on the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle (S350). The vehicle state final prediction unit (111) can obtain a prediction of the initial state of the vehicle. The prediction of the initial state of the vehicle is generated as a result of performing the process included in FIG. 2 and may be stored in any memory (not shown) included in the vehicle state prediction device. For example, the vehicle state final prediction unit (111) can determine the sum of the values ​​obtained by assigning different weights to the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle, respectively, as the final prediction of the current state of the vehicle.

[0082] FIG. 4 is a diagram illustrating the accuracy of dynamic data-based vehicle state prediction compared to basic data-based vehicle state prediction according to one embodiment of the present disclosure.

[0083] Referring to FIG. 4, the error (41) of the basic data-based vehicle state prediction and the error (42) of the dynamic data-based vehicle state prediction are shown.

[0084] When comparing the error (411) of the basic data-based prediction and the error (421) of the dynamic data-based prediction with respect to the speed of the vehicle, the error (411) of the basic data-based prediction is greater than the error (421) of the dynamic data-based prediction.

[0085] When comparing the error (413) of the basic data-based prediction and the error (423) of the dynamic data-based prediction regarding the mass of the vehicle, the error (312) of the basic data-based prediction is greater than the error (423) of the dynamic data-based prediction. In particular, the error (312) of the basic data-based prediction is about four times greater than the error (423) of the dynamic data-based prediction.

[0086] When comparing the error (415) of the basic data-based prediction and the error (425) of the dynamic data-based prediction regarding the vehicle's torque, the error (415) of the basic data-based prediction is greater than the error (425) of the dynamic data-based prediction. In particular, the error (415) of the basic data-based prediction is about twice as large as the error (425) of the dynamic data-based prediction.

[0087] Thus, a vehicle state prediction device according to one embodiment of the present disclosure can improve the accuracy of the prediction by predicting the vehicle state by reflecting the latest driving conditions of the vehicle based on dynamic data.

[0088] FIG. 5 is a flowchart schematically illustrating a dynamic data-based vehicle state prediction method according to one embodiment of the present disclosure.

[0089] Referring to FIG. 5, the vehicle state prediction device can collect driving data of the vehicle (S510).

[0090] The vehicle state prediction device can detect changes in the vehicle's driving environment based on driving data (S520).

[0091] The vehicle state prediction device can generate dynamic data based on driving data (S530).

[0092] The vehicle state prediction device can predict the current state of the vehicle based on dynamic data (S540).

[0093] A vehicle state prediction device can generate a final prediction of the vehicle's current state based on a prediction of the vehicle's initial state and a prediction of the vehicle's current state (S550).

[0094] FIG. 6 is a schematic block diagram of an exemplary computing device that can be used to implement the devices and methods described in the present disclosure.

[0095] The computing device (60) may include some or all of memory (600), a processor (620), storage (640), an input / output interface (660), and a communication interface (680). The computing device (60) may be a stationary computing device such as a desktop computer or server, as well as a mobile computing device such as a laptop computer or smartphone. The computing device (60) may include any specialized hardware accelerator capable of processing operations on an artificial intelligence model in an efficient manner. For example, the computing device (60) may include a graphic processing unit (GPU), a tensor processing unit (TPU), or a neural processing unit (NPU).

[0096] Memory (600) may store a program that enables the processor (620) to perform a method or operation according to various embodiments of the present disclosure. For example, the program may include a plurality of instructions executable by the processor (620), and the aforementioned method or operation may be performed by executing the plurality of instructions by the processor (620). Memory (600) may be a single memory or a plurality of memories. In this case, information required to perform a method or operation according to various embodiments of the present disclosure may be stored in a single memory or divided and stored in a plurality of memories. If memory (600) is composed of a plurality of memories, the plurality of memories may be physically separated. Memory (600) may include at least one of volatile memory and non-volatile memory. Volatile memory includes Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc., and non-volatile memory includes flash memory, etc.

[0097] The processor (620) may include at least one core capable of executing at least one instruction. The processor (620) may execute instructions stored in memory (600). The processor (620) may be a single processor or multiple processors.

[0098] Storage (640) retains stored data even if the power supplied to the computing device (60) is cut off. For example, storage (640) may include non-volatile memory and may include storage media such as magnetic tape, optical disc, or magnetic disc. A program stored in storage (640) may be loaded into memory (600) before being executed by the processor (620). Storage (640) may store a file written in a programming language, and a program generated from the file by a compiler, etc., may be loaded into memory (600). Storage (640) may store data to be processed by the processor (620) and / or data processed by the processor (620).

[0099] The input / output interface (660) may provide an interface with input devices such as a keyboard, mouse, etc. and / or output devices such as a display device, printer, etc. The user may trigger the execution of a program by the processor (620) through the input device and / or check the processing results of the processor (620) through the output device.

[0100] The communication interface (680) can provide access to an external network. The computing device (60) can communicate with other devices through the communication interface (680).

[0101] Each component of the device or method according to the present invention may be implemented in hardware or software, or in a combination of hardware and software. Additionally, the function of each component may be implemented in software, and a microprocessor may be implemented to execute the function of the software corresponding to each component.

[0102] Various embodiments of the systems and techniques described herein may be realized as digital electronic circuits, integrated circuits, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include being implemented as one or more computer programs executable on a programmable system. A programmable system comprises a storage system, at least one input device, and at least one programmable processor (which may be a special-purpose processor or a general-purpose processor) coupled to receive data and instructions from and transmit data and instructions to at least one output device. Computer programs (which are also known as programs, software, software applications, or code) include instructions for the programmable processor and are stored on a "computer-readable recording medium."

[0103] Computer-readable recording media include all types of recording devices in which data that can be read by a computer system is stored. Such computer-readable recording media may be non-volatile or non-transitory media such as ROM, CD-ROM, magnetic tape, floppy disk, memory card, hard disk, magneto-optical disk, and storage device, and may also include transitory media such as data transmission media. Additionally, computer-readable recording media may be distributed across networked computer systems, and computer-readable code may be stored and executed in a distributed manner.

[0104] Although the flowcharts and timing diagrams of this specification describe each process as being executed sequentially, this is merely an illustrative explanation of the technical concept of one embodiment of the present disclosure. In other words, a person skilled in the art to which one embodiment of the present disclosure belongs may modify and adapt the flowcharts and timing diagrams in various ways, such as changing the order described in the flowcharts and timing diagrams or executing one or more of the processes in parallel, without departing from the essential characteristics of one embodiment of the present disclosure; therefore, the flowcharts and timing diagrams are not limited to a chronological order.

[0105] The above description is merely an illustrative explanation of the technical concept of the present embodiment, and a person skilled in the art to which the present embodiment belongs would be able to make various modifications and variations within the scope of the essential characteristics of the present embodiment. Accordingly, the present embodiments are intended to explain, not limit, the technical concept of the present embodiment, and the scope of the technical concept of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present embodiment.

Claims

Claim 1 A method for predicting the state of a vehicle based on dynamic data, comprising: a process of collecting driving data of the vehicle; a process of detecting a change in the driving environment of the vehicle based on the driving data; a process of generating dynamic data based on the driving data; a process of predicting the current state of the vehicle based on the dynamic data; and a process of generating a final prediction of the current state of the vehicle based on a prediction of the initial state of the vehicle and a prediction of the current state of the vehicle. Claim 2 A method according to claim 1, wherein the process of detecting a change in the driving environment of the vehicle includes a process of determining whether at least one of a first condition, a second condition, and a third condition is satisfied based on the driving data of the vehicle. Claim 3 In paragraph 2, the method wherein the first condition is a condition set based on the rate of change of the driving data during a predetermined time interval. Claim 4 In paragraph 2, the second condition is a method in which the second condition is a condition set based on the size of the driving data. Claim 5 In paragraph 2, the third condition is a condition set based on the pattern of the driving data. Claim 6 A method according to claim 1, wherein the process of generating the dynamic data includes adding one or more characteristics different from the characteristics included in the basic data based on a change in the driving environment of the vehicle, wherein each of the characteristics included in the basic data is a characteristic extracted based on the initial driving environment of the vehicle. Claim 7 In paragraph 6, the prediction of the initial state of the vehicle is a method generated based on the basic data of the vehicle. Claim 8 A device for predicting the state of a vehicle based on dynamic data, comprising: at least one memory for storing instructions; and at least one processor, wherein the at least one processor performs the following steps by executing the instructions: collecting driving data of the vehicle; detecting a change in the driving environment of the vehicle based on the driving data; generating dynamic data based on the driving data; predicting the current state of the vehicle based on the dynamic data; and generating a final prediction of the current state of the vehicle based on the prediction of the initial state of the vehicle and the prediction of the current state of the vehicle. Claim 9 In claim 8, the process of detecting a change in the driving environment of the vehicle comprises a process of determining whether at least one of a first condition, a second condition, and a third condition is satisfied based on the driving data of the vehicle. Claim 10 In claim 9, the device wherein the first condition is a condition set based on the rate of change of the driving data during a predetermined time interval. Claim 11 In claim 9, the device, wherein the second condition is a condition set based on the size of the driving data. Claim 12 In claim 9, the device, wherein the third condition is a condition set based on the pattern of the driving data. Claim 13 In claim 8, the process of generating the dynamic data includes adding one or more characteristics different from the characteristics included in the basic data based on changes in the driving environment of the vehicle, wherein each of the characteristics included in the basic data is a characteristic extracted based on the initial driving environment of the vehicle. Claim 14 In paragraph 13, the prediction of the initial state of the vehicle is a device generated based on the basic data of the vehicle.

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