阅读说明：本技术 一种基于多维连续信号分析的模拟驾驶评判方法 (Simulation driving judgment method based on multi-dimensional continuous signal analysis ) 是由 赵行健 吴子宇 于 2021-07-26 设计创作，主要内容包括：本发明公开了一种基于多维连续信号分析的模拟驾驶评判方法,属于信号分析领域,旨在解决驾驶模拟器上缺少判断学员巽寮效果的评判算法,不能对学员的操作数据进行记录计算分析并展示评判结果的问题；本发明提供的算法能够匹配评判学员在驾驶模拟器上的实时传感器操作数据与预先录入到本地的传感器信号数据,得出一个匹配度相似度,并对评判结果进行展示；同时采用了标量公示接收多维连续信号,可以对不同维度和不同播放时间戳的多维连续传感器信号数据进行详细传递和记录；通过对比评判误差的方式,可以对多维连续信号数据进行详细对比分析,提高匹配度和评判结果的准确性,并可对学员的操作习惯进行分析,给出学员应当着重改进或必须改正的问题。(The invention discloses a simulated driving judging method based on multi-dimensional continuous signal analysis, which belongs to the field of signal analysis and aims to solve the problems that a driving simulator lacks a judging algorithm for judging the effect of a student's son house, and the operation data of the student cannot be recorded, calculated and analyzed, and the judging result can not be displayed; the algorithm provided by the invention can match the real-time sensor operation data of the judge student on the driving simulator with the sensor signal data which is pre-recorded to the local, so as to obtain a matching degree similarity, and display the judging result; meanwhile, a scalar public display is adopted to receive multi-dimensional continuous signals, and multi-dimensional continuous sensor signal data with different dimensions and different playing time stamps can be transmitted and recorded in detail; by means of comparing and judging errors, detailed comparison and analysis can be carried out on multi-dimensional continuous signal data, the matching degree and the accuracy of judging results are improved, the operation habits of students can be analyzed, and the problems that the students should focus on improvement or need to correct are given.)

1. A simulation driving judgment method based on multi-dimensional continuous signal analysis is characterized by comprising the following steps: the method comprises the following steps of (1) loading local data: the local data is a json format file which is stored in the local of the simulator computer in advance through a series of operations, and a program loads a corresponding json data file according to the name;

step (2) receiving sensor signals: the program receives a multi-dimensional continuous signal from the simulator sensor in the form of the following scalar: f_n(t)＝(f₁(t)，f₂(t)，f₃(t)，f₄(t),……，f_n(t))，(t≥0，n∈N^*) Wherein f is_n(t) represents different sensor signal data, n represents a dimension, and t represents a timestamp of a current playing of the video;

step (3) analyzing sensor signals: at each analysis time point t_(i)Obtaining multi-dimensional continuous signal data F of the current sensor_n(t_(i)) With t_(i)As a reference point, the sum t is truncated_(i)The c pieces of data at the closest time point are the evaluation data group: f_n(t_i-2)，F_n(t_i-1)，F_n(t_i)，F_n(t_i+1)，F_n(t_i+2) And the value range of c is temporarily set to 1-5. Comparing the current data F_n(t_(i)) All signals f in₁(t_(i))～f_n(t_(i)) When the error of the signal of the float value type is within the error value delta range, the signal of the float value type is matched to pass, and the signal of the bool value type and the signal of the int value type need to be completely matched to pass. The current sensor signals can be roughly divided into 3 types of signals of a boost value type, an int value type and a float value type;

generating evaluation data: at each analysis time point t_(i)And two states of matching pass and matching fail exist in each continuous signal matching result. When the matching is passed, calculating scores according to the weights of different signals, obtaining a total matching degree by combining the total received signal data quantity, further performing big data analysis by combining a general misoperation record library according to the operation habits of the student, judging which aspect of the student is more insufficient, and giving the problem that the student should focus on improvement or needs to be corrected;

and (5) displaying results: the microprocessor analyzes and matches the program to obtain an evaluation result, uploads corresponding result data to a background, and finally displays the result data through the multimedia equipment;

step (6) other settings: for multi-dimensional continuous signals, unlike discrete signals, in training data entry

When in use, a pool parameter C which can be automatically configured or manually set is additionally set₁，C₂，C₃，……，C_n. When C is present_nWhen the signal is true, the signal is required to be included in the judgment range when the system judges; when C is_nFor false, the corresponding signal f will be ignored in the evaluation process_n。

2. The method according to claim 1,

the method is characterized in that: the sensors in the step (2) specifically comprise a power supply sensor, an ignition sensor, a left turn light sensor, a right turn light sensor, a dipped headlight sensor, a high beam light sensor, an exchange far and near light sensor, a safety belt sensor, a hand brake sensor, an emergency light (double flash) sensor, a horn sensor, a windscreen wiper sensor, a steering wheel steering sensor, a clutch pedal sensor, a brake pedal sensor, an accelerator pedal sensor and a parking brake sensor.

3. The method according to claim 1, wherein the method comprises the following steps: and (3) dividing the sensor signal processing modules of the sensors in the step (2) into at least one sensor set.

4. The method according to claim 1, wherein the method comprises the following steps: the sensor set and the sensor signal processing module in the step (2) may be separated or integrated.

5. The method according to claim 1, wherein the method comprises the following steps: the bool value type signals in the step (3) comprise a power supply, an ignition, a left turn lamp, a right turn lamp, a dipped headlight, a high beam, a switching high and low beam, a safety belt, a hand brake, an emergency lamp (double flash), a loudspeaker and a wiper; the int value type signal comprises a gear value of 0-6 of the vehicle; the float value type signal comprises a steering wheel shaft (-1), a clutch shaft (0-1), a brake shaft (0-1) and a throttle shaft (0-1).

6. The method according to claim 1, wherein the method comprises the following steps: the communication interface of the multimedia device in the step (5) selects at least one of the following interfaces: USB interface, WIFI interface, RJ45 interface, IIC interface, RS232 interface, RS422 interface, RS485 interface or bluetooth interface.

7. The method according to claim 1, wherein the method comprises the following steps: and (5) connecting the communication interface of the multimedia equipment with the microprocessor, and connecting the microprocessor with the sensor information processing module.

8. The method according to claim 1, wherein the method comprises the following steps: the multimedia device in the step (5) can display weather information, road condition information, traffic density information and the like in a simulation mode, and can perform voice prompt.

Technical Field

The invention relates to the field of signal analysis, in particular to a simulation driving judgment method based on multi-dimensional continuous signal analysis.

Background

The driving simulator is a teaching device for driving training, which utilizes virtual reality simulation technology to create a virtual driving training environment, and a student interacts with driving simulation software through an operation part of the driving simulator so as to carry out driving training. The driving simulator in a broad sense includes an automobile driving simulator, an airplane driving simulator, a ship driving simulator, and the like. All simulation equipment used for driving can be called as a driving simulator. Of course, car driving simulators are the most widely used relative to other driving simulators. In modern society, automobiles are already a very common means of transportation and transportation because automobile driving simulators are more widely used. The cabin of the automobile driving simulator comprises a cockpit, a visual computer, a visual screen (19 inches display), an operation sensor, a data acquisition card, an earphone, a microphone and the like. The cockpit contains the same operating components as the real vehicle, a "five large" steering mechanism: steering wheel, clutch, service brake, throttle and manual brake. Real car derailleur: reverse gear, first gear, second gear, third gear, fourth gear, fifth gear and neutral gear (automatic gear only includes forward gear, reverse gear and parking gear). Real car operating switch: the automobile safety device comprises a left steering lamp, a right steering lamp, an emergency lamp, a loudspeaker, an ignition switch, a main electric switch, a safety belt, an automobile door, a windshield wiper, a high beam, a dipped headlight and a high beam and a low beam which are alternated. The cabin automobile can be used for networking training and also can be used for stand-alone training. The driving simulator teaching is that various road conditions and scenes are simulated by a device similar to motor vehicle driving and a computer to set corresponding programs so as to achieve the aim of training. The specific arrangement is that after a citizen who learns to drive a car completes a theoretical test, the citizen can drive the car to go on the road to practice driving skills in reality by playing the 'electronic game' -a driving simulator. The working principle of the simulator is as follows: the driver manipulates the operating member so that the sensor directly connected to the operating member is changed, thereby causing a change in the electric signal. The signal acquisition and processing subsystem periodically acquires the electric signals on the sensor according to certain precision and carries out processing such as filtering and the like. The processed signals are used as the input of a vehicle dynamics model subsystem, and the current state of the vehicle, such as the information of the engine speed, the engine output torque, the vehicle speed, the current position of the vehicle and the like, is calculated through vehicle dynamics model simulation operation. And the result calculated by the vehicle dynamics model is sent to a display system for graphic display, a sound system for sound simulation and an instrument system for instrument display. The automobile driving simulator almost completely clones the real automobile learning environment, can eliminate the fear of driving beginners, standardizes the operation of the driver in due time, and provides powerful help for driving training in driving schools. The driving simulation technology is utilized to train the new student, so that the new student can be trained safely and efficiently, and the method has the characteristics of low cost, zero emission and environmental protection; if the driving simulation training method is fully utilized, remarkable economic benefits and social benefits can be brought. The driving analog signal processing system is an electric signal system of the driving simulator, and the structure and the composition of the driving analog signal processing system directly influence the cost and the simulation experience of the driving simulator. Compared with the actual vehicle test, the driving simulator has the following advantages: (1) the safety is high: the test of dangerous driving state and the test and training in limit state can be safely carried out; (2) the reproducibility is high: the same test conditions are easy to ensure, and the test under the same conditions can be repeatedly carried out; (3) the test conditions are easily set: the method is easy to set and change the environment such as automobile characteristics, road surface conditions, roads, obstacles and the like, and does not need to manufacture a large amount of test equipment, thereby saving the cost; (4) easy data determination and analysis: the computer can easily store parameters difficult to measure in a real vehicle test, and can display required data immediately for efficient processing. A simulation driving training course based on one-way video interaction needs a judgment algorithm capable of judging the training effect of a student. The algorithm can match real-time sensor operation data of a judge student on a driving simulator with sensor signal data which is pre-recorded to the local to obtain a matching degree similarity, so that a judging result which can be displayed is obtained.

Disclosure of Invention

In view of the problems in the prior art, the invention discloses a simulation driving judgment method based on multi-dimensional continuous signal analysis, which adopts the technical scheme that the method comprises the following steps of (1) loading local data: the local data is a json format file which is stored in the local of the simulator computer in advance through a series of operations, and a program in the microprocessor loads a corresponding json data file according to the name, so that the multidimensional continuous signal can be conveniently matched with the multidimensional continuous signal data which is stored in the local in advance in the follow-up manner, and the method is more convenient and quicker;

step (2) receiving sensor signals: the program receives a multi-dimensional continuous signal from the simulator sensor in the form of the following scalar: f_n(t)＝(f₁(t)，f₂(t)，f₃(t)，f₄(t),……，f_n(t))，(t≥0，n∈N^*) Wherein f is_n(t) represents different sensor signal data, n represents a dimension, t represents a timestamp of the current playing of the video, and multidimensional continuous sensor signal data with different dimensions and different playing timestamps can be transmitted in detail by using a scalar formula;

step (3) analyzing sensor signals: at each analysis time point t_(i)Obtaining multi-dimensional continuous signal data F of the current sensor_n(t_(i)) With t_(i)As a reference point, the sum t is truncated_(i)The c pieces of data at the closest time point are the evaluation data group: f_n(t_i-2)，F_n(t_i-1)，F_n(t_i)，F_n(t_i+1)，F_n(t_i+2) And the value range of c is temporarily set to 1-5. Comparing the current data F_n(t_(i)) All signals f in₁(t_(i))～f_n(t_(i)) When the error of the signal of the float value type is within the error value delta range, the signal of the float value type is matched to pass, and the signal of the bool value type and the signal of the int value type need to be completely matched to pass. At present, sensor signals can be roughly divided into 3 types of signals of a pool value type, an int value type and a float value type, through the mode of comparing and judging errors, multi-dimensional continuous signal data transmitted to a program by different sensors on a driving simulator can be analyzed in detail, the matching degree between the signals is judged, and an accurate judgment result is obtained;

generating evaluation data: at each analysis time point t_(i)Matching the result for each successive signalThere are two states match pass and match fail. When the matching is passed, the score is calculated according to the weight of different signals, the total matching degree is obtained by combining the data quantity of the total received signals, further, the big data analysis can be carried out by combining a general misoperation record library according to the operation habits of the student, the situation that the student is in which aspect is more insufficient is judged, the problem that the student should be emphasized to improve or needs to correct is given, and the operation habits of the student can be contrastingly calculated and analyzed by the matching degree judging mode, so that the place where the student needs to improve is given, and the teaching efficiency is improved;

and (5) displaying results: the microprocessor analyzes and matches the program to obtain the judgment result, uploads the corresponding result data to the background, and finally displays the judgment result through the multimedia equipment, so that the judgment result of the student can be stored in the database and can be analyzed and displayed in detail, and the comparison and exercise of the subsequent student can be facilitated;

step (6) other settings: for multi-dimensional continuous signals, unlike discrete signals, a pool parameter C which can be automatically configured or manually set is additionally set during the recording of training data₁，C₂，C₃，……，C_n. When C is present_nWhen the signal is true, the signal is required to be included in the judgment range when the system judges; when C is_nFor false, the corresponding signal f will be ignored in the evaluation process_n。

As a preferred technical solution of the present invention, the sensors in step (2) specifically include a power supply sensor, an ignition sensor, a left turn light sensor, a right turn light sensor, a dipped headlight sensor, a high beam light sensor, an exchange high beam light sensor, a safety belt sensor, a handbrake sensor, an emergency light (double flash) sensor, a horn sensor, a wiper sensor, a steering wheel sensor, a clutch pedal sensor, a brake pedal sensor, an accelerator pedal sensor and a parking brake sensor, and through the arrangement of various sensors, a student can conveniently and continuously transmit multidimensional continuous signals of the sensors to the sensor signal processing module when practicing driving the simulator.

As a preferable aspect of the present invention, in the step (2), the sensor signal processing module of the sensor is divided into at least one sensor set, and the physical operation of the operation member of the driving simulation system can be converted into an electric signal by setting the sensor set.

As a preferred technical solution of the present invention, in the step (2), the sensor set and the sensor signal processing module may be separated or integrated, and through the arrangement of the sensor set and the sensor signal processing module, the sensor set can transmit the electrical signal of the sensor to the sensor signal processing module for real-time processing.

As a preferred technical scheme of the present invention, the signal of the bool value type in step (3) includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, a switching high beam light and a low beam light, a safety belt, a hand brake, an emergency light (double flash), a horn and a wiper; the int value type signal comprises a gear value of 0-6 of the vehicle; the float value type signals comprise steering wheel shafts (-1), clutch shafts (0-1), brake shafts (0-1) and throttle shafts (0-1), and the signal of the boost value type, the signal of the int value type and the signal of the float value type can be subjected to attribution classification when physical operation of a driving simulation system operation component is converted into an electric signal.

As a preferred technical solution of the present invention, in the step (5), the communication interface of the multimedia device selects at least one of the following interfaces: USB interface, WIFI interface, RJ45 interface, IIC interface, RS232 interface, RS422 interface, RS485 interface or bluetooth interface, through communication interface's setting, can make things convenient for microprocessor to utilize multimedia device communication interface to carry out signal interaction with external equipment.

As a preferred technical solution of the present invention, in the step (5), the multimedia device communication interface is connected to the microprocessor, the microprocessor is connected to the sensor information processing module, and the multi-dimensional continuous signals can be processed and analyzed in real time through the setting of the microprocessor.

As a preferred technical solution of the present invention, the multimedia device in step (5) may display weather information, road condition information, traffic density information, and the like in a simulated manner, and may perform voice prompt, and through the setting of the multimedia device, the trainee may provide different weather information, road condition information, traffic density information, and the like in a voice manner when driving the simulator, so as to improve the reality of the practice, and may analyze, evaluate, and display the final evaluation result.

The invention has the beneficial effects that: the invention provides a simulation driving judgment method based on multi-dimensional continuous signal analysis, which has the following advantages: (1) the algorithm can match real-time sensor operation data of a judging student on a driving simulator with sensor signal data which is pre-recorded to the local to obtain a matching degree similarity, so that a judging result which can be displayed is obtained, and the judging result is displayed; (2) the scalar public display is adopted to receive the multi-dimensional continuous signals, the multi-dimensional continuous sensor signal data with different dimensions and different playing time stamps can be transmitted in detail, and the physical operation of each dimension and playing time stamp can be checked conveniently; (3) by means of comparing and judging errors, detailed comparison and analysis can be carried out on multi-dimensional continuous signal data transmitted to a program by different sensors on a driving simulator, the matching degree and the accuracy of a judging result are improved, further, big data analysis can be carried out by combining a general misoperation record library according to the operation habits of a student, the fact that the student is in which aspect is more insufficient is judged, and the problem that the student needs to be improved or corrected is solved.

Drawings

FIG. 1 is a schematic flow chart of the present invention;

FIG. 2 is a schematic diagram of the signal types of the sensor of the present invention;

FIG. 3 is a schematic diagram of a bool value type signal according to the present invention;

FIG. 4 is a diagram illustrating int value type signals according to the present invention;

FIG. 5 is a schematic diagram of a float value type signal according to the present invention;

FIG. 6 is a schematic diagram of a continuous signal curve according to the present invention.

Detailed Description

Example 1

As shown in fig. 1 to 6, the invention discloses a simulation driving judgment method based on multi-dimensional continuous signal analysis, which adopts the technical scheme that the method comprises the following steps of (1) loading local data: the local data is a json format file which is stored in the local of the simulator computer in advance through a series of operations, and a program loads the corresponding json data file according to the name, so that the loading mode can be convenient for matching the multidimensional continuous signal with the multidimensional continuous signal data which is stored in the local in advance, and the analysis speed is accelerated;

step (2) receiving sensor signals: the program receives a multi-dimensional continuous signal from the simulator sensor in the form of the following scalar: f_n(t)＝(f₁(t)，f₂(t)，f₃(t)，f₄(t),……，f_n(t))，(t≥0，n∈N^*) Wherein f is_n(t) represents different sensor signal data, n represents a dimension, t represents a timestamp of the current playing of the video, and a scalar formula can transmit multi-dimensional continuous sensor signal data with different dimensions and different playing timestamps in detail;

step (3) analyzing sensor signals: at each analysis time point t_(i)Obtaining multi-dimensional continuous signal data F of the current sensor_n(t_(i)) With t_(i)As a reference point, the sum t is truncated_(i)The c pieces of data at the closest time point are the evaluation data group: f_n(t_i-2)，F_n(t_i-1)，F_n(t_i)，F_n(t_i+1)，F_n(t_i+2) And the value range of c is temporarily set to 1-5. Comparing the current data F_n(t_(i)) All signals f in₁(t_(i))～f_n(t_(i)) When the error of the signal of the float value type is within the error value delta range, the signal of the float value type is matched to pass, and the signal of the bool value type and the signal of the int value type need to be completely matched to pass. At present, the sensor signals can be roughly divided into 3 types of signals of a pool value type, an int value type and a float value type, the comparison and judgment errors can carry out detailed comparison and analysis on multi-dimensional continuous signal data, the matching degree between the signals can be judged, and an accurate signal can be obtainedJudging results;

generating evaluation data: at each analysis time point t_(i)And two states of matching pass and matching fail exist in each continuous signal matching result. When the matching is passed, the score is calculated according to the weight of different signals, the total matching degree is obtained by combining the data quantity of the total received signals, further, the big data analysis can be carried out by combining a general misoperation record library according to the operation habits of the student, the fact that the student is in which aspect is more insufficient is judged, the problem that the student should be improved or needs to be corrected is given, and the teaching efficiency can be greatly improved by comparing, calculating and analyzing the operation habits of the student;

and (5) displaying results: the microprocessor analyzes and matches the program to obtain an evaluation result, uploads corresponding result data to a background, and finally displays the evaluation result through multimedia equipment, so that the evaluation result can be conveniently displayed for a student to compare and practice;

step (6) other settings: for multi-dimensional continuous signals, unlike discrete signals, a pool parameter C which can be automatically configured or manually set is additionally set during the recording of training data₁，C₂，C₃，……，C_n. When C is present_nWhen the signal is true, the signal is required to be included in the judgment range when the system judges; when C is_nFor false, the corresponding signal f will be ignored in the evaluation process_n。

As a preferred technical solution of the present invention, the sensors in step (2) specifically include a power supply sensor, an ignition sensor, a left turn light sensor, a right turn light sensor, a dipped headlight sensor, a high beam light sensor, an exchange high beam light sensor, a safety belt sensor, a handbrake sensor, an emergency light (double flash) sensor, a horn sensor, a wiper sensor, a steering wheel sensor, a clutch pedal sensor, a brake pedal sensor, an accelerator pedal sensor and a parking brake sensor, and when a student exercises driving the simulator, the sensors can continuously transmit multi-dimensional continuous signals of the sensors to the sensor signal processing module.

As a preferred technical solution of the present invention, in the step (2), the sensor signal processing module of the sensor is divided into at least one sensor set, and the sensor set can convert the physical operation of the operation component of the driving simulation system into an electrical signal.

As a preferred technical solution of the present invention, in the step (2), the sensor set and the sensor signal processing module may be separated or integrated, and the sensor set can transmit the electrical signal of the sensor to the sensor signal processing module for real-time processing.

As a preferred technical scheme of the present invention, the signal of the bool value type in step (3) includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, a switching high beam light and a low beam light, a safety belt, a hand brake, an emergency light (double flash), a horn and a wiper; the int value type signal comprises a gear value of 0-6 of the vehicle; the float value type signals comprise steering wheel shafts (-1), clutch shafts (0-1), brake shafts (0-1) and throttle shafts (0-1), and various signal types can conveniently classify the types of the sensor electric signals.

As a preferred technical solution of the present invention, in the step (5), the communication interface of the multimedia device selects at least one of the following interfaces: USB interface, WIFI interface, RJ45 interface, IIC interface, RS232 interface, RS422 interface, RS485 interface or bluetooth interface, communication interface makes things convenient for microprocessor to utilize multimedia device communication interface and external equipment to carry out signal interaction.

As a preferred technical solution of the present invention, in the step (5), the multimedia device communication interface is connected to the microprocessor, the microprocessor is connected to the sensor information processing module, and the microprocessor can perform real-time processing and analysis on the multi-dimensional continuous signal.

As a preferred technical solution of the present invention, in the step (5), the multimedia device may simulate and display weather information, road condition information, traffic density information, and the like, and may perform voice prompt, and the multimedia device may provide different weather information, road condition information, traffic density information, and the like by voice when the trainee drives the simulator, so as to improve the reality of the exercise, and may analyze, evaluate and display the final evaluation result.

The microprocessor adopts an STM32 chip for controlling the sensor, and the technical advices can be obtained by those skilled in the art by referring to textbooks or technical manuals published by manufacturers by the pins and the connection mode of the STM 32; the circuit connections according to the invention are conventional means used by the person skilled in the art and can be suggested by a limited number of tests, which are common knowledge.

Components not described in detail herein are prior art.

Although the present invention has been described in detail with reference to the specific embodiments, the present invention is not limited to the above embodiments, and various changes and modifications without inventive changes may be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.