CN113487935B - Simulation driving judgment method based on multi-dimensional discrete signal analysis - Google Patents
Simulation driving judgment method based on multi-dimensional discrete signal analysis Download PDFInfo
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B9/00—Simulators for teaching or training purposes
- G09B9/02—Simulators for teaching or training purposes for teaching control of vehicles or other craft
- G09B9/04—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles
- G09B9/052—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles characterised by provision for recording or measuring trainee's performance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
Abstract
The invention discloses a simulation driving judgment method based on multi-dimensional discrete signal analysis, which relates to the technical field of signal analysis and aims to solve the problem that more or less people have habits of improper operation when the existing people learn automobile driving and solve the problem that a student receives multi-dimensional discrete signals from a sensor through discrete signal data in local data during training through a simulation training course of one-way video interaction, matches the multi-dimensional discrete signals with the local data according to corresponding signal reactions of the student in different time periods, scores the discrete signal data according to different weight ratios of a bool value signal and an int value signal, displays the operation result of the student, obtains a judgment result, is favorable for the student to judge the training condition of the student when the student performs video simulation driving training, and gives the problem that the student should improve or correct.
Description
Technical Field
The invention relates to the technical field of signal analysis, in particular to a simulated driving judgment method based on multi-dimensional discrete signal analysis.
Background
Signals that are not continuous in time but are still continuous in amplitude are referred to as discrete signals, which still belong to the analog signal, since the amplitude of the discrete signal is still continuous.
A discrete signal is a signal sampled on a continuous signal, and, unlike a continuous signal whose argument is continuous, a discrete signal is a sequence, i.e. whose argument is "discrete", and each value of this sequence can be regarded as a sample of the continuous signal.
Since the discrete signal is only a sequence of samples and cannot obtain the sampling rate therefrom, the sampling rate must be additionally stored, and the discrete signal with time as an argument is a discrete-time signal.
A discrete signal is not equivalent to a digital signal, which is not only discrete, but also quantized, i.e., its arguments are not only discrete, but its values are also discrete, so that the accuracy of the discrete signal can be infinite.
The precision of the digital signal is finite and infinite, i.e. the discrete signal, which is continuous in value, is also called the sampled signal, so the discrete signal includes both the digital signal and the sampled signal.
Multidimensional processing means the generation of a multi-functional signal waveform and the sequential transmission and reception of such signals in a suitable manner, such signals being encoded.
Furthermore, the processor should process data belonging to multiple domains (vector processing mode), and there is no unified theory of design and corresponding vector generation processing structure suitable for processing such encoded waveforms of multiple domains.
The simulated driving is also called as automobile driving simulation or automobile virtual driving, the simulated driving enables an experiencer to experience visual sense, auditory sense and somatosensory automobile driving experience which is close to a real effect in a virtual driving environment, the driving simulation effect is vivid, energy is saved, safety and economy are achieved, the limit of time, weather and places is avoided, the driving training efficiency is high, the training period is short, and the like, and the method is very wide in application in the aspects of new automobile model development and driving training.
A simulation driving training course based on one-way video interaction needs an evaluation algorithm capable of judging the training effect of a student, and the algorithm can match real-time sensor operation data of the student on a driving simulator with sensor discrete signal data which are pre-recorded to the local to obtain a matching degree similarity.
Therefore, an evaluation result which can be displayed is obtained, and the video simulation driving training method is beneficial for the trainees to judge the training conditions of the trainees when performing video simulation driving training and provides the problems which should be improved or corrected by the trainees.
When existing people learn to drive automobiles, some habitual problems of improper operation exist more or less, and need to be solved.
Disclosure of Invention
In view of the problems in the prior art, the invention discloses a simulation driving judgment method based on multi-dimensional discrete signal analysis, which adopts the technical scheme that the method comprises the following steps:
step 1: reading local data, loading local multidimensional discrete signal data, and preventing misoperation by taking the local discrete data as a standard when performing corresponding operation through the locally stored multidimensional discrete signal data;
step 2: receiving a sensor signal, receiving a multidimensional discrete signal from a sensor, receiving different data received by the sensor signal, and recording a timestamp of the current data;
and step 3: analyzing the sensor signals, analyzing, matching and comparing the current sensor signals with the local signals according to the timestamps, analyzing the signals of different timestamps by analyzing the sensor signals, and correspondingly comparing the signals with the local data;
and 4, step 4: generating evaluation data, obtaining evaluation scores according to different weight ratios of different signals, and distinguishing the importance ratios of different signals when driving is simulated through different weight ratios of different signals;
and 5: and displaying the result, namely displaying the judgment result obtained by analyzing and matching, and displaying the judgment result so that the current driver can know the deficiency of the driver, and the subsequent driver can pay attention to the error points.
As a preferred technical solution of the present invention, in step 1, the local data is a json format file stored in the local of the simulator computer in advance through a series of operations, the program loads the corresponding json data file according to the name, and the json format file in the local of the computer corresponds to the json data file corresponding to the name, so as to facilitate the use of the subsequent simulated driving.
As a preferred embodiment of the present invention, in the step 2, the multidimensional discrete signal from the simulator sensor is received by a program, and is in the form of the following scalar: f n (t)＝f 1 (t),f 2 (t),f 3 (t),f 4 (t),……f n (t)(t≥0，n∈N * ) Wherein f is n (t) represents different sensor signal data, n represents dimension, t represents time stamp of video playing currently, discrete signal value emitted by current simulator sensor only includes 0 or 1, namely f n (t) is epsilon {0,1}, and is received by the receiving sensor in different time periodsMulti-dimensional discrete signals to facilitate receiving sensor recordings.
As a preferred technical solution of the present invention, in the step 3, the program receives the discrete signals from the simulator in real time, and when different discrete signals f are detected n (t i ) And the value f of the last moment n (t i -1) discrete sensor signal f currently received when the contrast changes n (t i ) Analyzing and comparing with the discrete signal data prestored in the local, setting a time deviation value delta t, and according to the current video time stamp t i Traversing the local discrete signal data at t i - Δ t to t i Searching discrete signal data f needing matching in + delta t range n (t k )，f n (t k -1)，ΛΛ，f n (t k + j), where t is k >t i -Δt,t k +j<t i + Δ t, forming an evaluation data set, when the data signal value is the same as the current signal value in the evaluation data set, the matching comparison is passed, and a time deviation range is set up here, so as to allow the trainee to have some tolerance on time deviation in the training operation process, the timestamp of data entry is absolute, but in the actual operation training, some time error on the millisecond level must exist, which can be tolerated, and there are two discrete signals of the current sensor: the signal of the bool value type and the signal of the int value type are received by the simulator through a program, and when the discrete signal at the moment and the discrete signal at the last moment are changed, the discrete signals are compared with the local discrete signals so as to be convenient for analysis and comparison.
As a preferred technical solution of the present invention, the above-mentioned bool value type signal includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, an exchange high and low beam light, a safety belt, a hand brake, an emergency light, a horn, and a wiper, and the signal passing through the bool value includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, an exchange high and low beam light, a safety belt, a hand brake, an emergency light, a horn, and a wiper, so that an operator can start different signals to respond according to different simulation environments during simulation, and can quickly respond in a real driving environment.
As a preferable technical solution of the present invention, the int value type signal includes a first gear, a second gear, a third gear, a fourth gear, a fifth gear and a sixth gear of the vehicle gear.
As a preferred technical solution of the present invention, in the step 4, in each analysis process, there are two states of match pass and match fail in the matching result of the discrete signal each time, and when the match passes, the weight ω of different signals is used according to the match pass 1 ，ω 2 ，ω 3 ，ω 4 ，ΛΛ，ω n The score is calculated, the total matching degree is obtained by combining the total received signal data quantity, further, 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 more insufficient in which aspect is judged, the problem that the student needs to be improved or corrected is given, and the importance degree of different signals is realized by different weight ratios of different signals.
As a preferred technical solution of the present invention, in the step 5, the program displays the evaluation result obtained by analyzing and matching, and uploads the corresponding result data to the background server, so that the operator can conveniently know, by the evaluation result, what time period the operator starts up when performing simulated driving, and generates a corresponding report by combining the local multidimensional data, so as to facilitate the subsequent operation process, exercise with emphasis, and specification operation in time.
The invention has the beneficial effects that: according to the invention, through a one-way video interaction simulation training course, when a student trains, the student can match with local data by receiving multi-dimensional discrete signals from a sensor and according to corresponding signal reactions of the student in different time periods when making corresponding operation reactions through most of discrete signal data in the local data, scores the local data according to different weight ratios of a bool value signal and an int value signal, and displays the operation result of the student so as to know the emphasis point of the student and the weak point needing to be strengthened for training, thereby obtaining a judgment result for display, being beneficial to judging the training condition of the student when the student conducts video simulation driving training, and providing the problem that the student should be improved or corrected.
Further, by dividing into the boost value signal and the int signal, the weight ratio of different signals can be distinguished, so as to divide the importance of different signals.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a second diagram of the structure of the present invention;
FIG. 3 is a third diagram of the structure of the present invention;
FIG. 4 is a fourth view of the structure of the present invention;
FIG. 5 is a fifth view of the structure of the present invention;
FIG. 6 shows the signal values of a signal according to the present invention at different time axes.
Detailed Description
Example 1
As shown in fig. 1 to 6, the invention discloses a simulation driving judgment method based on multi-dimensional discrete signal analysis, which adopts the technical scheme that the method comprises the following steps:
step 1: the local data are read, and the local multidimensional discrete signal data are loaded, so that an operator can conveniently perform corresponding operation, and the local discrete data are taken as the reference coefficient to prevent misoperation;
step 2: receiving a sensor signal, receiving a multidimensional discrete signal from a sensor, receiving different data received by the sensor signal, and recording a timestamp of the current data;
and step 3: analyzing the sensor signal, analyzing, matching and comparing the current sensor signal with the local signal according to the timestamp, and recording different time points of the multi-dimensional discrete signal so as to know the use times of different signals and under the simulation condition;
and 4, step 4: generating evaluation data, obtaining evaluation scores according to different weight ratios of different signals, and distinguishing the importance ratios of different signals when driving is simulated through different weight ratios of different signals;
and 5: and displaying a result, namely displaying the judgment result obtained by analyzing and matching, and displaying the judgment result so that the current driver can know the deficiency of the driver, and the subsequent driver can pay attention to the error points.
As a preferred technical solution of the present invention, in step 1, the local data is a json format file stored in the local of the simulator computer in advance through a series of operations, the program loads the corresponding json data file according to the name, and the json format file in the local of the computer corresponds to the json data file corresponding to the name, so as to facilitate the use of the subsequent simulated driving.
As a preferred embodiment of the present invention, in the step 2, the multidimensional discrete signal from the simulator sensor is received by a program, and is in the form of the following scalar: f n (t)＝f 1 (t),f 2 (t),f 3 (t),f 4 (t),……f n (t)(t≥0，n∈N * ) Wherein f is n (t) represents different sensor signal data, n represents dimension, t represents time stamp of video playing currently, discrete signal value emitted by simulator sensor only includes 0 or 1 at present, i.e. f n (t) is equal to {0,1}, and is expressed as a plurality of parallel different signals in most of discrete signals.
As a preferred technical solution of the present invention, in the step 3, the program receives the discrete signal from the simulator in real time, and when a different discrete signal f is detected n (t i ) Value f from last moment n (t i -1) discrete sensor signal f currently received when the contrast changes n (t i ) Analyzing and comparing with discrete signal data pre-stored locally, setting a time deviation value delta t, and according to the current video time stamp t i Traversing local discrete signal data at t i - Δ t to t i Searching discrete signal data f needing matching in + delta t range n (t k )，f n (t k -1)，ΛΛ，f n (t k + j), where t is k >t i -Δt,t k +j<t i + Δ t, forming a judging data group, when the data signal value is the same as the current signal value in the judging data group, the matching comparison is passed, and a time deviation range is set up to allow the trainee to have tolerance on time deviation in the training operation process. The time stamp of data entry is absolute, but in the actual operation training, must have some time error on the millisecond level, and this can be tolerated, through allowing the time error on the millisecond level, makes it more closely to driving environment in reality, and the discrete signal of sensor now has two kinds: the signal of the bool value type and the signal of the int value type are divided into two types through the signals so as to distinguish, and corresponding weight ratio is set for the two different signals so as to ensure the reality of simulated driving.
As a preferred technical solution of the present invention, the above-mentioned bool value type signal includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, an exchange high and low beam light, a safety belt, a hand brake, an emergency light, a horn, and a wiper, and the signal passing through the bool value includes a power supply, an ignition, a left turn light, a right turn light, a dipped headlight, a high beam light, an exchange high and low beam light, a safety belt, a hand brake, an emergency light, a horn, and a wiper, so that an operator can start different signals to respond according to different simulation environments during simulation, and can quickly respond in a real driving environment.
As a preferred technical solution of the present invention, the int value type signal includes a first gear, a second gear, a third gear, a fourth gear, a fifth gear and a sixth gear of the vehicle, and the int value type signal includes a first gear, a second gear, a third gear, a fourth gear, a fifth gear and a sixth gear, so that an operator can perform corresponding operations according to different situations when simulating driving, and subsequent skilled operations are facilitated.
As a preferred technical solution of the present invention, in the step 4, in each analysis process, there are two states of match pass and match fail in the matching result of the discrete signal each time, and when the match passes, the weight ω of different signals is used according to the match pass 1 ，ω 2 ，ω 3 ，ω 4 ，ΛΛ，ω n And calculating the score, and obtaining the 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, and judging which aspect of the student is more insufficient, thereby giving the problem that the student should focus on improvement or needs to be corrected.
As a preferred technical solution of the present invention, in the step 5, the program displays the evaluation result obtained by analyzing and matching, and uploads the corresponding result data to the background server, so that the operator can conveniently know, by the evaluation result, what time period the operator starts up when performing simulated driving, and generates a corresponding report by combining the local multidimensional data, so as to facilitate the subsequent operation process, exercise with emphasis, and specification operation in time.
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.
Claims (5)
1. A simulated driving judgment method based on multi-dimensional discrete signal analysis is characterized by comprising the following steps: the method comprises the following steps:
step 1: reading local data and loading local multidimensional discrete signal data;
step 2: receiving a sensor signal, and receiving a multi-dimensional discrete signal from a sensor;
in the step 2, the multidimensional discrete signal from the simulator sensor is received by the program, and is in the form of the following scalar:
(t≥0，n∈N * ) Wherein f is n (t) represents different sensor signal data, n represents dimension, t represents time stamp of video playing currently, discrete signal value sent by simulator sensor only includes 0 or 1, i.e. f n (t)∈{0,1}；
And step 3: analyzing the sensor signal, and analyzing, matching and comparing the current sensor signal with the local signal according to the timestamp;
in said step 3, the program receives the discrete signal from the simulator in real time, and when detecting a different discrete signal f n (t i ) Value f from last moment n (t i -1) discrete sensor signal f currently received when the contrast changes n (t i ) Analyzing and comparing with the discrete signal data prestored in the local, setting a time deviation value delta t, and according to the current video time stamp t i Traversing the local discrete signal data at t i - Δ t to t i Searching discrete signal data f needing matching in + delta t range n (t k )，f n (t k -1)，ΛΛ，f n (t k + j) where t k >t i -Δt,t k +j<t i + Δ t, forming an evaluation data set, when the data signal value is the same as the current signal value in the evaluation data set, the matching comparison is passed, and a time deviation range is set up here, so as to allow the trainee to have some tolerance on time deviation during the training operation, the time stamp of data entry is absolute, but in the actual operation training, some time error on the millisecond level must exist, which can be tolerated, and there are two discrete signals of the sensor: a signal of a bool value type and a signal of an int value type;
and 4, step 4: generating evaluation data, and obtaining evaluation scores according to different weight ratios of different signals;
in the step 4, in each analysis process, the matching result of each discrete signal has two states of matching passing and not passing; when the match passes, the weights ω of the different signals are used 1 ，ω 2 ，ω 3 ，ω 4 ，ΛΛ，ω n Calculating scores, obtaining total matching degree by combining the total received signal data quantity, and performing big data analysis by combining a general misoperation record library according to the operation habits of the students to judge which aspect of the students is more insufficient and solve the problem that the students need to improve or need to correct;
and 5: and displaying the result, namely displaying the judgment result obtained by analyzing and matching.
2. The method for evaluating the simulated driving based on the multidimensional discrete signal analysis as claimed in claim 1, wherein: in the step 1, the local data is a json format file which is stored in the local of the simulator computer through a series of operations in advance, and the program loads the corresponding json data file according to the name.
3. The method according to claim 1, wherein the method comprises the following steps: the boul value type signals 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, a horn and a windscreen wiper.
4. The method according to claim 1, wherein the method comprises the following steps: the int value type signals comprise first gear, second gear, third gear, fourth gear, fifth gear and sixth gear of the vehicle.
5. The method for evaluating the simulated driving based on the multidimensional discrete signal analysis as claimed in claim 1, wherein: in the step 5, the program displays the evaluation result obtained by analyzing and matching, and uploads the corresponding result data to the background server.
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| WO2010136878A1 (en) * | 2009-05-27 | 2010-12-02 | Toyota Jidosha Kabushiki Kaisha | Driving operation evaluation apparatus, driving operation evaluation system, and driving operation evaluation method |
| JP2021081903A (en) * | 2019-11-18 | 2021-05-27 | 株式会社ジェイテクト | Operation evaluation device, operation evaluation system, on-vehicle device, out-vehicle evaluation device, operation evaluation program, and evaluation mapping data generation method |
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| WO2012080741A1 (en) * | 2010-12-15 | 2012-06-21 | Andrew William Wright | Method and system for logging vehicle behaviour |
| US9688283B2 (en) * | 2014-02-25 | 2017-06-27 | Cartasite, Llc | Enhanced driver and vehicle performance and analysis |
| US10300922B2 (en) * | 2017-09-29 | 2019-05-28 | Denso International America, Inc. | Risk assessment system for assessing current driver behavior relative to past behavior and behaviors of other drivers |
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| WO2010136878A1 (en) * | 2009-05-27 | 2010-12-02 | Toyota Jidosha Kabushiki Kaisha | Driving operation evaluation apparatus, driving operation evaluation system, and driving operation evaluation method |
| JP2021081903A (en) * | 2019-11-18 | 2021-05-27 | 株式会社ジェイテクト | Operation evaluation device, operation evaluation system, on-vehicle device, out-vehicle evaluation device, operation evaluation program, and evaluation mapping data generation method |
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