CN110728199A - Intelligent driving test car practice system and method based on MR - Google Patents
Intelligent driving test car practice system and method based on MR Download PDFInfo
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Abstract
The invention discloses an intelligent driving test car practice system and method based on MR.A 3D depth camera is used for acquiring depth data in real time, a 3D identification module, a driving test simulation module and a data processing module are matched for identifying the identity, action and operation of a student, a driving test information system is connected in real time, modeling can be carried out in real time, and a rendered three-dimensional real environment is output; the training condition of the trainees is fed back in real time, the scene images are output, the trainees can visually observe the defects of the trainees, the trainees can accurately judge the problems of the trainees even if the trainees are not nearby, and the trainees can make progress. And operation, the action of real-time supervision student at the driving process can give suggestion, interaction, realize self-service intelligent driving, promote driving efficiency, reinforcing driving experience.
Description
Technical Field
The invention relates to an MR technology, in particular to an MR-based intelligent driving test car system and method.
Background
As is well known, driving license examination requires a sufficient driving simulation exercise, but the driving exercise usually has inconvenience in time, place, road conditions, and the like. People's life rhythm is fast, draws out a large amount of idle time in present work environment to practise and really is a difficult problem, consequently drags many people's the process of taking pictures slowly, influences the learning effect. Many students report driving schools, and the psychology of the students is influenced by the reputations of the coaches in different degrees; dozens of students need to be brought by a coach, the coach greedy is early, dark and dry, and the coach can also be decocted.
If the virtual scene simulation exercise can be carried out and the real-time guidance information can be obtained, the learning vehicle can be greatly helped. In the prior art, the system and the method for simulating the automobile driving are based on the drawn scene picture, a driver can only perform monotonous operation, the experience difference with the real road driving is very large, and the system and the method can only be used for relaxing games and getting joy and cannot obtain the expected simulation exercise effect.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention aims to provide an MR-based intelligent driving test car training system and method.
The technical scheme is as follows: an MR-based intelligent driving test car practice system comprises a data processing module, a 3D depth camera, a 3D identification module, a driving test simulation module and an output module, wherein the data processing module, the 3D depth camera, the 3D identification module, the driving test simulation module and the output module are arranged on a training car and are in communication connection with the data processing module;
the 3D depth camera is used for acquiring a human body depth image and a scene depth image;
the data processing module is used for carrying out feature extraction and driving test information matching on the depth image;
the 3D recognition module is used for recognizing face information, action information and scene reconstruction according to the characteristic information;
the driving test simulation module is used for calling driving test information and vehicle control information;
the output module is used for outputting the picture and the voice signal.
Further, the driving test information comprises student identity information, qualified action information and qualified vehicle control information.
Furthermore, the 3D depth camera collects a human depth image and transmits the human depth image to the data processing module, the data processing module extracts features of the depth image and transmits the feature information to the 3D recognition module, the 3D recognition module recognizes face information and action information according to the feature information, the driving test simulation module calls driving test information and vehicle control information, the data processing module performs information matching on the face information and student identity information called by the driving test simulation module, performs information matching on the action information and called qualified action information, performs information matching on the vehicle control information and called qualified vehicle control information, and a matching result is output through the output module;
the 3D depth camera collects a scene depth image and then transmits the scene depth image to the data processing module, the data processing module extracts features of the depth image and transmits feature information to the 3D identification module, the 3D identification module conducts scene reconstruction according to the feature information, and the output module outputs a 3D picture of the scene reconstruction.
Furthermore, the 3D depth camera comprises a 3D depth camera arranged in the vehicle and used for collecting the face depth image and a 3D depth camera arranged outside the vehicle and used for collecting the scene depth image.
Further, install and be used for gathering the 3D degree of depth camera of scene depth image outside the car including setting up 4 3D degree of depth cameras in the front of vehicle/back/left/right position respectively, 3D identification module still is used for splicing the scene image of 4 degree of depth camera acquisitions.
Further, the output module includes a display for outputting a picture and a speaker for outputting a voice signal, which are provided in the vehicle.
An MR-based intelligent driving test driving method comprises an information matching method and a scene reconstruction display method, wherein the information matching method comprises the following steps:
collecting a human body depth image;
performing feature extraction on the depth image;
recognizing face information and action information according to the characteristic information;
calling driving test information and vehicle control information; performing information matching on the face information and the student identity information, performing information matching on the action information and the qualified action information, performing information matching on the vehicle control information and the qualified vehicle control information, and outputting a matching result;
the scene reconstruction matching is as follows:
acquiring a scene depth image;
performing feature extraction on the depth image;
and reconstructing a scene according to the characteristic information and outputting a scene reconstructed picture.
Further, the method further comprises an interaction method, which specifically comprises the following steps:
after the features of the depth image are extracted, facial expression features are identified according to feature information, facial expression paraphrases are matched in a facial expression library, and corresponding interactive pictures or voice information is output according to the facial expression paraphrases.
Further, the method further comprises an error prompting method, which specifically comprises the following steps:
when the information matching of the action information and the qualified action information fails, outputting error action prompt information; and outputting wrong operation prompt information when the information matching of the vehicle control information and the qualified vehicle control information fails.
Further, the method also comprises a process recording method, which specifically comprises the following steps:
generating a matching result information set of information matching;
capturing a scene reconstruction picture set when the matching of the action information and the qualified action information fails, the matching of the vehicle control information and the qualified vehicle control information fails, and the training is finished;
and generating a training record by the information set and the picture set.
Has the advantages that: according to the invention, the 3D depth camera is used for acquiring depth data in real time, the 3D identification module, the driving test simulation module and the data processing module are matched for identifying the identity, action and operation of a student, a driving test information system is connected in real time, modeling can be carried out in real time, and a rendered three-dimensional real environment is output; the training condition of the trainees is fed back in real time, the scene images are output, the trainees can visually observe the defects of the trainees, the trainees can accurately judge the problems of the trainees even if the trainees are not nearby, and the trainees can make progress. And operation, the action of real-time supervision student at the driving process can give suggestion, interaction, realize self-service intelligent driving, promote driving efficiency, reinforcing driving experience.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solution is described in detail below with reference to a preferred embodiment and the accompanying drawings.
As shown in fig. 1, an MR-based intelligent driving test driving system comprises a data processing module, a 3D depth camera, a 3D identification module, a driving test simulation module and an output module, which are mounted on a training vehicle, wherein the 3D depth camera, the 3D identification module, the driving test simulation module and the output module are all in communication connection with the data processing module; in addition, the system also comprises a storage module which is used for storing information to be called in the driving test module, training records and the like.
The 3D depth camera is used for acquiring a human body depth image and a scene depth image; the 3D degree of depth camera that adopts in this embodiment is used for obtaining depth data and color data in real time to carry out preliminary data processing, generate 3D data information, the structured light 3D degree of depth camera that this embodiment adopted contains:
an infrared emitter for emitting infrared light;
an infrared receiver for receiving infrared light;
the 3D identification chip is used for generating depth data and generating 3D information according to the depth data;
the RGB camera is used for acquiring color data so as to generate a real-time picture;
the 3D depth camera comprises a camera for capturing a color image and a camera for capturing a depth image, the camera for capturing the depth image uses a structured light measurement system, depth information data is obtained by projecting a known infrared mode into a scene based on light coding through deformation of the mode captured by another infrared CMOS imager, and distance information of all objects in a field angle is output to obtain the depth image.
The 3D depth camera in the system comprises a 3D depth camera installed in the vehicle and used for collecting the face depth image and a 3D depth camera installed outside the vehicle and used for collecting the scene depth image. Install the 3D degree of depth camera that is used for gathering scene depth image outside the car in this embodiment including setting up 4 3D degree of depth cameras in the front/back/left/right position of vehicle respectively, 3D identification module still is used for splicing the scene image of 4 degree of depth camera collections.
The data processing module is used for carrying out feature extraction and driving test information matching on the depth image, specifically carrying out face information extraction, living body detection, surrounding distance monitoring and 3D feature extraction according to the depth data;
the 3D recognition module is used for recognizing face information, action information and scene reconstruction according to the characteristic information; the 3D recognition module comprises a face recognition unit for recognizing face information according to the characteristic information, an action recognition unit for recognizing action information according to the characteristic information and a scene reconstruction unit for reconstructing a scene according to the characteristic information;
the face recognition unit performs living body detection and face recognition according to the depth information, extracts face feature data and an expression feature library according to the 3D information and performs feature retrieval to perform expression recognition;
the action recognition unit extracts the driving practice action data and the 3D feature library according to the 3D information to perform feature retrieval for recognition;
the scene reconstruction unit carries out three-dimensional reconstruction on the surrounding environment of the practice vehicle;
in other embodiments, the 3D identification module may also include:
the face retrieval module is used for retrieving the identity of a person according to the depth and the color information;
the distance monitoring module is used for calculating the distance according to the depth data;
and the 3D feature extraction module is used for extracting 3D information according to the 3D information of the depth data so as to finish 3D feature recognition, living body detection and character expression recognition.
The driving test simulation module is used for calling driving test information and vehicle control information; the driving test information comprises student identity information, qualified action information and qualified vehicle control information. The qualified vehicle control information specifically refers to control operations on the vehicle within a specified time, such as a steering operation, a turn-on operation of a turn signal, a braking operation, and the like; the qualified action information specifically refers to actions that the trainee should do within a specified time, such as swinging head to observe a rearview mirror, operating a gear without lowering head, and the like.
The output module is used for outputting pictures and voice signals and rendering a three-dimensional real environment of the driving practice, presenting a driving practice operation sequence, correcting motion errors and the like. The output module in this embodiment includes a display and a speaker that are arranged in the car and used for outputting pictures, and the display displays real-time monitoring pictures and recognized 3D characteristic information. In addition, an AI voice interaction system is arranged in the output module and used for human-computer voice interaction and real-time prompt in the driving practice process, and the input and intelligent answer of the question of the learner are carried out according to the driving practice operation and the intelligent voice prompt of the driving practice process with the changed driving practice scene.
When the device is used, the 3D depth camera collects a human depth image and then transmits the human depth image to the data processing module, the data processing module extracts features of the depth image and transmits the feature information to the 3D recognition module, the 3D recognition module recognizes face information and action information according to the feature information, the driving test simulation module calls driving test information and vehicle control information, the data processing module performs information matching on the face information and student identity information called by the driving test simulation module, performs information matching on the action information and called qualified action information, performs information matching on the vehicle control information and called qualified vehicle control information, and a matching result is output through the output module;
the 3D depth camera collects a scene depth image and then transmits the scene depth image to the data processing module, the data processing module extracts features of the depth image and transmits feature information to the 3D identification module, the 3D identification module conducts scene reconstruction according to the feature information, and the output module outputs a 3D picture of the scene reconstruction.
The MR-based intelligent driving test driving method using the system comprises an information matching method and a scene reconstruction display method, wherein the information matching method comprises the following steps:
collecting a human body depth image;
performing feature extraction on the depth image; outputting 3D data information by a 3D identification chip, carrying out feature detection on the depth data blocks, iterating to an optimal value, summarizing features, and obtaining feature data;
recognizing face information and action information according to the characteristic information;
calling driving test information and vehicle control information; performing information matching on the face information and the student identity information, performing information matching on the action information and the qualified action information, performing information matching on the vehicle control information and the qualified vehicle control information, and outputting a matching result;
the information matching of the face information and the student identity information specifically comprises the following steps:
1) and performing living body detection by using the acquired depth image data.
2) And carrying out face recognition by using the acquired color data and depth data.
3) And carrying out identity authentication on the identity information of the student by using the face recognition result, and calling various training information of the student after the authentication is successful.
The scene reconstruction matching is as follows:
acquiring a scene depth image; namely, color data and depth data collected by 3D sensors around the vehicle are obtained. As the training of the subject three needs to go on the road and needs to be accompanied by a coach, the training of the subject two is aimed at, and the periphery of the vehicle is the environment around the vehicle in the training field;
performing feature extraction on the depth image;
scene reconstruction is carried out according to the characteristic information, 3D modeling is adopted, real-time reconstruction of the driving practice environment is completed, the reconstructed scene is rendered by utilizing corresponding color data, and the reality of the scene is restored;
and 3D pictures reconstructed by the scene are output, the 3D pictures can clearly see the state of the vehicle in the real environment, whether line pressing, surrounding distance and the like can be intuitively reflected, and the 3D effect can be drawn by adopting the opengl directshow and other technologies during drawing.
The method for acquiring the depth image of the human body or the scene is the prior art, such as:
1) the 3D depth camera comprises a camera for capturing a color image and a camera for capturing a depth image, the camera for capturing the depth image uses a structured light measurement system, depth information data is obtained by projecting a known infrared mode into a scene based on light coding through deformation of the mode captured by another infrared CMOS imager, and distance information of all objects in a field angle is output to obtain the depth image.
2) The color image and the depth image are registered and synchronized, the depth camera and the color camera acquire the completely same scene through a registration algorithm, pixel mapping of the color image and the depth can be carried out, and synchronous output of the color image and the depth can be guaranteed through a synchronization function.
3) And the color image and the depth image are transmitted to a data processing module.
The method further comprises an interaction method, which specifically comprises the following steps:
after the features of the depth image are extracted, facial expression features are identified according to feature information, facial expression paraphrases are matched in a facial expression library, and corresponding interactive pictures or voice information is output according to the facial expression paraphrases. For example, the stress of the student is detected, and a picture is output or AI voice is adopted to sooth the student.
The method further comprises an error prompting method, which specifically comprises the following steps:
and in the driving practice process, the driving practice action extraction is carried out on the acquired depth data and the RGB data. Specifically, the method comprises the following steps: outputting 3D data information by a 3D identification chip, carrying out feature detection on the depth data blocks, iterating to an optimal value, summarizing features, and obtaining feature data;
matching the extracted action data with the driving practice actions of the learner in the driving practice action characteristic library;
the matched action is connected with a driving test information system, the correctness is checked, and when the information matching of the action information and the qualified action information fails, wrong action prompt information is output; outputting error operation prompt information when the information matching of the vehicle control information and the qualified vehicle control information fails;
for example, the AI voice prompt is performed and corrected, and if the action accuracy is high, the AI voice encouragement is performed.
The method also comprises a process recording method, which specifically comprises the following steps:
generating a matching result information set of information matching;
capturing a scene reconstruction picture set when the matching of the action information and the qualified action information fails, the matching of the vehicle control information and the qualified vehicle control information fails, and the training is finished;
generating a training record by the information set and the picture set; training completion degree evaluation can be generated, and advantages and disadvantages are marked;
and (4) archiving the training records, wherein the training records can comprise information such as training process, training data and evaluation results.
The above is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that several modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should be considered as the protection scope of the present invention.
Claims (10)
1. An MR-based intelligent driving test practicing system is characterized by comprising a data processing module, a 3D depth camera, a 3D identification module, a driving test simulation module and an output module which are arranged on a training vehicle, wherein the 3D depth camera, the 3D identification module, the driving test simulation module and the output module are all in communication connection with the data processing module;
the 3D depth camera is used for acquiring a human body depth image and a scene depth image;
the data processing module is used for carrying out feature extraction and driving test information matching on the depth image;
the 3D recognition module is used for recognizing face information, action information and scene reconstruction according to the characteristic information;
the driving test simulation module is used for calling driving test information and vehicle control information;
the output module is used for outputting the picture and the voice signal.
2. The MR-based intelligent driving exam training system of claim 1, wherein the driving exam information comprises trainee identity information, qualified action information, and qualified vehicle control information.
3. The MR-based intelligent driving test system according to claim 2, wherein the 3D depth camera collects a depth image of a human body and transmits the depth image to the data processing module, the data processing module performs feature extraction on the depth image and transmits feature information to the 3D recognition module, the 3D recognition module recognizes face information and action information according to the feature information, the driving test simulation module calls driving test information and vehicle control information, the data processing module performs information matching on the face information and student identity information called by the driving test simulation module, performs information matching on the action information and qualified called action information, and performs information matching on the vehicle control information and qualified called vehicle control information, and the matching result is output through the output module;
the 3D depth camera collects a scene depth image and then transmits the scene depth image to the data processing module, the data processing module extracts features of the depth image and transmits feature information to the 3D identification module, the 3D identification module conducts scene reconstruction according to the feature information, and the output module outputs a 3D picture of the scene reconstruction.
4. The MR-based intelligent driving exam training system of claim 1, wherein the 3D depth cameras comprise a 3D depth camera mounted inside the vehicle for acquiring depth images of human faces and a 3D depth camera mounted outside the vehicle for acquiring depth images of scenes.
5. The MR-based intelligent driving exam training system according to claim 4, wherein the 3D depth cameras installed outside the vehicle for acquiring the scene depth images comprise 4 3D depth cameras respectively arranged at the front/back/left/right of the vehicle, and the 3D identification module is further configured to splice the scene images acquired by the 4 depth cameras.
6. The MR-based intelligent driving exam training system of claim 1, wherein the output module comprises a display for outputting pictures and a speaker for outputting voice signals disposed within the vehicle.
7. An MR-based intelligent driving test driving method is characterized by comprising an information matching method and a scene reconstruction display method, wherein the information matching method comprises the following steps:
collecting a human body depth image;
performing feature extraction on the depth image;
recognizing face information and action information according to the characteristic information;
calling driving test information and vehicle control information; performing information matching on the face information and the student identity information, performing information matching on the action information and the qualified action information, performing information matching on the vehicle control information and the qualified vehicle control information, and outputting a matching result;
the scene reconstruction matching is as follows:
acquiring a scene depth image;
performing feature extraction on the depth image;
and reconstructing a scene according to the characteristic information and outputting a scene reconstructed picture.
8. The MR-based intelligent driving test method according to claim 7, further comprising an interaction method, specifically:
after the features of the depth image are extracted, facial expression features are identified according to feature information, facial expression paraphrases are matched in a facial expression library, and corresponding interactive pictures or voice information is output according to the facial expression paraphrases.
9. The MR-based intelligent driving test method according to claim 7, further comprising an error prompt method, specifically:
when the information matching of the action information and the qualified action information fails, outputting error action prompt information; and outputting wrong operation prompt information when the information matching of the vehicle control information and the qualified vehicle control information fails.
10. The MR-based intelligent driving test method according to claim 7, further comprising a process recording method, specifically:
generating a matching result information set of information matching;
capturing a scene reconstruction picture set when the matching of the action information and the qualified action information fails, the matching of the vehicle control information and the qualified vehicle control information fails, and the training is finished;
and generating a training record by the information set and the picture set.
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| CN113901895A (en) * | 2021-09-18 | 2022-01-07 | 武汉未来幻影科技有限公司 | Door opening action recognition method and device for vehicle and processing equipment |
| CN114051116A (en) * | 2021-08-30 | 2022-02-15 | 武汉未来幻影科技有限公司 | Video monitoring method, device and system for driving test vehicle |
| CN114550525A (en) * | 2020-11-24 | 2022-05-27 | 郑州畅想高科股份有限公司 | Locomotive component overhauls real standard system based on mix reality technique |
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