SE2051569A1 - Intelligent training aiding method and system for instructional cars - Google Patents
Intelligent training aiding method and system for instructional carsInfo
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- SE2051569A1 SE2051569A1 SE2051569A SE2051569A SE2051569A1 SE 2051569 A1 SE2051569 A1 SE 2051569A1 SE 2051569 A SE2051569 A SE 2051569A SE 2051569 A SE2051569 A SE 2051569A SE 2051569 A1 SE2051569 A1 SE 2051569A1
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- 238000012549 training Methods 0.000 title claims abstract description 183
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- 238000001514 detection method Methods 0.000 claims description 19
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- 238000012544 monitoring process Methods 0.000 claims description 4
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- 238000010801 machine learning Methods 0.000 abstract 1
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- 238000012360 testing method Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 2
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Classifications
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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
- G09B9/042—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles providing simulation in a real vehicle
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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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
- G09B19/00—Teaching not covered by other main groups of this subclass
- G09B19/16—Control of vehicles or other craft
- G09B19/167—Control of land vehicles
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- Electrically Operated Instructional Devices (AREA)
Abstract
The invention relates to an intelligent training aiding method and system for instructional cars. The method comprises: acquiring image information of a training field in real time through a camera, and uploading the image information into a machine learning model; detecting an image of the training field by a model to determine the type of the training field and determine whether or not there is an instructional car in the image of the training field; if there is an instructional car in the image of the training field, generating a driving path by calculation and analysis of the model; calculating and analyzing the driving path by the model to determine whether or not the driving path deviates from a preset path; if so, generating correction information by the model and sending the information to the instructional car; and displaying and broadcasting the correction information in the car, so that a learner in the car can adjust personal driving actions according to the correction information to make the driving path close to the preset path. The method can replace instructors to some extent to intelligently aid the learner in driving training and can improve the training level of the learner to a certain extent.
Description
INTELLIGENT TRAINING AIDING METHOD AND SYSTEM FORINSTRUCTIONAL CARS Technical Field[0001] The embodiments of the invention relates to the technical field ofintelligent teaching aiding techniques for driving schools, in particular to an intelligent training aiding method and system for instructional cars.
Background[0002] With the development of the society and the gradual improvement of people°s living standard, more and more families possess cars, and more and morepeople start to leam driving. Due to the fact that the number of driving instructors indriving schools is relatively smaller While the number of driving leamers is great, theWorkload of the driving instructors is large, and the same leaming instructor has torepeat the same technical action over and over in one day and may finally getimpatient, thus leading to contradictions between the leamers and the instructors. Inaddition, due to the great number of driving leamers and the limitations of traditionalface-to-face teaching methods in leaming time and frequency, the leamers cannotflexibly select the instructors, so the improvement of the driving skills of the leamers is limited to some extent. 3. 3. id="p-3" id="p-3" id="p-3" id="p-3" id="p-3" id="p-3" id="p-3" id="p-3" id="p-3" id="p-3"
id="p-3"
[0003] Most existing intelligent products used by driving schools, such asdriving training simulators, are virtual products. Because of the drastic differencebetween the driving training simulators and the cars in operating folloWability and thepoor practicability of necessary teaching materials, the training effect is unsatisfactory;and driving teachings in different regions are different, but such products cannot makechanges to adapt to different driving schools, so the training simulators in the driving schools perform practically no function. 4. 4. id="p-4" id="p-4" id="p-4" id="p-4" id="p-4" id="p-4" id="p-4" id="p-4" id="p-4" id="p-4"
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[0004] In the drive leaming process, it generally takes a lot of time of the leamers to carry out training for the second subject, especially for reverse parking in the second subject. Due to the complicated driving actions of reverse parking, theleamers have to put more effort into training to improve their proficiency; moreover,during reverse parking, the leamers need to slightly adjust the direction of cars toreversely park the cars in parking spaces, but it is difficult for the beginners to masterthe adjustment direction and angle, and it is also impossible for the instructors toprompt the leamers in real time; if the instructors is in a bad mood, the leamers Willfeel more strained, Which is not benef1cial to the improvement of the driving skills ofthe leamers. In view of this, it is necessary to solve one or more problems of the related art. . . id="p-5" id="p-5" id="p-5" id="p-5" id="p-5" id="p-5" id="p-5" id="p-5" id="p-5" id="p-5"
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[0005] It should be noted that this section aims to introduce the backgroundor context of the implementation of the invention stated in the claims, but the description in this section does not necessarily belong to the prior part.
Summa[0006] The objective of the embodiments of the invention is to provide anintelligent training aiding method and system for instructional cars to, at least to someextent, solve one or more problems caused by the limitations and draWbacks of the related art. 7. 7. id="p-7" id="p-7" id="p-7" id="p-7" id="p-7" id="p-7" id="p-7" id="p-7" id="p-7" id="p-7"
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[0007] In a first aspect, the embodiments of the invention provide an intelligent training aiding method for instructional cars, comprising: 8. 8. id="p-8" id="p-8" id="p-8" id="p-8" id="p-8" id="p-8" id="p-8" id="p-8" id="p-8" id="p-8"
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[0008] Acquiring image information of a training field in real time througha camera disposed above the training field, and uploading the image information of the training field into a machine leaming model; 9. 9. id="p-9" id="p-9" id="p-9" id="p-9" id="p-9" id="p-9" id="p-9" id="p-9" id="p-9" id="p-9"
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[0009] Detecting an image of the training field by a target detection modelpre-established in the machine leaming model to determine the type of the trainingfield and determine Whether or not there is an instructional car in the image of the training field; . . id="p-10" id="p-10" id="p-10" id="p-10" id="p-10" id="p-10" id="p-10" id="p-10" id="p-10" id="p-10"
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[0010] Wherein, the machine leaming model detects the image of thetraining field by means of a pre-established instructional car target detection model based on deep leaming; 11. 11. id="p-11" id="p-11" id="p-11" id="p-11" id="p-11" id="p-11" id="p-11" id="p-11" id="p-11" id="p-11"
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[0011] If there is an instructional car in the image of the training field,calculating and analyzing multiple movement positions of the instructional car by themachine leaming model to generate a driving path based on the multiple movement positions; 12. 12. id="p-12" id="p-12" id="p-12" id="p-12" id="p-12" id="p-12" id="p-12" id="p-12" id="p-12" id="p-12"
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[0012] Calculating and analyzing the driving path by the machine leaming model to determine Whether or not the driving path deviates from a preset path; 13. 13. id="p-13" id="p-13" id="p-13" id="p-13" id="p-13" id="p-13" id="p-13" id="p-13" id="p-13" id="p-13"
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[0013] Wherein, the machine leaming model is used after parameters are cured therein by leaming and training multiple driving paths of the instructional car; 14. 14. id="p-14" id="p-14" id="p-14" id="p-14" id="p-14" id="p-14" id="p-14" id="p-14" id="p-14" id="p-14"
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[0014] If the driving path deviates from the preset path, generatingcorrection information according to the degree of deviation of the driving path andsending the correction information to a receiving module on the instructional car, by the machine leaming model; . . id="p-15" id="p-15" id="p-15" id="p-15" id="p-15" id="p-15" id="p-15" id="p-15" id="p-15" id="p-15"
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[0015] Sending the correction information to a control module in theinstructional car by the receiving module, and controlling the correction informationto be displayed and broadcast in the instructional car by the control module, so that aleamer can adjust personal driving actions according to the correction information to make the driving path close to the preset path; 16. 16. id="p-16" id="p-16" id="p-16" id="p-16" id="p-16" id="p-16" id="p-16" id="p-16" id="p-16" id="p-16"
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[0016] Wherein, the correction information includes voice promptinformation and video demonstration information, and the video demonstrationinformation is demonstrative animation information about a pre-tuming direction and angle of a steering Wheel in the instructional car; 17. 17. id="p-17" id="p-17" id="p-17" id="p-17" id="p-17" id="p-17" id="p-17" id="p-17" id="p-17" id="p-17"
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[0017] Every time the instructional car completes a training subject, storing an actual driving path of the instructional car in the machine leaming model; 18. 18. id="p-18" id="p-18" id="p-18" id="p-18" id="p-18" id="p-18" id="p-18" id="p-18" id="p-18" id="p-18"
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[0018] Comparing the actual driving path stored in the machine leamingmodel With the preset path to generate path comparison information, and sending the path comparison information to a terminal for timely correction of the leamer. 19. 19. id="p-19" id="p-19" id="p-19" id="p-19" id="p-19" id="p-19" id="p-19" id="p-19" id="p-19" id="p-19"
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[0019] In one embodiment, the method further comprises: . . id="p-20" id="p-20" id="p-20" id="p-20" id="p-20" id="p-20" id="p-20" id="p-20" id="p-20" id="p-20"
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[0020] In the process of completing the training subject by the instructionalcar, monitoring the driving speed of the instructional car in real time and deterrnining Whether or not the driving speed is Within a preset range; and 21. 21. id="p-21" id="p-21" id="p-21" id="p-21" id="p-21" id="p-21" id="p-21" id="p-21" id="p-21" id="p-21"
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[0021] If the driving speed exceeds the preset range, generating a prompt message, and sending the prompt message to the instructional car. 22. 22. id="p-22" id="p-22" id="p-22" id="p-22" id="p-22" id="p-22" id="p-22" id="p-22" id="p-22" id="p-22"
id="p-22"
[0022] In one embodiment, the method further comprises: 23. 23. id="p-23" id="p-23" id="p-23" id="p-23" id="p-23" id="p-23" id="p-23" id="p-23" id="p-23" id="p-23"
id="p-23"
[0023] When the driving path of the instructional car deviates from thepreset path beyond a preset deviation and the machine leaming model recognizes thatthe instructional car is under a dangerous condition, sending a brake signal to the instructional car to control the instructional car to stop in time. 24. 24. id="p-24" id="p-24" id="p-24" id="p-24" id="p-24" id="p-24" id="p-24" id="p-24" id="p-24" id="p-24"
id="p-24"
[0024] In one embodiment of the invention, the path comparisoninformation is video comparison information of an actual driving video of the instructional car and a preset virtual driving video of the instructional car. . . id="p-25" id="p-25" id="p-25" id="p-25" id="p-25" id="p-25" id="p-25" id="p-25" id="p-25" id="p-25"
id="p-25"
[0025] In one embodiment of the invention, the type of the training fieldincludes: a reverse parking training subject, a parallel parking training subject, a zigzag driving training subject and a left or right tuming training subject. 26. 26. id="p-26" id="p-26" id="p-26" id="p-26" id="p-26" id="p-26" id="p-26" id="p-26" id="p-26" id="p-26"
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[0026] In a second aspect, the embodiments of the invention provide an intelligent training aiding system for instructional cars, comprising: 27. 27. id="p-27" id="p-27" id="p-27" id="p-27" id="p-27" id="p-27" id="p-27" id="p-27" id="p-27" id="p-27"
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[0027] A camera module disposed above a training field and used foracquiring image information of the training field in real time and uploading the image information of the training field into a machine leaming model; 28. 28. id="p-28" id="p-28" id="p-28" id="p-28" id="p-28" id="p-28" id="p-28" id="p-28" id="p-28" id="p-28"
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[0028] The machine leaming model used for detecting an image of thetraining field through a target detection model pre-established in the machine leamingmodel to determine the type of the training field and determine Whether or not there isan instructional car in the image of the training field, Wherein the machine leamingmodel detects the image of the training field by means of a pre-establishedinstructional car target detection model based on deep leaming; if there is aninstructional car in the image of the training field, the machine leaming modelcalculates and analyzes multiple movement positions of the instructional car to generate a driving path based on the multiple movement positions; 29. 29. id="p-29" id="p-29" id="p-29" id="p-29" id="p-29" id="p-29" id="p-29" id="p-29" id="p-29" id="p-29"
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[0029] The machine leaming model is used for calculating and analyzingthe driving path to determine Whether or not the driving path deviates from a presetpath, Wherein the machine leaming model is used after parameters are cured thereinby leaming and training multiple driving paths of the instructional car; if the drivingpath deviates from the preset path, the machine leaming model generates correctioninformation according to the degree of deviation of the driving path and sends thecorrection information to a receiving module on the instructional car; every time theinstructional car completes a training subject, an actual driving path of the instructional car is stored in the machine leaming model; . . id="p-30" id="p-30" id="p-30" id="p-30" id="p-30" id="p-30" id="p-30" id="p-30" id="p-30" id="p-30"
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[0030] An instructional car module comprising a receiving module and acontrol module, Wherein the receiving module is used for sending the receivedcorrection information to the control module in the instructional car, and the controlmodule is used controlling the correction information to be displayed and broadcast inthe instructional car, so that a leamer in the instructional car can adjust personaldriving actions according to the correction information to make the driving path close to the preset path; 31. 31. id="p-31" id="p-31" id="p-31" id="p-31" id="p-31" id="p-31" id="p-31" id="p-31" id="p-31" id="p-31"
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[0031] The correction information includes voice prompt inforrnation andvideo demonstration inforrnation, and the video demonstration information isdemonstrative animation information about a pre-tuming direction and angle of a steering Wheel in the instructional car; 32. 32. id="p-32" id="p-32" id="p-32" id="p-32" id="p-32" id="p-32" id="p-32" id="p-32" id="p-32" id="p-32"
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[0032] A comparison module used for comparing the actual driving pathstored in the machine leaming model With the preset path to generate path comparisoninformation and sending the path comparison information to a terminal for timely correction of the leamer. 33. 33. id="p-33" id="p-33" id="p-33" id="p-33" id="p-33" id="p-33" id="p-33" id="p-33" id="p-33" id="p-33"
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[0033] In one embodiment of the invention, the machine leaming module isused for monitoring the driving speed of the instructional car in real time in theprocess of completing the training subject by the instructional car, deterrniningWhether or not the driving speed is Within a preset range, generating a promptmessage if the driving speed exceeds the preset range, and sending the prompt message to the instructional car. 34. 34. id="p-34" id="p-34" id="p-34" id="p-34" id="p-34" id="p-34" id="p-34" id="p-34" id="p-34" id="p-34"
id="p-34"
[0034] In one embodiment of the invention, the machine leaming model isalso used for sending a brake signal to the instructional car to control the instructionalcar to stop in time When the driving path of the instructional car deviates from thepreset path beyond a preset deviation and the machine leaming model recognizes that the instructional car is under a dangerous condition. . . id="p-35" id="p-35" id="p-35" id="p-35" id="p-35" id="p-35" id="p-35" id="p-35" id="p-35" id="p-35"
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[0035] In one embodiment of the invention, the path comparisoninformation is video comparison information of an actual driving video of the instructional car and a preset virtual driving video of the instructional car. 36. 36. id="p-36" id="p-36" id="p-36" id="p-36" id="p-36" id="p-36" id="p-36" id="p-36" id="p-36" id="p-36"
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[0036] In one embodiment of the invention, the type of the training fieldincludes: a reverse parking training subject, a parallel parking training subject, a zigzag driving training subject and a left or right tuming training subject. 37. 37. id="p-37" id="p-37" id="p-37" id="p-37" id="p-37" id="p-37" id="p-37" id="p-37" id="p-37" id="p-37"
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[0037] The technical solution provided by the embodiments of the invention may have the following beneficial effects: 38. 38. id="p-38" id="p-38" id="p-38" id="p-38" id="p-38" id="p-38" id="p-38" id="p-38" id="p-38" id="p-38"
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[0038] According to the intelligent training aiding method and system forinstructional cars provided by the embodiments of the invention, image inforrnation iscaptured in real time through a camera disposed above a training field and is uploadedto a machine leaming model, then an actual driving path of an instructional car iscalculated and analyzed through the machine leaming model to obtain a deviation ofthe actual driving path from a preset path, and correction inforrnation is generatedaccording to the deviation and can be displayed and broadcast in the instructional carin real time, so that a leamer can visually adjust the turning direction and angle of thesteering wheel according to video demonstrations in the correction inforrnation; avoice prompt is broadcast in the instructional car at the same time, so that the leamercan correct personal driving actions under the combined action of the videodemonstrations and the voice prompt. The intelligent training aiding method canreplace instructors to some extent to intelligently aid the leamer in driving training and can improve the training level of the leamer to a certain extent.
Brief Description of the Drawings 39. 39. id="p-39" id="p-39" id="p-39" id="p-39" id="p-39" id="p-39" id="p-39" id="p-39" id="p-39" id="p-39"
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[0039] The drawings, incorporated into the specification and constitutingone part of the specification, illustrate feasible embodiments of the disclosure and areused to explain the principle of the disclosure together with the specification.Obviously, the drawings in the following description merely illustrate someembodiments of the disclosure, and those ordinarily skilled in the art can obtain otherdrawings according to the following ones without creative labor. 40. 40. id="p-40" id="p-40" id="p-40" id="p-40" id="p-40" id="p-40" id="p-40" id="p-40" id="p-40" id="p-40"
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[0040] FIG. l illustrates a flow diagram of an intelligent training aidingmethod for instructional cars in an illustrative embodiment of the invention; 41. 41. id="p-41" id="p-41" id="p-41" id="p-41" id="p-41" id="p-41" id="p-41" id="p-41" id="p-41" id="p-41"
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[0041] FIG. 2 illustrates a schematic diagram of the modules of anintelligent training aiding system for instructional cars in the illustrative embodimentof the invention; 42. 42. id="p-42" id="p-42" id="p-42" id="p-42" id="p-42" id="p-42" id="p-42" id="p-42" id="p-42" id="p-42"
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[0042] FIG. 3 is a schematic diagram of the tuming direction and angle of a steering Wheel displayed on a display screen of an instructional car in the illustrativeembodiment of the invention;[0043] FIG. 4 is a schematic diagram of a preset driving path of theinstructional car in a training field in the illustrative embodiment of the invention;[0044] FIG. 5 is a schematic diagram of an actual driving path of theinstructional car in the training field in the illustrative embodiment of the invention;[0045] FIG. 6 is a comparison diagram of the actual driving path and thepreset path of the instructional car in the training field in the illustrative embodiment of the invention.
Detailed Description of Embodiments 46. 46. id="p-46" id="p-46" id="p-46" id="p-46" id="p-46" id="p-46" id="p-46" id="p-46" id="p-46" id="p-46"
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[0046] Illustrative embodiments Will be more comprehensively describedbelow With reference to the accompanying draWings. Clearly, the illustrativeembodiments may be implemented in different forms, and should not be limited to theforms expounded herein. These illustrative embodiments are provided to make theinvention more comprehensive and completed and to comprehensively convey theconception of the illustrative embodiments to those skilled in the art. The features,structures or properties described below can be combined in one or more embodiments in any suitable manners. 47. 47. id="p-47" id="p-47" id="p-47" id="p-47" id="p-47" id="p-47" id="p-47" id="p-47" id="p-47" id="p-47"
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[0047] In addition, the accompanying draWings are merely illustrativedraWings of the embodiments of the invention, and are not necessarily draWn to scale.Identical reference signs in the draWings represent identical or similar parts, sorepeated descriptions of these identical reference signs are omitted. Some blockdiagrams in the accompanying draWings illustrate functional entities and do not necessarily correspond to physically or logically independent entities. 48. 48. id="p-48" id="p-48" id="p-48" id="p-48" id="p-48" id="p-48" id="p-48" id="p-48" id="p-48" id="p-48"
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[0048] This illustrative embodiment provides an intelligent training aiding method for instructional cars. Referring to FIG. l, the method may comprise: 49. 49. id="p-49" id="p-49" id="p-49" id="p-49" id="p-49" id="p-49" id="p-49" id="p-49" id="p-49" id="p-49"
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[0049] S101: image information of a training field is acquired in real timethrough a camera disposed above the training field, and the image information of the training field is uploaded into a machine leaming model. 50. 50. id="p-50" id="p-50" id="p-50" id="p-50" id="p-50" id="p-50" id="p-50" id="p-50" id="p-50" id="p-50"
id="p-50"
[0050] S102: an image of the training field is detected by a target detectionmodel pre-established in the machine leaming model to determine the type of thetraining field and to determine Whether or not there is an instructional car in the imageof the training field, Wherein the machine leaming model detects the image of thetraining field by means of a pre-established instructional car target detection model based on deep leaming. 51. 51. id="p-51" id="p-51" id="p-51" id="p-51" id="p-51" id="p-51" id="p-51" id="p-51" id="p-51" id="p-51"
id="p-51"
[0051] S103: if there is an instructional car in the image of the training field,multiple movement positions of the instructional car are calculated and analyzed bythe machine leaming model to generate a driving path based on the multiple movement positions. 52. 52. id="p-52" id="p-52" id="p-52" id="p-52" id="p-52" id="p-52" id="p-52" id="p-52" id="p-52" id="p-52"
id="p-52"
[0052] S104: the machine leaming model calculates and analyzes thedriving path to determine Whether or not the driving path deviates from a preset path,Wherein the machine leaming model is used after parameters are cured therein by leaming and training multiple driving paths of the instructional car. 53. 53. id="p-53" id="p-53" id="p-53" id="p-53" id="p-53" id="p-53" id="p-53" id="p-53" id="p-53" id="p-53"
id="p-53"
[0053] S105: if the driving path deviates from the preset path, the machineleaming model generates correction information according to the degree of deviationof the driving path and sends the correction information to a receiving module on the instructional car. 54. 54. id="p-54" id="p-54" id="p-54" id="p-54" id="p-54" id="p-54" id="p-54" id="p-54" id="p-54" id="p-54"
id="p-54"
[0054] S106: the receiving module sends the received correctioninformation to a control module in the instructional car, and the control modulecontrols the correction information to be displayed and broadcast in the instructionalcar, so that a leamer can adjust personal driving actions according to the correction information to make the driving path close to the preset path, Wherein the correction information includes voice prompt information and video demonstration information,and the video demonstration information is demonstrative animation information about a pre-tuming direction and angle of a steering Wheel in the instructional car. 55. 55. id="p-55" id="p-55" id="p-55" id="p-55" id="p-55" id="p-55" id="p-55" id="p-55" id="p-55" id="p-55"
id="p-55"
[0055] S107: every time the instructional car completes one training subject, an actual driving path of the instructional car is stored in the machine leaming model. 56. 56. id="p-56" id="p-56" id="p-56" id="p-56" id="p-56" id="p-56" id="p-56" id="p-56" id="p-56" id="p-56"
id="p-56"
[0056] S108: the actual driving path stored in the machine leaming model iscompared With the preset path to generate path comparison information, and the path comparison information is sent to a terminal for timely correction of the leamer. 57. 57. id="p-57" id="p-57" id="p-57" id="p-57" id="p-57" id="p-57" id="p-57" id="p-57" id="p-57" id="p-57"
id="p-57"
[0057] According to the intelligent training aiding method for instructionalcars, image information is captured in real time through a camera disposed above atraining field and is uploaded to a machine leaming model, then an actual driving pathof the instructional car is calculated and analyzed through the machine leaming modelto obtain a deviation of the actual driving path from a preset path, and correctioninformation is generated according to the deviation and can be displayed andbroadcast in the instructional car in real time, so that a leamer can visually adjust thetuming direction and angle of the steering Wheel according to video demonstrations inthe correction information; a voice prompt is broadcast in the instructional car at thesame time, so that the leamer can correct personal driving actions under the combinedaction of the video demonstrations and voice prompt. The intelligent training aidingmethod can replace instructors to some extent to intelligently aid the leamer in driving training and can improve the training level of the leamer to a certain extent. 58. 58. id="p-58" id="p-58" id="p-58" id="p-58" id="p-58" id="p-58" id="p-58" id="p-58" id="p-58" id="p-58"
id="p-58"
[0058] BeloW, the steps of the intelligent training aiding method forinstructional cars in this illustrative embodiment Will be described in further detail With reference to FIG. 1 to FIG. 6. 59. 59. id="p-59" id="p-59" id="p-59" id="p-59" id="p-59" id="p-59" id="p-59" id="p-59" id="p-59" id="p-59"
id="p-59"
[0059] S101: image information of a training field is acquired in real time through a camera disposed above the training field, and the image information of the ll training field is uploaded into a machine leaming model. 60. 60. id="p-60" id="p-60" id="p-60" id="p-60" id="p-60" id="p-60" id="p-60" id="p-60" id="p-60" id="p-60"
id="p-60"
[0060] Illustratively, the camera is fixedly disposed above the training field;when a leamer carries out training for the second subject of the driver test, the cameraabove the training field will be started to acquire image inforrnation of the trainingfield in real time and upload the acquired image inforrnation of the training field to themachine leaming model, wherein the image inforrnation may be, but is not limited to,image frame data. The machine leaming model reads the data, namely collects thedata, and then processes and corrects the data in advance such as through featureextraction, feature dimension reduction, feature null-value processing, featuretransformation, feature norrnalization, target null-value processing and target valuetransformation; in the model test stage, the data can be classified in advance through across validation method; after the data is processed, a training model is established bymeans of the processed data, and the model can be evaluated by different parameterssuch as score, precision rate and recall ratio. Specific details can be understood with reference to the prior art and will not be detailed anymore here. 61. 61. id="p-61" id="p-61" id="p-61" id="p-61" id="p-61" id="p-61" id="p-61" id="p-61" id="p-61" id="p-61"
id="p-61"
[0061] S102: an image of the training field is detected by a target detectionmodel pre-established in the machine leaming model to determine the type of thetraining field and to determine whether or not there is an instructional car in the imageof the training field, wherein the machine leaming model detects the image of thetraining field by means of a pre-established instructional car target detection model based on deep leaming. 62. 62. id="p-62" id="p-62" id="p-62" id="p-62" id="p-62" id="p-62" id="p-62" id="p-62" id="p-62" id="p-62"
id="p-62"
[0062] Illustratively, the instructional car target detection model based ondeep leaming can be established through the following steps: an instructional cartarget detection deep-leaming network is established, wherein the instructional cartarget detection deep-leaming network comprises an instructional car feature extractorformed by eight convolutional layers, eight ReLU activation layers and three poolinglayers, and a first convolutional layer and a second convolutional layer connected to the instructional car feature extractor; image samples of the training field under ll 12 different angles, illuminations and image qualities are obtained; an area Where theinstructional car starts training is marked out in each image sample of the trainingfield through a rectangular box, and coordinate information of the rectangular frame isrecorded; an instructional car target detection training dataset is generated With eachimage sample of the training field and the coordinate information of the rectangularbox in the image sample of the training field as a set of training data; the instructionalcar target detection deep-leaming netWork is trained by means of the generatedinstructional car target detection training datasets, and the instructional car targetdetection model based on deep leaming is obtained after parameter updating. Specificdetails can be understood With reference to the prior art. After an image of the trainingfield is detected by the target detection model, the type of the training field Will beobtained. In one example, the type of the training field includes: a reverse parkingtraining subject, a parallel parking training subject, a zigzag driving training subjectand a left or right tuming training subject. The intelligent training aiding method provided by this embodiment can be used for training of each of these subjects. 63. 63. id="p-63" id="p-63" id="p-63" id="p-63" id="p-63" id="p-63" id="p-63" id="p-63" id="p-63" id="p-63"
id="p-63"
[0063] S103: if there is an instructional car in the image of the training field,multiple movement positions of the instructional car are calculated and analyzed bythe machine leaming model to generate a driving path based on the multiple movement positions. 64. 64. id="p-64" id="p-64" id="p-64" id="p-64" id="p-64" id="p-64" id="p-64" id="p-64" id="p-64" id="p-64"
id="p-64"
[0064] Illustratively, the machine leaming model can receive imageinformation of the training field acquired by the camera in real time. In the case Wherethere is an instructional car in the training field, parameters can be cured in themachine leaming model for use by means of a large number of positive and negativesamples of the instructional car, Wherein the positive samples are samplescorresponding to a category to be correctly sorted out, and in this embodiment, thepositive samples are the instructional car in the image information, the negativesamples are other objects, except the instructional car, in the training field such as cement grounds, grass laWns and yelloW lines; the machine leaming model calculates 12 and analyzes multiple received movement positions of the instructional car to generatea driving path based on the multiple movement positions. For example, when theleamer carries out training in the field for reverse parking, the camera uploadsacquired image frames to the machine leaming model, and the machine leamingmodel calculates and processes each image frame to form a driving path together withthe previous image frame and previous images. In this way, the driving path isgenerated at the moment the leamer backs up the instructional car, and changes along with the movement of the instructional car. 65. 65. id="p-65" id="p-65" id="p-65" id="p-65" id="p-65" id="p-65" id="p-65" id="p-65" id="p-65" id="p-65"
id="p-65"
[0065] S104: the machine leaming model calculates and analyzes thedriving path to determine whether or not the driving path deviates from a preset path,wherein the machine leaming model is used after parameters are cured therein by leaming and training multiple driving paths of the instructional car. 66. 66. id="p-66" id="p-66" id="p-66" id="p-66" id="p-66" id="p-66" id="p-66" id="p-66" id="p-66" id="p-66"
id="p-66"
[0066] Illustratively, the machine leaming model is used after parametersare cured therein by leaming and training multiple driving paths of the instructionalcar, that is to say, after being trained by a large number of samples, the machineleaming model can figure out how the instructional car should be driven to bereversely parked successfully or how to adjust the tuming direction and angle of thesteering wheel to reversely park the instructional car under the condition where theinstructional car deviates from an original path, and this adjustment is converted intospeeches to aiding the leamer in training. As shown in FIG. 4, when the leamer drivesthe instructional car for reverse-parking training, the machine leaming model willcalculate and analyze the driving path of the instructional car according to capturedimage information and will determine whether or not the driving path deviates from apreset path, at this moment, the machine leaming model will supervise the leamer likean instructor; when the driving path is different from a common path, the instructorwill timely remind the leamer to adjust the direction to keep the instructional car close to the common path. 67. 67. id="p-67" id="p-67" id="p-67" id="p-67" id="p-67" id="p-67" id="p-67" id="p-67" id="p-67" id="p-67"
id="p-67"
[0067] S105: if the driving path deviates from the preset path, the machine 13 leaming model generates correction inforrnation according to the degree of deviationof the driving path and sends the correction inforrnation to a receiving module on the instructional car. 68. 68. id="p-68" id="p-68" id="p-68" id="p-68" id="p-68" id="p-68" id="p-68" id="p-68" id="p-68" id="p-68"
id="p-68"
[0068] Illustratively, if the machine leaming model finds by calculation thatthe driving path of the instructional car deviates from the preset path or the leamerWill fail in the training subject along the current driving path, the machine leamingmodel Will generate correction inforrnation according to the degree of deviation of thedriving path from the preset path and send the correction inforrnation to theinstructional car. For example, When carrying out training for reverse parking, if theleamer tums the steering Wheel too early, the instructional car Will inevitably roll onlines of a target parking space along the current path; at this moment, the machineleaming model Will generate correction inforrnation by calculation and analysis andsend the correction inforrnation to the receiving module on the instructional car to prompt the leamer to adjust the direction. 69. 69. id="p-69" id="p-69" id="p-69" id="p-69" id="p-69" id="p-69" id="p-69" id="p-69" id="p-69" id="p-69"
id="p-69"
[0069] S106: the receiving module sends the received correctioninforrnation to a control module in the instructional car, and the control modulecontrols the correction inforrnation to be displayed and broadcast in the instructionalcar, so that a leamer can adjust personal driving actions according to the correctioninforrnation to make the driving path close to the preset path, Wherein the correctioninforrnation includes voice prompt inforrnation and video demonstration inforrnation,and the video demonstration inforrnation is demonstrative animation inforrnation about a pre-tuming direction and angle of the steering Wheel in the instructional car. 70. 70. id="p-70" id="p-70" id="p-70" id="p-70" id="p-70" id="p-70" id="p-70" id="p-70" id="p-70" id="p-70"
id="p-70"
[0070] Illustratively, the control module is disposed in the instructional carand is used for controlling a display screen in the instructional car to display thecorrection inforrnation and controlling a speaker in the car to broadcast the voiceprompt. For example, When carrying out training for reverse parking, if the leamertums the steering Wheel too early, the instructional car Will inevitably roll on lines of a target parking space along the current path, and the leamer Will fail in this training 14 subject; at this moment, the machine leaming model will generate correctioninformation by calculation and analysis and send the correction information to thereceiving module on the instructional car, the receiving module sends the correctioninformation to the control module in the car, and the control module controls thedisplay screen to display the correction information. As shown in FIG. 3, contentsdisplayed on the display screen includes Video demonstration information in thecorrection information, the video demonstration information is specif1cally about apre-tuming direction and angle of the steering wheel in the instructional car, and thetuming direction of the steering wheel is marked out by an arrow. By displaying avideo on the display screen, the leaner can be instructed how to operate the steeringwheel more visually, and the training efficiency of the leamer is improved; moreover,the instructional car will give a voice prompt according to the voice promptinformation in the correction information, such as "tum the steering wheel back byhalf a circle". After the instructional car travels along this path by a certain distance,the machine leaming model will generate another correction information bycalculation and analysis and sends the correction information to the instructional car,at this moment, a video animation about a different tuming angle and direction of thesteering wheel will be displayed on the display screen, a voice prompt such as "tumthe steering wheel rightward to the end", and under the combined action of the videoprompt displayed on the display screen and the voice prompt, the leamer can correctlypark the instructional car in the parking space. It should be noted that the voiceprompt information in the correction information can be selected and set according to the actual condition and is not limited to those mentioned in this embodiment. 71. 71. id="p-71" id="p-71" id="p-71" id="p-71" id="p-71" id="p-71" id="p-71" id="p-71" id="p-71" id="p-71"
id="p-71"
[0071] S107: every time the instructional car completes one training subject,an actual driving path of the instructional car is stored in the machine leaming model;S108: the actual driving path stored in the machine leaming model is compared withthe preset path to generate path comparison information, and the path comparison information is sent to a terminal for timely correction of the leamer. 16 72. 72. id="p-72" id="p-72" id="p-72" id="p-72" id="p-72" id="p-72" id="p-72" id="p-72" id="p-72" id="p-72"
id="p-72"
[0072] Illustratively, as shown in FIG. 5 and FIG. 6, every time the leamercompletes a training subject, an actual driving path of the training car will be stored ina storage module in the machine leaming model and will be compared with the presetpath to generate path comparison information. In one example, the path comparisoninformation is video comparison information of an actual driving video of theinstructional car and a preset virtual driving video of the instructional car. Specifically,the camera disposed above the training field can acquire the actual path of theinstructional car in a training subject to obtain a video, namely the actual drivingvideo of the instructional car, and the video is stored and is compared with the presetvirtual driving video of the instructional car by a comparison module to generate acomparison video which is sent to a terminal such as a mobile phone of the leamer, sothat the leamer can more visually realize his/her own driving problems during training by watching the path comparison video. 73. 73. id="p-73" id="p-73" id="p-73" id="p-73" id="p-73" id="p-73" id="p-73" id="p-73" id="p-73" id="p-73"
id="p-73"
[0073] After S103, the training aiding method further comprises: 74. 74. id="p-74" id="p-74" id="p-74" id="p-74" id="p-74" id="p-74" id="p-74" id="p-74" id="p-74" id="p-74"
id="p-74"
[0074] Sl03l: in the process of completing the training subject by theinstructional car, the driving speed of the instructional car is monitored in real time, and whether or not the driving speed is within a preset range is deterrnined;. 75. 75. id="p-75" id="p-75" id="p-75" id="p-75" id="p-75" id="p-75" id="p-75" id="p-75" id="p-75" id="p-75"
id="p-75"
[0075] 81032: if the driving speed exceeds the preset range, a prompt message is generated and is sent to the instructional car. 76. 76. id="p-76" id="p-76" id="p-76" id="p-76" id="p-76" id="p-76" id="p-76" id="p-76" id="p-76" id="p-76"
id="p-76"
[0076] Illustratively, during a test of the second subject, particularly duringreverse parking and parallel parking, the leamer can adjust the state of the car moreeasily by decreasing the speed of the car, so in the process of completing this trainingsubject, the driving speed of the instructional car can be monitored in real time, andwhether or not the driving speed is within a preset range is deterrnined. For example,the instructional car will give a voice prompt to remind the leamer to slow down whenthe driving speed of the instructional car is over 5km/h. The preset range can be set according to the actual condition, and the invention has no limitation in this aspect. 16 17 77. 77. id="p-77" id="p-77" id="p-77" id="p-77" id="p-77" id="p-77" id="p-77" id="p-77" id="p-77" id="p-77"
id="p-77"
[0077] After S104, the training aiding method further comprises: 78. 78. id="p-78" id="p-78" id="p-78" id="p-78" id="p-78" id="p-78" id="p-78" id="p-78" id="p-78" id="p-78"
id="p-78"
[0078] Sl04l: When the driving path of the instructional car deviates fromthe preset path beyond a preset deviation and the machine leaming model recognizesthat the instructional car is under a dangerous condition, a brake signal is sent to the instructional car to control the instructional car to stop in time. 79. 79. id="p-79" id="p-79" id="p-79" id="p-79" id="p-79" id="p-79" id="p-79" id="p-79" id="p-79" id="p-79"
id="p-79"
[0079] Illustratively, in the driving training process of the leamer, toprevent an accident such as the situation Where the instructional car is out of controldue to mistaken slam on the brake, the machine leaming model Will determine that theinstructional car is under a dangerous condition When the instructional car deviatesfrom the preset path excessively or abnorrnally, and send a brake signal to thereceiving module in the instructional car, and a brake module in the instructional carWill receive the brake signal sent from the receiving module to control theinstructional car to stop, so that the safety of the leamer in the driving training process is guaranteed. 80. 80. id="p-80" id="p-80" id="p-80" id="p-80" id="p-80" id="p-80" id="p-80" id="p-80" id="p-80" id="p-80"
id="p-80"
[0080] This illustrative embodiment further provides an intelligent trainingaiding system for instructional cars. Referring to FIG. 2, the system may comprise acamera module, a machine leaming model, an instructional car module and a comparison module. 81. 81. id="p-81" id="p-81" id="p-81" id="p-81" id="p-81" id="p-81" id="p-81" id="p-81" id="p-81" id="p-81"
id="p-81"
[0081] The camera module is disposed above a training field and used foracquiring image information of the training field in real time and uploading the image information of the training field into a machine leaming model; 82. 82. id="p-82" id="p-82" id="p-82" id="p-82" id="p-82" id="p-82" id="p-82" id="p-82" id="p-82" id="p-82"
id="p-82"
[0082] The machine leaming model is used for detecting an image of thetraining field through a target detection model pre-established in the machine leamingmodel to determine the type of the training field and determine Whether or not there isan instructional car in the image of the training field, Wherein the machine leaming model detects the image of the training field by means of a pre-established 17 instructional car target detection model based on deep leaming; if there is aninstructional car in the image of the training field, the machine leaming modelcalculates and analyzes multiple movement positions of the instructional car to generate a driving path based on the multiple movement positions; 83. 83. id="p-83" id="p-83" id="p-83" id="p-83" id="p-83" id="p-83" id="p-83" id="p-83" id="p-83" id="p-83"
id="p-83"
[0083] The machine leaming model is used for calculating and analyzingthe driving path to determine Whether or not the driving path deviates from a presetpath, Wherein the machine leaming model is used after parameters are cured thereinby leaming and training multiple driving paths of the instructional car; if the drivingpath deviates from the preset path, the machine leaming model generates correctioninformation according to the degree of deviation of the driving path and sends thecorrection information to a receiving module on the instructional car; every time theinstructional car completes one training subject, an actual driving path of the instructional car is stored in the machine leaming model. 84. 84. id="p-84" id="p-84" id="p-84" id="p-84" id="p-84" id="p-84" id="p-84" id="p-84" id="p-84" id="p-84"
id="p-84"
[0084] The instructional car module is used for sending the receivedcorrection information to the control module in the instructional car, and the controlmodule is used controlling the correction information to be displayed and broadcast inthe instructional car, so that a leamer in the instructional car can adjust personaldriving actions according to the correction information to make the driving path closeto the preset path; the correction information includes voice prompt information andvideo demonstration information, and the video demonstration information isdemonstrative animation information about a pre-tuming direction and angle of a steering Wheel of the instructional car. 85. 85. id="p-85" id="p-85" id="p-85" id="p-85" id="p-85" id="p-85" id="p-85" id="p-85" id="p-85" id="p-85"
id="p-85"
[0085] The comparison module is used for comparing the actual drivingpath stored in the machine leaming model With the preset path to generate pathcomparison information and sending the path comparison information to a terminal for timely correction of the leamer. 86. 86. id="p-86" id="p-86" id="p-86" id="p-86" id="p-86" id="p-86" id="p-86" id="p-86" id="p-86" id="p-86"
id="p-86"
[0086] The specific implementation of the system can be understood With 18 reference to the above embodiment and Will not be detailed anymore here. 87. 87. id="p-87" id="p-87" id="p-87" id="p-87" id="p-87" id="p-87" id="p-87" id="p-87" id="p-87" id="p-87"
id="p-87"
[0087] In one example, the machine leaming module is used for monitoringthe driving speed of the instructional car in real time in the process of completing thetraining subject by the instructional car, deterrnining Whether or not the driving speedis Within a preset range, generating a prompt message When the driving speed exceeds the preset range, and sending the prompt message to the instructional car. 88. 88. id="p-88" id="p-88" id="p-88" id="p-88" id="p-88" id="p-88" id="p-88" id="p-88" id="p-88" id="p-88"
id="p-88"
[0088] In one example, the machine leaming model is also used for sendinga brake signal to the instructional car to control the instructional car to stop in timeWhen the driving path of the instructional car deviates from the preset path beyond apreset deviation and the machine leaming model recognizes that the instructional car is under a dangerous condition. 89. 89. id="p-89" id="p-89" id="p-89" id="p-89" id="p-89" id="p-89" id="p-89" id="p-89" id="p-89" id="p-89"
id="p-89"
[0089] In one example, the path comparison information is videocomparison information of an actual driving video of the instructional car and a preset virtual driving video of the instructional car. 90. 90. id="p-90" id="p-90" id="p-90" id="p-90" id="p-90" id="p-90" id="p-90" id="p-90" id="p-90" id="p-90"
id="p-90"
[0090] In one example, the type of the training field includes: a reverseparking training subject, a parallel parking training subject, a zigzag driving training subject and a left or right tuming training subject. 91. 91. id="p-91" id="p-91" id="p-91" id="p-91" id="p-91" id="p-91" id="p-91" id="p-91" id="p-91" id="p-91"
id="p-91"
[0091] According to the intelligent training aiding method and system forinstructional cars, image information is captured in real time through a cameradisposed above a training field and is uploaded to a machine leaming model, then anactual driving path of the instructional car is calculated and analyzed through themachine leaming model to obtain a deviation of the actual driving path from a presetpath, and correction information is generated according to the deviation and can bedisplayed and broadcast in the instructional car in real time, so that a leamer canvisually adjust the tuming direction and angle of the steering Wheel according to video demonstrations in the correction information; a voice prompt is broadcast in the 19 instructional car at the same time, so that the leamer can correct personal drivingactions under the combined action of the video demonstrations and voice prompt. Theintelligent training aiding method can replace instructors to some extent tointelligently aid the leamer in driving training and can improve the training level of the leamer to a certain extent. 92. 92. id="p-92" id="p-92" id="p-92" id="p-92" id="p-92" id="p-92" id="p-92" id="p-92" id="p-92" id="p-92"
id="p-92"
[0092] It should be noted that the terms such as "central", "lengthwise", 77 CC "crosswise", "length", "Width", "thickness , upper", "lower", "front", "back", "left", 77 CC 77 CC "right", "vertical", "horizontal", "top", "bottom", "inner , outer , clockwise" and"anticlockwise" in the above description are used to indicate directional or positionalrelations on the basis of the drawings merely for the purpose of facilitating andsimplifying the description of the embodiments of the invention, do not indicate orimply that devices or elements referred to must be in a specific direction or must be configured or operated in a specific direction, and thus should not be construed as limitations of the embodiments of the invention. 93. 93. id="p-93" id="p-93" id="p-93" id="p-93" id="p-93" id="p-93" id="p-93" id="p-93" id="p-93" id="p-93"
id="p-93"
[0093] In addition, the terms "first" and "second" are merely for thepurpose of description, should not be construed as indications or implications ofrelative importance or implicit indications of the number of technical features referredto. Thus, in case where a feature defined by "first" or "second", it may explicitly orimplicitly indicate that one or more said features are included. In the description of theembodiments of the invention, ""multiple" refers to two or more, unless otherwise specifically defined. 94. 94. id="p-94" id="p-94" id="p-94" id="p-94" id="p-94" id="p-94" id="p-94" id="p-94" id="p-94" id="p-94"
id="p-94"
[0094] In the embodiments of the invention, unless otherwise expresslystated or defined, the terms such as "install", "link", "connect" and "fix" should bebroadly understood. For example, "connect" may refer to fixed connection, detachableconnection or integral connection, or mechanical connection or electrical connection,or direct connection or indirect connection via an interrnediate, or intemalcommunication of two elements or interaction of two elements. Those ordinarily skilled in the art can appreciate the specific meaning of these terms in the invention as 21 the case may be. 95. 95. id="p-95" id="p-95" id="p-95" id="p-95" id="p-95" id="p-95" id="p-95" id="p-95" id="p-95" id="p-95"
id="p-95"
[0095] In the embodiments of the invention, unless otherwise expresslystated or defined, the expression that a first feature is located "above" or "below" asecond feature may include the case where the first feature directly makes contactwith the second feature and the case where the first feature makes contact with thesecond feature through another feature rather than directly making contact with thesecond feature. In addition, the expression that a first feature is located "over" or"above" a second feature or located on an "upper side" of the second feature meansthat the first feature is located over or above the second feature or means that the firstfeature is horizontally higher than the second feature. The expression that a firstfeature is located "under" or "below" a second feature or located on a "lower side" ofa second feature means that the first feature is located under or below the second feature or means that the first feature is horizontally lower than the second feature. 96. 96. id="p-96" id="p-96" id="p-96" id="p-96" id="p-96" id="p-96" id="p-96" id="p-96" id="p-96" id="p-96"
id="p-96"
[0096] In the specification, the description of the reference term "oneembodiment", "some embodiments", "example", "specific example" or "someexamples" is intended to point out that the specific features, structures, materials ofcharacteristics incorporated in said embodiment or example are included in at leastone embodiment or example of the invention. In this specification, the illustrativedescription of this term does not necessarily refer to one embodiment or example. Inaddition, the specific features, structures, materials or characteristics referred to maybe incorporated in one or more embodiments or examples in any suitable manners.Moreover, those skilled in the art can integrate and combine different embodiments or examples described in this specification. 97. 97. id="p-97" id="p-97" id="p-97" id="p-97" id="p-97" id="p-97" id="p-97" id="p-97" id="p-97" id="p-97"
id="p-97"
[0097] By reading the specification and implementing the invention, thoseskilled in the art can easily come up with other embodiments of the invention. Theapplication is intended to include any transforrnations, usage, or adaptive variations ofthe invention, which follow the basic principle of the invention and include common knowledge or technical means, not disclosed by the invention, of the prior art. The 22 specification and en1bodin1ents are nierely for an illustrative purpose, and the essential scope and spirit of the invention should be defined by the appended clainis.
Claims (12)
1. l. An intelligent training aiding method for instructional cars, comprising: acquiring image information of a training field in real time through a cameradisposed above the training field, and uploading the image information of the training fieldinto a machine leaming model; detecting an image of the training field by a target detection model pre-established inthe machine leaming model to determine the type of the training field and determine Whetheror not there is an instructional car in the image of the training field; Wherein, the machine leaming model detects the image of the training field by meansof a pre-established instructional car target detection model based on deep leaming; if there is an instructional car in the image of the training field, calculating andanalyzing multiple movement positions of the instructional car by the machine leaming modelto generate a driving path based on the multiple movement positions; calculating and analyzing the driving path by the machine leaming model todetermine Whether or not the driving path deviates from a preset path; Wherein, the machine leaming model is used after parameters are cured therein byleaming and training multiple driving paths of the instructional car; if the driving path deviates from the preset path, generating correction informationaccording to the degree of deviation of the driving path, and sending the correctioninformation to a receiving module on the instructional car, by the machine leaming model; sending the correction information to a control module in the instructional car by thereceiving module, and controlling the correction information to be displayed and broadcast inthe instructional car by the control module, so that a leamer can adjust personal drivingactions according to the correction information to make the driving path close to the presetpath; Wherein, the correction information includes voice prompt information and videodemonstration information, and the video demonstration information is demonstrativeanimation information about a pre-tuming direction and angle of a steering Wheel in the instructional car;
2. The intelligent training aiding method for instructional cars according to claim l,further comprising: in the process of completing a training subject by the instructional car, monitoring adriving speed of the instructional car in real time and deterrnining Whether or not the drivingspeed is Within a preset range; and if the driving speed exceeds the preset range, generating a prompt message, and sending the prompt message to the instructional car.
3. The intelligent training aiding method for instructional cars according to claim 2,further comprising: When the driving path of the instructional car deviates from the preset path beyond apreset deviation and the machine leaming model recognizes that the instructional car is undera dangerous condition, sending a brake signal to the instructional car to control the instructional car to stop in time.
4. The intelligent training aiding method for instructional cars according to claim l,further comprising: every time the instructional car completes one training subject, storing an actualdriving path of the instructional car in the machine leaming model; comparing the actual driving path stored in the machine leaming model With thepreset path to generate path comparison information, and sending the path comparison information to a terminal for timely correction of the leamer.
5. The intelligent training aiding method for instructional cars according to claim 4,Wherein the path comparison information is video comparison information of an actual driving video of the instructional car and a preset virtual driving video of the instructional car.
6. The intelligent training aiding method for instructional cars according to claim l, Wherein the type of the training field includes: a reverse parking training subject, a parallel parking training subject, a zigzag driving training subject and a left or right turning training subject.
7. An intelligent training aiding system for instructional cars, comprising: a camera module disposed above a training field and used for acquiring imageinformation of the training field in real time and uploading the image information of thetraining field into a machine leaming model; the machine leaming model used for detecting an image of the training field by atarget detection model pre-established in the machine leaming model to determine the type ofthe training field and determine Whether or not there is an instructional car in the image of thetraining field, Wherein the machine leaming model detects the image of the training field bymeans of a pre-established instructional car target detection model based on deep leaming; ifthere is an instructional car in the image of the training field, the machine leaming modelcalculates and analyzes multiple movement positions of the instructional car to generate adriving path based on the multiple movement positions; the machine leaming model is used for calculating and analyzing the driving path todetermine Whether or not the driving path deviates from a preset path, Wherein the machineleaming model is used after parameters are cured therein by leaming and training multipledriving paths of the instructional car; if the driving path deviates from the preset path, themachine leaming model generates correction information according to the degree of deviationof the driving path and sends the correction information to a receiving module on theinstructional car; every time the instructional car completes one training subject, an actualdriving path of the instructional car is stored in the machine leaming model; an instructional car module comprising a receiving module and a control module,Wherein the receiving module is used for sending the received correction information to thecontrol module in the instructional car, and the control module is used controlling thecorrection information to be displayed and broadcast in the instructional car, so that a leamerin the instructional car can adjust personal driving actions according to the correctioninformation to make the driving path close to the preset path; the correction information includes voice prompt information and video demonstration information, and the Video demonstration information is demonstrativeanimation information about a pre-tuming direction and angle of a steering Wheel in the instructional car.
8. The intelligent training aiding system for instructional cars according to claim 7,Wherein the machine leaming module is used for monitoring a driving speed of theinstructional car in real time in the process of completing the training subject by theinstructional car, deterrnining Whether or not the driving speed is Within a preset range,generating a prompt message When the driving speed exceeds the preset range, and sending the prompt message to the instructional car.
9. The intelligent training aiding system for instructional cars according to claim 8,Wherein the machine leaming model is also used for sending a brake signal to the instructionalcar to control the instructional car to stop in time When the driving path of the instructional cardeviates from the preset path beyond a preset deviation and the machine leaming model recognizes that the instructional car is under a dangerous condition.
10. l0. The intelligent training aiding system for instructional cars according to claim 7,further comprising: a comparison module used for comparing the actual driving path stored in themachine leaming model With the preset path to generate path comparison information and sending the path comparison information to a terminal for timely correction of the leamer.
11. ll. The intelligent training aiding system for instructional cars according to claim 10,Wherein the path comparison information is video comparison information of an actual driving video of the instructional car and a preset virtual driving video of the instructional car.
12. The intelligent training aiding system for instructional cars according to claim 7,Wherein the type of the training field includes: a reverse parking training subject, a parallel parking training subject, a zigzag driving training subject and a left or right tuming training subject.
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| CN202010696315.XA CN111667742A (en) | 2020-07-20 | 2020-07-20 | Intelligent auxiliary training method and system for learner-driven vehicle |
| PCT/CN2020/105268 WO2022016583A1 (en) | 2020-07-20 | 2020-07-28 | Intelligent training aiding method and system for learner-driven vehicle |
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| CN112201113A (en) * | 2020-10-21 | 2021-01-08 | 广东星唯信息技术有限公司 | Method for monitoring backing and warehousing of learner-driven vehicle by RTK mapping technology |
| CN114399932A (en) * | 2021-12-28 | 2022-04-26 | 南京财经大学 | Talent training simulation training system based on intelligent learning |
| CN114581826B (en) * | 2022-02-28 | 2024-09-27 | 西南大学 | Weak supervision target detection method and system based on deep learning |
| CN114822169B (en) * | 2022-05-06 | 2023-06-09 | 辽宁科技大学 | Auxiliary driving exercise method and device for learner-driven vehicle |
| US11643108B1 (en) * | 2022-06-23 | 2023-05-09 | Motional Ad Llc | Generating corrected future maneuver parameters in a planner |
| CN115578914B (en) * | 2022-11-23 | 2023-03-14 | 湖南视觉伟业智能科技有限公司 | Training system and method for backing in place during driving training |
| CN116645848B (en) * | 2023-04-17 | 2024-06-11 | 武汉未来幻影科技有限公司 | Vehicle operation control method and related equipment |
| CN116729422B (en) * | 2023-06-07 | 2024-03-08 | 广州市德赛西威智慧交通技术有限公司 | Deviation correction method for vehicle track, vehicle driving assistance method and device |
| CN118447566A (en) * | 2024-04-11 | 2024-08-06 | 顺德职业技术学院 | Operation control method and system under automobile practical training |
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| US8930227B2 (en) * | 2012-03-06 | 2015-01-06 | State Farm Mutual Automobile Insurance Company | Online system for training novice drivers and rating insurance products |
| CN105590449B (en) * | 2015-12-29 | 2017-12-12 | 南京邮电大学 | A kind of learner-driven vehicle traveling monitoring system and monitoring method |
| CN208376741U (en) * | 2017-03-24 | 2019-01-15 | 多伦科技股份有限公司 | A kind of intelligent robot coach DAS (Driver Assistant System) |
| CN108109481A (en) * | 2017-12-21 | 2018-06-01 | 云南冲浪科技有限公司 | A kind of vehicle driver training intelligent coach system |
| CN108389477A (en) * | 2018-03-05 | 2018-08-10 | 广州星唯信息科技有限公司 | A kind of correction guidance method for driving training field training |
| CN108806371B (en) * | 2018-08-31 | 2020-06-26 | 成都的卢青创网络科技有限公司 | Intelligent judgment method and system based on driving test subject training |
| CN109272821A (en) * | 2018-10-22 | 2019-01-25 | 广州星唯信息科技有限公司 | A kind of place driving evaluation method based on high-precision vision positioning |
| CN110599853B (en) * | 2019-08-05 | 2022-07-05 | 深圳华桥智能设备科技有限公司 | Intelligent teaching system and method for driving school |
| CN111047948B (en) * | 2019-11-27 | 2022-06-10 | 康忠文 | Auxiliary method and system for learning to train |
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