CN113257074A - Intelligent driving training system based on driver operation behavior perception - Google Patents
Intelligent driving training system based on driver operation behavior perception Download PDFInfo
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Abstract
The invention relates to the technical field of driving simulation training, and particularly discloses an intelligent driving training system based on driver operation behavior perception. The system comprises a scene acquisition module, a course selection module, a scene rendering module, a simulation training module, an emergency module, an action acquisition module, an optimal processing module and an action correction module. The scene acquisition module acquires roads in a city as training scenes which are not stored in a scene library, the course selection module is used for selecting courses, the scene rendering module generates training scenes corresponding to the courses, and training is carried out in the training scenes through the simulation training module. In the training process, the emergency module makes the emergency in the training scene, the action acquisition module acquires the user action, the optimal processing module gives an optimal processing mode, and the action correction module corrects the user action.
Description
Technical Field
The invention relates to the technical field of driving simulation training, in particular to an intelligent driving training system based on driver operation behavior perception.
Background
With the rapid development of national economy, more and more families have automobiles, and the requirement of people for driving skill training shows the increase of well-spraying type. At present, the training of driving skills is to guide a student in a way that a coach speaks to teach himself or herself on a special training field or road.
However, this method has some disadvantages because the trainee can train on a special training field or road, the trainer tells the trainee what operation should be performed when the trainee arrives at a certain position, the trainee can only master how to drive under the current environment by repeating the exercise for many times, the trainee cannot drive under different environments, the driving experience is accumulated, and the interference of pedestrians or other vehicles does not occur in the exercise process. For the teaching of the emergency, only theoretical learning is basically carried out, and the students do not feel how to deal with the emergency in the learning process, so that the situation that many people dare to go on the road after trying the driving license or traffic accidents easily occur after novices go on the road appears.
Disclosure of Invention
The invention aims to provide an intelligent driving training system based on driver operation behavior perception, which can help a user to carry out driving training in different environments, generate an emergency in the training process and train the reaction capability of the user when dealing with the emergency.
The basic scheme provided by the invention is as follows: the system comprises a scene acquisition module, a scene library, a course selection module, a scene rendering module, a simulation training module, an emergency module, an action acquisition module, an optimal processing module and an action correction module;
the scene acquisition module is used for acquiring urban roads as training scenes and storing the training scenes in a scene library;
the course selection module is used for selecting courses, and each course at least corresponds to one training scene;
the scene rendering module is used for acquiring training scenes from a scene library according to the courses and generating corresponding training scenes;
the simulation training module is used for carrying out driving training in a training scene;
the emergency module is used for randomly generating an emergency in a virtual scene in the training process;
the action acquisition module is used for acquiring user operation actions;
the optimal processing module is used for generating an optimal processing mode according to the current training scene and the emergency;
and the action correcting module corrects the user action according to the user operation action and the optimal processing mode.
The principle and the advantages of the invention are as follows: various training scenes are collected and stored in a scene library. The user selects courses according to the self requirements, then a corresponding training scene is generated by applying a virtual reality technology, the user conducts driving training in the training scene, the collected training scene is a road in a city, and each course has a plurality of scenes for providing training. The emergency module randomly generates an emergency in the training process, exercises the reaction of a user when dealing with the emergency, detects the action of the user operation in real time through the action acquisition module, the optimal processing module gives out the current optimal processing mode, and the action correction module compares the operation action of the user with the optimal processing mode in advance, so that the operation of the user is detected and corrected in real time. Compared with the prior art, the method and the system can enable the user to carry out driving training in different scenes, help the user accumulate driving experiences in different scenes, add emergencies in the training process, exercise the strain capacity of the user facing the emergencies, detect and correct the actions of the user in real time, and help the user to know own errors in time.
Further, the emergency module comprises a weather event module and a road surface event module;
the weather event module is used for changing weather in the training scene and generating a weather emergency;
and the road surface event module is used for changing the driving road surface condition in the training scene and generating a road surface emergency.
Through changing weather and road surface condition, make the user drive training under various environment, because under different weather conditions, road surface condition, the operation that the user need carry out also can be different, compare in prior art, trained the operation of user under different environment, can promote user's driving level better.
Furthermore, the emergency module also comprises an accident collecting module and an accident generating module;
the accident collection module is used for acquiring various traffic accident cases in the current scene through big data according to the courses selected by the user, and the scene rendering module is also used for restoring the scene before the accident happens and recording the scene as an accident emergency;
and the accident generation module is used for generating accident emergencies in the training process.
By collecting real cases and restoring the scenes when the cases occur in the training process, the user is trained to process the cases when encountering accident emergencies, wherein the accident emergencies are from the real cases and are possibly encountered by the user in the driving process in the future, and the training is carried out in advance to prevent the same accidents from occurring in the driving process in the future.
Furthermore, the emergency module further comprises an accident broadcasting module, and the accident broadcasting module is used for broadcasting the data of the traffic accident case sending scene corresponding to the accident emergency to the user when the user does not have the traffic accident corresponding to the accident emergency in the training accident emergency.
When a user faces an accident emergency, the traffic accident corresponding to the accident emergency is not avoided, and the user is shown not to well process the accident emergency, so that the video data, the picture data or the text data of the case site are broadcasted to the user, the impression of the user is enhanced by knowing the result of the error operation, and the user is helped to execute correct operation when the user meets the accident emergency next time.
Further, when the user does not avoid the traffic accident corresponding to the accident emergency, the accident generation module marks the accident emergency, the marked accident emergency can repeatedly appear in the training process until the user can avoid the traffic accident corresponding to the accident emergency, the marking is cancelled, and the action correction module is also used for only executing the operation in the optimal processing mode when the user next appears after the user fails to avoid the traffic accident for three times.
And if the user still has the traffic accident in the face of the same accident emergency for more than three times, the user can teach the accident emergency by a forced teaching mode.
The system further comprises a state detection module, wherein the state detection module comprises a variable quantity recording module and a state judgment module;
the state detection module is used for detecting the state parameters of the user in the user training process;
the variable quantity recording module is used for recording the variable quantity of the state parameters when the user faces the accident emergency;
and the state judgment module is used for judging the tension degree of the user according to the variable quantity of the state parameters when the user faces the accident emergency.
Detecting the state parameters of the user, recording the variation of the state parameters when the user faces the accident emergency, and judging whether the user is in a tension state when the user faces the accident emergency.
Further, the state parameters comprise heartbeat speed, breathing frequency and pupil size, and the state detection module further comprises a heartbeat detection module, a breathing detection module and a pupil detection module;
the heartbeat detection module is used for detecting the heartbeat speed of the user;
the breath detection module is used for detecting the breath frequency of the user;
and the pupil detection module is used for detecting the pupil size of the user.
The state parameters of the heartbeat speed, the breathing frequency and the pupil size can obviously change when in a tense state, and the tense degree of the user facing an accident emergency can be better judged by detecting the data.
Further, the event generation module is also used for marking the accident emergency when the user is in a tension state facing the accident emergency. The event generation module marks accident emergencies which enable the user to be in a tension state, the marked accident emergencies can repeatedly appear in the training process, and the marking is cancelled until the user is not in the tension state when facing the accident emergencies.
The method comprises the steps of detecting whether a user is in a tension state when facing an accident emergency or not, wherein the tension state is too high when facing the accident emergency, so that the user can be in a busy or disorderly state and make wrong operation.
The system further comprises a before-driving standard module, wherein the before-driving standard module comprises a vehicle winding detection module, a safety belt detection module and a vehicle door detection module;
the system comprises a vehicle winding detection module, a vehicle winding detection module and a vehicle monitoring module, wherein the vehicle winding detection module is used for detecting whether a user winds a vehicle for a circle to check;
the safety belt detection module is used for detecting whether a user fastens a safety belt or not;
the vehicle door detection module is used for detecting whether the vehicle door is closed or not;
and the pre-driving standard module is used for starting the system after the detection reaches the standard.
The operation specification before detecting the user and traveling can inspect tire pressure through winding the car a week, whether there is the barrier in locomotive rear of a vehicle, whether check safety belt is tied and whether the door is closed reach safe driving's purpose. The method helps users to develop good habits, so that the users can check the habits before driving in the future.
Furthermore, the system also comprises a grading module, a grading module and a broadcasting module;
the scoring module is used for scoring the user according to the operation action of the user, the response to the emergency and the result;
the grading module is used for grading the user according to the user grades;
and the broadcasting module is used for broadcasting the training record of the user to other users with the same rating as the user.
The users are scored and graded, and errors made by each user are pushed to other users, because the emergencies encountered by each user are different, and the processing modes are also different, the users with similar levels can learn each other, and the learning efficiency is improved.
Drawings
FIG. 1 is a logic diagram of a first embodiment of the present invention;
FIG. 2 is a logic block diagram of the emergency module according to the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
an embodiment substantially as shown in figure 1:
the system comprises a scene acquisition module, a scene library, a course selection module, a scene rendering module, a pre-driving standard module, an emergency module, a simulation training module, an optimal processing module, an action acquisition module and an action correction module.
In this embodiment, the established courses include backing up and warehousing, parking at a side position, parking at an edge, lane changing, straight-line driving, curve driving, mountain road driving, left-turning at an intersection, right-turning at an intersection, passing through a pedestrian crossing line, passing through a bus stop, meeting, overtaking, end turning, and the like, the scene acquisition module generates a plurality of scene models after acquiring road information through each camera on an urban road, stores the scene models in a scene library, corresponds the courses to the scene models, for example, the scene model of a straight road corresponds to a straight-line driving course, the scene model of a road section with more intersections corresponds to a course with left-turning at an intersection and a course with right-turning at an intersection, and each course at least includes one scene.
The user can select the course that wants to exercise through course selection module, after the user selects the course, select a scene that this course corresponds from the scene storehouse at random, the scene is rendered the module and is generated this scene, and simulation training module presents this scene in the user's front through virtual reality technique, and in this embodiment, presents the training scene to the user through VR glasses to operate in the training scene through driving the simulator, give the more real experience of user.
The driving front standard module comprises a winding detection module, a safety belt detection module and a vehicle door detection module, the winding detection module comprises at least two sensors, the winding detection module is located at the front end and the rear end of the driving simulator, and a user needs to touch the two sensors respectively to complete detection. The safety belt detection module detects whether a user has the action of fastening the safety belt or not through the camera, and the vehicle door detection module detects whether the vehicle door is closed or not through the distance sensor on the vehicle door of the driving simulator. In order to develop the operating specifications of the user before driving, the system can be started for driving training only after the user completes the detection.
The emergency module is shown in fig. 2 and comprises a weather event module, a road surface event module, an accident collecting module, an accident generating module and an accident broadcasting module. The weather event module generates weather emergency events, and randomly changes the weather in the scene in the training process, wherein the weather events comprise sunny days, rainy days, snowy days and heavy fog. The road surface event module generates a road surface emergency, the road surface conditions in the scene including a cement-concrete road surface, an asphalt-concrete road surface, a broken stone road surface and a stone road surface are changed randomly in the training process, and the weather and the road surface conditions can be combined randomly to appear in the training scene by changing the weather and the road surface conditions, so that the driving ability of a user under different conditions is trained.
The action collection module comprises a plurality of cameras, the operation actions of the user are shot in the user training process, the optimal processing module is used for generating an optimal processing mode according to the current driving environment, the action correction module sends correction guidance to the user in a voice mode by comparing the operation actions of the user with the optimal processing mode, and each action of the user is monitored and corrected in real time. For example, when the user changes lanes, the optimal processing mode is to start the turn light first, observe the rearview mirror, change lanes, and prompt the user to observe the rearview mirror through voice if the camera does not shoot that the user has the action of observing the rearview mirror.
The accident collecting module searches traffic accidents occurring in the current environment of the user through big data, wherein the environment comprises one or more of course content, weather and road surface conditions which are performed by the user, for example, if the user performs overtaking training, the traffic accidents occurring due to overtaking are searched; and if the environment in the training scene is the foggy weather, searching for traffic accidents caused by the foggy weather. Or the conditions can be combined and then searched for traffic accidents caused by overtaking in heavy fog. The searched traffic accident is recorded as an accident emergency. The accident generation module restores the accident emergency in the training scene, the role played in the user accident emergency is the main cause party causing the traffic accident at that time, the user is required to make a decision to avoid the traffic accident, and the reaction and judgment of the user facing the accident emergency are trained. For example, when a user performs right turn training at an intersection, a searched accident emergency is a collision caused by that a relatively-running right-turning vehicle does not lead a left-turning vehicle when passing through the intersection without traffic light control or traffic police, and 3s before the user reaches the turning intersection, the user generates head-on running in the relative running direction, and turns on a vehicle with a left-turning light.
If the user cannot perform the operation of avoiding the traffic accident corresponding to the accident emergency when dealing with the accident emergency, the accident broadcasting module broadcasts one or more of video data, picture data and text data after the accident happens, such as videos and photos shot at a scene of the accident or news reporting the traffic accident, to the user. In this way, the user is made aware of the danger of the faulty operation, deepening his impression.
And then the accident generation module marks the accident emergency, and the accident emergency can repeatedly appear in the subsequent process of the user until the user cancels the mark after the accident emergency does not appear any more. If the user still has a traffic accident when facing the accident emergency more than three times, the user can only execute the operation given by the optimal processing mode when meeting the accident emergency next time. For example, the optimal processing module gives a brake-on command, and at this time, when the user operates the driving simulator, only the command for executing the brake-on command is valid, and other commands such as accelerator-on command and steering wheel-turning command are invalid.
In other embodiments of the present application, the user may also play a role in the accident emergency as a victim, which may cause property loss or life safety hazards, although the victim may not assume legal responsibility during the handling process. By enabling the user to act as a victim, the user can make decisions in the face of illegal driving of vehicles or illegal passing pedestrians, and the loss is reduced. For example, when the user performs straight-line driving training, a vehicle changing lane to the right suddenly is generated on the left side of the user, and the optimal processing module gives a mode of decelerating by slightly stepping on the brake.
After the user training is finished, the scoring module scores the driving behavior of the user, the scoring standard scores the behavior of the user in the driving training process of the user, the scoring standard is 100 points full, if operation normative errors such as changing lanes and not turning on a steering lamp occur in the training process, 5 points are deducted, if an accident emergency is responded, a traffic accident occurs as a cause, 15 points are deducted, and if a traffic accident occurs as a victim, 10 points are deducted. The grading module is used for grading the users according to the scores of the users, the user grading of 90-100 points is A, the user grading of 70-90 points is B, the user grading of 60-70 points is C, the user grading below 60 points is D, and the broadcast module broadcasts the deduction reason of each user to other users with the same grade.
Example two
The difference between the second embodiment and the first embodiment is that the medical device further comprises a state acquisition module, and the state acquisition module comprises a heartbeat detection module, a respiration detection module, a pupil detection module, a variation recording module and a state judgment module.
The heartbeat detection module is an electrocardiograph installed on the driving simulator and is used for detecting the heartbeat of a user in real time. The call detection module calculates the breathing frequency of the user by recording the breathing sound of the user. The pupil detection module is a macro camera in VR glasses for real-time detection of the pupil size of the user. When a user is in a calm state, the heartbeat, the respiration and the pupil size of the user are in a normal range, the normal heartbeat speed is 60-90 times per minute, the normal respiration frequency is 16-20 times per minute, the normal pupil diameter is 2-4 mm, when the user faces an accident emergency, the data can be changed, and the variable quantity recording module is used for recording the variable quantity of the data when the user faces the accident emergency. When the user deals with the accident emergency, the heartbeat speed is increased by more than 30 times per minute, the respiratory rate is increased by more than 5 times per minute, and the pupil diameter is increased by 0.5 mm, the state judgment module judges that the user is in a tension state currently. The accident generation module marks accident emergencies which enable the user to be in a tension state, and the accident emergencies are repeatedly generated in the subsequent training process until the user is not in the tension state any more when facing the accident emergencies, the marks are cancelled.
EXAMPLE III
The difference between this embodiment and the second embodiment is that the training device further includes a behavior determining module, where the behavior determining module is configured to determine whether a behavior of the user in the training process is a good habit or a bad habit, and the accident generating module is further configured to randomly generate an accident emergency that makes the user in a nervous state when the behavior of the user is determined to be the bad habit.
Specifically, the behavior judgment module judges whether the behavior in the user training process is a good habit or a bad habit according to the collected behavior in the user training process through the trained neural network model, for example, the user does not turn on a turn signal in the driving process, and the behavior judgment module judges that the behavior is the bad habit. When the user has the behavior of changing lanes and not turning on the turn lights, the accident generation module randomly generates an accident emergency which enables the user to be in a tension state.
When the user has bad habits, an accident emergency which enables the user to be in a tension state in the previous training is generated and is reminded. The accident emergency is not necessarily caused by bad habits of the user, for example, the lane change is carried out when the user does not turn on the turn lamp, and the most easily occurring accident is that the rear vehicle does not know that the user is going to change the lane, so that the user is scratched. When the user performs lane change without turning on the turn signal, the user may observe the lane change direction, and after observation, the user considers that the lane change is possible, and only the action of turning on the turn signal is not performed. The randomly generated accident emergency which enables the user to be in the tension state can lead the user to be free of precaution, the bad habit of the user is related to the accident emergency which enables the user to be in the tension state, the bad habit of the user and the accident emergency which enables the user to be in the tension state have no causal relationship when the bad habit is executed, the effect of giving up the user is achieved, the user is alerted in the mode, and the bad habit is corrected. Meanwhile, even if the user does not completely correct the bad habit, the user can keep high attention when the bad habit is executed because of a stressful event every time the bad habit is executed.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. The utility model provides an intelligence driving training system based on driver's operation action perception which characterized in that: the system comprises a scene acquisition module, a scene library, a course selection module, a scene rendering module, a simulation training module, an emergency module, an action acquisition module, an optimal processing module and an action correction module;
the scene acquisition module is used for acquiring urban roads as training scenes and storing the training scenes in a scene library;
the course selection module is used for selecting courses, and each course at least corresponds to one training scene;
the scene rendering module is used for acquiring training scenes from a scene library according to the courses and generating corresponding training scenes;
the simulation training module is used for carrying out driving training in a training scene;
the emergency module is used for randomly generating an emergency in a virtual scene in the training process;
the action acquisition module is used for acquiring user operation actions;
the optimal processing module is used for generating an optimal processing mode according to the current training scene and the emergency;
and the action correcting module corrects the user action according to the user operation action and the optimal processing mode.
2. The intelligent driving training system based on the perception of the driver's operational behavior according to claim 1, characterized in that: the emergency module comprises a weather event module and a road surface event module;
the weather event module is used for changing weather in the training scene and generating a weather emergency;
and the road surface event module is used for changing the driving road surface condition in the training scene and generating a road surface emergency.
3. The intelligent driving training system based on the perception of the driver's operational behavior according to claim 2, characterized in that: the emergency module also comprises an accident collecting module and an accident generating module;
the accident collection module is used for acquiring various traffic accident cases in the current scene through big data according to the courses selected by the user, and the scene rendering module is also used for restoring the scene before the accident happens and recording the scene as an accident emergency;
and the accident generation module is used for generating accident emergencies in the training process.
4. The intelligent driving training system based on the perception of the driver's operational behavior according to claim 3, characterized in that: the emergency module also comprises an accident broadcasting module which is used for broadcasting the data of the traffic accident case scene corresponding to the accident emergency to the user when the user does not have the traffic accident corresponding to the accident emergency in the training accident emergency.
5. The intelligent driving training system based on the perception of the driver's operational behavior according to claim 4, characterized in that: when the user does not avoid the traffic accident corresponding to the accident emergency, the accident generation module marks the accident emergency, the marked accident emergency can repeatedly appear in the training process until the user can avoid the traffic accident corresponding to the accident emergency, the marking is cancelled, and the action correction module is also used for only executing the operation of an optimal processing mode when the accident emergency appears next time after the user fails to avoid the traffic accident for three times.
6. The intelligent driving training system based on the perception of the driver's operational behavior according to claim 4, characterized in that: the device also comprises a state detection module, wherein the state detection module comprises a variable quantity recording module and a state judgment module;
the state detection module is used for detecting the state parameters of the user in the user training process;
the variable quantity recording module is used for recording the variable quantity of the state parameters when the user faces the accident emergency;
and the state judgment module is used for judging the tension degree of the user according to the variable quantity of the state parameters when the user faces the accident emergency.
7. The intelligent driving training system based on driver operation behavior perception according to claim 6, characterized in that: the state parameters comprise heartbeat speed, respiratory frequency and pupil size, and the state detection module further comprises a heartbeat detection module, a respiratory detection module and a pupil detection module;
the heartbeat detection module is used for detecting the heartbeat speed of the user;
the breath detection module is used for detecting the breath frequency of the user;
and the pupil detection module is used for detecting the pupil size of the user.
8. The intelligent driving training system based on driver operation behavior perception according to claim 7, characterized in that: the event generation module is further configured to mark the emergency when the user is in a stressed state facing the emergency. The event generation module marks accident emergencies which enable the user to be in a tension state, the marked accident emergencies can repeatedly appear in the training process, and the marking is cancelled until the user is not in the tension state when facing the accident emergencies.
9. An intelligent driving training system based on driver's operational behavior perception according to any one of claims 5-8, characterized in that: the safety belt safety system further comprises a pre-driving standard module, wherein the pre-driving standard module comprises a vehicle winding detection module, a safety belt detection module and a vehicle door detection module;
the system comprises a vehicle winding detection module, a vehicle winding detection module and a vehicle monitoring module, wherein the vehicle winding detection module is used for detecting whether a user winds a vehicle for a circle to check;
the safety belt detection module is used for detecting whether a user fastens a safety belt or not;
the vehicle door detection module is used for detecting whether the vehicle door is closed or not;
and the pre-driving standard module is used for starting the system after the detection reaches the standard.
10. The intelligent driving training system based on driver operation behavior perception according to claim 8, characterized in that: the system also comprises a grading module, a grading module and a broadcasting module;
the scoring module is used for scoring the user according to the operation action of the user, the response to the emergency and the result;
the grading module is used for grading the user according to the user grades;
and the broadcasting module is used for broadcasting the training record of the user to other users with the same rating as the user.
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