A processing method, system and device for driving skill assessment learning and a storage medium

Abstract

The application discloses a processing method for driving skill evaluation learning, comprising the following steps: monitoring current driving behavior in the driving process; obtaining current automatic driving planning of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving planning; when it is determined that there is bad driving behavior, prompting; after the driving is finished, comparing and displaying each bad driving behavior and corresponding correct driving behavior in the automatic driving planning, or / and performing simulation driving learning operation. The application also discloses corresponding systems, devices and storage media. By using the prompting, playback, comparison and simulation driving, the driving skill of the driver can be evaluated and improved, so that the safety of driving is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving of vehicles, and in particular to a processing method, system and device for driving skill evaluation and learning, and a storage medium. BACKGROUND

[0002] With the popularization of vehicles, the number of drivers increases, but the training basis in driving schools is single, and the drivers have different levels and many driving habit problems. Many drivers often cannot choose the best way to deal with dangerous situations due to lack of experience. It is very important to correctly guide novice drivers to avoid accidents and develop bad habits.

[0003] If a driver develops bad driving habits after graduating from a driving school, it is easy to cause traffic accidents when driving, and it is difficult to correct these bad driving habits once they are developed.

[0004] In the current technical solution, the intelligent driving system only provides a single function of alarming for the bad driving habits of the driver. For example, in the case of changing lanes and overtaking and having a car behind, if the driver chooses to slow down and change lanes, the system will only issue a danger prompt through sound reminders or steering wheel vibration. In the current driving, the driver can perceive the danger, but cannot experience the situation in person after the event, and cannot deeply understand and correct the driving problems of the driver. Therefore, there are many safety hazards in driving due to the bad driving habits of the driver. SUMMARY

[0005] The technical problem to be solved by the present application is that the present application provides a processing method, system and device for driving skill evaluation and learning, and a storage medium, which can evaluate and improve the driving skills of the driver through prompting, playback, comparison and simulated driving, thereby improving the safety of driving.

[0006] As one aspect of the present application, a processing method for driving skill evaluation and learning is provided, which at least includes the following steps:

[0007] monitoring the current driving behavior during driving;

[0008] obtaining the current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and prompting when it is determined that there is bad driving behavior;

[0009] after driving, comparing and displaying each bad driving behavior and the correct driving behavior in the corresponding automatic driving plan, or / and performing simulated driving learning operation.

[0010] In the step of monitoring the current driving behavior during driving, the method further includes:

[0011] starting a driving behavior evaluation function of the vehicle;

[0012] starting a perception sensor of an intelligent driving system, obtaining road environment information around the vehicle and driving behavior information of the driver, and performing fusion processing;

[0013] extracting a current driving scene according to the information after the fusion processing.

[0014] wherein, obtaining a current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and when it is determined that there is a bad driving behavior, prompting, further comprising:

[0015] automatically generating or retrieving a corresponding automatic driving plan according to the current driving scene, obtaining a correct driving behavior corresponding to the current driving scene in the automatic driving plan;

[0016] comparing the correct driving behavior with the current driving behavior;

[0017] determining whether there is a bad driving behavior or a risk point according to the comparison result;

[0018] when the judgment result is that there is a bad driving behavior or a risk point, performing identification and reminding, and storing the current video.

[0019] wherein, the comparison and display of each bad driving behavior and the correct driving behavior in the corresponding automatic driving plan are further included, comprising:

[0020] after detecting that the vehicle stops and is in the parking gear, scoring the driving behavior this time;

[0021] generating a corresponding driving suggestion for the bad driving behavior or the risk point existing in the driving process, and comparing and displaying the video of the bad driving behavior corresponding to each driving scene, and the video or animation of the correct driving behavior.

[0022] wherein, further comprising:

[0023] converting the actual driving scene data containing the bad driving behavior or the risk point to obtain virtual scene data and store it.

[0024] wherein, after the driving is completed, a simulated driving learning operation is performed, further comprising:

[0025] after the vehicle stops, the simulated driving mode is started, the corresponding light-emitting LED glass of the vehicle is powered on, and the electronic power steering, the electronic power steering, and the electronic power steering are switched to the simulated driving mode, and are disconnected from the corresponding actuators;

[0026] Select the virtual scene data used in this simulation driving, and play back the virtual scene in the light-emitting LED glass;

[0027] In response to the operation of the driver on the drive-by-wire throttle, drive-by-wire steering and drive-by-wire shifting, the driving state corresponding to the current vehicle in the virtual scene is changed;

[0028] After exiting the simulation driving, the light-emitting LED glass is powered off, and the drive-by-wire shifting, drive-by-wire steering and drive-by-wire throttle are restored to the initial state associated with the corresponding actuators.

[0029] Further comprising:

[0030] During the simulation driving, the real-time state of the vehicle in the virtual scene is monitored, and when it is detected that there is a dangerous driving behavior, an operation reminder is performed;

[0031] After the simulation driving ends, the operation score and prompt of this simulation driving are performed.

[0032] Correspondingly, as another aspect of the present application, a processing system for driving technology evaluation learning is also provided, which at least includes:

[0033] A driving monitoring unit for monitoring the current driving behavior during driving;

[0034] A driving behavior comparison processing unit for obtaining the current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and performing identification and reminding when it is determined that there is a bad driving behavior;

[0035] An evaluation learning unit for comparing and displaying the correct driving behavior in the corresponding automatic driving plan after the driving ends, or / and performing simulation driving processing.

[0036] Further comprising:

[0037] A correct behavior obtaining unit for automatically generating or calling the corresponding automatic driving plan to obtain the corresponding correct driving behavior in the automatic driving plan;

[0038] A comparison unit for comparing the correct driving behavior with the current driving behavior;

[0039] A bad driving behavior determination unit for determining whether there is a bad driving behavior or a risk point according to the comparison result;

[0040] An identification and reminding unit for identifying and reminding when it is determined that there is a bad driving behavior or a risk point, and storing the current video.

[0041] Further comprising:

[0042] A virtual scene conversion unit is configured to convert actual driving scene data containing bad driving behaviors or risk points to obtain virtual scene data and store the virtual scene data.

[0043] The evaluation learning unit further comprises:

[0044] The simulation driving mode starting unit is configured to start the simulation driving mode after the vehicle stops, control the power supply of the light-emitting LED glass, and control the line control throttle, the line control steering, and the line control gear shift to switch to the simulation driving mode and disconnect from the corresponding actuators.

[0045] The virtual scene playback unit is configured to select virtual scene data used in the current simulation driving and play back the virtual scene on the light-emitting LED glass.

[0046] The simulation driving response unit is configured to change the driving state of the current vehicle in the virtual scene in response to the operation of the line control throttle, the line control steering, and the line control gear shift by the driver.

[0047] The simulation driving mode exiting unit is configured to control the power supply of the light-emitting LED glass and control the line control gear shift, the line control steering, and the line control throttle to return to the initial state after exiting the simulation driving and associate with the corresponding actuators.

[0048] As still another aspect of the present application, a computing processing device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the foregoing method when executing the computer program.

[0049] As still another aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the foregoing method when executed by a processor.

[0050] The embodiments of the present application have the following advantages:

[0051] The present application provides a processing method, system and device for driving skill evaluation and learning, and a storage medium.

[0052] This invention utilizes the perception sensors and control planning functions of its own intelligent driving system to record video and data at the current moment when encountering poor driving habits of the driver. This data is then compared with the correct driving behaviors in the driving scenarios in its own scenario library to generate comparison results. The results inform the driver of the risk points and the correct operating methods to avoid the risks, thereby providing effective guidance to the driver and improving the driver's driving skills and driving safety.

[0053] This invention collects external environmental data from actual driving scenarios and generates corresponding virtual scenes. Once the vehicle enters simulated driving mode, this virtual scene is displayed on the vehicle's illuminated LED windows, allowing the driver to simulate driving. During the simulation, the system provides real-time reminders and corrections for any bad driving habits. It enables repeated training for drivers in specific dangerous driving scenarios, correcting bad driving habits through practical experience and thus improving driving skills and road safety. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0055] Figure 1 This is a schematic diagram of the main flow of an embodiment of a processing method for driving skill assessment and learning provided by the present invention;

[0056] Figure 2 This is a schematic diagram illustrating the effect of simulated driving in this invention;

[0057] Figure 3 A more detailed flowchart illustrating one application scenario of the method provided by the present invention;

[0058] Figure 4 A more detailed flowchart illustrating another application scenario of the method provided by the present invention;

[0059] Figure 5 This is a schematic diagram of an embodiment of the processing system for driving skill assessment and learning provided by the present invention;

[0060] Figure 6 for Figure 5 A schematic diagram of the structure of the driving behavior comparison and processing unit;

[0061] Figure 7 for Figure 5A schematic diagram of the structure of the middle evaluation learning unit. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings.

[0063] In order to facilitate understanding of the present application, first, the technical terms involved in the present application are explained as follows:

[0064] The related technologies involved in the present application include intelligent driving system perception system, intelligent driving system planning control system, light-emitting LED glass technology, shift-by-wire, throttle-by-wire, steering-by-wire technology, human-computer interaction system, occupant monitoring system and high-precision map, etc. Among them:

[0065] Intelligent driving system perception system: it is a core component of the intelligent driving system, which perceives the environment and road information around the vehicle through the use of various perception sensors (such as cameras, laser radars, millimeter wave radars and ultrasonic radars, etc.). Through analysis and processing of the perception data, the system can identify and track other vehicles, pedestrians, traffic lights, road signs and obstacles, etc.

[0066] Intelligent driving system planning control system: it is another important component of intelligent driving technology. It is responsible for analyzing the environment and road information obtained by the perception system, and making decisions and generating appropriate driving paths based on these information. The system usually uses decision algorithms, path planning algorithms and vehicle control algorithms, etc. Among them, the decision algorithm is to determine the driving strategy that the vehicle should take according to the current environment and road state, such as acceleration, deceleration, lane change and parking, etc. The path planning algorithm generates the driving path of the vehicle according to the target and the current position. These algorithms take into account factors such as road conditions, traffic flow, road speed limits and traffic rules to ensure the safety and efficiency of the path. The vehicle control algorithm converts the path and decision into specific vehicle actions, such as acceleration, braking, reversing, steering, etc., to control the vehicle to travel according to the predetermined path.

[0067] Light-emitting LED glass technology: light-emitting LED glass is to embed LED light sources into glass to form various patterns. This glass not only has excellent brightness and energy-saving characteristics, but also can block 99% of ultraviolet rays and more than 40% of infrared rays, and has partial sound insulation function. In the present application, the front windshield glass and the glass on the left and right sides of the first row of seats can adopt light-emitting LED glass.

[0068] Shift-by-wire: it is an advanced shift technology that removes the traditional mechanical connection and only uses electronic control to realize the transmission mechanism.

[0069] Drive-by-wire throttle: It is also known as "electronic throttle", mainly composed of throttle pedal, pedal displacement sensor, electronic control unit (ECU), data bus, motor and throttle actuator. When the driver steps on the throttle pedal, the pedal displacement sensor will monitor the change of the pedal position in real time and quickly transmit this information to the electronic control unit (ECU). Then, the ECU will calculate and process according to this information and other system data (such as vehicle speed, vehicle distance, throttle opening, engine speed, etc.) and generate a control signal. This control signal is sent to the servo motor relay through the data bus, and then drives the throttle actuator to work, realizing throttle control.

[0070] Drive-by-wire steering: It cancels the physical connection between the steering wheel and the front wheels in the traditional mechanical steering system, and replaces it with electrical signals to transmit the steering wheel angle. This technology enables the steering machine to receive signals and directly act on the wheels to complete the steering action.

[0071] Human-machine interaction system: It is a system for information exchange and operation between people and cars. It realizes the interaction between people and cars through various input devices (such as keyboard, mouse, touch screen, voice recognition, etc.) and output devices (such as display, audio device, etc.).

[0072] Driver monitoring system: It realizes functions such as action behavior, line of sight, emotion, age recognition, photographing, video recording, etc. by arranging multiple cameras on the inside rearview mirror, A-pillar or steering column.

[0073] High-precision map: Compared with ordinary maps, it has higher requirements in terms of fineness and richness, mainly serving the field of autonomous driving. It is a high-precision map for autonomous driving, which expresses road, lane, roadside traffic signs and ground markings as prior data for serving autonomous driving, and the map accuracy can reach centimeter level. The core keywords of high-precision map are high-precision data and high-precision positioning ability based on visual recognition, point cloud matching, etc. It can provide dynamic and real-time data services for autonomous driving, such as dynamic traffic information, traffic facility information, construction or temporary emergency information, etc. on the basis of accurate positioning.

[0074] As shown in Figure 1 , a main flow diagram of one embodiment of a processing method for driving technology evaluation and learning provided by the application is shown; in combination with Figure 2 , in this embodiment, the method at least includes the following steps:

[0075] Step S10, monitoring the current driving behavior during driving;

[0076] In specific examples, the step S10 further includes:

[0077] starting a driving behavior evaluation function of the vehicle;

[0078] starting a perception sensor of an intelligent driving system, obtaining road environment information around the vehicle and driving behavior information of the driver, and performing fusion processing; the driving behavior information can include: driver acceleration, deceleration, gear shifting, steering information, etc.

[0079] extracting the current driving scene according to the information after the fusion processing; the driving scene can be, for example: a turning scene, a red light stopping scene, a lane changing and overtaking scene, a following a preceding vehicle scene, etc.

[0080] Step S11, according to the automatic driving plan corresponding to the vehicle, and according to the automatic driving plan, the current driving behavior is evaluated in real time, and when it is determined that there is a bad driving behavior, it is identified and reminded;

[0081] In a specific example, the step S11 further comprises:

[0082] generating or retrieving the corresponding automatic driving plan according to the current driving scene, obtaining the correct driving behavior corresponding to the current driving scene; in a specific example, the correct driving behavior can be stored in a scene library for pre-calibration;

[0083] comparing the correct driving behavior with the current driving behavior;

[0084] determining whether there is a bad driving behavior or a risk point according to the comparison result;

[0085] when the judgment result is that there is a bad driving behavior or a risk point, identification and reminding are performed, and the current video is stored.

[0086] Step S12, after driving, the bad driving behavior corresponding to each driving scene and the correct driving behavior in the corresponding automatic driving plan are compared and displayed, or / and simulated driving learning operation is performed.

[0087] In a specific example, the step S12 further comprises:

[0088] detecting that the vehicle stops and is in the parking gear, and scoring the driving behavior this time;

[0089] generating corresponding driving suggestions for the bad driving behavior or risk point existing in the driving process, and comparing and displaying the video of the bad driving behavior corresponding to each driving scene and the video or animation of the corresponding correct driving behavior.

[0090] In another specific example, further comprising:

[0091] The actual driving scene data containing bad driving behaviors or risk points is converted to obtain virtual scene data and stored.

[0092] Meanwhile, the step S12 further includes:

[0093] After the vehicle stops, the simulation driving mode is started, the corresponding light-emitting LED glass of the vehicle is powered on, and the drive-by-wire throttle, drive-by-wire steering and drive-by-wire gear shift are switched to the simulation driving mode and disconnected from the corresponding actuators; it can be understood that the drive-by-wire throttle, drive-by-wire steering and drive-by-wire gear shift are switched to the simulation driving mode, so that the operation of the driver on the vehicle will not send commands (such as acceleration, deceleration, gear shifting, steering) to the corresponding actuators, that is, the operation of the driver on the vehicle will only obtain the corresponding signals, but will not change the vehicle itself, thereby realizing the simulation driving operation;

[0094] The virtual scene data used in the simulation driving is selected, and the virtual scene is played back on the light-emitting LED glass; the virtual scene can be played back on the front windshield, the first row of light-emitting LED glass on the left and right sides of the vehicle to reproduce the corresponding driving scene of the virtual scene;

[0095] In response to the operation of the driver on the drive-by-wire throttle, drive-by-wire steering and drive-by-wire gear shift, the driving state of the current vehicle in the virtual scene is changed, that is, the position, speed and direction of the vehicle in the virtual scene are changed;

[0096] After the simulation driving is exited, the light-emitting LED glass is powered off, and the drive-by-wire gear shift, drive-by-wire steering and drive-by-wire throttle are restored to the initial state and associated with the corresponding actuators.

[0097] Further comprising:

[0098] During the simulation driving, the real-time state of the vehicle in the virtual scene is monitored, and when it is detected that there is a dangerous driving behavior, an operation reminder is given;

[0099] After the simulation driving is ended, the operation score and prompt of the simulation driving are given.

[0100] It can be understood that in the method provided by the application, the bad driving scene can be extracted in real time through automatic driving, and the scene can be recorded and reviewed; the driver's bad driving habits can be summarized more in the vehicle operation scene;

[0101] In the method of the application, the cockpit monitoring technology is combined to collect the dangerous behaviors in the driving process by capturing the actions and eyes of the driver; in the whole process, the vehicle is not controlled, and only the driving behavior of the person is analyzed;

[0102] In the method of the present application, the surrounding environment information is extracted by automatic driving sensors, and the corresponding driving scene is generated in real time. Based on this scene, the driver's behavior and the performance of the actual vehicle are collected and compared with the automatic driving algorithm to confirm the dangerous driving behavior.

[0103] The method of the present application is implemented by comparing the driving behavior of the driver with the correct behavior and effectively guiding the driver to improve the driving technology of the driver. At the same time, the collected road environment data can be converted into a virtual scene, and the driver can be effectively guided by simulating driving in the virtual scene to improve the driving technology of the driver.

[0104] In order to better understand the present application, the following two application scenarios are used to describe the steps of the method involved in the present application.

[0105] I. Application scenario one

[0106] As shown in Figure 3 The method provided by the present application comprises the following steps:

[0107] Step 1: Function start

[0108] When the vehicle is in P gear, the user activates the driving behavior evaluation function involved in the present application by pressing the sound panel button or voice activation.

[0109] Step 2: Intelligent driving perception system starts

[0110] Start the perception sensors of the intelligent driving system, such as ultrasonic radar, millimeter wave radar, camera, laser radar and cabin monitoring camera, etc.

[0111] Step 3: Driver normally drives

[0112] When the driver normally drives, the surrounding environment, road information and driver's behavior are confirmed by the perception sensors, the information of different sensors is fused, and the road information is given to the planning and control module.

[0113] Step 4: Extract the actual scene

[0114] After the vehicle obtains the fused perception information and gives the information to the planning and control module, the corresponding scene will be extracted, such as lane changing, overtaking, following the front vehicle and double-vehicle side-by-side driving on the highway, etc.

[0115] Specifically, the intelligent perception system of the vehicle needs to be started to collect the surrounding environmental information; meanwhile, the planning and control module also needs to plan the path according to the environmental information, without controlling the vehicle; when the actual driving behavior and the correct behavior given by the system are quite different, the system will record the environmental video;

[0116] Step 5: Driving behavior comparison

[0117] When the corresponding scene is exited, the correct driving behavior of the driver in this scene is called at this time, and then the risk points of the driving behavior are confirmed by comparing the actual steering wheel angle, speed, turn signal, throttle and driver's line of sight and other information of the vehicle. And the video will be recorded and saved through the internal and external cameras.

[0118] In this step, the system calls the corresponding scene from the library according to the extracted scene for comparison; incorrect driving behavior is identified and timely reminded through HMI and other means;

[0119] Step 6: Give current driving suggestions

[0120] When driving stops, such as putting the car in P gear, according to the risk points given in the previous step, appropriate suggestions will be given, such as when two cars are driving side by side for too long, they should speed up or slow down to stagger the distance with the car next to them; for example, when overtaking and changing lanes, do not slow down to overtake, which will cause collision risk to the car behind, etc. When playing the stored video on the left, the correct video or animation demonstration will be performed on the right.

[0121] Specifically, each driving risk is recorded according to the time point, and there are corresponding real videos and correct driving scene explanations; all content will be stored and can be played back by clicking;

[0122] Step 7: Overall score and playback

[0123] When giving current driving suggestions, there will also be an overall assessment of driving behavior, which can be scored or graded. All risk scenarios can be marked in a driving cycle, and the time points are distributed according to the timeline. Clicking on these nodes can play back. In this step, you can also list the scenes that are often made mistakes, and give priority. You can also identify the progress or exit of each driving from the last one.

[0124] Step 8: Function exit

[0125] After the user confirms to take over, the function exits.

[0126] It can be understood that when the above function is turned on, the intelligent driving system only records behavior and does not control the vehicle. The present application is different from the current driving evaluation technology. The corresponding scene and driving habit of the driver can be extracted through the existing sensor and planning control module of the intelligent driving system, and the comparison is made, and the suggestion is given, so as to correct the bad driving habit of the driver.

[0127] II. Application scenario two

[0128] As shown in Figure 4 The method provided by the present application comprises the following steps:

[0129] Step 1: function is turned on

[0130] The user turns on the function through the sound panel button or voice activation.

[0131] Step 2: intelligent driving perception system starts

[0132] The perception sensors of the intelligent driving system are started, such as ultrasonic radar, millimeter wave radar, camera, laser radar and cabin monitoring camera.

[0133] Step 3: driver drives

[0134] When the driver drives normally, the system records the surrounding environment and road information from the start of the vehicle.

[0135] Step 4: actual scene data is obtained

[0136] When the system confirms the surrounding environment and road information through the perception system, the information of different sensors is fused, and the fused perception information is given.

[0137] It can be understood that the vehicle needs to have a high-precision map with basic road scene information; at the same time, a perception fusion module is arranged in the vehicle, which can convert the road traffic participants into corresponding images.

[0138] Step 5: convert the actual scene into a virtual scene

[0139] When the fused perception information is received, the target object and road condition are converted into a virtual scene in combination with the data of the high-precision map, and the dangerous moment is marked when dangerous working conditions are encountered, and the occurrence time point is recorded.

[0140] Specifically, the corresponding virtual scene is generated by the conversion module arranged on the vehicle in combination with the high-precision map and the traffic participant information;

[0141] In the virtual scene, the relative positions of the same time traffic participants and vehicles can be recorded; the collision time is determined according to the corresponding position, speed and other information of the self and the traffic participants, so as to obtain the time of dangerous driving behavior; if dangerous driving occurs, the time is time-stamped.

[0142] Step 6: P after parking

[0143] When the driving stops, such as hanging P, this time the scene conversion is ended, the converted scene is stored, and a driving behavior evaluation analysis report is given, and driving suggestions are given for each dangerous point;

[0144] And mark the time point of dangerous driving behavior, cut and save the scene from one minute (time can be set) before the time to one minute (time can be set) after the time, which can be played back.

[0145] Step 7: Start simulation driving

[0146] After selecting the segment to be simulated, click to start simulation driving, at which time the vehicle will collect the data generated by the driver's operation through the line control shift, line control steering and line control throttle, at which time the driver's operation experience is consistent with the actual road driving, but will not produce any action on the real vehicle.

[0147] The glass of the vehicle is a light-emitting LED glass, first control the front glass to automatically close the window on the left and right sides, and the LED glass will be powered on, at which time the glass presents the scene in the vehicle in the simulation scene.

[0148] Due to the data of each traffic participant stored before, the movement mode of each traffic participant in the virtual scene at each time is the same as that in the actual scene, for example, in the actual scene, a small car changes lanes from the left lane to the right lane at a speed of 80km / h, and the virtual scene is also the same performance.

[0149] In the virtual scene, the movement trajectory of the traffic participants is consistent with that in the real scene;

[0150] At this time, the vehicle executes the action in the selected scene, and before the driver takes over the vehicle, the vehicle advances according to the previously recorded trajectory, the traffic participants are also displayed on the glass, and the risk points are reminded when dangerous conditions occur, and the corresponding driving suggestions are given.

[0151] Step 8: Play back the scene according to the driver's operation

[0152] From the perspective of the driver, the driver drives in the virtual scene. The feedback of the vehicle in the virtual scene is also consistent with the operation of the driver, such as the vehicle accelerates when the driver steps on the accelerator; the vehicle decelerates when the driver steps on the brake; and the vehicle turns when the driver turns the steering wheel.

[0153] At the moment of danger, the instrument and the host have corresponding operation reminders, such as the need to accelerate, decelerate, turn on the turn signal, and shift gears at this time. For example, when turning on the turn signal while changing lanes, the rear vehicle is close, and there is a risk of collision, the driver will be reminded to look in the rearview mirror, and then change lanes after the rear vehicle passes.

[0154] Step 9: Output suggestions after completion, can be practiced multiple times

[0155] When the driver completes the operation, the current operation will also be scored to confirm where it is not enough. Multiple practice can be performed to confirm the progress points and points that need to be corrected, and to correct bad driving habits.

[0156] Step 10: Function exit

[0157] When the driver clicks the function exit, the light-emitting LED glass is powered off, and the transparent state is restored; the glass also returns to the initial position. The vehicle's line-controlled shifting, line-controlled steering, and line-controlled throttle are restored to the initial state, and the association between the vehicle and the actuator is restored.

[0158] As shown in Figure 5 , a structural schematic diagram of one embodiment of a processing system for driving skill evaluation learning provided by the present application is shown. In combination with Figure 6 and Figure 7 , in this embodiment, the processing system for driving skill evaluation learning 1 at least includes:

[0159] A driving monitoring unit 10 for monitoring the current driving behavior during driving;

[0160] A driving behavior comparison processing unit 11 for obtaining the current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and identifying and reminding when it is determined that there is a bad driving behavior;

[0161] An evaluation learning unit 12 for comparing and displaying each bad driving behavior and the corresponding correct driving behavior in the automatic driving plan after driving, or / and performing a simulated driving learning operation.

[0162] The driving monitoring unit 10 is configured to start the driving behavior evaluation function of the vehicle, start the perception sensor of the intelligent driving system, obtain the road environment information around the vehicle and the driving behavior information of the driver, and perform fusion processing, and extract the current driving scene according to the information after the fusion processing.

[0163] As shown in the specific example, the driving behavior comparison processing unit 11 further includes: Figure 6

[0164] The correct behavior obtaining unit 110 is configured to automatically generate or call the corresponding automatic driving plan according to the current driving scene, and obtain the correct driving behavior corresponding to the current driving scene in the automatic driving plan;

[0165] The comparison unit 111 is configured to compare the correct driving behavior with the current driving behavior;

[0166] The bad driving behavior determining unit 112 is configured to determine whether there is a bad driving behavior or a risk point according to the comparison result;

[0167] The identification reminding unit 113 is configured to identify and remind when the judgment result is that there is a bad driving behavior or a risk point, and store the current video.

[0168] In one example, the evaluation learning unit 12 is configured to score the driving behavior after the vehicle stops and the parking gear is engaged, and generate a corresponding driving suggestion for the bad driving behavior or the risk point existing in the driving process, and display the video of the bad driving behavior corresponding to each driving scene and the video or animation of the correct driving behavior corresponding to each driving scene.

[0169] In one specific example, the system of the present application further includes:

[0170] The virtual scene conversion unit 13 is configured to convert the actual driving scene data containing the bad driving behavior or the risk point to obtain virtual scene data and store it.

[0171] In another example, the evaluation learning unit 12 further includes:

[0172] The simulation driving mode starting unit 120 is configured to start the simulation driving mode after the vehicle stops, control the power-on of the corresponding light-emitting LED glass of the vehicle, and control the line control throttle, the line control steering, and the line control shift to switch to the simulation driving mode and disconnect from the corresponding actuators;

[0173] The virtual scene playback unit 121 is configured to select the virtual scene data used in the current simulation driving, and play back the virtual scene on the light-emitting LED glass; ​

[0174] The simulation driving response unit 122 is configured to change the driving state corresponding to the current vehicle in the virtual scene in response to the driver's operation of the drive-by-wire throttle, drive-by-wire steering and drive-by-wire gear shifting.

[0175] The simulation driving mode exit unit 123 is configured to control the light-emitting LED glass to be powered off, and control the drive-by-wire gear shifting, drive-by-wire steering and drive-by-wire throttle to return to the initial state after exiting the simulation driving mode.

[0176] For more details, refer to the foregoing description of the simulation driving method and the simulation driving system, which will not be repeated here. Figures 1 to 4

[0177] As still another aspect of the present application, there is provided a computing processing device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method as described above when executing the computer program. Figures 1 to 4 For more details, refer to the foregoing description of the simulation driving method and the simulation driving system, which will not be repeated here. Figures 1 to 4

[0178] As still another aspect of the present application, there is provided a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as described above. Figures 1 to 4 For more details, refer to the foregoing description of the simulation driving method and the simulation driving system, which will not be repeated here. Figures 1 to 4

[0179] The embodiments of the present application have the following advantages:

[0180] The present application provides a processing method, system and device for driving skill evaluation and learning, and a storage medium.

[0181] The present application uses the perception sensors of the intelligent driving system and the control planning function of the intelligent driving system to record the video and data at the current time when encountering the bad driving habits of the driver, and compare the correct driving behaviors of the driving scenes in the scene library to generate a comparison result, tell the driver the risk points and the correct operation mode to avoid risks, thereby effectively guiding the driver to improve the driving skills and the safety of driving.

[0182] ​​​The application collects external environment data of actual driving scene in driving process, and generates corresponding virtual scene; after the vehicle enters the simulation driving mode, the virtual scene can be displayed on the light-emitting LED glass of the vehicle, and the driver is allowed to drive in simulation mode. In the process of simulation driving, the driver's bad habits can be reminded in real time, and corrected in time. The driver can be repeatedly trained for dangerous driving scene, the driver's bad driving habits are corrected through actual training, so as to improve the driving technology of the driver and the safety of driving.

[0183] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, apparatus, or computer program products. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0184] The application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The apparatus for performing the functions specified in one or more flows and / or blocks.

[0185] The above disclosure is only a preferred embodiment of the application, and of course cannot limit the scope of the application, so the equivalent changes made according to the claims of the application are still within the scope of the application.

Claims

1. A processing method for driving skill assessment learning, characterized by, At least comprising the following steps: monitoring the current driving behavior during driving; obtaining the current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and prompting when it is determined that there is bad driving behavior; after the driving ends, comparing and displaying each bad driving behavior and the correct driving behavior in the corresponding automatic driving plan, or / and performing a simulated driving learning operation; wherein, after the driving ends, the simulated driving learning operation further comprises: after the vehicle stops, turning on the simulated driving mode, controlling the corresponding light-emitting LED glass to be powered on, and controlling the drive-by-wire throttle, drive-by-wire steering, and drive-by-wire gear shift to switch to the simulated driving mode and disconnect from the corresponding actuators; selecting the virtual scene data used in this simulated driving, and playing back the virtual scene on the light-emitting LED glass; in response to the driver's operation on the drive-by-wire throttle, drive-by-wire steering, and drive-by-wire gear shift, changing the corresponding driving state of the current vehicle in the virtual scene; after exiting the simulated driving, controlling the light-emitting LED glass to be powered off, and controlling the drive-by-wire gear shift, drive-by-wire steering, and drive-by-wire throttle to return to the initial state and be associated with the corresponding actuators.

2. The method of claim 1, wherein, monitoring the current driving behavior during driving further comprises: starting the driving behavior evaluation function of the vehicle; starting the perception sensors of the intelligent driving system to obtain the road environment information around the vehicle and the driving behavior information of the driver, and performing fusion processing; extracting the current driving scene according to the fusion processed information.

3. The method of claim 2, wherein, obtaining the current automatic driving plan of the vehicle, and performing real-time evaluation on the current driving behavior according to the automatic driving plan, and prompting when it is determined that there is bad driving behavior, further comprises: automatically generating or retrieving the corresponding automatic driving plan according to the current driving scene, obtaining the correct driving behavior corresponding to the current driving scene in the automatic driving plan; comparing the correct driving behavior with the current driving behavior; determining whether there is bad driving behavior or risk point according to the comparison result; when the judgment result is that there is bad driving behavior or risk point, performing identification and reminding, and storing the current video.

4. The method of claim 3, wherein, after the driving ends, comparing and displaying each bad driving behavior and the correct driving behavior in the corresponding automatic driving plan, further comprises: after the vehicle stops and is put into the parking gear, scoring the driving behavior this time; generating corresponding driving suggestions for the bad driving behavior or risk point existing in the driving process, and comparing and displaying the video of the bad driving behavior of each driving scene, and the video or animation of the correct driving behavior.

5. The method according to any one of claims 1 to 4, characterized in that, further comprising: converting the actual driving scene data containing bad driving behavior or risk points to obtain virtual scene data and store it.

6. The method of claim 5, wherein, further comprising: during the simulated driving, monitoring the real-time state of the vehicle in the virtual scene, and prompting when it is detected that there is dangerous driving behavior; after the simulated driving ends, scoring and prompting the simulated driving this time.

7. A processing system for driving skill assessment learning, characterized by, at least comprising: a driving monitoring unit for monitoring the current driving behavior during driving; The driving behavior comparison processing unit is configured to obtain a current automatic driving plan of the vehicle, and to perform real-time evaluation on a current driving behavior according to the automatic driving plan, and to provide a prompt when it is determined that there is an undesirable driving behavior; The evaluation learning unit is configured to display a comparison between each undesirable driving behavior and a correct driving behavior in the corresponding automatic driving plan after driving is completed, or / and to perform a simulated driving learning operation; The evaluation learning unit further includes: The simulated driving mode starting unit is configured to start a simulated driving mode after the vehicle is stopped, to control a corresponding light-emitting LED glass to be powered on, and to control a drive-by-wire throttle, a drive-by-wire steering, and a drive-by-wire gear shift to be switched to the simulated driving mode and disconnected from corresponding actuators; The virtual scene playback unit is configured to select virtual scene data used in the current simulated driving, and to play back the virtual scene on the light-emitting LED glass; The simulated driving response unit is configured to change a driving state of a current vehicle in the virtual scene in response to an operation of the drive-by-wire throttle, the drive-by-wire steering, and the drive-by-wire gear shift by a driver; The simulated driving mode exiting unit is configured to control the light-emitting LED glass to be powered off, and to control the drive-by-wire gear shift, the drive-by-wire steering, and the drive-by-wire throttle to return to an initial state and be associated with corresponding actuators after the simulated driving is exited.

8. The system of claim 7, wherein, The driving behavior comparison processing unit further includes: The correct behavior obtaining unit is configured to automatically generate or call corresponding automatic driving plans to obtain corresponding correct driving behaviors in the automatic driving plans; The comparison unit is configured to compare the correct driving behaviors with a current driving behavior; The undesirable driving behavior determining unit is configured to determine whether there is an undesirable driving behavior or a risk point according to a comparison result; The identification and reminding unit is configured to provide an identification and a prompt when it is determined that there is an undesirable driving behavior or a risk point, and to store a current video.

9. The system of claim 7 or 8, wherein, Further including: The virtual scene conversion unit is configured to convert actual driving scene data containing an undesirable driving behavior or a risk point to obtain virtual scene data and store the virtual scene data.

10. A computing processing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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