Intelligent driving guidance method and device based on multi-dimensional data

Abstract

The invention discloses an intelligent driving guidance method and device based on multi-dimensional data, and the method comprises the steps: obtaining the environment image data of a current environment where a target vehicle is located, obtaining the real-time operation information of the target vehicle, and carrying out the data processing operation of the environment image data, and obtaining an image processing result; determining vehicle driving information of the target vehicle based on the real-time operation information and the image processing result; according to the vehicle driving information, generating a driving influence parameter corresponding to the target vehicle; and according to the driving influence parameter, generating a driving guidance parameter of a driving student corresponding to the target vehicle. Therefore, the linear driving of the trainee can be intelligently guided, the intelligence and efficiency of driving teaching guidance can be improved, and the accuracy and reliability of driving teaching guidance for the trainee can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of driving guidance, and in particular to a driving intelligent guidance method and device based on multi-dimensional data. Background Art

[0002] The third subject test in driving training requires trainees to drive in a straight line in a complex traffic environment and be able to fine-tune the steering wheel in time to keep the vehicle moving straight. However, it is often difficult for trainees to master the fine-tuning skills of the steering wheel when they are just starting to learn to drive, and they are prone to deviating from the lane or making excessive adjustments. In addition, the trainees' sight habits (such as looking too close) and changes in vehicle speed may also affect the vehicle's straight-line driving stability.

[0003] However, the current driving guidance method for driving school students is usually manual guidance by the instructor. This manual guidance method may have problems such as line of sight deviation or operation deviation, thus affecting the effect of guiding students to drive. It can be seen that it is particularly important to provide a new driving guidance method to realize intelligent driving teaching and thus improve the intelligence and accuracy of driving teaching guidance. Summary of the invention

[0004] The present invention provides a driving intelligent guidance method and device based on multi-dimensional data, which can provide intelligent guidance for students' straight-line driving, which is beneficial to improving the intelligence and efficiency of driving teaching guidance, and is also beneficial to improving the accuracy and reliability of driving teaching guidance for students.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a driving intelligent guidance method based on multi-dimensional data, the method comprising:

[0006] Acquire environmental image data of the current environment where the target vehicle is located, and acquire real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain image processing results;

[0007] Determining vehicle driving information of the target vehicle based on the real-time operation information and the image processing result;

[0008] Generating a driving influence parameter corresponding to the target vehicle according to the vehicle driving information;

[0009] According to the driving influencing parameters, driving guidance parameters for the driver trainee corresponding to the target vehicle are generated.

[0010] As an optional implementation manner, in the first aspect of the present invention, before generating the driving influence parameter corresponding to the target vehicle according to the vehicle driving information, the method further includes:

[0011] Acquire driving state information of the driver trainee corresponding to the target vehicle, wherein the driving state information includes operation state information, sight state information, and head detection state information of the driver trainee;

[0012] Determining driving sight line information corresponding to the driving trainee according to the driving state information;

[0013] Wherein, generating the driving influence parameter corresponding to the target vehicle according to the vehicle driving information includes:

[0014] A driving influence parameter corresponding to the target vehicle is generated according to the vehicle driving information and the driving sight line information.

[0015] As an optional implementation manner, in the first aspect of the present invention, determining the driving sight line information corresponding to the driving trainee according to the driving state information includes:

[0016] Analyzing the driving state information based on a predetermined infrared eye tracking model to obtain a line of sight analysis result corresponding to the driver trainee, and analyzing the driving state information based on a predetermined head movement detection model to obtain a head movement analysis result corresponding to the driver trainee;

[0017] A data fusion operation is performed on the sight line analysis result and the head movement analysis result to obtain driving sight line information corresponding to the driving trainee.

[0018] As an optional implementation, in the first aspect of the present invention, performing a data processing operation on the environment image data to obtain an image processing result includes:

[0019] Performing an image processing operation on the environmental image data through a preset processing algorithm to obtain a first processing result, wherein the preset processing algorithm includes one or more of a radar-visual fusion algorithm, an edge detection algorithm, and an object tracking algorithm;

[0020] Performing a feature recognition operation on the environment image data to obtain a second processing result; wherein the feature recognition operation includes one or more of a lane recognition operation, a vehicle recognition operation, and a pedestrian recognition operation;

[0021] An image processing result is generated according to the first processing result and the second processing result.

[0022] As an optional implementation manner, in the first aspect of the present invention, after generating the driving guidance parameters of the driver trainee corresponding to the target vehicle according to the driving influence parameters, the method further includes:

[0023] Acquiring the real-time driving status of the driver trainee, and determining guidance demand information of the driver trainee according to the real-time driving status;

[0024] Determining the instruction method information corresponding to the driver trainee according to the instruction requirement information, and generating driving instruction information for the driver trainee based on the instruction method information and the driving instruction parameters;

[0025] The driving guidance information is used to guide the driver to perform driving guidance operations that match the driving guidance parameters.

[0026] As an optional implementation, in the first aspect of the present invention, before determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result, the method further includes:

[0027] Determine the real-time environment information of the current environment according to the image processing result, wherein the real-time environment information includes one or more of the real-time pedestrian information and the real-time vehicle information of the current environment;

[0028] Based on the real-time environment information, predict a motion trajectory corresponding to the current environment, wherein the motion trajectory includes one or more of a vehicle motion trajectory in the current environment and a pedestrian motion trajectory in the current environment;

[0029] Wherein, determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result includes:

[0030] Based on the real-time operation information and the motion trajectory, vehicle driving information of the target vehicle is determined.

[0031] As an optional implementation manner, in the first aspect of the present invention, generating the driving guidance parameters of the driver trainee corresponding to the target vehicle according to the driving influence parameters includes:

[0032] Determining driving influencing parameters of the target vehicle according to the driving influencing parameters, wherein the driving influencing parameters include an offset influencing parameter and a speed influencing parameter of the target vehicle;

[0033] Inputting the driving influencing parameter into a preset intelligent guidance model to obtain a model output result, wherein the model output result at least includes an influence relationship between the offset of the target vehicle and the driving speed of the target vehicle;

[0034] Based on the model output result, at least one target guidance parameter matching the model output result is determined in a predetermined guidance parameter set, and based on all the target guidance parameters, driving guidance parameters for the driver trainee corresponding to the target vehicle are generated.

[0035] The second aspect of the present invention discloses a driving intelligent guidance device based on multi-dimensional data, the device comprising:

[0036] An acquisition module, used to acquire environmental image data of the current environment where the target vehicle is located, and to acquire real-time operation information of the target vehicle;

[0037] A processing module, used for performing a data processing operation on the environmental image data to obtain an image processing result;

[0038] A determination module, configured to determine the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result;

[0039] A generation module is used to generate driving influence parameters corresponding to the target vehicle according to the vehicle driving information; and to generate driving guidance parameters for a driving student corresponding to the target vehicle according to the driving influence parameters.

[0040] As an optional implementation, in the second aspect of the present invention, the acquisition module is further used to acquire the driving state information of the driver corresponding to the target vehicle before the generation module generates the driving influence parameter corresponding to the target vehicle according to the vehicle driving information, wherein the driving state information includes the operation state information, the sight state information, and the head detection state information of the driver;

[0041] The determination module is further used to determine the driving sight line information corresponding to the driving trainee according to the driving state information;

[0042] The specific manner in which the generating module generates the driving influence parameter corresponding to the target vehicle according to the vehicle driving information includes:

[0043] A driving influence parameter corresponding to the target vehicle is generated according to the vehicle driving information and the driving sight line information.

[0044] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generating module determines the driving sight line information corresponding to the driving trainee according to the driving state information includes:

[0045] Analyzing the driving state information based on a predetermined infrared eye tracking model to obtain a line of sight analysis result corresponding to the driver trainee, and analyzing the driving state information based on a predetermined head movement detection model to obtain a head movement analysis result corresponding to the driver trainee;

[0046] A data fusion operation is performed on the sight line analysis result and the head movement analysis result to obtain driving sight line information corresponding to the driving trainee.

[0047] As an optional implementation, in the second aspect of the present invention, the processing module performs a data processing operation on the environment image data, and a specific manner of obtaining an image processing result includes:

[0048] Performing an image processing operation on the environmental image data through a preset processing algorithm to obtain a first processing result, wherein the preset processing algorithm includes one or more of a radar-visual fusion algorithm, an edge detection algorithm, and an object tracking algorithm;

[0049] Performing a feature recognition operation on the environment image data to obtain a second processing result; wherein the feature recognition operation includes one or more of a lane recognition operation, a vehicle recognition operation, and a pedestrian recognition operation;

[0050] An image processing result is generated according to the first processing result and the second processing result.

[0051] As an optional implementation, in the second aspect of the present invention, the acquisition module is further used to acquire the real-time driving state of the driving student after the generation module generates the driving guidance parameters of the driving student corresponding to the target vehicle according to the driving influence parameters;

[0052] The determination module is further used to determine the guidance requirement information of the driving trainee according to the real-time driving state; and determine the instruction method information corresponding to the driving trainee according to the guidance requirement information;

[0053] The generating module is further used to generate driving guidance information for the driving trainee based on the indication mode information and the driving guidance parameters;

[0054] The driving guidance information is used to guide the driver to perform driving guidance operations that match the driving guidance parameters.

[0055] As an optional implementation, in the second aspect of the present invention, the determination module is further used to determine the real-time environmental information of the current environment according to the image processing result before determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result, wherein the real-time environmental information includes one or more of real-time pedestrian information and real-time vehicle information of the current environment;

[0056] The device also includes:

[0057] A prediction module, configured to predict a motion trajectory corresponding to the current environment based on the real-time environment information, wherein the motion trajectory includes one or more of a vehicle motion trajectory in the current environment and a pedestrian motion trajectory in the current environment;

[0058] The specific manner in which the determination module determines the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result includes:

[0059] Based on the real-time operation information and the motion trajectory, vehicle driving information of the target vehicle is determined.

[0060] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generating module generates the driving guidance parameters of the driving trainee corresponding to the target vehicle according to the driving influence parameters includes:

[0061] Determining driving influencing parameters of the target vehicle according to the driving influencing parameters, wherein the driving influencing parameters include an offset influencing parameter and a speed influencing parameter of the target vehicle;

[0062] Inputting the driving influencing parameter into a preset intelligent guidance model to obtain a model output result, wherein the model output result at least includes an influence relationship between the offset of the target vehicle and the driving speed of the target vehicle;

[0063] Based on the model output result, at least one target guidance parameter matching the model output result is determined in a predetermined guidance parameter set, and based on all the target guidance parameters, driving guidance parameters for the driver trainee corresponding to the target vehicle are generated.

[0064] The third aspect of the present invention discloses another intelligent driving guidance device based on multi-dimensional data, the device comprising:

[0065] A memory storing executable program code;

[0066] a processor coupled to the memory;

[0067] The processor calls the executable program code stored in the memory to execute the intelligent driving guidance method based on multi-dimensional data disclosed in the first aspect of the present invention.

[0068] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the driving intelligent guidance method based on multi-dimensional data disclosed in the first aspect of the present invention.

[0069] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0070] In the embodiment of the present invention, the environmental image data of the current environment where the target vehicle is located is obtained, and the real-time operation information of the target vehicle is obtained, and the data processing operation is performed on the environmental image data to obtain the image processing result; based on the real-time operation information and the image processing result, the vehicle driving information of the target vehicle is determined; according to the vehicle driving information, the driving influence parameters corresponding to the target vehicle are generated; according to the driving influence parameters, the driving guidance parameters of the driving trainee corresponding to the target vehicle are generated. It can be seen that the implementation of the present invention can provide intelligent guidance for the trainee's straight-line driving, which is conducive to improving the intelligence and efficiency of driving teaching guidance, and is also conducive to the accuracy and reliability of driving teaching guidance for trainees. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0072] Figure 1 It is a flowchart of a driving intelligent guidance method based on multi-dimensional data disclosed in an embodiment of the present invention;

[0073] Figure 2 is a flow chart of another driving intelligent guidance method based on multi-dimensional data disclosed in an embodiment of the present invention;

[0074] Figure 3 is a structural schematic diagram of a driving intelligent guidance device based on multi-dimensional data disclosed in an embodiment of the present invention;

[0075] Figure 4 is a structural schematic diagram of another driving intelligent guidance device based on multi-dimensional data disclosed in an embodiment of the present invention;

[0076] Figure 5 It is a structural schematic diagram of another driving intelligent guidance device based on multi-dimensional data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0079] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0080] The present invention discloses a driving intelligent guidance method and device based on multi-dimensional data, which can provide intelligent guidance for students' straight-line driving, which is beneficial to improving the intelligence and efficiency of driving teaching guidance, and is also beneficial to improving the accuracy and reliability of driving teaching guidance for students. The following are detailed descriptions.

[0081] Embodiment 1

[0082] See also Figure 1 , Figure 1 : is a flowchart of a driving intelligent guidance method based on multi-dimensional data disclosed in an embodiment of the present invention. Figure 1 The described driving intelligent guidance method based on multi-dimensional data can be applied to a driving intelligent guidance device based on multi-dimensional data, wherein the driving intelligent guidance device based on multi-dimensional data can be integrated in a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the driving intelligent guidance method based on multi-dimensional data may include the following operations:

[0083] 101. Obtain environmental image data of a current environment where a target vehicle is located, obtain real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain an image processing result.

[0084] In an embodiment of the present invention, optionally, the target vehicle includes a body sensor module, wherein the body sensor module is used to obtain environmental information around the vehicle, and the body sensor module may include at least a front camera, a side camera, and a radar sensor, wherein the front camera is installed at the front of the vehicle to detect lane lines and traffic participants in front; the side cameras are installed on both sides of the vehicle to detect traffic participants on the side and changes in lane lines; the radar sensors are installed at the front and both sides of the vehicle to detect objects and traffic participants that are close to the vehicle.

[0085] In an embodiment of the present invention, optionally, the current environment in which the target vehicle is located may include the current environment in which the target vehicle is currently located; the real-time operation information of the target vehicle may include one or more of the steering wheel rotation angle information, steering wheel rotation direction information, and vehicle driving speed information corresponding to the target vehicle.

[0086] 102. Determine the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result.

[0087] In the embodiment of the present invention, optionally, the determining of the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result may include:

[0088] Based on the real-time operation information and the image processing result, determining the driving operation information of the target vehicle in the current environment, and determining the vehicle driving information of the target vehicle according to the driving operation information;

[0089] The vehicle driving information of the target vehicle may include one or more of the lane driving information, angle driving information, and direction driving information of the target vehicle.

[0090] 103. Generate driving influence parameters corresponding to the target vehicle according to the vehicle driving information.

[0091] In an embodiment of the present invention, optionally, the driving influencing parameters corresponding to the target vehicle include one or more of the influencing parameters of the driving speed of the target vehicle on the driving direction, the influencing parameters of the steering wheel angle on the driving direction, and the influencing parameters of the steering wheel force on the driving direction.

[0092] 104. Generate driving guidance parameters for the driver trainee corresponding to the target vehicle according to the driving influence parameters.

[0093] In the embodiment of the present invention, optionally, the driving guidance parameters for the driving trainee include one or more of a steering wheel angle guidance parameter, a steering wheel force guidance parameter, and a driving speed guidance parameter for the driving trainee.

[0094] It can be seen that the implementation Figure 1 The described driving intelligent guidance method based on multi-dimensional data can obtain environmental image data of the current environment where the target vehicle is located and obtain real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain image processing results, determine the vehicle driving information of the target vehicle according to the real-time operation information and the image processing results, and generate driving influence parameters corresponding to the target vehicle, and then generate driving guidance parameters for the driving trainee corresponding to the target vehicle according to the driving influence parameters. By acquiring vehicle environmental image data and operation information in real time, the surrounding environment can be more accurately perceived, and potential dangers and obstacles can be identified in time, thereby improving driving safety. In combination with real-time operation information and image processing results, the driving information of the vehicle can be more accurately determined, which is conducive to improving the accuracy and reliability of generating driving guidance parameters. In addition, the driving influence parameters and driving guidance parameters generated according to the vehicle driving information can more intelligently adjust the driving strategy and optimize the driving experience, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for trainees.

[0095] Embodiment 2

[0096] See also Figure 2 , Figure 2 is a flow chart of another driving intelligent guidance method based on multi-dimensional data disclosed in an embodiment of the present invention. Figure 2 The described driving intelligent guidance method based on multi-dimensional data can be applied to a driving intelligent guidance device based on multi-dimensional data, wherein the driving intelligent guidance device based on multi-dimensional data can be integrated in a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the driving intelligent guidance method based on multi-dimensional data may include the following operations:

[0097] 201. Obtain environmental image data of a current environment where a target vehicle is located, obtain real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain an image processing result.

[0098] 202. Determine vehicle driving information of the target vehicle based on the real-time operation information and the image processing result.

[0099] 203. Obtain driving status information of the driver trainee corresponding to the target vehicle.

[0100] In the embodiment of the present invention, the driving status information includes the operation status information, the sight status information, and the head detection status information of the driver trainee.

[0101] In the embodiment of the present invention, optionally, the driving status information of the driver trainee corresponding to the target vehicle may be obtained in real time, or may be obtained when it is detected that the driver trainee needs driving guidance, which is not specifically limited in the embodiment of the present invention.

[0102] In an embodiment of the present invention, optionally, the operation status information may include operation information of the driver trainee operating the target vehicle; the line of sight status information may include line of sight range information of the driver trainee while driving the target vehicle; the head detection status information may include one or more of head rotation angle information, head rotation direction information, and head rotation number information of the driver trainee while driving the target vehicle.

[0103] 204. Determine driving sight line information corresponding to the driver trainee according to the driving state information.

[0104] In the embodiment of the present invention, optionally, the driving vision information corresponding to the driving trainee may include one or more of the driving vision range information and the driving vision angle information of the driving trainee.

[0105] 205. Generate driving influence parameters corresponding to the target vehicle according to the vehicle driving information and the driving sight line information.

[0106] In the embodiment of the present invention, optionally, generating the driving influence parameter corresponding to the target vehicle according to the vehicle driving information and the driving sight line information may include:

[0107] Extracting first key information from vehicle driving information and extracting second key information from driving sight information, and determining driving key information of the target vehicle based on the first key information and the second key information; wherein the driving key information includes one or more of safety key information, comfort information, and efficiency information, the safety key information includes obstacle detection and traffic sign recognition information, safety distance information, and safety speed information; the comfort information includes stability information and vibration information; and the efficiency information includes fuel efficiency information and energy consumption information;

[0108] According to the key driving information, the driving impact parameters corresponding to the target vehicle are generated.

[0109] 206. Generate driving guidance parameters for the driver trainee corresponding to the target vehicle according to the driving influence parameters.

[0110] In the embodiment of the present invention, for the detailed description of step 201-step 202 and step 206, please refer to the other descriptions of step 101-step 102 and step 104 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0111] It can be seen that the implementation Figure 2 The described driving intelligent guidance method based on multi-dimensional data can obtain the driving state information of the driving trainee corresponding to the target vehicle and determine the driving sight line information corresponding to the driving trainee, determine the driving sight line information corresponding to the driving trainee according to the driving state information, and generate the driving influence parameter corresponding to the target vehicle according to the vehicle driving information and the driving sight line information. It can provide more personalized driving training and guidance by analyzing the operation state, sight line state and head detection state information of the driving trainee, which is conducive to improving the accuracy and reliability of generating driving guidance parameters for each driving trainee, and by analyzing the operation state information of the driving trainee, it can more accurately evaluate its driving skill level, including operation accuracy, reaction speed, etc., which is conducive to improving driving skills in a targeted manner, and can also be conducive to improving the driving safety of the driving trainee during the driving process. By real-time feedback of the driving state of the driving trainee and providing personalized driving guidance, the driving experience can be improved, and by analyzing the driving state of the driving trainee, it can better adapt to different driving environments and conditions and improve the adaptability of driving, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for students.

[0112] In an optional embodiment, determining the driving sight line information corresponding to the driving trainee according to the driving state information includes:

[0113] Analyzing the driving state information based on a predetermined infrared eye tracking model to obtain a line of sight analysis result corresponding to the driver trainee, and analyzing the driving state information based on a predetermined head movement detection model to obtain a head movement analysis result corresponding to the driver trainee;

[0114] A data fusion operation is performed on the sight line analysis results and the head movement analysis results to obtain the driving sight line information corresponding to the driving trainee.

[0115] In this optional embodiment, the optional, predetermined infrared eye tracking model is a non-invasive eye tracking technology that detects and tracks the pupil position by illuminating the eye with an infrared light source and recording the reflected image. It can illuminate the pupil using an infrared or near-infrared light source to produce a corneal reflection, and then record the reflection through an infrared camera to define the pupil center, infer eye rotation, and determine the gaze direction.

[0116] In this optional embodiment, optionally, the predetermined head movement detection model is to obtain the posture angles of the head through the facial image, including pitch, yaw and roll, and find the model transformation that best suits the observed head movement to estimate the head posture.

[0117] In this optional embodiment, optionally, performing a data fusion operation on the sight line analysis result and the head movement analysis result to obtain the driving sight line information corresponding to the driving trainee may include:

[0118] Extracting first feature information from the gaze analysis result, and extracting second feature information from the head movement analysis result, and performing feature processing operations on the first feature information and the second feature information to obtain key feature information, wherein the feature processing operations include one or more of feature classification operations, feature aggregation operations, and feature synthesis operations;

[0119] Perform data fusion operations on all key feature information to obtain the corresponding driving sight line information of the driving trainee.

[0120] It can be seen that the implementation of this optional embodiment can analyze the driving state information based on the infrared eye tracking model to obtain the corresponding line of sight analysis results, and analyze the driving state information based on the head movement detection model to obtain the corresponding head movement analysis results, and perform data fusion operations on the line of sight analysis results and the head movement analysis results to obtain the driving line of sight information corresponding to the driver. The infrared eye tracking model and the head movement detection model can be combined to more accurately capture the line of sight direction and head posture of the driver, thereby providing more accurate driving line of sight information. According to the specific line of sight and head movement data of the driver, the coach can provide more personalized feedback and guidance, which is conducive to improving the accuracy and reliability of the subsequent generation of driving guidance parameters for the driver, and through automated data fusion, the influence of human factors on the line of sight and head movement analysis results is reduced, and the reliability of the analysis results is improved. It can further improve the accuracy and reliability of the driving guidance parameters generated for the driver, which is conducive to further improving the accuracy and reliability of the generated driving guidance parameters and the intelligence and efficiency of the generated driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for the students.

[0121] In another optional embodiment, performing a data processing operation on the environment image data to obtain an image processing result includes:

[0122] Performing an image processing operation on the environmental image data through a preset processing algorithm to obtain a first processing result, wherein the preset processing algorithm includes one or more of a radar-visual fusion algorithm, an edge detection algorithm, and an object tracking algorithm;

[0123] Performing a feature recognition operation on the environment image data to obtain a second processing result; wherein the feature recognition operation includes one or more of a lane recognition operation, a vehicle recognition operation, and a pedestrian recognition operation;

[0124] An image processing result is generated according to the first processing result and the second processing result.

[0125] In this optional embodiment, optionally, the radar-vision fusion algorithm refers to a technology that combines the data of radar (Radar) and vision (Camera) sensors to improve the accuracy and robustness of target detection and tracking. This algorithm usually includes image target detection, radar target detection, radar and camera calibration, and data fusion processing. The fusion effect can be used in various application scenarios such as traffic event analysis; the edge detection algorithm is a basic problem in image processing and computer vision, and its purpose is to identify points in the image with obvious brightness changes. These points usually reflect important events and changes in the image; the object tracking algorithm is used to track the movement of target objects in images or videos, and predict their position and other related information.

[0126] In this optional embodiment, optionally, a feature recognition operation is performed on the environmental image data, and the second processing result can be obtained by performing feature recognition through one or more of an edge detection algorithm, a deep learning model, and a local feature matching algorithm; wherein the edge detection algorithm may include one or more of a Sobel operator and a Canny algorithm; the deep learning model may be implemented through one or more of a convolutional neural network (CNN), FastR-CNN, Faster R-CNN, and YOLO; the local feature matching algorithm may include one or more of algorithms such as SIFT, SURF, and ORB.

[0127] In this optional embodiment, optionally, the first processing result may include information corresponding to the current environment, such as one or more of environmental location information and environmental obstacle information; the second processing result may include one or more of identified lane lines and traffic participants (such as pedestrians, vehicles, bicycles, etc.).

[0128] In this optional embodiment, optionally, generating the image processing result according to the first processing result and the second processing result may include:

[0129] According to the first processing result and the second processing result, an image association relationship between the current environment and the driving condition of the target vehicle is determined, and an image processing result is generated based on the image association relationship.

[0130] It can be seen that the implementation of this optional embodiment can perform an image processing operation on the environmental image data through a processing algorithm to obtain a first processing result, and perform a feature recognition operation on the environmental image data to obtain a second processing result, and generate an image processing result based on the first processing result and the second processing result. By using a radar-vision fusion algorithm and combining radar and visual data, the accuracy of perception of the current environment can be improved, and edge detection and object tracking algorithms can help the system better understand the environmental layout and the motion state of dynamic objects. Feature recognition operations including lane, vehicle and pedestrian recognition can improve the system's ability to recognize key traffic elements. By comprehensively using a variety of image processing and feature recognition technologies, the system's ability to understand the environment can be improved, safety and decision-making quality can be enhanced, and the accuracy and reliability of driving guidance parameters generated for driving trainees can be further improved, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters as well as the intelligence and efficiency of generating driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for trainees.

[0131] In yet another optional embodiment, after generating the driving instruction parameters of the driver trainee corresponding to the target vehicle according to the driving influence parameters, the method further includes:

[0132] Obtain the real-time driving status of the driving trainee, and determine the guidance demand information of the driving trainee based on the real-time driving status;

[0133] Determine the instruction mode information corresponding to the driver trainee according to the instruction demand information, and generate driving instruction information for the driver trainee based on the instruction mode information and the driving instruction parameters;

[0134] The driving guidance information is used to guide the driver to perform driving guidance operations that match the driving guidance parameters.

[0135] In this optional embodiment, optionally, the real-time driving status information of the driving trainee includes one or more of the driving trainee's driving fatigue status information, driving attention information, driving behavior information, and driving physiological status information; wherein the driving behavior information may include making and receiving phone calls and smoking behavior, and the driving physiological status information may include the driving trainee's heart rate information.

[0136] In this optional embodiment, the guidance requirement information may optionally include one or more of voice guidance requirements, image guidance requirements, and video guidance requirements.

[0137] In this optional embodiment, optionally, the instruction method information corresponding to the driving trainee matches the guidance requirement information.

[0138] In this optional embodiment, the driving guidance information may optionally include one or more of driving voice guidance information, driving image guidance information, driving text guidance information, and driving video guidance information.

[0139] In this optional embodiment, optionally, for example, when it is detected that the vehicle deviates from the lane, the trainee is reminded to make adjustments through voice prompts and display screens; the trainee's steering wheel operation is analyzed, and situations where the steering wheel is not dared to move or the adjustment range is too large due to nervousness are identified, and real-time adjustment suggestions are provided; when it is detected that the trainee's line of sight is too close, the trainee is reminded to adjust the line of sight appropriately and maintain correct line of sight habits; when it is detected that the speed is too fast or too slow, appropriate speed adjustment suggestions are provided to ensure that the vehicle maintains stable straight driving within an appropriate speed range; when traffic participants are detected in front or on the side, the trainee is reminded to slow down or avoid them.

[0140] It can be seen that the implementation of this optional embodiment can obtain the real-time driving status of the driving trainee and determine the guidance demand information of the driving trainee, determine the instruction method information corresponding to the driving trainee according to the instruction demand information, and generate the driving guidance information of the driving trainee based on the instruction method information and the driving guidance parameters. It can provide personalized driving guidance according to the real-time driving status and guidance demand information of the driving trainee, so that the teaching is more in line with the actual ability and learning progress of the trainee. By real-time monitoring of the driving status, timely adjustment of the teaching content and methods, help the trainee master driving skills faster and improve learning efficiency. Through operational guidance that matches the driving guidance parameters, help the trainee better understand and practice safe driving skills. In addition, by accurately identifying the guidance needs of the trainees, it is possible to more effectively allocate teaching resources and improve the utilization efficiency of teaching resources. By real-time monitoring of the status of the driving trainees and providing personalized guidance, not only the efficiency and quality of driving teaching are improved, but also the safety and learning experience of the trainees are enhanced, which can be conducive to further improving the driving guidance effect and learning effect of the driving trainees.

[0141] In yet another optional embodiment, before determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result, the method further includes:

[0142] Determine the real-time environment information of the current environment according to the image processing result, wherein the real-time environment information includes one or more of the real-time pedestrian information and the real-time vehicle information of the current environment;

[0143] Based on the real-time environment information, predict the motion trajectory corresponding to the current environment, wherein the motion trajectory includes one or more of the vehicle motion trajectory and the pedestrian motion trajectory in the current environment;

[0144] Wherein, based on the real-time operation information and the image processing result, the vehicle driving information of the target vehicle is determined, including:

[0145] Based on the real-time operation information and motion trajectory, the vehicle driving information of the target vehicle is determined.

[0146] In this optional embodiment, optionally, the above-mentioned predicting the motion trajectory corresponding to the current environment based on the real-time environment information may include:

[0147] Acquire historical environment information of the current environment within a preset historical time period, and perform information fitting operations on the historical environment information and the real-time environment information to obtain information fitting results, wherein the information fitting results include information comparison results between the historical environment information and the real-time environment information;

[0148] The information fitting result is input into a predetermined trajectory prediction model to obtain the motion trajectory corresponding to the current environment.

[0149] In this optional embodiment, optionally, the above-mentioned determining the vehicle driving information of the target vehicle based on the real-time operation information and the motion trajectory may include:

[0150] generating real-time driving impact information based on the real-time operation information and the motion trajectory, and generating vehicle control adjustment parameters of the target vehicle according to the real-time driving impact information;

[0151] determining vehicle driving information of the target vehicle according to a vehicle control adjustment parameter of the target vehicle;

[0152] Among them, the vehicle control adjustment parameters may include one or more of vehicle speed control adjustment parameters, vehicle driving direction adjustment parameters, and vehicle driving angle adjustment parameters.

[0153] It can be seen that the implementation of this optional embodiment can determine the real-time environmental information of the current environment according to the image processing results, predict the motion trajectory corresponding to the current environment based on the real-time environmental information, and determine the vehicle driving information of the target vehicle based on the real-time operation information and the motion trajectory. By real-time monitoring and predicting the motion trajectories of vehicles and pedestrians in the environment, potential collision risks can be identified in advance, thereby improving driving safety. By analyzing the image processing results to determine the real-time environmental information, the system can more accurately perceive the surrounding environment, including the position, speed and direction of pedestrians and vehicles. Based on the real-time environmental information and the predicted motion trajectory, a more optimized path planning can be provided for the vehicle, and at the same time, better driving guidance parameters can be provided for the driving trainee. In combination with the real-time operation information and the predicted motion trajectory, the accuracy of the vehicle driving information can be improved, and the accuracy and reliability of the driving guidance of the driving trainee can be improved. By real-time monitoring and predicting the environmental information, combined with the vehicle operation information, the accuracy and reliability of the vehicle driving information can be significantly improved, thereby improving driving safety and efficiency, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and is also conducive to improving the accuracy and reliability of driving teaching guidance for trainees, and can also be conducive to improving the learning effect of students in driving learning.

[0154] In yet another optional embodiment, generating driving guidance parameters for a driver trainee corresponding to a target vehicle according to the driving influence parameters includes:

[0155] Determining driving influencing parameters of the target vehicle according to the driving influencing parameters, wherein the driving influencing parameters include an offset influencing parameter and a speed influencing parameter of the target vehicle;

[0156] Inputting the driving influencing parameters into a preset intelligent guidance model to obtain a model output result, wherein the model output result at least includes an influence relationship between an offset of the target vehicle and a driving speed of the target vehicle;

[0157] Based on the model output result, at least one target guidance parameter matching the model output result is determined in a predetermined guidance parameter set, and based on all the target guidance parameters, a driving guidance parameter for the driver trainee corresponding to the target vehicle is generated.

[0158] In this optional embodiment, for example, the effect of speed on straight-line driving is evaluated from the perspective of physics, sensor data, algorithm design, and vehicle control system, combining experimental data with mathematical modeling. The specific analysis elements are: the relationship between lateral offset and speed, and the specific numerical quantification of the effect of steering wheel control on the straight-line driving of the vehicle at different vehicle speeds. Finally, clear and real-time feedback is given to the trainees.

[0159] In this optional embodiment, optionally, the determining, based on the model output result, at least one target guidance parameter matching the model output result from a predetermined guidance parameter set may include:

[0160] Determine the target keyword in the model output result, and calculate the matching degree between each guidance parameter included in the predetermined guidance parameter set and the target keyword, and based on all the matching degrees, select the target guidance parameter whose matching degree with the target keyword is greater than or equal to a preset matching degree threshold from all at least the guidance parameters.

[0161] In this optional embodiment, optionally, the driving guidance parameters of the driver student corresponding to the target vehicle may at least include all target guidance parameters.

[0162] It can be seen that the implementation of this optional embodiment can determine the driving influencing parameters of the target vehicle according to the driving influencing parameters, input the driving influencing parameters into the intelligent guidance model to obtain the model output results, and determine the matching target guidance parameters in the guidance parameter set based on the model output results, and generate the driving guidance parameters of the driving trainee corresponding to the target vehicle based on all the target guidance parameters. The guidance parameters that can be customized according to the driving influencing parameters can provide more personalized guidance for driving trainees, improve learning efficiency and driving skills, and prevent potential driving errors such as lane departure and speeding by analyzing the offset influencing parameters and the speed influencing parameters, thereby improving driving safety. The model output results can reflect the difference between the driving behavior and the expected goal in real time, and provide students with Instant feedback helps them adjust their driving behavior in a timely manner, which is conducive to improving the intelligence and efficiency of driving guidance for driving trainees, as well as improving the effectiveness and learning effect of driving trainees learning driving behavior. By generating guidance parameters that match driving influencing parameters through an intelligent guidance model, not only the efficiency and quality of driving teaching are improved, but also the safety and learning experience of trainees are enhanced. It can also significantly improve the accuracy and reliability of vehicle driving information, thereby improving driving safety and efficiency, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters, as well as the intelligence and efficiency of generating driving guidance parameters. It is also conducive to improving the accuracy and reliability of driving teaching guidance for trainees, and can also help improve the learning effect of trainees in driving learning.

[0163] Embodiment 3

[0164] See also Figure 3 , Figure 3 Schematic diagram of a driving intelligent guidance device based on multi-dimensional data disclosed in an embodiment of the present invention. Figure 3 As shown, the driving intelligent guidance device based on multi-dimensional data may include:

[0165] The acquisition module 301 is used to acquire the environmental image data of the current environment where the target vehicle is located, and to acquire the real-time operation information of the target vehicle;

[0166] The processing module 302 is used to perform data processing operations on the environmental image data to obtain an image processing result;

[0167] A determination module 303, for determining vehicle driving information of a target vehicle based on the real-time operation information and the image processing result;

[0168] The generating module 304 is used to generate driving influence parameters corresponding to the target vehicle according to the vehicle driving information; and to generate driving guidance parameters of the driving trainee corresponding to the target vehicle according to the driving influence parameters.

[0169] It can be seen that the implementation Figure 3 The described device can obtain environmental image data of the current environment where the target vehicle is located and obtain real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain image processing results, determine the vehicle driving information of the target vehicle according to the real-time operation information and the image processing results, and generate driving influence parameters corresponding to the target vehicle, and then generate driving guidance parameters for the driving trainee corresponding to the target vehicle according to the driving influence parameters. By acquiring vehicle environmental image data and operation information in real time, the surrounding environment can be more accurately perceived, and potential dangers and obstacles can be identified in time, thereby improving driving safety. In combination with real-time operation information and image processing results, the driving information of the vehicle can be more accurately determined, which is conducive to improving the accuracy and reliability of generating driving guidance parameters. In addition, the driving influence parameters and driving guidance parameters generated according to the vehicle driving information can more intelligently adjust the driving strategy and optimize the driving experience, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for trainees.

[0170] In an optional embodiment, if Figure 3 As shown, the acquisition module 301 is further used to acquire the driving state information of the driver corresponding to the target vehicle before the generation module 304 generates the driving influence parameters corresponding to the target vehicle according to the vehicle driving information, wherein the driving state information includes the operation state information, the sight state information, and the head detection state information of the driver;

[0171] The determination module 303 is further used to determine the driving sight line information corresponding to the driving trainee according to the driving state information;

[0172] The specific manner in which the generating module 304 generates the driving influence parameters corresponding to the target vehicle according to the vehicle driving information includes:

[0173] According to the vehicle driving information and driving sight information, the driving influence parameters corresponding to the target vehicle are generated.

[0174] It can be seen that the implementation Figure 3 The described device can obtain the driving state information of the driver corresponding to the target vehicle and determine the driving sight line information corresponding to the driver, determine the driving sight line information corresponding to the driver according to the driving state information, generate the driving influence parameter corresponding to the target vehicle according to the vehicle driving information and the driving sight line information, and can provide more personalized driving training and guidance by analyzing the operation state, sight line state and head detection state information of the driver, which is conducive to improving the accuracy and reliability of generating driving guidance parameters for each driver, and by analyzing the operation state information of the driver, it can more accurately evaluate the driving skill level of the driver, including the accuracy of operation, reaction speed, etc., which is conducive to improving the driving skills in a targeted manner, and can also be conducive to improving the driving safety of the driver during the driving process, and can improve the driving experience by providing real-time feedback on the driving state of the driver and providing personalized driving guidance, and by analyzing the driving state of the driver, it can better adapt to different driving environments and conditions, improve the adaptability of driving, and then help to further improve the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and also help to improve the accuracy and reliability of driving teaching guidance for students.

[0175] In another optional embodiment, Figure 3 As shown, the specific manner in which the generating module 304 determines the driving sight information corresponding to the driving trainee according to the driving state information includes:

[0176] Analyzing the driving state information based on a predetermined infrared eye tracking model to obtain a line of sight analysis result corresponding to the driver trainee, and analyzing the driving state information based on a predetermined head movement detection model to obtain a head movement analysis result corresponding to the driver trainee;

[0177] A data fusion operation is performed on the sight line analysis results and the head movement analysis results to obtain the driving sight line information corresponding to the driving trainee.

[0178] It can be seen that the implementation Figure 3The described device can analyze driving state information based on an infrared eye tracking model to obtain corresponding sight line analysis results, and can analyze driving state information based on a head movement detection model to obtain corresponding head movement analysis results, and perform data fusion operations on the sight line analysis results and the head movement analysis results to obtain driving sight line information corresponding to the driver. The infrared eye tracking model and the head movement detection model can be combined to more accurately capture the sight line direction and head posture of the driver, thereby providing more accurate driving sight line information. According to the specific sight line and head movement data of the driver, the coach can provide more personalized feedback and guidance, which is conducive to improving the accuracy and reliability of the subsequent generation of driving guidance parameters for the driver, and reduces the influence of human factors on the sight line and head movement analysis results through automated data fusion, improves the reliability of the analysis results, can further improve the accuracy and reliability of the generation of driving guidance parameters for the driver, and thus is conducive to further improving the accuracy and reliability of the generation of driving guidance parameters as well as the intelligence and efficiency of the generation of driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for the students.

[0179] In yet another optional embodiment, Figure 3 As shown, the processing module 302 performs data processing operations on the environmental image data, and the specific manner of obtaining the image processing result includes:

[0180] Performing an image processing operation on the environmental image data through a preset processing algorithm to obtain a first processing result, wherein the preset processing algorithm includes one or more of a radar-visual fusion algorithm, an edge detection algorithm, and an object tracking algorithm;

[0181] Performing a feature recognition operation on the environment image data to obtain a second processing result; wherein the feature recognition operation includes one or more of a lane recognition operation, a vehicle recognition operation, and a pedestrian recognition operation;

[0182] An image processing result is generated according to the first processing result and the second processing result.

[0183] It can be seen that the implementation Figure 3The described device can perform an image processing operation on environmental image data through a processing algorithm to obtain a first processing result, and perform a feature recognition operation on the environmental image data to obtain a second processing result, and generate an image processing result based on the first processing result and the second processing result. It can improve the perception accuracy of the current environment by using a radar-vision fusion algorithm in combination with radar and visual data, and can help the system better understand the environmental layout and the motion state of dynamic objects through edge detection and object tracking algorithms. Through feature recognition operations including lane, vehicle and pedestrian recognition, the system's ability to recognize key traffic elements can be improved. By comprehensively using multiple image processing and feature recognition technologies, the system's ability to understand the environment can be improved, safety and decision-making quality can be enhanced, and the accuracy and reliability of driving guidance parameters generated for driving trainees can be further improved, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters as well as the intelligence and efficiency of generating driving guidance parameters, and is also conducive to the accuracy and reliability of driving teaching guidance for trainees.

[0184] In yet another optional embodiment, Figure 3 As shown, the acquisition module 301 is further used to acquire the real-time driving state of the driving trainee after the generation module 304 generates the driving guidance parameters of the driving trainee corresponding to the target vehicle according to the driving influence parameters;

[0185] The determination module 303 is further used to determine the guidance requirement information of the driving trainee according to the real-time driving state; and determine the instruction method information corresponding to the driving trainee according to the guidance requirement information;

[0186] The generating module 304 is further used to generate driving guidance information for the driving trainee based on the indication mode information and the driving guidance parameters;

[0187] The driving guidance information is used to guide the driver to perform driving guidance operations that match the driving guidance parameters.

[0188] It can be seen that the implementation Figure 3The described device can obtain the real-time driving status of the driving trainee and determine the guidance demand information of the driving trainee, determine the indication method information corresponding to the driving trainee according to the guidance demand information, and generate the driving guidance information of the driving trainee based on the indication method information and the driving guidance parameters, and can provide personalized driving guidance according to the real-time driving status and guidance demand information of the driving trainee, so that the teaching is more in line with the actual ability and learning progress of the trainee, and can timely adjust the teaching content and methods by real-time monitoring of the driving status to help the trainee master driving skills faster and improve learning efficiency, and help the trainee better understand and practice safe driving skills through operation guidance matching the driving guidance parameters, and, by accurately identifying the guidance needs of the trainee, can more effectively allocate teaching resources and improve the utilization efficiency of teaching resources, and can provide personalized guidance by real-time monitoring of the status of the driving trainee, which not only improves the efficiency and quality of driving teaching, but also enhances the safety and learning experience of the trainee, and can be conducive to further improving the driving guidance effect and learning effect of the driving trainee.

[0189] In yet another optional embodiment, Figure 4 As shown, the determination module 303 is further used to determine the real-time environment information of the current environment according to the image processing result before determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result, wherein the real-time environment information includes one or more of the real-time pedestrian information and the real-time vehicle information of the current environment;

[0190] The device also includes:

[0191] A prediction module 305 is used to predict a motion trajectory corresponding to the current environment based on the real-time environment information, wherein the motion trajectory includes one or more of a vehicle motion trajectory in the current environment and a pedestrian motion trajectory in the current environment;

[0192] The specific manner in which the determination module 303 determines the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result includes:

[0193] Based on the real-time operation information and motion trajectory, the vehicle driving information of the target vehicle is determined.

[0194] It can be seen that the implementation Figure 4The described device can determine the real-time environmental information of the current environment according to the image processing result, predict the motion trajectory corresponding to the current environment based on the real-time environmental information, and determine the vehicle driving information of the target vehicle based on the real-time operation information and the motion trajectory. By real-time monitoring and predicting the motion trajectory of vehicles and pedestrians in the environment, potential collision risks can be identified in advance, thereby improving driving safety. By analyzing the image processing results to determine the real-time environmental information, the system can more accurately perceive the surrounding environment, including the position, speed and direction of pedestrians and vehicles. Based on the real-time environmental information and the predicted motion trajectory, more optimized path planning can be provided for the vehicle, and more optimal driving guidance parameters can be provided for the driving trainee. Combined with the real-time operation information and the predicted motion trajectory, the accuracy of the vehicle driving information can be improved, and the accuracy and reliability of the driving guidance of the driving trainee can be improved. By real-time monitoring and predicting the environmental information, combined with the vehicle operation information, the accuracy and reliability of the vehicle driving information can be significantly improved, thereby improving driving safety and efficiency, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters and the intelligence and efficiency of generating driving guidance parameters, and is also conducive to improving the accuracy and reliability of driving teaching guidance for trainees, and is also conducive to improving the learning effect of students in driving learning.

[0195] In yet another optional embodiment, Figure 3 As shown, the specific manner in which the generating module 304 generates the driving guidance parameters of the driving trainee corresponding to the target vehicle according to the driving influence parameters includes:

[0196] Determining driving influencing parameters of the target vehicle according to the driving influencing parameters, wherein the driving influencing parameters include an offset influencing parameter and a speed influencing parameter of the target vehicle;

[0197] Inputting the driving influencing parameters into a preset intelligent guidance model to obtain a model output result, wherein the model output result at least includes an influence relationship between an offset of the target vehicle and a driving speed of the target vehicle;

[0198] Based on the model output result, at least one target guidance parameter matching the model output result is determined in a predetermined guidance parameter set, and based on all the target guidance parameters, a driving guidance parameter for the driver trainee corresponding to the target vehicle is generated.

[0199] It can be seen that the implementation Figure 3The described device can determine the driving influencing parameters of the target vehicle according to the driving influencing parameters, input the driving influencing parameters into the intelligent guidance model to obtain the model output results, and determine the matching target guidance parameters in the guidance parameter set based on the model output results, and generate the driving guidance parameters of the driving trainee corresponding to the target vehicle based on all the target guidance parameters. The guidance parameters that can be customized according to the driving influencing parameters can provide more personalized guidance for the driving trainee, improve the learning efficiency and driving skills, and prevent potential driving errors such as lane departure and speeding by analyzing the offset influencing parameters and the speed influencing parameters, thereby improving driving safety. The model output results can reflect the difference between the driving behavior and the expected target in real time, and provide the trainee with instant feedback Feedback can help them adjust their driving behavior in time, which is conducive to improving the intelligence and efficiency of driving guidance for driving students, as well as improving the effectiveness and learning effect of driving students learning driving behavior. The guidance parameters that match the driving influencing parameters are generated by the intelligent guidance model, which not only improves the efficiency and quality of driving teaching, but also enhances the safety and learning experience of students. It can also significantly improve the accuracy and reliability of vehicle driving information, thereby improving driving safety and efficiency, which is conducive to further improving the accuracy and reliability of generating driving guidance parameters, as well as the intelligence and efficiency of generating driving guidance parameters, and is also conducive to improving the accuracy and reliability of driving teaching guidance for students, and can also help improve the learning effect of students in driving learning.

[0200] Embodiment 4

[0201] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another driving intelligent guidance device based on multi-dimensional data disclosed in an embodiment of the present invention. Figure 5 As shown, the driving intelligent guidance device based on multi-dimensional data may include:

[0202] A memory 401 storing executable program codes;

[0203] a processor 402 coupled to the memory 401;

[0204] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the intelligent driving guidance method based on multi-dimensional data described in the first embodiment of the present invention or the second embodiment of the present invention.

[0205] Embodiment 5

[0206] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps of the driving intelligent guidance method based on multi-dimensional data described in Embodiment 1 or Embodiment 2 of the present invention.

[0207] Embodiment 6

[0208] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the driving intelligent guidance method based on multi-dimensional data described in Example 1 or Example 2.

[0209] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0210] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0211] Finally, it should be noted that the driving intelligent guidance method and device based on multi-dimensional data disclosed in the embodiment of the present invention discloses only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical schemes recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the various embodiments of the present invention.

Claims

1. A driving intelligent guidance method based on multi-dimensional data, characterized in that: The method comprises: Acquire environmental image data of the current environment where the target vehicle is located, and acquire real-time operation information of the target vehicle, and perform data processing operations on the environmental image data to obtain image processing results; Determining vehicle driving information of the target vehicle based on the real-time operation information and the image processing result; generating a driving influence parameter corresponding to the target vehicle according to the vehicle driving information; According to the driving influencing parameters, driving guidance parameters for the driver trainee corresponding to the target vehicle are generated.

2. The driving intelligent guidance method based on multi-dimensional data according to claim 1 is characterized in that: Before generating the driving influence parameter corresponding to the target vehicle according to the vehicle driving information, the method further includes: Acquire driving state information of the driver trainee corresponding to the target vehicle, wherein the driving state information includes operation state information, sight state information, and head detection state information of the driver trainee; Determining driving sight line information corresponding to the driving trainee according to the driving state information; Wherein, generating the driving influence parameter corresponding to the target vehicle according to the vehicle driving information includes: A driving influence parameter corresponding to the target vehicle is generated according to the vehicle driving information and the driving sight line information.

3. The driving intelligent guidance method based on multi-dimensional data according to claim 2 is characterized in that: The determining, according to the driving state information, driving sight line information corresponding to the driving trainee includes: Analyzing the driving state information based on a predetermined infrared eye tracking model to obtain a line of sight analysis result corresponding to the driver trainee, and analyzing the driving state information based on a predetermined head movement detection model to obtain a head movement analysis result corresponding to the driver trainee; A data fusion operation is performed on the sight line analysis result and the head movement analysis result to obtain driving sight line information corresponding to the driving trainee.

4. The driving intelligent guidance method based on multi-dimensional data according to claim 1, characterized in that: The performing of a data processing operation on the environment image data to obtain an image processing result includes: Performing an image processing operation on the environmental image data through a preset processing algorithm to obtain a first processing result, wherein the preset processing algorithm includes one or more of a radar-visual fusion algorithm, an edge detection algorithm, and an object tracking algorithm; Performing a feature recognition operation on the environment image data to obtain a second processing result; wherein the feature recognition operation includes one or more of a lane recognition operation, a vehicle recognition operation, and a pedestrian recognition operation; An image processing result is generated according to the first processing result and the second processing result.

5. The driving intelligent guidance method based on multi-dimensional data according to claim 1, characterized in that: After generating the driving guidance parameters of the driver trainee corresponding to the target vehicle according to the driving influence parameters, the method further includes: Acquiring the real-time driving status of the driver trainee, and determining guidance demand information of the driver trainee according to the real-time driving status; Determining the instruction method information corresponding to the driver trainee according to the instruction requirement information, and generating driving instruction information for the driver trainee based on the instruction method information and the driving instruction parameters; The driving guidance information is used to guide the driver to perform driving guidance operations that match the driving guidance parameters.

6. The driving intelligent guidance method based on multi-dimensional data according to claim 1, characterized in that: Before determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result, the method further includes: Determine the real-time environment information of the current environment according to the image processing result, wherein the real-time environment information includes one or more of the real-time pedestrian information and the real-time vehicle information of the current environment; Based on the real-time environment information, predict a motion trajectory corresponding to the current environment, wherein the motion trajectory includes one or more of a vehicle motion trajectory in the current environment and a pedestrian motion trajectory in the current environment; Wherein, determining the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result includes: Based on the real-time operation information and the motion trajectory, vehicle driving information of the target vehicle is determined.

7. The driving intelligent guidance method based on multi-dimensional data according to claim 1, characterized in that: The step of generating a driving guidance parameter for the driver trainee corresponding to the target vehicle according to the driving influence parameter includes: Determining driving influencing parameters of the target vehicle according to the driving influencing parameters, wherein the driving influencing parameters include an offset influencing parameter and a speed influencing parameter of the target vehicle; Inputting the driving influencing parameter into a preset intelligent guidance model to obtain a model output result, wherein the model output result at least includes an influence relationship between the offset of the target vehicle and the driving speed of the target vehicle; Based on the model output result, at least one target guidance parameter matching the model output result is determined in a predetermined guidance parameter set, and based on all the target guidance parameters, driving guidance parameters for the driver trainee corresponding to the target vehicle are generated.

8. A driving intelligent guidance device based on multi-dimensional data, characterized in that: The device comprises: An acquisition module, used to acquire environmental image data of the current environment where the target vehicle is located, and to acquire real-time operation information of the target vehicle; A processing module, used for performing a data processing operation on the environmental image data to obtain an image processing result; A determination module, configured to determine the vehicle driving information of the target vehicle based on the real-time operation information and the image processing result; A generating module is used to generate driving influence parameters corresponding to the target vehicle according to the vehicle driving information; and to generate driving guidance parameters for a driving student corresponding to the target vehicle according to the driving influence parameters.

9. A driving intelligent guidance device based on multi-dimensional data, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the driving intelligent guidance method based on multi-dimensional data as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute the driving intelligent guidance method based on multi-dimensional data as described in any one of claims 1-7.

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