Special credible artificial intelligence method for vehicle driving and control

Abstract

The invention belongs to the technical field of artificial intelligence, and discloses a credible artificial intelligence method special for vehicle driving and control, which comprises the following steps: in a vehicle driving process, acquiring a video stream acquired by a camera of a vehicle; extracting feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predicting feature information of a next video frame based on the feature information of the current video frame; on the basis of the extracted feature information of the next video frame and the predicted error between the feature information of the next video frame, determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model; and carrying out online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on an online autonomous learning strategy to obtain a vehicle artificial intelligence perception network model after online training. According to the invention, online feedback learning and feedback training of the vehicle artificial intelligence perception network model are realized, and the perception accuracy of the model is improved.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, and in particular relates to a trusted artificial intelligence method dedicated to vehicle driving and control. Background Art

[0002] In recent years, with the rapid development of artificial intelligence, artificial intelligence has gradually been applied to vehicles. Specifically, the driving data of the vehicle can be collected offline, and the collected driving data can be used as training data for model training to obtain a pre-trained model. During the driving process of the vehicle, the prediction results are obtained through the pre-trained model, and the vehicle is controlled accordingly based on the prediction results.

[0003] From the above description, it can be seen that in the related technology, a large amount of vehicle driving data is required to conduct offline training of the pre-trained model, resulting in a high training cost of the pre-trained model; and during the driving process of the vehicle, if a driving scenario not covered by the pre-trained model is encountered, the pre-trained model cannot make accurate predictions, resulting in a low prediction accuracy of the pre-trained model, thereby posing a safety hazard to the vehicle. Summary of the invention

[0004] The purpose of the present invention is to realize online feedback and autonomous learning of the vehicle artificial intelligence perception network model. The perception accuracy of the vehicle artificial intelligence perception network model after online training is higher, which improves the safety of the vehicle, and online training does not require special collection of training data, which reduces the training cost of the vehicle artificial intelligence perception network model. Through the online autonomous learning method of the present invention, the reliability and safety of intelligent perception and decision-making in unknown scenarios and working conditions are improved.

[0005] In a first aspect, an embodiment of the present invention provides a trusted artificial intelligence method dedicated to vehicle driving and control, the method comprising:

[0006] During the driving of the vehicle, obtaining a video stream captured by a camera of the vehicle;

[0007] Extracting feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predicting feature information of a next video frame based on feature information of a current video frame, wherein the current video frame is any video frame of the video stream;

[0008] Determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame;

[0009] Based on the online autonomous learning strategy, online feedback learning and feedback training are performed on the vehicle artificial intelligence perception network model to obtain the vehicle artificial intelligence perception network model after online training.

[0010] Optionally, extracting feature information of each video frame of the video stream by using a vehicle artificial intelligence perception network model includes:

[0011] Inputting the video stream into a vehicle artificial intelligence perception network model;

[0012] The vehicle artificial intelligence perception network model is used to extract feature information of multiple dimensions for each video frame.

[0013] Optionally, predicting feature information of a next video frame based on feature information of a current video frame includes:

[0014] For feature information of each dimension, feature information of the dimension of the next video frame is predicted based on the feature information of the dimension of the current video frame.

[0015] Optionally, determining the online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame includes:

[0016] For feature information of each dimension, calculating an error between first feature information and second feature information, where the first feature information is the extracted feature information of the dimension of the next video frame, and the second feature information is the predicted feature information of the dimension of the next video frame;

[0017] An online autonomous learning strategy of the vehicle artificial intelligence perception network model is determined based on the calculated multiple errors.

[0018] Optionally, the method further includes:

[0019] Outputting multiple perception results through the vehicle artificial intelligence perception network model; each perception result is used to complete a perception task, and different perception results complete different perception tasks;

[0020] For each perception result, obtaining a calibration result corresponding to the perception result from a pre-built database;

[0021] Calculating a loss function value of the vehicle artificial intelligence perception network model based on the perception results and the calibration results;

[0022] Based on the calculated loss function value, the online autonomous learning strategy of the vehicle artificial intelligence perception network model is adjusted to obtain an adjusted online autonomous learning strategy.

[0023] Optionally, performing online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training includes:

[0024] Based on the adjusted online autonomous learning strategy, online feedback learning and feedback training are performed on the vehicle artificial intelligence perception network model to obtain a new online trained vehicle artificial intelligence perception network model.

[0025] In a second aspect, an embodiment of the present invention provides an artificial intelligence method specifically for vehicle-type driving controllability, the method comprising:

[0026] Acquire a video stream collected by a camera of the vehicle during driving of the vehicle;

[0027] Inputting the video stream into the online trained vehicle artificial intelligence perception network model to output a perception result through the online trained vehicle artificial intelligence perception network model;

[0028] The vehicle is controlled accordingly based on the perception result.

[0029] In a third aspect, an embodiment of the present invention provides an artificial intelligence device dedicated to vehicle driving and control, the device comprising:

[0030] A video stream acquisition module, used to acquire the video stream collected by the camera of the vehicle during the driving process of the vehicle;

[0031] A feature information extraction and prediction module, used to extract feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predict feature information of a next video frame based on feature information of a current video frame, wherein the current video frame is any video frame of the video stream;

[0032] An online autonomous learning strategy determination module, used to determine the online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame;

[0033] The vehicle artificial intelligence perception network model training module is used to perform online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0034] In a fourth aspect, an embodiment of the present invention provides an artificial intelligence device dedicated to vehicle driving control, the device comprising:

[0035] A video stream acquisition module is used to acquire the video stream collected by the camera of the vehicle during the driving process of the vehicle;

[0036] A perception result acquisition module, used for inputting the video stream into the online trained vehicle artificial intelligence perception network model to output the perception result through the online trained vehicle artificial intelligence perception network model;

[0037] A vehicle control module is used to control the vehicle accordingly based on the perception result.

[0038] In a fifth aspect, an embodiment of the present invention provides an electronic device, including:

[0039] at least one processor;

[0040] a memory for storing the at least one processor-executable instruction;

[0041] The at least one processor is configured to execute the instructions to implement the method described in any one of the first aspect or the second aspect.

[0042] In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in any one of the first aspect or the second aspect.

[0043] In a seventh aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in either the first aspect or the second aspect.

[0044] The technical solution provided by the embodiment of the present invention is to obtain a video stream captured by a camera of the vehicle during vehicle driving; input the video stream into a vehicle artificial intelligence perception network model to be trained online, and perform the following steps through the vehicle artificial intelligence perception network model: extract feature information of multiple dimensions of each video frame in the video stream; extract feature information of each video frame of the video stream through the vehicle artificial intelligence perception network model, and predict feature information of the next video frame based on the feature information of the current video frame; determine an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the error between the extracted feature information of the next video frame and the predicted feature information of the next video frame; perform online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0045] It can be seen that in the embodiment of the present invention, the vehicle artificial intelligence perception network model can be trained online in real time during the movement of the vehicle, realizing online feedback and autonomous learning of the vehicle artificial intelligence perception network model. The perception accuracy of the vehicle artificial intelligence perception network model after online training is higher, which improves the safety of the vehicle, and online training does not require special collection of training data, which reduces the training cost of the vehicle artificial intelligence perception network model. Through the online autonomous learning method of the present invention, the reliability and safety of intelligent perception and decision-making in unknown scenarios and working conditions are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a trusted artificial intelligence method specifically for vehicle driving and control provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the process of online feedback learning and feedback training of a vehicle artificial intelligence perception network model provided by an embodiment of the present invention;

[0048] Figure 3 A flowchart of a trusted artificial intelligence method specifically for vehicle driving and control provided by an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of an online enhancement process of a vehicle artificial intelligence perception network model provided by an embodiment of the present invention;

[0050] Figure 5 A flowchart of an artificial intelligence method specifically used for vehicle-type driving controllable provided by an embodiment of the present invention;

[0051] Figure 6 A schematic diagram of the structure of an artificial intelligence device dedicated to vehicle driving and control provided by an embodiment of the present invention;

[0052] Figure 7 A trusted artificial intelligence device dedicated to vehicle driving and control provided by an embodiment of the present invention;

[0053] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below through examples.

[0055] In recent years, with the rapid development of artificial intelligence, artificial intelligence has gradually been applied to vehicles. However, the artificial intelligence of existing technologies does not have the ability to learn online or update in real time. As a pre-trained model, it can only be continuously iterated based on pre-training, which means that its knowledge and capabilities are trained based on data before a specific point in time. In addition, during use, generative artificial intelligence cannot actively learn or memorize new information, nor can it update its knowledge base in real time based on user interaction. This means that if a scene not covered by pre-training is encountered during driving, the safety of the vehicle will be greatly challenged.

[0056] From the above description, it can be seen that the training cost of the pre-trained model in the prior art is huge. It is necessary to prepare a large amount of offline data for pre-training, and then put the trained model into use. In addition, due to the extremely complex internal structure of the model, the training process cannot be analyzed, and the reliability of the results still needs to be considered. In addition, during driving, if a scene not covered by pre-training is encountered, the prediction accuracy of the pre-trained model is low, which will cause incorrect control of the vehicle and will bring serious consequences. Therefore, the reliability and safety of the application of artificial intelligence in vehicles is a huge challenge.

[0057] In order to solve the above technical problems, the embodiment of the present invention provides a vehicle artificial intelligence capable of online learning. Compared with the existing artificial intelligence technology, the present invention reduces the complexity of the model through online feedback learning technology, and can analyze the model to a certain extent, so that its safety and reliability meet the driving requirements. This will not only promote the development of artificial intelligence theory, but also promote the industrial application of intelligent technology in the field of intelligent driving. And it can improve the reliability and safety of intelligent perception and decision-making in unknown scenes and working conditions.

[0058] The following is a detailed description of a trusted artificial intelligence method specifically for vehicle driving and control provided by an embodiment of the present invention.

[0059] like Figure 1 As shown, it is a flowchart of a trusted artificial intelligence method for vehicle driving and control provided by an embodiment of the present invention. The method may include the following steps:

[0060] S110, while the vehicle is traveling, obtaining a video stream captured by a camera of the vehicle.

[0061] Specifically, the embodiment of the present invention trains the vehicle artificial intelligence perception network model through online learning technology, so the continuous video stream is the learning and perception basis of the vehicle artificial intelligence perception network model. During the driving process of the vehicle, the vehicle's camera will collect video streams in real time, so the video streams collected by the vehicle's camera can be obtained.

[0062] S120, extracting feature information of each video frame of the video stream through the vehicle artificial intelligence perception network model, and predicting feature information of the next video frame based on the feature information of the current video frame.

[0063] The current video frame is any video frame of the video stream.

[0064] As an implementation method of the embodiment of the present invention, extracting feature information of each video frame of a video stream through a vehicle artificial intelligence perception network model may include the following steps, namely step a1 and step a2:

[0065] Step a1: input the video stream into the vehicle artificial intelligence perception network model.

[0066] Step a2: extract feature information of multiple dimensions of each video frame through the vehicle artificial intelligence perception network model.

[0067] Among them, before online training of the vehicle artificial intelligence perception network model, the vehicle artificial intelligence perception network model can be pre-trained, and the pre-training process is an offline training process. In order to improve the perception accuracy of the vehicle artificial intelligence perception network model obtained by pre-training, in an embodiment of the present invention, during the driving process of the vehicle, the video stream collected by the vehicle's camera is input into the pre-trained vehicle artificial intelligence perception network model to perform online training on the pre-trained vehicle artificial intelligence perception network model. Because the vehicle will encounter various new scenes during driving, by online training of the vehicle artificial intelligence perception network model, the vehicle artificial intelligence perception network model can be adapted to various new scenes, thereby improving the perception accuracy of the vehicle artificial intelligence perception network model. In addition, online training does not require the special collection of a large amount of training data, which can reduce the training cost of the vehicle artificial intelligence perception network model.

[0068] As an implementation method of the embodiment of the present invention, extracting feature information of each video frame of a video stream through a vehicle artificial intelligence perception network model may include the following steps, namely step b1 and step b2:

[0069] Step b1, input the video stream into the vehicle artificial intelligence perception network model.

[0070] Step b2: extracting feature information of multiple dimensions of each video frame through the vehicle artificial intelligence perception network model.

[0071] Specifically, after obtaining the video stream, the video stream can be input into the vehicle artificial intelligence perception network model. After receiving the video stream, the vehicle artificial intelligence perception network model can extract feature information of multiple dimensions of the video stream, where the multiple dimensions may include two-dimensional, three-dimensional or four-dimensional, etc. The two-dimensional feature information may be plane feature information, the three-dimensional feature information may be spatial feature information, and the four-dimensional feature information may be time and space feature information.

[0072] Accordingly, in one implementation, predicting feature information of the next video frame based on feature information of the current video frame may include the following step c1:

[0073] Step c1: for feature information of each dimension, predict feature information of the dimension of the next video frame based on the feature information of the dimension of the current video frame.

[0074] Specifically, assuming that there is two-dimensional feature information, three-dimensional feature information, and four-dimensional feature information, the two-dimensional feature information of the next video frame can be predicted based on the two-dimensional feature information of the current video frame, the three-dimensional feature information of the next video frame can be predicted based on the three-dimensional feature information of the current video frame, and the four-dimensional feature information of the next video frame can be predicted based on the four-dimensional feature information of the current video frame.

[0075] S130, determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame.

[0076] Specifically, after predicting the feature information of the next video frame, the error between the extracted feature information of the next video frame and the predicted feature information of the next video frame can be calculated, and based on the error between the two, the online autonomous learning strategy of the vehicle artificial intelligence perception network model can be determined.

[0077] As an implementation of an embodiment of the present invention, based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame, determining the online autonomous learning strategy of the vehicle artificial intelligence perception network model may include the following steps, namely step d1 and step d2:

[0078] Step d1, for each dimension of feature information, calculate the error between the first feature information and the second feature information.

[0079] The first feature information is the extracted feature information of the dimension of the next video frame, and the second feature information is the predicted feature information of the dimension of the next video frame.

[0080] Step d2, determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the calculated multiple errors.

[0081] Specifically, it is assumed that the extracted feature information has three dimensions, namely two-dimensional feature information, three-dimensional feature information and four-dimensional feature information. Then, the error between the extracted two-dimensional feature information of the next video frame and the predicted two-dimensional feature information of the next video frame can be calculated, and the error between the extracted three-dimensional feature information of the next video frame and the predicted three-dimensional feature information of the next video frame can also be calculated. The error between the extracted four-dimensional feature information of the next video frame and the predicted four-dimensional feature information of the next video frame can also be calculated. And based on the three calculated errors, the online autonomous learning strategy of the vehicle artificial intelligence perception network model is determined. Among them, the online autonomous learning strategy can be the model parameter adjustment information of the vehicle artificial intelligence perception network model.

[0082] S140, performing online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0083] Specifically, after obtaining the online autonomous learning strategy of the vehicle artificial intelligence perception network model, the vehicle artificial intelligence perception network model can be subjected to online feedback learning and feedback training according to the online autonomous learning strategy. This process is the model parameter adjustment process of the vehicle artificial intelligence perception network model, thereby obtaining the vehicle artificial intelligence perception network model after online training. Assuming that the vehicle artificial intelligence perception network model needs to adjust three parameters a, b and c, the online autonomous learning strategy can be: keep a unchanged, increase b, and decrease c. Then, based on the online autonomous learning strategy, a can be kept unchanged, b can be increased, and c can be decreased. The perception accuracy of the vehicle artificial intelligence perception network model after online training is higher, and online training does not require special collection of training data, which reduces the training cost of the vehicle artificial intelligence perception network model.

[0084] The technical solution provided by the embodiment of the present invention is to obtain a video stream captured by a camera of the vehicle during vehicle driving; input the video stream into a vehicle artificial intelligence perception network model to be trained online, and perform the following steps through the vehicle artificial intelligence perception network model: extract feature information of multiple dimensions of each video frame in the video stream; extract feature information of each video frame of the video stream through the vehicle artificial intelligence perception network model, and predict feature information of the next video frame based on the feature information of the current video frame; determine an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the error between the extracted feature information of the next video frame and the predicted feature information of the next video frame; perform online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0085] It can be seen that in the embodiment of the present invention, the vehicle artificial intelligence perception network model can be trained online in real time during the movement of the vehicle, realizing online feedback and autonomous learning of the vehicle artificial intelligence perception network model. The perception accuracy of the vehicle artificial intelligence perception network model after online training is higher, which improves the safety of the vehicle, and online training does not require special collection of training data, which reduces the training cost of the vehicle artificial intelligence perception network model. Through the online autonomous learning method of the present invention, the reliability and safety of intelligent perception and decision-making in unknown scenarios and working conditions are improved.

[0086] Below Figure 2 Take this as an example to illustrate: Figure 2 The image space in is the image contained in each video frame in the video stream captured by the vehicle's camera, and the video input is to input the video stream into the vehicle's artificial intelligence perception network model. Figure 2 The feature network in is the above-mentioned vehicle artificial intelligence perception network model, which is used to extract the feature information of each video frame in the video stream. The feature space corresponding to time t-1 can be understood as the feature information of the current video frame, and the feature space corresponding to the predicted time t can be the feature information of the next video frame. The feature space corresponding to time t can be the real feature information of the current video frame extracted. By calculating the error between the predicted feature information of the current video frame and the real two-dimensional feature information, a gradient space can be constructed. The gradient space is an online autonomous learning strategy. Figure 2 The feedback network shown feeds back the online autonomous learning strategy to the feature network, and performs online feedback learning and feedback training on the vehicle artificial intelligence perception network model through the autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0087] exist Figure 1 Based on the embodiment shown, in one implementation, as Figure 3 As shown, the method may also include the steps of:

[0088] S310, outputting multiple perception results through the vehicle artificial intelligence perception network model.

[0089] Each perception result is used to complete a perception task, and different perception results complete different perception tasks.

[0090] Specifically, the vehicle artificial intelligence perception network model can form multiple perception task heads, that is, output multiple perception results, where the multiple perception results can include target recognition results, obstacle recognition results, motion feature perception results, operational space recognition results, semantic information results, lane line recognition results, traffic sign recognition results, etc., without specific limitation here.

[0091] S320: For each perception result, obtain a calibration result corresponding to the perception result from a pre-built database.

[0092] S330, calculating the loss function value of the vehicle artificial intelligence perception network model based on the perception results and calibration results.

[0093] Specifically, for each perception result, the calibration result corresponding to the perception result can be obtained from a pre-built database, and the loss function value of the vehicle artificial intelligence perception network model can be calculated based on the perception result and the calibration result to evaluate the perception result.

[0094] S340, based on the calculated loss function value, adjusting the online autonomous learning strategy of the vehicle artificial intelligence perception network model to obtain an adjusted online autonomous learning strategy.

[0095] Specifically, if the calculated loss function value is large, it means that the accuracy of the perception result is low. Therefore, based on the calculated loss function value, the online autonomous learning strategy of the vehicle artificial intelligence perception network model can be adjusted, that is, the model parameters of the vehicle artificial intelligence perception network model can be adjusted, so as to realize the online enhanced learning of the vehicle artificial intelligence perception network model.

[0096] by Figure 4 Take this as an example to illustrate: Figure 4 The image space in is the image contained in each video frame in the video stream captured by the vehicle's camera, and the video input is to input the video stream into the vehicle's artificial intelligence perception network model. The feature information network is used to extract the feature information of each video frame, the feature space refers to the extracted feature information, and the typical target recognition network is used to output the perception results. The typical knowledge base is a pre-constructed database. The loss function value of the vehicle's artificial intelligence perception network model is calculated based on the perception results and the calibration results in the typical knowledge base. If the loss function value is large, the online autonomous learning strategy of the vehicle's artificial intelligence perception network model is adjusted, and the adjusted online autonomous learning strategy is obtained.

[0097] exist Figure 4 Based on the illustrated embodiment, in one implementation, S140, performing online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on an online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training may include the following steps:

[0098] S350, based on the adjusted online autonomous learning strategy, online feedback learning and feedback training are performed on the vehicle artificial intelligence perception network model to obtain a new online trained vehicle artificial intelligence perception network model.

[0099] Specifically, the adjusted online autonomous learning strategy is obtained by integrating online feedback learning and online reinforcement learning. By performing feedback learning and feedback training on the vehicle artificial intelligence perception network model through the adjusted online autonomous learning strategy, the accuracy of the perception results of the vehicle artificial intelligence perception network model can be further improved.

[0100] It can be seen that in the embodiments of the present invention, during the movement of the vehicle, online feedback learning and online reinforcement learning can be performed on the vehicle artificial intelligence perception network model in real time. The perception results output by the vehicle artificial intelligence perception network model after online training are more accurate, which improves the safety of the vehicle. In addition, online training does not require special collection of training data, which reduces the training cost of the vehicle artificial intelligence perception network model.

[0101] The embodiment of the present invention also provides an artificial intelligence method specifically for vehicle driving control, such as Figure 5 As shown, the method may include the following steps:

[0102] S510, obtaining a video stream collected by a camera of the vehicle during the driving process of the vehicle.

[0103] S520, inputting the video stream into the online trained vehicle artificial intelligence perception network model to output the perception result through the online trained vehicle artificial intelligence perception network model.

[0104] S530: Control the vehicle accordingly based on the perception result.

[0105] Specifically, in actual applications, during the driving process of the vehicle, the video stream collected by the vehicle's camera is input into the online trained vehicle artificial intelligence perception network model obtained through the above embodiment. After receiving the video stream, the online trained vehicle artificial intelligence perception network model extracts the feature information of each video frame in the video stream, and outputs the perception result based on the feature information, and then controls the vehicle accordingly according to the perception result. For example, if the perception result is that there is an obstacle in front of the vehicle, then the vehicle can be controlled to avoid the obstacle. It can be understood that there are multiple perception results and multiple ways to control the vehicle, which are not listed here one by one.

[0106] The technical solution of the embodiment of the present invention can perform online feedback learning and online reinforcement learning on the vehicle artificial intelligence perception network model in real time during the movement of the vehicle. The perception results output by the vehicle artificial intelligence perception network model after online training are more accurate, thereby improving the safety of the vehicle. In addition, online training does not require special collection of training data, thereby reducing the training cost of the vehicle artificial intelligence perception network model.

[0107] The embodiment of the present invention also provides an artificial intelligence device 60 dedicated to vehicle driving and control, such as Figure 6 As shown, the device comprises:

[0108] The video stream acquisition module 610 is used to acquire the video stream collected by the camera of the vehicle during the driving process of the vehicle;

[0109] A feature information extraction and prediction module 620, configured to extract feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predict feature information of a next video frame based on feature information of a current video frame, wherein the current video frame is any video frame of the video stream;

[0110] An online autonomous learning strategy determination module 630, for determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame;

[0111] The model online training module 640 is used to perform online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

[0112] The embodiment of the present invention provides an artificial intelligence device 70 specifically for vehicle driving control, such as Figure 7 As shown, the device comprises:

[0113] The video stream acquisition module 710 is used to acquire the video stream collected by the camera of the vehicle during the driving process of the vehicle;

[0114] A perception result acquisition module 720 is used to input the video stream into the online trained vehicle artificial intelligence perception network model to output the perception result through the online trained vehicle artificial intelligence perception network model;

[0115] The vehicle control module 730 is used to control the vehicle accordingly based on the perception result.

[0116] The embodiment of the present invention further provides an electronic device 800, such as Figure 8 As shown, including:

[0117] at least one processor 801;

[0118] a memory 802 for storing the at least one processor executable instruction;

[0119] Wherein, the at least one processor is configured to execute the instructions to implement the above method.

[0120] In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the above method.

[0121] In a seventh aspect, an embodiment of the present invention provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0122] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A trusted artificial intelligence method specifically for vehicle driving and control, characterized in that: The method comprises: During the driving of the vehicle, obtaining a video stream captured by a camera of the vehicle; Extracting feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predicting feature information of a next video frame based on feature information of a current video frame, wherein the current video frame is any video frame of the video stream; Determining an online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame; Based on the online autonomous learning strategy, online feedback learning and feedback training are performed on the vehicle artificial intelligence perception network model to obtain the vehicle artificial intelligence perception network model after online training.

2. The method according to claim 1, characterized in that The extracting feature information of each video frame of the video stream by using the vehicle artificial intelligence perception network model includes: Inputting the video stream into a vehicle artificial intelligence perception network model; The vehicle artificial intelligence perception network model is used to extract feature information of multiple dimensions from each video frame.

3. The method according to claim 2, characterized in that The predicting feature information of the next video frame based on the feature information of the current video frame includes: For feature information of each dimension, feature information of the dimension of the next video frame is predicted based on the feature information of the dimension of the current video frame.

4. The method according to claim 2, characterized in that: The determining of the online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame includes: For feature information of each dimension, calculating an error between first feature information and second feature information, where the first feature information is the extracted feature information of the dimension of the next video frame, and the second feature information is the predicted feature information of the dimension of the next video frame; An online autonomous learning strategy of the vehicle artificial intelligence perception network model is determined based on the calculated multiple errors.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Outputting multiple perception results through the vehicle artificial intelligence perception network model; each perception result is used to complete a perception task, and different perception results complete different perception tasks; For each perception result, obtaining a calibration result corresponding to the perception result from a pre-built database; Calculating a loss function value of the vehicle artificial intelligence perception network model based on the perception results and the calibration results; Based on the calculated loss function value, the online autonomous learning strategy of the vehicle artificial intelligence perception network model is adjusted to obtain an adjusted online autonomous learning strategy.

6. The method according to claim 5, characterized in that The online feedback learning and feedback training of the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training includes: Based on the adjusted online autonomous learning strategy, online feedback learning and feedback training are performed on the vehicle artificial intelligence perception network model to obtain a new online trained vehicle artificial intelligence perception network model.

7. An artificial intelligence method specifically for vehicle driving control, characterized in that: The method comprises: Acquire a video stream collected by a camera of the vehicle during driving of the vehicle; Inputting the video stream into the online trained vehicle artificial intelligence perception network model according to any one of claims 1 to 6, so as to output a perception result through the online trained vehicle artificial intelligence perception network model; The vehicle is controlled accordingly based on the perception result.

8. A trusted artificial intelligence device dedicated to vehicle driving and control, characterized in that: The device comprises: A video stream acquisition module is used to acquire the video stream collected by the camera of the vehicle while the vehicle is traveling; A feature information extraction and prediction module, used to extract feature information of each video frame of the video stream through a vehicle artificial intelligence perception network model, and predict feature information of a next video frame based on feature information of a current video frame, wherein the current video frame is any video frame of the video stream; An online autonomous learning strategy determination module, used to determine the online autonomous learning strategy of the vehicle artificial intelligence perception network model based on the extracted feature information of the next video frame and the error between the predicted feature information of the next video frame; The vehicle artificial intelligence perception network model training module is used to perform online feedback learning and feedback training on the vehicle artificial intelligence perception network model based on the online autonomous learning strategy to obtain the vehicle artificial intelligence perception network model after online training.

9. An artificial intelligence device specifically used for vehicle driving control, characterized in that: The device comprises: A video stream acquisition module is used to acquire the video stream collected by the camera of the vehicle during the driving process of the vehicle; A perception result acquisition module, used to input the video stream into the online trained vehicle artificial intelligence perception network model according to any one of claims 1 to 6, so as to output a perception result through the online trained vehicle artificial intelligence perception network model; A vehicle control module is used to control the vehicle accordingly based on the perception result.

10. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6 or claim 7.

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