Real-time reminding method and system based on vehicle driving behaviors and electronic equipment

Abstract

The invention relates to a real-time reminding method and system based on vehicle driving behaviors and electronic equipment, and belongs to the field of intelligent driving. Comprising the steps of collecting monitoring data of vehicle driving in real time, inputting a pre-established violation behavior recognition model, and pre-judging violation behaviors in vehicle driving; if it is judged that the violation behavior exists or the non-standard driving action causes the safety risk, scanning a manifest file of an application program based on the vehicle-mounted voice service to verify the application capability; after verification is completed, a first reminding message is sent to the driving side so as to correct the driving behavior; and responding to the road congestion message displayed by the navigation equipment, generating a navigation plan in combination with the vehicle position and the environmental road information around the vehicle position, and sending a second reminding message to the driving side to re-plan the route. According to the scheme, the problems of lagging and unsafety caused when violation behaviors in driving cannot be judged in time or safety risks exist are solved, the optimal route can be planned again after correction, and the driving experience of a user is further improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent driving technology, and specifically relates to a real-time reminder method, system and electronic equipment based on vehicle driving behavior. Background Art

[0002] Currently, some in-vehicle navigation or traffic monitoring devices can provide some violation reminder functions, but generally, the driver is not notified until after the violation has occurred. At this point, the violation has already occurred, and the driver needs to bear the subsequent time and financial costs of the violation. In addition, when sending various reminders to the driver regarding violations, it is necessary to use the voice service of the smart cockpit. The voice service of the smart cockpit has been gradually improved. It can control the vehicle and applications through voice services, making the cockpit interaction more intelligent. Specific implementation solutions include:

[0003] 1. The voice service implements all vertical categories (navigation route calculation, multimedia playback, vehicle control) independently, which makes the voice system extremely large.

[0004] 2. Voice outputs semantics intuitively, and specific operations are taken over by the corresponding application. This implementation requires the voice to send the recognized semantics to the corresponding application according to the agreed protocol, and the application implements the logic. At the same time, the application will send a command to the voice to inform the execution result based on the execution result. The voice broadcasts the TTS based on the application's execution result.

[0005] The problem with the above solution is how the in-vehicle voice service determines whether the application can execute the command before sending it. If not, the command does not need to be sent to the application; the voice service can handle it internally. One approach is to implement semantic versioning within the voice service, but this requires a table to maintain the correspondence between the voice service and the supported semantics of the application. This table must be updated every time either version is updated, and this table is relatively complex. Summary of the Invention

[0006] Based on this, it is necessary to provide a real-time reminder method, system and electronic equipment based on vehicle driving behavior to address the above technical problems, which can effectively determine violations or unsafe actions during driving, and solve the delay and unsafe problems caused by violations or safety risks; at the same time, after verifying the application capabilities through the in-vehicle voice service, real-time reminders are given to the driver side, thereby improving the semantic recognition ability of the judgment results and the accuracy of voice command execution.

[0007] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a parking space planning method based on automatic parking, the method comprising:

[0009] Real-time collection of vehicle driving monitoring data, input into the pre-established violation behavior recognition model, and predict vehicle violations during driving;

[0010] If a violation or irregular driving action is determined to pose a safety risk, the in-vehicle voice service scans the application's manifest file to verify its capabilities.

[0011] After the verification is completed, a first reminder message is sent to the driver to correct the driving behavior;

[0012] In response to the road congestion message displayed by the navigation device, a navigation plan is generated by combining the vehicle position and the environmental road information around the vehicle position, and a second reminder message is sent to the driver side to replan the route.

[0013] Optionally, the real-time collection of vehicle driving monitoring data includes: when the vehicle enters a traffic control area, starting a real-time monitoring mode, capturing traffic police gesture image data on the road ahead through an external camera of the vehicle, and collecting the current video stream through an internal camera of the vehicle;

[0014] The spatial frame image in the video stream is converted into a plane image, and the position of the lane line in the video stream is determined based on the plane image; the monitoring data of the vehicle driving in the video stream is determined according to the position of the lane line, and the monitoring data and the corresponding traffic police gesture image data are synchronized to the cloud.

[0015] Optionally, the vehicle driving monitoring data includes vehicle speed, driving trajectory, distance from lane line, vehicle position, driving direction, and turn signal status.

[0016] Optionally, the traffic violation identification model is constructed based on historical monitoring data of vehicle travel:

[0017] According to the traffic regulations corresponding to the violations, the vehicle status of the traffic control area is divided, and a rule database is established based on the violations under different vehicle statuses; wherein the rule database is used to store traffic rules and blacklist and whitelist data of violations under different vehicle statuses;

[0018] Normalizing the input parameters in the rule database and historical monitoring data to construct training samples;

[0019] Define the target parameters of violation behavior and normalize them;

[0020] The input parameters are used as input parameters for judging traffic violations, the target parameters are used as targets, a model is constructed, and the training samples are used to train the model to obtain a traffic violation recognition model for predicting traffic violations during vehicle driving.

[0021] Optionally, the determination of safety risks caused by the irregular driving action includes: reading vehicle driving monitoring data, and determining the current driving behavior according to the offset between the vehicle position and the lane line during driving, and the vehicle's lane pressing or tire steering conditions;

[0022] Based on the traffic rules in the rule database and the corresponding traffic police gesture image data, it identifies whether the current driving action is standard; records the duration of irregular driving behavior, and when the duration exceeds the preset threshold, determines the safety risk caused by irregular driving behavior.

[0023] Optionally, scanning the manifest file of the application based on the in-vehicle voice service to verify the application capabilities includes:

[0024] Collect semantic NLU corresponding to function points through the in-vehicle voice service, and create a function key for the capability corresponding to each semantic NLU;

[0025] Create a function key list to represent the mapping relationship between semantic NLU and function key;

[0026] The application generates a semantic NLU from the function keys it supports. Each semantic NLU is mapped to a function key in the capability list based on the application to be distributed, the operation to be performed, and the slot information. This is written into the application manifest file in the form of a list of metadata tags to generate a maintenance capability list. The function keys supported by the application include speech recognition and semantic understanding.

[0027] The manifest file includes a maintenance capability list pre-loaded into the manifest file;

[0028] When loading and installing an application, the in-vehicle voice service scans the application's manifest file, parses the metadata tags in the maintenance capability manifest, obtains the function keys maintained in the metadata tags, and updates them to the memory;

[0029] Before the in-vehicle voice service performs semantic distribution, it queries the value corresponding to the function key in the capability list maintained by the system, and determines whether the semantic NLU is executable based on the mapping relationship between the semantic NLU and the function key;

[0030] When the value is false, it means that there is no application that can be taken over, and the first reminder message is generated by the voice broadcast TTS of the in-vehicle voice service.

[0031] Optionally, the first reminder message includes: a reminder of the driver's illegal behavior, a reminder of the traffic police's gesture intention, a reminder of the action standard of driving behavior, and a reminder to learn driving rules.

[0032] Optionally, responding to the road congestion message displayed by the navigation device, generating a navigation plan based on the vehicle position and environmental road information around the vehicle position, and sending a second reminder message to the driver to re-plan the route includes:

[0033] When receiving a road congestion message displayed by a navigation device, the vehicle's current position is located, and all routes from the vehicle's current position to the destination area are displayed; the routes covering the destination area are sorted from near to far from the vehicle's current position, and route information matching a preset driving time range is obtained to determine candidate routes; and a congestion degree is evaluated based on the volume of pedestrian and vehicle traffic around the vehicle's position; the candidate routes are screened based on the evaluation results and a navigation plan is generated; and a second reminder message is fed back to the on-board terminal; wherein the second reminder message includes navigation information contained in the navigation planning result of the re-planned route.

[0034] In a second aspect, the present invention provides a real-time reminder system based on vehicle driving behavior, the system comprising:

[0035] The recognition module is used to collect real-time monitoring data of vehicle driving, input the pre-established violation behavior recognition model, and predict the violation behavior of the vehicle during driving;

[0036] The application verification module is used to scan the application manifest file based on the in-vehicle voice service to verify the application capabilities if it determines that there is a violation of traffic regulations or irregular driving behavior that poses a safety risk;

[0037] A first reminder module is used to send a first reminder message to the driver after the verification is completed to correct the driving behavior;

[0038] The second reminder module is used to respond to the road congestion message displayed by the navigation device, generate a navigation plan based on the vehicle position and the environmental road information around the vehicle position, and send a second reminder message to the driver side to replan the route.

[0039] In a third aspect, the present invention provides an electronic device, comprising:

[0040] at least one processor; and,

[0041] a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform any one of the methods according to the first aspect.

[0043] The above-mentioned real-time reminder method, system and electronic device based on vehicle driving behavior collects vehicle driving monitoring data in real time, inputs a pre-established violation behavior recognition model, and predicts violations during vehicle driving; if it is determined that there is a violation or irregular driving action that causes a safety risk, the application capability is verified based on the on-board voice service scanning the application's manifest file; after the verification is completed, a first reminder message is sent to the driver to correct the driving behavior. Verifying the application capability through the on-board voice service can further confirm that the on-board voice service sends a voice command to the application, so that before the application responds to the voice command and sends the first reminder message to the driver, it first determines whether the application has the ability to execute the voice command. This improves the locomotive's semantic recognition capability for the judgment result and the accuracy of the voice command execution, ensuring real-time reminders to the driver.

[0044] After the incorrect behavior is corrected, it can respond to the traffic congestion message displayed by the navigation device, generate a navigation plan based on the vehicle's location and the surrounding road information, and send a second reminder message to the driver to re-plan the route. The above solution solves the unsafe issues caused by the inability to promptly identify violations or safety risks during driving, as well as the lag in executing voice reminders caused by the voice device waiting for feedback from the application to determine whether the semantic understanding is correct. It can not only prevent violations and standardize driving behavior, but also alleviate driving anxiety, reduce the time and money costs of violations and traffic accidents, and improve the user's driving experience.

[0045] Furthermore, after a traffic violation is corrected, the optimal route can be re-planned, improving driving reliability. This effectively addresses the issue of triggering navigation planning messages to re-plan routes when traffic congestion is indicated and the current route is not properly planned. This ensures users receive the latest navigation suggestions, providing an intelligent driving experience and further improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0047] Figure 1 This is a flow chart of the real-time reminder method based on vehicle driving behavior provided by the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of the real-time reminder system based on vehicle driving behavior provided by the present invention;

[0049] Figure 3 It is a diagram of the internal structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0050] The following embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only examples and are not intended to limit the scope of protection of the present invention.

[0051] It should be noted that, unless otherwise specified, the technical or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0052] The present invention provides a real-time reminder method, system and electronic device based on vehicle driving behavior. The technical solution adopted can predict the driving safety risks caused by violations and irregular driving behaviors. Before the voice service issues a reminder, the voice service will verify the capabilities of the application and update the capabilities maintained in the manifest file to the memory for use by the voice service; so that whether the semantics are executable can be known before the semantics are distributed, and the application does not need to perform semantic version control, which reduces the cost of communication between applications. After the verification is completed, a reminder message is sent to the driver to correct the driving behavior; this solves the problem that reminders after violations are not forward-looking.

[0053] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0054] Example 1: Please refer to Figure 1 Embodiment 1 of the present invention provides a real-time reminder method based on vehicle driving behavior, the specific steps of the method include:

[0055] S101 collects vehicle driving monitoring data in real time, inputs it into a pre-established violation behavior recognition model, and predicts violations of vehicle driving;

[0056] If it is determined in S102 that there is a violation of traffic rules or irregular driving action that causes a safety risk, the manifest file of the application is scanned based on the in-vehicle voice service to verify the application capabilities;

[0057] After S103 verification is completed, a first reminder message is sent to the driver to correct the driving behavior;

[0058] S104 responds to the road congestion message displayed by the navigation device, generates a navigation plan based on the vehicle position and the environmental road information around the vehicle position, and sends a second reminder message to the driver to re-plan the route.

[0059] In the above step S101, real-time collection of vehicle driving monitoring data includes: when the vehicle enters a traffic control area, starting a real-time monitoring mode, capturing traffic police gesture image data on the road ahead through the vehicle's external camera, and collecting the current video stream through the vehicle's internal camera;

[0060] The spatial frame image in the video stream is converted into a plane image, and the position of the lane line in the video stream is determined based on the plane image; the monitoring data of the vehicle driving in the video stream is determined according to the position of the lane line, and the monitoring data and the corresponding traffic police gesture image data are synchronized to the cloud.

[0061] In step S101 of the above embodiment, the monitoring data of vehicle driving includes vehicle speed, driving trajectory, distance from lane line, vehicle position, driving direction, and turn signal status.

[0062] In the above embodiment, the traffic violation recognition model described in step S101 is constructed based on the historical monitoring data of vehicle driving, and the in-vehicle voice service obtains the recognition result output by the traffic violation recognition model; based on the pre-packaged technical parameters of the in-vehicle voice service, the recognition result is analyzed by ASR to obtain the ASR recognition result;

[0063] The ASR recognition results obtained through ASR analysis are forwarded to the corresponding application. Through operations such as voice recognition and semantic translation, the NLU recognition results are translated into semantic NLU in a unified standard format. Before the in-vehicle voice service performs semantic distribution, the value corresponding to the function key in the capability list maintained by the system is queried. Based on the mapping relationship between the semantic NLU and the function key, it is determined whether the semantic NLU is executable. Whether it is executable is determined by the True or False result found and displayed by the system. If False is displayed, it indicates whether there is an application that can take over the semantics. If there is no application that can take over, it is translated into a semantic NLU in a unified standard format and returned to the vehicle end for semantic distribution through the in-vehicle voice service.

[0064] Furthermore, the specific construction process of the violation behavior identification model includes: dividing the vehicle status of the traffic control area according to the traffic regulations corresponding to the violation behavior, and establishing a rule database based on the violation behavior under different vehicle states; wherein the rule database is used to store traffic rules and blacklist and whitelist data of violation behaviors under different vehicle states;

[0065] Normalize the input parameters in the rule database and historical monitoring data to construct training samples;

[0066] Define the target parameters of violation behavior and normalize them;

[0067] The input parameters are used as input parameters for judging traffic violations, the target parameters are used as targets, a model is constructed, and the training samples are used to train the model to obtain a traffic violation recognition model for predicting traffic violations during vehicle driving.

[0068] In the above embodiment, determining the safety risk caused by the irregular driving action in step S102 includes: reading the vehicle driving monitoring data, and determining the current driving behavior based on the offset between the vehicle position and the lane line during driving, and the vehicle's lane crossing or tire steering;

[0069] Based on the traffic rules in the rule database and the corresponding traffic police gesture image data, it identifies whether the current driving action is standard; records the duration of irregular driving behavior, and when the duration exceeds the preset threshold, determines the safety risk caused by irregular driving behavior.

[0070] In the above embodiment, the step S102 of scanning the manifest file of the application based on the in-vehicle voice service and verifying the application capability specifically includes:

[0071] Collect semantic NLU corresponding to function points through the in-vehicle voice service, and create a function key for the capability corresponding to each semantic NLU;

[0072] Create a list of functional keys to represent the mapping between semantic NLU and functional keys. The mapping between semantic NLU and keys is represented by a triplet + slot. The triplet is composed of domain + intention + type. The domain represents the vertical category (navigation, multimedia, phone, etc.) to which the semantic information applies. Intention refers to the intent, which can be distinguished as query or search. Type is more detailed and can indicate turning certain applications on or off. Triplet definitions can be customized based on specific business needs. Slots are the most detailed part and can be subdivided into categories (such as navigating home and navigating to work). Home and work are different slot values. Based on the triplet + slot, the values ​​are mapped to the key in the list.

[0073] The application generates a semantic NLU from the function keys it supports. Each semantic NLU is mapped to a function key in the capability list based on the application to be distributed, the operation to be performed, and the slot information. This is written into the application manifest file in the form of a list of metadata tags to generate a maintenance capability list. The function keys supported by the application include speech recognition and semantic understanding.

[0074] The manifest file includes a maintenance capability list pre-loaded into the manifest file;

[0075] When loading and installing an application, the in-vehicle voice service scans the application's manifest file, parses the metadata tags in the maintenance capability manifest, obtains the function keys maintained in the metadata tags, and updates them to the memory;

[0076] Before the in-vehicle voice service performs semantic distribution, it queries the value corresponding to the function key in the capability list maintained by the system, and determines whether the semantic NLU is executable based on the mapping relationship between the semantic NLU and the function key;

[0077] When the value is false, it means that there is no application that can be taken over, and the first reminder message is generated by the voice broadcast TTS of the in-vehicle voice service.

[0078] In the above embodiment, the first reminder message includes: a reminder of the driver's illegal behavior in the event of a safety risk caused by illegal behavior or irregular driving action, a reminder of the traffic police's gesture intention, a reminder of the action standard of driving behavior, and a reminder to learn driving rules.

[0079] In the above embodiment, in step S104, responding to the road congestion message displayed by the navigation device, generating a navigation plan based on the vehicle location and environmental road information surrounding the vehicle location, and sending a second reminder message to the driver to re-plan the route includes:

[0080] When receiving a road congestion message displayed by a navigation device, the vehicle's current position is located, and all routes from the vehicle's current position to the destination area are displayed; the routes covering the destination area are sorted from near to far from the vehicle's current position, and route information matching a preset driving time range is obtained to determine candidate routes; and a congestion degree is evaluated based on the volume of pedestrian and vehicle traffic around the vehicle's position; the candidate routes are screened based on the evaluation results and a navigation plan is generated; and a second reminder message is fed back to the on-board terminal; wherein the second reminder message includes navigation information contained in the navigation planning result of the re-planned route.

[0081] Example 2: Based on the same inventive concept, this embodiment of the present application also provides a real-time reminder system based on vehicle driving behavior for implementing the above-mentioned real-time reminder method based on vehicle driving behavior. The implementation solution provided by this system is similar to the implementation solution described in the method of Example 1 above. Therefore, the specific limitations of one or more system embodiments based on vehicle driving behavior provided below can be found in the above-mentioned limitations on the real-time reminder method based on vehicle driving behavior, and will not be repeated here.

[0082] In one embodiment, the present invention also provides a real-time reminder system based on vehicle driving behavior, such as Figure 2 As shown, it includes: an identification module 11, an application verification module 12, a first reminder module 13 and a second reminder module 14, wherein:

[0083] The recognition module 11 is used to collect real-time monitoring data of vehicle driving, input the pre-established violation behavior recognition model, and predict the violation behavior of the vehicle during driving;

[0084] The application verification module 12 is configured to scan the application manifest file based on the in-vehicle voice service to verify the application capabilities if it is determined that there is a violation of traffic rules or irregular driving behavior that causes a safety risk;

[0085] The first reminder module 13 is used to send a first reminder message to the driver after the verification is completed to correct the driving behavior;

[0086] The second reminder module 14 is used to respond to the road congestion message displayed by the navigation device, generate a navigation plan based on the vehicle position and the environmental road information around the vehicle position, and send a second reminder message to the driver to re-plan the route.

[0087] At the same time, the present application also provides a computer-readable storage medium and an electronic device. In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of steps S101 to S104 of the method are implemented.

[0088] In one embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, it implements any one of the methods from S101 to S104. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0089] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0090] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0094] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A real-time reminder method based on vehicle driving behavior, characterized in that: The method comprises: Real-time collection of vehicle driving monitoring data, input into the pre-established violation behavior recognition model, and predict vehicle violations during driving; If a violation or irregular driving action is determined to pose a safety risk, the in-vehicle voice service scans the application's manifest file to verify its capabilities. After the verification is completed, a first reminder message is sent to the driver to correct the driving behavior; In response to the road congestion message displayed by the navigation device, a navigation plan is generated by combining the vehicle position and the environmental road information around the vehicle position, and a second reminder message is sent to the driver side to replan the route.

2. The method according to claim 1, wherein The real-time collection of vehicle driving monitoring data includes: when the vehicle enters a traffic control area, turning on the real-time monitoring mode, capturing traffic police gesture image data on the road ahead through the vehicle's external camera, and collecting the current video stream through the vehicle's internal camera; The spatial frame image in the video stream is converted into a plane image, and the position of the lane line in the video stream is determined based on the plane image; the monitoring data of the vehicle driving in the video stream is determined according to the position of the lane line, and the monitoring data and the corresponding traffic police gesture image data are synchronized to the cloud.

3. The method according to claim 2, wherein The vehicle driving monitoring data includes vehicle speed, driving trajectory, distance from lane line, vehicle position, driving direction, and turn signal status.

4. The method according to claim 1, wherein The traffic violation identification model is constructed based on historical monitoring data of vehicle driving: According to the traffic regulations corresponding to the violations, the vehicle status of the traffic control area is divided, and a rule database is established based on the violations under different vehicle statuses; wherein the rule database is used to store traffic rules and blacklist and whitelist data of violations under different vehicle statuses; Normalizing the input parameters in the rule database and historical monitoring data to construct training samples; Define the target parameters of violation behavior and normalize them; The input parameters are used as input parameters for judging traffic violations, the target parameters are used as targets, a model is constructed, and the training samples are used to train the model to obtain a traffic violation recognition model for predicting traffic violations during vehicle driving.

5. The method according to claim 1, wherein Determining the safety risk caused by the irregular driving action includes: reading the vehicle driving monitoring data, and determining the current driving behavior based on the offset between the vehicle position and the lane line during driving, as well as the vehicle's lane crossing or tire steering conditions; Based on the traffic rules in the rule database and the corresponding traffic police gesture image data, it identifies whether the current driving action is standard; records the duration of irregular driving behavior, and when the duration exceeds the preset threshold, determines the safety risk caused by irregular driving behavior.

6. The method according to claim 1, wherein The in-vehicle voice service scans the application manifest file and verifies the application capabilities, including: Collect semantic NLU corresponding to function points through the in-vehicle voice service, and create a function key for the capability corresponding to each semantic NLU; Create a function key list to represent the mapping relationship between semantic NLU and function key; The application generates a semantic NLU from the function keys it supports. Each semantic NLU is mapped to a function key in the capability list based on the application to be distributed, the operation to be performed, and the slot information. This is written into the application manifest file in the form of a list of metadata tags to generate a maintenance capability list. The function keys supported by the application include speech recognition and semantic understanding. The manifest file includes a maintenance capability list pre-loaded into the manifest file; When loading and installing an application, the in-vehicle voice service scans the application's manifest file, parses the metadata tags in the maintenance capability manifest, obtains the function keys maintained in the metadata tags, and updates them to the memory; Before the in-vehicle voice service performs semantic distribution, it queries the value corresponding to the function key in the capability list maintained by the system, and determines whether the semantic NLU is executable based on the mapping relationship between the semantic NLU and the function key; When the value is false, it means that there is no application that can be taken over, and the first reminder message is generated by the voice broadcast TTS of the in-vehicle voice service.

7. The method according to claim 6, wherein The first reminder message includes: a reminder of the driver's illegal behavior, a reminder of the traffic police's gesture intention, a reminder of the action standard of driving behavior, and a reminder to learn driving rules.

8. The method according to claim 1, wherein The responding to the road congestion message displayed by the navigation device, generating a navigation plan based on the vehicle location and environmental road information around the vehicle location, and sending a second reminder message to the driver to re-plan the route includes: When receiving a road congestion message displayed by a navigation device, the vehicle's current position is located, and all routes from the vehicle's current position to the destination area are displayed; the routes covering the destination area are sorted from near to far from the vehicle's current position, and route information matching a preset driving time range is obtained to determine candidate routes; and a congestion degree is evaluated based on the volume of pedestrian and vehicle traffic around the vehicle's position; the candidate routes are screened based on the evaluation results and a navigation plan is generated; and a second reminder message is fed back to the on-board terminal; wherein the second reminder message includes navigation information contained in the navigation planning result of the re-planned route.

9. A real-time reminder system based on vehicle driving behavior, characterized in that: The system comprises: The recognition module is used to collect real-time monitoring data of vehicle driving, input the pre-established violation behavior recognition model, and predict the violation behavior of the vehicle during driving; The application verification module is used to scan the application manifest file based on the in-vehicle voice service to verify the application capabilities if it determines that there is a violation of traffic regulations or irregular driving behavior that poses a safety risk; A first reminder module is used to send a first reminder message to the driver after the verification is completed to correct the driving behavior; The second reminder module is used to respond to the road congestion message displayed by the navigation device, generate a navigation plan based on the vehicle position and the environmental road information around the vehicle position, and send a second reminder message to the driver side to replan the route.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

Images