Safety processing method and system for incoming call in driving process, storage medium and vehicle

Abstract

The invention provides a safe processing method for an incoming call in a driving process. The safe processing method comprises the following steps: acquiring real-time road condition information of a vehicle; analyzing and evaluating the received road condition information by adopting a trained risk evaluation large model to obtain a risk evaluation value of a currently answered call; when an incoming call is received, performing corresponding processing on the incoming call according to the current risk assessment value: when the risk assessment value is lower than a first preset threshold value, reminding and determining whether to answer the incoming call according to the selection of a driver; and when the risk assessment value is higher than or equal to a second preset threshold value, automatically answering the incoming call, and informing the caller of a driver that the current road condition is not suitable for answering the call and the expected answerable time assessment value through a voice synthesis technology. The invention further discloses a corresponding system, a storage medium and a vehicle. By implementing the method and the device, the safety, the flexibility and the intelligence of answering the call in the driving process can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of driving incoming call processing, and particularly to a safe processing method, system, storage medium and vehicle for incoming calls during driving by integrating map navigation technology and large model algorithms. Background Art

[0002] During the vehicle driving process, it is inevitable to face the scenario of handling incoming calls. In the existing technical solutions, a common solution is to directly block all incoming calls during vehicle driving and then prompt the driver that there are missed calls after the vehicle stops. Another solution is to let the driver manually select whether to answer the call when it comes in, but no risk prompt based on road conditions is provided.

[0003] However, the above existing technical solutions all have deficiencies. The solution of directly blocking incoming calls may cause the driver to miss urgent or important communications, affecting normal work and life. And only relying on the driver to manually select whether to answer or not, since it is difficult for the driver to accurately judge the road condition risk in a short time, it is easy to make wrong decisions, increasing the probability of traffic accidents. In addition, these solutions cannot dynamically adapt to the changes in road conditions and lack flexibility and intelligence. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to propose a safe processing system, method, storage medium and vehicle for incoming calls during driving, which can process incoming calls according to the current answering risk, and inform the caller of the corresponding estimated available answering time value when it cannot be answered, improving the safety, flexibility and intelligence of answering calls.

[0005] As one aspect of the present invention, a safe processing method for incoming calls during driving is provided, which includes the following steps:

[0006] Obtain the real-time road condition information of the vehicle;

[0007] Analyze and evaluate the received road condition information by using a trained risk assessment large model to judge the risk degree of answering the call currently and obtain the current risk assessment value;

[0008] When an incoming call is received, process the incoming call according to the current risk assessment value:

[0009] When the risk assessment value is lower than the first preset threshold, remind the driver that there is an incoming call and ask whether to answer it, and determine whether to answer the incoming call according to the driver's choice;

[0010] When the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answer time value, automatically answer the incoming call, and inform the caller that the current road conditions of the driver are not suitable for answering the call and the estimated available answer time value. The second preset threshold is greater than or equal to the first preset threshold.

[0011] Among them, obtaining the real-time road condition information of the vehicle includes:

[0012] The map navigation module of the vehicle obtains real-time road condition information through high-precision satellite positioning technology, traffic data platforms, and the vehicle's own sensors. The road condition information includes the vehicle's position, speed, driving direction, road type, traffic congestion status, road construction information, the impact of weather conditions on road conditions, and the distribution of surrounding vehicles.

[0013] Among them, using the trained risk assessment large model to analyze and evaluate the received road condition information, judge the risk degree of answering the call currently, and obtain the current risk assessment value, including:

[0014] Pre-build a risk assessment large model based on deep learning algorithms and large-scale training data, and conduct training to obtain the trained risk assessment large model;

[0015] Extract features and preprocess the input road condition data. According to the vehicle's speed change trend, safe distance from the vehicle in front, curvature and slope of the road, dynamic changes in traffic flow, and behavior patterns of vehicles in adjacent lanes, use multi-layer neural networks and reinforcement learning algorithms to analyze the road condition information and output the current risk assessment value.

[0016] Among them, it is characterized in that it further includes:

[0017] According to the predetermined calculation period, continuously calculate the estimated available answer time value. If the estimated available answer time value advances or delays by more than the predetermined time threshold, use voice synthesis technology to inform the caller of the change in the estimated available answer time value of the driver.

[0018] Among them, it further includes:

[0019] During the incoming call processing, record the detailed data of each incoming call processing, including road condition information, risk assessment value, processing method, and subsequent feedback information.

[0020] Correspondingly, as one aspect of the present invention, there is also provided a safe processing system for incoming calls during driving, which includes:

[0021] A map navigation module for obtaining the real-time road condition information of the vehicle;

[0022] The large model processing module, connected to the map navigation module, is used to deeply analyze and comprehensively evaluate the received road condition information by using the trained risk assessment large model, judge the risk level of answering the call currently, and output the corresponding risk assessment value;

[0023] The communication module is used to establish a connection with the mobile phone network, convert and convey voice information when automatically answering an incoming call, and implement voice synthesis and speech recognition functions;

[0024] The driver interaction module is used to convey suggestions and relevant information for answering the call to the driver, and provide operation options and incoming call information display for the driver;

[0025] The processor module is used to, when receiving an incoming call, process the incoming call according to the current risk assessment value: when the risk assessment value is lower than the first preset threshold, remind the driver of the incoming call and ask whether to answer through the driver interaction module, and determine whether to answer the incoming call according to the driver's choice;

[0026] And when the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answering time value, and automatically answer the incoming call, inform the caller that the current road condition of the driver is not suitable for answering the call and the estimated available answering time value, and the second preset threshold is greater than or equal to the first preset threshold.

[0027] Among them, the map navigation module obtains real-time road condition information through high-precision satellite positioning technology, traffic data platform and the vehicle's own sensors, and the road condition information includes the vehicle's position, speed, driving direction, road type, traffic congestion condition, road construction information, the impact of weather conditions on the road condition, and the distribution of surrounding vehicles.

[0028] Among them, the large model processing module is further used to extract features and preprocess the input road condition data by using the trained risk assessment model, analyze the road condition information according to the vehicle's speed change trend, the safety distance from the vehicle in front, the curvature and slope of the road, the dynamic change of traffic flow, and the behavior patterns of vehicles in adjacent lanes by using a multi-layer neural network and reinforcement learning algorithm, and output the current risk assessment value;

[0029] Among them, the trained risk assessment large model is pre-constructed based on deep learning algorithms and large-scale training data to obtain a risk assessment large model, and is trained.

[0030] Among them, the processor module further includes:

[0031] A dynamic adjustment unit, which is used to continuously calculate the estimated available answering time value according to a predetermined calculation period. If the estimated available answering time value advances or delays beyond a predetermined time threshold, the change in the estimated available answering time value of the driver will be informed to the caller through voice synthesis technology.

[0032] Furthermore, it further includes:

[0033] A memory module, which is used to store the detailed data of each incoming call processing, including road condition information, risk assessment value, processing method, and subsequent feedback information.

[0034] Correspondingly, as another aspect of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor module, the steps of the method as described above are implemented.

[0035] Correspondingly, as another aspect of the present invention, there is also provided a vehicle, which includes:

[0036] One or more processor modules;

[0037] A memory, which is used to store one or more computer programs;

[0038] When the one or more computer programs are executed by the one or more processor modules, the one or more processor modules implement the method as described above.

[0039] Implementing the embodiments of the present invention has the following beneficial effects:

[0040] The present invention provides a safe processing system, method, storage medium, and vehicle for incoming calls during driving. It can calculate the risk assessment value of answering a call currently according to the collected road condition information and in combination with a risk assessment large model, and perform corresponding processing on the incoming call according to the risk assessment value. When it is not possible to answer, the caller will be informed of the corresponding estimated available answering time value, improving the safety, flexibility, and intelligence of answering calls. At the same time, in the present invention, the estimated value of the available answering time can also be dynamically adjusted according to the real-time change of the road condition and timely feedback to the caller to relieve the caller's anxiety.

[0041] In the present invention, by accurately judging the road condition and the risk of incoming calls, it is avoided that the driver answers the call in a high-risk situation, thereby effectively reducing the incidence of traffic accidents caused by distraction.

[0042] In the present invention, on the premise of ensuring safety, the necessary communication needs of the driver are met, improving the convenience and efficiency of travel.

[0043] In the present invention, it is possible to flexibly adjust the incoming call processing strategy according to the dynamic change of the real-time road condition to adapt to various complex driving scenarios.

[0044] In the present invention, by using a friendly interaction method and intelligent services, the troubles and pressures of the driver during driving are reduced, and the driving comfort and pleasure are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, obtaining other drawings based on these drawings still belongs to the scope of the present invention.

[0046] Figure 1 It is a schematic diagram of the main process of an embodiment of a safe handling method for incoming calls during driving provided by the present invention.

[0047] Figure 2 It is a schematic diagram of the structure of an embodiment of a safe handling system for incoming calls during driving provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings.

[0049] As Figure 1 shown, it shows a schematic diagram of the main process of an embodiment of a safe handling method for incoming calls during driving provided by the present invention. In this embodiment, the safe handling method for incoming calls during driving at least includes the following steps:

[0050] Step S10, obtaining real-time road condition information of the vehicle;

[0051] Specifically, in a specific example, the map navigation module of the vehicle obtains real-time road condition information through high-precision satellite positioning technology, a traffic data platform, and sensors of the vehicle itself. The road condition information includes multi-dimensional road condition information such as the position, speed, driving direction, road type, traffic congestion condition, road construction information, the impact of weather conditions on road conditions, and the distribution of surrounding vehicles. At the same time, the collected road condition information can be preprocessed to remove noise and abnormal data and extract key features, such as the average vehicle speed, traffic flow, and distance from the vehicle in front on the current road.

[0052] In a specific example, the map navigation module obtains road condition information multiple times per second by closely integrating with the satellite positioning system, the traffic data platform, and the vehicle's own sensors (such as speed sensors, gyroscopes, etc.). The information includes the vehicle's precise position (with an accuracy reaching the sub-meter level), real-time speed (with an error within ±0.1 km / h), road congestion level (divided into multiple levels such as unobstructed, slightly congested, moderately congested, and severely congested), the status of traffic lights (red, green, yellow, and countdown), etc. At the same time, through cloud computing technology, the road condition information is updated in real time to ensure the freshness and accuracy of the data. In other examples, weather data and other factors that may affect road conditions can also be combined to ensure that the information obtained is comprehensive and accurate.

[0053] Step S11: Analyze and evaluate the received road condition information using the trained large risk assessment model to determine the risk level of the current incoming call and obtain the current risk assessment value.

[0054] It can be understood that in the embodiments of the present invention, a large risk assessment model is pre-constructed based on deep learning algorithms and large-scale training data and trained to obtain the trained large risk assessment model.

[0055] Extract and preprocess the features of the input road condition data. Analyze the road condition information according to the vehicle's speed change trend, the safe distance from the vehicle in front, the curvature and slope of the road, the dynamic change of traffic flow, and the behavior patterns of vehicles in adjacent lanes using a multi-layer neural network and a reinforcement learning algorithm, and output the current risk assessment value.

[0056] In a specific example, the large risk assessment model comprehensively considers various factors such as the instantaneous change in vehicle speed (such as sudden acceleration and sudden deceleration), the real-time distance from the vehicle in front and its change rate, the impact of the curvature and slope of the road on vehicle control, and the short-term fluctuations in traffic flow (such as sudden lane changes, merging, or exiting of adjacent vehicles), and uses the pre-trained weights and biases for calculation to output a quantified risk assessment value. The risk assessment value ranges from 0 to 100, where 0 indicates extremely low risk and 100 indicates extremely high risk.

[0057] Step S12: When an incoming call is received, handle the incoming call accordingly based on the current risk assessment value:

[0058] When the risk assessment value is lower than the first preset threshold, remind the driver of the incoming call and ask whether to answer it, and determine whether to answer the incoming call according to the driver's choice;

[0059] When the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answer time value and automatically answer the incoming call. Through voice synthesis technology, inform the caller that the current road conditions of the driver are not suitable for answering the call and the estimated available answer time value. The second preset threshold is greater than or equal to the first preset threshold.

[0060] In one example, the first preset threshold is set to 30. When the risk assessment value is lower than 30, it is determined that the road conditions are relatively good. At this time, the driver will be reminded in time that there is an incoming call and asked whether to answer. If the driver chooses to answer, the system will automatically reduce the volume of the in-car audio, reduce unnecessary sounds such as the air-conditioning wind sound, and at the same time enable noise reduction technology to reduce environmental interference. The basic information of the caller and the answer options will be displayed on the touch screen.

[0061] When the second preset threshold is set to 70, when the risk assessment value is higher than or equal to 70, it is determined that the road conditions are complex and there is a risk. At this time, the incoming call will be automatically answered. The large model uses voice synthesis technology to inform the caller in a natural and fluent voice that the driver is driving and the current road conditions are complex and not suitable for answering the call. For example, "Hello, the driver is driving on the highway. The current traffic flow is large, and there are continuous curves ahead. The driver is temporarily unable to answer your call. Do you want to leave a message, or I can inform you that the driver may be available to answer in about 15 minutes."

[0062] When the risk assessment value is between the first preset threshold and the second preset threshold, the answer strategy can be set flexibly. For example, the incoming call can be directly answered or directly rejected.

[0063] At the same time, in other embodiments, the first preset threshold and the second preset threshold can also be set to the same value. For example, they can both be set to 50. Then, in the method of the present invention, when the risk assessment value is lower than 50, the driver will be reminded in time that there is an incoming call and asked whether to answer. When the risk assessment value is higher than or equal to 50, the incoming call will be automatically answered and the driver will be informed that the call cannot be made and the estimated available answer time value.

[0064] It can be understood that in the embodiments of the present invention, the estimated available answer time value can be calculated in the following manner:

[0065] According to the current road condition information and historical data, combined with a preset algorithm to determine an initial estimated available answer time. For example, if the current is in a congested section of the highway, with a large traffic flow and slow vehicle speed, based on the average passing time in similar road conditions in historical data, it is estimated that the driver may return to a relatively safe driving state and be able to answer the call in about 15 minutes. Then this time will be informed to the caller.

[0066] In a specific example, the method of the present invention further includes:

[0067] According to a predetermined calculation period (such as 30 seconds), continuously calculate the estimated answerable time valuation. If the estimated answerable time valuation advances or delays beyond a predetermined time threshold, inform the caller of the change in the estimated answerable time valuation of the driver through voice synthesis technology.

[0068] It can be understood that the map navigation module continuously monitors the road condition changes and re-evaluates the road conditions every 30 seconds. Continuously obtain new information such as vehicle position, speed, road congestion level, traffic flow, etc.

[0069] Analyze the newly obtained road condition information, compare it with the road conditions evaluated in the previous cycle, and determine whether the road conditions have improved or deteriorated. For example, check factors indicating improved road conditions such as whether the vehicle speed has increased, the traffic flow has decreased, or the congestion has eased, or factors indicating deteriorated road conditions such as whether an accident has occurred or road construction has led to increased congestion.

[0070] The large model processing module re-performs risk assessment based on the new road condition information. If the road conditions improve, the risk assessment value may decrease; if the road conditions deteriorate, the risk assessment value may increase.

[0071] If the road conditions have improved, such as congestion alleviation, traffic flow reduction, vehicle speed increase, etc., the large model will recalculate the estimated answerable time based on the new road condition information and notify the caller in a timely manner. For example, originally it was estimated that the call could be answered after 10 minutes, but after 5 minutes, the road conditions improved significantly. After re-evaluation by the large model, (the system can automatically send a text message to the caller's mobile phone number) to inform the caller that the driver can conveniently answer the call in about another 5 minutes.

[0072] If the road conditions deteriorate, the estimated answerable time will also be extended accordingly, and the situation will be explained to the caller, such as "Due to an accident ahead, the estimated answerable time needs to be extended to 15 minutes".

[0073] In a specific example, the method of the present invention further includes:

[0074] During the call handling process, record the detailed data of each call handling, including road condition information, risk assessment value, handling method, and subsequent feedback information (such as the content of the caller's message, etc.). These stored information can be used for subsequent optimization and effect evaluation of the model.

[0075] As Figure 2 shown, a structural schematic diagram of an embodiment of a safety handling system for incoming calls during driving provided by the present invention is shown. In this embodiment, the system at least includes:

[0076] The map navigation module 1 is used to obtain the real-time road conditions of the vehicle; in a specific example, the map navigation module 1 obtains real-time road conditions through high-precision satellite positioning technology, a traffic data platform, and the vehicle's own sensors, and the road conditions include the vehicle's position, speed, driving direction, road type, traffic congestion, road construction information, the impact of weather conditions on road conditions, and the distribution of surrounding vehicles;

[0077] The large model processing module 2 is connected to the map navigation module and is used to deeply analyze and comprehensively evaluate the received road conditions information by using a trained risk assessment large model, judge the risk degree of the current incoming call, and output the corresponding risk assessment value;

[0078] The communication module 3 is used to connect to the mobile phone network, convert and convey voice information when automatically answering an incoming call, and implement voice synthesis and voice recognition functions;

[0079] The driver interaction module 4 is used to convey suggestions and relevant information about answering the call to the driver, and provide operation options and incoming call information display for the driver;

[0080] The processor module 5 is used to, when receiving an incoming call, process the incoming call according to the current risk assessment value: when the risk assessment value is lower than the first preset threshold, remind the driver of the incoming call through the driver interaction module and ask whether to answer, and determine whether to answer the incoming call according to the driver's choice;

[0081] And when the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answer time estimate and automatically answer the incoming call, and inform the caller through voice synthesis technology that the current road conditions are not suitable for answering the call and the estimated available answer time estimate, and the second preset threshold is greater than or equal to the first preset threshold.

[0082] In a specific example, the large model processing module 2 is further used to extract features and preprocess the input road condition data by using a trained risk assessment model, and analyze the road condition information according to the vehicle's speed change trend, the safety distance from the vehicle in front, the curvature and slope of the road, the dynamic change of traffic flow, and the behavior patterns of vehicles in adjacent lanes by using a multi-layer neural network and a reinforcement learning algorithm, and output the current risk assessment value, and the risk assessment value ranges from 0 to 100, where 0 indicates extremely low risk and 100 indicates extremely high risk;

[0083] In a specific example, the communication module 3 supports multiple communication protocols, including but not limited to GSM, CDMA, LTE, etc., and can achieve a stable connection to the mobile phone network. When automatically answering an incoming call, it can clearly and accurately convert and convey voice information, and at the same time has voice synthesis and voice recognition functions to ensure smooth communication with the caller.

[0084] The driver interaction module 4 may include a voice prompt system and a touch display screen. The voice prompt system can convey suggestions and relevant information for answering calls to the driver in a clear and friendly voice. The touch display screen is used to provide the driver with more detailed operation options and incoming call information display when necessary.

[0085] Among them, the trained risk assessment large model is pre-constructed based on deep learning algorithms and large-scale training data to build a risk assessment large model, and then trained to obtain it.

[0086] In a specific example, the processor module 5 further includes:

[0087] A dynamic adjustment unit, configured to continuously calculate the estimated answerable time estimate according to a predetermined calculation period. If the estimated answerable time estimate advances or delays by more than a predetermined time threshold, the voice synthesis technology is used to inform the caller of the change in the estimated answerable time estimate of the driver.

[0088] In a specific example, the system provided by the present invention further includes:

[0089] A memory module 6, configured to store detailed data of each incoming call processing, including road condition information, risk assessment values, processing methods, and subsequent feedback information.

[0090] For more details, reference can be made to and combined with the foregoing description of Figure 1 and will not be elaborated herein.

[0091] As another aspect of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by the processor module, the steps of the method described in Figure 1 are implemented. For more details, reference can be made to and combined with the foregoing description of Figure 1 and will not be elaborated herein.

[0092] As another aspect of the present invention, a vehicle is further provided, which includes:

[0093] One or more processor modules;

[0094] A memory, configured to store one or more computer programs;

[0095] When the one or more computer programs are executed by the one or more processor modules, the one or more processor modules implement the method described in Figure 1 For more details, reference can be made to and combined with the foregoing description of Figure 1 and will not be elaborated herein.

[0096] Implementing the embodiments of the present invention has the following beneficial effects:

[0097] The present invention provides a safe handling system, method, storage medium and vehicle for incoming calls during driving. It can calculate the risk assessment value of answering a call currently according to the collected road condition information and in combination with a risk assessment large model, and perform corresponding processing on the incoming call according to the risk assessment value. When it is not possible to answer, it will inform the caller of the estimated value of the expected available answering time, improving the safety, flexibility and intelligence of answering calls. At the same time, in the present invention, the estimated value of the expected available answering time can also be dynamically adjusted according to the real-time change of the road condition and fed back to the caller in time to relieve the caller's anxiety.

[0098] In the present invention, by accurately judging the road condition and the risk of incoming calls, it is avoided that the driver answers the call in a high-risk situation, thus effectively reducing the incidence of traffic accidents caused by distraction.

[0099] In the present invention, on the premise of ensuring safety, it meets the necessary communication needs of the driver and improves the convenience and efficiency of travel.

[0100] In the present invention, it is possible to flexibly adjust the incoming call handling strategy according to the dynamic change of the real-time road condition and adapt to various complex driving scenarios.

[0101] In the present invention, by using a friendly interaction method and intelligent services, it reduces the driver's troubles and pressures during driving and improves the comfort and pleasure of driving.

[0102] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor module of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to generate a machine, so that the instructions executed by the processor module of the computer or other programmable data processing devices generate a device for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 a block or multiple blocks.

[0103] The above-disclosed is only a preferred embodiment of the present invention, and of course, it cannot be used to limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A safety processing method for incoming calls during driving, characterized in that Including the following steps: Obtain the real-time traffic condition information of the vehicle; Use the trained large risk assessment model to analyze and evaluate the received traffic condition information, judge the risk degree of answering the call currently, and obtain the current risk assessment value; When receiving an incoming call, perform corresponding processing on the incoming call according to the current risk assessment value: When the risk assessment value is lower than the first preset threshold, remind the driver of the incoming call and ask whether to answer it, and determine whether to answer the incoming call according to the driver's choice; When the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answering time value and automatically answer the incoming call, and inform the caller that the current traffic condition of the driver is not suitable for answering the call and the estimated available answering time value, and the second preset threshold is greater than or equal to the first preset threshold.

2. The method according to claim 1, characterized in that, Obtaining the real-time traffic condition information of the vehicle includes: The map navigation module of the vehicle obtains real-time traffic condition information through high-precision satellite positioning technology, traffic data platform and the vehicle's own sensors. The traffic condition information includes the vehicle's position, speed, driving direction, road type, traffic congestion condition, road construction information, the influence of weather conditions on the traffic condition, and the distribution of surrounding vehicles.

3. The method according to claim 2, characterized in that The step of using the trained large risk assessment model to analyze and evaluate the received traffic condition information, judge the risk degree of answering the call currently, and obtain the current risk assessment value includes: Pre-build a large risk assessment model based on deep learning algorithms and large-scale training data, and perform training to obtain the trained large risk assessment model; Extract and preprocess the features of the input traffic condition data, and analyze the traffic condition information according to the vehicle's speed change trend, safe distance from the vehicle in front, curvature and slope of the road, dynamic change of traffic flow, and behavior patterns of vehicles in adjacent lanes, and output the current risk assessment value by using multi-layer neural network and reinforcement learning algorithms.

4. The method according to any one of claims 1 to 3, characterized in that Further include: Continuously calculate the estimated available answering time value according to a predetermined calculation period. If the estimated available answering time value advances or delays beyond the predetermined time threshold, inform the caller of the change in the estimated available answering time value of the driver.

5. The method according to claim 4, characterized in that, Further include: During the incoming call processing, record the detailed data of each incoming call processing, including traffic condition information, risk assessment value, processing method and subsequent feedback information.

6. A safety processing system for incoming calls during driving, characterized in that, Include: A map navigation module for obtaining the real-time traffic condition information of the vehicle; A large model processing module, connected to the map navigation module, for deeply analyzing and comprehensively evaluating the received traffic condition information by using the trained large risk assessment model, judging the risk degree of answering the call currently, and outputting the corresponding risk assessment value; A communication module for realizing the connection with the mobile phone network, converting and transmitting voice information when automatically answering an incoming call, and realizing voice synthesis and voice recognition functions; A driver interaction module for conveying suggestions and relevant information for answering the call to the driver, and providing operation options and incoming call information display for the driver; A processor module, which is used to, when receiving an incoming call, process the incoming call according to the current risk assessment value: when the risk assessment value is lower than the first preset threshold, remind the driver of the incoming call through the driver interaction module and ask whether to answer the call, and determine whether to answer the incoming call according to the driver's selection; and when the risk assessment value is higher than or equal to the second preset threshold, calculate the estimated available answer time estimate and automatically answer the incoming call, and inform the caller that the current road conditions are not suitable for answering the call and the estimated available answer time estimate, where the second preset threshold is greater than or equal to the first preset threshold.

7. The system according to claim 6, wherein The map navigation module obtains real-time road condition information through high-precision satellite positioning technology, a traffic data platform, and the vehicle's own sensors. The road condition information includes the vehicle's position, speed, driving direction, road type, traffic congestion status, road construction information, the impact of weather conditions on road conditions, and the distribution of surrounding vehicles.

8. The system according to claim 7, characterized in that The large model processing module is further used to extract features and preprocess the input road condition data using a trained risk assessment model, analyze the road condition information according to the vehicle's speed change trend, the safety distance from the vehicle in front, the curvature and slope of the road, the dynamic change of traffic flow, and the behavior patterns of vehicles in adjacent lanes by using a multi-layer neural network and a reinforcement learning algorithm, and output the current risk assessment value; wherein, the trained risk assessment large model is pre-constructed based on a deep learning algorithm and large-scale training data to obtain a risk assessment large model and then trained.

9. The system according to any one of claims 6 to 8, characterized in that, The processor module further includes: A dynamic adjustment unit, which is used to continuously calculate the estimated available answer time estimate according to a predetermined calculation period. If the estimated available answer time estimate advances or delays by more than a predetermined time threshold, inform the caller of the change in the estimated available answer time estimate through voice synthesis technology.

10. The system according to claim 9, wherein, Further includes: A memory module, which is used to store the detailed data of each incoming call processing, including road condition information, risk assessment value, processing method, and subsequent feedback information.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor module, it implements the steps of the method according to any one of claims 1 to 5.

12. A vehicle, characterized in that, Includes: One or more processor modules; A memory, which is used to store one or more computer programs; When the one or more computer programs are executed by the one or more processor modules, the one or more processor modules implement the method according to any one of claims 1 to 5.

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