Vehicle driving emergency situation identification and display method and device

Abstract

The invention discloses a vehicle driving emergency condition identification and display method and device, and the method comprises the steps: collecting the operation state information and surrounding environment information of a vehicle through a multi-source sensor, and recognizing risk events in all preset events in combination with an artificial intelligence model; performing full-screen display on the risk event and the corresponding emergency measure on a vehicle display screen according to a preset display strategy; timing is conducted from the beginning of full-screen display, and if no active braking signal is detected within the preset time, a vehicle subsystem is controlled to execute forced braking measures according to the distance between the vehicle and the front obstacle. The multi-source sensor is used for forming complementary sensing capacity, failure or misjudgment of a single sensor in a complex scene is reduced, for the situation that active braking is not adopted in time, related vehicle subsystems are cooperatively dispatched to conduct forced braking measures, the risk that the vehicle is out of control and danger avoiding fails in the complex emergency situation is reduced, and the safety of the vehicle is improved. Therefore, the risk avoiding reliability of the vehicle is effectively improved.

Description

Technical Field

[0001] This application relates to the field of vehicle safety control technology, specifically to a method and device for identifying and displaying emergency situations while driving a vehicle. Background Technology

[0002] With the booming development of the global automotive industry, automobiles have become an indispensable means of transportation in people's daily lives. The continuous increase in car ownership has also made road traffic safety issues increasingly prominent. With the rapid development of technology, especially breakthroughs in artificial intelligence, sensor technology, and big data analysis, the application of intelligent monitoring and response mechanisms in vehicle safety has gradually become a research hotspot. Although existing vehicle safety control technologies have improved vehicle safety to some extent, there are still issues with the reliability of vehicle avoidance in emergency situations: The failure or misjudgment of single sensors in complex scenarios, such as rain, fog, or construction, can lead to the missed detection or false alarms of dangerous events, causing vehicles to miss the best opportunity for avoidance and reducing driving safety; Furthermore, the independent operation of various vehicle subsystems can lead to mutual interference in complex emergency situations, resulting in loss of vehicle control or failure to avoid danger. Summary of the Invention

[0003] This application provides a method and device for identifying and displaying emergency situations while driving a vehicle, which can solve the technical problem of poor reliability in vehicle avoidance in existing vehicle emergency safety control technologies.

[0004] To achieve the above objectives, in a first aspect, this application provides a method for identifying and displaying emergency situations while driving a vehicle, the method comprising: The system utilizes multi-source sensors to collect vehicle operating status information and surrounding environment information, and combines this with an artificial intelligence model to identify risk events among various preset events; these risk events include multiple risk levels.

[0005] According to a preset display strategy, the risk events and corresponding emergency measures are displayed in full screen on the vehicle's display screen.

[0006] Starting from the full-screen display, if no active braking signal is detected within a preset time, the vehicle subsystem will execute forced braking measures based on the distance between the vehicle and the obstacle in front.

[0007] Furthermore, in one embodiment, the operating status information includes the vehicle's acceleration and angular velocity; the surrounding environment information includes objects around the vehicle, the speed and orientation of the surrounding objects, the distance between the vehicle and the surrounding objects, and traffic elements.

[0008] The multi-source sensors include radar, camera, and inertial measurement unit; wherein, the camera identifies objects and traffic elements around the vehicle, the radar collects information about the surrounding environment, and the inertial measurement unit collects information about the operating status.

[0009] The preset events include collisions, mechanical failures, and sudden illness of the driver.

[0010] Furthermore, in one embodiment, the step of identifying risk events among various preset events using an artificial intelligence model includes: By combining the operational status information and surrounding environment information with an artificial intelligence model, the risk value of each preset event is predicted.

[0011] All risk events in each preset event are determined based on the risk value.

[0012] Furthermore, in one embodiment, determining the risk event among the preset events based on the risk value includes: The risk level of the risk event is divided into three levels.

[0013] If the risk value of the preset event is greater than or equal to the preset first risk threshold and less than the preset second risk threshold, the corresponding preset event is a low-risk event.

[0014] If the risk value of the preset event is greater than or equal to the second risk threshold and less than the preset third risk threshold, the corresponding preset event is a medium-risk event.

[0015] If the risk value of the preset event is greater than or equal to the third risk threshold, the corresponding preset event is a high-risk event.

[0016] The first risk threshold is less than the second risk threshold, which is less than the third risk threshold.

[0017] Furthermore, in one embodiment, the preset display strategy is: The vehicle display screen is divided into a main alarm area and a secondary alarm area. The high-risk events are displayed in the main alarm area in descending order of risk value from top to bottom, and the corresponding emergency measures are displayed at the bottom.

[0018] The low-risk and medium-risk events are displayed in the secondary alarm area in descending order of risk value from top to bottom.

[0019] Furthermore, in one embodiment, the active braking signal is a brake pedal opening greater than or equal to a preset opening threshold, or an equivalent braking request.

[0020] Furthermore, in one embodiment, the step of controlling the vehicle subsystem to perform forced braking based on the distance between the vehicle and the obstacle ahead includes: If the distance between the vehicle and the obstacle in front is greater than a preset distance threshold, the vehicle's automatic braking system will be activated to brake the vehicle.

[0021] Furthermore, in one embodiment, during braking control, if the distance between the vehicle and the obstacle in front is less than or equal to a preset distance threshold, the vehicle's steering assist system is activated to control the vehicle's steering.

[0022] Furthermore, in one embodiment, the method further includes: when the amount of newly added vehicle historical accident data reaches a preset incremental threshold, using the newly added vehicle historical accident data to iteratively train the artificial intelligence model.

[0023] Secondly, this application provides a vehicle emergency driving situation identification and display device, the device comprising: The risk identification module is used to collect vehicle operating status information and surrounding environment information using multi-source sensors, and combine them with artificial intelligence models to identify risk events among various preset events; the risk events include multiple risk levels.

[0024] The risk display module is used to display the risk event and the corresponding emergency measures on the vehicle display screen in full screen according to a preset display strategy.

[0025] The forced braking module is used to start timing from the beginning of the full-screen display. If no active braking signal is detected within a preset time, the vehicle subsystem is controlled to perform forced braking measures based on the distance between the vehicle and the obstacle in front.

[0026] The beneficial effects of the technical solutions provided in this application include: This application utilizes multi-source sensors to collect vehicle operating status information and surrounding environmental information, and combines this with an artificial intelligence model to identify risk events among various preset events. By leveraging the complementary sensing capabilities of multiple sensors, it reduces the failure or misjudgment of a single sensor in complex scenarios. According to a preset display strategy, the risk events and corresponding emergency measures are displayed in full screen on the vehicle's display screen. Starting from the beginning of the full-screen display, if no active braking signal is detected within a preset time, the vehicle subsystem is controlled to execute forced braking measures based on the distance between the vehicle and the obstacle ahead. By coordinating and scheduling relevant vehicle subsystems to perform forced braking measures, the risk of vehicle loss of control and failure to avoid obstacles in complex emergency situations is reduced, thereby effectively improving the reliability of vehicle obstacle avoidance. Attached Figure Description

[0027] Figure 1 This is a flowchart of a method for identifying and displaying emergency situations while driving, as described in this application.

[0028] Figure 2This is a block diagram of a vehicle emergency recognition and display device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0031] In a first aspect, embodiments of this application provide a method for identifying and displaying emergency situations while driving a vehicle.

[0032] In one embodiment, see Figure 1 As shown, Figure 1 This is a flowchart of a vehicle emergency identification and display method according to an embodiment of this application. The vehicle emergency identification and display method includes: S1. Collect vehicle operating status information and surrounding environment information using multi-source sensors, and identify risk events among preset events using an artificial intelligence model; where risk events include multiple risk levels, and the artificial intelligence model is a deep learning model or a machine learning model.

[0033] S2. Display the risk events and corresponding emergency measures on the vehicle display screen in full screen according to the preset display strategy.

[0034] S3. Starting from the start of full-screen display, if no active braking signal is detected within the preset time, the vehicle subsystem is controlled to perform forced braking measures based on the distance between the vehicle and the obstacle in front.

[0035] In this embodiment, a multi-source sensor system enables comprehensive and accurate perception of the vehicle's operating status and surrounding environment, effectively overcoming the shortcomings of single sensors that are prone to misjudgment or failure in complex scenarios. An artificial intelligence model can identify events of different risk levels in real time and, based on a preset display strategy, prioritize the display of risk events and their corresponding emergency measures in full-screen mode. This ensures that critical information is transmitted to the driver immediately and without interference, significantly improving the timeliness and effectiveness of information transmission. Simultaneously, active response monitoring is performed after full-screen display is initiated. If no active braking behavior from the driver is detected within a set time, the vehicle subsystem will automatically trigger emergency operations such as forced braking or assisted steering based on the distance between the vehicle and obstacles ahead.

[0036] This method can monitor vehicle operating status and surrounding environmental information in real time, identify potential hazards in advance, and deliver hazard information and countermeasures to users in an efficient and intuitive way. It can also assist in taking timely and effective mandatory countermeasures, thereby eliminating accident risks in their infancy. It achieves closed-loop management from hazard identification and information prompts to vehicle control, effectively reducing the risk of collisions caused by slow driver reaction or improper operation, and significantly enhancing the vehicle's ability to avoid risks and overall safety performance in emergency situations.

[0037] Furthermore, in one embodiment, in step S1 above, the operating status information includes the vehicle's acceleration and angular velocity; the surrounding environment information includes objects around the vehicle, the speed and orientation of the surrounding objects, the distance between the vehicle and the surrounding objects, and traffic elements (such as road markings).

[0038] The multi-source sensors include radar, cameras, and inertial measurement units; among them, cameras identify objects and traffic elements around the vehicle, radar collects the aforementioned surrounding environmental information, and inertial measurement units (IMUs) collect the aforementioned operational status information.

[0039] Preset events include collisions, mechanical failures, and sudden illnesses of the driver.

[0040] In this embodiment, the radar utilizes electromagnetic waves to detect and range target objects, featuring high precision and reliability. It can operate stably under various weather conditions, accurately acquiring information such as the distance between the vehicle and surrounding objects, the speed and orientation of objects around the vehicle. Millimeter-wave radar is widely used in the field of autonomous driving, capable of real-time monitoring of the position and movement of vehicles, pedestrians, and other obstacles ahead, providing crucial data for vehicle driving decisions. In complex traffic environments, millimeter-wave radar can simultaneously track multiple targets and is highly sensitive to changes in target speed and distance, enabling timely detection of potential collision risks.

[0041] Cameras capture information about the vehicle's surroundings in the form of images, providing rich visual details and recognizing various traffic signs, markings, vehicles, and pedestrians. With the continuous development of computer vision technology, cameras are playing an increasingly important role in vehicle safety monitoring. Forward-looking cameras can identify traffic signs and traffic lights on the road ahead, providing timely alerts to the driver; surround-view cameras can provide a 360-degree panoramic image of the vehicle's surroundings, helping drivers better understand the situation around the vehicle in scenarios such as parking and reversing, thus avoiding collisions.

[0042] An IMU (Integrated Measurement Unit) precisely senses a vehicle's motion state, including acceleration, deceleration, cornering, and tilting, by measuring its acceleration and angular velocity. The IMU plays a crucial role in vehicle dynamic stability control, monitoring changes in vehicle attitude in real time. When it detects a tendency to lose control, it promptly sends signals to the vehicle control system to take appropriate measures to maintain stability. For example, in an emergency requiring emergency braking at high speed, the IMU can monitor changes in acceleration during braking in real time, helping the vehicle control system to rationally distribute braking force and prevent dangerous situations such as skidding or fishtailing.

[0043] Furthermore, in one embodiment, in step S1 above, the risk events among the preset events are identified using an artificial intelligence model. The specific steps are as follows: By combining the above-mentioned operational status information and surrounding environment information with an artificial intelligence model, the risk value of each preset event is predicted.

[0044] Based on the above risk values, all risk events in each preset event are determined.

[0045] In this embodiment, by combining operational status information and surrounding environment information with an artificial intelligence model, the risk value of each preset event is predicted, and the corresponding preset event is determined to be a risk event based on the risk value. The artificial intelligence model can efficiently extract features from massive amounts of sensor data. Through learning and training on a large amount of historical data, it can identify various dangerous events, thereby quickly and accurately determining whether the current driving situation poses a risk during real-time monitoring. This not only improves the vehicle's sensitivity to potential hazards but also provides reliable data support for subsequent risk level classification, display strategy formulation, and forced braking decisions, significantly enhancing the vehicle's active safety performance in emergency situations.

[0046] Furthermore, in one embodiment, the risk events in each preset event are determined based on the risk value. The risk level of each risk event includes three levels: low-risk event, medium-risk event, and high-risk event. The specific method for determining the three levels of events is as follows: If the risk value of the aforementioned preset event is greater than or equal to the preset first risk threshold and less than the preset second risk threshold, the corresponding preset event is a low-risk event.

[0047] If the risk value of the aforementioned preset event is greater than or equal to the second risk threshold and less than the preset third risk threshold, the corresponding preset event is a medium-risk event.

[0048] If the risk value of the aforementioned preset event is greater than or equal to the third risk threshold, the corresponding preset event is a high-risk event. The first risk threshold is less than the second risk threshold, which is less than the third risk threshold. These risk thresholds are determined based on the risk values ​​from historical accident data.

[0049] Furthermore, in one embodiment, the preset display strategy in step S2 above is: The vehicle display screen is divided into a main alarm area and a secondary alarm area. High-risk events are displayed in the main alarm area in descending order of risk value from top to bottom, and the corresponding emergency measures are displayed at the bottom.

[0050] Low-risk and medium-risk events are displayed in the secondary alarm zone in descending order of risk value from top to bottom.

[0051] Because current vehicle displays typically support multiple display modes, such as split-screen mode, which simultaneously displays navigation information, multimedia entertainment information, and vehicle status information, this split-screen display mode can meet the driver's needs for multiple information under normal driving conditions. However, in emergency situations, too much information displayed may distract the driver, affecting their ability to obtain and process critical information and making it easy to miss risk warnings.

[0052] In this embodiment, by setting a full-screen display strategy for risk warnings, when a risk event is detected, the vehicle display screen switches to full-screen display, showing the corresponding risk warning and its corresponding countermeasures. Countermeasures may include braking force and timing, avoidance direction, etc. The full-screen vehicle display screen is divided into a main warning area and a secondary warning area. The secondary warning area is located on one side of the display screen and occupies a smaller area than the main warning area. If high-risk events and other levels of risk events exist simultaneously, the main warning area is used to display high-risk events, displayed in descending order of risk value on the display screen, with the font size decreasing sequentially and the font color being red. The corresponding countermeasures are displayed at the bottom of the main warning area. The secondary warning area is used to display low-risk and medium-risk events, displayed in descending order of risk value on the display screen. Once a high-risk event disappears, the highest-risk event among the remaining risk events is moved to the main warning zone. This ensures that the main warning zone always displays the most important events in an adaptive rotation, providing clear, concise, and prioritized visual warnings throughout the entire driving cycle. This significantly improves human-machine interaction efficiency and driving safety in emergency scenarios. If no high-risk events occur simultaneously, the highest-risk event among other risk events is displayed in the main warning zone, while the remaining risk events are displayed in the secondary warning zones.

[0053] Furthermore, in one embodiment, in step S3 above, the active braking signal is a brake pedal opening greater than or equal to a preset opening threshold, or an equivalent braking request.

[0054] Furthermore, in one embodiment, in step S3 above, the vehicle subsystem is controlled to perform forced braking measures based on the distance between the vehicle and the obstacle in front. The specific steps are as follows: If the distance between the vehicle and the obstacle in front is greater than a preset distance threshold, the vehicle's automatic braking system will be activated to brake the vehicle.

[0055] During braking control, if the distance between the vehicle and the obstacle in front is less than or equal to a preset distance threshold, the vehicle's steering assist system is activated to control the vehicle's steering.

[0056] In this embodiment, when a high-risk event warning is issued, the vehicle's lighting system immediately activates the hazard lights. If the driver fails to take timely active braking measures, the vehicle's automatic braking system is activated to forcibly brake the vehicle, reducing its speed and the impact of a collision. During braking, the system monitors the distance and relative speed between the vehicle and surrounding obstacles in real time, dynamically adjusting the braking force to ensure optimal braking performance. If, during braking, the distance between the vehicle and an obstacle ahead is detected to be less than or equal to a preset distance threshold, the vehicle's steering assist system is activated to forcibly steer the vehicle. The steering assist system controls the vehicle's steering mechanism to complete the turn. During the turn, the steering assist is automatically adjusted based on the vehicle's speed, steering angle, and surrounding environmental information to ensure smooth and safe steering. In certain dangerous situations, simple braking may not be enough to prevent a collision. In such cases, the steering assist system plays a crucial role, enabling the lighting system, automatic braking system, and steering assist system to work together to address the danger and effectively reduce the risk of loss of vehicle control and failed hazard avoidance in complex emergency situations.

[0057] Furthermore, in one embodiment, the above-mentioned vehicle driving emergency identification and display method further includes: when the amount of newly added vehicle historical accident data reaches a preset incremental threshold, using the newly added vehicle historical accident data to iteratively train the above-mentioned artificial intelligence model.

[0058] In this embodiment, historical vehicle accident data is continuously monitored, including the time and location of the accident, the vehicle's driving status (speed, direction, acceleration, etc.), surrounding environmental information (weather, road conditions, traffic flow, etc.), and the countermeasures taken. This data is stored in the vehicle's local database or a cloud server, forming a large historical accident dataset. When the amount of newly added historical vehicle accident data reaches a preset incremental threshold, the artificial intelligence model is iteratively trained using this new data. This improves the model's accuracy and efficiency in complex scenarios such as rain, fog, and construction, enabling it to take appropriate countermeasures, reduce accident risks, and enhance driving safety.

[0059] Secondly, embodiments of this application also provide a vehicle emergency driving recognition and display device, see [link to relevant documentation]. Figure 2 As shown, Figure 2This is a block diagram of a vehicle emergency identification and display device according to an embodiment of this application. The vehicle emergency identification and display device includes a risk identification module, a risk display module, and a forced braking module. Specifically: The risk identification module is used to collect vehicle operating status information and surrounding environment information using multi-source sensors, and combine them with artificial intelligence models to identify risk events in various preset events; risk events include multiple risk levels.

[0060] The risk display module is used to display risk events and corresponding emergency measures on the vehicle's display screen in full screen according to a preset display strategy.

[0061] The forced braking module is used to start timing from the beginning of the full-screen display. If no active braking signal is detected within a preset time, the vehicle subsystem is controlled to perform forced braking measures based on the distance between the vehicle and the obstacle in front.

[0062] This application achieves accurate environmental perception in all weather and all postures through multi-source fusion of radar, camera, and inertial measurement unit, overcoming the shortcomings of single sensors in scenarios such as rain, fog, and backlight. It introduces a "hazard level-full-screen switching" mechanism, interrupting the entertainment navigation split-screen in milliseconds to display high-risk event warnings and corresponding countermeasures in the main warning area, while the secondary warning area displays other risk events, ensuring the driver's line of sight captures the most critical information immediately and eliminating the risk of information dispersion. It continuously collects historical vehicle accident data and utilizes dynamic iterative algorithms to make risk identification more accurate, achieving increasingly safer driving. If no active braking is detected within a preset time after full-screen display, it immediately coordinates automatic braking and steering assistance based on the distance between the vehicle and obstacles ahead, forming a closed loop of perception-decision-control. Therefore, it significantly reduces the accident rate, providing drivers and passengers with full-cycle, personalized, and continuously evolving safety protection, improving driving safety, and making the driving process more relaxed, comfortable, and reliable.

[0063] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0064] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0065] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0066] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0067] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0069] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for identifying and displaying emergency situations while driving a vehicle, characterized in that, The method includes: The system utilizes multi-source sensors to collect vehicle operating status information and surrounding environment information, and combines this with an artificial intelligence model to identify risk events among various preset events; the risk events include multiple risk levels. According to the preset display strategy, the risk events and corresponding emergency measures are displayed in full screen on the vehicle display screen; Starting from the full-screen display, if no active braking signal is detected within a preset time, the vehicle subsystem will execute forced braking measures based on the distance between the vehicle and the obstacle in front.

2. The vehicle emergency driving identification and display method as described in claim 1, characterized in that, The operating status information includes the vehicle's acceleration and angular velocity; the surrounding environment information includes objects around the vehicle, the speed and position of the surrounding objects, the distance between the vehicle and the surrounding objects, and traffic elements. The multi-source sensor includes a radar, a camera, and an inertial measurement unit; wherein, the camera identifies objects and traffic elements around the vehicle, the radar collects information about the surrounding environment, and the inertial measurement unit collects information about the operating status. The preset events include collisions, mechanical failures, and sudden illness of the driver.

3. The vehicle emergency driving identification and display method as described in claim 1, characterized in that, The method of identifying risk events among preset events using artificial intelligence models includes: By combining the operational status information and surrounding environment information with an artificial intelligence model, the risk value of each preset event is predicted. All risk events in each preset event are determined based on the risk value.

4. The vehicle emergency driving identification and display method as described in claim 3, characterized in that, The step of determining the risk events among the preset events based on the risk value includes: The risk levels of the aforementioned risk events are classified into three levels; If the risk value of the preset event is greater than or equal to the preset first risk threshold and less than the preset second risk threshold, the corresponding preset event is a low-risk event. If the risk value of the preset event is greater than or equal to the second risk threshold and less than the preset third risk threshold, the corresponding preset event is a medium-risk event. If the risk value of the preset event is greater than or equal to the third risk threshold, the corresponding preset event is a high-risk event; The first risk threshold is less than the second risk threshold, which is less than the third risk threshold.

5. The vehicle emergency driving identification and display method as described in claim 4, characterized in that, The preset display strategy is as follows: The vehicle display screen is divided into a main alarm area and a secondary alarm area. The high-risk events are displayed in the main alarm area in descending order of risk value from top to bottom, and the corresponding emergency measures are displayed at the bottom. The low-risk and medium-risk events are displayed in the secondary alarm area in descending order of risk value from top to bottom.

6. The vehicle emergency driving identification and display method as described in claim 1, characterized in that, The active braking signal is a brake pedal opening greater than or equal to a preset opening threshold, or an equivalent braking request.

7. The vehicle emergency driving identification and display method as described in claim 1, characterized in that, The method of controlling the vehicle subsystem to perform forced braking based on the distance between the vehicle and the obstacle in front includes: If the distance between the vehicle and the obstacle in front is greater than a preset distance threshold, the vehicle's automatic braking system will be activated to brake the vehicle.

8. The vehicle emergency driving identification and display method as described in claim 7, characterized in that, During braking control, if the distance between the vehicle and the obstacle in front is less than or equal to a preset distance threshold, the vehicle's steering assist system is activated to control the vehicle's steering.

9. The method for identifying and displaying vehicle emergency driving conditions as described in claim 1, characterized in that, The method also includes: when the amount of newly added vehicle historical accident data reaches a preset incremental threshold, using the newly added vehicle historical accident data to iteratively train the artificial intelligence model.

10. A vehicle emergency identification and display device, characterized in that, The device includes: The risk identification module is used to collect vehicle operating status information and surrounding environment information using multi-source sensors, and combine them with an artificial intelligence model to identify risk events among various preset events; the risk events include multiple risk levels. The risk display module is used to display the risk event and the corresponding emergency measures on the vehicle display screen in full screen according to a preset display strategy. The forced braking module is used to start timing from the beginning of the full-screen display. If no active braking signal is detected within a preset time, the vehicle subsystem is controlled to perform forced braking measures based on the distance between the vehicle and the obstacle in front.

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