Vehicle prompting method, device and electronic equipment

Abstract

The application discloses a vehicle prompting method and device and electronic equipment. The method comprises the following steps: determining the attention direction of a driver according to the eye movement data and the head posture data of the driver; determining a plurality of first vehicles in the attention focus area of the driver according to the environmental data of the vehicle where the driver is and the attention direction; determining a target vehicle from the plurality of first vehicles according to at least one of the eye movement data, the gesture data of the driver, the voice data of the driver, the environmental data and the vehicle type of the first vehicle, wherein the target vehicle comprises a vehicle to which the driver pays attention; and displaying prompt information corresponding to the target vehicle in a display picture, wherein the display picture comprises a visual field picture of the driver, so that the prompt information of the vehicle to which the driver pays attention is highlighted, and the safety of vehicle driving is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a vehicle alerting method, apparatus, and electronic device. Background Technology

[0002] With the development of science and technology, augmented reality (AR) technology has been gradually applied to the automotive field. For example, the augmented reality head-up display (AR-HUD) system projects some information onto the windshield, reducing the driver's eye shift to some extent. However, because this technology mainly focuses on the vehicle's proactive information delivery, it fails to accurately capture and utilize the driver's attention direction. In complex road conditions, the driver may still be distracted by information overload and find it difficult to quickly focus on key vehicle information. Summary of the Invention

[0003] In view of this, embodiments of this application propose a vehicle alerting method, apparatus, and electronic device to improve the above-mentioned problems.

[0004] In a first aspect, embodiments of this application provide a vehicle prompting method, the method comprising: determining the driver's attention direction based on the driver's eye movement data and head posture data; determining a plurality of first vehicles within the driver's attention focus area based on environmental data of the vehicle in which the driver is located and the attention direction; determining a target vehicle from the plurality of first vehicles based on at least one of the eye movement data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicles, wherein the target vehicle includes the vehicle in which the driver's attention is focused; and displaying prompt information corresponding to the target vehicle on a display screen, wherein the display screen includes the driver's field of vision.

[0005] Secondly, embodiments of this application provide a vehicle alerting device, comprising: an attention direction determination module, a first vehicle determination module, a focus vehicle determination module, and a focus vehicle alerting module. The attention direction determination module is used to determine the driver's attention direction based on the driver's eye movement data and head posture data; the first vehicle determination module is used to determine multiple first vehicles within the driver's attention focus area based on environmental data of the vehicle in which the driver is located and the attention direction; the focus vehicle determination module is used to determine a target vehicle from the multiple first vehicles based on at least one of the eye movement data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicles, wherein the target vehicle includes the vehicle that the driver's attention is focused on; and the focus vehicle alerting module is used to display alert information corresponding to the target vehicle on a display screen, wherein the display screen includes the driver's field of vision.

[0006] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory is coupled to the processor, the memory stores instructions, and when the instructions are executed by the processor, the processor executes the vehicle alerting method provided in the first aspect above.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing program code, which can be invoked by a processor to execute the vehicle notification method provided in the first aspect above.

[0008] In the solution of this application, the driver's attention direction is determined by the driver's eye movement data and head posture data. Then, based on the driver's attention direction and the surrounding environment data of the driver's vehicle, multiple vehicles within the driver's attention focus area are identified. Furthermore, by combining at least one of the driver's gesture data, voice data, environmental data of the vehicle, and vehicle type of the vehicles within the attention focus area, the vehicle that the driver is focusing on is identified from the multiple vehicles within the driver's attention focus area. When displaying the driver's field of vision, a prompt message for the vehicle that the driver is focusing on is displayed, thereby highlighting the prompt message for the vehicle that the driver is focusing on and improving the safety of driving. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic flowchart of a vehicle alerting method according to an embodiment of this application is shown; Figure 2 A schematic flowchart of a vehicle alerting method according to an embodiment of this application is shown; Figure 3 A structural block diagram of a vehicle alert system provided in an embodiment of this application is shown; Figure 4 A module block diagram of a vehicle alert device according to an embodiment of this application is shown; Figure 5 A block diagram of an electronic device for performing a vehicle prompting method according to an embodiment of this application is shown. Detailed Implementation

[0011] 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.

[0012] To better understand the solutions of the embodiments of this application, the technical terms used in the embodiments of this application will be explained below.

[0013] Augmented Reality Glasses (AR glasses) are smart wearable devices that combine the real world and the virtual world by using technologies such as augmented reality, audio-visual fusion, and waveguide lenses to collect data from real-world scenes through cameras and sensors.

[0014] Virtual Reality (VR) headsets use head-mounted displays to shut out a person's vision and hearing from the outside world, guiding the user to feel as if they are in a virtual environment. The display principle is that the left and right screens display images for the left and right eyes respectively, and the human eye obtains this information with differences and then generates a sense of three-dimensionality in the brain.

[0015] Augmented Reality Head-up Display (AR-HUD) is an in-vehicle display technology that accurately overlays virtual information (such as navigation arrows, vehicle speed, warning signs, etc.) onto the real road scene in front of the driver.

[0016] The implementation details of the technical solutions in the embodiments of this application are described in detail below: In the current field of intelligent transportation and augmented reality interaction technologies, in-vehicle display and interaction systems are experiencing rapid development. In the application of AR technology in the automotive sector, AR glasses can integrate high-precision eye-tracking sensors and head posture sensors, enabling close-range and high-precision monitoring of the driver's eye movements and head gestures. Compared to other devices, AR glasses are closer to the driver's eyes and head, and the monitoring data is less affected by external environmental interference, allowing for more accurate determination of the driver's attention direction.

[0017] Among related technologies, there are already solutions that utilize AR glasses to enhance the driving experience. Users can wear AR glasses to visually integrate navigation instructions, hazard warnings, entertainment content, charging station information, and parking status into the real environment. However, this technology mainly focuses on the vehicle actively pushing information and does not accurately capture and utilize the driver's attention direction. In complex road conditions, drivers may still be distracted by too much information and find it difficult to quickly focus on key vehicle information.

[0018] For example, traditional AR-HUD systems are limited by the projection area on the windshield, resulting in small information display size, poor clarity and brightness, and a tendency to produce ghosting. Therefore, existing AR-HUD systems present excessive and cluttered information in complex road conditions, making it difficult to highlight key information. Drivers are easily distracted by irrelevant information, leading to delayed access to crucial information. For instance, in congested urban areas, simultaneously displaying vehicle speed, navigation instructions, and distances to surrounding vehicles can overwhelm drivers, making it difficult to quickly focus on vehicles requiring immediate attention (such as those about to collide).

[0019] Furthermore, existing interaction methods primarily rely on vehicles proactively providing information, lacking precise capture and utilization of the driver's attention direction. During driving, a driver's attention naturally focuses on vehicles or targets that significantly influence driving decisions, and there may even be brief periods of distraction. However, current technology cannot intelligently adjust information display based on the driver's attention distribution, thus affecting driving safety and efficiency. Therefore, in judging driver attention, existing interaction methods mainly rely on vehicles proactively pushing information, without fully considering the driver's natural attention distribution. Although some devices attempt to monitor driver status, such as fatigue or distraction, they still lack precise capture and in-depth analysis of the targets the driver's gaze naturally focuses on during driving. They cannot intelligently adjust information presentation based on the driver's real-time focus, greatly limiting the effectiveness of interaction and the improvement of driving safety.

[0020] The existing system also has shortcomings in handling special scenarios. For example, when encountering emergency rescue vehicles, law enforcement vehicles, or other special vehicles, the system cannot quickly guide the driver's attention to identify and focus on these special vehicles in a timely manner. After the driver focuses on the special vehicle, the system cannot provide further personalized services based on the driver's attention status, such as predicting the special vehicle's driving path or alerting the driver of potential interaction risks with the vehicle. This makes it difficult to meet the driving needs in complex traffic scenarios.

[0021] In addition, Google showcased AR glasses navigation in the Android Auto 14.2 beta. Users can activate navigation while wearing AR glasses, displaying precise Google Maps location and real-time updates directly in front of them, reducing the need for drivers to look down at their phones or central control displays and minimizing distraction. However, this technology also lacks monitoring and analysis of driver attention, and cannot intelligently adjust information display based on the driver's focus. In information-rich driving scenarios, the efficiency with which drivers obtain crucial information still needs improvement.

[0022] Other related technologies propose integrating sensors into AR / VR devices to detect user head movements and eye positions, using algorithms to analyze the data and generate audio cues to enhance user interaction with the virtual environment. However, this technology is mainly applied to virtual environments and lacks the ability to identify the focal vehicle and provide drivers with rich interactive services related to it in real driving scenarios. It cannot directly solve the problem of drivers' attention to and information interaction with surrounding real vehicles during driving.

[0023] In summary, the application of AR technology in the automotive field has several drawbacks, including cluttered and unfocused information display, inability to accurately capture and utilize driver attention, insufficient interactive services in special scenarios, and consequently, lower vehicle driving safety.

[0024] To address the aforementioned problems, the inventors, through extensive research, have developed the vehicle alerting method, device, and electronic device provided in this application. By displaying alert information for the vehicle the driver is focused on while viewing the driver's field of vision, the system highlights the alert information for the vehicle the driver is paying attention to, thereby improving driving safety. The specific vehicle alerting method will be described in detail in subsequent embodiments.

[0025] The embodiments involved in this application will now be described with reference to the accompanying drawings.

[0026] Please see Figure 1 , Figure 1 A flowchart illustrating a vehicle alert method according to an embodiment of this application is shown. In a specific embodiment, this vehicle alert method can be applied to, for example... Figure 4The vehicle warning device 200 and the electronic device 100 equipped with the vehicle warning device 200 are shown. Figure 5 The following will use an electronic device as an example to illustrate the specific process of this embodiment. The following will focus on... Figure 1 The process shown will be described in detail. The vehicle notification method may specifically include the following steps: Step S110: Determine the driver's attention direction based on the driver's eye movement data and head posture data.

[0027] In some implementations, the electronic device in this embodiment can acquire eye movement data and head posture data of users inside the vehicle (e.g., the driver and other passengers of the vehicle), and can determine the direction of attention of users inside the vehicle based on the eye movement data and head posture data. Based on at least one of the direction of attention of users inside the vehicle, vehicle environment data, gesture data of users inside the vehicle, and voice data, it can determine the vehicle that the user's attention is focused on, and can display prompt information corresponding to the vehicle that the user's attention is focused on while displaying the user's field of vision.

[0028] As one possible implementation, the electronic device may include a head-mounted display device; wherein the head-mounted display device may be equipped with an eye-tracking sensor and a head posture sensor. The eye-tracking sensor can be used to capture subtle eye movements of the wearer of the head-mounted display device; the head posture sensor can be used to acquire information on the direction and angle of the wearer's head rotation. The wearer can be understood as a user inside the vehicle, including but not limited to the driver and other occupants of the vehicle.

[0029] For example, a head-mounted display device can use eye-tracking sensors to detect real-time eye movement data such as the wearer's eye movement trajectory, fixation point position, eye rotation angle, and fixation duration. The fixation point can be the intersection of the wearer's binocular visual axes captured by the eye-tracking sensor, thereby determining the precise location the wearer is looking at in three-dimensional space. Fixation duration can include the duration for which the wearer's eyes focus on a specific area and can be used to distinguish between the wearer's dominant and peripheral vision, improving the accuracy of determining the vehicle on which the wearer's attention is focused. Eye rotation angle can include the horizontal and vertical rotation of the eyes.

[0030] For example, a head-mounted display device can detect head posture data such as yaw angle (left and right rotation), pitch angle (up and down rotation), and roll angle (left and right tilt) of the wearer's head in real time through a head posture sensor.

[0031] It is understandable that simple eye movement data changes with head movement; therefore, in this embodiment, the head-mounted display device can determine the wearer's attention direction based on the wearer's eye movement data and head posture data. The attention direction can be understood as the spatial orientation of the target of attention determined by the wearer's gaze and head direction in the current environment, or as the direction the wearer is looking (e.g., left front, right rear, etc.).

[0032] The head-mounted display device can vector-superimpose the rotation angle of the eyeballs within the eye sockets with the rotation angle of the head to determine the wearer's attention direction. For example, attention direction = head orientation + relative eyeball offset; where, if the wearer's head turns 20 degrees to the left, and the eyes turn 10 degrees to the right relative to the head, the wearer's attention direction can be determined to be 10 degrees to the left. This allows for gaze compensation based on head movement data, improving the accuracy of attention direction capture. Optionally, during the acquisition of the wearer's attention direction, the head-mounted display device can also generate a three-dimensional spatial ray along the fused angle direction, with the center of the wearer's eyes (or head) as the origin. This coordinate fusion determines the wearer's true attention direction in the world coordinate system, further improving the accuracy and realism of attention direction capture.

[0033] In some implementations, the electronic device may also communicate with sensors that collect eye movement data and head posture data of the driver in the vehicle, and may acquire the driver's eye movement data and head posture data collected by the sensors, and may determine the driver's attention direction based on the eye movement data and head posture data.

[0034] Step S120: Based on the environmental data of the vehicle where the driver is located and the direction of attention, determine multiple first vehicles within the driver's attention focus area.

[0035] In some embodiments, the electronic device can be a head-mounted display device and can communicate with the vehicle in which the wearer of the head-mounted display device is located. The wearer of the head-mounted display device can be the driver or other occupants of the vehicle, without limitation. The vehicle can acquire environmental data detected by vehicle sensors (e.g., cameras, radar, etc.) in real time and transmit it to the head-mounted display device in real time. The head-mounted display device can also communicate with surrounding vehicles to obtain environmental data acquired by those vehicles. For example, the head-mounted display device can acquire environmental data perceived by surrounding vehicles through V2X (vehicle-to-everything) communication. For instance, the head-mounted display device can interact with the sensor system (e.g., cameras, radar, etc.) of the wearer's vehicle and surrounding vehicles through a wireless communication module; wherein, the vehicle sensors collect environmental data such as the position, speed, and direction of travel of vehicles in the surrounding environment in real time and transmit it to the head-mounted display device.

[0036] In some implementations, after acquiring environmental data of the vehicle in which the driver is located and the driver's attention direction, the electronic device can determine multiple first vehicles within the driver's attention focus area based on the environmental data and the attention direction. Here, a first vehicle can be understood as a vehicle that is potentially the driver's attention is focused on, or a potential focus vehicle.

[0037] It is understandable that, due to the slight, unconscious tremors in human vision (which can also be understood as physiological nystagmus), and the complex driving environment, head-mounted display devices cannot determine the wearer's gaze target based on a single ray; instead, they need to construct a spatial cone (or visual cone). Therefore, in this embodiment, the electronic device can determine a preset area in the environmental data that matches the direction of attention as the driver's focus area, and can identify vehicles within this focus area as the first vehicle.

[0038] For example, the electronic device can use the line of sight corresponding to the direction of attention as the central axis to set a spatial cone model with a horizontal viewing range (e.g., 5°~10° to the left and right) and a vertical viewing range (e.g., 3°~5° up and down) as the driver's attention focus area, and can identify vehicles within this attention focus area as the first vehicle. Optionally, the electronic device can also identify the central concave area (the cone center) of the spatial cone model as the driver's primary visual area, and the peripheral area (the cone edge) of the spatial cone model as the driver's peripheral visual area, thereby distinguishing between primary and peripheral vision. Vehicles within the primary visual area are identified as higher-priority focus vehicles, while vehicles within the peripheral visual area are identified as lower-priority vehicles that pose a potential threat or are located in the surrounding environment, thereby improving the efficiency and accuracy of the driver's attention focus vehicle identification.

[0039] In some implementations, electronic devices can acquire an environmental semantic map corresponding to the driver's vehicle based on the acquired environmental data of the driver's vehicle. For example, the electronic device can acquire environmental data of the driver's vehicle through V2X (vehicle-to-everything) or onboard sensors (cameras, radar), and can obtain the coordinates, dimensions, and bounding boxes of surrounding vehicles from this environmental data. It can also perform spatial intersection calculations between the attention focus area (e.g., a spatial line-of-sight cone model) and the 3D bounding boxes of surrounding vehicles to calculate whether the driver's vehicle has collided with another vehicle. If a vehicle's bounding box intersects with the line-of-sight cone, or if the vehicle's projection distance on the line-of-sight ray is the shortest, the vehicle can be determined to be within the attention focus area and identified as the first vehicle. Thus, based on the driver's attention direction, vehicles within the driver's attention focus area are selected from the acquired environmental data including information about surrounding vehicles and designated as the first vehicle, reducing the computational load for determining the driver's attention focus vehicle and increasing the speed of identification.

[0040] Step S130: Determine a target vehicle from the plurality of first vehicles based on at least one of the eye-tracking data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicle, wherein the target vehicle includes the vehicle on which the driver's attention is focused.

[0041] In some implementations, the electronic device can determine the target vehicle from a plurality of first vehicles based on the driver's eye movement data, head posture data, gesture data, voice data, environmental data of the vehicle in question, and the vehicle type of the first vehicle, thereby achieving multimodal detection of the focus vehicle. The target vehicle may include the vehicle on which the driver's attention is focused (which can also be understood as the focus vehicle). The number of vehicles on which the driver's attention is focused can be one or more, and is not limited here.

[0042] For example, electronic devices can identify the first vehicle whose gaze duration is longer than a preset duration based on eye-tracking data; they can also identify the first vehicle in the direction of head turn based on head posture data; they can identify the first vehicle specified by gesture data; they can identify the first vehicle specified by voice data; they can identify the first vehicle with a distance less than a preset distance based on vehicle environment data; and they can identify the first vehicle of a preset type (e.g., ambulance, fire truck, armored truck, etc.) based on vehicle type. This better meets the interaction needs of special scenarios, such as when drivers need to quickly identify and focus on specific vehicles, like emergency rescue vehicles or law enforcement vehicles, enhancing personalized service capabilities. Simultaneously, supporting voice commands and gesture recognition allows drivers to interact more naturally and conveniently with the information displayed on the electronic device while driving, obtaining personalized services according to their needs and improving the user experience.

[0043] However, the process of multimodal detection of the focal vehicle suffers from problems such as multimodal detection conflicts, false detections, or missed detections of focal vehicles. For example, please refer to Table 1, which shows a table of defects in multimodal detection of the focal vehicle provided in one embodiment of this application.

[0044] Table 1 Based on this, in this embodiment, considering the problem of multimodal detection conflict in the process of determining the vehicle that the driver's attention is focused on through multimodal signals, after determining the first vehicle, the electronic device can determine the target vehicle from multiple first vehicles based on at least one of the driver's eye movement data, driver's gesture data, driver's voice data, environmental data of the driver's vehicle, and the vehicle type of the first vehicle.

[0045] As an implementable approach, electronic devices can prioritize multimodal signals to accurately determine the vehicle on which the driver's attention is focused. For example, prioritizing eye-tracking data (e.g., gaze duration), the electronic device can rank the priority based on the eye-tracking data, including gaze duration, and determine whether the first vehicle is briefly glanced at or continuously gazed upon based on the driver's gaze duration and head stability. Specifically, the electronic device can trigger a primary focus when the driver's continuous gaze on the vehicle exceeds a first duration (e.g., 0.5 seconds) and a secondary focus when the driver's continuous gaze on the vehicle is less than a second duration (e.g., 0.3 seconds). The first duration is greater than the second duration. The electronic device can identify vehicles in the primary visual region as primary focuses and vehicles in the peripheral visual regions as secondary focuses. The focus weight corresponding to the primary focus is greater than the focus weight corresponding to the secondary focus. The electronic device can determine the target vehicle from multiple first vehicles based on the focus weight corresponding to the first vehicle. Optionally, the electronic device can identify the target vehicle as the first vehicle whose focus weight is greater than a preset weight.

[0046] For example, the electronic device can also prioritize vehicles based on eye-tracking data and vehicle location. Specifically, the electronic device can identify vehicles in the primary visual region as primary focal points and vehicles in the peripheral visual regions as secondary focal points. The focusing weight of a primary focal point is greater than that of a secondary focal point. The electronic device can then determine the target vehicle from multiple primary vehicles based on the focusing weight of the primary vehicle. Optionally, the electronic device can identify the target vehicle as a primary vehicle whose focusing weight is greater than a preset weight.

[0047] Step S140: Display the prompt information corresponding to the target vehicle on the display screen, wherein the display screen includes the driver's field of vision.

[0048] In some implementations, after the electronic device identifies a target vehicle, it can display corresponding prompts for the target vehicle on a display screen. This display screen includes the driver's field of vision from the vehicle, which is communicatively connected to the electronic device. For example, the electronic device can be a head-mounted display device. Based on this, the head-mounted display device can display the prompts for the target vehicle on its display screen. This display screen can include the field of vision of the wearer of the head-mounted display device. The prompts for the target vehicle include, but are not limited to, the target vehicle's driving data (e.g., real-time speed, distance to the driver's vehicle, driving direction, vehicle type, etc.) and the target vehicle's identification data (e.g., the target vehicle's brand and model, license plate number, whether it is an autonomous vehicle, etc.). By prominently displaying the prompts for the target vehicle in the driver's field of vision, the driver's attention is quickly guided, improving driving safety.

[0049] For example, the electronic device can display information about the target vehicle (such as the target vehicle's real-time speed, distance to the current vehicle, direction of travel, and vehicle type, such as sedan, truck, bus, etc.) in a highlighted manner on the display screen. This information can be displayed using a simple and clear graphical interface, such as using different colored lines to represent the target vehicle's trajectory, displaying the target vehicle's speed and distance to the driver's vehicle in prominent numbers, etc.

[0050] In some implementations, after identifying a target vehicle, the electronic device can monitor its driving status in real time and output alarm information when the driving status meets warning conditions, thereby improving driving safety. The alarm information can be used to prompt the target vehicle to trigger a safety alarm mechanism. Warning conditions include, but are not limited to, the target vehicle changing lanes or the target vehicle's speed change rate exceeding a threshold. The electronic device can output alarm information in ways including, but not limited to, voice broadcasting and visual display. For example, when the electronic device determines that the target vehicle's speed change rate exceeds a threshold and the distance change rate between the target vehicle and the driver's vehicle exceeds a distance change rate threshold, it can output alarm information on the electronic device's display screen by turning the target vehicle's outline red and flashing it at a first frequency, while simultaneously emitting a rapid alarm sound at a second frequency.

[0051] In some implementations, while displaying prompts for the target vehicle on the electronic device's screen, the electronic device can also interact with the driver to manage the prompts. Specifically, the driver can switch between different prompts for different target vehicles on the electronic device's screen using gestures or voice commands, or adjust the display method and layout of the prompts. For example, the driver can input a voice command, such as "View detailed information about the focused vehicle," and the electronic device can respond by displaying more detailed information about the target vehicle on the screen (e.g., vehicle brand and model, license plate number, whether it is an autonomous vehicle, etc.).

[0052] In some implementations, while displaying prompts for the target vehicle on the screen, the electronic device can also simplify the display of prompts for non-target vehicles. For example, the electronic device can highlight and enhance the prompts for the target vehicle, including but not limited to displaying detailed data, such as using highly saturated colors and dynamic outlines to display the target vehicle's speed, distance, and trajectory lines. Conversely, the electronic device can abstract and reduce the display of prompts for non-target vehicles, including but not limited to removing detailed numerical displays, retaining necessary placeholders, using semi-transparency, simplifying the illustrations, or only displaying outlines. This allows for precise identification of the vehicle the driver is focused on and timely provision of crucial information and alerts, helping the driver react more quickly and avoid potential traffic accidents. For instance, on a highway, a driver can quickly notice a vehicle suddenly changing lanes and obtain its speed and distance, allowing for timely adjustments to their driving behavior and improving driving safety. Furthermore, highlighting the most important vehicle information prevents drivers from getting lost among a sea of ​​vehicle data, reduces irrelevant information interference, and allows drivers to more efficiently obtain information valuable for driving decisions. In complex urban road conditions, drivers no longer need to search through cluttered information to find key details, allowing them to quickly focus on the vehicles they need attention, saving information processing time and improving information acquisition efficiency. In addition, rich interactive methods, such as voice commands and gesture recognition, enable drivers to interact more naturally and conveniently with the key vehicle information displayed on electronic devices, obtaining detailed information or adjusting the information display according to their needs, thus enhancing the user experience during driving. For example, drivers can obtain the necessary information through simple voice commands without taking their hands off the steering wheel, making operation smoother and enhancing the interaction between the driver and electronic devices.

[0053] One embodiment of this application provides a vehicle alert method that determines the driver's attention direction using the driver's eye movement data and head posture data. Then, based on the driver's attention direction and the surrounding environment data of the vehicle, it identifies multiple vehicles within the driver's attention focus area. Finally, it combines at least one of the driver's gesture data, voice data, environmental data of the vehicle, and vehicle types of the vehicles within the attention focus area to determine the vehicle the driver is focused on from among the multiple vehicles. When displaying the driver's field of vision, it displays alert information for the vehicle the driver is focused on, thereby highlighting the alert information for the vehicle the driver is focused on and improving driving safety.

[0054] Please see Figure 2 , Figure 2 A schematic flowchart of a vehicle alerting method according to an embodiment of this application is shown. This method is applied to the aforementioned electronic device, and will be discussed below. Figure 2 The process shown will be described in detail. The vehicle notification method may specifically include the following steps: Step S210: Determine the driver's attention direction based on the driver's eye movement data and head posture data.

[0055] Step S220: Based on the environmental data of the vehicle where the driver is located and the direction of attention, determine multiple first vehicles within the driver's attention focus area.

[0056] For a detailed description of steps S210-S220, please refer to the previous description of steps S110-S120, which will not be repeated here.

[0057] Step S230: Determine the target vehicle from the plurality of first vehicles based on the environmental data and at least one of the vehicle types of the first vehicles, wherein the target vehicle includes the vehicle that the driver's attention is focused on in the event of emergency driving of the vehicle in which the driver is located.

[0058] In some implementations, after obtaining environmental data of the driver's vehicle and the vehicle type of the first vehicle, the electronic device can determine a target vehicle from a plurality of first vehicles based on at least one of the environmental data and the vehicle type of the first vehicle. The target vehicle may include the vehicle on which the driver's attention is focused during an emergency driving situation. In an emergency driving situation, the vehicle on which the driver's attention is focused can be understood as the target vehicle at the emergency level, or as a source of vehicle security threat. Regardless of the driver's current attention direction, a security hijacking mechanism is used to forcibly override the vehicle on which the driver's attention is focused, determined by the driver's current attention direction.

[0059] As one feasible approach, electronic devices can identify a first vehicle of a preset type as the target vehicle. These preset types include, but are not limited to, emergency rescue vehicles and law enforcement vehicles. This allows for rapid identification of specific vehicles in special scenarios such as emergency rescue vehicles and law enforcement vehicles.

[0060] As another feasible approach, electronic devices can identify the first vehicle with a probability greater than a collision probability threshold based on environmental data, and designate it as the target vehicle. This allows for collision warnings when a vehicle rapidly approaches the driver's vehicle.

[0061] Specifically, before identifying a first vehicle with a collision probability greater than a collision probability threshold as the target vehicle based on environmental data, the electronic device determines the distance and relative speed between each first vehicle and the driver's vehicle based on the environmental data. Furthermore, it obtains the probability of each first vehicle colliding with the driver's vehicle based on the corresponding distance and relative speed. This collision probability is then compared with the collision probability threshold to determine the target vehicle.

[0062] Step S240: Based on the eye-tracking data, the gesture data, and the voice data, determine the target vehicle from the plurality of first vehicles, wherein the target vehicle includes the vehicle that the driver's attention is focused on when the driver's vehicle is not in an emergency driving situation.

[0063] In some implementations, after identifying a first vehicle, the electronic device can combine the driver's eye movement data, gesture data, and voice data to determine a target vehicle from the first vehicle. The target vehicle includes the vehicle the driver is focused on when the driver is not in an emergency. The vehicle the driver is focused on when the driver is not in an emergency can be understood as the target vehicle at the intent level, or as the vehicle the driver is focused on, determined by resolving conflicts between gaze, gesture, and voice, under the premise of no emergency driving situation or driving safety, respecting the driver's active intent.

[0064] In some implementations, the electronic device can determine the gaze duration corresponding to each first vehicle based on eye-tracking data, determine a first designated probability corresponding to each first vehicle based on gesture data, determine a second designated probability corresponding to each first vehicle based on voice data, and determine a target vehicle from a plurality of first vehicles based on the gaze duration, the first designated probability, and the second designated probability. The first designated probability can represent the probability that a vehicle is designated by the driver's gesture; the second designated probability can represent the probability that a vehicle is designated by the driver's voice.

[0065] The electronic device can determine the focusing probability of each first vehicle based on the gaze duration, the first weight corresponding to the gaze duration, the first specified probability, the second weight corresponding to the first specified probability, the second specified probability, and the third weight corresponding to the second specified probability. It can also identify the first vehicle whose focusing probability is greater than a focusing probability threshold as the target vehicle. The first weight is greater than the second weight, and the second weight is greater than the third weight. Therefore, combined with the priority settings in Table 1 (which prioritize gaze data over gesture data, and gesture data over voice), the accuracy of determining the vehicle on which the driver's attention is focused is improved.

[0066] In some implementations, considering that the accuracy of measuring driver gesture data and eye movement data will decrease in low light conditions, in this embodiment, the third weight can be negatively correlated with the ambient light intensity of the driver's environment. This automatically increases the weight of the voice data when the driver is in a low light environment, adjusts the rule parameters of the intent layer priority at the environment layer, and improves the accuracy of the vehicle that the driver's attention is focused on.

[0067] Step S250: Display the prompt information corresponding to the target vehicle on the display screen, wherein the display screen includes the driver's field of vision, and the prompt information includes the driving data of the target vehicle and the identity data of the target vehicle.

[0068] In some implementations, after the electronic device identifies the target vehicle, it can display corresponding prompts on the screen. The screen may include the driver's field of vision, and the prompts may include the target vehicle's driving data and identification data. Driving data includes, but is not limited to, the target vehicle's real-time speed, distance to the driver's vehicle, direction of travel, and vehicle type (e.g., sedan, truck, bus). Identification data includes, but is not limited to, the target vehicle's brand and model, license plate number, and whether it is an autonomous vehicle. This improves the user experience of the electronic device.

[0069] For example, please refer to Figure 3This document illustrates a structural block diagram of a vehicle alert system according to an embodiment of this application. The vehicle alert system may include a multimodal detection module, a focus detection engine module, a dynamic focus decision module, a highlight display module, and a simplified information module. The multimodal detection module may include an eye-tracking module, a head posture perception module, a gesture recognition module, and a voice command module. The eye-tracking module may include an eye-tracking sensor, which can be used to capture the eye movement trajectory of the driver (e.g., a wearer of a head-mounted display device) in real time, including data such as eye rotation angle, gaze point position, and gaze duration. The head posture perception module may include a head posture sensor, which can be used to acquire information on the driver's head rotation direction and angle. The gesture recognition module can be used to acquire the driver's hand posture data in real time, and the voice command module can be used to acquire the driver's voice commands in real time.

[0070] The processing unit of the multimodal detection module can receive data transmitted from the eye-tracking sensor and the head posture sensor, and perform comprehensive analysis on the two types of data. For example, when it is detected that the driver's eyes are focused in a certain direction and the head turns in that direction simultaneously, it is clear that the driver's attention is focused on that area, thereby accurately determining the driver's attention direction and sending it to the focus detection engine module.

[0071] The focus detection engine module can also connect to the sensor systems (such as cameras and radar) and cockpit systems of the driver's vehicle via a wireless communication module, and can also connect to surrounding vehicle information via V2V. The sensor systems of the driver's vehicle can collect environmental data such as the position, speed, and direction of travel of vehicles in the surrounding environment in real time, and continuously transmit this data to electronic devices to calculate the safe distance within the vehicle's space. The electronic devices then use this safe distance to determine the probability of a collision between the driver's vehicle and the vehicle in question.

[0072] The focus detection engine module can also be used to filter out the vehicle information of the first vehicle within the driver's focus area from the received surrounding vehicle information based on the direction of attention.

[0073] The dynamic focus decision module can analyze the first selected vehicles using a preset priority ranking and multimodal conflict arbitration algorithm to determine the vehicle that the driver's attention is focused on. For example, vehicles that are close to the driver's vehicle, have abnormal speed changes, or have potential conflicts with the driver's driving route, and thus have a high driving impact, are identified as target vehicles and sent to the highlighting module and the simplified information module.

[0074] The highlighting module can prominently display target vehicle information (such as real-time speed, distance to the driver's vehicle, direction of travel, vehicle type, etc.) on the electronic device's screen using a graphical interface. It can also represent the target vehicle's trajectory with different colored lines and display the target vehicle's speed and distance using prominent numbers. Furthermore, the highlighting module can update the target vehicle's information in real time and output alarm messages when the target vehicle experiences significant events such as sudden acceleration, deceleration, or lane changes. For example, it can flash the target vehicle's outline in red and emit a rapid alarm sound to alert the driver. The highlighting module can also support driver interaction via voice commands or gesture recognition to obtain more detailed environmental information, switch between viewing information from different vehicles, and adjust the information display method and layout.

[0075] The simplified information module can be used to abstract and reduce the dimensions of non-target vehicle content in the display screen of electronic devices, such as removing detailed numerical displays, retaining necessary space placeholders, semi-transparency, simplified diagrams, or only displaying outlines.

[0076] By integrating high-precision eye-tracking sensors and head posture sensors into electronic devices (such as head-mounted displays), and comprehensively analyzing eye-tracking data, head posture data, gesture recognition, and voice commands, the system accurately captures the driver's (e.g., the wearer of the head-mounted display) attention, providing a reliable basis for subsequently identifying the focus vehicle. Furthermore, based on the driver's attention direction and combined with surrounding vehicle information collected by vehicle sensors, the electronic device uses a preset algorithm to prioritize vehicles within the attention focus area and arbitrate information conflicts, thereby identifying the focus vehicle. This effectively solves the problems of cluttered information display and lack of emphasis on key vehicles, ensuring that the driver can quickly focus on critical vehicles. Moreover, for the identified focus vehicle, the electronic device highlights its relevant information in a prominent graphical interface and automatically increases its priority and issues visual and auditory alerts when important events occur in nearby vehicles. Simultaneously, it supports driver interaction with the focus vehicle information through voice commands and gesture recognition to obtain detailed information or adjust the display method, improving driving safety, information acquisition efficiency, and interactive experience.

[0077] The vehicle notification method provided in one embodiment of this application is compared to... Figure 1The vehicle alerting method shown in this embodiment can also determine a target vehicle from a plurality of first vehicles based on environmental data and at least one of the vehicle types of the first vehicles, wherein the target vehicle includes the vehicle that the driver's attention is focused on when the driver's vehicle is in an emergency driving situation; or, the target vehicle can be determined from a plurality of first vehicles based on eye-tracking data, gesture data, and voice data, wherein the target vehicle includes the vehicle that the driver's attention is focused on when the driver's vehicle is not in an emergency driving situation, thereby determining the vehicle that the driver's attention is focused on in both emergency driving situations and non-emergency driving situations, thus improving the accuracy of determining the vehicle that the driver's attention is focused on.

[0078] Meanwhile, the prompt information in this embodiment may include the target vehicle's driving data and the target vehicle's identity data, thereby highlighting the driving data and identity data of the vehicle that the driver's attention is focused on, so that the driver can efficiently obtain valuable information for driving, improving driving safety and user experience.

[0079] Please see Figure 4 , Figure 4 A module block diagram of a vehicle alerting device according to an embodiment of this application is shown. This vehicle alerting device 200 is applied to the aforementioned electronic device, and will be discussed below. Figure 4 The process is described in detail below. The vehicle prompting device 200 includes: an attention direction determination module 210, a first vehicle determination module 220, a focus vehicle determination module 230, and a focus vehicle prompting module 240, wherein: The attention direction determination module 210 is used to determine the driver's attention direction based on the driver's eye movement data and head posture data.

[0080] The first vehicle determination module 220 is used to determine multiple first vehicles within the driver's attention focus area based on the environmental data of the vehicle where the driver is located and the direction of attention.

[0081] The focus vehicle determination module 230 is used to determine a target vehicle from the plurality of first vehicles based on at least one of the eye-tracking data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicle, wherein the target vehicle includes the vehicle on which the driver's attention is focused.

[0082] The focus vehicle prompt module 240 is used to display prompt information corresponding to the target vehicle on a display screen, wherein the display screen includes the driver's field of vision.

[0083] Furthermore, the focus vehicle determination module 230 may include: a focus vehicle determination first unit, or a focus vehicle determination second unit, wherein: A focus vehicle determination first unit is configured to determine the target vehicle from the plurality of first vehicles based on the environmental data and at least one of the vehicle types of the first vehicles, wherein the target vehicle includes the vehicle on which the driver's attention is focused in the event of emergency driving of the vehicle in which the driver is located.

[0084] The second unit for determining the focus vehicle is used to determine the target vehicle from the plurality of first vehicles based on the eye movement data, the gesture data, and the voice data, wherein the target vehicle includes the vehicle that the driver's attention is focused on when the driver's vehicle is not in an emergency driving situation.

[0085] Furthermore, the first unit for determining the focal vehicle may include: a vehicle type determination unit, and / or a collision probability determination unit, wherein: The vehicle type determination unit is used to identify the first vehicle of a preset type as the target vehicle.

[0086] The collision probability determination unit is used to determine, based on the environmental data, a first vehicle whose probability of colliding with the vehicle where the driver is located is greater than a collision probability threshold, as the target vehicle.

[0087] Furthermore, before determining, based on the environmental data, a first vehicle with a probability greater than a collision probability threshold being involved in a collision with the driver's vehicle as the target vehicle, the vehicle warning device 200 may further include: a relative vehicle speed determination unit and a collision probability determination unit, wherein: The relative vehicle speed determination unit is used to determine the distance and relative speed between each first vehicle and the vehicle where the driver is located based on the environmental data.

[0088] The collision probability determination unit is used to obtain the probability of each first vehicle colliding with the vehicle where the driver is located based on the distance between each first vehicle and the relative vehicle speed.

[0089] Furthermore, the second unit for determining the focus vehicle may include: a gaze duration determination unit, a gesture designation probability determination unit, a voice designation probability determination unit, and a target vehicle determination unit, wherein: The gaze duration determination unit is used to determine the gaze duration corresponding to each first vehicle based on the eye movement data.

[0090] The gesture designation probability determination unit is used to determine a first designation probability corresponding to each first vehicle based on the gesture data, wherein the first designation probability represents the probability that the vehicle is designated by the driver's gesture.

[0091] A voice-designated probability determination unit is used to determine a second designation probability corresponding to each first vehicle based on the voice data, wherein the second designation probability represents the probability that the vehicle is designated by the driver's voice.

[0092] The target vehicle determination unit is used to determine the target vehicle from the plurality of first vehicles based on the gaze duration, the first specified probability, and the second specified probability.

[0093] Furthermore, the target vehicle determination unit may include: a focusing probability determination unit and a target vehicle determination subunit, wherein: The focusing probability determination unit is used to determine the focusing probability of each first vehicle based on the gaze duration, the first weight corresponding to the gaze duration, the first specified probability, the second weight corresponding to the first specified probability, the second specified probability, and the third weight corresponding to the second specified probability, wherein the first weight is greater than the second weight, and the second weight is greater than the third weight.

[0094] The target vehicle determination subunit is used to determine the first vehicle whose focusing probability is greater than the focusing probability threshold as the target vehicle.

[0095] Furthermore, the third weight is negatively correlated with the ambient light intensity of the driver's environment.

[0096] Furthermore, before determining the driver's attention direction based on the driver's eye movement data and head posture data, the vehicle prompting device 200 may further include: a data acquisition unit, wherein: The data acquisition unit is used to acquire the eye movement data and the head posture data through a head-mounted display device.

[0097] Furthermore, the focus vehicle notification module 240 may include: a head-mounted display device display unit, wherein: A head-mounted display device display unit is used to display prompt information corresponding to the target vehicle on the display screen of the head-mounted display device.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this application, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0100] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0101] Please see Figure 5 The diagram illustrates a structural block diagram of a vehicle according to an embodiment of this application. The electronic device 100 can be an AR or VR headset or other electronic device with processing capabilities. The electronic device 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more application programs. The one or more application programs may be stored in the memory 120 and configured to be executed by one or more processors 110. The one or more programs are configured to perform the methods described in the foregoing method embodiments.

[0102] The processor 110 may include one or more processing cores. The processor 110 connects to various parts within the electronic device 100 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and by calling data stored in the memory 120. Optionally, the processor 110 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 110 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 110 and may be implemented separately using a communication chip.

[0103] The memory 120 may include random access memory (RAM) or read-only memory (ROM). The memory 120 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described below. The data storage area may also store data created by the electronic device 100 during use (such as phonebook data, audio and video data, chat log data, etc.).

[0104] In this embodiment, a computer-readable medium stores program code, which can be called by a processor to execute the methods described in the above method embodiments.

[0105] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media includes non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0106] In this application, "multiple" refers to two or more.

[0107] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0108] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0109] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0110] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0111] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle notification method, characterized in that, The method includes: The driver's attention direction is determined based on the driver's eye movement data and head posture data; Based on the environmental data of the vehicle where the driver is located and the direction of attention, multiple first vehicles within the driver's attention focus area are determined; A target vehicle is determined from the plurality of first vehicles based on at least one of the eye-tracking data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicle, wherein the target vehicle includes the vehicle on which the driver's attention is focused; The display screen shows the prompt information corresponding to the target vehicle, and the display screen includes the driver's field of vision.

2. The method according to claim 1, characterized in that, The step of determining the target vehicle from the plurality of first vehicles based on at least one of the eye-tracking data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicle includes: Based on the environmental data and at least one of the vehicle types of the first vehicles, the target vehicle is determined from the plurality of first vehicles, wherein the target vehicle includes the vehicle on which the driver's attention is focused in the event of an emergency driving situation in the driver's vehicle; or, The target vehicle is determined from the plurality of first vehicles based on the eye-tracking data, the gesture data, and the voice data, wherein the target vehicle includes the vehicle that the driver's attention is focused on when the driver's vehicle is not in an emergency driving situation.

3. The method according to claim 2, characterized in that, Determining the target vehicle from the plurality of first vehicles based on the environmental data and at least one of the vehicle types of the first vehicles includes: The first vehicle of a preset type is identified as the target vehicle; and / or, Based on the environmental data, a first vehicle with a probability greater than the collision probability threshold is identified as the target vehicle.

4. The method according to claim 3, characterized in that, Before determining, based on the environmental data, a first vehicle with a probability greater than a collision probability threshold to be involved in a collision with the driver's vehicle as the target vehicle, the method further includes: The distance and relative speed between the first vehicle and the driver's vehicle are determined based on the environmental data. Based on the distance between the first vehicles and their relative speeds, the probability of a collision between the first vehicle and the vehicle in which the driver is located is obtained.

5. The method according to claim 2, characterized in that, The step of determining the target vehicle from the plurality of first vehicles based on the eye-tracking data, the gesture data, and the voice data includes: The gaze duration corresponding to each first vehicle is determined based on the eye-tracking data. A first designated probability is determined for each first vehicle based on the gesture data, wherein the first designated probability represents the probability that the vehicle is designated by the driver's gesture; The second designation probability corresponding to each first vehicle is determined based on the voice data, wherein the second designation probability represents the probability that the vehicle is designated by the driver's voice. The target vehicle is determined from the plurality of first vehicles based on the gaze duration, the first specified probability, and the second specified probability.

6. The method according to claim 5, characterized in that, The step of determining the target vehicle from the plurality of first vehicles based on the gaze duration, the first specified probability, and the second specified probability includes: Based on the gaze duration, the first weight corresponding to the gaze duration, the first specified probability, the second weight corresponding to the first specified probability, the second specified probability, and the third weight corresponding to the second specified probability, the focusing probability corresponding to each first vehicle is determined, wherein the first weight is greater than the second weight, and the second weight is greater than the third weight. The first vehicle whose focusing probability is greater than the focusing probability threshold is identified as the target vehicle.

7. The method according to claim 6, characterized in that, The third weight is negatively correlated with the ambient light intensity of the driver's environment.

8. The method according to any one of claims 1-7, characterized in that, Before determining the driver's attention direction based on the driver's eye movement data and head posture data, the method further includes: The eye movement data and head posture data are collected using a head-mounted display device; The step of displaying the prompt information corresponding to the target vehicle on the display screen includes: The head-mounted display device displays a prompt message corresponding to the target vehicle.

9. A vehicle warning device, characterized in that, The device includes: The attention direction determination module is used to determine the driver's attention direction based on the driver's eye movement data and head posture data. The first vehicle determination module is used to determine multiple first vehicles within the driver's attention focus area based on the environmental data of the vehicle where the driver is located and the direction of attention. A focus vehicle determination module is used to determine a target vehicle from a plurality of first vehicles based on at least one of the eye-tracking data, the driver's gesture data, the driver's voice data, the environmental data, and the vehicle type of the first vehicle, wherein the target vehicle includes the vehicle on which the driver's attention is focused; The focus vehicle prompt module is used to display prompt information corresponding to the target vehicle on the display screen, wherein the display screen includes the driver's field of vision.

10. An electronic device, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the method as described in any one of claims 1-8.

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