Vehicle interaction method, apparatus, and vehicle

Abstract

The application provides a vehicle interaction method and device and a vehicle, relates to the technical field of vehicles, and aims to solve the problem that the reaction speed of a driver is slow in a risk scenario in the prior art. The method comprises the following steps: acquiring environment state data of a vehicle, determining a risk type and a risk area based on the environment state data, and controlling a display device of the vehicle to display a virtual image based on the risk type and the risk area. When the display device displays the virtual image, the virtual image corresponds to the target display area of the display device and the risk area, and the virtual image performs a risk protection action corresponding to the risk type in the target display area. The technical scheme provided in the application embodiment reduces the cognitive load and reaction time of the driver, and improves the risk protection effect and user experience.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to a vehicle interaction method, device, and vehicle. Background Technology

[0002] With the widespread application of advanced driver assistance systems (ADAS), augmented reality head-up display (AR-HUD) technology has become an important means of vehicle safety interaction. It can convert data such as lane lines and obstacle distances into visual symbols or dynamic arrows superimposed on the road surface to assist drivers in making driving decisions.

[0003] However, the symbols and arrows currently displayed to drivers are relatively abstract. Drivers need to understand the specific risk scenarios based on the visual symbols, which requires a certain amount of cognitive and reaction time. In emergency risk scenarios, the driver's cognitive delay can easily lead to misjudgment and untimely operation, resulting in significant safety risks. Summary of the Invention

[0004] This application provides a vehicle interaction method, device, and vehicle, aiming to solve problems in the prior art such as insufficiently intuitive and personalized risk warnings, resulting in inadequate risk prevention effects and a lack of emotional connection and sense of belonging in the human-vehicle interaction experience.

[0005] Firstly, this application provides a vehicle interaction method applied to a vehicle, which includes a display device. The vehicle interaction method provided in this application includes: acquiring environmental state data of the vehicle; determining risk types and risk areas based on the environmental state data; and controlling the display device to display a virtual avatar based on the risk type and risk area. When the display device displays the virtual avatar, the virtual avatar corresponds to a risk area in the target display area of ​​the display device, and the virtual avatar performs risk protection actions corresponding to the risk type in the target display area.

[0006] The technical solution provided in this application controls a virtual avatar to perform risk protection actions associated with the type of risk, and aligns the display area of ​​the virtual image with the risk area. This transforms abstract risk information into a visual image with clear spatial orientation and semantic meaning. Drivers can intuitively perceive the specific source and nature of the risk without complex symbol decoding, reducing the driver's cognitive load and reaction time, and improving the risk protection effect. Simultaneously, it avoids the anxiety caused by traditional audible and visual alarms, increasing the driver's trust and acceptance of the risk protection system, thereby improving the user experience.

[0007] In some possible implementations, the display device is controlled to display a virtual image based on the risk type and risk area. Specifically, this can be achieved by: determining the spatial orientation of the risk area relative to the vehicle, defining the edge area of ​​the display device near the spatial orientation as the target display area, and controlling the display device to display a virtual image in the target display area.

[0008] The above implementation maps the spatial orientation of the risk area to a target display area at the edge of the display device, with the virtual image appearing at the edge of the driver's field of vision, avoiding obstruction of the driver's central field of vision. Simultaneously, by utilizing the strong correlation of spatial orientation, it reduces the time it takes for the driver to locate the source of the risk, improving the immediacy and spatial directionality of risk warnings while ensuring clear visibility.

[0009] In some possible implementations, controlling the display device to show a virtual image based on risk type and risk area can be specifically implemented as follows: determining the risk protection action corresponding to the risk type from a preset action library based on the risk type, and controlling the virtual image displayed on the display device to perform the risk protection action. The risk types include at least one of the following: longitudinal collision risk, lateral blind spot risk, pedestrian collision risk, and lane departure risk; the risk protection actions include at least one of the following: push-away action, pull-back action, pedestrian protection action, and right-side-up action.

[0010] The above implementation method maps specific risk types such as longitudinal collisions and lateral blind spots to anthropomorphic actions such as pushing away, pulling back, and protecting, transforming abstract risk data into intuitive action language with clear semantic meaning. This allows drivers to instantly understand the specific nature of the risk (such as whether the vehicle in front is too close or someone has intruded), thereby significantly reducing the cognitive load on drivers in emergency situations, shortening the time from perceiving the risk to reacting, and enhancing the warning effect and emotional resonance of safety warnings.

[0011] In some possible implementations, determining risk types and risk areas based on environmental status data can be specifically implemented as follows: determining risk events and risk levels based on environmental status data, and determining the risk type and risk area corresponding to the risk event with the highest safety risk among the risk events.

[0012] The above implementation method identifies the highest risk level among risk events and locks down the corresponding risk type and area, thereby achieving risk screening in scenarios with multiple concurrent risks. This avoids the problem of warning information overload or confusion when multiple risk events exist at the same time, ensuring that drivers can receive the most urgent and dangerous safety information as soon as possible in complex working conditions, and improving the decision-making efficiency and warning accuracy of the vehicle safety system.

[0013] In some possible implementations, the vehicle interaction method provided in this application further includes: determining the safety protection boundary of a risk event based on environmental state data, and controlling a display device to render the safety protection boundary as a visual graphic for display. The safety protection boundary includes at least one of the following: longitudinal safety distance, lateral safety range, and pedestrian safety range, and the visual graphic is a block-shaped luminous graphic.

[0014] The above implementation renders abstract calculation boundaries such as longitudinal safety distance and lateral safety range as block-shaped luminous graphics (such as a halo carpet), transforming invisible "meters" or "seconds" into ground coverage areas that drivers can intuitively perceive. This achieves a concrete display of safety margin, allowing drivers to directly judge whether the braking distance is sufficient through graphic length and coverage, eliminating the cognitive delay caused by traditional numerical display reminders, and improving drivers' real-time perception of vehicle safety status.

[0015] In some possible implementations, the display parameters of the visualization graphics corresponding to risk events of different risk levels are different, and the display parameters include at least one of the following: color, flashing frequency, and transparency.

[0016] The above implementation method dynamically adjusts the color, flashing frequency, and transparency of the visualization graphics according to the risk level, intuitively reflecting the urgency of the risk (e.g., from green to red, with an increased frequency), ensuring that drivers can accurately assess the urgency of the current situation based on the changes in the graphics, avoiding the problem of uneven warning intensity caused by a single warning mode in low-risk and high-risk scenarios, and thus avoiding misjudgment or delusion caused by ambiguous warning signals.

[0017] In some possible implementations, the vehicle interaction method provided in this application further includes: monitoring the driver's posture information in the vehicle, determining the driver's interaction intention based on the posture information, and adjusting the display state of the virtual image and / or visual graphics in the display device based on the interaction intention.

[0018] The above implementation dynamically adjusts the prompting strategy according to the driver's actual state (such as whether he pays attention or ignores it), avoiding repeated alarms when the driver has already confirmed or ignored the risk, reducing information interference, and improving the intelligence level and collaborative efficiency of safety protection.

[0019] In some possible implementations, the posture information includes: gaze state and head posture. The driver's interaction intention is determined based on the posture information. Specifically, this can be implemented by: determining the time the driver gazes at the virtual image based on the gaze state; and determining the driver's interaction intention based on the head posture when the driver's gaze time at the virtual image exceeds a preset time threshold.

[0020] The above implementation method only performs intention determination after the driver has been continuously paying attention to the virtual image, which improves the accuracy and reliability of interactive intention recognition and effectively avoids the problem of accidental triggering caused by the driver's unconscious scanning or gaze drift.

[0021] In some possible implementations, determining the driver's interaction intent based on head posture can be specifically achieved as follows: when the head posture meets the characteristics of a nodding motion, the interaction intent is determined to be risk confirmation; when the head posture meets the characteristics of a head shaking motion, the interaction intent is determined to be risk ignoring.

[0022] The above implementation maps nodding to risk confirmation and head shaking to risk ignoring. In driving scenarios where both hands are on the wheel and the driver's eyes are focused on the road, only a slight head movement is needed to respond to the warning information, without the need for cumbersome touch or voice operations. This ensures improved ease of operation, avoids the problem of drivers being distracted by manual operation, and effectively reduces driving safety risks caused by interactive operation.

[0023] In some possible implementations, adjusting the display state of the virtual avatar on the display device based on the interaction intent can be specifically implemented as follows: when the interaction intent is to confirm a risk, the virtual avatar is controlled to perform a positive feedback action. When the interaction intent is to ignore a risk, the virtual avatar is controlled to perform a feedback received action, and within a preset time period after performing the feedback received action, the risk protection action corresponding to the risk type ignored by the driver is not performed. Positive feedback actions include at least one of the following: a thumbs-up, a smile, or a blink; feedback received actions include at least one of the following: a nod, a blink, or disappearance.

[0024] The above implementation method, based on the driver's intention to confirm or ignore the risk, triggers positive feedback actions or suppresses repeated alarms, achieving dynamic adaptation of the warning strategy. When the driver confirms the risk, positive emotional feedback is given through actions such as thumbs-ups and smiles, enhancing the driver's trust in the safety system. When the driver ignores the risk, the feedback action is executed and repeated alarms are temporarily suppressed, avoiding the problem of driver frustration caused by repeated prompts.

[0025] Secondly, this application provides a vehicle interaction device, which includes an acquisition module, a processing module, and a control module.

[0026] The aforementioned acquisition module is used to acquire environmental status data of the vehicle.

[0027] The aforementioned processing module is used to determine the risk type and risk area based on environmental status data.

[0028] The aforementioned control module is used to control the display device of the vehicle to display a virtual image based on the risk type and risk area. When the display device displays the virtual image, the target display area of ​​the virtual image corresponds to the risk area, and the virtual image performs risk protection actions corresponding to the risk type within the target display area.

[0029] In some possible implementations, the control module described above is specifically used to: determine the spatial orientation of the risk area relative to the vehicle, determine the edge area of ​​the display device near the spatial orientation as the target display area, and control the display device to display a virtual image in the target display area.

[0030] In some possible implementations, the aforementioned control module is specifically used to: determine the risk protection action corresponding to the risk type from a preset action library based on the risk type, and control the virtual image displayed on the display device to perform the risk protection action. The risk types include at least one of the following: longitudinal collision risk, lateral blind spot risk, pedestrian collision risk, and lane departure risk; the risk protection actions include at least one of the following: push-away action, pull-back action, pedestrian protection action, and right-side-up action.

[0031] In some possible implementations, the above processing module is specifically used to: determine risk events and risk levels based on environmental state data, and determine the risk type and risk area corresponding to the risk event with the highest safety risk among the risk events.

[0032] In some possible implementations, the processing module is further configured to: determine the safety protection boundary of a risk event based on environmental state data. The control module is further configured to: control the display device to render the safety protection boundary as a visual graphic for display. The safety protection boundary includes at least one of the following: a longitudinal safety distance, a lateral safety range, and a pedestrian safety range; the visual graphic is a block-shaped luminous graphic.

[0033] In some possible implementations, the display parameters of the visualization graphics corresponding to risk events of different risk levels are different, and the display parameters include at least one of the following: color, flashing frequency, and transparency.

[0034] In some possible implementations, the acquisition module is further configured to: monitor the driver's posture information in the vehicle. The processing module is further configured to: determine the driver's interaction intention based on the posture information. The control module is further configured to: adjust the display state of the virtual image and / or visual graphics in the display device based on the interaction intention.

[0035] In some possible implementations, the posture information includes: gaze state and head posture. Specifically, the processing module described above is used to: determine the duration of the driver's gaze at the virtual avatar based on the gaze state; and when the driver's gaze time at the virtual avatar exceeds a preset time threshold, determine the driver's interaction intention based on the head posture.

[0036] In some possible implementations, the above processing module is specifically used to: determine the interaction intent as risk confirmation when the head posture meets the characteristics of a nodding motion; and determine the interaction intent as risk ignoring when the head posture meets the characteristics of a shaking motion.

[0037] In some possible implementations, the control module is specifically used to: control the virtual avatar to perform a positive feedback action when the interaction intent is to confirm a risk; and control the virtual avatar to perform a feedback received action when the interaction intent is to ignore a risk, and within a preset time period after performing the feedback received action, not perform the risk protection action corresponding to the risk type ignored by the driver. Positive feedback actions include at least one of the following: a thumbs-up, a smile, or a blink; feedback received actions include at least one of the following: a nod, a blink, or disappearing.

[0038] The technical effects of the various implementation methods in the second aspect are consistent with the technical effects of the various implementation methods in the first aspect.

[0039] Thirdly, this application provides a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement the vehicle interaction method in any of the embodiments of the first aspect described above.

[0040] Fourthly, this application provides a computer program product, including a computer program; when the computer program is executed, it is able to implement the vehicle interaction method in any of the embodiments of the first aspect described above.

[0041] Fifthly, this application provides a vehicle that includes the vehicle interaction device in any embodiment of the second aspect described above, or that executes the vehicle interaction method in any embodiment of the first aspect described above. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in some embodiments of this application 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.

[0043] Figure 1 A schematic diagram of the structure of a human-computer interaction system provided in some embodiments of this application; Figure 2A flowchart illustrating a vehicle interaction method provided in some embodiments of this application; Figure 3 A flowchart illustrating another vehicle interaction method provided in some embodiments of this application; Figure 4 This application provides a schematic diagram of the structure of a vehicle interaction system according to some embodiments; Figure 5 This is a structural schematic diagram of a vehicle interaction device provided in some embodiments of this application. Detailed Implementation

[0044] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0046] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0047] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document 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 alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more.

[0048] The embodiments of this application are described below with reference to the accompanying drawings.

[0049] This application provides a vehicle.

[0050] Alternatively, a vehicle may also be referred to as a vehicle, mobile carrier, electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), fuel cell vehicle (FCV), autonomous vehicle, intelligent and connected vehicle (ICV), driverless vehicle, new energy vehicle, etc.

[0051] In this application's embodiments, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a motorcycle, a tricycle, a special vehicle (such as an ambulance, fire truck, police car, etc.), a driverless taxi, an intelligent connected bus, an autonomous logistics vehicle, an electric truck, etc. The method provided in this application's embodiments is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, port vehicles, etc., and this application does not impose specific limitations on them. For example, the vehicle can be an electric sport utility vehicle (SUV) including a hub motor, kingpin steering, fully active air suspension, and EMB.

[0052] In some possible examples, the vehicle is equipped with a control system, which typically adopts a layered distributed architecture. From the bottom layer to the top layer, it can be roughly divided into a perception layer, a control layer, a coordination layer, and an interaction layer. The layers communicate with each other through an in-vehicle network.

[0053] The perception layer mainly consists of various sensors distributed inside and outside the vehicle, including but not limited to external environment cameras, millimeter-wave radar, lidar, ultrasonic sensors, in-vehicle driver monitoring cameras, microphone arrays, and various vehicle status sensors (such as wheel speed sensors, inertial measurement units, temperature sensors, etc.). The perception layer is responsible for collecting multi-dimensional data such as the vehicle's own operating status, driver behavior, and external driving environment in real time.

[0054] The control layer consists of dozens to hundreds of electronic control units (ECUs), distributed across multiple functional domains including powertrain, chassis, body, intelligent driving, and infotainment. Each ECU embeds real-time control software that performs closed-loop control of the vehicle's actuators based on preset control strategies or upper-level commands, and generates corresponding alarm signals when abnormal conditions are detected. Typical ECUs include the engine control unit, transmission control unit, brake control unit, steering control unit, vehicle stability control unit, airbag control unit, intelligent driving domain controller, and in-vehicle infotainment unit.

[0055] The coordination layer typically exists in the form of a domain controller or a central computing platform, responsible for cross-domain data fusion, global state management, and collaborative decision-making. The coordination layer centrally processes and schedules the sensing data and control commands that were originally scattered across various functional domains, connecting downwards to various electronic control units and supporting human-machine interaction functions upwards.

[0056] The interaction layer mainly includes in-cabin display devices (such as instrument panel, central control screen, head-up display), voice interaction system, haptic feedback device, etc., which are responsible for presenting vehicle status, warning information and driving suggestions to the driver in the form of visual, auditory or tactile, while receiving the driver's touch, voice and other input commands.

[0057] Data transmission and interaction between different layers are achieved through the vehicle bus network. Common vehicle bus protocols include CAN (Controller Area Network), CAN FD (CAN with Flexible Data-Rate), LIN (Local Interconnect Network), FlexRay, and vehicle Ethernet, which supports high-bandwidth data transmission. Among these, CAN and CAN FD buses are widely used for communication in real-time control domains such as powertrain and chassis, while vehicle Ethernet is gradually being applied to high-bandwidth sensor data transmission in the intelligent driving domain and vehicle interaction scenarios in the cockpit domain.

[0058] In some possible implementations, the vehicle includes a human-machine interaction system.

[0059] Among them, the human-machine interaction system is used to establish an information exchange and control channel between the driver, passengers and the vehicle, so that the driver and passengers can perceive the vehicle status and functional information, and at the same time ensure that the driver's and passengers' operating intentions are accurately executed by the vehicle. For example, the human-machine interaction system can realize functions such as information display, operation control and driving assistance.

[0060] In some possible implementations, the human-computer interaction system is a comprehensive security platform that integrates proactive perception, intelligent decision-making, and emotional interaction.

[0061] For example, a human-machine interaction system can monitor potential risks in the vehicle's surrounding environment and use dashboard icons, beeps, or simple HUD symbols to provide risk warnings. For instance, vehicle operating status and environmental risk information can be transmitted in real time through visual, auditory, and tactile channels (such as beeps, flashing red warnings, etc.).

[0062] With the widespread application of ADAS, AR-HUD technology has become an important means of vehicle safety interaction. It can convert data such as lane lines and obstacle distances into visual symbols or dynamic arrows superimposed on the road surface to assist drivers in making driving decisions.

[0063] However, the symbols and arrows currently displayed to drivers are relatively abstract. Drivers need to understand the specific risk scenarios based on the visual symbols, which requires a certain amount of cognitive and reaction time. In emergency risk scenarios, the driver's cognitive delay can easily lead to misjudgment and untimely operation, resulting in significant safety risks.

[0064] In view of this, such as Figure 1 As shown, this application provides a display device 100 as the core interactive terminal of a human-computer interaction system, based on which human-computer interaction is realized. The display device 100 has AR display capabilities, enabling it to accurately overlay and map virtual information onto real driving scenarios. Based on the spatial orientation of the risk area, it directly renders a virtual image at the corresponding position in the driver's field of vision and drives it to perform protective actions matching the risk type. Simultaneously, it transforms the abstract safety distance into a visualized luminous boundary graphic.

[0065] The display device 100 provided in this application embodiment no longer serves merely as a passive carrier of information, but as a key medium for the visualization and semantic intuitiveness of risk space, enabling drivers to intuitively perceive the specific location and source of risk without shifting their gaze or performing logical decoding, thereby achieving natural, efficient, and low-cognitive-load human-computer interaction.

[0066] Specifically, the display device 100 of this application can drive a virtual avatar to perform anthropomorphic behaviors with clear action meanings, such as "pushing away," "pulling back," and "straightening," transforming abstract collision or deviation risks into visual language that drivers can intuitively understand, thus reducing the driver's cognitive decoding time. Simultaneously, it can also transform the invisible "safe distance" into block-shaped luminous graphics (with a halo) spread across the road surface, whose color, flashing frequency, and transparency dynamically adjust according to the risk level, allowing drivers to intuitively judge the current safety margin and urgency.

[0067] For example, the display device 100 may include an image projection unit and an optical combination unit. The image projection unit is used to generate high-definition image data containing virtual images, risk boundary graphics and semantic action instructions. The optical combination unit is used to optically fuse the generated virtual image with the real road scene seen by the driver through the windshield or glasses using prisms, freeform mirrors, waveguide technology, etc., to ensure that the virtual content maintains spatial consistency and anchoring relationship with the real physical environment.

[0068] In some possible implementations, the human-machine interaction system also includes a controller 200, which communicates with the display device 100 via vehicle Ethernet, CAN bus, or the like.

[0069] For example, the controller 200 can analyze the surrounding scene of the vehicle in real time, and combine the environmental status data collected by vehicle radar, cameras and other devices to dynamically calculate the coordinates of the risk area in three-dimensional space, thereby controlling the display device 100 to render and display the virtual image at the corresponding position in the driver's field of vision, and transforming the abstract risk data into a three-dimensional interactive interface that the driver can intuitively perceive and that is seamlessly integrated with the real world.

[0070] For example, the controller 200 can also control the display device 200 to adjust the actions performed by the virtual image and the display parameters (such as brightness and color) of the block-shaped luminous graphics based on the driver's line of sight, head posture and other information. When the driver nods to confirm, positive emotional feedback is given, and repeated alarms are intelligently suppressed when the driver shakes his head to ignore it. This improves the level of intelligence and emotional experience of human-computer interaction while ensuring driving safety.

[0071] For example, please refer to Figure 2 , Figure 2 This is a flowchart illustrating a vehicle interaction method provided in an embodiment of this application. The vehicle interaction method provided in this application can be applied to the vehicle or human-machine interaction system described above, and can also be applied to other interactive devices capable of providing risk warnings.

[0072] like Figure 2 As shown, a vehicle interaction method according to an embodiment of this application includes: Step S201: Obtain the vehicle's environmental status data.

[0073] Optionally, environmental status data includes obstacle information (such as the real-time location coordinates, relative speed, driving trajectory, physical size and vehicle type of surrounding vehicles, the location, direction of movement and speed of surrounding pedestrians, and the location coordinates and attributes of fixed obstacles), road information (such as the curvature of the current lane, lane width, road slope, roadside boundary, lane line type, and number of lanes), traffic rule information (such as the speed limit of the current road segment, traffic light status, and traffic sign content), and weather data (such as visibility, rainfall / snowfall intensity, road surface slipperiness, light intensity, and fog concentration).

[0074] For example, vehicle-mounted sensors (such as cameras, millimeter-wave radar, ultrasonic radar, and lidar) can be used to acquire real-time environmental status data, including the vehicle's status, the position, speed, and movement trajectory of surrounding vehicles, pedestrians, non-motorized vehicles, lane lines, and obstacles.

[0075] Step S202: Determine the risk type and risk area based on environmental status data.

[0076] Optionally, the risk types include: longitudinal collision risk, lateral blind spot risk, pedestrian collision risk, and lane departure risk.

[0077] For example, based on environmental state data, information corresponding to each risk type is calculated, such as time to collision (TTC), collision distance, blind spot detection status, lane departure risk, emergency deceleration warning, and abnormal behavior of the vehicle in front (such as sudden lane change or emergency braking), and the current risk event is determined according to preset judgment rules.

[0078] For example, if the vehicle in front exhibits sudden braking behavior, the distance between the two vehicles is less than a preset distance threshold, and the Time To Compatibility (TTC) is less than a preset event threshold (e.g., 2.5s), a risk event of longitudinal collision risk is identified. If there is an obstacle in the vehicle's left blind spot and the vehicle intends to change lanes to the left, a risk event of left blind spot risk is identified. When a pedestrian / non-motorized vehicle enters a preset danger zone around the vehicle, a risk event of vulnerable road user (VRU) approach risk is identified.

[0079] The risk area is the three-dimensional coordinate range of a risk event in the real physical space.

[0080] For example, raw point cloud data or depth images collected by vehicle-mounted LiDAR, millimeter-wave radar, and stereo vision cameras are filtered, segmented, and clustered to extract the surface geometric features of the target object (such as pedestrians, other vehicles, or obstacles) corresponding to the risk event. These surface geometric features are then transformed into three-dimensional coordinates (length, width, and height) in a real-world coordinate system, thus initially determining the static three-dimensional coordinate range of the target object in the real physical space. Based on the real-time motion state data of the target object (such as velocity vectors and acceleration), a dynamic spatiotemporal volume that evolves over time is generated. This dynamic spatiotemporal volume represents all spatial positions that the target object may occupy within the predicted time period. The set of all spatial positions that the target object may occupy within the predicted time period is determined as the three-dimensional coordinate range of the risk event in the real physical space.

[0081] In some possible implementations, multiple risk events may exist at the same time. Based on environmental status data, multiple corresponding risk levels are determined, and the risk type and risk area corresponding to the risk event with the highest safety risk among the risk events are determined.

[0082] Among them, the risk level is an indicator that quantifies the urgency of a risk event (such as high, medium, low or 0-100 points). The higher the risk level (the larger the value), the higher the urgency of the risk event.

[0083] For example, the Time Toll Collection (TTC) and collision probability corresponding to each risk event are calculated based on obstacle distance, relative speed, acceleration, vehicle motion state, weather, road conditions, etc. The smaller the TTC, the higher the collision probability and the higher the risk level. For example, the risk level value = α × reciprocal of TTC + β × target object type weight + γ × vehicle state factor.

[0084] Among them, α, β, and γ are preset weight coefficients that can be flexibly adjusted according to needs. Target object type weight refers to the weight corresponding to different types of target objects. For example, the target object type weight is 1.0 when the target object is a pedestrian, 0.9 when the target object is a non-motorized vehicle, 0.7 when the target object is a vehicle, and 0.5 when the target object is a static obstacle. The vehicle state factor is used to characterize the vehicle's operating condition. For example, the vehicle state factor is 1.2 when the vehicle intends to change lanes, 1.0 when the vehicle is driving normally in a straight line, and 0.8 when the vehicle is braking.

[0085] Step S203: Control the display device to display the virtual image based on the risk type and risk area.

[0086] When a virtual avatar is displayed on a display device, the virtual avatar corresponds to a risk area in the target display area of ​​the display device, and the virtual avatar performs risk protection actions corresponding to the risk type in the target display area. Optionally, the virtual avatar can be a virtual character, a virtual pet, a virtual animation, etc.

[0087] For example, the spatial orientation of the risk area relative to the vehicle is determined, and the edge area of ​​the display device closest to this spatial orientation is defined as the target display area. The display device is then controlled to display a virtual image within the target display area. For instance, the virtual image can be displayed in a holographic floating form on the edge area of ​​the AR display window of the display device.

[0088] For example, the mapping relationship between risk areas and target display areas is shown in Table 1: Table 1

[0089] In some possible implementations, risk protection actions corresponding to risk types are determined from a preset action library based on risk types, and the virtual image displayed on the display device is controlled to perform the risk protection actions.

[0090] Optionally, risk protection actions include: pushing away, pulling back, pedestrian protection, and righting actions.

[0091] For example, while controlling the virtual image displayed on the control display device to perform risk protection actions, risk warnings can also be issued to users in conjunction with risk warning methods such as voice prompts and text display.

[0092] For example, the risk protection actions corresponding to different risk types are shown in Table 2: Table 2

[0093] In some possible implementations, the safety protection boundary of a risk event is determined based on environmental state data (such as vehicle speed, braking capacity, and the motion state of objects around the vehicle), and the control display device renders the safety protection boundary as a visual graphic for display.

[0094] The visual graphics are block-shaped glowing graphics. For example, rectangular blocks with different colored halos.

[0095] Optionally, the safety protection boundary includes: longitudinal safety distance, lateral safety range, and VRU boundaries (pedestrian safety range, non-motorized vehicle safety range), etc. Specifically, the longitudinal safety distance is the safe distance threshold and danger distance threshold between the vehicle and the vehicle in front / obstacles in front; the lateral safety boundary is the coverage area of ​​the lane lines and blind spots on both sides of the vehicle; the pedestrian safety range is the safe distance threshold and danger distance threshold between the vehicle and pedestrians; and the non-motorized vehicle safety range is the safe distance threshold and danger distance threshold between the vehicle and non-motorized vehicles.

[0096] For example, when rendering safety protection boundaries as visual graphics, block-shaped luminous graphics are laid out on the longitudinal safe distance range (between the safe distance threshold and the danger distance threshold), blind spot coverage range, and pedestrian safety range of the road surface in front of the vehicle, and the blind spot coverage range and pedestrian outline are highlighted by the bright outline.

[0097] In some possible implementations, the display parameters of the visualization graphics corresponding to risk events of different risk levels are different.

[0098] Optionally, display parameters include: color, blink frequency, and transparency.

[0099] For example, the display parameters corresponding to different risk levels are shown in Table 3: Table 3

[0100] In some possible implementations, the driver's posture information in the vehicle is monitored, the driver's interaction intention is determined based on the posture information, and the display state of the virtual avatar and / or visual graphics on the display device is adjusted based on the interaction intention. For example, a driver monitoring camera can capture the driver's gaze direction and nodding / shaking movements, and a voice acquisition module can receive the driver's confirmation or ignore instructions for the virtual avatar's prompts.

[0101] Optionally, the posture information includes gaze state and head posture.

[0102] For example, the driver's gaze duration on the virtual avatar is determined based on the gaze state. When the driver's gaze duration on the virtual avatar exceeds a preset time threshold (e.g., 0.8s), the driver's interaction intention is determined based on head posture.

[0103] For example, when the head posture matches the characteristics of a nodding motion, the interaction intent is determined to be to acknowledge the risk. When the head posture matches the characteristics of a shaking motion, the interaction intent is determined to be to ignore the risk.

[0104] For example, the conditions for satisfying the characteristics of a nodding motion are shown in Table 4: Table 4

[0105] The conditions for satisfying the characteristics of head-shaking movements are shown in Table 5: Table 5

[0106] In some possible implementations, if the driver's head posture meets the characteristics of a nodding action and the data collected by the acceleration sensor indicates that the vehicle is not in a bumpy state, the interaction intent is determined as a risk to avoid the problem of misidentification of interaction intent caused by bumps.

[0107] In some possible implementations, the driver's interaction intent is determined based on the driver's posture information and the voice commands issued by the driver.

[0108] For example, the driver's interaction intent is determined based on the driver's posture information, voice commands, and the confidence level corresponding to the voice commands.

[0109] For example, the confidence threshold for voice commands is 0.85. Voice commands include voice commands that acknowledge risk (such as "okay", "understand", "confirm") and voice commands that ignore risk (such as "no need", "cancel", "shut up").

[0110] Specifically, when identifying driver risk confirmation based on driver posture information, the interaction intent is determined to be risk confirmation. When identifying driver risk ignoring based on driver posture information, the interaction intent is determined to be risk ignoring. When the confidence score of the voice command "OK" is greater than 0.85, the interaction intent is determined to be risk confirmation. When the confidence score of the voice command "Cancel" is greater than 0.85, the interaction intent is determined to be risk ignoring.

[0111] In some possible implementations, the intensity of the risk warning is adjusted based on voice commands issued by the driver.

[0112] In some possible implementations, voice commands from the driver are only received when the driver is looking at the virtual avatar, thus avoiding false triggering of interaction intent recognition.

[0113] In some possible implementations, only voice commands issued from the driver's area are recognized to avoid interference from multiple users interacting.

[0114] In some possible implementations, when the interaction intent is to confirm a risk, the virtual avatar is controlled to perform a positive feedback action and reduce the intensity of the risk warning (e.g., reduce display brightness). When the interaction intent is to ignore a risk, the virtual avatar is controlled to perform a feedback received action, and the display is suppressed (stopped) for a preset period of time after the feedback received action, or the risk protection action corresponding to the type of risk ignored by the driver is suppressed (stopped).

[0115] Optionally, positive feedback actions include at least one of the following: liking, smiling, blinking, and receiving feedback actions include at least one of the following: nodding, blinking, disappearing.

[0116] For example, when the interaction intent is to confirm a risk, the control display device performs the actions shown in Table 6: Table 6

[0117] When the interaction intent is to confirm a risk, the control display device will perform the actions shown in Table 7: Table 7

[0118] In some possible implementations, after detecting a decrease in risk level or after the user performs an action to reduce risk, the virtual avatar is controlled to perform positive action feedback and reduce the intensity of the risk warning.

[0119] In some possible implementations, the intensity of the risk warning is increased if no interaction intent from the driver is detected within a preset time after the risk protection action is displayed.

[0120] For example, if no driver interaction intent is detected within 5 seconds after the risk protection action is displayed, the virtual avatar re-executes the risk protection action. If no driver interaction intent is detected within 8 seconds after the risk protection action is displayed, the virtual avatar stops executing the risk protection action and enters standby mode.

[0121] For example, the driver's interaction intent is identified according to a preset time period. If the driver's interaction intent is not identified in multiple (e.g., 3) identification processes after the risk protection action is displayed, a more intense risk warning method such as a buzzer warning is added.

[0122] In some possible implementations, such as Figure 3 As shown in the embodiments of this application, another vehicle interaction method includes: Step S301: Obtain the vehicle's environmental status data.

[0123] Step S302: Calculate the security protection boundary.

[0124] Step S303: Render the security protection boundary into a visual graphic and display it through a display device.

[0125] Step S304: Assess the risk level of the risk event and determine the risk type and risk area corresponding to the risk event with the highest risk level.

[0126] Step S305: Determine the target display area of ​​the virtual avatar based on the risk area, and anchor the virtual avatar to the target display area.

[0127] Step S306: Determine risk protection actions based on risk type and control the virtual avatar to perform risk protection actions.

[0128] Step S307: Collect the driver's posture information in the vehicle and determine the driver's interaction intention based on the posture information.

[0129] Step S308: Determine whether the driver's gaze time at the virtual image exceeds 0.8s based on the posture information. If yes, proceed to step S309; ​​otherwise, proceed to step S312.

[0130] Step S309: Determine the interaction intent based on the driver's head posture.

[0131] Step S310: When the head posture meets the characteristics of a nodding action, determine that the interaction intention is to confirm the risk, control the virtual image to perform positive action feedback and reduce the intensity of the risk warning.

[0132] Step S311: When the head posture meets the characteristics of head shaking, determine that the interaction intention is to ignore the risk, control the virtual image to perform the action of receiving feedback, and suppress the display of risk protection actions corresponding to the risk type ignored by the driver within a preset time period after the action of receiving feedback.

[0133] Step S312: Maintain the current risk indication intensity.

[0134] The virtual avatar's actions and target display area are adjusted and updated in real time based on environmental status data and user feedback.

[0135] In some possible implementations, embodiments of this application provide a vehicle interaction system, such as... Figure 4 As shown, the vehicle interaction system includes: an environment perception unit 400, a safety boundary calculation unit 401, an AR boundary rendering unit 402, a risk level assessment unit 403, a spatial dynamic anchoring unit 404, an action linkage unit 405, and an interaction intent monitoring unit 406.

[0136] For example, the vehicle interaction system provided in the embodiments of this application is used for human-computer interaction in different scenarios.

[0137] Scenario 1: Following too closely in front of another car.

[0138] When the vehicle is traveling at 80 km / h, the environmental perception unit 400 detects a distance of 25 meters between the vehicle and the vehicle in front. The safety boundary calculation unit 401 calculates a safe distance threshold of 40 meters and a dangerous distance threshold of 30 meters based on the braking model. 25 meters has entered the dangerous distance threshold. The AR boundary rendering unit 402 renders a red "halo" area extending from the rear of the vehicle in front towards the vehicle in front in the AR display window area at the bottom of the windshield, visually indicating the risk of "following too closely". The risk level assessment unit 403 determines that the highest risk type is "longitudinal collision risk". The spatial dynamic anchoring unit 404 moves the virtual pet to the bottom center of the AR display window area. The action linkage unit 405 matches the "push away" action in the action library, controlling the virtual pet to perform a risk protection action of pushing forward with both hands, while simultaneously providing a voice prompt "Too close, keep your distance". When the interaction intent monitoring unit 406 detects that the driver stares at the virtual pet for more than 0.8 seconds and nods, the warning intensity is reduced, and the red halo turns into a yellow halo. When the system detects that the driver has actively slowed down and increased the distance between the two vehicles, the distance of the "halo" area along the direction of the vehicle in front gradually shortens and changes color to green, and the virtual pet makes a "thumbs up" gesture to provide positive feedback.

[0139] Scenario 2: A vehicle is approaching from the left blind spot.

[0140] When the vehicle prepares to change lanes, the environmental perception unit 400 detects a rapidly approaching vehicle in the left blind spot. The AR boundary rendering unit 402 overlays the approaching vehicle as a highlighted outline in the AR display window area inside the vehicle's A-pillar, simulating a "see-through A-pillar" effect. The risk level assessment unit 403 determines the highest risk type to be "left blind spot collision risk." The spatial dynamic anchoring unit 404 moves the virtual pet to the left edge of the AR display window. The action linkage unit 405 matches the "pull back" action from the action library, controlling the virtual pet to perform a pulling action from left to right, accompanied by a rapid voice prompt "Car on the left." When the interaction intent monitoring unit 406 detects that the driver stares at the virtual pet for more than 0.8 seconds and shakes their head, it indicates "the driver knows but does not need a reminder," temporarily suppressing the same type of risk warning. When the driver cancels the lane change action, the virtual pet performs a "reassuring" action, and the lighting effect returns to the default lighting effect.

[0141] Specifically, when confirming the driver's interaction intent, the driver monitoring camera captures the driver's facial orientation and gaze location in real time. After the virtual pet performs a risk protection action to provide a risk warning, it enters an "interaction waiting state." If the driver's gaze remains fixed on the virtual pet for more than 0.8 seconds, and their head is nodding upwards (with a pitch angle exceeding a preset threshold), the interaction intent is determined to be "confirming risk," and the interaction log is recorded. If the driver's gaze remains fixed on the virtual pet for more than 0.8 seconds, and their head is shaking left and right, the interaction intent is determined to be "ignoring risk," and this confirmed and ignored risk type is added to a short-term suppression list to avoid interference from repeated warnings. When the driver's gaze is not fixed on the virtual pet, the current risk warning intensity is maintained, and the intensity is increased if necessary (e.g., the virtual pet repeatedly performs the risk protection action, or the amplitude of the virtual pet's risk protection action is increased).

[0142] The foregoing mainly describes the solutions of the embodiments of this application from the perspectives of methods and systems. It is understood that, in order to achieve the above functions, the system includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments applied herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the preset application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each preset application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0143] This application embodiment can divide the system into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one functional module. The integrated module can be implemented in hardware or software. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. The following description uses the example of dividing each functional module according to each function.

[0144] This application also provides a vehicle interaction device. For example, please refer to... Figure 5 , Figure 5 This is a schematic diagram of the structure of a vehicle interaction device provided in an embodiment of this application. The vehicle interaction device includes an acquisition module 501, a processing module 502, and a control module 503.

[0145] The aforementioned acquisition module 501 is used to acquire environmental status data of the vehicle.

[0146] The aforementioned processing module 502 is used to determine the risk type and risk area based on environmental status data.

[0147] The aforementioned control module 503 is used to control the display device of the vehicle to display a virtual image based on the risk type and risk area. When the display device displays the virtual image, the target display area of ​​the virtual image corresponds to the risk area, and the virtual image performs risk protection actions corresponding to the risk type within the target display area.

[0148] In some possible implementations, the control module 503 is specifically used to: determine the spatial orientation of the risk area relative to the vehicle, determine the edge area of ​​the display device near the spatial orientation as the target display area, and control the display device to display a virtual image in the target display area.

[0149] In some possible implementations, the control module 503 is specifically used to: determine the risk protection action corresponding to the risk type from a preset action library based on the risk type, and control the virtual image displayed on the display device to perform the risk protection action. The risk types include at least one of the following: longitudinal collision risk, lateral blind spot risk, pedestrian collision risk, and lane departure risk; the risk protection actions include at least one of the following: push-away action, pull-back action, pedestrian protection action, and right-side-up action.

[0150] In some possible implementations, the processing module 502 is specifically used to: determine risk events and risk levels based on environmental status data, and determine the risk type and risk area corresponding to the risk event with the highest safety risk among the risk events.

[0151] In some possible implementations, the processing module 502 is further configured to: determine the safety protection boundary of the risk event based on environmental state data. The control module 503 is further configured to: control the display device to render the safety protection boundary as a visual graphic for display. The safety protection boundary includes at least one of the following: a longitudinal safety distance, a lateral safety range, and a pedestrian safety range; the visual graphic is a block-shaped luminous graphic.

[0152] In some possible implementations, the display parameters of the visualization graphics corresponding to risk events of different risk levels are different, and the display parameters include at least one of the following: color, flashing frequency, and transparency.

[0153] In some possible implementations, the acquisition module 501 is further configured to: monitor the driver's posture information in the vehicle. The processing module 502 is further configured to: determine the driver's interaction intention based on the posture information. The control module 503 is further configured to: adjust the display state of the virtual image and / or visual graphics in the display device based on the interaction intention.

[0154] In some possible implementations, the posture information includes: gaze state and head posture. The aforementioned processing module 502 is specifically used to: determine the time the driver gazes at the virtual image based on the gaze state, and when the time the driver gazes at the virtual image exceeds a preset time threshold, determine the driver's interaction intention based on the head posture.

[0155] In some possible implementations, the processing module 502 is specifically used to: determine the interaction intent as risk confirmation when the head posture meets the characteristics of a nodding motion; and determine the interaction intent as risk ignoring when the head posture meets the characteristics of a shaking motion.

[0156] In some possible implementations, the control module 503 is specifically used to: control the virtual avatar to perform a positive feedback action when the interaction intent is to confirm a risk; and control the virtual avatar to perform a feedback received action when the interaction intent is to ignore a risk, and within a preset time period after performing the feedback received action, not perform the risk protection action corresponding to the risk type ignored by the driver. Positive feedback actions include at least one of the following: a thumbs-up, a smile, or a blink; feedback received actions include at least one of the following: a nod, a blink, or disappearing.

[0157] Some embodiments of this application also provide an in-vehicle terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it can realize vehicle interaction.

[0158] Some embodiments of this application also provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing at least one piece of program code that, when loaded and executed by a processor, causes a computer to perform a vehicle interaction method as described in any of the above embodiments.

[0159] For example, the aforementioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this application may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0160] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the vehicle interaction method of any of the above embodiments.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0162] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

[0163] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle interaction method, characterized in that, Applied to vehicles, wherein the vehicles include display devices; The vehicle interaction method includes: Obtain the environmental status data of the vehicle; Based on the environmental status data, the risk type and risk area are determined; Based on the risk type and the risk area, the display device is controlled to display a virtual image; wherein, when the display device displays the virtual image, the target display area of ​​the virtual image on the display device corresponds to the risk area, and the virtual image performs risk protection actions corresponding to the risk type in the target display area.

2. The vehicle interaction method according to claim 1, characterized in that, The method of controlling the display device to display a virtual image based on the risk type and the risk area includes: Determine the spatial orientation of the risk area relative to the vehicle; The edge region of the display device near the spatial orientation is defined as the target display area; The display device is controlled to display the virtual image in the target display area.

3. The vehicle interaction method according to claim 1, characterized in that, The method of controlling the display device to display a virtual image based on the risk type and the risk area includes: Based on the risk type, determine the corresponding risk protection action from the preset action library; Control the virtual avatar displayed on the display device to perform the risk protection action; The risk types include at least one of the following: longitudinal collision risk, lateral blind spot risk, pedestrian collision risk, and lane departure risk. The risk protection actions include at least one of the following: pushing away action, pulling back action, pedestrian protection action, and righting action.

4. The vehicle interaction method according to claim 1, characterized in that, The process of determining the risk type and risk area based on the environmental status data includes: Based on the environmental status data, risk events and their risk levels are determined. Identify the risk type and risk area corresponding to the risk event with the highest security risk among the risk events.

5. The vehicle interaction method according to claim 4, characterized in that, The vehicle interaction method also includes: The safety protection boundary for the risk event is determined based on the environmental status data; the safety protection boundary includes at least one of the following: longitudinal safety distance, lateral safety range, and pedestrian safety range; The display device is controlled to render the security protection boundary as a visual graphic for display; the visual graphic is a block-shaped luminescent graphic.

6. The vehicle interaction method according to claim 5, characterized in that, The display parameters of the visualization graphics corresponding to risk events of different risk levels are different; the display parameters include at least one of the following: color, flashing frequency, and transparency.

7. The vehicle interaction method according to claim 5, characterized in that, The vehicle interaction method also includes: Monitor the driver's posture information in the vehicle; The driver's interaction intention is determined based on the posture information; The display state of the virtual image and / or visual graphics in the display device is adjusted based on the interaction intent.

8. The vehicle interaction method according to claim 7, characterized in that, The posture information includes: gaze state and head posture; determining the driver's interaction intention based on the posture information includes: The duration for which the driver gazes at the virtual image is determined based on the gaze state. When the driver gazes at the virtual image for more than a preset time threshold, the driver's interaction intention is determined based on the head posture.

9. The vehicle interaction method according to claim 8, characterized in that, Determining the driver's interaction intent based on the head posture includes: When the head posture meets the characteristics of a nodding action, the interaction intent is determined to be a risk confirmation. When the head posture meets the characteristics of a head shaking motion, the interaction intention is determined to be to ignore the risk.

10. The vehicle interaction method according to claim 9, characterized in that, Adjusting the display state of the virtual avatar on the display device based on the interaction intent includes: When the interaction intent is to confirm risk, the virtual avatar is controlled to perform a positive feedback action; the positive feedback action includes at least one of the following: liking, smiling, blinking; When the interaction intent is to ignore the risk, the virtual image is controlled to perform a feedback action, and within a preset time period after the feedback action is performed, the risk protection action corresponding to the risk type ignored by the driver is not performed; the feedback action includes at least one of the following: nodding, blinking, disappearing.

11. A vehicle interaction device, characterized in that, The vehicle interaction device includes: The acquisition module is used to acquire environmental status data of the vehicle; The processing module is used to determine the risk type and risk area based on the environmental status data; A control module is used to control the display device of the vehicle to display a virtual image based on the risk type and the risk area; wherein, when the display device displays the virtual image, the virtual image corresponds to the risk area in the target display area of ​​the display device, and the virtual image performs risk protection actions corresponding to the risk type in the target display area.

12. A vehicle, characterized in that, Includes the vehicle interaction device as described in claim 11.

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