Safety prompting method and device of vehicle, controller, vehicle and medium

Abstract

Embodiments of the present application provide a risk avoidance prompting method and device for a vehicle, a controller, a vehicle and a medium. The method comprises: acquiring road condition data of an environment in which the vehicle is located and performance characteristics of the vehicle; determining a risk situation of the environment in which the vehicle is located according to the road condition data; the risk situation is used to indicate the position of each risk point existing in the environment; the risk point refers to a target object that threatens the safety of the vehicle; and according to the risk situation and the performance characteristics, a risk avoidance operation prompt is given to a driver. The method is used to improve the reliability of the risk avoidance prompt of the vehicle, thereby improving the driving safety.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more particularly to a method, device, controller, vehicle, and medium for providing vehicle hazard avoidance warnings. Background Technology

[0002] When vehicles are traveling on high-risk road sections such as mountain roads, road construction zones, and accident-prone areas, it is usually necessary to send avoidance operation prompts to the driver in order to improve driving safety, targeting road risks such as falling rocks, slope collapses, and road construction.

[0003] Most existing prompting solutions only focus on the risk itself, such as generating corresponding operation prompts according to fixed templates for different risk types. However, different vehicle models have inherent differences in parameters such as power performance, braking efficiency, ground clearance, and off-road capability. Moreover, the actual performance of the same model of vehicle will change accordingly as the mileage increases and parts age and wear out.

[0004] It is evident that the existing technology, which uses a fixed template to provide indiscriminate warnings to all vehicles, suffers from the problem of the warning content failing to adapt to the individual performance differences of each vehicle. This can even lead to situations where the driver is unable to follow the warning instructions due to the vehicle's own limitations, potentially causing driving safety hazards. Therefore, a new risk warning method is urgently needed to improve the reliability of hazard avoidance warnings and thus ensure driving safety. Summary of the Invention

[0005] This application provides a method, device, controller, vehicle, and medium for providing vehicle hazard avoidance warnings, thereby improving the reliability of vehicle hazard avoidance warnings and thus enhancing driving safety.

[0006] In a first aspect, embodiments of this application provide a method for providing hazard avoidance warnings for vehicles, the method comprising:

[0007] Acquire road condition data of the vehicle's environment and the vehicle's performance characteristics;

[0008] Based on road condition data, the risk situation of the vehicle's environment is determined; the risk situation is used to indicate the location of each risk point in the environment; a risk point refers to a target that poses a threat to vehicle safety.

[0009] Based on the risk situation and performance characteristics, provide the driver with prompts for hazard avoidance maneuvers.

[0010] In one possible embodiment, the road condition data includes first road condition data of the first road segment where the vehicle is currently located, and second road condition data of the second road segment that the vehicle is expected to reach in the future. Obtaining road condition data of the vehicle's environment includes:

[0011] The vehicle's perception system collects road condition data for the first road segment, and obtains the road condition data for the first road segment from other vehicles' historical uploads via the cloud server, thus obtaining the first road condition data.

[0012] The second road condition data is obtained by retrieving historical road condition data uploaded by other vehicles from the second road section through a cloud server.

[0013] In one possible embodiment, based on the risk situation and performance characteristics, the driver is prompted to take evasive action, including:

[0014] For each risk point indicated in the risk situation, if the relative distance between the risk point and the vehicle is less than the first preset distance, a risk avoidance operation prompt is generated based on the location and performance characteristics of the risk point.

[0015] Send hazard avoidance prompts to drivers.

[0016] In one possible embodiment, the method further includes:

[0017] For each risk point, if the relative distance between the risk point and the vehicle is less than the second preset distance, a warning prompt will be given to the driver for the risk point.

[0018] The first preset distance is less than the second preset distance.

[0019] In one possible embodiment, before providing risk warnings to the driver based on the risk situation, the method further includes:

[0020] Obtain the driver's driving operation profile; the driving operation profile is determined based on the driver's historical driving operation records;

[0021] Based on the vehicle's performance characteristics and / or the driver's driving operation profile, determine the first preset distance and the second preset distance.

[0022] In one possible embodiment, the driver is given a warning alert regarding potential risks, including:

[0023] The intensity of the warning is determined based on the relative distance between the risk point and the vehicle;

[0024] Drivers are given warnings based on the intensity of the warning; the intensity of the warning is positively correlated with the relative distance.

[0025] In one possible embodiment, before prompting the driver to take evasive action, the method further includes:

[0026] The content of the hazard avoidance operation prompts was revised using the driver's driving operation profile.

[0027] In one possible embodiment, the risk situation is further used to indicate the expected time of occurrence of the accident corresponding to each risk point, and the vehicle is equipped with a streaming rearview mirror; the method further includes:

[0028] If the risk situation indicates that there is a risk point in the first road segment, then the risk point with the shortest expected occurrence time of the accident shall be identified as the first target risk point;

[0029] The target camera is determined based on the relative positional relationship between the primary target risk point and the vehicle;

[0030] The image captured by the target camera is displayed in the streaming rearview mirror.

[0031] In one possible embodiment, when the relative positional relationship is such that the location of the risk is in front of the vehicle, displaying the image captured by the target camera in the streaming rearview mirror includes:

[0032] The image captured by the target camera is preprocessed to obtain a processed image, which is then displayed on the streaming media rearview mirror.

[0033] The preprocessing includes cropping the image of the target area and perspective distortion correction; the target area is the area in front that is obscured by the streaming media rearview mirror.

[0034] In one possible embodiment, the method further includes;

[0035] If the risk situation indicates that there are no risk points in the first road segment but there are risk points in the second road segment, then the risk location will be indicated across the entire area via the streaming rearview mirror.

[0036] Secondly, embodiments of this application provide a vehicle hazard avoidance warning device, the device comprising:

[0037] The acquisition module is used to acquire road condition data of the vehicle's environment and the vehicle's performance characteristics.

[0038] The first determination module is used to determine the risk situation of the vehicle's environment based on road condition data; the risk situation indicates the location of each risk point in the environment; a risk point refers to a target that poses a threat to vehicle safety.

[0039] The first prompt module is used to provide drivers with prompts for hazard avoidance actions based on the risk situation and performance characteristics.

[0040] In one possible embodiment, the traffic data includes first traffic data of the first road segment where the vehicle is currently located, and second traffic data of the second road segment that the vehicle is expected to reach in the future. The acquisition module is specifically used for:

[0041] The vehicle's perception system collects road condition data for the first road segment, and obtains the road condition data for the first road segment from other vehicles' historical uploads via the cloud server, thus obtaining the first road condition data.

[0042] The second road condition data is obtained by retrieving historical road condition data uploaded by other vehicles from the second road section through a cloud server.

[0043] In one possible embodiment, the first prompting module is specifically used for:

[0044] For each risk point indicated in the risk situation, if the relative distance between the risk point and the vehicle is less than the first preset distance, a risk avoidance operation prompt is generated based on the location and performance characteristics of the risk point.

[0045] Send hazard avoidance prompts to drivers.

[0046] In one possible embodiment, the device further includes:

[0047] The second alert module is specifically used to provide a warning to the driver for each risk point if the relative distance between the risk point and the vehicle is less than the second preset distance.

[0048] The first preset distance is less than the second preset distance.

[0049] In one possible embodiment, the device further includes:

[0050] The second determination module is used to obtain the driver's driving operation profile; the driving operation profile is determined based on the driver's historical driving operation records.

[0051] Based on the vehicle's performance characteristics and / or the driver's driving operation profile, determine the first preset distance and the second preset distance.

[0052] In one possible embodiment, the second prompting module is specifically used for:

[0053] The intensity of the warning is determined based on the relative distance between the risk point and the vehicle;

[0054] Drivers are given warnings based on the intensity of the warning; the intensity of the warning is positively correlated with the relative distance.

[0055] In one possible embodiment, the device further includes a correction module for correcting the content of the hazard avoidance operation prompts using a driver's driving operation profile.

[0056] In one possible embodiment, the risk situation is further used to indicate the expected time of occurrence of the accident corresponding to each risk point. The vehicle is equipped with a streaming rearview mirror, and the device also includes:

[0057] The display module includes:

[0058] The first determining unit is used to determine the risk point with the shortest expected occurrence time of the accident as the first target risk point if the risk situation indicates that there is a risk point in the first road segment;

[0059] The second determining unit is used to determine the target camera based on the relative positional relationship between the first target risk point and the vehicle;

[0060] The display unit is used to display the images captured by the target camera in the streaming rearview mirror.

[0061] In one possible embodiment, when the relative positional relationship is such that the location where the risk occurs is in front of the vehicle, the display unit is specifically used for:

[0062] The image captured by the target camera is preprocessed to obtain a processed image, which is then displayed on the streaming media rearview mirror.

[0063] The preprocessing includes cropping the image of the target area and perspective distortion correction; the target area is the area in front that is obscured by the streaming media rearview mirror.

[0064] In one possible embodiment, the device further includes;

[0065] The third prompt module is used to provide a full-area risk location prompt via the streaming media rearview mirror if the risk situation indicates that there is no risk point in the first road segment but there is a risk point in the second road segment.

[0066] Thirdly, embodiments of this application provide a controller, including: a memory and a processor;

[0067] The memory stores the instructions that the computer executes;

[0068] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0069] Fourthly, according to Embodiment 2 of this application, a vehicle is provided, including a vehicle body and the controller provided above.

[0070] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0071] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0072] The vehicle hazard avoidance warning method, device, controller, vehicle, and medium provided in this application acquire road condition data of the vehicle's environment and the vehicle's performance characteristics. Based on the road condition data, they determine the risk situation for indicating the location of each risk point in the environment. Then, combining the risk situation and performance characteristics, they provide hazard avoidance operation prompts to the driver. This enables the hazard avoidance warning to simultaneously consider external environmental risks and the vehicle's own traffic capacity, thereby improving the pertinence and reliability of risk perception and warning in complex and dangerous road conditions, and ultimately improving driving safety. Attached Figure Description

[0073] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0074] Figure 1 This is a flowchart illustrating a vehicle hazard avoidance warning method provided in Embodiment 1 of this application;

[0075] Figure 2 This is a flowchart illustrating a vehicle hazard avoidance warning method provided in Embodiment 2 of this application;

[0076] Figure 3 This is a schematic diagram of the structure of a vehicle avoidance warning device provided in Embodiment 4 of this application;

[0077] Figure 4 This is a structural schematic diagram of a vehicle hazard avoidance warning device provided in Embodiment 5 of this application;

[0078] Figure 5 This is a schematic diagram of the structure of a controller provided in Embodiment 5 of this application.

[0079] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0080] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0081] To address the problems mentioned in the background art, the inventors propose a vehicle hazard avoidance warning method. During vehicle operation, the method first acquires road condition data of the vehicle's environment and the vehicle's performance characteristics. Then, based on the road condition data, it determines the risk situation of the vehicle's environment and uses this risk situation to indicate the location of each risk point in the environment. Risk points are target objects that threaten vehicle safety. On this basis, the method further provides hazard avoidance operation prompts to the driver based on the determined risk situation and the vehicle's performance characteristics.

[0082] It is evident that this technical approach does not merely provide static warnings about risks, but rather links the location of risk points in the environment with the vehicle's own performance to generate more adaptive prompts. This allows drivers to receive risk avoidance suggestions that are more in line with the vehicle's current capabilities when facing different dangerous environments, thereby improving the effectiveness of decision-making and driving safety during passage through dangerous road sections.

[0083] It should be noted that the method provided in this application can be executed solely by the vehicle-side controller, which can be a vehicle controller, an in-vehicle smart cockpit controller, etc.; alternatively, it can be executed in conjunction with a cloud server. For example, the cloud server acquires road condition data and vehicle performance characteristics of the vehicle's environment, determines the risk level of the vehicle's environment based on the road condition data, and finally determines the prompt content for hazard avoidance operation based on the risk level and performance characteristics, sending the prompt content to the vehicle-side controller so that the vehicle-side controller can prompt the driver to perform hazard avoidance operation. This application does not strictly limit the specific entity executing the method.

[0084] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0085] Figure 1 This is a flowchart illustrating a vehicle hazard avoidance warning method provided in Embodiment 1 of this application, as shown below. Figure 1 As shown, the method provided in this embodiment includes:

[0086] S101: Obtain road condition data of the vehicle's environment and the vehicle's performance characteristics.

[0087] In this step, the vehicle's environment can cover both the current road segment and the road segment it is expected to reach. The acquired road condition data refers to relevant data that characterizes the road conditions of the vehicle's environment, and its function is to provide basic input for the subsequent determination of risk situations; the acquired performance characteristics refer to vehicle characteristics that characterize the vehicle's own capabilities and status, and their function is to ensure that the subsequently generated risk avoidance operation prompts can match the vehicle's actual current travel capabilities.

[0088] In one possible implementation, the road condition data includes first road condition data of the first road segment where the vehicle is currently located, and second road condition data of the second road segment that the vehicle is expected to reach in the future; correspondingly, the road condition data of the vehicle's environment can be obtained by steps a to b as follows:

[0089] Step a: Collect road condition data for the first road segment through the vehicle's perception system, and obtain the road condition data for the first road segment that was historically uploaded by other vehicles through the cloud server to obtain the first road condition data.

[0090] Regarding the method of dividing road segments in this implementation, for example, the real-time location of the vehicle can be used as a reference to designate the roads covered by the preset mileage before and after the vehicle as the first road segment, and the road segments to be traveled beyond the preset mileage and planned to be traveled by the vehicle in the future as the second road segment.

[0091] In this step, the primary traffic data sources include real-time data collected by the vehicle perception system and historical data uploaded by other vehicles from the cloud server.

[0092] As a concrete example, while the vehicle is in motion, it can collect road condition data through a sensing system and send the results to the controller. Simultaneously, the cloud server determines the road segment the vehicle is on based on its current location, queries and retrieves historical road condition data collected by other vehicles on that segment, and sends the retrieved data to the controller. The controller then merges the real-time data collected by the vehicle with historical data uploaded by other vehicles to form the initial road condition data.

[0093] Step b: Obtain the road condition data collected on the second road segment from other vehicles' historical uploads via the cloud server to obtain the second road condition data.

[0094] In this step, the second road segment refers to the route the vehicle is expected to reach in the future, which can be obtained through the vehicle's navigation data. The traffic data for the second road segment comes from historical data uploaded by other vehicles and aggregated on a cloud server.

[0095] As a specific example, for the second road segment, the on-board processing unit can determine the road segment that the vehicle is expected to reach in the future based on the navigation path or map planning results, and then send a request to the cloud server to obtain the road condition data collected in the second road segment that other vehicles have uploaded in the past, and generate the second road condition data.

[0096] In this implementation, the perception system includes, for example, cameras (such as forward-looking cameras and surround-view cameras), radar (such as millimeter-wave radar and lidar), ultrasonic sensors, and inertial measurement units. Accordingly, the road condition data collected by the vehicle (including the vehicle itself and other vehicles) may include image data of the current road segment acquired by cameras and spatial point cloud of the current road segment constructed using lidar.

[0097] Optionally, in practical applications, before the vehicle departs, it can obtain historical data uploaded by other vehicles corresponding to the expected road segments via a cloud server and preload it locally. Based on this, during the journey, the vehicle can directly obtain real-time historical data uploaded by other vehicles for the first and second road segments corresponding to its current location from its local machine. This method effectively avoids cloud data acquisition failures due to network issues during the journey, thus ensuring the effectiveness of road condition data acquisition.

[0098] This implementation method acquires road condition data synchronously through the cloud and the vehicle, ensuring the comprehensiveness of the acquired data and providing more accurate data support for subsequent risk assessment. At the same time, it also acquires road condition data on the expected route through the cloud, providing a data foundation for predicting risks in subsequent road segments and ensuring the forward-looking nature of risk warnings.

[0099] Optionally, in specific implementation, traffic data can also be collected through multiple channels, such as public network public opinion information and commercial map service interfaces, including real-time traffic reports from traffic management platforms, traffic control announcements, road network construction reports, and dynamic traffic information such as temporary accidents, road congestion, and slope hazards reported by map service providers through crowdsourcing.

[0100] In addition, performance characteristics can be derived from the collection of current vehicle performance data, or from the factory configuration database or cloud-based vehicle records. Specifically, performance characteristics may include vehicle power performance, braking capacity, vehicle height, curb weight, wheelbase, tire grip, steering response, minimum turning radius, maximum passable gradient, current load status, current speed, acceleration, and moose test performance. Among these, power performance can be characterized by the torque output capacity of the engine or electric motor, acceleration response time, and current power redundancy; braking capacity can be characterized by the braking system status, estimated braking distance, brake fade status, and tire adhesion conditions.

[0101] S102: Determine the risk level of the vehicle's environment based on road condition data.

[0102] In this step, the acquired road condition data needs to be analyzed and processed to obtain the risk situation of the vehicle's environment. The risk situation indicates the location of each risk point in the environment; a risk point refers to a target that poses a threat to vehicle safety. The location of each risk point can be represented by latitude, longitude, and altitude, directly indicating the relative distance between the risk point and the vehicle.

[0103] In detail, a risk point is a target that poses a threat to vehicle safety. This can be a stationary target or a hazard source with an evolving tendency, such as rocks that have fallen onto the road, rock masses at the risk of falling on slopes, landslide debris, debris flow-covered areas, slope slip zones, construction debris, and temporary barriers created by accidents. A risk situation should at least indicate the location of each risk point present in the environment.

[0104] In practice, the implementing entity needs to perform fusion analysis on the road condition data obtained in step S101 to identify targets in the current environment and related information about those targets. For example, for image data, the implementing entity can identify road obstacles, road surface cracks, falling rocks, and landslide traces through image segmentation, target detection, semantic recognition, and edge extraction. For radar data, the implementing entity can identify the spatial location and volume range of targets through clustering, point cloud segmentation, and spatial contour fitting. Subsequently, the implementing entity can uniformly map the above identification results to the vehicle reference coordinate system or road mileage coordinate system to obtain the location of each risk point in the environment.

[0105] Optionally, the risk situation can further indicate information such as the risk type, size of the risk point, the area of ​​the risk point that occupies the road, the risk change trend, the risk level, and the expected time of the accident (i.e. the time when the risk point comes into contact with the vehicle, which can be determined based on the risk change trend, the relative distance between the risk point and the vehicle, etc.) for each risk point.

[0106] Optionally, the vehicle can also upload the road condition data collected by the vehicle to the cloud to be shared with other vehicles for the identification of risk points.

[0107] S103: Provide the driver with hazard avoidance instructions based on the risk situation and performance characteristics.

[0108] In this step, it is necessary to combine the aforementioned risk information and performance characteristics to generate hazard avoidance operation prompts adapted to road and vehicle conditions, and then display these prompts to the driver. These hazard avoidance operation prompts refer to driving suggestions that the driver can understand and execute, used to guide the driver's driving behavior.

[0109] In practical applications, the hazard avoidance operation prompts can be voice prompts, instrument panel image prompts, head-up display prompts, voice prompts, streaming rearview mirror prompts, steering wheel vibration prompts, or multimodal combination prompts. Prompts may include, for example, slowing down, maintaining your lane and proceeding slowly, braking in advance, stopping, detouring, quickly passing through the risk area, no overtaking, being aware of falling rocks on the left, and being aware of the edge of a landslide on the right front.

[0110] In practice, the implementing entity can first extract the risk factors most relevant to the current decision based on the risk situation, such as at least one of the following: the relative distance between the risk point and the vehicle, the risk type of the risk point, the degree of road occupation by the risk point, the risk level, and the expected time of the accident; at the same time, extract the vehicle's current capability parameters that can be used for traffic decision-making from the performance characteristics, including at least one of the following: power response capability, current braking capability, vehicle passability, steering maneuverability, and current power supply capability.

[0111] Subsequently, the implementing entity generates hazard avoidance prompts for each risk point, based on the corresponding risk factor and the vehicle's performance parameters, adapting to the current road and vehicle conditions. For example, for a rockfall risk point, if the risk situation indicates that there are rocks close ahead occupying part of the lane, and the vehicle's performance characteristics indicate that the vehicle has sufficient power reserves, a short braking distance, and a quick steering response, the driver can be prompted to slow down in advance and quickly pass through using the passable side. When the same risk situation corresponds to a heavy-duty truck, a vehicle with a long braking distance, or limited steering maneuverability, the prompt can be to brake in advance, reduce speed, and stop and wait in a safe area.

[0112] To achieve the above adaptation process, the executing entity can adopt rule-based decision-making, scoring-based decision-making, or model-based reasoning. For example, when using rule-based decision-making, mapping rules between risk factors and vehicle performance characteristics and avoidance operations can be established in advance.

[0113] For example, when using a scoring-based decision-making approach, the risk pressure value and vehicle capacity value can be calculated separately, and a warning level and warning content can be generated based on their relationship. Although this embodiment does not limit the specific formula, the risk pressure value can be determined by comprehensively considering distance, risk type, lane occupancy degree, and trend, while the vehicle capacity value can be determined by comprehensively considering power, braking, and vehicle characteristics. When the risk pressure value is higher than the vehicle capacity value, a more conservative warning is output; when the vehicle capacity value is significantly higher than the risk pressure value, a warning can be output suggesting passing through the risk zone while controlling vehicle speed.

[0114] For example, when using model inference, risk conditions and performance characteristics can be used as inputs, and the trained decision model can output risk avoidance operation prompts.

[0115] The vehicle hazard avoidance warning method provided in this application obtains road condition data of the vehicle's environment and the vehicle's performance characteristics, determines the risk situation of each risk point in the environment based on the road condition data, and then provides hazard avoidance operation prompts to the driver in combination with the risk situation and performance characteristics. This enables the hazard avoidance warning to take into account both external environmental risks and the vehicle's own traffic capacity, thereby improving the pertinence and reliability of risk perception and warning in complex and dangerous road conditions, and thus improving the effect of driving safety.

[0116] Furthermore, Figure 2 This is a flowchart illustrating a vehicle hazard avoidance warning method provided in Embodiment 2 of this application, as shown below. Figure 2 As shown, based on the above embodiments, the method provided in this application specifically includes the following:

[0117] S201: Obtain road condition data of the vehicle's environment and the vehicle's performance characteristics.

[0118] S202: Determine the risk level of the vehicle's environment based on road condition data.

[0119] The risk situation is used to indicate the location of each risk point in the environment; a risk point refers to a target that poses a threat to vehicle safety.

[0120] For details regarding steps S201 and S202, please refer to the foregoing embodiments; they will not be repeated here.

[0121] S203: For each risk point indicated by the risk situation, if the relative distance between the risk point and the vehicle is less than the second preset distance, a warning prompt will be given to the driver for the risk point.

[0122] In this step, after the vehicle is powered on (including the idling and driving phases), for each risk point indicated in the risk situation, it is necessary to calculate the relative distance between the risk point and the vehicle in real time, and when the relative distance between the risk point and the vehicle is less than a second preset distance, a warning prompt will be given to the driver for the risk point.

[0123] The first preset distance is used to characterize the trigger threshold for the risk avoidance operation prompt.

[0124] In practice, the warning can be displayed through icons, text, and color changes on the vehicle's screen, or through voice broadcasts and beeping alerts, so that drivers can be aware of the corresponding risk points ahead and adjust their speed, direction, or traffic strategy in advance.

[0125] In one possible implementation, providing drivers with warnings about potential risks may include the following steps 3.1 to 3.2:

[0126] Step 3.1: Determine the warning intensity based on the relative distance between the risk point and the vehicle.

[0127] Step 3.2: Provide drivers with warnings for risk points according to the warning intensity; where the warning intensity is positively correlated with the relative distance.

[0128] The warning intensity is used to characterize the degree of alert to the driver, and can be reflected through warning dimensions and the intensity of the warning in each dimension. Warning dimensions refer to the different types of prompts that constitute the driver's alert, including visual display dimensions, audio-visual alert dimensions, text pop-up dimensions, and other types of prompts. The intensity of the warning corresponding to different warning dimensions is reflected in ways such as voice volume, image flashing frequency, or vibration amplitude of the seat, steering wheel, and pedals.

[0129] For example, in practical applications, if the relative distance between the risk point and the vehicle is large, the controller outputs a weak warning prompt through the human-machine interface terminal to avoid unnecessary interference to the driver. As the vehicle continues to approach the risk point, the controller gradually increases the intensity of the warning prompt according to the distance change, and the voice broadcast volume, indicator icon brightness or vibration amplitude are increased synchronously. When the relative distance further decreases, the intensity of the warning prompt continues to increase to prompt the driver to take timely actions such as slowing down, changing lanes, detouring or stopping.

[0130] Specifically, the intensity adjustment can be achieved in segments based on a preset distance threshold, or it can be achieved based on continuous changes in distance. The controller can update the output parameters in real time according to the distance mapping relationship.

[0131] This implementation links the intensity of the warning alert with the relative distance between the risk point and the vehicle, allowing drivers to intuitively perceive the approach of the risk through changes in the alert intensity. This enables them to identify changes in danger earlier and adjust their driving behavior accordingly. This improves the perceptibility of the warning information while reducing excessive intervention at long distances, thus enhancing the relevance, real-time nature, and safety of alerts in complex and dangerous road conditions.

[0132] S204: If the relative distance between the risk point and the vehicle is less than the first preset distance, then based on the location and performance characteristics of the risk point, a risk avoidance operation prompt is generated and pushed to the driver.

[0133] The first preset distance is less than the second preset distance.

[0134] In this step, for each risk point, when the relative distance between the risk point and the vehicle is less than a first preset distance, a risk avoidance operation prompt is generated based on the location and performance characteristics of the risk point, and the risk avoidance operation prompt is pushed to the driver.

[0135] The first preset distance is used to represent the proximity threshold. When the relative distance between the risk point and the vehicle is less than the threshold, the risk point is considered to have a strong impact on the current driving. This application does not impose specific restrictions on the specific setting of the first preset distance.

[0136] For specific methods of generating risk avoidance operation prompts based on the location and performance characteristics of risk points, please refer to the aforementioned embodiments, which will not be repeated here.

[0137] The vehicle hazard avoidance warning method provided in this application avoids premature intervention in long-distance risks and insufficient warnings for high-threat risks by using a distance threshold as the trigger condition for sending warnings and hazard avoidance operation prompts for risk points. Furthermore, because the first preset distance is set to be less than the second preset distance, the system can form a clear distance hierarchy between the warning phase and the hazard avoidance phase, matching the prompting rhythm with the proximity of the risk and avoiding interference caused by prompts that are too late or too early. This improves the risk perception capability and operational reliability during driving on dangerous road sections.

[0138] Furthermore, the flowchart of a vehicle hazard avoidance warning method provided in Embodiment 3 of this application is shown. Based on the above embodiments, the vehicle in which the method provided in this application is applied is equipped with a streaming media rearview mirror.

[0139] This streaming rearview mirror is an image display structure on a vehicle, typically including a physical mirror display mode and a screen display mode. The physical mirror display mode refers to a display mode that presents the rear view based on optical reflection principles; the screen display mode refers to a display mode that uses a built-in display panel to visualize the real-time external scene captured by the vehicle's camera. The streaming rearview mirror can be the vehicle's center rearview mirror, left rearview mirror, or right rearview mirror. (The request requests restrictions on this step.)

[0140] Accordingly, the aforementioned methods also include:

[0141] Step 301: Based on the risk situation, determine whether there are risk points in the first and second road segments.

[0142] If the risk status indicates that there is a risk point in the first road segment, proceed to step 302; if the risk status indicates that there is no risk point in the first road segment but there is a risk point in the second road segment, proceed to step 305; if the risk status indicates that there is no risk point in either the first or the second road segment, proceed to step 306.

[0143] In practice, it can be determined whether there are risk points in each road segment by verifying whether the latitude and longitude of each risk point fall within the geographical boundary of the corresponding road segment.

[0144] Step 302: Identify the risk point with the shortest expected occurrence time of the accident as the first target risk point.

[0145] In this step, if the risk situation indicates that there are risk points in the first road segment, it is necessary to first determine the expected occurrence time of the accident at each risk point in the first road segment based on the risk situation, and then determine the risk point with the shortest expected occurrence time of the accident as the first target risk point.

[0146] Step 303: Determine the target camera based on the relative positional relationship between the first target risk point and the vehicle.

[0147] In this step, the controller can determine the target camera based on the relative positional relationship between the first target risk point and the vehicle. This relative positional relationship may include front, left front, right front, side rear, or rear, so that the selected camera covers the target risk area.

[0148] The target camera can be installed at the front of the vehicle, on the exterior rearview mirror housing, on the roof, or at the rear, and connected to the vehicle body via a fixed bracket.

[0149] Step 304: Display the image captured by the target camera in the streaming rearview mirror.

[0150] In this step, the image output by the target camera needs to be transmitted to the streaming media rearview mirror display via a video link.

[0151] In one possible implementation, when the relative positional relationship is such that the location of the risk is in front of the vehicle, the image captured by the target camera is displayed in the streaming rearview mirror, including:

[0152] The image captured by the target camera is preprocessed to obtain the processed image, which is then displayed in the streaming rearview mirror. The preprocessing includes cropping the image of the target area and perspective distortion correction. The target area is the area in front that is obscured by the streaming rearview mirror.

[0153] The processed image refers to the result of cropping and geometric correction of the target camera image. Its purpose is to enable the driver to observe the area in front that is blocked by the rearview mirror more clearly through the streaming media rearview mirror.

[0154] In detail, preprocessing is a process of correcting the original image to improve the visibility and accuracy of the displayed content. Specifically, cropping the target area involves extracting the image content of a specified area in front of the target camera, while perspective distortion correction corrects the viewing angle of the extracted image to eliminate distortion caused by the shooting angle.

[0155] In practical implementation, the target camera can be positioned near the centerline of the vehicle at the front, with the lens facing the direction of travel. The captured images include the road ahead and environmental information related to the location of the risk. The streaming media rearview mirror can employ a structure integrating a liquid crystal display module with the mirror housing. The display module's surface faces the driver, allowing for direct presentation of forward-facing video footage on the mirror surface.

[0156] After determining that the risk location is in front of the vehicle, the controller acquires the original image output by the target camera. Based on the installation position, field of view, and obstruction boundary of the streaming rearview mirror, it determines the image range corresponding to the obstructed area in front, and then crops this range to obtain the target area image. Subsequently, the system performs perspective distortion correction on the target area image based on the installation height, pitch angle, and perspective relationship of the target area in the image, so that the road edges, obstacle outlines, and risk point locations are displayed as close as possible to the driver's intuitive observation habits. The processed image is output in real time to the display interface of the streaming rearview mirror to replace or supplement the conventional rearview observation field of view. In this way, the driver can intuitively observe the risk of falling rocks at a high position in front that is blocked by the rearview mirror (such as a central streaming rearview mirror).

[0157] The method provided in this implementation specifically extracts the area of ​​the vehicle's forward field of vision obstructed by the original physical rearview mirror, and corrects geometric distortion of the image through perspective distortion correction, so that the imaging of the streaming media rearview mirror closely matches the real perspective effect of the human eye. Drivers can intuitively view road conditions and risks ahead based on the displayed screen, quickly identifying potential hazards, eliminating redundant content, improving the relevance of the display, and saving display resources.

[0158] The method provided in steps 302 to 304 prioritizes risk points based on the estimated time of accident occurrence, allowing the central streaming rearview mirror to prioritize displaying visual information corresponding to the most pressing risks, thereby dynamically visualizing the risk points of nearby vehicles. Furthermore, since the displayed content is directly related to the spatial location of the target risk point, drivers can quickly obtain road images relevant to hazard avoidance decisions in the rearview mirror, thus improving judgment efficiency and traffic safety in complex and dangerous road conditions.

[0159] Step 305: Provide global risk location alerts via streaming rearview mirror.

[0160] In this step, if the risk situation indicates that there are no risk points in the first road segment but there are risk points in the second road segment, then the risk location will be indicated across the entire area via the streaming rearview mirror.

[0161] Among them, the overall risk location indication refers to providing a comprehensive indication of the spatial distribution of risk points within the second road segment. For example, the location and distance of each risk point relative to the vehicle in the road segment ahead can be displayed on the streaming rearview mirror.

[0162] As a specific example, the controller can directly extract the location information of each risk point in the second road segment from the road condition data and convert it into the orientation and distance of the risk point relative to the vehicle. For example, a circular compass-style interface can be used to mark the orientation of the risk point relative to the vehicle, and the distance of each risk point relative to the vehicle can be marked in numerical form.

[0163] Optionally, in practical applications, in addition to streaming media rearview mirrors, other devices can be used to achieve full-area risk point reminders, such as central control displays, head-up displays, and in-vehicle voice systems.

[0164] The method provided in this step outputs overall risk location information for subsequent road segments to the driver via a streaming rearview mirror when there are no risk points near the vehicle, enabling the driver to develop a spatial awareness of the risk locations of the second road segment in advance. This assists the driver in making decisions such as slowing down, changing lanes, detouring, or stopping before entering dangerous road segments, and improves traffic safety and the accuracy of warnings in complex road conditions.

[0165] Step 306: Control the streaming media rearview mirror to switch to physical mirror display mode.

[0166] In this step, if the risk situation indicates that there are no risk points in either the first or second road segment, the streaming rearview mirror will be switched to physical mirror display mode.

[0167] In practice, if the controller determines that there are no risk points in the current road segment or the expected future road segment based on the risk situation, it will directly control the streaming media rearview mirror to switch to physical mirror display mode.

[0168] The method provided in this step can reduce vehicle power consumption and restore the natural viewing field of view of traditional rearview mirrors by automatically shutting down camera imaging and switching physical reflection mode in risk-free scenarios.

[0169] The vehicle hazard avoidance warning method provided in this application dynamically adjusts the display content of the streaming media rearview mirror for different risk distribution scenarios. It enables the rearview mirror to adaptively switch between three display modes: normal field of view (physical mirror mode), focused field of view (fixed-point display of target risk points), and full-field field of view (marking the distribution of multiple risk points at a distance). This ensures that the driver can obtain the corresponding field of view information as needed according to real-time road conditions, which not only meets the normal rearview needs of daily driving, but also accurately captures the location of danger when passing through dangerous road sections, taking into account both observation comfort and the effectiveness of risk warning.

[0170] Furthermore, in some possible implementations, based on the above embodiments, the vehicle risk avoidance warning method provided in this application may further include the following steps 401 and 403 before providing risk warnings to the driver according to the risk situation:

[0171] Step 401: Obtain the driver's driving operation profile.

[0172] Among them, the driving operation profile is determined based on the driver's historical driving operation records.

[0173] Specifically, historical driving operation records include two categories: data on various past driving behaviors and historical accident records. For example, historical driving behaviors can include characteristics such as acceleration, deceleration, steering, and braking during past journeys; these behaviors can be used to determine the driver's driving style and habit profile. Historical accident records refer to archived data on accidents reported by the driver, minor collisions, traffic violations, and dangerous driving incidents such as speeding, lane violations, and route deviations when passing through similar high-risk road sections. These records can be used to determine the driver's risk avoidance ability profile. For example, the more dangerous driving incidents a driver has, the worse their risk avoidance ability.

[0174] In practice, after the vehicle is powered on, the controller can identify the driver's identity through facial recognition and other identity verification methods, and retrieve the driver's operation profile corresponding to that identity.

[0175] Step 402: Determine the first preset distance and the second preset distance based on the vehicle's performance characteristics and / or the driver's driving operation profile.

[0176] In this step, the controller will jointly determine the first and second preset distances for this trip based on the driver's operation profile and the vehicle's performance characteristics.

[0177] For example, for drivers with a conservative operating style, early braking intervention, and poor risk avoidance ability, the corresponding first and second preset distances can be set relatively large to extend their judgment and handling time; for drivers with an aggressive operating style, fast reaction speed, and superior risk avoidance ability, the corresponding first and second preset distances can be relatively small to avoid premature warnings that trigger unnecessary intervention.

[0178] When introducing vehicle performance characteristics, for vehicles whose performance characteristics indicate superior risk avoidance capabilities such as power performance, braking performance, and passability, the corresponding first and second preset distances can be relatively small to avoid premature warnings that trigger unnecessary interventions. For vehicles whose performance characteristics indicate poor or superior risk avoidance capabilities such as power performance, braking performance, and passability, the corresponding first and second preset distances can be relatively large so that risk warnings are triggered when the risk point is still far away, thus allowing sufficient reaction time for the vehicle to decelerate, change lanes, or stop.

[0179] Regarding the specific implementation of the solution, the controller can generate the final distance value using weighted calculation, rule mapping, or table lookup matching, and store it in the vehicle controller for subsequent risk warning calls. For example, a distance lookup table corresponding to different driver profiles and different vehicle performance can be pre-entered into the vehicle storage. After retrieving the driver profile and vehicle parameters, the vehicle controller can directly index and look up the table to quickly obtain the first preset distance and the second preset distance.

[0180] The method provided in this implementation determines the threshold for alerts based on vehicle capabilities and driver profiles, ensuring that the timing of alerts is adapted to the current vehicle state and the driver's individual characteristics. This improves the targeting and accuracy of alerts in complex and dangerous road conditions. Adopting this approach reduces problems such as premature, delayed, or mismatched alerts due to uniform thresholds, helping to improve the driver's risk perception efficiency and enhance vehicle safety in hazardous road sections.

[0181] In addition, in some possible implementations, the vehicle avoidance prompt method provided in this application may further include, before prompting the driver to perform avoidance operations, modifying the content of the avoidance operation prompt using the driver's driving operation profile.

[0182] For example, for drivers with aggressive driving style and poor risk avoidance ability, the risk avoidance operation tips are supplemented with more detailed practical guidance on deceleration range, steering correction range, etc.; for drivers with gentle driving habits and excellent risk avoidance ability, the risk avoidance operation tips are simplified, retaining only the key tips.

[0183] The method provided in this implementation adjusts the content of the risk avoidance operation prompts based on the driver's driving operation profile, so that the prompts are accurately matched with the driver's driving level and operating habits. This achieves the effect of providing detailed guidance to high-risk drivers to avoid driving risks, while avoiding redundant prompts to high-quality drivers and improving the driving experience.

[0184] Furthermore, in some possible implementations, based on the above embodiments, the vehicle avoidance warning method provided in this application may also include the following method: modifying the driver's avoidance operation behavior according to the consistency between the driver's avoidance operation behavior and the avoidance operation warning.

[0185] Specifically, after the avoidance operation prompt is sent, the vehicle's operating parameters can be continuously monitored to obtain the driver's avoidance operation behavior; based on the consistency between the avoidance operation prompt and the driver's avoidance operation behavior, the driver's avoidance operation behavior can be corrected.

[0186] As a specific example, the deviation between the driver's avoidance operation and the avoidance operation prompt can be quantitatively calculated by comparing the target operation parameters of the vehicle indicated by the avoidance operation prompt with the vehicle's real-time collected vehicle operating parameters such as throttle opening, brake master cylinder pressure, and wheel angle. When the calculated deviation exceeds the preset level, the controller directly takes over the control of the entire vehicle and activates the autonomous driving avoidance strategy to avoid safety hazards caused by driver delays. If the deviation does not exceed the preset level, only a light auxiliary correction is implemented for the driver's operation. For example, when the driver performs an acceleration avoidance action according to the avoidance operation prompt, the vehicle control amount is finely adjusted based on the optimal power output and driving trajectory parameters calculated by the autonomous driving system in the current scenario.

[0187] The method provided in this implementation can avoid safety hazards caused by driver delays by taking timely autonomous driving takeover actions when the driver's overall operation deviates significantly. At the same time, by using gradual small-scale auxiliary corrections in scenarios where the degree of driver operation deviation is small, it can balance the smoothness of vehicle driving and the driver's native control experience.

[0188] In some possible implementations, based on the above embodiments, the vehicle hazard avoidance warning method provided in this application may further include the following steps 501 and 502:

[0189] Step 501: Identify the risk point with the smallest relative distance to the vehicle as the second target risk point.

[0190] Step 502: Adjust the vehicle's control performance parameters dynamically in real time based on the relative distance between the second target risk point and the vehicle.

[0191] Among them, the vehicle's control performance parameters are used to represent the control quantities related to the vehicle's risk avoidance response. The control quantities may include power output, braking response, steering response, and vehicle stability control-related parameters. The system adjusts these parameters in real time based on the changes in the distance to the second target risk point so that the vehicle maintains an appropriate control state when approaching the risk area.

[0192] In its implementation, the control unit continuously identifies the risk point with the smallest relative distance to the vehicle among the various risk points indicated by the risk situation, using it as the second target risk point. When a decrease in the relative distance between the second target risk point and the vehicle is detected, the controller increases the adjustment range of the vehicle's control performance parameters, such as increasing braking response intensity, lowering the power output threshold, or increasing steering intervention sensitivity, while simultaneously lowering the vehicle height and tightening suspension tuning parameters to enhance overall vehicle handling agility. Conversely, when an increase in the relative distance is detected, the corresponding adjustment range is reduced, gradually raising the vehicle height and loosening suspension constraint parameters. These parameter adjustments can be achieved by outputting the vehicle controller to the power control module, braking control module, stability control module, and suspension control module.

[0193] The method provided by this implementation can always adjust the control parameters around the most pressing risk point, so that the vehicle control state can change smoothly with the approach of the risk, and the vehicle control performance can be kept consistent with the current level of danger, thereby improving the vehicle's response time and control accuracy to nearby risks.

[0194] Figure 3 This is a structural schematic diagram of a vehicle hazard avoidance warning device provided in Embodiment 4 of this application, as shown below. Figure 3 As shown, the vehicle hazard avoidance warning device 60 provided in this embodiment includes:

[0195] The acquisition module 601 is used to acquire road condition data of the vehicle's environment and the vehicle's performance characteristics.

[0196] The first determining module 602 is used to determine the risk situation of the vehicle's environment based on road condition data; the risk situation is used to indicate the location of each risk point in the environment; a risk point refers to a target object that poses a threat to the vehicle's safety.

[0197] The first prompt module 603 is used to prompt the driver to take evasive action based on the risk situation and performance characteristics.

[0198] The vehicle avoidance warning device 60 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0199] Figure 4 This is a structural schematic diagram of a vehicle hazard avoidance warning device provided in Embodiment 5 of this application, as shown below. Figure 4 As shown, based on the above embodiments, the vehicle hazard avoidance warning device 60 provided in this embodiment further includes:

[0200] The second prompt module 604 is specifically used to provide a warning prompt to the driver for each risk point if the relative distance between the risk point and the vehicle is less than the second preset distance.

[0201] The first preset distance is less than the second preset distance.

[0202] The second determining module 605 is used to obtain the driver's driving operation profile; the driving operation profile is determined based on the driver's historical driving operation records.

[0203] Based on the vehicle's performance characteristics and / or the driver's driving operation profile, determine the first preset distance and the second preset distance.

[0204] The correction module 606 is used to correct the content of the avoidance operation prompts using the driver's driving operation profile.

[0205] Display module 607 includes:

[0206] The first determining unit is used to determine the risk point with the shortest expected occurrence time of the accident as the first target risk point if the risk situation indicates that there is a risk point in the first road segment;

[0207] The second determining unit is used to determine the target camera based on the relative positional relationship between the first target risk point and the vehicle;

[0208] The display unit is used to display the images captured by the target camera in the streaming rearview mirror.

[0209] The third prompt module 608 is used to provide a full-area risk location prompt via the streaming media rearview mirror if the risk situation indicates that there is no risk point in the first road segment but there is a risk point in the second road segment.

[0210] In one possible embodiment, the road condition data includes first road condition data of the first road segment where the vehicle is currently located, and second road condition data of the second road segment that the vehicle is expected to reach in the future. The acquisition module 601 is specifically used for:

[0211] The vehicle's perception system collects road condition data for the first road segment, and obtains the road condition data for the first road segment from other vehicles' historical uploads via the cloud server, thus obtaining the first road condition data.

[0212] The second road condition data is obtained by retrieving historical road condition data uploaded by other vehicles from the second road section through a cloud server.

[0213] In one possible embodiment, the first prompting module 603 is specifically used for:

[0214] For each risk point indicated in the risk situation, if the relative distance between the risk point and the vehicle is less than the first preset distance, a risk avoidance operation prompt is generated based on the location and performance characteristics of the risk point.

[0215] Send hazard avoidance operation prompts to drivers.

[0216] In one possible embodiment, the second prompting module 604 is specifically used for:

[0217] The intensity of the warning is determined based on the relative distance between the risk point and the vehicle;

[0218] Drivers are given warnings based on the intensity of the warning; the intensity of the warning is positively correlated with the relative distance.

[0219] In one possible embodiment, when the relative positional relationship is such that the location where the risk occurs is in front of the vehicle, the display unit is specifically used for:

[0220] The image captured by the target camera is preprocessed to obtain a processed image, which is then displayed on the streaming media rearview mirror.

[0221] The preprocessing includes cropping the image of the target area and perspective distortion correction; the target area is the area in front that is obscured by the streaming media rearview mirror.

[0222] The vehicle avoidance warning device 60 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0223] Figure 5 This is a schematic diagram of the structure of a controller provided in Embodiment 5 of this application. Figure 5 As shown, the controller 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the controller 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0224] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0225] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0226] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0227] The memory may include read-only memory and random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0228] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0229] This application also provides a vehicle, including a vehicle body and the aforementioned controller;

[0230] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method.

[0231] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the above-described method.

[0232] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, EEPROM, EPROM, PROM, ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0233] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0234] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0235] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0236] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0237] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. 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.

[0238] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0239] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

[0240] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for providing hazard avoidance warnings for vehicles, characterized in that, The method includes: Acquire road condition data of the vehicle's environment and the vehicle's performance characteristics; Based on the road condition data, the risk situation of the vehicle's environment is determined; the risk situation is used to indicate the location of each risk point in the environment; the risk point refers to a target that poses a threat to the vehicle's safety. Based on the described risk situation and performance characteristics, the driver is prompted to take evasive action.

2. The method according to claim 1, characterized in that, The road condition data includes first road condition data of the first road segment where the vehicle is currently located, and second road condition data of the second road segment that the vehicle is expected to reach in the future. The acquisition of road condition data of the vehicle's environment includes: The vehicle's perception system collects road condition data for the first road segment, and obtains road condition data for the first road segment from other vehicles' historical uploads via a cloud server to obtain the first road condition data. The second road condition data is obtained by acquiring historical road condition data uploaded by other vehicles on the second road segment through a cloud server.

3. The method according to claim 1 or 2, characterized in that, The step of providing the driver with hazard avoidance prompts based on the risk situation and performance characteristics includes: For each risk point indicated in the aforementioned risk situation, if the relative distance between the risk point and the vehicle is less than a first preset distance, then the risk avoidance operation prompt is generated based on the location of the risk point and the performance characteristics. The driver is prompted to perform the hazard avoidance maneuver.

4. The method according to claim 3, characterized in that, The method further includes: For each risk point, if the relative distance between the risk point and the vehicle is less than a second preset distance, a warning prompt will be given to the driver for the risk point. The first preset distance is less than the second preset distance.

5. The method according to claim 4, characterized in that, Before providing risk warnings to the driver based on the risk situation, the method further includes: Obtain the driver's driving operation profile; the driving operation profile is determined based on the driver's historical driving operation records; The first preset distance and the second preset distance are determined based on the performance characteristics of the vehicle and / or the driving operation profile of the driver.

6. The method according to claim 4 or 5, characterized in that, The provision of early warnings to the driver regarding the aforementioned risk points includes: The warning intensity is determined based on the relative distance between the risk point and the vehicle; The driver is given a warning alert for the risk point according to the warning intensity; the warning intensity is positively correlated with the relative distance.

7. The method according to claim 5, characterized in that, Before prompting the driver to take evasive action, the method further includes: The content of the hazard avoidance operation prompts is revised using the driver's driving operation profile.

8. The method according to claim 2, characterized in that, The risk information is also used to indicate the estimated time of occurrence of an accident corresponding to each risk point. The vehicle is equipped with a streaming rearview mirror, and the method further includes: If the risk situation indicates that there is a risk point in the first road segment, then the risk point with the shortest expected occurrence time of the accident is determined as the first target risk point; The target camera is determined based on the relative positional relationship between the first target risk point and the vehicle; The image captured by the target camera is displayed in the streaming rearview mirror.

9. The method according to claim 8, characterized in that, When the relative positional relationship is such that the location where the risk occurs is in front of the vehicle, displaying the image captured by the target camera in the streaming rearview mirror includes: The image captured by the target camera is preprocessed to obtain a processed image, and the processed image is displayed on the streaming media rearview mirror; The preprocessing includes cropping the image of the target area and perspective distortion correction processing; the target area is the front area that is obscured by the streaming media rearview mirror.

10. The method according to claim 8, characterized in that, The method further includes: If the risk situation indicates that there are no risk points in the first road segment but there are risk points in the second road segment, then the streaming media rearview mirror will provide a full-area risk location alert.

11. A vehicle hazard avoidance warning device, characterized in that, The device includes: The acquisition module is used to acquire road condition data of the vehicle's environment and the vehicle's performance characteristics; The determination module is used to determine the risk situation of the vehicle's environment based on the road condition data; the risk situation is used to indicate the location of each risk point in the environment; the risk point refers to a target object that poses a threat to vehicle safety; The prompting module is used to prompt the driver to take evasive action based on the risk situation and the performance characteristics.

12. A controller, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-10.

13. A vehicle, characterized in that, The vehicle includes a vehicle body and the controller as described in claim 12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

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