A vehicle early warning method, device, electronic equipment and storage medium

Abstract

This invention discloses a vehicle early warning method, device, electronic device, and storage medium. A specific implementation includes: determining target data; processing the target data to generate a risk feature vector corresponding to the target data; determining a risk rating result based on the risk feature vector and a collision risk prediction model; and determining an early warning method based on the risk rating result. By determining the target data, the problem of incomplete blind spot coverage in underground parking garage exit areas in existing technologies is solved. By generating a risk feature vector to determine the risk rating result, early warning of vehicles in underground parking garage exit areas is achieved, improving the accuracy of early warning and the safety protection level of underground parking garage exit areas.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a vehicle warning method, device, electronic device, and storage medium. Background Technology

[0002] With the continuous growth of urban parking demand, underground parking garages have become increasingly common in urban buildings. The exit of an underground parking garage is the only connection between the garage and the surface space, and it is also the high-risk scenario with the highest probability and most serious consequences of conflicts between vehicles exiting the garage and pedestrians. This is mainly because underground parking garage exits have numerous inherent blind spots, limiting the driver's visibility and severely impairing their ability to perceive pedestrians.

[0003] Basement exits have three distinct blind spots compared to ordinary roads: First, the uphill elevation difference blind spot—when drivers exit from the basement upwards, the elevation difference of the slope obstructs their view, making it impossible to detect pedestrians above the slope in advance; second, the blind spot caused by pillars obstructing views at curves—the structural pillars of the basement block views of curves, creating blind spots behind the pillars during vehicle turns; third, the blind spot in the transition area between the exit and the pedestrian walkway—at the junction of the basement exit and the pedestrian walkway, pedestrians may quickly enter the vehicle's path from the side, making it difficult for drivers to detect them in advance. The combination of these three types of blind spots makes basement exits one of the most frequent conflict and hazard points within urban buildings. Summary of the Invention

[0004] This invention provides a vehicle early warning method, device, electronic device, and storage medium to achieve early warning of vehicles in the underground parking garage exit area and improve the safety protection level of the underground parking garage exit area.

[0005] According to one aspect of the present invention, a vehicle warning method is provided, applied to an edge gateway, the edge gateway being installed in a management center for managing a parking garage exit area, the edge gateway establishing a communication connection with the vehicle to be warned, comprising: Determine target data, which includes data corresponding to the vehicle to be warned and the environment in which the vehicle to be warned is located, and the vehicle to be warned includes vehicles operating in the parking garage exit area; The target data is processed to generate a risk feature vector corresponding to the target data. The risk feature vector indicates the collision risk between the vehicle to be warned and the target object in the environment. The target object includes objects in the parking garage exit area that have a collision risk with the vehicle to be warned. Based on the risk feature vector and the collision risk prediction model, a risk rating result is determined, which indicates the probability of a collision between the vehicle to be warned and the target object; Based on the risk rating results, the early warning method is determined.

[0006] According to another aspect of the present invention, a vehicle warning device is provided, configured on an edge gateway, the edge gateway being installed in a management center for managing a parking garage exit area, the edge gateway establishing a communication connection with the vehicle to be warned, comprising: The first determining module is used to determine target data, which includes data corresponding to the vehicle to be warned and the environment in which the vehicle to be warned is located. The vehicle to be warned includes vehicles operating in the parking garage exit area. The generation module is used to process the target data and generate a risk feature vector corresponding to the target data. The risk feature vector indicates the collision risk between the vehicle to be warned and the target object in the environment. The target object includes objects in the parking garage exit area that have a collision risk with the vehicle to be warned. The second determining module is used to determine a risk rating result based on the risk feature vector and the collision risk prediction model, wherein the risk rating result indicates the probability of a collision between the vehicle to be warned and the target object; The third determining module is used to determine the early warning method based on the risk rating results.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle warning method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle warning method according to any embodiment of the present invention.

[0009] The technical solution of this invention involves: determining target data; processing the target data to generate a risk feature vector corresponding to the target data; determining a risk rating result based on the risk feature vector and a collision risk prediction model; and determining a warning method based on the risk rating result. By determining the target data, the problem of incomplete blind spot coverage in the underground parking garage exit area in existing technologies is solved. By generating a risk feature vector to determine the risk rating result, early warning of vehicles in the underground parking garage exit area is achieved, improving the accuracy of warnings and the safety protection level of the underground parking garage exit area.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a vehicle warning method provided according to Embodiment 1 of the present invention; Figure 2 This is a structural diagram of a basement exit area provided according to Embodiment 1 of the present invention; Figure 3 This is a flowchart of a target data determination method provided in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the structure of a vehicle warning device according to Embodiment 3 of the present invention; Figure 5 This is a block diagram of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] It is understood that before using the technical methods disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0016] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to autonomously choose whether to provide personal information to the software or hardware, such as the electronic device, application program, server, or storage medium performing the operations of this invention, based on the prompt message.

[0017] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0018] It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of the present invention. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present invention.

[0019] Example 1 Figure 1 This is a flowchart of a vehicle warning method according to Embodiment 1 of the present invention. This embodiment is applicable to situations where warnings are issued in the exit area of ​​an underground parking garage. The method can be executed by a vehicle warning device, which can be implemented in hardware and / or software. The vehicle warning device can be configured in an electronic device, which can be equipped with an edge gateway. The edge gateway is installed in a management center, which manages the exit area of ​​the underground parking garage. The edge gateway establishes a communication connection with the vehicle to be warned. Figure 1 As shown, the method includes: S110. Determine the target data.

[0020] The target data includes the data corresponding to the vehicle to be warned and the environment in which the vehicle to be warned is located. The vehicle to be warned includes vehicles operating in the parking garage exit area.

[0021] In this embodiment, the edge gateway is deployed at the management center in the parking garage exit area. It can communicate with the vehicles to be warned via a wired local area network and is responsible for data fusion, model inference, and early warning control. The vehicles to be warned can be understood as vehicles passing through the parking garage exit area. Target data can be understood as the data corresponding to the vehicles to be warned and their surrounding environment. Target data may include data collected by various sensors in the parking garage exit area, such as image data.

[0022] Specifically, at least one sensor is deployed in the parking garage exit area, and the data collected by the sensor is transmitted to an edge gateway. This sensor can collect data on vehicles awaiting warning as they pass through the parking garage exit area, as well as data on the surrounding environment. By preprocessing the data collected by the sensor, the target data can be obtained.

[0023] For example, Figure 2 This is a structural diagram of a basement exit area provided according to Embodiment 1 of the present invention. Figure 2 As shown, a three-dimensional detection matrix is ​​constructed in the underground parking garage exit area. To address the blind spots caused by the uphill elevation difference, the blind spots caused by pillars blocking the curves, and the blind spots in the transition area of ​​the exit connecting road, a three-dimensional detection matrix with no blind spots is deployed according to the following scheme: segmented deployment of points along the slope, blind spot filling deployment along the curves, and full coverage deployment of points in the exit transition area. Low-power distributed millimeter-wave radar nodes, wide dynamic range binocular vision nodes, and infrared supplementary lighting nodes are deployed to form a three-dimensional detection matrix with full-path coverage without blind spots from the underground parking garage exit area to the connecting road.

[0024] S120. Process the target data to generate a risk feature vector corresponding to the target data.

[0025] The risk feature vector indicates the collision risk between the vehicle to be warned and a target object in the environment, the target object including objects in the parking garage exit area that pose a collision risk to the vehicle to be warned.

[0026] In this embodiment, the risk feature vector can be understood as a vector indicating the collision risk between the vehicle to be warned and a target object in the environment where the vehicle to be warned is located. The risk feature vector can be a vector that integrates information about the state of the vehicle to be warned, the state of the target object, and the environment in which the vehicle to be warned is located. The target object can be understood as an object in the parking garage exit area that has a collision risk with the vehicle to be warned. The target object can include pedestrians, non-motorized vehicles, vehicles, and static obstacles, etc.

[0027] Specifically, the target data can be identified first to determine the target objects that pose a collision risk to the vehicle to be warned. Then, the state of the target objects, the state of the vehicle to be warned, and the environment in which the vehicle to be warned is located can be extracted from the target data, and the above information can be fused to obtain the risk feature vector corresponding to the target data.

[0028] S130. Determine the risk rating result based on the risk feature vector and collision risk prediction model.

[0029] The risk rating result indicates the probability of a collision between the vehicle to be warned and the target object.

[0030] In this embodiment, the collision risk prediction model can be understood as a model used to determine the probability of a collision between the vehicle to be warned and the target object. The collision risk prediction model can be a model built based on a two-layer long short-term memory network.

[0031] Specifically, the risk feature vector is input into the collision risk prediction model, which can predict the probability of a collision between the vehicle to be warned and the target object. Then, based on the probability of a collision, it outputs a four-level risk rating result: blue, yellow, orange, and red.

[0032] S140. Based on the risk rating results, determine the early warning method.

[0033] In this embodiment, the early warning method can be understood as an early warning method set based on the risk rating results. Different early warning methods are formulated for the four levels of risk rating results: blue, yellow, orange, and red.

[0034] Specifically, the warning method is determined based on the risk rating results to achieve tiered warnings. The edge gateway can distribute different warning methods to various nodes, including vehicle warning terminals, object warning terminals, and the management center. Finally, the edge gateway receives the warning reception confirmation signal from the nodes, and the management center issues a closed-loop shutdown command after completing the handling. If no confirmation is received within the time limit, the warning level is automatically upgraded, forming a complete warning closed loop.

[0035] The technical solution of this invention involves: determining target data; processing the target data to generate a risk feature vector corresponding to the target data; determining a risk rating result based on the risk feature vector and a collision risk prediction model; and determining a warning method based on the risk rating result. By determining the target data, the problem of incomplete blind spot coverage in the underground parking garage exit area in existing technologies is solved. By generating a risk feature vector to determine the risk rating result, early warning of vehicles in the underground parking garage exit area is achieved, improving the accuracy of warnings and the safety protection level of the underground parking garage exit area.

[0036] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0037] In one embodiment, processing the target data to generate a risk feature vector corresponding to the target data includes: The target data is identified to determine the target object and the target state parameters corresponding to the target object, wherein the target state parameters include data corresponding to the state of the target object; The vehicle status parameters and environmental status parameters contained in the target data are determined. The vehicle status parameters include the operating status of the vehicle to be warned, and the environmental status parameters include the parameters of the environment in which the vehicle to be warned is located and the parameters of the environment in which the target object is located. By integrating the target state parameters, the vehicle state parameters, and the environmental state parameters, a risk feature vector corresponding to the target data is obtained.

[0038] In this embodiment, target state parameters can be understood as data corresponding to the state of the target object. These parameters may include position, direction of movement, speed, distance from the conflict zone, and an estimated intrusion probability. Vehicle state parameters can be understood as data corresponding to the operating state of the vehicle to be warned. These parameters may include vehicle speed, acceleration, brake pedal status, steering angle, and distance from the conflict zone. Environmental state parameters can be understood as parameters of the environment in which the vehicle to be warned is located and parameters of the environment in which the target object is located. These environmental state parameters may include light intensity, light pattern, weather conditions, time of day, road surface slippage, slope gradient coefficient, and curve obstruction coefficient.

[0039] Specifically, target identification is performed on the preprocessed target data to distinguish target objects such as pedestrians, non-motorized vehicles, vehicles, and static obstacles, and target state data such as position, direction of movement, speed of movement, and predicted trajectory of each target object are obtained. The vehicle state parameters and environmental state parameters contained in the target data are determined, and the target state parameters, vehicle state parameters, and environmental state parameters are fused simultaneously to obtain the risk feature vector corresponding to the target data.

[0040] For example, vehicle state parameters (vehicle speed, acceleration, brake pedal status, steering angle, distance to the conflict zone), target state parameters (target object's position, direction of movement, speed of movement, distance to the conflict zone, probability of intrusion), and environmental state parameters (light intensity, weather, time of day, road surface slippage, slope elevation difference coefficient, curve occlusion coefficient). These vehicle state parameters, target state parameters, and environmental state parameters are simultaneously fused to calculate a 17-dimensional risk feature vector for the current frame (4-dimensional vehicle state parameters + 8-dimensional target state parameters + 5-dimensional environmental state parameters).

[0041] In one embodiment, determining the risk rating result based on the risk feature vector and the collision risk prediction model includes: The risk feature vector is input into the collision risk prediction model to obtain the collision risk value, which includes the probability value of the collision between the vehicle to be warned and the target object; Based on the collision risk value, a risk rating result is determined.

[0042] In this embodiment, the collision risk value can be understood as the probability value of a collision between the vehicle to be warned and the target object.

[0043] For example, a collision risk prediction model is first constructed based on a two-layer Long Short-Term Memory (LSTM) network: the first LSTM layer has 128 hidden units, the second LSTM layer has 64 hidden units, the temporal input window length is 30 frames, and the output layer uses Softmax to output a four-level risk probability distribution of blue / yellow / orange / red. The collision risk prediction model supports online incremental learning, updating the weights every 24 hours based on actual data samples to continuously adapt to the scene distribution of the parking garage exit area. The collision risk prediction model runs on an edge gateway, requiring no cloud server, with an inference latency of less than 50ms, and can predict collision risks 1-3 seconds in advance. Then, a 17-dimensional risk feature vector is input into the collision risk prediction model (corresponding to approximately 1 second of historical sequence) with a temporal window of 30 frames to obtain the collision risk value, and the risk rating result is determined based on the magnitude of the collision risk value.

[0044] Optionally, determining the risk rating result based on the collision risk value includes: If the collision risk value is less than the first risk threshold, the risk rating result will be determined as a blue level. If the collision risk value is greater than or equal to the first risk threshold and less than the second risk threshold, the risk rating result will be determined as yellow level; If the collision risk value is greater than or equal to the second risk threshold and less than the third risk threshold, the risk rating result will be determined as orange level; If the collision risk value is greater than or equal to the third risk threshold, the risk rating result will be determined as red level.

[0045] In this embodiment, the first risk threshold, the second risk threshold, and the third risk threshold are all set thresholds used to determine the risk rating result. The value of the third risk threshold is greater than the value of the second risk threshold, and the value of the second risk threshold is greater than the value of the first risk threshold.

[0046] For example, if the collision risk value R is less than the first risk threshold of 0.3, the risk rating result is determined to be blue; if the collision risk value R is greater than or equal to the first risk threshold of 0.3 and less than the second risk threshold of 0.6, the risk rating result is determined to be yellow; if the collision risk value R is greater than or equal to the second risk threshold of 0.6 and less than the third risk threshold of 0.85, the risk rating result is determined to be orange; and if the collision risk value R is greater than or equal to the third risk threshold of 0.85, the risk rating result is determined to be red.

[0047] For example: A vehicle awaiting warning passes through the underground parking garage exit area at approximately 8 km / h, while simultaneously, a target object (e.g., a pedestrian) is rapidly walking from the sidewalk towards the exit area at approximately 1.5 m / s. A yellow warning is detected when the pedestrian is 3.5 m from the conflict zone (R=0.42). As the pedestrian continues to approach, the warning is upgraded to an orange warning (R=0.68) when the pedestrian is 1.8 m from the conflict zone.

[0048] In one embodiment, determining the early warning method based on the risk rating result includes: If the risk rating result is blue, the warning method is determined to be to display the location of the vehicle to be warned in the management center; If the risk rating result is yellow, the warning method is determined to be to issue a first warning message to the vehicle warning terminal and the object warning terminal. The object warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the target object. The vehicle warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the vehicle to be warned. If the risk rating result is orange, the warning method is determined to be to send a second warning message to the vehicle warning terminal, the object warning terminal, and the management center; If the risk rating result is red, the warning method is determined to be to send a third warning message to the vehicle warning terminal, the object warning terminal, and the management center. The warning level of the third warning information is greater than that of the second warning information, and the warning level of the second warning information is greater than that of the first warning information.

[0049] In this embodiment, the object warning terminal can be a terminal installed in the underground parking garage exit area. The object warning terminal may include a flashing light strip and a speaker. It can communicate with an edge gateway and is used to issue warnings to target objects. The vehicle warning terminal can be a terminal installed in the underground parking garage exit area, higher than the height of the vehicle to be warned. It may include a display screen and a speaker. It can communicate with an edge gateway and is used to issue warnings to the vehicle to be warned. The first, second, and third warning messages are all pre-defined warning methods. The first warning message may cause the vehicle warning terminal to issue a single voice prompt and pop up a yellow notification box on the screen, while simultaneously triggering the yellow flashing light on the ground of the object warning terminal. The second warning message may cause the vehicle warning terminal to issue a continuous voice alarm, while simultaneously triggering the red flashing light and voice broadcast of the object warning terminal, and causing the management center to trigger the speed limit on the barrier gate. The third warning message may issue a full-band audio-visual warning to both the vehicle and object warning terminals, the management center forcibly closes the barrier gate and pushes an emergency notification to the on-duty personnel, and simultaneously triggers on-site video recording and archiving. The warning level of the third warning message is greater than that of the second warning message, and the warning level of the second warning message is greater than that of the first warning message.

[0050] Specifically, the vehicle warning terminal is installed above the exit ramp in the underground parking garage exit area, using an HMI display screen and speaker above vehicle height. It also sends a notification via Bluetooth to the OBD interface of the vehicle entering the sensing area. The object warning terminal is installed on the ground at the exit of the road in the underground parking garage exit area, including a ground LED flashing light strip and a speaker. The management center connects to the edge gateway, displays the real-time status of the underground parking garage exit area on a large monitoring screen, receives push notifications, and operates the barrier gate linkage control. When the risk rating result is blue, the warning method is determined to be displaying the location of the vehicle to be warned at the management center; that is, only marking the location of the vehicle to be warned on the large screen in the management center, without triggering an audible or visual warning. When the risk rating result is yellow, the warning method is determined to issue a first warning message to both the vehicle warning terminal and the object warning terminal; that is, the vehicle warning terminal issues a single voice prompt to the driver and pops up a yellow prompt box on the HMI screen, while simultaneously triggering the yellow flashing light on the ground of the object warning terminal. If the risk rating is orange, the warning method is determined to be issuing a second warning message to the vehicle warning terminal, the target warning terminal, and the management center. Specifically, the vehicle warning terminal issues a continuous voice alarm to the driver, triggers a red flashing light and voice broadcast on the target warning terminal, and the management center triggers the speed limiter at 5 km / h. If the risk rating is red, the warning method is determined to be issuing a third warning message to the vehicle warning terminal, the target warning terminal, and the management center. Specifically, the vehicle warning terminal issues a full-band audio-visual warning to the driver, the target warning terminal issues a full-band audio-visual warning, the management center forcibly closes the speed limiter and sends an emergency notification to on-duty personnel, and simultaneously triggers on-site video recording for archiving.

[0051] In addition, when an orange or higher alert is triggered, the edge gateway simultaneously sends an alert message to the target alert terminal, the vehicle alert terminal, and the management center. The message includes the risk level, target location, timestamp, and event ID. Upon receiving and responding to the alert (the vehicle slows down, pedestrians stop, or a pop-up window appears on the property management screen), each terminal sends an ACK confirmation message to the edge gateway. If any terminal fails to send an ACK confirmation message within 5 seconds, the edge gateway automatically upgrades the alert level by one level. When the conflict risk is detected as eliminated (the target leaves the conflict zone or the vehicle stops) and the property management center operator confirms the completion of the handling, the system issues a closed-loop shutdown command, logs the alert event, and completes the full alert closed loop.

[0052] Example 2 Figure 3 This is a flowchart of a target data determination method according to Embodiment 2 of the present invention. This embodiment focuses on the target data determination method described in the above embodiment. Figure 3 As shown, the method includes: S210, Collect data from multiple sources.

[0053] The multi-source data includes data collected by at least one sensor deployed in the basement exit area.

[0054] In this embodiment, multi-source data can be understood as data collected by at least one sensor located in the basement exit area. Multi-source data may include millimeter-wave radar point cloud data, visual image data, etc. collected by each sensor.

[0055] Specifically, at least one sensor is deployed in the basement exit area, and this sensor can cover the entire basement exit area. The sensor can be a detection node, which can be used to collect multi-source data such as millimeter-wave radar point cloud data and visual image data.

[0056] For example, such as Figure 2 As shown, the node deployment scheme of the three-dimensional detection matrix is ​​as follows: the first detection node N1 is deployed at the bottom of the ramp to cover the blind spot at the beginning of the ramp; the second detection node N2 is deployed in the middle of the ramp to cover the three-dimensional blind spot due to the elevation difference of the ramp; detection nodes N3 and N4 are deployed on both sides of the pillars at the curve, and signal cross coverage is used to eliminate the blind spot blocked by the pillars; the fifth detection node N5 is deployed in the exit transition area to cover the area where the underground parking garage exit connects with the municipal pedestrian walkway; each node uses three-dimensional spatial coordinate calibration technology to construct a unified three-dimensional detection coordinate system.

[0057] For example, the exit ramp of a certain underground parking garage is about 15m long and has a slope of about 12%. It has a 90° left turn and a structural column on the inside of the curve. The exit area of ​​the underground parking garage is directly connected to the ground road. The following detection nodes are deployed in the exit area of ​​the underground parking garage: Node N1 (bottom of the ramp): equipped with millimeter-wave radar (77GHz, detection range 0~20m, horizontal viewing angle 120°) + binocular vision camera (1920×1080, frame rate 30fps), installed at a height of 1.2m, covering the blind spot at the beginning of the ramp; Node N2 (middle section of the ramp, about 7m from the exit): equipped with millimeter-wave radar + infrared supplementary lighting module, installed at a height of 1.0m, working with N1 to form a three-dimensional detection coverage of the ramp section, calibrating the spatial coordinate relationship between nodes, and establishing a unified three-dimensional coordinate system; Node N3 (left side of the curve pillar) + Node N4 (right side of the curve pillar): each equipped with a millimeter-wave radar node, installed at a height of 0.8m, with the radar detection angle range set to cover the area obstructed by the pillar, forming cross-signal coverage; Node N5 (exit transition area, 2m from the exit): equipped with binocular vision camera + infrared supplementary lighting module, installed at a height of 1.5m, covering the transition area connecting with the municipal sidewalk.

[0058] For example, in a parking garage exit area, the exit ramp is 20m long with an 8% slope and features two consecutive bends. The bends have four structural columns, creating extremely complex blind spots. In this scenario, the detection node scheme is expanded from the standard scheme to seven nodes (N1-N7). Detection nodes are added on both sides of the four columns at the two bends to ensure overlapping coverage of the blind spots behind all columns. Node coordinate calibration employs a multi-node joint calibration method based on a checkerboard target, achieving a calibration accuracy better than 2cm.

[0059] S220. The multi-source data is preprocessed to obtain preprocessed data.

[0060] In this embodiment, the preprocessed data can be understood as the data obtained after preprocessing multi-source data collected by at least one sensor.

[0061] Specifically, the multi-source data, such as millimeter-wave radar point cloud data and visual image data collected by various sensors, are preprocessed through time alignment, noise reduction filtering, and adaptive light compensation to obtain preprocessed data.

[0062] S230. Based on the scene where the vehicle to be warned is located, perform anti-interference processing on the preprocessed data to obtain target data.

[0063] Specifically, targeting the "white hole" and "black hole" effects in the parking garage exit area, preprocessed data undergoes specialized anti-interference processing to obtain target data. The white hole effect occurs when a vehicle under warning moves from a dimly lit environment to a brightly lit one, such as when it exits the parking garage during the day. The black hole effect occurs when a vehicle under warning moves from a brightly lit environment to a dimly lit one, such as when it exits the parking garage at night. By real-time detection of ambient brightness, image contrast, and histogram distribution, light pattern recognition is performed to determine whether the vehicle under warning is experiencing the white hole or black hole effect, and different anti-interference processing methods are determined for each, resulting in target data.

[0064] Optionally, the step of performing anti-interference processing on the preprocessed data based on the environment of the vehicle to be warned to obtain target data includes: When the scene where the vehicle to be warned is located is in a white hole mode, the contrast of the preprocessed data is increased to obtain the target data; When the scenario where the vehicle to be warned is located is in a black hole mode, the preprocessed data is corrected to obtain the target data.

[0065] For example, the camera exposure parameters of the sensor are set to automatic, and the image brightness and histogram are detected in real time. When the proportion of the right side (highlight area) of the image histogram exceeds a threshold (default 60%), it is identified as a white hole mode. If the scene of the vehicle to be warned is in white hole mode, indicating that the vehicle is leaving the warehouse during the day, the contrast of the preprocessed data can be improved to obtain the target data. This includes: performing Local Histogram Equalization (CLAHE) to improve the image contrast to a recognizable state, and simultaneously using millimeter-wave radar to cross-validate the target, increasing the radar confidence weight to 0.7. Furthermore, the camera parameters can be adjusted, for example: switching the wide dynamic range camera to HDR mode, expanding the dynamic range to over 120dB, and activating the polarizing filter to reduce strong light reflection.

[0066] When the image brightness is below the threshold (default 20 lux), it is identified as a black hole mode. If the scene of the vehicle to be warned is in black hole mode, it indicates that the vehicle is leaving the warehouse at night. The preprocessed data is then corrected to obtain the target data, including adaptive Gamma correction (Gamma value increased from the default 1.0 to 1.8). Additionally, camera parameters can be adjusted, such as automatically activating the infrared fill light module (850nm, fill light distance 15m), increasing the sensor ISO gain to 3200, and adjusting the shutter speed to 1 / 60s.

[0067] S240. Process the target data to generate a risk feature vector corresponding to the target data.

[0068] S250. Based on the risk feature vector and collision risk prediction model, determine the risk rating result.

[0069] S260. Based on the risk rating results, determine the early warning method.

[0070] The technical solution of this invention involves collecting multi-source data, including data collected by at least one sensor deployed in the parking garage exit area; preprocessing the multi-source data to obtain preprocessed data; and performing anti-interference processing on the preprocessed data based on the scene of the vehicle to be warned, to obtain target data. By collecting multi-source data, the problem of incomplete blind spot coverage in the parking garage exit area in the prior art is solved, and the preprocessing and anti-interference processing of the multi-source data improves the accuracy of early warning and the safety protection level of the parking garage exit area.

[0071] Example 3 Figure 4 This is a structural schematic diagram of a vehicle warning device according to Embodiment 3 of the present invention. Figure 4 As shown, the device includes: The first determining module 310 is used to determine target data, the target data including the vehicle to be warned and the data corresponding to the environment in which the vehicle to be warned is located, the vehicle to be warned including vehicles running in the parking garage exit area; The generation module 320 is used to process the target data and generate a risk feature vector corresponding to the target data. The risk feature vector indicates the collision risk between the vehicle to be warned and the target object in the environment. The target object includes objects in the parking garage exit area that have a collision risk with the vehicle to be warned. The second determining module 330 is used to determine a risk rating result based on the risk feature vector and the collision risk prediction model, wherein the risk rating result indicates the probability of a collision between the vehicle to be warned and the target object; The third determining module 340 is used to determine the early warning method based on the risk rating result.

[0072] The vehicle warning device provided in this embodiment of the invention determines target data through a first determining module; processes the target data through a generating module to generate a risk feature vector corresponding to the target data; determines a risk rating result through a second determining module based on the risk feature vector and a collision risk prediction model; and determines a warning method based on the risk rating result through a third determining module. Through the cooperation between these modules, the target data is determined, solving the problem of incomplete blind spot coverage in underground parking garage exit areas in existing technologies. By generating risk feature vectors to determine risk rating results, early warning of vehicles in underground parking garage exit areas is achieved, improving warning accuracy and the safety protection level of underground parking garage exit areas.

[0073] In one embodiment, the first determining module includes: A data acquisition unit is used to acquire multi-source data, including data acquired by at least one sensor deployed in the basement exit area; The preprocessing unit is used to preprocess the multi-source data to obtain preprocessed data; An anti-interference processing unit is used to perform anti-interference processing on the preprocessed data based on the scene where the vehicle to be warned is located, so as to obtain target data.

[0074] In one embodiment, the anti-interference processing unit is specifically used for: When the scene where the vehicle to be warned is located is in a white hole mode, the contrast of the preprocessed data is increased to obtain the target data; When the scenario where the vehicle to be warned is located is in a black hole mode, the preprocessed data is corrected to obtain the target data.

[0075] In one embodiment, the generating unit 320 is specifically used for: The target data is identified to determine the target object and the target state parameters corresponding to the target object, wherein the target state parameters include data corresponding to the state of the target object; The vehicle status parameters and environmental status parameters contained in the target data are determined. The vehicle status parameters include the operating status of the vehicle to be warned, and the environmental status parameters include the parameters of the environment in which the vehicle to be warned is located and the parameters of the environment in which the target object is located. By integrating the target state parameters, the vehicle state parameters, and the environmental state parameters, a risk feature vector corresponding to the target data is obtained.

[0076] In one embodiment, the second determining module 330 includes: The input unit is used to input the risk feature vector into the collision risk prediction model to obtain the collision risk value, which includes the probability value of the collision between the vehicle to be warned and the target object; The determining unit is used to determine the risk rating result based on the collision risk value.

[0077] In one embodiment, the determining unit is specifically used for: If the collision risk value is less than the first risk threshold, the risk rating result will be determined as a blue level. If the collision risk value is greater than or equal to the first risk threshold and less than the second risk threshold, the risk rating result will be determined as yellow level; If the collision risk value is greater than or equal to the second risk threshold and less than the third risk threshold, the risk rating result will be determined as orange level; If the collision risk value is greater than or equal to the third risk threshold, the risk rating result will be determined as red level.

[0078] In one embodiment, the third determining module 340 is specifically used for: If the risk rating result is blue, the warning method is determined to be to display the location of the vehicle to be warned in the management center; If the risk rating result is yellow, the warning method is determined to be to issue a first warning message to the vehicle warning terminal and the object warning terminal. The object warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the target object. The vehicle warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the vehicle to be warned. If the risk rating result is orange, the warning method is determined to be to send a second warning message to the vehicle warning terminal, the object warning terminal, and the management center; If the risk rating result is red, the warning method is determined to be to send a third warning message to the vehicle warning terminal, the object warning terminal, and the management center. The warning level of the third warning information is greater than that of the second warning information, and the warning level of the second warning information is greater than that of the first warning information.

[0079] The vehicle warning device provided in this embodiment of the invention can execute the vehicle warning method provided in any embodiment of the invention. Through the cooperation and coordination between the modules, it completes the warning of vehicles and has the corresponding functional modules and beneficial effects of the execution method.

[0080] Example 4 According to embodiments of the present invention, the present invention also provides an electronic device and a computer-readable storage medium.

[0081] Figure 5 This is a block diagram of an electronic device according to Embodiment 4 of the present invention, which implements the vehicle warning method described in the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 5As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM), communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 can also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0083] Multiple components in the electronic device are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless transceiver, etc. The communication unit 419 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as vehicle warning methods.

[0085] In some embodiments, the vehicle warning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the vehicle warning method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the vehicle warning method by any other suitable means (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] The technical solution of this invention provides a vehicle early warning method, device, electronic device, and storage medium. The method involves: determining target data; processing the target data to generate a risk feature vector corresponding to the target data; determining a risk rating result based on the risk feature vector and a collision risk prediction model; and determining an early warning method based on the risk rating result. By determining the target data, the problem of incomplete blind spot coverage in underground parking garage exit areas in existing technologies is solved. By generating a risk feature vector to determine the risk rating result, early warning of vehicles in underground parking garage exit areas is achieved, improving the accuracy of early warning and the safety protection level of underground parking garage exit areas.

[0093] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0094] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle early warning method, characterized in that, Applied to an edge gateway, the edge gateway is installed in a management center, which manages the parking garage exit area. The edge gateway establishes a communication connection with the vehicle to be warned, including: Determine target data, which includes data corresponding to the vehicle to be warned and the environment in which the vehicle to be warned is located, and the vehicle to be warned includes vehicles operating in the parking garage exit area; The target data is processed to generate a risk feature vector corresponding to the target data. The risk feature vector indicates the collision risk between the vehicle to be warned and the target object in the environment. The target object includes objects in the parking garage exit area that have a collision risk with the vehicle to be warned. Based on the risk feature vector and the collision risk prediction model, a risk rating result is determined, which indicates the probability of a collision between the vehicle to be warned and the target object; Based on the risk rating results, the early warning method is determined.

2. The method according to claim 1, characterized in that, The determination of target data includes: Collect multi-source data, including data collected by at least one sensor deployed in the basement exit area; The multi-source data is preprocessed to obtain preprocessed data; Based on the scene where the vehicle to be warned is located, the preprocessed data is subjected to anti-interference processing to obtain the target data.

3. The method according to claim 2, characterized in that, The step involves performing anti-interference processing on the preprocessed data based on the environment of the vehicle to be warned, to obtain target data, including: When the scene where the vehicle to be warned is located is in a white hole mode, the contrast of the preprocessed data is increased to obtain the target data; When the scenario where the vehicle to be warned is located is in a black hole mode, the preprocessed data is corrected to obtain the target data.

4. The method according to claim 1, characterized in that, The step of processing the target data to generate a risk feature vector corresponding to the target data includes: The target data is identified to determine the target object and the target state parameters corresponding to the target object, wherein the target state parameters include data corresponding to the state of the target object; The vehicle status parameters and environmental status parameters contained in the target data are determined. The vehicle status parameters include the operating status of the vehicle to be warned, and the environmental status parameters include the parameters of the environment in which the vehicle to be warned is located and the parameters of the environment in which the target object is located. By integrating the target state parameters, the vehicle state parameters, and the environmental state parameters, a risk feature vector corresponding to the target data is obtained.

5. The method according to claim 1, characterized in that, The step of determining the risk rating result based on the risk feature vector and collision risk prediction model includes: The risk feature vector is input into the collision risk prediction model to obtain the collision risk value, which includes the probability value of the collision between the vehicle to be warned and the target object; Based on the collision risk value, a risk rating result is determined.

6. The method according to claim 5, characterized in that, The process of determining the risk rating result based on the collision risk value includes: If the collision risk value is less than the first risk threshold, the risk rating result will be determined as a blue level. If the collision risk value is greater than or equal to the first risk threshold and less than the second risk threshold, the risk rating result will be determined as yellow level; If the collision risk value is greater than or equal to the second risk threshold and less than the third risk threshold, the risk rating result will be determined as orange level; If the collision risk value is greater than or equal to the third risk threshold, the risk rating result will be determined as red level.

7. The method according to claim 1, characterized in that, The determination of the early warning method based on the risk rating result includes: If the risk rating result is blue, the warning method is determined to be to display the location of the vehicle to be warned in the management center; If the risk rating result is yellow, the warning method is determined to be to issue a first warning message to the vehicle warning terminal and the object warning terminal. The object warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the target object. The vehicle warning terminal includes a terminal that communicates with the edge gateway and is used to issue a warning to the vehicle to be warned. If the risk rating result is orange, the warning method is determined to be to send a second warning message to the vehicle warning terminal, the object warning terminal, and the management center; If the risk rating result is red, the warning method is determined to be to send a third warning message to the vehicle warning terminal, the object warning terminal, and the management center. The warning level of the third warning information is greater than that of the second warning information, and the warning level of the second warning information is greater than that of the first warning information.

8. A vehicle warning device, characterized in that, Configured on an edge gateway, which is installed in a management center for managing the parking garage exit area, the edge gateway establishes a communication connection with the vehicle to be warned, including: The first determining module is used to determine target data, which includes data corresponding to the vehicle to be warned and the environment in which the vehicle to be warned is located. The vehicle to be warned includes vehicles operating in the parking garage exit area. The generation module is used to process the target data and generate a risk feature vector corresponding to the target data. The risk feature vector indicates the collision risk between the vehicle to be warned and the target object in the environment. The target object includes objects in the parking garage exit area that have a collision risk with the vehicle to be warned. The second determining module is used to determine a risk rating result based on the risk feature vector and the collision risk prediction model, wherein the risk rating result indicates the probability of a collision between the vehicle to be warned and the target object; The third determining module is used to determine the early warning method based on the risk rating results.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle warning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the vehicle warning method according to any one of claims 1-7.

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