Risk detection method and device, equipment, storage medium and product

Abstract

The invention discloses a risk detection method and device, equipment, a storage medium and a product, and relates to the technical field of auxiliary driving. According to the method, the front set range is sensed in real time in the vehicle driving process, the driving condition of the front target vehicle is tracked and detected based on the sensing information, and meanwhile, the abnormal condition of the road surface is detected. According to the method, the driving condition of the target vehicle is tracked, the road surface is monitored in combination with the road surface abnormal information, the risk condition of the road surface in front can be comprehensively evaluated, it is guaranteed that the vehicle can respond to possible dangers in time, and therefore the life and property safety of a driver and passengers is protected.

Description

Technical Field

[0001] The present application relates to the field of assisted driving technology, and in particular to a risk detection method, device, equipment, storage medium and product. Background Art

[0002] In the related art, after natural disasters and force majeure disasters occur, the road surface will also be affected, for example, the bridge deck breaks, highway collapses, etc. These positional factors on the road surface may affect driving safety and cause greater safety hazards.

[0003] Therefore, how to conduct risk assessment on the road ahead during driving is an urgent problem to be solved. Summary of the invention

[0004] The main purpose of this application is to provide a risk detection method, device, equipment, storage medium and product, aiming to solve the technical problem of how to conduct risk assessment on the road ahead during driving.

[0005] To achieve the above objectives, the present application proposes a risk detection method, which includes: During the driving process of the vehicle, the perception information within the set range in front of the vehicle is obtained in real time; Based on the perception information, track the target vehicle in front of the vehicle to obtain tracking information; Based on the perception information, obtain road surface abnormality information within a set range in front of the vehicle; The road surface risk condition of the road ahead of the vehicle is determined based on the tracking information and / or the road surface abnormality information.

[0006] In some embodiments, determining the road surface risk condition of the road ahead of the vehicle according to the tracking information includes: When the tracking information indicates that the target vehicle disappears within the set range for a set time, the curvature and slope of the road section corresponding to the disappearance position of the target vehicle are obtained according to the map data information; When the curvature is greater than a preset curvature, or the slope is greater than a preset slope, it is determined that the road surface risk condition of the road ahead of the vehicle is normal.

[0007] In some embodiments, after obtaining the curvature and slope of the road section corresponding to the disappearance position of the target vehicle according to the map data information, the method further includes: When the curvature is less than or equal to a preset curvature and the slope is less than or equal to a preset slope, constructing an occupation grid map within a set range in front of the vehicle based on the perception information; Based on the occupancy grid map, a road surface risk condition of the road ahead of the vehicle is determined.

[0008] In some embodiments, determining the road surface risk condition of the road ahead of the vehicle based on the occupancy grid map includes: In the occupied grid map, if the number of unoccupied grids is greater than a set value, it is determined that the road surface risk condition of the road ahead of the vehicle is abnormal; If the number of unoccupied grids is less than or equal to a set value, it is determined that the road surface risk condition of the road ahead of the vehicle is normal.

[0009] In some embodiments, determining the road surface risk condition of the road ahead of the vehicle according to the tracking information and the road surface abnormality information includes: Based on a preset model, weighted calculation is performed on the tracking information and the road surface abnormality information, wherein the preset model is respectively provided with different preset risk coefficients for the tracking information and the road surface abnormality information, and the different preset risk coefficients are obtained by training with sample data; The road surface risk condition of the road ahead of the vehicle is determined according to the result of the weighted calculation.

[0010] In some embodiments, after determining the road surface risk condition of the road ahead of the vehicle based on the disappearance position of the target vehicle and the map data information, the method further includes: When it is determined that the road surface risk condition ahead of the vehicle is abnormal, a warning message is issued to prompt the user to brake and a timer is started; If no braking command is detected after the timing reaches the set time, the vehicle is controlled to brake.

[0011] In addition, to achieve the above purpose, the present application also proposes a risk detection device, which includes: The three-dimensional perception module is used to obtain the perception information within a set range in front of the vehicle in real time while the vehicle is driving; A vehicle tracking module, used to track a target vehicle in front of the vehicle based on the sensing information to obtain tracking information; A road surface detection module, used to obtain abnormal road surface information within a set range in front of the vehicle based on the perception information; The risk determination module is used to determine the road surface risk condition of the road ahead of the vehicle based on the tracking information and / or the road surface abnormality information.

[0012] In addition, to achieve the above objectives, the present application also proposes a risk detection device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the risk detection method described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the risk detection method described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the risk detection method described above are implemented.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: By sensing the set range ahead in real time during the vehicle's driving, the driving conditions of the target vehicle ahead are tracked and detected based on the sensing information, and abnormal conditions on the road surface are detected at the same time. By tracking the driving conditions of the target vehicle and monitoring the road surface in combination with abnormal road surface information, the risk conditions of the road ahead can be comprehensively assessed to ensure that the vehicle can respond to possible dangers in a timely manner, thereby protecting the lives and property of the driver and passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a process flow of a risk detection method provided by an embodiment of the present application is shown; Figure 2 A flow chart of a risk detection method provided by an exemplary embodiment of the present application is shown; Figure 3 A schematic diagram of the structure of a risk detection device provided in an embodiment of the present application is shown; Figure 4 A schematic diagram of the structure of a risk detection device provided in an embodiment of the present application is shown.

[0019] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of the embodiment of the present application is: in the process of driving the vehicle, the perception information within the set range in front of the vehicle is obtained in real time; based on the perception information, the target vehicle in front of the vehicle is tracked to obtain tracking information; based on the perception information, the abnormal road surface information within the set range in front of the vehicle is obtained; according to the tracking information and / or the abnormal road surface information, the road surface risk condition of the road in front of the vehicle is determined.

[0023] In the related art, after natural disasters and force majeure disasters occur, the road surface may also be affected, for example, the bridge deck breaks, highway collapses, etc. In previous natural disasters, there have been bridge deck breaks that caused vehicles to fall into the water, highway collapses that caused vehicles to fall, etc., causing significant loss of life and property.

[0024] In order to protect the lives and property of drivers and passengers, the safety of the road ahead should be ensured during vehicle driving to prevent the recurrence of the above-mentioned cases.

[0025] In summary, how to conduct risk assessment on the road ahead during driving is a problem that needs to be solved urgently.

[0026] Based on this, the present application provides a solution that enables the vehicle to assess the risks of the road ahead in real time during driving, ensure the vehicle can drive safely, and protect the lives and property of the driver and passengers.

[0027] Reference Figure 1 , Figure 1 The schematic diagram of the process of the risk detection method provided by an embodiment of the present application is shown. The risk detection method can be applied to the risk detection device, including the following steps S110 to S140: Step S110, during the driving of the vehicle, real-time sensing information within a set range in front of the vehicle is obtained.

[0028] The self-vehicle refers to the current vehicle, that is, the vehicle where the risk detection device is located. The risk detection device can be set by default to automatically start when the vehicle starts, so that the road surface risk of driving safety can be evaluated throughout the driving process of the self-vehicle.

[0029] In some embodiments, the risk detection device can be automatically controlled to start when the power domain controller of the vehicle is powered on. It is understandable that the vehicle can generally include multiple domain controllers, such as a central domain controller, a power domain controller, a cockpit domain controller, etc., wherein the power domain controller is related to the power output of the vehicle, so the risk detection device can be started at the same time as the power domain controller.

[0030] Perception information refers to visual information and three-dimensional point cloud information.

[0031] Among them, visual information refers to image or video information, which can usually be obtained through a three-dimensional perception module (such as a camera, etc.). Taking a camera as an example, the camera can be pre-arranged at a specified position of the vehicle (such as above the front windshield of the vehicle), and the angle of the camera can be adjusted so that it can capture the road section within a certain range directly in front. Three-dimensional point cloud information can be collected by laser radar, millimeter wave radar and other equipment installed on the vehicle.

[0032] When the vehicle starts to drive, the power domain controller controls the power output, so that the risk detection device starts to obtain the sensing information within the set range in front of the vehicle. The set range can be a range set by the staff, or it can be the maximum sensing range of the relevant sensing device in the risk detection device, which is not limited in this embodiment.

[0033] Step S120: Based on the perception information, track the target vehicle in front of the vehicle to obtain tracking information.

[0034] It is understandable that during driving, there may be other vehicles ahead traveling in the same direction.

[0035] In this embodiment, the three-dimensional point cloud information and visual information (such as images) can be fused first to achieve a more accurate tracking effect. Before fusion, the image and point cloud data need to be preprocessed to improve the quality and consistency of the data. Image preprocessing usually includes steps such as denoising, contrast enhancement, and color correction. After the preprocessing is completed, the registration can be achieved by extracting feature points in the image and point cloud and calculating the correspondence between them. After that, global optimization is performed to adjust the parameters of the image and point cloud data to ensure the consistency of the two in space and time, so as to complete the accurate fusion.

[0036] Taking the example of at least one vehicle traveling in the same direction, after the fusion is completed, the fused image can be used to track the vehicles in front separately through target detection technology.

[0037] Specifically, tracking may include the following (1)-(3): (1) Use a target detection algorithm to identify targets of interest, such as the vehicle ahead in this application. The target detection algorithm can be based on traditional computer vision techniques, such as feature-based methods, machine learning-based methods, or deep learning-based methods, such as convolutional neural networks; (2) After the vehicle is identified, it is necessary to associate the same vehicle in different frames to form the vehicle's trajectory. For example, based on the Hungarian algorithm, joint probabilistic data association, etc., the similarity of vehicles in different frames can be calculated based on information such as the vehicle's location and appearance features, and they can be associated and identified as the same vehicle.

[0038] (3) In order to accurately track the vehicle, it is necessary to estimate the vehicle's state, including position, speed, acceleration, etc. This is usually achieved using filters, such as Kalman Filter or Particle Filter, which can predict the vehicle's future state and update the vehicle's state estimate based on the vehicle's motion model and observation data.

[0039] The target vehicle refers to any vehicle traveling in the same direction within a set range in front of the vehicle. It is not limited to a fixed vehicle, but a vehicle that appears in the tracking range during the entire driving process of the vehicle.

[0040] By tracking the target vehicle in front of the vehicle through the above-mentioned tracking method, it is possible to detect in real time whether the target vehicle is lost from the tracking range (ie, the aforementioned set range).

[0041] It is understandable that if the target vehicle is always within the tracking range, it means that the road ahead can be driven normally, and it can be determined that the road surface risk situation is normal. At this time, the vehicle can be controlled to continue driving and the risk detection method of the present application can be continued.

[0042] If the target vehicle disappears from the tracking range and the disappearance time is long, there is a high possibility of road safety risk. There are many reasons why the target vehicle is lost from the tracking range, such as: ① The target vehicle falls due to landslides or fractures in front of it, and thus disappears from the tracking range; ② The road ahead is a sharp turn, and the target vehicle disappears from the tracking range after turning; ③ There is a large ramp on the road ahead, and the target vehicle disappears from the tracking range when going downhill after going uphill.

[0043] To distinguish the above situations, this application designs a set time (e.g., 300 milliseconds) to detect the disappearance time of the target vehicle. If the target vehicle disappears from the tracking range for longer than the set time, it is necessary to further assess the road risk of the road ahead of the vehicle based on the disappearance position of the target vehicle and the map data information.

[0044] Based on this, the tracking information in this application may include: a. The target vehicle disappears from the set range for a set period of time; b. The target vehicle disappears from the set range for a set period of time; c. The target vehicle is always within the tracking range.

[0045] In some embodiments, when the tracking information indicates that the target vehicle disappears within the set range for a set period of time, the curvature and slope of the road section corresponding to the disappearance position of the target vehicle can be obtained based on the map data information; when the curvature is greater than the preset curvature, or the slope is greater than the preset slope, it is determined that the road surface risk condition of the road ahead of the vehicle is normal.

[0046] Among them, map data information refers to the relevant data of the road on which the vehicle is currently traveling, such as road location, slope, curvature and other information. It can be understood that the present application obtains the positioning information of the target vehicle in real time during the tracking process. In order to detect the above situations ② and ③, the present application is designed to determine the curvature and slope of the road section where the target vehicle disappeared through the disappearance position of the target vehicle and the map data information, so as to determine whether the disappearance of the target vehicle belongs to the above situations ② and ③.

[0047] If the curvature is greater than the preset curvature, it means that there is a sharp turn section at the location where the target vehicle disappears, and the target vehicle disappears from the tracking range because of turning. That is, it can be determined that the road risk condition of the road ahead of the vehicle is normal.

[0048] If the slope is greater than the preset slope, it means that there is a large ramp at the location where the target vehicle disappears. The target vehicle disappears from the tracking range because of the height difference between the target vehicle and the uphill and downhill slopes. That is, it can be determined that there is no abnormality in the road risk condition in front of the target vehicle.

[0049] If the curvature is less than or equal to the preset curvature, and the slope is less than or equal to the preset slope, it means that there are no sharp turns or large ramps on the road ahead. If the target vehicle disappears from the tracking range at this time, it means that there may be unknown dangers on the road ahead, which is the above-mentioned situation ①.

[0050] As an implementation method, risk assessment of the road ahead can be performed by constructing an occupancy grid map within a set range in front of the vehicle.

[0051] First, the perception data collected in the previous steps can be preprocessed. For example, data filtering and outlier removal can be performed. After the preprocessing is completed, useful features are extracted from the data, such as the edges of objects, surface normals, etc. These features are necessary features for map construction. Create an initial occupancy grid map, which can be a three-dimensional grid, and each grid cell represents a small area in the scene (the road section in front of the vehicle). During initialization, all grid cells can be set to an unknown state. Based on the previously extracted features and the processed perception data, the occupancy grid map is updated, and the state of each grid cell in the occupancy grid map is set. For example, the grid cells that are in an unknown state at initialization are marked as occupied or unoccupied.

[0052] In some embodiments, in the occupied grid map, if the number of unoccupied grids is greater than a set value, the road surface risk condition of the road ahead of the vehicle is determined to be abnormal; if the number of unoccupied grids is less than or equal to the set value, the road surface risk condition of the road ahead of the vehicle is determined to be normal.

[0053] Step S130, based on the perception information, obtain road surface abnormality information within a set range in front of the vehicle.

[0054] In some implementations, step S130 may be performed in parallel while the vehicle is traveling and performing step S120, so as to achieve a more accurate abnormality determination.

[0055] Specifically, the vehicle can detect the road surface within a set range in front of the vehicle based on the above-mentioned visual perception information and three-dimensional point cloud information during driving. It can be understood that through the fusion of visual perception and three-dimensional point cloud information, through the relevant target recognition algorithm, it is possible to approximately identify whether there is a collapse in the road surface ahead.

[0056] Step S140: determining the road risk condition of the road ahead of the vehicle based on the tracking information and / or the road abnormality information.

[0057] It is understandable that road surface risk assessment can also be achieved to a certain extent only through the above step S120 or the above step S130, but the accuracy is relatively low.

[0058] To further improve the accuracy of risk assessment, in this embodiment, a model for weighted calculation based on weights may be pre-set, so as to combine the result obtained in step S120 and the result obtained in step S130 to perform a comprehensive assessment.

[0059] Specifically, the result determined by the tracking information in step S120 is defined as the first result, and the result determined by the road surface abnormality information in step S130 is defined as the second result. Based on a preset model (ie, a weighted calculation model), a weighted calculation is performed on the first result and the second result.

[0060] In some embodiments, the staff can pre-set initial risk coefficients (i.e., weight values) for the first result and the second result. After training with a large number of samples, the risk coefficients can be tuned. For example, the initial risk coefficients are 3 and 7 respectively, and the final risk coefficients after tuning are 3.5 and 6.5 respectively.

[0061] In this embodiment, after the vehicle obtains the first result and the second result through step S120 and step S130, a weighted calculation model may be used to perform weighted calculation, and the road surface risk condition of the road ahead of the vehicle may be determined according to the result of the weighted calculation.

[0062] In some embodiments, when it is determined that the road surface risk condition ahead of the vehicle is abnormal, a warning message is issued to prompt the user to brake and a timer is started; if no braking command is detected after the timing reaches a set time, the vehicle is controlled to perform braking.

[0063] Specifically, when it is determined that the road risk condition in front of the vehicle is abnormal, an abnormal signal can be sent to the vehicle's central domain controller. After receiving the abnormal signal, the central domain controller automatically controls the vehicle's relevant electronic control unit to turn on the vehicle's hazard warning lights to alert the following vehicles; at the same time, it prompts the driver through steering wheel vibration, seat vibration, voice broadcast, etc.

[0064] If the driver still does not control the vehicle braking when the timing reaches the set time, the central domain controller can authorize the vehicle's brake assist module to automatically brake the vehicle.

[0065] In addition, if the road risk condition of the road ahead is determined to be abnormal, data such as the current location, time, and road abnormality type can be uploaded to the cloud server to alert relevant agencies, making it easier to send relevant alarm information to more vehicles and minimize the danger.

[0066] This embodiment provides a risk detection method, which senses the set range ahead in real time during vehicle driving, tracks and detects the driving condition of the target vehicle ahead based on the sensed information, and detects abnormal conditions on the road surface at the same time. By tracking the driving condition of the target vehicle and monitoring the road surface in combination with abnormal road surface information, the risk condition of the road ahead can be comprehensively evaluated to ensure that the vehicle can respond to possible dangers in a timely manner, thereby protecting the lives and property of the driver and passengers.

[0067] As an example, see Figure 2 , Figure 2 A flow chart of a risk detection method provided by an exemplary embodiment of the present application is provided.

[0068] The specific steps may include: S1, when the vehicle is driving, the front video data is collected through the front-view camera, and the 3D point cloud data is collected through the lidar.

[0069] During the driving process of the vehicle, after S1, steps S2 and S3 are executed simultaneously.

[0070] S2 combines video data and 3D point cloud data to track the vehicle ahead.

[0071] S3 determines whether the vehicle ahead has disappeared for a set time.

[0072] S31, if the vehicle ahead has not disappeared or the set time has not been reached, determining the first result is that there is no abnormality on the road ahead.

[0073] S32: If the vehicle ahead disappears for a set time, further map data is obtained to determine whether the curvature of the road section where the vehicle is located is greater than a set curvature, and whether the slope value is greater than a set slope.

[0074] S321: If the curvature is greater than the set curvature and the slope value is greater than the set slope, it is determined that the first result is that there is no abnormality on the road ahead.

[0075] S322: If the curvature is less than or equal to the set curvature, or the slope value is less than or equal to the set slope value, then the first result is determined to be that the road surface ahead is abnormal.

[0076] S4, combines the video data and 3D point cloud data to determine whether there is abnormal terrain on the road ahead.

[0077] S41, if there is abnormal terrain on the road ahead, determine the second result as an abnormal situation on the road ahead.

[0078] S42: If there is no abnormal terrain on the road ahead, determine the second result as no abnormal situation on the road ahead.

[0079] S5, performing weighted calculation on the first result and the second result using a pre-trained weighted calculation model to determine the risk of the road ahead.

[0080] S6, when it is determined that there is a risk on the road surface, the timer starts, the hazard warning lights are turned on, and the user is prompted to brake through voice broadcasts, steering wheel vibrations, etc.

[0081] S7: If the vehicle has not braked when the timing time reaches the set time, braking is automatically performed through the control system.

[0082] S8, upload the abnormal road conditions to the cloud server.

[0083] This application also provides a risk detection device, please refer to Figure 3 , the risk detection device 100 comprises: The three-dimensional perception module 110 is used to obtain perception information within a set range in front of the vehicle in real time during the driving process of the vehicle; A vehicle tracking module 120, configured to track a target vehicle in front of the vehicle based on the sensing information to obtain tracking information; A road surface detection module 130 is used to obtain road surface abnormality information within a set range in front of the vehicle based on the perception information; The risk determination module 140 is used to determine the road surface risk condition of the road ahead of the vehicle according to the tracking information and / or the road surface abnormality information.

[0084] The risk detection device 100 provided in the present application adopts the risk detection method in the above embodiment, which can solve the technical problem of how to perform risk assessment on the road ahead during driving. Compared with the prior art, the beneficial effects of the risk detection device 100 provided in the present application are the same as the beneficial effects of the risk detection method provided in the above embodiment, and the other technical features in the risk detection device 100 are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0085] The present application provides a risk detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the risk detection method in the above-mentioned embodiment one.

[0086] Reference below Figure 4 , which shows a schematic diagram of the structure of a risk detection device suitable for implementing the embodiment of the present application. The risk detection device in the embodiment of the present application may include but is not limited to a mobile terminal such as a mobile phone, a vehicle-mounted terminal, etc. and a fixed terminal such as a computer, etc. Figure 4 The risk detection device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0087] like Figure 4As shown, the risk detection device 200 may include a processing device 210 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 220 or a program loaded from a storage device 230 to a random access memory (RAM: Random Access Memory) 240. In RAM240, various programs and data required for the operation of the risk detection device are also stored. The processing device 210, ROM220, and RAM240 are connected to each other through a bus 250. An input / output (I / O) interface 260 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 260: an input device 270 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 280 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 230 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 290. The communication device 290 may allow the risk detection device to communicate with other devices wirelessly or by wire to exchange data. Although the risk detection device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0088] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 230, or installed from the ROM 220. When the computer program is executed by the processing device 210, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0089] The risk detection device provided by the present application adopts the risk detection method in the above embodiment, which can solve the technical problem of how to conduct risk assessment on the road ahead during driving. Compared with the prior art, the beneficial effects of the risk detection device provided by the present application are the same as the beneficial effects of the risk detection method provided by the above embodiment, and the other technical features in the risk detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0090] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0092] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the risk detection method in the above-mentioned embodiment.

[0093] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0094] The computer-readable storage medium may be included in the risk detection device; or may exist independently without being assembled into the risk detection device.

[0095] The computer-readable storage medium may carry one or more programs. When the one or more programs are executed by the risk detection device, the risk detection device may write computer program codes for performing the operations of the present application in one or more programming languages ​​or a combination thereof. The programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0096] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0097] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0098] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned risk detection method, and can solve the technical problem of how to perform risk assessment on the road ahead during driving. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the risk detection method provided in the above-mentioned embodiment, and will not be elaborated here.

[0099] The present application also provides a computer program product, including a computer program, which implements the steps of the risk detection method as described above when executed by a processor.

[0100] The computer program product provided by the present application can solve the technical problem of how to perform risk assessment on the road ahead during driving. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the risk detection method provided by the above embodiment, which will not be repeated here.

[0101] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A risk detection method, characterized in that: The risk detection method comprises: During the driving process of the vehicle, the perception information within the set range in front of the vehicle is obtained in real time; Based on the perception information, track the target vehicle in front of the vehicle to obtain tracking information; Based on the perception information, obtain road surface abnormality information within a set range in front of the vehicle; The road surface risk condition of the road ahead of the vehicle is determined based on the tracking information and / or the road surface abnormality information.

2. The risk detection method according to claim 1, characterized in that: Determining the road surface risk of the road ahead of the vehicle according to the tracking information includes: When the tracking information indicates that the target vehicle disappears within the set range for a set time, the curvature and slope of the road section corresponding to the disappearance position of the target vehicle are obtained according to the map data information; When the curvature is greater than a preset curvature, or the slope is greater than a preset slope, it is determined that the road surface risk condition of the road ahead of the vehicle is normal.

3. The risk detection method according to claim 2, characterized in that: After obtaining the curvature and slope of the road section corresponding to the disappearance position of the target vehicle according to the map data information, the method further includes: When the curvature is less than or equal to a preset curvature and the slope is less than or equal to a preset slope, constructing an occupation grid map within a set range in front of the vehicle based on the perception information; Based on the occupancy grid map, a road surface risk condition of the road ahead of the vehicle is determined.

4. The risk detection method according to claim 3, characterized in that: The determining of the road surface risk condition of the road ahead of the vehicle based on the occupied grid map includes: In the occupied grid map, if the number of unoccupied grids is greater than a set value, it is determined that the road surface risk condition of the road ahead of the vehicle is abnormal; If the number of unoccupied grids is less than or equal to a set value, it is determined that the road surface risk condition of the road ahead of the vehicle is normal.

5. The risk detection method according to claim 1, characterized in that: The determining, based on the tracking information and the road surface abnormality information, a road surface risk condition of a road ahead of the vehicle includes: Determine a first result based on the tracking information, and determine a second result based on the road surface abnormality information; Based on a preset model, weighted calculation is performed on the first result and the second result, wherein the preset model is respectively provided with different preset risk coefficients for tracking information and road surface abnormality information, and the different preset risk coefficients are obtained by training sample data; The road surface risk condition of the road ahead of the vehicle is determined according to the result of the weighted calculation.

6. The risk detection method according to claim 4, characterized in that: After determining the road surface risk of the road ahead of the vehicle based on the disappearance position of the target vehicle and the map data information, the method further includes: When it is determined that the road surface risk condition ahead of the vehicle is abnormal, a warning message is issued to prompt the user to brake and a timer is started; If no braking command is detected after the timing reaches the set time, the vehicle is controlled to brake.

7. A risk detection device, characterized in that: The risk detection device comprises: The three-dimensional perception module is used to obtain the perception information within a set range in front of the vehicle in real time while the vehicle is driving; A vehicle tracking module, used to track a target vehicle in front of the vehicle based on the sensing information to obtain tracking information; A road surface detection module, used to obtain abnormal road surface information within a set range in front of the vehicle based on the perception information; The risk determination module is used to determine the road surface risk condition of the road ahead of the vehicle based on the tracking information and / or the road surface abnormality information.

8. A risk detection device, characterized in that: The risk detection device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the risk detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the risk detection method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the risk detection method according to any one of claims 1 to 6 are implemented.

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