Vehicle hazard event analysis methods, devices, computer equipment, and storage media

Abstract

This application relates to a method, apparatus, computer device, and storage medium for analyzing vehicle hazard events. Belonging to the field of intelligent driving technology, the method includes: determining a first obstacle detection status when a target vehicle is detected to have experienced a target hazard event; determining a camera detection result based on camera detection information when no obstacle is detected in the first obstacle detection status; and determining a vehicle hazard event analysis report based on the camera detection result, millimeter-wave detection information, and lidar detection information of the target vehicle. This application not only facilitates users in quickly analyzing hazard events based on vehicle hazard event analysis reports but also allows for rapid identification of the causes of hazard events.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a method, apparatus, computer equipment, and storage medium for analyzing vehicle hazard events. Background Technology

[0002] With the development of intelligent connected vehicle technology, the functions of high-level intelligent driving systems are constantly increasing and the complexity of the systems is constantly increasing, inevitably bringing about intelligent driving safety issues.

[0003] How to conduct intelligent driving safety analysis on hazardous events (such as collisions) is one of the most important issues that urgently need to be addressed in the field of intelligent driving. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle hazard event analysis method, device, computer equipment, and storage medium that can effectively improve engine braking performance in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for analyzing vehicle hazard events. The method includes:

[0006] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0007] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0008] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0009] In one embodiment, the camera detection information includes image information;

[0010] Accordingly, based on the camera detection information, the camera detection result is determined, including:

[0011] Determine the second obstacle detection status reported by the camera;

[0012] If no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information.

[0013] In one embodiment, determining the camera detection result based on image information includes:

[0014] Based on the image information, determine the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles;

[0015] The camera detection results are determined based on ambient light information, as well as the background color and motion trajectory information of obstacles.

[0016] In one embodiment, based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined, including:

[0017] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle;

[0018] Based on the LiDAR detection information of the target vehicle, determine the fourth obstacle detection status of the target vehicle's LiDAR;

[0019] Based on the camera detection results, the third obstacle detection results from millimeter-wave radar, and the fourth obstacle detection results from lidar, a vehicle hazard event analysis report for the target vehicle is determined.

[0020] In one embodiment, the method further includes:

[0021] If a first hazard event is detected in the target vehicle, obtain the event type of the first hazard event;

[0022] In the case of a high-risk event, the first hazard event is designated as the target hazard event; where a high-risk event refers to an event that exceeds the risk threshold.

[0023] In one embodiment, the event type of the first hazardous event is obtained, including:

[0024] Obtain the target vehicle's driving strategy; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing;

[0025] Based on the driving strategy, determine the event type of the first hazard event.

[0026] Secondly, this application also provides a vehicle hazard event analysis device. The device includes:

[0027] The first determining module is used to determine the first obstacle detection status when a target vehicle is detected to have caused a target hazard event;

[0028] The second determining module is used to determine the camera detection result based on the camera detection information when the first obstacle detection condition is that no obstacle is detected.

[0029] The third determination module is used to determine the vehicle hazard event analysis report of the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle.

[0030] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps.

[0031] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0032] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0033] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0035] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0036] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0037] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0038] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0039] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0040] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0041] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0042] The aforementioned vehicle hazard event analysis method, apparatus, computer equipment, and storage medium, when a target hazard event is detected on a target vehicle, determine the first obstacle detection status. If no obstacle is detected in the first obstacle detection status, a camera detection result is determined based on camera detection information. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined. This application, when a target hazard event is detected on the target vehicle, can determine the camera detection result based on camera detection information even if no obstacle is detected in the first obstacle detection status. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is quickly and efficiently determined. This not only facilitates users in quickly analyzing hazard events based on vehicle hazard event analysis reports but also allows for rapid identification of the causes of hazard events. Attached Figure Description

[0043] Figure 1 This is a diagram illustrating the application environment of the vehicle hazard event analysis method provided in this embodiment.

[0044] Figure 2 This is a flowchart illustrating the first vehicle hazard event analysis method provided in this embodiment;

[0045] Figure 3 This is a schematic diagram of the process for determining the camera detection result provided in this embodiment;

[0046] Figure 4 This is a flowchart illustrating the process of determining a target hazard event provided in this embodiment;

[0047] Figure 5 This is a flowchart illustrating the second vehicle hazard event analysis method provided in this embodiment;

[0048] Figure 6 This is a structural block diagram of a vehicle hazard event analysis device provided in this embodiment;

[0049] Figure 7 This is an internal structural diagram of the computer device provided in this embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] The vehicle status analysis method provided in this application can be executed by an in-vehicle terminal or by a server. That is, this method can be applied to, for example... Figure 1The application environment is illustrated below. Taking a server application as an example, when server 104 detects a target hazard event occurring on target vehicle 102, it determines the first obstacle detection status. If the first obstacle detection status is that no obstacle is detected, it determines the camera detection result based on camera detection information. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, server 104 determines a vehicle hazard event analysis report for the target vehicle.

[0052] Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers. Target vehicle 102 can be a gasoline-powered vehicle or a new energy vehicle; in this application, the target vehicle primarily refers to a vehicle with autonomous driving capabilities.

[0053] In one embodiment, Figure 2 As shown, a method for analyzing vehicle hazard events is provided, which can be applied to... Figure 1 Taking the server in the example of this, such as Figure 2 As shown, it includes the following steps:

[0054] S201, if a target vehicle is detected to have caused a target hazard event, determine the first obstacle detection status.

[0055] The target vehicle refers to a vehicle with autonomous driving capabilities that experiences a target hazard event. The target hazard event refers to a hazardous event that occurs under a pre-defined driving plan (e.g., a collision). The first obstacle detection status refers to the target vehicle's obstacle detection status, which is determined by the target vehicle based on environmental information detected by various detection devices.

[0056] As an optional implementation of this application, the hazardous events are pre-classified, and a first obstacle detection status is determined when a target vehicle is detected to have committed a target hazardous event. For example, hazardous events are divided into passive hazardous events and active hazardous events. Passive hazardous events refer to events that harm the target vehicle (e.g., being collided with). Active hazardous events refer to hazardous events initiated by the target vehicle where the primary responsibility lies with it.

[0057] As an optional implementation of this application, when a target vehicle is detected to have experienced a target hazard event, a log retrieval request is sent to the target vehicle, so that the target vehicle sends corresponding logs to the server based on the log retrieval information. The corresponding logs contain information about the first obstacle detection.

[0058] S202, if no obstacle is detected in the first obstacle detection case, the camera detection result is determined based on the camera detection information.

[0059] As an optional implementation of this application, if no obstacle is detected in the first obstacle detection case, the camera detection information is input into the intelligent analysis tool based on the camera detection information, and the intelligent analysis tool outputs the camera detection result.

[0060] As another optional implementation of this application embodiment, the camera detection information in this embodiment includes image information. Based on this, if the first obstacle detection situation is that no obstacle is detected, a second obstacle detection situation fed back by the camera is determined. If the second obstacle detection situation is that no obstacle is detected, the camera detection result is determined based on the image information. Specifically, the second obstacle detection situation fed back by the camera to the target vehicle is obtained from the target vehicle. Here, the second obstacle detection situation refers to the camera's detection status regarding obstacles, for example, whether an obstacle is detected or not. The camera detection result refers to the detection result obtained from the camera based on the image information.

[0061] Optionally, if the first obstacle detection condition is that an obstacle is detected, the braking system triggering condition of the target vehicle is determined, and based on the braking system triggering condition, a vehicle hazard event analysis report of the target vehicle is determined.

[0062] S203, Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, determine the vehicle hazard event analysis report for the target vehicle.

[0063] Among these, millimeter-wave detection information refers to the detection results of millimeter waves targeting obstacles. LiDAR detection information refers to the detection results of lidar targeting obstacles. The vehicle hazard event analysis report is an event analysis report on a specific hazard event involving the target vehicle.

[0064] As an optional implementation of this application, based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, event analysis content is generated, and the event analysis content is added to the initial event analysis report to obtain a vehicle hazard event analysis report for the target vehicle. The initial event analysis report refers to the event analysis report without added event analysis content.

[0065] Another optional implementation of this application involves inputting the camera detection results, along with the millimeter-wave detection information and lidar detection information of the target vehicle, into an intelligent analysis tool, which then outputs a vehicle hazard event analysis report for the target vehicle. The intelligent analysis report may, but is not limited to, a neural network model.

[0066] The aforementioned vehicle hazard event analysis method, when a target hazard event is detected on the target vehicle, determines the first obstacle detection status. If no obstacle is detected in the first obstacle detection status, the camera detection result is determined based on camera detection information. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined. This application, when a target hazard event is detected on the target vehicle, can determine the camera detection result based on camera detection information even if no obstacle is detected in the first obstacle detection status. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle can be quickly and efficiently determined. This not only facilitates users in quickly analyzing hazard events based on vehicle hazard event analysis reports but also allows for rapid identification of the causes of hazard events.

[0067] In one embodiment, to more accurately determine the camera detection results, such as Figure 3 As shown, one possible implementation method for determining the camera detection result based on image information includes:

[0068] S301, based on the image information, determines the ambient light information, as well as the background color information and motion trajectory information of the obstacles.

[0069] Image information refers to images captured by the camera, specifically images containing obstacles. Ambient lighting information refers to the lighting information of the environment surrounding the target vehicle. Background color information refers to information related to the background color of the obstacle, mainly used to determine whether the obstacle was not detected because the background color was the same as or similar to the obstacle color. Trajectory information refers to the trajectory information of the obstacle, such as whether it is stationary or moving. If it is moving, the corresponding trajectory information needs to be analyzed and determined.

[0070] As an optional implementation of this application, the image information is input into an image analysis tool, which then determines the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles. The image analysis tool can be a neural network model, for example, a neural network model with strong image recognition capabilities.

[0071] It should be noted that the image information can be a series of consecutive frames. Based on a series of consecutive frames, it is easier to determine the trajectory information of the obstacle.

[0072] S302 determines the camera detection result based on ambient light information, as well as the background color information and motion trajectory information of obstacles.

[0073] As an optional implementation of this application, ambient light information, as well as the background color information and motion trajectory information of obstacles, are input into an analysis tool, which then outputs the camera detection results. The analysis tool can be a neural network model, which performs intelligent analysis based on the ambient light information, the background color information, and the motion trajectory information of obstacles, thereby outputting the camera detection results. The purpose of the intelligent analysis is to analyze why the camera failed to detect the obstacle.

[0074] As another optional implementation of this application, the ambient light information, as well as the background color information and motion trajectory information of the obstacle, are used as the camera detection results.

[0075] Optionally, based on this embodiment, and according to the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, an optional implementation method for determining the vehicle hazard event analysis report of the target vehicle is determined, including:

[0076] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar. Based on the lidar detection information of the target vehicle, determine the fourth obstacle detection status of the lidar. Based on the camera detection results, the third obstacle detection status of the millimeter-wave radar, and the fourth obstacle detection status of the lidar, determine the vehicle hazard event analysis report for the target vehicle. The third obstacle detection status refers to the millimeter-wave radar's detection status regarding obstacles—whether it detected or not. The third obstacle detection status refers to the lidar's detection status regarding obstacles—whether it detected or not.

[0077] In this embodiment, ambient light information, as well as the background color information and motion trajectory information of obstacles, are determined based on image information. By using the ambient light information, as well as the background color information and motion trajectory information of obstacles, the camera detection results can be determined more accurately.

[0078] In one embodiment, Figure 4 As shown, an optional implementation of a vehicle hazard event analysis method includes:

[0079] S401, if a first hazard event is detected in the target vehicle, obtain the event type of the first hazard event.

[0080] The first hazardous event refers to a hazardous event that occurs to the target vehicle, such as a collision.

[0081] Optionally, in this embodiment, upon detecting a first hazardous event involving the target vehicle, the vehicle's driving strategy is acquired; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing. Based on the driving strategy, the event type of the first hazardous event is determined.

[0082] It should be noted that, in this application, when the driving strategy is safe obstacle avoidance or automatic lane changing, the event type of the first hazard event is a high-risk event.

[0083] S402, in the case of an event type of high-risk event, the first hazard event is designated as the target hazard event.

[0084] High-risk events refer to events that exceed the risk threshold.

[0085] In this embodiment, when a first hazardous event is detected involving the target vehicle, the event type of the first hazardous event is obtained. If the event type is a high-risk event, the first hazardous event is taken as the target hazardous event, thus improving the accuracy of identifying the target hazardous event.

[0086] In one embodiment, Figure 5 As shown, an optional implementation of a vehicle hazard event analysis method includes:

[0087] S501, upon detecting a first hazard event involving the target vehicle, acquires the target vehicle's driving strategy. This driving strategy includes single-lane cruise, obstacle avoidance, safe parking, and automatic lane changing.

[0088] S502, Based on the driving strategy, determine the event type of the first hazard event.

[0089] S503 stipulates that when an event is classified as a high-risk event, the first hazard event shall be designated as the target hazard event. A high-risk event is defined as an event exceeding a risk threshold.

[0090] S504, in the event that a target vehicle is detected to have caused a target hazard, determine the first obstacle detection status.

[0091] S505, if the first obstacle detection result is that no obstacle is detected, determine the second obstacle detection result reported by the camera.

[0092] S506, in the case where no obstacle is detected in the second obstacle detection scenario, the ambient light information, as well as the background color information and movement trajectory information of the obstacle, are determined based on the image information fed back by the camera.

[0093] S507 determines the camera detection results based on ambient light information, as well as the background color information and motion trajectory information of obstacles.

[0094] S508 determines the third obstacle detection status of the target vehicle's millimeter-wave radar based on the target vehicle's millimeter-wave detection information.

[0095] S509, based on the LiDAR detection information of the target vehicle, determines the fourth obstacle detection status of the LiDAR of the target vehicle.

[0096] S510 determines the vehicle hazard event analysis report for the target vehicle based on the camera detection results, the third obstacle detection results of the millimeter-wave radar, and the fourth obstacle detection results of the lidar.

[0097] In this embodiment, when a target vehicle is detected to have experienced a target hazard event, a first obstacle detection status is determined. If no obstacle is detected in the first obstacle detection status, a camera detection result is determined based on camera detection information. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined. This application, when a target vehicle is detected to have experienced a target hazard event, can determine the camera detection result based on camera detection information even if no obstacle is detected in the first obstacle detection status. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle can be quickly and efficiently determined. This not only facilitates users in quickly analyzing hazard events based on vehicle hazard event analysis reports but also allows for rapid identification of the cause of the hazard event.

[0098] Based on the same inventive concept, this application also provides a vehicle hazard event analysis device for implementing the vehicle hazard event analysis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of an embodiment of the vehicle hazard event analysis device provided below can be found in the limitations of the vehicle hazard event analysis method described above, and will not be repeated here.

[0099] In one embodiment, Figure 6 As shown, a vehicle hazard event analysis device 1 is provided, comprising:

[0100] The first determining module 10 is used to determine the first obstacle detection status when a target vehicle is detected to have caused a target hazard event.

[0101] The second determining module 20 is used to determine the camera detection result based on the camera detection information when the first obstacle detection condition is that no obstacle is detected.

[0102] The third determining module 30 is used to determine the vehicle hazard event analysis report of the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle.

[0103] Based on the apparatus in this embodiment, when a target vehicle is detected to have experienced a target hazard event, a first obstacle detection status is determined. If no obstacle is detected in the first obstacle detection status, a camera detection result is determined based on camera detection information. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined. This application, when a target vehicle is detected to have experienced a target hazard event, can determine the camera detection result based on camera detection information even if no obstacle is detected in the first obstacle detection status. Based on the camera detection result, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle can be determined quickly and efficiently. This not only facilitates users in quickly analyzing hazard events based on vehicle hazard event analysis reports but also allows for rapid identification of the cause of the hazard event.

[0104] In one embodiment, the camera detection information includes image information;

[0105] Correspondingly, above Figure 6 The second determining module 20 is also specifically used for:

[0106] Determine the second obstacle detection status reported by the camera;

[0107] If no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information.

[0108] In one embodiment, the upper Figure 6 The second determining module 20 is also specifically used for:

[0109] Based on the image information, determine the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles;

[0110] The camera detection results are determined based on ambient light information, as well as the background color and motion trajectory information of obstacles.

[0111] In one embodiment, the upper Figure 6 The third determining module 30 is also specifically used for:

[0112] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle;

[0113] Based on the LiDAR detection information of the target vehicle, determine the fourth obstacle detection status of the target vehicle's LiDAR;

[0114] Based on the camera detection results, the third obstacle detection results from millimeter-wave radar, and the fourth obstacle detection results from lidar, a vehicle hazard event analysis report for the target vehicle is determined.

[0115] In one embodiment, the vehicle hazard event analysis device 1 further includes:

[0116] The acquisition module is used to acquire the event type of the first hazard event when the target vehicle is detected to have experienced a first hazard event.

[0117] The fourth determination module is used to identify the first hazard event as the target hazard event when the event type is a high-risk event; where a high-risk event refers to an event that exceeds the risk threshold.

[0118] In one embodiment, the acquisition module is further specifically used for:

[0119] Obtain the target vehicle's driving strategy; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing;

[0120] Based on the driving strategy, determine the event type of the first hazard event.

[0121] Each module in the aforementioned vehicle hazard event analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0122] In one embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for analyzing vehicle hazard events. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0123] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0124] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0125] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0126] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0127] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0128] In one embodiment, when the processor executes the computer program, it further performs the following steps: the camera detection information includes image information;

[0129] Accordingly, based on the camera detection information, the camera detection result is determined, including:

[0130] Determine the second obstacle detection status reported by the camera;

[0131] If no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information.

[0132] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the camera detection result based on the image information, including:

[0133] Based on the image information, determine the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles;

[0134] The camera detection results are determined based on ambient light information, as well as the background color and motion trajectory information of obstacles.

[0135] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining a vehicle hazard event analysis report for the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, including:

[0136] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle;

[0137] Based on the LiDAR detection information of the target vehicle, determine the fourth obstacle detection status of the target vehicle's LiDAR;

[0138] Based on the camera detection results, the third obstacle detection results from millimeter-wave radar, and the fourth obstacle detection results from lidar, a vehicle hazard event analysis report for the target vehicle is determined.

[0139] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0140] If a first hazard event is detected in the target vehicle, obtain the event type of the first hazard event;

[0141] In the case of a high-risk event, the first hazard event is designated as the target hazard event; where a high-risk event refers to an event that exceeds the risk threshold.

[0142] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the event type of the first hazardous event, including:

[0143] Obtain the target vehicle's driving strategy; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing;

[0144] Based on the driving strategy, determine the event type of the first hazard event.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0146] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0147] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0148] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0149] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: the camera detection information includes image information;

[0150] Accordingly, based on the camera detection information, the camera detection result is determined, including:

[0151] Determine the second obstacle detection status reported by the camera;

[0152] If no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information.

[0153] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the camera detection result based on the image information, including:

[0154] Based on the image information, determine the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles;

[0155] The camera detection results are determined based on ambient light information, as well as the background color and motion trajectory information of obstacles.

[0156] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining a vehicle hazard event analysis report for the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, including:

[0157] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle;

[0158] Based on the LiDAR detection information of the target vehicle, determine the fourth obstacle detection status of the target vehicle's LiDAR;

[0159] Based on the camera detection results, the third obstacle detection results from millimeter-wave radar, and the fourth obstacle detection results from lidar, a vehicle hazard event analysis report for the target vehicle is determined.

[0160] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0161] If a first hazard event is detected in the target vehicle, obtain the event type of the first hazard event;

[0162] In the case of a high-risk event, the first hazard event is designated as the target hazard event; where a high-risk event refers to an event that exceeds the risk threshold.

[0163] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: obtaining the event type of the first hazardous event, including:

[0164] Obtain the target vehicle's driving strategy; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing;

[0165] Based on the driving strategy, determine the event type of the first hazard event.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0167] In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined.

[0168] If no obstacle is detected in the first obstacle detection scenario, the camera detection result is determined based on the camera detection information.

[0169] Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

[0170] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: the camera detection information includes image information;

[0171] Accordingly, based on the camera detection information, the camera detection result is determined, including:

[0172] Determine the second obstacle detection status reported by the camera;

[0173] If no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information.

[0174] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the camera detection result based on the image information, including:

[0175] Based on the image information, determine the ambient lighting information, as well as the background color information and motion trajectory information of the obstacles;

[0176] The camera detection results are determined based on ambient light information, as well as the background color and motion trajectory information of obstacles.

[0177] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining a vehicle hazard event analysis report for the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, including:

[0178] Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle;

[0179] Based on the LiDAR detection information of the target vehicle, determine the fourth obstacle detection status of the target vehicle's LiDAR;

[0180] Based on the camera detection results, the third obstacle detection results from millimeter-wave radar, and the fourth obstacle detection results from lidar, a vehicle hazard event analysis report for the target vehicle is determined.

[0181] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0182] If a first hazard event is detected in the target vehicle, obtain the event type of the first hazard event;

[0183] In the case of a high-risk event, the first hazard event is designated as the target hazard event; where a high-risk event refers to an event that exceeds the risk threshold.

[0184] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: obtaining the event type of the first hazardous event, including:

[0185] Obtain the target vehicle's driving strategy; the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing;

[0186] Based on the driving strategy, determine the event type of the first hazard event.

[0187] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0188] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0189] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for analyzing vehicle hazard events, characterized in that, The method includes: In the event that a target vehicle is detected to have caused a target hazard, the status of the first obstacle detection is determined. If no obstacle is detected in the first obstacle detection scenario, a camera detection result is determined based on camera detection information; wherein, the camera detection information includes image information; determining the camera detection result based on the camera detection information includes: determining a second obstacle detection scenario reported by the camera; if no obstacle is detected in the second obstacle detection scenario, the camera detection result is determined based on the image information; determining the camera detection result based on the image information includes: determining ambient light information, background color information, and motion trajectory information of the obstacle based on the image information; determining the camera detection result based on the ambient light information, background color information, and motion trajectory information of the obstacle. Based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle, a vehicle hazard event analysis report for the target vehicle is determined.

2. The method according to claim 1, characterized in that, The step of determining a vehicle hazard event analysis report for the target vehicle based on the camera detection results, millimeter-wave detection information, and lidar detection information of the target vehicle includes: Based on the millimeter-wave detection information of the target vehicle, determine the third obstacle detection status of the millimeter-wave radar of the target vehicle; Based on the lidar detection information of the target vehicle, determine the fourth obstacle detection status of the lidar of the target vehicle; Based on the camera detection results, the third obstacle detection results of the millimeter-wave radar, and the fourth obstacle detection results of the lidar, a vehicle hazard event analysis report for the target vehicle is determined.

3. The method according to claim 1, characterized in that, The method further includes: If a first hazardous event is detected in the target vehicle, the event type of the first hazardous event is obtained; In the case where the event type is a high-risk event, the first hazardous event is taken as the target hazardous event; wherein, the high-risk event refers to an event that is higher than the risk threshold.

4. The method according to claim 3, characterized in that, The event type for obtaining the first hazardous event includes: The driving strategy of the target vehicle is obtained; wherein the driving strategy includes single-lane cruise, safe obstacle avoidance, safe parking, and automatic lane changing; Based on the driving strategy, determine the event type of the first hazardous event.

5. A vehicle hazard event analysis device, characterized in that, The device includes: The first determining module is used to determine the first obstacle detection status when a target vehicle is detected to have caused a target hazard event; The second determining module is configured to determine a camera detection result based on camera detection information when the first obstacle detection condition indicates no obstacle is detected; wherein the camera detection information includes image information; determining the camera detection result based on the camera detection information includes: determining a second obstacle detection condition reported by the camera; and determining the camera detection result based on the image information when the second obstacle detection condition indicates no obstacle is detected; determining the camera detection result based on the image information includes: determining ambient light information, background color information, and motion trajectory information of the obstacle based on the image information; and determining the camera detection result based on the ambient light information, background color information, and motion trajectory information of the obstacle. The third determining module is used to determine the vehicle hazard event analysis report of the target vehicle based on the camera detection results, as well as the millimeter-wave detection information and lidar detection information of the target vehicle.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle hazard event analysis method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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