Risk map construction method and device, vehicle, storage medium and product

Abstract

The invention discloses a risk map construction method and device, a vehicle, a storage medium and a product, and relates to the technical field of vehicle big data, the method is applied to an Internet of Vehicles system, and the method comprises the following steps: for any first vehicle in the Internet of Vehicles system, receiving a first risk event data packet sent by other vehicles, the other vehicles are vehicles except the first vehicle in the Internet of Vehicles system; determining first risk event information sensed by other vehicles based on the first risk event data packet; and constructing a vehicle risk map based on the first risk event information and second risk event information sensed by the first vehicle. According to the invention, the real-time performance of vehicle risk map construction can be improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle big data technology, and in particular to a risk map construction method, device, vehicle, storage medium and product. Background Art

[0002] With the continuous development of intelligent transportation and autonomous driving technologies, vehicle network environmental perception technology, as the core module for realizing vehicle intelligent decision-making and collaborative control, its performance is directly related to traffic efficiency, driving safety and user experience.

[0003] Currently, connected vehicle (IoV) environmental perception technology generally uses a centralized architecture, relying on cloud servers to centrally process and analyze the massive amounts of environmental data collected by vehicles. However, in areas with weak network coverage or when cloud server hardware failures occur, the entire IoV environmental perception system can fail, preventing vehicles from providing timely decision-making support and thus impacting driving safety.

[0004] Therefore, how to improve the real-time performance of vehicle risk map construction is an urgent problem that needs to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a risk map construction method, device, vehicle, storage medium and product, aiming to improve the real-time performance of vehicle risk map construction.

[0006] To achieve the above objectives, the present application provides a risk map construction method, which is applied to a vehicle networking system and includes:

[0007] For any first vehicle in the vehicle networking system, receiving a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle other than the first vehicle in the vehicle networking system;

[0008] determining first risk event information perceived by the other vehicle based on the first risk event data packet;

[0009] A vehicle risk map is constructed based on the first risk event information and the second risk event information sensed by the first vehicle.

[0010] In one embodiment, the method further comprises:

[0011] For any second vehicle in the vehicle networking system, sensing vehicle driving information and first road condition information within a preset driving area where the second vehicle is located through the second vehicle;

[0012] Determining a risk event type and a risk event confidence level of the second vehicle based on the vehicle driving information and / or the first road condition information;

[0013] At least the risk event type, the risk event confidence, and location information are used as risk event information perceived by the second vehicle, wherein the location information represents the preset driving area.

[0014] In one embodiment, the step of determining the risk event type and risk event confidence of the second vehicle based on the vehicle driving information and / or the first road condition information includes:

[0015] comparing the vehicle driving information and / or the first road condition information with a preset risk data range;

[0016] When the vehicle driving information and / or the first road condition information is within the preset risk data range, determining that the risk event type of the second vehicle is a preset event type corresponding to the preset risk data range;

[0017] Determine a difference between the vehicle driving information and / or the first road condition information and preset standard data, and determine a confidence level of a risk event for the second vehicle based on the difference, wherein the preset standard data is within the preset risk data range.

[0018] In one embodiment, the method further comprises:

[0019] When the second vehicle senses a change in the risk event information, determining a changed item in the risk event information, wherein the changed item is the risk event type and / or the risk event confidence level;

[0020] The change amount corresponding to the change item is sent to vehicles in the vehicle networking system other than the second vehicle, so as to update the vehicle risk map based on the change amount.

[0021] In one embodiment, the step of constructing a vehicle risk map based on the first risk event information and the second risk event information sensed by the first vehicle includes:

[0022] In a case where a plurality of third risk event information in the first risk event information and the second risk event information represent the same vehicle driving area, determining whether the third risk event information is the same;

[0023] In the case where the third risk event information is different, each vehicle in the vehicle networking system is used as a consensus node to vote on each third risk event;

[0024] The fourth risk event information with the most votes among the third risk event information is determined as valid information, and a vehicle risk map is constructed based on the fourth risk event information and the fifth risk event information, wherein the fifth risk event information is the risk event information in the first risk event information and the second risk event information, excluding the third risk event information.

[0025] In one embodiment, the vehicle networking system further includes a roadside unit, and the method further includes:

[0026] In a case where the risk event information perceived by the second vehicle represents a blind spot risk event, a blind spot risk heat map is constructed by a target roadside unit located in the blind spot of the second vehicle, and the blind spot risk heat map is broadcast to neighboring vehicles, wherein the neighboring vehicles are vehicles in the vehicle networking system whose distance from the target roadside unit is less than a preset distance threshold.

[0027] In one embodiment, the step of constructing a blind spot risk heat map using the target roadside unit in the blind spot of the second vehicle includes:

[0028] sending, by the second vehicle, a blind spot request command to a target roadside unit located in a blind spot of the second vehicle;

[0029] The target roadside unit collects second road condition information within the blind spot based on the blind spot request instruction, and generates a blind spot risk heat map based on the second road condition information.

[0030] In addition, to achieve the above-mentioned purpose, the present application further provides a risk map construction device, which is applied to a vehicle networking system and includes:

[0031] a data sharing module, configured to receive, for any first vehicle in the vehicle networking system, a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle in the vehicle networking system other than the first vehicle;

[0032] a data parsing module, configured to determine first risk event information perceived by the other vehicle based on the first risk event data packet;

[0033] A map construction module is used to construct a vehicle risk map based on the first risk event information and the second risk event information perceived by the first vehicle.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores a program for implementing the risk map construction method. The program for implementing the risk map construction method is executed by a processor to implement the steps of the risk map construction method as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which implements the steps of the risk map construction method as described above when executed by a processor.

[0036] The present application provides a risk map construction method, which is applied to a vehicle networking system. For any first vehicle in the vehicle networking system, the method receives a first risk event data packet sent by other vehicles, wherein the other vehicles refer to vehicles other than the first vehicle in the vehicle networking system; based on the first risk event data packet, the method determines the first risk event information perceived by the other vehicles; and finally, based on the first risk event information and the second risk event information perceived by the first vehicle, constructs a vehicle risk map.

[0037] In summary, this application enables each vehicle in the connected vehicle system to build a vehicle risk map based on received and perceived risk event information, without relying on cloud servers. Compared to traditional map construction methods that rely on cloud servers for centralized data processing and analysis, this application avoids the problem of connected vehicle perception system failures caused by weak network coverage or cloud server failures. Based on a decentralized architecture, it improves the real-time nature of vehicle risk map construction and ensures driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a flow chart of the first embodiment of the risk map construction method of the present application;

[0041] Figure 2 A schematic diagram of a vehicle risk map construction process involved in an embodiment of the risk map construction method of the present application;

[0042] Figure 3 A schematic diagram of an Internet of Vehicles system according to an embodiment of the risk map construction method of the present application;

[0043] Figure 4 This is a schematic diagram of the module structure of the risk map construction device of this application;

[0044] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the risk map construction method in the embodiment of the present application.

[0045] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0047] 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.

[0048] The main solution of this application is: for any first vehicle in the Internet of Vehicles system, receive a first risk event data packet sent by other vehicles, wherein the other vehicles are vehicles in the Internet of Vehicles system other than the first vehicle; determine the first risk event information perceived by the other vehicles based on the first risk event data packet; and construct a vehicle risk map based on the first risk event information and the second risk event information perceived by the first vehicle.

[0049] Currently, connected vehicle (IoV) environmental perception technology generally uses a centralized architecture, relying on cloud servers to centrally process and analyze the massive amounts of environmental data collected by vehicles. However, in areas with weak network coverage or when cloud server hardware failures occur, the entire IoV environmental perception system can fail, preventing vehicles from providing timely decision-making support and thus impacting driving safety.

[0050] Therefore, how to improve the real-time performance of vehicle risk map construction is an urgent problem that needs to be solved.

[0051] This application leverages information sharing between vehicles in the connected vehicle system, enabling each vehicle to construct a vehicle risk map based on received and perceived risk event information, without relying on cloud servers. Compared to traditional map construction methods that rely on cloud servers for centralized data processing and analysis, this application avoids the problem of connected vehicle perception system failures caused by weak network coverage or cloud server failures. Based on a decentralized architecture, it improves the real-time nature of vehicle risk map construction and ensures driving safety.

[0052] It should be noted that the execution entity of the risk map construction method in each embodiment of this application can be a vehicle networking system, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or a vehicle capable of performing the above functions, etc. This embodiment does not specifically limit this. The following uses the vehicle networking system as the execution entity as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, this application proposes a risk map construction method of the first embodiment, which is applied to the Internet of Vehicles system. Please refer to Figure 1 The risk map construction method includes steps S10 to S30:

[0054] Step S10: for any first vehicle in the vehicle networking system, receiving a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle other than the first vehicle in the vehicle networking system;

[0055] It should be noted that the IoV system includes multiple vehicles, and any two vehicles are connected to each other via V2V (Vehicle to Vehicle) communication. Any vehicle in the IoV system is referred to as a first vehicle, and vehicles other than the first vehicle in the IoV system are referred to as other vehicles.

[0056] For any vehicle (i.e., the first vehicle) in the vehicle networking system, a risk event data packet (hereinafter referred to as the first risk event data packet for distinction) sent by other vehicles is received. It should be understood that the risk event data packet refers to a data packet containing driving risk event information perceived by other vehicles.

[0057] For example, a vehicle may send compressed data packets via 802.11p / V2X broadcast.

[0058] Step S20, determining first risk event information perceived by the other vehicle based on the first risk event data packet;

[0059] The first vehicle parses the received first risk event data packet to obtain risk event information perceived by other vehicles (hereinafter referred to as first risk event information for distinction). It is understood that the Internet of Vehicles system includes multiple vehicles, so when there are multiple other vehicles that perceive the risk event, the first vehicle will also receive multiple data packets.

[0060] Step S30: constructing a vehicle risk map based on the first risk event information and the second risk event information sensed by the first vehicle.

[0061] It should be noted that each vehicle in the Internet of Vehicles system is able to sense nearby risk event information, and the risk event information sensed by the first vehicle (hereinafter referred to as the second risk event information for distinction) is the risk event information.

[0062] A vehicle risk map is constructed by the first vehicle based on the first risk event information and the second risk event information perceived by the first vehicle itself.

[0063] For example, after constructing the vehicle risk map, the driving strategy (such as speed reduction, lane change, etc.) is adjusted in combination with the risk map, and the driver is prompted through the vehicle's HUD (Head Up Display) / voice.

[0064] By sharing information between vehicles in the connected vehicle system, this embodiment enables each vehicle to construct a vehicle risk map based on received and perceived risk event information, without relying on cloud servers. Compared to traditional map construction methods that rely on cloud servers for centralized data processing and analysis, this embodiment avoids the problem of connected vehicle perception system failure caused by weak network coverage or cloud server failures. Based on a decentralized architecture, this embodiment improves the real-time nature of vehicle risk map construction and ensures driving safety.

[0065] In this embodiment, step S30 may include:

[0066] Step S301: When multiple third risk event information in the first risk event information and the second risk event information represent the same vehicle driving area, determining whether the third risk event information is the same;

[0067] It should be noted that the risk event information includes the location information of the risk event, that is, it can be understood that each risk event information represents a vehicle driving area.

[0068] If multiple pieces of risk event information (hereinafter referred to as third risk event information for distinction) in the first risk event information and the second risk event information represent the same vehicle driving area, it is determined whether the third risk event information is the same. In other words, it is determined whether the risk event type and risk event confidence level in each piece of third risk event information is consistent.

[0069] Step S302: When the third risk event information is different, each vehicle in the Internet of Vehicles system is used as a consensus node to vote on each third risk event;

[0070] When the information of each third risk event is different, each vehicle in the Internet of Vehicles system is used as a consensus node to participate in voting on each third risk event.

[0071] Step S303: Determine the fourth risk event information with the most votes among the third risk event information as valid information, and construct a vehicle risk map based on the fourth risk event information.

[0072] It should be noted that the risk event information other than the plurality of third risk event information in the first risk event information and the second risk event information is referred to as fifth risk event information.

[0073] The risk event information with the most votes among the third risk events is called fourth risk event information, and the fourth risk event information is determined as valid information, so as to construct a vehicle risk map based on the valid information and the fifth risk event information.

[0074] In one feasible implementation, a Byzantine fault-tolerant consensus algorithm is introduced. When multiple vehicles report risk events in the same area, more than half of the nodes must confirm the report before it is marked as valid, thereby suppressing malicious data interference. Furthermore, after constructing the vehicle risk map, this embodiment of the application dynamically reduces the weight of historical risk events based on a spatiotemporal decay model to ensure the real-time nature of the risk map. For example, the weight is decayed every five minutes.

[0075] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , the risk map construction method of this application also includes:

[0076] Step A10: for any second vehicle in the vehicle networking system, sensing vehicle driving information and first road condition information within a preset driving area where the second vehicle is located through the second vehicle;

[0077] In this embodiment, any vehicle in the connected vehicle system is referred to as the second vehicle. It should be noted that the perceptible range of each vehicle is pre-set, and this range typically depends on the detection range of sensors such as cameras and radars installed on the vehicle. For example, in one feasible embodiment, the perceptible range of a vehicle is the area of a circle centered on the vehicle's location and with a preset radius. The preset driving area where the second vehicle is located refers to the perceptible range of the second vehicle.

[0078] For any vehicle (ie, the second vehicle) in the Internet of Vehicles system, the second vehicle senses the vehicle driving information and the road condition information within the preset driving area where the second vehicle is located (hereinafter referred to as the first road condition information for distinction).

[0079] Step A20: determining a risk event type and a risk event confidence level of the second vehicle based on the vehicle driving information and / or the first road condition information;

[0080] The risk event type and risk event confidence of the second vehicle are determined based on the vehicle driving information and / or the first road condition information. It is understood that the risk event types that can be determined based on the vehicle driving information include but are not limited to sudden braking, road skidding, lane departure, speeding and following too closely. The risk event types that can be determined based on the first road condition information include but are not limited to road construction, flooded sections, obstacle intrusion, traffic light failure and low road visibility. In addition, the embodiment of the present application also proposes an event-driven broadcast optimization algorithm to dynamically adjust the data transmission priority and give priority to broadcasting emergency events.

[0081] In this embodiment, step A20 may include:

[0082] Step A201: comparing the vehicle driving information and / or the first road condition information with a preset risk data range;

[0083] Step A202: if the vehicle driving information and / or the first road condition information is within the preset risk data range, determining that the risk event type of the second vehicle is a preset event type corresponding to the preset risk data range;

[0084] Step A203, determining the difference between the vehicle driving information and / or the first road condition information and preset standard data, and determining the risk event confidence of the second vehicle based on the difference, wherein the preset standard data is within the preset risk data range.

[0085] It should be noted that a risk data range corresponding to each risk event type is pre-set. That is, when the detected vehicle driving information and / or road condition information meets a certain risk data range, the risk event perceived by the vehicle is considered to be the pre-set risk event type corresponding to this risk data range. Furthermore, a value within the risk data range that best represents the corresponding risk event type is set, i.e., the pre-set standard data.

[0086] The steps of determining the risk event type and risk event confidence level include: first, comparing the vehicle driving information and / or the first road condition information with a preset risk data range; if the vehicle driving information and / or the first road condition information is within the preset risk data range, determining the risk event type of the second vehicle as a preset event type corresponding to the preset risk data range; then, determining the difference between the vehicle driving information and / or the first road condition information and preset standard data, and determining the risk event confidence level of the second vehicle based on the difference. It should be noted that, within the range of risk event confidence levels, the larger the difference, the smaller the risk event confidence level, indicating a lower credibility of the risk event.

[0087] In step A30, at least the risk event type, the risk event confidence, and the location information are used as the risk event information perceived by the second vehicle, wherein the location information represents the preset driving area.

[0088] At least the risk event type, risk event confidence and location where the risk event occurs are used as risk event information perceived by the second vehicle, so that the second vehicle can generate a risk event data packet based on the perceived risk event information and send it to other vehicles in the Internet of Vehicles system.

[0089] In a feasible implementation, the location where the risk event occurs can be represented as a grid ID. Specifically, the longitude and latitude of the location where the risk event occurs is mapped to the grid ID. The grid ID refers to the location information.

[0090] This embodiment of the application processes and analyzes data from vehicles in the connected vehicle system to obtain risk event information consisting of at least the risk event type, risk event confidence, and location information. This information is then compressed into a data package and shared with other vehicles. This enables lightweight processing of multimodal data and conserves bandwidth resources typically used for inter-vehicle data transmission.

[0091] In this embodiment, the risk map construction method of the present application further includes:

[0092] Step B10: When the second vehicle senses a change in the risk event information, determining a changed item in the risk event information, wherein the changed item is the risk event type and / or the risk event confidence level;

[0093] When the second vehicle senses a change in the risk event information, it determines a changed item in the risk event information. It can be understood that the changed item is the risk event type and / or the risk event confidence.

[0094] Step B20: Send the change amount corresponding to the change item to vehicles other than the second vehicle in the Internet of Vehicles system, so as to update the vehicle risk map based on the change amount.

[0095] The change amount corresponding to the change item is sent to vehicles other than the second vehicle in the vehicle networking system through the second vehicle, so as to determine new risk event information based on the change amount, and update the vehicle risk map based on the new risk event information.

[0096] For example, Figure 2The figure shows a schematic diagram of the vehicle risk map construction process. First, each vehicle in the Internet of Vehicles system perceives the surrounding environment through on-board sensors to determine whether it has perceived risk event information. When risk event information is perceived, the data is compressed to generate a risk event data packet and sent to other vehicles. The vehicle constructs a vehicle risk map based on the risk event information it perceives and the risk event information sent by other vehicles. That is, each vehicle maintains a dynamic risk map locally, and the map storage structure uses a spatiotemporal hash table (the key is the grid ID + time window, and the value is the risk level and confidence level).

[0097] In this way, the embodiment of the present application sets up a risk event information update mechanism and only shares the changes in the risk event information, thereby further saving bandwidth resources occupied by data transmission between vehicles and reducing latency.

[0098] Based on the first and / or second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and / or second embodiments can be referred to above and will not be described in detail. On this basis, the vehicle networking system also includes a roadside unit, and the risk map construction method of the present application also includes:

[0099] Step C10: When the risk event information perceived by the second vehicle represents a blind spot risk event, a blind spot risk heat map is constructed through a target roadside unit located in the blind spot of the second vehicle, and the blind spot risk heat map is broadcast to neighboring vehicles, wherein the neighboring vehicles are vehicles in the Internet of Vehicles system whose distance from the target roadside unit is less than a preset distance threshold.

[0100] It should be noted that the Internet of Vehicles system also includes RSU (Road Side Unit), which is connected to the vehicles in the Internet of Vehicles system via V2I (Vehicle to Infrastructure) communication. The event type of risk event information also includes blind spot risk events. For example, Figure 3 The figure shows a schematic diagram of the vehicle networking system. The vehicle networking system includes multiple vehicles, which are connected through V2V communication. The vehicle networking system also includes roadside units, and the vehicles and roadside units are connected through V2I communication.

[0101] If the risk event information sensed by the second vehicle represents a blind spot risk event, a roadside unit (hereinafter referred to as a target roadside unit for distinction) located in the second vehicle's blind spot is determined, a blind spot risk heat map is constructed using the target roadside unit, and the blind spot risk heat map is broadcast to neighboring vehicles, where the neighboring vehicles are vehicles in the connected vehicle system whose distance from the target roadside unit is less than a preset distance threshold. The specific value of the preset distance threshold is not limited in this embodiment of the present application.

[0102] In this embodiment, step C10 may include:

[0103] Step C101, sending a blind spot request command to a target roadside unit located in the blind spot of the second vehicle through the second vehicle;

[0104] Step C102 : collecting second road condition information within the blind spot based on the blind spot request instruction through the target roadside unit, and generating a blind spot risk heat map based on the second road condition information.

[0105] When the risk event information perceived by the second vehicle represents a blind spot risk event, a blind spot request instruction is sent to a target roadside unit located in the blind spot of the second vehicle. After receiving the blind spot request instruction, the target roadside unit collects road condition information in the blind spot (hereinafter referred to as the second road condition information for distinction) based on the blind spot request instruction to generate a blind spot risk heat map based on the second road condition information.

[0106] In one feasible implementation, the second vehicle may also forward blind spot obstacle information to the vehicle behind the curve to form a chain warning.

[0107] In this way, the embodiment of the present application realizes the collaboration between the vehicle and the roadside unit, and requests blind spot data from the roadside unit or the vehicle in front when there is a blind spot in the vehicle driving scene, so as to improve driving safety.

[0108] The present application also provides a risk map construction device, please refer to Figure 4 The risk map construction device is applied to a vehicle networking system, and the risk map construction device includes:

[0109] The data sharing module 10 is configured to receive, for any first vehicle in the vehicle networking system, a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle in the vehicle networking system other than the first vehicle;

[0110] a data parsing module 20, configured to determine first risk event information perceived by the other vehicle based on the first risk event data packet;

[0111] The map construction module 30 is configured to construct a vehicle risk map based on the first risk event information and the second risk event information sensed by the first vehicle.

[0112] Optionally, the risk map construction device further includes an environment perception module, and the environment perception module is used to:

[0113] For any second vehicle in the vehicle networking system, sensing vehicle driving information and first road condition information within a preset driving area where the second vehicle is located through the second vehicle;

[0114] Determining a risk event type and a risk event confidence level of the second vehicle based on the vehicle driving information and / or the first road condition information;

[0115] At least the risk event type, the risk event confidence, and location information are used as risk event information perceived by the second vehicle, wherein the location information represents the preset driving area.

[0116] Optionally, the environment perception module is further configured to:

[0117] comparing the vehicle driving information and / or the first road condition information with a preset risk data range;

[0118] When the vehicle driving information and / or the first road condition information is within the preset risk data range, determining that the risk event type of the second vehicle is a preset event type corresponding to the preset risk data range;

[0119] Determine a difference between the vehicle driving information and / or the first road condition information and preset standard data, and determine a confidence level of a risk event for the second vehicle based on the difference, wherein the preset standard data is within the preset risk data range.

[0120] Optionally, the risk map construction device further includes a data updating module, which is configured to:

[0121] When the second vehicle senses a change in the risk event information, determining a changed item in the risk event information, wherein the changed item is the risk event type and / or the risk event confidence level;

[0122] The change amount corresponding to the change item is sent to vehicles in the vehicle networking system other than the second vehicle, so as to update the vehicle risk map based on the change amount.

[0123] Optionally, the map construction module 30 is further configured to:

[0124] In a case where a plurality of third risk event information in the first risk event information and the second risk event information represent the same vehicle driving area, determining whether the third risk event information is the same;

[0125] In the case where the third risk event information is different, each vehicle in the vehicle networking system is used as a consensus node to vote on each third risk event;

[0126] The fourth risk event information with the most votes among the third risk event information is determined as valid information, and a vehicle risk map is constructed based on the fourth risk event information and the fifth risk event information, wherein the fifth risk event information is the risk event information in the first risk event information and the second risk event information, excluding the third risk event information.

[0127] Optionally, the vehicle networking system further includes a roadside unit, and the risk map construction device further includes a blind spot recognition module, wherein the blind spot recognition module is configured to:

[0128] In a case where the risk event information perceived by the second vehicle represents a blind spot risk event, a blind spot risk heat map is constructed by a target roadside unit located in the blind spot of the second vehicle, and the blind spot risk heat map is broadcast to neighboring vehicles, wherein the neighboring vehicles are vehicles in the vehicle networking system whose distance from the target roadside unit is less than a preset distance threshold.

[0129] Optionally, the blind spot identification module is further configured to:

[0130] sending, by the second vehicle, a blind spot request command to a target roadside unit located in a blind spot of the second vehicle;

[0131] The target roadside unit collects second road condition information within the blind spot based on the blind spot request instruction, and generates a blind spot risk heat map based on the second road condition information.

[0132] The risk map construction device provided in the embodiments of this application, utilizing the risk map construction method described in the aforementioned embodiments, can address the technical problem of improving the real-time performance of vehicle risk map construction. Compared to the prior art, the beneficial effects of the risk map construction device provided in the embodiments of this application are the same as those of the risk map construction method described in the aforementioned embodiments. Other technical features of the risk map construction device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0133] The present application provides a vehicle, comprising: 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 map construction method of the above-mentioned embodiment one.

[0134] Reference below Figure 5 , which shows a structural schematic diagram of a vehicle suitable for implementing an embodiment of the present application. Figure 5 The vehicle shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present application.

[0135] like Figure 5 As shown, the vehicle may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for vehicle operation. Processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speakers, and vibrator; storage device 1003 including, for example, a magnetic tape or hard disk; and communication device 1009. Communication device 1009 may allow the vehicle to communicate with other devices wirelessly or by wire to exchange data. Although the figures show a vehicle with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may alternatively be implemented or have.

[0136] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0137] The vehicle provided in this application utilizes the risk map construction method described in the aforementioned embodiment, solving the technical problem of improving the real-time performance of vehicle risk map construction. Compared to the prior art, the vehicle provided in this application achieves the same beneficial effects as the risk map construction method described in the aforementioned embodiment. Other technical features of this vehicle are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0138] It should be understood that the various parts disclosed in this application can be implemented using 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.

[0139] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0140] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the risk map construction method in the above-mentioned embodiment.

[0141] The computer-readable storage medium provided herein 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 thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0142] The computer-readable storage medium may be included in the vehicle, or may exist independently without being installed in the vehicle.

[0143] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle, the vehicle: receives a first risk event data packet sent by other vehicles for any first vehicle in the vehicle networking system, wherein the other vehicles are vehicles in the vehicle networking system other than the first vehicle; determines the first risk event information perceived by the other vehicles based on the first risk event data packet; and constructs a vehicle risk map based on the first risk event information and the second risk event information perceived by the first vehicle.

[0144] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including 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 a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through 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., through the Internet using an Internet service provider).

[0145] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of 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 box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0146] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0147] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned risk map construction method. This computer-readable storage medium addresses the technical problem of improving the real-time performance of vehicle risk map construction. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the risk map construction method provided in the aforementioned embodiments and are not further elaborated here.

[0148] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the risk map construction method as described above.

[0149] The computer program product provided in this application can improve the real-time performance of vehicle risk map construction. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as those of the risk map construction method provided in the above embodiments, and will not be elaborated here.

[0150] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A risk map construction method, characterized in that: The risk map construction method is applied to a vehicle networking system, and the risk map construction method includes: For any first vehicle in the vehicle networking system, receiving a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle other than the first vehicle in the vehicle networking system; determining first risk event information perceived by the other vehicle based on the first risk event data packet; A vehicle risk map is constructed based on the first risk event information and the second risk event information sensed by the first vehicle.

2. The risk map construction method according to claim 1, characterized in that: The method further comprises: For any second vehicle in the vehicle networking system, sensing vehicle driving information and first road condition information within a preset driving area where the second vehicle is located through the second vehicle; Determining a risk event type and a risk event confidence level of the second vehicle based on the vehicle driving information and / or the first road condition information; At least the risk event type, the risk event confidence, and location information are used as risk event information perceived by the second vehicle, wherein the location information represents the preset driving area.

3. The risk map construction method according to claim 2, characterized in that: The step of determining the risk event type and risk event confidence of the second vehicle based on the vehicle driving information and / or the first road condition information includes: comparing the vehicle driving information and / or the first road condition information with a preset risk data range; When the vehicle driving information and / or the first road condition information is within the preset risk data range, determining that the risk event type of the second vehicle is a preset event type corresponding to the preset risk data range; Determine a difference between the vehicle driving information and / or the first road condition information and preset standard data, and determine a confidence level of a risk event for the second vehicle based on the difference, wherein the preset standard data is within the preset risk data range.

4. The risk map construction method according to claim 2, characterized in that: The method further comprises: When the second vehicle senses a change in the risk event information, determining a changed item in the risk event information, wherein the changed item is the risk event type and / or the risk event confidence level; The change amount corresponding to the change item is sent to vehicles in the vehicle networking system other than the second vehicle, so as to update the vehicle risk map based on the change amount.

5. The risk map construction method according to claim 1, characterized in that: The step of constructing a vehicle risk map based on the first risk event information and the second risk event information sensed by the first vehicle includes: In a case where a plurality of third risk event information in the first risk event information and the second risk event information represent the same vehicle driving area, determining whether the third risk event information is the same; In the case where the third risk event information is different, each vehicle in the vehicle networking system is used as a consensus node to vote on each third risk event; The fourth risk event information with the most votes among the third risk event information is determined as valid information, and a vehicle risk map is constructed based on the fourth risk event information and the fifth risk event information, wherein the fifth risk event information is the risk event information in the first risk event information and the second risk event information, excluding the third risk event information.

6. The risk map construction method according to claim 2, characterized in that: The vehicle networking system further includes a roadside unit, and the method further includes: In a case where the risk event information perceived by the second vehicle represents a blind spot risk event, a blind spot risk heat map is constructed by a target roadside unit located in the blind spot of the second vehicle, and the blind spot risk heat map is broadcast to neighboring vehicles, wherein the neighboring vehicles are vehicles in the vehicle networking system whose distance from the target roadside unit is less than a preset distance threshold.

7. The risk map construction method according to claim 6, characterized in that: The step of constructing a blind spot risk heat map using the target roadside unit of the blind spot of the second vehicle includes: sending, by the second vehicle, a blind spot request command to a target roadside unit located in a blind spot of the second vehicle; The target roadside unit collects second road condition information within the blind spot based on the blind spot request instruction, and generates a blind spot risk heat map based on the second road condition information.

8. A risk map construction device, characterized in that: The risk map construction device is applied to a vehicle networking system, and the risk map construction device includes: a data sharing module, configured to receive, for any first vehicle in the vehicle networking system, a first risk event data packet sent by another vehicle, wherein the other vehicle is a vehicle in the vehicle networking system other than the first vehicle; a data parsing module, configured to determine first risk event information perceived by the other vehicle based on the first risk event data packet; A map construction module is used to construct a vehicle risk map based on the first risk event information and the second risk event information perceived by the first vehicle.

9. A vehicle, characterized in that: The vehicle 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 map construction method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the risk map construction method according to any one of claims 1 to 7 are implemented.

11. 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 map construction method according to any one of claims 1 to 7 are implemented.

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