CN112258838B

CN112258838B - Driving risk prompting method, device, storage medium and equipment

Info

Publication number: CN112258838B
Application number: CN202011123664.9A
Authority: CN
Inventors: 侯琛
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-10-20
Filing date: 2020-10-20
Publication date: 2023-10-13
Anticipated expiration: 2040-10-20
Also published as: CN112258838A

Abstract

The invention discloses a driving risk prompting method, a driving risk prompting device, a storage medium and driving risk prompting equipment. Wherein the method comprises the following steps: determining surrounding vehicles within a driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the surrounding vehicle sets, and screening a risk vehicle set from the surrounding vehicle sets, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles; issuing associated data corresponding to the risk vehicle set to a target vehicle; and then, constructing a new surrounding vehicle set according to data except the risk vehicles in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating a risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle. According to the scheme, the peripheral vehicle information is issued to the target vehicle according to the collision risk degree, and the driving safety is improved.

Description

Driving risk prompting method, device, storage medium and equipment

Technical Field

The invention relates to the technical field of auxiliary driving, in particular to a driving risk prompting method, a driving risk prompting device, a storage medium and driving risk prompting equipment.

Background

In the running process of the vehicle, the phenomena such as collision, rear-end collision and the like often occur. In accident cause statistics, more than 75% of accidents are caused by illegal driving of drivers, and mainly comprise distraction, fatigue driving, drunk driving, dialing of mobile phones and the like. In order to improve the driving safety of the vehicle, surrounding vehicle information can be sent to the target vehicle in a driving risk early warning mode, so that the driving of the target vehicle is assisted, and most collision accidents can be avoided.

In the related driving risk early warning scheme, information of all vehicles in a certain range (such as a safe distance range) around the target vehicle is generally acquired at intervals, and the acquired vehicles are regarded as collision risks with the target vehicle and then all the acquired vehicles are sent to the target vehicle.

However, in the driving risk early warning scheme provided by the related art, information of all vehicles attached to the target vehicle is sent to the target vehicle, so that differences of collision risks brought to the target vehicle by different vehicles are ignored, and the defect of low early warning accuracy exists.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides a driving risk prompting method, a device, a storage medium and equipment, which are used for at least solving the technical problem of low early warning accuracy of the existing driving risk early warning scheme.

According to an aspect of the embodiment of the present invention, there is provided a driving risk prompting method, including: determining surrounding vehicles within a driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the surrounding vehicle set, and screening a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles; determining associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle; issuing associated data corresponding to the risk vehicle set to the target vehicle; and constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating a risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

According to another aspect of the embodiment of the present invention, there is also provided a driving risk prompting device, including: a determination unit configured to determine a surrounding vehicle within a driving safety distance of a target vehicle, and obtain a surrounding vehicle set; the processing unit is used for executing data processing on the surrounding vehicle set, and screening a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles; the acquiring unit is used for acquiring associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle; the sending unit is used for sending the associated data corresponding to the risk vehicle set to the target vehicle; and the updating unit is used for constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, triggering the data processing unit to execute data processing on the constructed surrounding vehicle set, generating the risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

According to another aspect of the embodiments of the present invention, there is also provided a computer readable storage medium having at least one instruction or at least one program stored therein, the at least one instruction or at least one program being loaded and executed by a processor to implement the driving risk prompting method described above.

According to another aspect of the embodiments of the present invention, there is also provided a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor, causes the processor to execute the driving risk prompting method described above.

According to the method, a surrounding vehicle set is built according to surrounding vehicles within a driving safety distance of a target vehicle, differences of collision risks brought to the target vehicle by different surrounding vehicles are considered, surrounding vehicles with higher collision possibility to the target vehicle in each risk measurement angle in the surrounding vehicle set are used as risk vehicles, data processing of the surrounding vehicle set is completed, then the risk vehicles are removed from the surrounding vehicle set to obtain an updated surrounding vehicle set, data processing is carried out on the updated surrounding vehicle set, each round of data processing can obtain a risk vehicle set consisting of the risk vehicles, and vehicle information and vehicle control information of the risk vehicles in the risk vehicle set are returned to the target vehicle once each risk vehicle set is obtained. According to the invention, the risk priority of the risk vehicle set obtained by processing the data of each wheel corresponds to the generation time sequence of the risk vehicle set, the generation time sequence of the risk vehicle set is front, the risk vehicle set has higher possibility of colliding with the target vehicle, the risk vehicle set has lower possibility of colliding with the target vehicle, and the risk vehicle set generated by processing each theory of data is sequentially fed back to the target vehicle, so that the vehicle information is issued to the target vehicle from high to low according to the collision risk of the surrounding vehicles to the target vehicle, the target vehicle can quickly obtain effective information, and the driving safety is facilitated to be improved. The technical problem that the early warning accuracy of the existing driving risk early warning scheme is low is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a schematic diagram of a hardware environment of a driving risk prompting method according to an embodiment of the present application;

FIG. 2 is a data sharing system according to an embodiment of the present application;

FIG. 3 is a flow chart of an alternative driving risk prompting method according to an embodiment of the present application;

FIG. 4 is a flow chart of an alternative method of screening a set of obtained risk vehicles according to an embodiment of the present application;

FIG. 5 is a flow chart of a method of constructing a set of candidate vehicles corresponding to a vehicle location in accordance with an embodiment of the application;

FIG. 6 is a flow chart of a method of constructing a set of candidate vehicles corresponding to vehicle speeds in accordance with an embodiment of the application;

FIG. 7 is a flow chart of a method of constructing a set of candidate vehicles corresponding to a direction of travel of a vehicle in accordance with an embodiment of the application;

FIG. 8 is an alternative hardware platform for implementing a driving risk prompting method according to an embodiment of the present application;

fig. 9 is a schematic structural view of a driving risk presenting apparatus according to an embodiment of the present application;

Fig. 10 is a block diagram of an apparatus according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The embodiment of the invention provides a driving risk prompting method. First, the embodiment of the invention discloses an implementation environment of the driving risk prompting method in a feasible embodiment.

Referring to fig. 1, the implementation scenario at least includes an on-board device, an internet of vehicles cloud platform 30, a drive test detection apparatus 20, and a traffic management department cloud platform 40. The vehicle-mounted device is mounted on the vehicle 10, and the vehicle-mounted device can collect position information, running speed, running direction, video data and the like of the vehicle 10 in real time, and can communicate with the internet of vehicle cloud platform 20 based on Browser/Server (B/S) mode or Client/Server (C/S) mode, and report running data such as real-time position, running speed, running direction and the like to the internet of vehicle cloud platform 20. The road test detection device is used for collecting road surface data in real time, and is in communication with the internet of vehicles cloud platform 30, and can respond to a request of the internet of vehicles cloud platform for obtaining driving data of surrounding vehicles of the vehicle 10, obtain the speed, driving direction, GPS position and the like of each vehicle within a safe distance of the vehicle based on the position information of the vehicle 10, and report the speed, driving direction, GPS position and the like to the internet of vehicles cloud platform 30. The traffic management department cloud platform 40 communicates with the internet of vehicles cloud platform 30, and the internet of vehicles cloud platform 30 may obtain the historical traffic accident rate of the road section and the historical violation information of the vehicle from the traffic management department cloud platform 40.

The cloud platform 30 of internet of vehicles may include a database server and a service server, where the service server is in communication connection with the database server, the database server may also be disposed inside the service server, where the database server may be configured to store data content required by the service server, such as running data reported by vehicle devices, data obtained by detecting the drive test detection device 20, road section historical traffic accident rate and vehicle historical violation information provided by the cloud platform 40 of the traffic management department, and the database server may interact with the service server, so that the service server may perform analysis processing based on various data provided by the vehicle devices, the drive test detection device 20 and the cloud platform 40 of the traffic management department, evaluate collision risk possibly brought to a target vehicle by surrounding vehicles around any target vehicle, screen a risk vehicle set that causes a larger collision risk to the target vehicle in batches, and issue relevant information of each risk vehicle set to the target vehicle from large to small according to the risk size.

The internet of vehicles cloud platform 30 may include one independently operated server, or a distributed server, or a server cluster composed of a plurality of servers.

The traffic management cloud platform 40 may include a single independently operating server, or a distributed server, or a server cluster composed of a plurality of servers. The traffic management cloud platform 30 may include a network communication unit, a processor, a memory, and the like.

The in-vehicle apparatus may include: in-vehicle GPS devices, speed measuring devices, gyroscopes, cameras, automobile recorders, smartphones, tablet computers, notebook computers, digital assistants, smart wearable devices, vehicle terminals, and other types of physical devices, and may also include software running in the physical devices, such as application programs, and the like.

The drive test detection device can comprise a roadside camera, a monitor, a velocimeter, a radar detection device and the like. The road perception fusion device is a road test detection device which fuses various detection means. The road perception fusion device refers to double perception interaction of road external perception (perception of external information such as vehicles and people) and internal perception (perception of real service state and service performance of roads), and realizes human-vehicle-road cooperation. The external sensing technology is a series of technologies mainly used for detecting vehicle information, and the technologies mainly collect the vehicle information by arranging a fixed detection device (various detection devices and detection instruments) and comprise the following steps: coil detection, video detection, infrared detection, microblog detection and ultrasonic detection. The internal sensing technology is mainly a series of technologies for detecting and monitoring the running state and damage condition of road structure facilities. At present, the main detection technology at home and abroad is classified according to the technical principle, and comprises the following steps: ultrasonic detection techniques, elastic wave detection techniques, various radiation detection techniques, optical fiber sensing detection techniques, image recognition techniques, and piezoelectric sensing techniques.

The internet of vehicles cloud platform in the scenario of the driving risk prompting method related to the embodiment of the invention can be a data sharing system formed by connecting a plurality of nodes (any form of computing equipment in an access network, such as a server and a client) through network communication.

Referring to the data sharing system shown in fig. 2, the data sharing system 400 refers to a system for performing data sharing between nodes, and the data sharing system may include a plurality of nodes 101, and the plurality of nodes 101 may be respective clients in the data sharing system. Each node 101 may receive input information while operating normally and maintain shared data within the data sharing system based on the received input information. In order to ensure the information intercommunication in the data sharing system, information connection can exist between each node in the data sharing system, and the nodes can transmit information through the information connection. For example, when any node in the data sharing system receives input information, other nodes in the data sharing system acquire the input information according to a consensus algorithm, and store the input information as data in the shared data, so that the data stored on all nodes in the data sharing system are consistent.

Each node in the data sharing system has a node identifier corresponding to the node identifier, and each node in the data sharing system can store the node identifiers of other nodes in the data sharing system, so that the generated block can be broadcast to other nodes in the data sharing system according to the node identifiers of other nodes. Each node can maintain a node identification list shown in the following table, and the node names and the node identifications are correspondingly stored in the node identification list. The node identifier may be an IP (Internet Protocol, protocol interconnecting between networks) address, or any other information that can be used to identify the node.

Fig. 3 is a flow chart of an alternative driving risk prompting method according to an embodiment of the present invention. Referring to fig. 3, a flowchart of a driving risk prompting method is shown, where the method may be implemented by using the internet of vehicles cloud platform in the implementation environment shown in fig. 1 as an execution subject, and the method may include the following steps.

Step S301: a surrounding vehicle within a driving safety distance of the target vehicle is determined, and a surrounding vehicle set is obtained.

The method provided by the embodiment of the invention takes the Internet of vehicles as the background, and the Internet of vehicles refers to a huge interaction network formed by information such as vehicle positions, speeds, routes and the like. Based on an in-car network, an inter-car network and a vehicle-mounted mobile internet, a large system network for wireless communication and information exchange among vehicles, cars, roads, pedestrians and the internet is based on agreed communication protocols and data interaction standards, and is an integrated network capable of realizing intelligent traffic management, intelligent dynamic information service and intelligent control of vehicles, and is a typical application of the internet of things technology in the field of traffic systems.

The target vehicle may refer to any vehicle to be determined to issue driving risk prompt information. The driving risk prompt information is a prediction result of the vehicle networking cloud platform on the possible collision of other vehicles around the target vehicle, and related danger prediction information is sent to the target vehicle, so that the target vehicle can take driving operation behaviors avoiding dangers in advance, and driving safety is ensured.

The vehicle networking cloud platform determines driving safety distance according to the position information of the target vehicle in combination with the current speed of the target vehicle and/or the current road section of the target vehicle, takes the target vehicle as the center of a circle, takes the driving safety distance as the radius to circle a range, takes other vehicles which are in the range and are except the target vehicle as surrounding vehicles, and gathers all the surrounding vehicles to obtain a surrounding vehicle set. The driving safety distance refers to a necessary distance between a rear vehicle and a front vehicle during driving in order to avoid accidental collision with the front vehicle, a traffic management department makes relevant regulations of a safety distance for different road sections and different vehicle speed ranges, and in specific implementation, the traffic management department can inquire according to the road section where a target vehicle is currently located, determine the safety distance obtained by inquiry as the driving safety distance, inquire according to the current speed of the target vehicle, take the safety distance obtained by inquiry as the driving safety distance, and take the larger value of the safety distance corresponding to the road section where the target vehicle is currently located and the safety distance corresponding to the current speed as the driving safety distance.

In one possible embodiment, the target vehicle may acquire its own position information through the in-vehicle GPS device, report the position information to the internet-of-vehicle cloud platform, and the internet-of-vehicle cloud platform may detect, through the drive test detection device, a vehicle within a driving safety distance of a road on which the target vehicle is located based on the position information reported by the target vehicle. Among them, the drive test detection apparatus includes sensors, such as cameras, radars, lasers, ultrasonic waves, etc., installed near the road, capable of recording information of the road, and these sensors can obtain information of the road by detecting light, heat, pressure, or other variables. The road test detection device can record the information of the road in real time, and can record the information of the road at intervals of reference time, wherein the reference time can be set according to actual conditions, such as 5 seconds and the like.

Step S303: and executing data processing on the surrounding vehicle set, and screening a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from the preset risk measurement angles.

In the existing driving risk early warning scheme, information of surrounding vehicles of the target vehicle is issued to the target vehicle in any sequence, the difference of collision risks brought to the target vehicle by different vehicles within a driving safety distance is ignored, the information of the surrounding vehicles is issued to the target vehicle in disorder, and the target vehicle cannot distinguish the size of collision hazards possibly brought by the vehicles, so that the driving safety is not facilitated.

In contrast, the embodiment of the invention evaluates the collision risk brought by each peripheral vehicle to the target vehicle based on the preset risk measurement angle by combining the driving information of each peripheral vehicle in the target vehicle and the peripheral vehicle set and the historical traffic accident rate corresponding to the road section where the target vehicle is located, and screens the vehicles which bring the collision risk to the target vehicle from each risk measurement angle from the peripheral vehicle set. The specific process is shown in fig. 4, and comprises the following steps:

step S401, acquiring motion parameters of the target vehicle and the surrounding vehicles corresponding to each risk measurement angle.

The risk measurement angle refers to a parameter for measuring the collision risk of a vehicleThe reference index may be, for example, a vehicle speed, a vehicle position, a traveling direction, a driving violation duty ratio of the vehicle, or the like. The motion parameter corresponding to the risk metric angle refers to a specific value corresponding to the risk metric angle of the target vehicle or the surrounding vehicles. For example, the surrounding vehicle set { a } ₁ ,a ₂ ,a ₃ ,...,a _n When the risk measurement angle is the speed of the vehicle at the current time t, recording the speed of each peripheral vehicle as v _1.t ,v _2.t ,v _3.t ,...v _n.t The speed of the target vehicle is v _t When the risk measurement angle is the vehicle position, recording the position of each surrounding vehicle as p _1.t ,p _2.t ,p _3.t ,...p _n.t The vehicle position of the target vehicle is p _t Recording the traveling direction of each surrounding vehicle as theta when the risk measurement angle is the traveling direction _1.t ,θ _2.t ,θ _3.t ,...θ _n.t The traveling direction of the target vehicle is θ _t 。

The risk measurement angle in the embodiment of the invention at least comprises a vehicle speed, a vehicle position and a running direction, and for the motion parameters of the surrounding vehicles, the motion parameters of the surrounding vehicles can be obtained through detection of the drive test device, the cloud platform of the internet of vehicles can obtain the detection result of the drive test device on the motion parameters of the surrounding vehicles through a mode of sending instructions to the drive test device, and the cloud platform of the internet of vehicles can also obtain the related information of the surrounding vehicles around the target vehicles through a mode of reporting the motion parameters of the surrounding vehicles to the cloud platform of the internet of vehicles through the drive test device; the target vehicle may be detected by the in-vehicle detection device to obtain the vehicle speed, the traveling direction, and the vehicle position of the host vehicle, and in practice, the motion parameter of the target vehicle may be detected by the road test device in the above manner.

The drive test device detects and obtains relevant information of each vehicle, including but not limited to identification information, position information, running speed, running direction and the like of the vehicle. Each vehicle has unique identification information, for example, the identification information may be an IMEI code (International MobileEquipment Identity, mobile equipment international identity) of the vehicle in the internet of vehicles system, and the internet of vehicles cloud platform may parse the identification information of the vehicle from the acquired related information of the vehicle, so as to determine which vehicle the vehicle is. Optionally, the related information of the target vehicle may be information recorded by a speed measuring device, a gyroscope and a GPS device, or may refer to information recorded by a terminal of the target vehicle.

When the risk measurement angle is the driving violation duty ratio of the vehicle, the internet of vehicles cloud platform can query the driving violation duty ratio corresponding to the vehicle from the traffic management department cloud platform 40 according to the associated information (such as license plate number and owner identity information) of the target vehicle and the surrounding vehicles. The driving violation duty may be expressed as a ratio between the number of driving violations in a travel period and the number of days corresponding to the travel period.

Step S403, obtaining the historical traffic accident rate of the road section where the target vehicle is currently located.

Specifically, a road section where the target vehicle is currently located can be obtained through a road test detection device, or the road section where the target vehicle is currently located can be matched through the current position of the target vehicle, and then the vehicle networking cloud platform queries from the traffic management department cloud platform according to the road section where the target vehicle is currently located to obtain the historical traffic accident rate P of the road section _traffic 。

Step S405, according to the motion parameters of each peripheral vehicle, the motion parameters of the target vehicle and the historical traffic accident rate, a candidate risk vehicle set corresponding to each risk measurement angle is screened out from the peripheral vehicle set.

In a possible embodiment, step S405 may specifically include the following steps:

Step one: and determining the lower limit of the number of the risk vehicles which can be fed back to the target vehicle according to the historical traffic accident rate and the number of the surrounding vehicles.

Wherein, the method of determining the lower limit of the number of risk vehicles that can be fed back to the target vehicle may include: according to historyDetermining the traffic accident rate of the non-road traffic accident; determining a lower limit x of the number of risk vehicles which can be fed back to the target vehicle according to the non-road traffic accident rate and the number of surrounding vehicles, wherein the ratio of the lower limit x of the number of risk vehicles to the number n of surrounding vehicles is not less than the non-road traffic accident rate, and the non-road traffic accident rate can be expressed as 1-P _traffic 。

The lower limit x of the number of the risk vehicles meets the condition that x/n is more than or equal to 1-P _traffic . If x satisfies x/n.gtoreq.1-P _traffic Representing a ratio between the number of nearby vehicles issued to the target vehicle and the total number of nearby vehicles in the collection of nearby vehicles of not less than 1-P _traffic The number of nearby vehicles that are not issued to the target vehicle and the total number of nearby vehicles in the collection of nearby vehicles do not exceed the historical traffic accident rate P _traffic . So configured, it is based primarily on the following considerations: if the ratio of the other surrounding vehicle information amount not acquired by the target vehicle to the total vehicle information amount exceeds the historical traffic accident rate P _traffic The target vehicle may be unfavorable for improving driving safety due to lack of surrounding vehicle information, and thus unfavorable for reducing the existing traffic accident rate P _traffic 。

Step two: for any risk measurement angle, determining priority ranking of collision risks brought to the target vehicle by all surrounding vehicles from the risk measurement angle according to the motion parameters of all surrounding vehicles and the motion parameters of the target vehicle; and determining a set of candidate risk vehicles corresponding to each risk metric angle based on the priority ranks corresponding to each surrounding vehicle and the lower limit of the number of risk vehicles.

In particular, when the risk measure angle is the vehicle position, a candidate vehicle set corresponding to the vehicle position may be constructed according to the steps shown in fig. 5. Comprising the following steps:

s501, calculating the distance between each surrounding vehicle and the target vehicle according to the current vehicle position of each surrounding vehicle and the current vehicle position of the target vehicle.

S503, sorting the priority of collision risks of all the surrounding vehicles based on the distance between the surrounding vehicles and the target vehicle. Specifically, the driving distances corresponding to the surrounding vehicles can be ranked from large to small.

S505, constructing a candidate vehicle set corresponding to the vehicle position according to the top k surrounding vehicles ranked in front, wherein k is not smaller than the lower limit of the number of the risk vehicles and not larger than the total number of the surrounding vehicles.

When the risk metric angle is vehicle speed, a set of candidate vehicles corresponding to the vehicle speed may be constructed in accordance with the steps shown in fig. 6. Comprising the following steps:

s601, dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle.

Wherein the set of front vehicles includes: a nearby vehicle that is in front of the target vehicle in the target vehicle traveling direction, or a nearby vehicle that is in front of the target vehicle in the target vehicle traveling direction and that is side by side with the target vehicle. When the nearby vehicle is classified, the nearby vehicle that is alongside the target vehicle may be classified into a front vehicle set or a rear vehicle set.

S603, for the two vehicle sets obtained in step S601, it is determined whether any one of the two vehicle sets is a front vehicle set, if it is a front vehicle set, the process goes to step S605, and if it is not a front vehicle set, it is determined that it is a rear vehicle set, and the process goes to step S607.

S605, for the front vehicle set, sorting the priority of collision risks of the surrounding vehicles according to the speed difference between the speed of the target vehicle and the speed of each surrounding vehicle in the front vehicle set; the k1 surrounding vehicles ranked first are selected to form a first candidate vehicle set corresponding to the vehicle speed.

Specifically, a vehicle speed difference between a vehicle speed of a target vehicle and a vehicle speed of each peripheral vehicle in a front vehicle set can be calculated, and a first vehicle speed difference corresponding to each peripheral vehicle is obtained; the first vehicle speed differences of all surrounding vehicles are ranked from big to small, and k1 surrounding vehicles ranked in front are selected to form a first candidate vehicle set corresponding to the vehicle speed.

S607, for the rear vehicle set, sorting the priority of collision risk of the surrounding vehicles according to the speed difference between the speed of each surrounding vehicle in the rear vehicle set and the speed of the target vehicle; the k2 surrounding vehicles ranked first are selected to form a second candidate vehicle set corresponding to the vehicle speed.

Specifically, a vehicle speed difference between the vehicle speed of each peripheral vehicle in the rear vehicle set and the vehicle speed of the target vehicle may be calculated, and a second vehicle speed difference corresponding to each peripheral vehicle may be obtained; sorting the second vehicle speed differences of the surrounding vehicles from big to small; the k2 surrounding vehicles ranked first are selected to constitute a second set of candidate vehicles corresponding to vehicle speeds.

Wherein the sum of k1 and k2 simultaneously satisfies the condition: 1) Not less than the lower limit of the number of risk vehicles, 2) not more than the total number of surrounding vehicles.

S609, constructing a candidate vehicle set corresponding to the vehicle speed according to the first candidate vehicle set obtained in step S605 and the second candidate vehicle set obtained in step S607.

Specifically, the union of the first candidate vehicle set and the second candidate vehicle set is taken as a candidate vehicle set corresponding to the vehicle speed.

When the risk metric angle is the vehicle traveling direction, a set of candidate vehicles corresponding to the vehicle traveling direction may be constructed in accordance with the steps shown in fig. 7. Comprising the following steps:

s701, dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle.

The front-rear vehicle division may be specifically performed in combination with the traveling directions and the vehicle positions of the target vehicle and the surrounding vehicles that travel in the same direction as the target vehicle and that are located before the vehicle position of the target vehicle are divided into a front vehicle set, the surrounding vehicles that travel in the same direction as the target vehicle and that are located after the vehicle position of the target vehicle and that travel side by side with the target vehicle are classified into a rear vehicle set, or the surrounding vehicles that travel in the same direction as the target vehicle and that are located before the vehicle position of the target vehicle and that travel side by side with the target vehicle are divided into a front vehicle set, and the surrounding vehicles that travel in the same direction as the target vehicle and that are located after the vehicle position of the target vehicle are classified into a rear vehicle set.

S703, for the two vehicle sets obtained in step S701, it is determined whether any one of the two vehicle sets is a front vehicle set, and if the front vehicle set is the front vehicle set, the process goes to step S705, and if the front vehicle set is not the front vehicle set, it is determined that the front vehicle set is a rear vehicle set, and the process goes to step S707.

S705, for the set of front vehicles, determining a first direction angle formed between a line connecting each of the peripheral vehicles and the target vehicle and a lane line of a road on which the target vehicle is located, and a second direction angle formed between a traveling direction of the target vehicle and a traveling direction of each of the peripheral vehicles; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, the priority of collision risk of each peripheral vehicle is ordered; the k3 surrounding vehicles ranked first are selected to form a third candidate vehicle set corresponding to the vehicle speed.

In one possible embodiment, according to the current vehicle position of the target vehicle and each peripheral vehicle, determining a first direction angle corresponding to each peripheral vehicle in the front vehicle set, where the first direction angle is an included angle formed between a connecting line of the peripheral vehicle and the target vehicle and a lane line of a road where the target vehicle is located; determining a second direction angle corresponding to each peripheral vehicle in the front vehicle set according to the running direction of the target vehicle and the running direction of each peripheral vehicle, wherein the second direction angle is an included angle formed between the running direction of the target vehicle and the running direction of each peripheral vehicle; calculating a first angle difference between a first direction angle and a second direction angle corresponding to each peripheral vehicle, and sequencing each peripheral vehicle from small to large according to the first angle difference; the k3 nearby vehicles ranked first are selected to constitute a third set of candidate vehicles corresponding to vehicle speed.

S707 of determining, for the set of rear vehicles, a third direction angle formed between a line connecting each of the peripheral vehicles and the target vehicle and a lane line of a road on which the target vehicle is located, and a fourth direction angle formed between a traveling direction of each of the peripheral vehicles and a traveling direction of the target vehicle; according to the angle difference between the third direction angle and the fourth direction angle corresponding to each peripheral vehicle, the priority of collision risk of each peripheral vehicle is ordered; the k4 surrounding vehicles ranked first are selected to form a fourth candidate vehicle set corresponding to the vehicle speed.

In one possible embodiment, according to the current vehicle positions of the target vehicle and each peripheral vehicle, determining a third direction angle corresponding to each peripheral vehicle in the rear vehicle set, wherein the third direction angle is an included angle formed between a connecting line of the peripheral vehicle and the target vehicle and a lane line of a road on which the target vehicle is located; determining a fourth direction angle corresponding to each peripheral vehicle in the rear vehicle set according to the running direction of the target vehicle and the running direction of each peripheral vehicle, wherein the fourth direction angle is an included angle formed between the running direction of the peripheral vehicle and the running direction of the target vehicle; calculating a second angle difference between a third direction angle and a fourth direction angle corresponding to each peripheral vehicle in the rear vehicle set, and sequencing each peripheral vehicle from small to large according to the second angle difference; the k4 surrounding vehicles ranked first are selected to form a fourth candidate vehicle set corresponding to the vehicle speed.

Wherein the sum of k3 and k4 satisfies the condition: 1) Not less than a lower limit of the number of the risk vehicles, 2) not more than a total number of the surrounding vehicles.

S709, constructing a candidate vehicle set corresponding to the vehicle traveling direction according to the third candidate vehicle set obtained in step S705 and the fourth candidate vehicle set obtained in step S707.

Specifically, the union of the third candidate vehicle set and the fourth candidate vehicle set is taken as the candidate vehicle set corresponding to the vehicle traveling direction.

When the risk measurement angle is the driving violation duty ratio, if the driving violation duty ratio corresponding to the surrounding vehicle is higher than the sum of the driving violation duty ratios corresponding to the target vehicle, the collision risk between the target vehicle and the surrounding vehicle is larger. Specifically, the set of candidate vehicles corresponding to the risk metric angle may be constructed according to the following steps. Comprising the following steps:

obtaining the driving violation duty ratio of each surrounding vehicle; based on the driving violation duty ratio of each peripheral vehicle, sequencing the driving violation duty ratio of each peripheral vehicle from big to small; and constructing a candidate vehicle set corresponding to the vehicle position according to the top k surrounding vehicles which are ranked in front, wherein k is not less than the lower limit of the number of the risk vehicles and not more than the total number of the surrounding vehicles.

Step S407, determining the risk vehicle sets according to the surrounding vehicles simultaneously existing in each candidate risk vehicle set.

In the embodiment of the invention, the vehicle representatives in the candidate risk vehicle sets bring higher collision risk to the target vehicle from the corresponding risk measurement angles, and surrounding vehicles simultaneously existing in each candidate risk vehicle set form the risk vehicle set, so that the surrounding vehicles in the risk vehicle set are vehicles which bring higher collision risk to the target vehicle from each risk measurement angle.

Step S305: and determining associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle.

The associated data corresponding to the risk vehicle set refers to vehicle information and vehicle control information of each risk vehicle in the risk vehicle set. The vehicle information includes, but is not limited to, a speed of the vehicle, an acceleration of the vehicle, a type of the vehicle, failure information of the vehicle, an engine oil usage of the vehicle, and the like. The vehicle control information specifically refers to related information of a control system of the vehicle, the control system of the vehicle refers to a driver for driving a person, the vehicle control information refers to driving age, driving qualification, violation data and the like of the driver, the control system of the vehicle refers to the driver and the semi-automatic driving system for semi-automatic driving, the violation data in the vehicle control information refers to the sum of failure rate of the semi-automatic driving system and violation data corresponding to the driver, the control system of the vehicle refers to the automatic driving system for full-automatic driving, and the violation data in the vehicle control information refers to failure rate of the automatic driving system.

Step S307: and issuing the associated data corresponding to the risk vehicle set to the target vehicle.

Step S309: and constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating a risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

In the embodiment of the invention, the vehicle networking cloud platform determines each vehicle slice associated with collision risk and issues the vehicle slices to the target vehicle, and specifically, the vehicle networking cloud platform evaluates the collision risk brought by each peripheral vehicle to the target vehicle from the preset risk measurement angles and determines a candidate vehicle set { S } ₁ ,S ₂ ,...S _n Each subset in the candidate vehicle set is a candidate risk vehicle set corresponding to a risk measurement angle, then the intersection among the subsets in the candidate vehicle set is extracted to obtain a risk vehicle set, and the risk vehicle set is recorded as S _final,1 . Set S _final,1 And issuing the associated data of the first slice to the target vehicle for the first slice. Will S _final,1 Is removed from the set of nearby vehicles within the driving safety distance of the target vehicle (where n becomes n- |S) _final,1 I), and returns to step S301 to obtain a second vehicle slice, and so on, one vehicle slice is obtained per round of data processing. Considering that the collision risks brought by the vehicle slices obtained through data processing of all wheels are different in significance, the collision risk brought by the vehicle slices to the target vehicle is more significant, so that the vehicle networking cloud platform firstly transmits the association information of the first vehicle slice to the target vehicle, then transmits the association information of the second vehicle slice until the association information of the last vehicle slice is completed, the delay of the association information of the first vehicle slice is small, the available network transmission resources are more sufficient than the information transmitted later, and the target vehicle can quickly acquire the collision risk of the vehicleThe vehicle forms the information of the vehicle with safety threat, which is beneficial to improving the driving safety.

Where a vehicle slice represents multi-vehicle information that slices multiple frames of vehicle information into a group, rather than one-by-one, i.e., vehicles are delivered in aggregate. For example, there are 4 frames of vehicle information, the first two frames of vehicle information are regarded as one group (vehicle set), the second two frames of vehicle information are cut into one group (vehicle set), and the vehicle information is not transmitted frame by frame. One frame of vehicle information indicates movement state information of the vehicle at a certain time.

It should be noted that, in the above embodiment, the relevant information of the vehicles in the risk vehicle set is issued to the target vehicle immediately after the risk vehicle set is obtained by screening, and the priority of the risk vehicle set obtained by each round of data processing is not required to be ordered, but the automatic issuing of the surrounding vehicle information to the target vehicle according to the priority is realized.

As an extension of the solution of the present invention, in another embodiment, the driving risk prompting method may further include: s11: determining surrounding vehicles within a driving safety distance of the target vehicle to obtain a surrounding vehicle set; s13: performing data processing on the surrounding vehicle set, and screening a risk vehicle set from the surrounding vehicle set; updating the surrounding vehicle set according to the information except the risk vehicle set in the surrounding vehicle set, and returning to the step of executing data processing on the surrounding vehicle set to generate a risk vehicle set corresponding to each round of data processing; s15: according to the generation time sequence of the risk vehicle sets, carrying out risk level sequencing on each risk vehicle set obtained through processing; s16: determining associated data corresponding to each risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of surrounding vehicles; s17: and sequentially issuing the associated data of each risk vehicle set to the target vehicle according to the risk grade corresponding to the risk vehicle set.

Wherein, S17 may specifically include: sequencing each risk vehicle set according to the generation time sequence of each risk vehicle set, wherein the risk grade of each risk vehicle set is in direct proportion to the generation time sequence; and sequentially issuing the vehicle information of all surrounding vehicles in the risk vehicle set to the target vehicle according to the order of the risk level from high to low.

The specific means for data processing in step S13 in this embodiment is the same as that of the above embodiment, and please refer to the above embodiment specifically.

An embodiment of the present invention provides a driving risk prompting method, fig. 8 shows an optional hardware platform for implementing the driving risk prompting method, please refer to fig. 8, a vehicle requests related information of surrounding vehicles to a vehicle networking cloud platform 840, the vehicle networking cloud platform 840 selectively issues information of the vehicles around the requested vehicle to the requested vehicle according to a vehicle motion state, a high-precision positioning device 810 may be used to determine the positions of the requested vehicle and surrounding vehicles, a road perception fusion device 830 may be used to obtain motion state information of each vehicle, and a traffic management department cloud platform 820 is used to provide a historical traffic accident rate.

In the application scenario of fig. 8, the specific implementation process and effect of the driving risk prompting method will be described by taking the preset risk measurement angle as an example of the position, the speed and the running direction of the vehicle.

The implementation targets are as follows: and counting the ratio of the number of vehicles successfully issued to the host vehicle, wherein the number of vehicles with high collision risk is counted in the process of issuing the surrounding vehicle information to the host vehicle by adopting the prior art and the method provided by the application.

The implementation steps are as follows:

(1) And the vehicle networking cloud platform acquires the motion parameters of the vehicle and the vehicle within the road driving safety distance of the vehicle.

The vehicle acquires a GPS (global positioning system) of the vehicle through the GPS device in the vehicle, and reports the GPS to the cloud platform of the vehicle networking. The cloud platform of the internet of vehicles detects the speed and GPS of vehicles within the driving safety distance of the road where the vehicle is located based on the GPS reported by the vehicles through the road test detection device. Respectively recording the vehicle speed at the current time t as v _1,t ,v _2,t ,...,v _n,t GPS is p _1,t ,p _2,t ,...,p _n,t The running direction is theta _1,t ,θ _2,t ,...,θ _n,t The method comprises the steps of carrying out a first treatment on the surface of the The vehicle is obtained from the device in the vehicleThe speed of the vehicle at the current time t, GPS and the running direction are respectively recorded as v _0,t ,p _0,t ,θ _0,t Reporting the cloud information to an Internet of vehicles cloud platform; where n is the number of vehicles other than the host vehicle within the host vehicle driving safety distance.

(2) The internet of vehicles cloud platform determines which vehicles pose a significant risk of collision to the host vehicle from a location perspective.

The cloud platform of the internet of vehicles acquires the historical traffic accident rate of the road section where the vehicle is currently located from the traffic management department and marks the historical traffic accident rate as p _traffic The method comprises the steps of carrying out a first treatment on the surface of the The risk of collision between vehicles is greater if the distance between the vehicles is smaller, i.e., the risk of collision between vehicles is inversely proportional to the distance between the vehicles. Because the storage resources, the computing resources, the network transmission resources and the like of the internet of vehicles cloud platform are limited, the internet of vehicles cloud platform cannot issue the motion state information of all vehicles to the vehicle, and only part of vehicles can be issued. In order to ensure that the issued vehicle can assist the host vehicle in improving driving safety, the probability of issuing vehicle information far away from the host vehicle should be smaller than that of issuing vehicle close to the host vehicle. Thus, according to the vehicle position p _0,t And a position p of the vehicle within a driving safety distance _1,t ,p _2,t ,...,p _n,t Calculating the distance between the vehicle and the vehicles, respectively denoted as s _1,t ,s _2,t ,...,s _n,t The method comprises the steps of carrying out a first treatment on the surface of the Then the first k are selected from small to large, so that k/n is more than or equal to 1-p _traffic This set of vehicles is referred to as the first set, denoted S ₁ 。

(3) The cloud platform of the internet of vehicles determines which vehicles bring significant collision risks to the vehicle from the angle of the speed of the vehicle.

The vehicle networking cloud platform divides the vehicle within the driving safety distance into two parts, wherein one part of the vehicle is positioned in front of the vehicle and is respectively recorded asThe other part is positioned behind the vehicle and is respectively marked as +.>For the vehicle in front of the host vehicle, if The greater the difference in vehicle speed between the host vehicle and the preceding vehicle, the greater the risk of collision between the vehicles, i.e., the risk of collision between the vehicles is proportional to the difference in vehicle speed between the host vehicle and the preceding vehicle. For a vehicle behind the host vehicle, if the difference in vehicle speed between the host vehicle and the rear vehicle is larger, the risk of collision between the vehicles may be larger, i.e., the risk of collision between the vehicles is proportional to the difference in vehicle speed between the host vehicle and the rear vehicle. Thus, according to the own vehicle speed v _0,t And the speed v of the front vehicle of the host vehicle _1,t ,v _2,t ,...,v _n,t Calculating the speed difference between the vehicle and the front vehicle, which are respectively marked as +.>According to the speed v of the vehicle _0,t And the speed of the rear vehicle of the vehicle +.>Calculating the speed difference between the rear vehicle and the own vehicle, which are respectively marked as +.>For the front vehicle of the own vehicle, from the determinedMiddle from large to small before k ₁ For the rear vehicle of the own vehicle, the following is determined from +.>In from big to small, select the previous k ₂ Satisfy k ₁ +k ₂ =k and k/n+.1-p _traffic . This set of vehicles is referred to as the second set, denoted S ₂ 。

(4) The internet of vehicles cloud platform determines which vehicles pose significant risks to the host vehicle from a direction of travel perspective.

Vehicle networking cloud platform is according to car position p _0,t And a position p of the vehicle within a driving safety distance _1,t ,p _2,t ,...,p _n,t Determining the included angle (acute angle) between the connecting line between the vehicle and the vehicles and the lane line, and respectively marking as alpha _1,t ,α _2,t ,...,α _n,t Note that the direction corresponding to the front car of the car isThe direction corresponding to the rear vehicle of the vehicle isThe cloud platform of the internet of vehicles is based on the running direction theta of the vehicle _0,t And the driving direction of the front vehicle->Determining the angle difference between the running direction of the vehicle and the running direction of the preceding vehicle, and respectively marking the angle difference as +.>According to the traveling direction theta of the vehicle _0,t And the driving direction of the rear vehicle->Determining the angle difference between the running directions of the rear vehicle and the own vehicle, and respectively recording asFor the front vehicle, if->Respectively and->Differences betweenThe smaller the risk of collision between the preceding vehicle and the host vehicle is, the greater, for the following vehicle, if +.>Respectively and->Difference between->The smaller the risk of collision between the rear vehicle and the host vehicle is, the greater. Thus, from->Medium to large selection k ₃ From->Medium to large selection k ₄ Satisfying the requirement of k ₃ + _k 4=k and k/n is not less than 1-p _traffic The method comprises the steps of carrying out a first treatment on the surface of the This set of vehicles is referred to as the third set, denoted S ₃ 。

(5) The cloud platform of the internet of vehicles determines vehicles with larger collision risk with the vehicle (the vehicle information is the information which should be issued to the vehicle): determining a first vehicle set S by using a cloud platform of the Internet of vehicles ₁ Second vehicle set S ₂ Third set of vehicles S ₃ The intersection of (a) and (b), namely the vehicle set which can bring significant collision risk to the vehicle from the angles of position, speed and running direction, is marked as S _final,1 . And transmitting the information of the vehicles to the host vehicle. Vehicle set S _final,1 Is the first slice. Next, set S _final,1 Is removed from the vehicle collection within the safe distance of the driver (at this time, n becomes n-S) _final,1 I), and returning to the step 1 to obtain a second vehicle slice, and so on to obtain each vehicle slice. The cloud platform of the internet of vehicles firstly issues a first vehicle slice to the vehicle, and then issues a second vehicle slice until all vehicle slices are issued.

The scheme of the application is applied to a simulator, and vehicles enter the intersection where the traffic lights are positioned in fig. 8 in different driving states. And counting the ratio of the number of vehicles successfully issued to the host vehicle, wherein the number of vehicles with high collision risk is counted in the process of issuing the surrounding vehicle information to the host vehicle by adopting the prior art and the method provided by the application. Comparing the prior art with the performance of the present application, it is apparent that the performance of the present application is superior to the prior art.

Table 1 experimental results

The embodiment of the application also discloses a driving risk prompting device, as shown in fig. 9, the driving risk prompting device 900 includes: a determining unit 910 for determining a surrounding vehicle within a driving safety distance of the target vehicle, obtaining a surrounding vehicle set; a processing unit 920, configured to perform data processing on the surrounding vehicle set, and screen a risk vehicle set from the surrounding vehicle set, where risk vehicles in the risk vehicle set are vehicles that may bring collision risk to the target vehicle from preset risk measurement angles; an obtaining unit 930, configured to obtain association data corresponding to the risk vehicle set, where the association data includes vehicle information and vehicle control information of the risk vehicle; a sending unit 940, configured to send the association data corresponding to the risk vehicle set to the target vehicle; and an updating unit 950, configured to construct a new surrounding vehicle set according to data except the risk vehicle in the surrounding vehicle set, trigger the data processing unit to perform data processing on the constructed surrounding vehicle set, generate a risk vehicle set corresponding to each round of data processing, and sequentially issue associated data corresponding to each risk vehicle set to the target vehicle.

Wherein, the processing unit 920 may further include: the first acquisition module is used for acquiring motion parameters of the target vehicle and the surrounding vehicles, which correspond to the risk measurement angles; the second acquisition module is used for acquiring the historical traffic accident rate of the road section where the target vehicle is currently located; the screening module is used for screening candidate risk vehicle sets corresponding to each risk measurement angle from the surrounding vehicle sets according to the motion parameters of each surrounding vehicle, the motion parameters of the target vehicle and the historical traffic accident rate; and the determining module is used for determining the risk vehicle sets according to the surrounding vehicles which are simultaneously in each candidate risk vehicle set.

Further, the screening module specifically includes: a first determining submodule, configured to determine a lower limit of the number of risk vehicles that can be fed back to the target vehicle according to the historical traffic accident rate and the number of surrounding vehicles; the second determining submodule is used for determining priority ordering of collision risks of all surrounding vehicles for the target vehicle from any risk measurement angle according to the motion parameters of all the surrounding vehicles and the motion parameters of the target vehicle; and determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranks corresponding to the surrounding vehicles and the lower limit of the number of the risk vehicles.

In a possible embodiment, the first determination submodule is further configured to: determining a non-road traffic accident rate according to the historical traffic accident rate; and determining a lower limit of the number of the risk vehicles which can be fed back to the target vehicle according to the non-road traffic accident rate and the number of the surrounding vehicles, wherein the ratio of the lower limit of the number of the risk vehicles to the number of the surrounding vehicles is not smaller than the non-road traffic accident rate.

In one possible embodiment, the second determination submodule may be configured to: calculating the distance between each peripheral vehicle and the target vehicle according to the current vehicle position of each peripheral vehicle and the current vehicle position of the target vehicle; prioritizing collision risk of each of the nearby vehicles based on a distance between each of the nearby vehicles and the target vehicle; and constructing a candidate vehicle set corresponding to the vehicle position according to the top k surrounding vehicles which are ranked in front, wherein k is not less than the lower limit of the number of the risk vehicles and not more than the total number of the surrounding vehicles.

In one possible embodiment, the second determination submodule may be configured to: dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle; for the front vehicle set, prioritizing collision risk of the nearby vehicles according to a vehicle speed difference between a vehicle speed of the target vehicle and a vehicle speed of each of the nearby vehicles in the front vehicle set; selecting k1 surrounding vehicles ranked in front to form a first candidate vehicle set corresponding to the vehicle speed; for the set of rear vehicles, prioritizing collision risk of each of the surrounding vehicles according to a vehicle speed difference between a vehicle speed of the surrounding vehicle and a vehicle speed of the target vehicle in the set of rear vehicles; selecting k2 surrounding vehicles ranked in front to form a second candidate vehicle set corresponding to the vehicle speed; constructing a candidate vehicle set corresponding to the vehicle speed according to the first candidate vehicle set and the second candidate vehicle set; wherein the sum of k1 and k2 satisfies a lower limit of the number of the risk vehicles and is not greater than the total number of the surrounding vehicles.

In a possible embodiment, the second determining submodule may be further configured to: dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle; for the front vehicle set, determining a first direction angle formed between a connecting line of each of the peripheral vehicles and the target vehicle and a lane line of a road on which the target vehicle is located, and a second direction angle formed between a traveling direction of the target vehicle and a traveling direction of each of the peripheral vehicles; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, the priority of collision risk of each peripheral vehicle is ordered; selecting k3 surrounding vehicles ranked in front to form a third candidate vehicle set corresponding to the vehicle speed; for the rear vehicle set, determining a third direction angle formed between a connecting line of each peripheral vehicle and the target vehicle and a lane line of a road on which the target vehicle is located, and a fourth direction angle formed between a traveling direction of each peripheral vehicle and a traveling direction of the target vehicle; according to the angle difference between the third direction angle and the fourth direction angle corresponding to each peripheral vehicle, the priority of collision risk of each peripheral vehicle is ordered; selecting k4 surrounding vehicles ranked in front to form a fourth candidate vehicle set corresponding to the vehicle speed; constructing a candidate vehicle set corresponding to the vehicle running direction according to the third candidate vehicle set and the fourth candidate vehicle set; wherein the sum of k3 and k4 satisfies a lower limit of the number of the risk vehicles and is not greater than the total number of the surrounding vehicles.

According to the method, a surrounding vehicle set is built according to surrounding vehicles within a driving safety distance of a target vehicle, differences of collision risks brought to the target vehicle by different surrounding vehicles are considered, surrounding vehicles with higher collision possibility to the target vehicle in each risk measurement angle in the surrounding vehicle set are used as risk vehicles, data processing of the surrounding vehicle set is completed, then the risk vehicles are removed from the surrounding vehicle set to obtain an updated surrounding vehicle set, data processing is carried out on the updated surrounding vehicle set, each round of data processing can obtain a risk vehicle set consisting of the risk vehicles, and vehicle information and vehicle control information of the risk vehicles in the risk vehicle set are returned to the target vehicle once each risk vehicle set is obtained. In the embodiment of the invention, the risk priority of the risk vehicle set obtained by processing the data of each wheel corresponds to the generation time sequence of the risk vehicle set, the higher the possibility that the risk vehicle set generates the time sequence and contains the risk vehicle to collide with the target vehicle, the lower the possibility that the risk vehicle set generates the time sequence and contains the risk vehicle to collide with the target vehicle, the risk vehicle set generated by processing each theory of data is sequentially fed back to the target vehicle, so that the vehicle information is issued to the target vehicle from high to low according to the collision risk of the surrounding vehicles to the target vehicle, the target vehicle can quickly obtain effective information, and the driving safety is facilitated. The technical problem that the early warning accuracy of the existing driving risk early warning scheme is low is solved.

Specifically, the driving risk prompting device and the driving risk prompting method according to the embodiments of the present invention are all based on the same inventive concept. Please refer to the method embodiment for details, which will not be described herein.

The embodiment of the invention provides electronic equipment, which comprises a processor and a memory, wherein at least one instruction and at least one section of program are stored in the memory, and the at least one instruction or the at least one section of program is loaded and executed by the processor to realize a driving risk prompting method corresponding to the method shown in fig. 3-8.

The memory may be used to store software programs and modules that the processor executes to perform various functional applications and data processing by executing the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for functions, and the like; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide access to the memory by the processor.

Embodiments of the present invention also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the driving risk prompting method provided in the above-described various alternative implementations. The method at least comprises the following steps:

determining surrounding vehicles within a driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the surrounding vehicle set, and screening a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles; determining associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle; issuing associated data corresponding to the risk vehicle set to the target vehicle; and constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating a risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

Further, fig. 10 shows a schematic diagram of a hardware structure of an apparatus for implementing the method provided by the embodiment of the present application, where the apparatus may participate in forming or including the device or the system provided by the embodiment of the present application. As shown in fig. 10, the apparatus 10 may include one or more processors 102 (shown as 102a, 102b, … …,102 n) that may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 10 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the device 10 may also include more or fewer components than shown in fig. 10, or have a different configuration than shown in fig. 10.

It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Further, the data processing circuitry may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the device 10 (or mobile device). As referred to in embodiments of the application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination connected to the interface).

The memory 104 may be used to store software programs and modules of application software, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, i.e., implement a driving risk prompting method as described above, according to the program instructions/data storage device corresponding to the method in the embodiment of the present invention. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory located remotely from processor 102, which may be connected to device 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission means 106 is arranged to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communications provider of device 10. In one example, the transmission device 106 includes a network adapter (NetworkInterfaceController, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a radio frequency (RadioFrequency, RF) module for communicating wirelessly with the internet.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the device 10 (or mobile device).

It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device and server embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and references to the parts of the description of the method embodiments are only required.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

1. A driving risk prompting method, characterized by comprising:

determining surrounding vehicles within a driving safety distance of the target vehicle to obtain a surrounding vehicle set;

acquiring motion parameters of the target vehicle and the surrounding vehicles corresponding to each risk measurement angle;

acquiring a historical traffic accident rate of a road section where the target vehicle is currently located;

determining a non-road traffic accident rate according to the historical traffic accident rate;

determining a lower limit of the number of risk vehicles which can be fed back to a target vehicle according to the non-road traffic accident rate and the number of surrounding vehicles, wherein the ratio between the lower limit of the number of risk vehicles and the number of surrounding vehicles is not less than the non-road traffic accident rate;

For any risk measurement angle, determining priority ranking of collision risks brought by all surrounding vehicles to the target vehicle from the risk measurement angle according to the motion parameters of all surrounding vehicles and the motion parameters of the target vehicle; determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranks corresponding to the surrounding vehicles and the lower limit of the number of the risk vehicles;

determining a risk vehicle set according to surrounding vehicles which exist in each candidate risk vehicle set at the same time, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles;

determining associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle, and the vehicle control information refers to related information of a control system of the vehicle;

issuing associated data corresponding to the risk vehicle set to the target vehicle;

and constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating a risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

2. The method of claim 1, wherein when the risk metric angle is a vehicle position, the determining, according to the motion parameter of each peripheral vehicle and the motion parameter of the target vehicle, a priority ranking of each peripheral vehicle from the risk metric angle for bringing the target vehicle with collision risk; and determining a set of candidate risk vehicles corresponding to each risk metric angle based on the priority ranks corresponding to each surrounding vehicle and the lower limit of the number of risk vehicles, comprising:

calculating the distance between each peripheral vehicle and the target vehicle according to the current vehicle position of each peripheral vehicle and the current vehicle position of the target vehicle;

the method comprises the steps of sorting the collision risk of each peripheral vehicle according to the sorting result from big to small based on the distance between each peripheral vehicle and the target vehicle;

and constructing a candidate vehicle set corresponding to the vehicle position according to the top k surrounding vehicles which are ranked in front, wherein k is not less than the lower limit of the number of the risk vehicles and not more than the total number of the surrounding vehicles.

3. The method of claim 1, wherein when the risk metric angle is vehicle speed, the determining, based on the motion parameters of each peripheral vehicle and the motion parameters of the target vehicle, a priority ranking of each peripheral vehicle from the risk metric angle for collision risk to the target vehicle; and determining a set of candidate risk vehicles corresponding to each risk metric angle based on the priority ranks corresponding to each surrounding vehicle and the lower limit of the number of risk vehicles, comprising:

Dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle;

for the front vehicle set, sorting the priority of collision risk of the surrounding vehicles according to the result of sorting from big to small according to the speed difference between the speed of the target vehicle and the speed of each surrounding vehicle in the front vehicle set; selecting k1 surrounding vehicles ranked in front to form a first candidate vehicle set corresponding to the vehicle speed;

for the rear vehicle set, sorting the priority of collision risk of the surrounding vehicles according to the result of sorting from big to small according to the speed difference between the speed of each surrounding vehicle and the speed of the target vehicle in the rear vehicle set; selecting k2 surrounding vehicles ranked in front to form a second candidate vehicle set corresponding to the vehicle speed;

constructing a candidate vehicle set corresponding to the vehicle speed according to the first candidate vehicle set and the second candidate vehicle set;

wherein the sum of k1 and k2 satisfies a lower limit of the number of the risk vehicles and is not greater than the total number of the surrounding vehicles.

4. The method according to claim 1, wherein when the risk measure angle term is a vehicle traveling direction, determining a priority ranking of each peripheral vehicle for bringing a collision risk to the target vehicle from the risk measure angle according to a motion parameter of each peripheral vehicle and a motion parameter of the target vehicle; and determining a set of candidate risk vehicles corresponding to each risk metric angle based on the priority ranks corresponding to each surrounding vehicle and the lower limit of the number of risk vehicles, comprising:

for the front vehicle set, determining a first direction angle formed between a connecting line of each of the peripheral vehicles and the target vehicle and a lane line of a road on which the target vehicle is located, and a second direction angle formed between a traveling direction of the target vehicle and a traveling direction of each of the peripheral vehicles; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, according to the result of the sorting from small to large, sorting the priority of collision risk of each peripheral vehicle; selecting k3 surrounding vehicles ranked in front to form a third candidate vehicle set corresponding to the vehicle speed;

For the rear vehicle set, determining a third direction angle formed between a connecting line of each peripheral vehicle and the target vehicle and a lane line of a road on which the target vehicle is located, and a fourth direction angle formed between a traveling direction of each peripheral vehicle and a traveling direction of the target vehicle; according to the angle difference between the third direction angle and the fourth direction angle corresponding to each peripheral vehicle, according to the result of the sorting from small to large, sorting the priority of collision risk of each peripheral vehicle; selecting k4 surrounding vehicles ranked in front to form a fourth candidate vehicle set corresponding to the vehicle speed;

constructing a candidate vehicle set corresponding to the vehicle running direction according to the third candidate vehicle set and the fourth candidate vehicle set;

wherein the sum of k3 and k4 satisfies a lower limit of the number of the risk vehicles and is not greater than the total number of the surrounding vehicles.

5. A driving risk prompting device, characterized by comprising:

a determination unit configured to determine a surrounding vehicle within a driving safety distance of a target vehicle, and obtain a surrounding vehicle set;

the processing unit is used for acquiring motion parameters of the target vehicle and the surrounding vehicles, which correspond to the risk measurement angles; acquiring a historical traffic accident rate of a road section where the target vehicle is currently located; determining a non-road traffic accident rate according to the historical traffic accident rate; determining a lower limit of the number of risk vehicles which can be fed back to a target vehicle according to the non-road traffic accident rate and the number of surrounding vehicles, wherein the ratio between the lower limit of the number of risk vehicles and the number of surrounding vehicles is not less than the non-road traffic accident rate; for any risk measurement angle, determining priority ranking of collision risks brought by all surrounding vehicles to the target vehicle from the risk measurement angle according to the motion parameters of all surrounding vehicles and the motion parameters of the target vehicle; determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranks corresponding to the surrounding vehicles and the lower limit of the number of the risk vehicles; determining a risk vehicle set according to surrounding vehicles which exist in each candidate risk vehicle set at the same time, wherein the risk vehicles in the risk vehicle set are vehicles which bring collision risks to the target vehicle from preset risk measurement angles;

The acquiring unit is used for acquiring associated data corresponding to the risk vehicle set, wherein the associated data comprises vehicle information and vehicle control information of the risk vehicle, and the vehicle control information refers to related information of a control system of the vehicle;

the sending unit is used for sending the associated data corresponding to the risk vehicle set to the target vehicle;

and the updating unit is used for constructing a new surrounding vehicle set according to the data except the risk vehicles in the surrounding vehicle set, triggering the data processing unit to execute data processing on the constructed surrounding vehicle set, generating the risk vehicle set corresponding to each round of data processing, and sequentially issuing associated data corresponding to each risk vehicle set to the target vehicle.

6. A computer readable storage medium having stored therein at least one instruction or at least one program loaded and executed by a processor to implement a driving risk prompting method according to any of claims 1-4.

7. A computer device, characterized in that it comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the driving risk prompting method according to any one of claims 1 to 4.