CN112258838A

CN112258838A - Driving risk prompting method and device, storage medium and equipment

Info

Publication number: CN112258838A
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: 2021-01-22
Anticipated expiration: 2040-10-20
Also published as: CN112258838B

Abstract

The invention discloses a driving risk prompting method, a driving risk prompting device, a storage medium and equipment. Wherein, the method comprises the following steps: determining surrounding vehicles within the driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the peripheral vehicle set, and screening out a risk vehicle set from the peripheral vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which can bring collision risks to target vehicles from preset risk measurement angles; issuing the associated data corresponding to the risk vehicle set to the 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 wheel data processing, and issuing associated data corresponding to each risk vehicle set to the target vehicle in sequence. According to the scheme of the invention, 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 and 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 driving risk prompting storage medium and driving risk prompting equipment.

Background

In the running process of the vehicle, 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, mobile phone dialing and the like. In order to improve the driving safety of the vehicle, the information of the surrounding vehicles 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 a related driving risk early warning scheme, information of all vehicles in a certain range (such as a safe distance range) around a target vehicle is generally acquired at certain time intervals, and the acquired vehicles are considered to have collision risks with the target vehicle and then are all sent to the target vehicle.

However, in the driving risk early warning scheme provided by the related art, the information of all vehicles attached to the target vehicle is sent to the target vehicle, so that the difference of collision risks brought to the target vehicle by different vehicles is ignored, and the defect that the early warning accuracy is not high exists.

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

Disclosure of Invention

The embodiment of the invention provides a driving risk prompting method, a driving risk prompting device, a storage medium and equipment, and aims to at least solve the technical problem that the early warning accuracy of the existing driving risk early warning scheme is not high.

According to an aspect of an embodiment of the present invention, there is provided a driving risk notification method, including: determining surrounding vehicles within the driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the surrounding vehicle set, and screening out 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 comprise vehicle information and vehicle control information of the risk vehicles; issuing the 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 risky vehicle in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

According to another aspect of the embodiments of the present invention, there is also provided a driving risk prompting device, including: the determining unit is used for determining the surrounding vehicles within the driving safety distance of the target vehicle to obtain a surrounding vehicle set; the processing unit is used for performing data processing on the surrounding vehicle set and screening out a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which can 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 vehicles; the sending unit is used for issuing 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 data except the risky vehicle in the surrounding vehicle set, triggering the data processing unit to perform data processing on the constructed surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

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

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, the computer program, when executed by the processor, causing the processor to execute the driving risk prompting method described above.

In the embodiment of the invention, a peripheral vehicle set is constructed according to peripheral vehicles within the driving safety distance of a target vehicle, the difference of collision risks brought to the target vehicle by different peripheral vehicles is considered, the peripheral vehicles which can bring high collision possibility to the target vehicle at all risk measurement angles in the peripheral vehicle set are taken as risk vehicles, data processing on the peripheral vehicle set is completed, then the risk vehicles are removed from the peripheral vehicle set, an updated peripheral vehicle set is obtained, data processing is further performed on the updated peripheral 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 when each risk vehicle set is obtained. According to the invention, the risk priority of the risk vehicle set obtained by data processing of each wheel corresponds to the generation time sequence of the risk vehicle set, the risk vehicle set is generated at the front time sequence, the probability that the risk vehicle included in the risk vehicle set collides with the target vehicle is higher, the probability that the risk vehicle included in the risk vehicle set is generated at the rear time sequence, and the probability that the risk vehicle included in the risk vehicle set collides with the target vehicle is lower. The technical problem that the early warning accuracy of the existing driving risk early warning scheme is not high is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a schematic diagram of a hardware environment of a driving risk notification method according to an embodiment of the present invention;

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

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

FIG. 4 is a flow chart of an alternative method of screening a collection of at-risk vehicles in accordance with an embodiment of the present invention;

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

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 present invention;

FIG. 7 is a flowchart of a method of constructing a set of candidate vehicles corresponding to a direction of travel of a vehicle, according to an embodiment of the present invention;

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

fig. 9 is a schematic structural diagram of a driving risk presentation device according to an embodiment of the present invention;

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

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or 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, the vehicle-mounted device can acquire position information, driving speed, driving direction, video data and the like of the vehicle 10 in real time, and the vehicle-mounted device can communicate with the internet of vehicles cloud platform 20 based on a Browser/Server mode (Browser/Server, B/S) or a Client/Server mode (Client/Server, C/S) and report driving data such as real-time position, driving speed and driving direction to the internet of vehicles cloud platform 20. The drive test detection device is used for collecting road surface data in real time, communicates with the internet of vehicles cloud platform 30, can respond to a request that the internet of vehicles cloud platform obtains driving data of surrounding vehicles of the vehicle 10, obtains the speed, the driving direction, the GPS position and the like of each vehicle within a vehicle safety distance based on the position information of the vehicle 10, and reports the speed, the driving direction, the GPS position and the like to the internet of vehicles cloud platform 30. The traffic management department cloud platform 40 is in communication with the internet of vehicles cloud platform 30, and the internet of vehicles cloud platform 30 can acquire historical traffic accident rate of road sections and historical violation information of vehicles from the traffic management department cloud platform 40.

The car networking cloud platform 30 may include a database server and a service server, the service server is in communication connection with the database server, the database server may also be disposed inside the service server, the database server may be configured to store data content required by the service server, such as driving data reported by a vehicle-mounted device, data obtained by detection by the drive test detection device 20, and road section historical traffic accident rate and vehicle historical violation information provided by the traffic management department cloud platform 40, and the database server may interact with the service server, so that the service server may analyze and process various data provided by the vehicle-mounted device, the drive test detection device 20, and the traffic management department cloud platform 40, and evaluate collision risks that peripheral vehicles around any target vehicle may bring to the target vehicle, and screening out the risk vehicle sets causing larger collision risks to the target vehicles in batches, and issuing the associated information of each risk vehicle set to the target vehicles in batches according to the risk sizes.

The vehicle networking cloud platform 30 may comprise a server operating independently, or a distributed server, or a server cluster consisting of a plurality of servers.

The traffic authority cloud platform 40 may include a single server operating independently, or a distributed server, or a server cluster consisting of multiple servers. The traffic authority cloud platform 30 may include a network communication unit, a processor, and a memory, among others.

The in-vehicle apparatus may include: the system comprises an in-vehicle GPS device, a speed measuring device, a gyroscope, a camera, a vehicle data recorder, a smart phone, a tablet computer, a notebook computer, a digital assistant, a smart wearable device, a vehicle-mounted terminal, and other types of physical devices, and may also include software running in the physical devices, such as an application program.

The road test detection device may include a roadside camera, a monitor, a velocimeter, a radar detection device, etc. The road perception fusion device is a road detection device which integrates a plurality of detection means. The road perception fusion device is a double perception interaction of road external perception (perceiving external information such as vehicles and people) and internal perception (perceiving real service state and service performance of roads), and realizes human-vehicle-road cooperation. The external perception technology is a series of technologies mainly used for detecting vehicle information, and the technology mainly collects the vehicle information by setting a fixed detection device (various detection equipment and detection instruments) and comprises the following steps: coil detection, video detection, infrared detection, microblog detection and ultrasonic detection. The internal perception technology is mainly a series of technologies for detecting and monitoring the running state and the damage condition of the road structure facility. At present, the main detection technologies at home and abroad are classified according to technical principles, and comprise: ultrasonic detection technology, elastic wave detection technology, various ray detection technologies, optical fiber sensing detection technology, image recognition technology and piezoelectric sensing technology.

The car networking cloud platform in the scene of the driving risk prompting method related by the embodiment of the invention can be a data sharing system formed by connecting a plurality of nodes (any type of computing equipment in an access network, such as a server and a client) in a network communication mode.

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, the data sharing system may include a plurality of nodes 101, and the plurality of nodes 101 may refer to 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 information intercommunication in the data sharing system, information connection can exist between each node in the data sharing system, and information transmission can be carried out between the nodes through the information connection. For example, when an arbitrary 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 shared data, so that the data stored on all the nodes in the data sharing system are consistent.

Each node in the data sharing system has a node identifier corresponding thereto, and each node in the data sharing system may store a node identifier of another node in the data sharing system, so that the generated block is broadcast to the other node in the data sharing system according to the node identifier of the other node in the following. Each node may maintain a node identifier list as shown in the following table, and store the node name and the node identifier in the node identifier list correspondingly. The node identifier may be an IP (Internet Protocol) address and 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 invention. Referring to fig. 3, a flowchart of a driving risk prompting method, which may be implemented by using the car networking cloud platform in the implementation environment of fig. 1 as an execution subject, is shown, and the method may include the following steps.

Step S301: and determining the surrounding vehicles within the driving safety distance of the target vehicle, and obtaining a surrounding vehicle set.

The method provided by the embodiment of the invention takes the Internet of vehicles as a background, wherein the Internet of vehicles refers to a huge interactive network formed by information such as vehicle positions, speeds, routes and the like. The large-system network is an integrated network which can realize intelligent traffic management, intelligent dynamic information service and intelligent vehicle control 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 the driving risk prompt message. The driving risk prompt information is a pre-judgment result that the vehicle networking cloud platform sends related danger pre-judgment information to the target vehicle based on the fact that other vehicles around the target vehicle may cause collision to the target vehicle, so that the target vehicle can take driving operation behaviors avoiding danger in advance, and driving safety is guaranteed.

The Internet of vehicles cloud platform determines a driving safety distance according to the position information of the target vehicle and by combining the current speed of the target vehicle and/or the current road section where the target vehicle is located, a range is defined by taking the target vehicle as a circle center and taking the driving safety distance as a radius circle, other vehicles within the range except the target vehicle are taken as surrounding vehicles, and all the surrounding vehicles are collected to obtain a surrounding vehicle set. The driving safety distance refers to a necessary separation distance kept by a rear vehicle and a front vehicle during driving in order to avoid accidental collision with the front vehicle, traffic management departments set relevant regulations of safety vehicle distances according to different road sections and different vehicle speed ranges, during specific implementation, inquiry can be carried out according to the road section where a target vehicle is located currently, the safety vehicle distance obtained through inquiry is determined as the driving safety distance, inquiry can also be carried out according to the current vehicle speed of the target vehicle, the safety vehicle distance obtained through inquiry is used as the driving safety distance, and the larger value of the safety vehicle distance corresponding to the road section where the target vehicle is located currently and the safety vehicle distance corresponding to the current vehicle speed can also be used as the driving safety distance.

In a feasible embodiment, the target vehicle can acquire the position information of the target vehicle through the in-vehicle GPS device and report the position information to the internet-of-vehicle cloud platform, and the internet-of-vehicle cloud platform can detect the vehicle within the driving safety distance of the road where the target vehicle is located through the drive test detection device based on the position information reported by the target vehicle. Among them, the drive test detection device includes sensors, such as a camera, a radar, a laser, and an ultrasonic wave, installed near a road, which can obtain information of the road by detecting light, heat, pressure, or other variables. The drive test detection device can record the information of the road in real time, and can also record the information of the road at intervals of reference time, wherein the reference time can be set according to the actual situation, such as 5 seconds.

Step S303: and performing data processing on the surrounding vehicle set, and screening out 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.

In the existing driving risk early warning scheme, the information of the vehicles around the target vehicle can be 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 vehicles around the target vehicle is issued to the target vehicle in an unordered manner, the target vehicle cannot distinguish the collision hazards possibly brought by the vehicles, and the driving safety is not favorably improved.

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

step S401, obtaining motion parameters corresponding to the risk measurement angles of the target vehicle and the surrounding vehicles.

The risk measurement angle is a reference index for measuring the collision risk of the vehicle, and may be, for example, a vehicle speed, a vehicle position, a driving direction, a ratio of illegal driving of the vehicle, or the like. The motion parameter corresponding to the risk metric angle refers to a specific numerical value of the target vehicle or the nearby vehicle corresponding to the risk metric angle. For example, the set of surrounding vehicles { a }₁,a₂,a₃,...,a_nAnd when the current time t and the risk measurement angle are the vehicle speed, recording the vehicle speed v of each surrounding vehicle_1.t,v_2.t,v_3.t,...v_n.tThe speed of the target vehicle is v_tWhen the risk measurement angle is the vehicle position, the position of each surrounding vehicle is recorded as p_1.t,p_2.t,p_3.t,...p_n.tThe vehicle position of the target vehicle is p_tWhen the risk measurement angle is the driving direction, the driving direction of each peripheral vehicle is recorded as theta_1.t,θ_2.t,θ_3.t,...θ_n.tThe traveling direction of the target vehicle is theta_t。

The risk measurement angle in the embodiment of the invention at least comprises a vehicle speed, a vehicle position and a driving direction, motion parameters of surrounding vehicles can be obtained by detection of a drive test device, a vehicle networking cloud platform can obtain detection results of the drive test detection device on the motion parameters of the surrounding vehicles by sending instructions to the drive test detection device, and the vehicle networking cloud platform can also obtain related information of the surrounding vehicles around a target vehicle by adopting a mode that the drive test detection device reports the motion parameters of the surrounding vehicles to the vehicle networking cloud platform; the vehicle speed, the driving direction and the vehicle position of the vehicle can be detected and obtained by the in-vehicle detection device, and in fact, the motion parameters of the target vehicle can also be detected and obtained by the drive test device in the above manner.

The relevant information of each vehicle detected and obtained by the drive test device includes, but is not limited to, identification information, position information, driving speed, driving direction and the like of the vehicle. Each vehicle has unique identification information, for example, the identification information may be an International Mobile Equipment Identity (IMEI) code of a vehicle in the car networking system, and the car networking cloud platform may analyze the identification information of the vehicle from the acquired relevant information of the vehicle, so as to determine which vehicle the vehicle is. Alternatively, the information related to 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 proportion of the illegal driving of the vehicle, the internet of vehicles cloud platform can inquire the proportion of the illegal driving 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 proportion of the illegal driving can be expressed as the ratio of the number of times of the illegal driving in the trip period to the number of days corresponding to the trip period.

And S403, acquiring the historical traffic accident rate of the current road section of the target vehicle.

Specifically, the current road section of the target vehicle can be obtained through the drive test detection device, or the current road section of the target vehicle can be matched through the current position of the target vehicle, and then the internet of vehicles cloud platform is used for matching the current road section of the target vehicle according to the current position of the target vehicleThe historical traffic accident rate P of the road section is obtained by inquiring the cloud platform of the traffic management department of the previous road section_traffic。

Step S405, screening out candidate risk vehicle sets corresponding to the risk measurement angles from the surrounding vehicle sets according to the motion parameters of the surrounding vehicles, the motion parameters of the target vehicle and the historical traffic accident rate.

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

the method comprises the following steps: and determining a 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.

The method for determining the lower limit of the number of the risk vehicles which can be fed back to the target vehicle can comprise the following steps: determining a non-road traffic accident rate according to the historical traffic accident rate; determining a lower limit x 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 x of the number of the risk vehicles to the number n of the surrounding vehicles is not less than the non-road traffic accident rate which can be expressed as 1-P_traffic。

The lower limit x of the number of the risk vehicles satisfies that x/n is more than or equal to 1-P_traffic. If x satisfies x/n ≧ 1-P_trafficIndicating that the ratio between the number of peripheral vehicles issued to the target vehicle and the total number of peripheral vehicles in the set of peripheral vehicles is not less than 1-P_trafficThe number of the peripheral vehicles which are not issued to the target vehicle and the total number of the peripheral vehicles in the peripheral vehicle set do not exceed the historical traffic accident rate P_traffic. So set up, mainly based on the following considerations: if the ratio of the information amount of other peripheral vehicles not acquired by the target vehicle to the total vehicle information amount exceeds the historical traffic accident rate P_trafficThen, the target vehicle may be disadvantageous for improving driving safety due to lack of surrounding vehicle information, and further, for reducing the existing traffic accident rate P_traffic。

Step two: for any risk measurement angle, determining the priority ranking of the peripheral vehicles for bringing collision risks to the target vehicle from the risk measurement angle according to the motion parameters of the peripheral 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 ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles.

In particular implementation, when the risk metric angle is a vehicle position, a candidate vehicle set corresponding to the vehicle position may be constructed according to the steps shown in fig. 5. The method comprises the following steps:

s501, 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.

And S503, carrying out priority ranking on collision risks of the surrounding vehicles based on the distance between the surrounding vehicles and the target vehicle. Specifically, the travel distances corresponding to the peripheral vehicles may be sorted from large to small.

And S505, constructing a candidate vehicle set corresponding to the vehicle position according to the front k vehicles around the vehicle in the 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 vehicles around the vehicle.

When the risk metric angle is vehicle speed, a candidate vehicle set corresponding to the vehicle speed may be constructed according to the steps shown in fig. 6. The method comprises 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 target vehicle and the current vehicle position of each peripheral vehicle.

Wherein the set of front vehicles includes: a nearby vehicle that is ahead of the target vehicle in the target vehicle traveling direction, or a nearby vehicle that is ahead of the target vehicle and is alongside the target vehicle in the target vehicle traveling direction. When dividing the nearby vehicle, the nearby vehicle that is alongside the target vehicle may be categorized into the front vehicle group or the rear vehicle group.

S603, as for the two vehicle sets obtained in the step S601, whether any one set is a front vehicle set is determined, if the set is the front vehicle set, the step S605 is executed, if the set is not the front vehicle set, the rear vehicle set is judged, and the step S607 is executed.

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

Specifically, a vehicle speed difference between the vehicle speed of the target vehicle and the vehicle speed of each peripheral vehicle in the front vehicle set may be calculated, and a first vehicle speed difference corresponding to each peripheral vehicle is obtained; the first vehicle speed difference of each nearby vehicle is sorted from big to small, and k1 preceding vehicles in the sort order are selected to form a first candidate vehicle set corresponding to the vehicle speed.

S607, for the rear vehicle set, performing priority ranking of collision risks on the surrounding vehicles according to the vehicle speed difference between the vehicle speed of each surrounding vehicle in the rear vehicle set and the vehicle speed of the target vehicle; the top k2 preceding vehicles in the sequence 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 is obtained; sorting the second vehicle speed differences of the surrounding vehicles from large to small; the top k2 preceding vehicles in the neighborhood are selected to constitute a second candidate vehicle set corresponding to the vehicle speed.

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

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

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

When the risk metric angle is the vehicle traveling direction, a candidate vehicle set corresponding to the vehicle traveling direction may be constructed according to the steps shown in fig. 7. The method comprises the following steps:

and S701, dividing each peripheral vehicle in the peripheral vehicle set into a front vehicle set or a rear vehicle set according to the target vehicle and the current vehicle position of each peripheral vehicle.

Specifically, the front-rear vehicle division may be performed in conjunction with the traveling directions and vehicle positions of the target vehicle and the nearby vehicle, the nearby vehicle traveling in the same direction as the target vehicle and having a vehicle position before the vehicle position of the target vehicle may be divided into the front vehicle set, the nearby vehicle traveling in the same direction as the target vehicle and having a vehicle position after the vehicle position of the target vehicle and traveling alongside the target vehicle may be divided into the rear vehicle set, or the nearby vehicle traveling in the same direction as the target vehicle and having a vehicle position before the vehicle position of the target vehicle and traveling alongside the target vehicle may be divided into the front vehicle set, and the nearby vehicle traveling in the same direction as the target vehicle and having a vehicle position after the vehicle position of the target vehicle may be divided into the rear vehicle set.

And S703, determining whether any one set of the two vehicle sets obtained in the step S701 is a front vehicle set, if so, turning to the step S705, otherwise, determining that the set is a rear vehicle set, and turning to the step S707.

S705, for the front vehicle set, determining a first direction angle formed between a connecting line of each peripheral vehicle and the target vehicle and a lane line of a road where the target vehicle is located, and a second direction angle formed between the running direction of the target vehicle and the running direction of each peripheral vehicle; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, carrying out priority ranking on collision risks of each peripheral vehicle; the top k3 preceding vehicles in the sequence are selected to form a third candidate vehicle set corresponding to the vehicle speed.

In one possible embodiment, a first direction angle corresponding to each peripheral vehicle in the front vehicle set can be determined according to a target vehicle and the current vehicle position of each peripheral vehicle, wherein 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 the 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 according to the first angle difference from small to large; the top k3 preceding vehicles in the neighborhood are selected to constitute a third candidate vehicle set corresponding to the vehicle speed.

S707, 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 where the target vehicle is located, and a fourth direction angle formed between a driving direction of each peripheral vehicle and a driving 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, carrying out priority ranking on collision risks of each peripheral vehicle; the top k4 preceding vehicles in the neighborhood are selected to form a fourth candidate vehicle set corresponding to the vehicle speed.

In a feasible embodiment, determining a third direction angle corresponding to each peripheral vehicle in the rear vehicle set according to the current vehicle positions of the target vehicle and each peripheral vehicle, 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 where 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 each 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 top k4 preceding vehicles in the neighborhood are selected to form a fourth candidate vehicle set corresponding to the vehicle speed.

Wherein the sum of k3 and k4 simultaneously satisfies the condition: 1) not less than the lower limit of the number of the risky vehicles, 2) not more than the total number of the nearby vehicles.

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

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

When the risk measurement angle is the violation driving ratio, if the sum of the violation driving ratio corresponding to the peripheral vehicle and the violation driving ratio corresponding to the target vehicle is higher, the collision risk between the target vehicle and the peripheral vehicle is higher. Specifically, a candidate vehicle set corresponding to the risk metric angle may be constructed according to the following steps. The method comprises the following steps:

acquiring the violation driving proportion of each peripheral vehicle; based on the violation driving occupation ratios of the peripheral vehicles, the violation driving occupation ratios of the peripheral vehicles are sorted from large to small; and constructing a candidate vehicle set corresponding to the vehicle position according to the front k ranked vehicles, 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 set according to the surrounding vehicles simultaneously existing in the candidate risk vehicle sets.

In the embodiment of the invention, the vehicles in the candidate risk vehicle set represent vehicles which can bring higher collision risks to the target vehicle from the corresponding risk measurement angles, and the surrounding vehicles which simultaneously exist in each candidate risk vehicle set form the risk vehicle set, so that the surrounding vehicles in the risk vehicle set are vehicles which can bring higher collision risks 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 vehicles.

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, oil usage of the vehicle, and the like. The vehicle control information specifically refers to relevant information of a vehicle control system, for someone driving, the vehicle control system refers to a driver, the vehicle control information refers to driving age, driving qualification, violation data and the like of the driver, for semi-automatic driving, the vehicle control system refers to the driver and the semi-automatic driving system, the violation data in the vehicle control information refers to the sum of the failure rate of the semi-automatic driving system and the violation data corresponding to the driver, for full-automatic driving, the vehicle control system refers to an automatic driving system, and the violation data in the vehicle control information refers to the 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 risky vehicle in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

In the embodiment of the invention, the Internet of vehicles cloud platform determines each vehicle slice associated with the collision risk and issues the vehicle slice to the target vehicle, specifically, the Internet of vehicles cloud platform evaluates the collision risk brought to the target vehicle by each peripheral vehicle from each preset risk measurement angle, and determines the candidate vehicle set { S }₁,S₂,...S_nAnd fourthly, each subset in the candidate vehicle set is a candidate risk vehicle set corresponding to a risk measurement angle, then the intersection of all subsets in the candidate vehicle set is extracted, and the risk vehicle set is obtained and recorded as S_final,1. Set S_final,1Sending the associated data of the first slice to the first sliceA target vehicle. Will S_final,1Is removed from the set of surrounding vehicles within the driving safety distance of the target vehicle (when n is changed into n- | S_final,1And | the process returns to step S301 to obtain a second vehicle slice, and so on, and one vehicle slice is obtained through each round of data processing. Considering that the collision risk brought by the vehicle slices obtained by data processing of each wheel to the vehicle is different in degree of significance, and the earlier obtained vehicle slices bring more significant collision risk to the target vehicle, the vehicle networking cloud platform firstly issues the associated information of the first vehicle slice to the target vehicle, then issues the associated information of the second vehicle slice until the associated information of the last vehicle slice is completed, the time delay of the associated information of the first vehicle slice is small, the available network transmission resources are more sufficient, the target vehicle can quickly obtain the information of the vehicle forming a security threat to the vehicle, and the driving security can be improved.

The vehicle slice represents multi-vehicle information obtained by cutting multi-frame vehicle information into a group instead of information of one vehicle, namely, the vehicles are delivered in a set form. For example, there are 4 frames of vehicle information, the first two frames of vehicle information are regarded as one group (vehicle set), and the last two frames of vehicle information are cut into one group (vehicle set), rather than transmitting the vehicle information frame by frame. One frame of vehicle information represents the motion state information of the vehicle at a certain time.

It should be noted that, in the above embodiment, after the risk vehicle set is obtained by screening, the associated information of the vehicles in the risk vehicle set is immediately issued to the target vehicle, and the information of the peripheral vehicles is automatically issued to the target vehicle according to the priority without sorting the priority of the risk vehicle set obtained by processing data of each round.

As an extension of the aspect of the present invention, in another embodiment, the driving risk prompting method may further include: s11: determining surrounding vehicles within the driving safety distance of the target vehicle to obtain a surrounding vehicle set; s13: performing data processing on the surrounding vehicle set, and screening out a risk vehicle set from the surrounding vehicle set; updating the surrounding vehicle set according to information except the risk vehicle set in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, and generating a risk vehicle set corresponding to each wheel of data processing; s15: according to the generation time sequence of the risk vehicle sets, carrying out risk grade 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 level corresponding to the risk vehicle set.

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

The specific means of the data processing in step S13 in this embodiment is the same as that in 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 a vehicle networking cloud platform 840 for related information of surrounding vehicles, the vehicle networking cloud platform 840 selectively issues information of vehicles around the requested vehicle to the requested vehicle according to a vehicle motion state, wherein a high-precision positioning device 810 may be used to determine positions of the requested vehicle and the surrounding vehicles thereof, 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 are described by taking the preset risk measurement angle as the position, speed and driving direction of the vehicle as an example.

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

The implementation steps are as follows:

(1) the vehicle networking cloud platform obtains the vehicle and the motion parameters of the vehicle within the driving safety distance of the road where the vehicle is located.

The vehicle acquires the GPS of the vehicle through the GPS device in the vehicle and reports the GPS to the Internet of vehicles cloud platform. The Internet of vehicles cloud platform detects the speed and the GPS of the vehicle within the driving safety distance of the road where the vehicle is located through the GPS reported by the vehicle through the drive test detection device. Respectively recording the vehicle speeds v at the current time t_1,t,v_2,t,...,v_n,tGPS is p_1,t,p_2,t,...,p_n,tIn the direction of travel theta_1,t,θ_2,t,...,θ_n,t(ii) a The vehicle obtains the vehicle speed, GPS and driving direction of the vehicle at the current time t through the in-vehicle equipment, and the driving directions are respectively marked as v_0,t,p_0,t,θ_0,tReporting the information to the Internet of vehicles cloud platform; where n is the number of vehicles other than the host vehicle within the driving safety distance of the host vehicle.

(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 Internet of vehicles cloud platform obtains the historical traffic accident rate of the current road section of the vehicle from the traffic management department and records the historical traffic accident rate as p_traffic(ii) a The risk of collision between vehicles is greater if the distance between the vehicles is smaller, i.e. the risk of collision between the 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 can issue part of vehicles. In order to ensure that the issued vehicles can assist the host vehicle to improve the driving safety, the probability that the vehicle information which is too far away from the host vehicle is issued to the host vehicle should be smaller than the vehicle which is close to the host vehicle. Therefore, according to the vehicle position p_0,tPosition p of vehicle within driving safety distance_1,t,p_2,t,...,p_n,tCalculating the distances between the vehicle and the vehicles, and respectively recording the distances as s_1,t,s_2,t,...,s_n,t(ii) a Then the first k are selected from small to large, and k/n is more than or equal to 1-p_trafficThis set of vehicles is referred to as the first set, denoted S₁。

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

The vehicle within the driving safety distance of the vehicle is divided into two parts by the Internet of vehicles cloud platform, and one part of the vehicle is positioned in front of the vehicle and is respectively recorded as

The other part is positioned behind the vehicle and is respectively recorded as

For the vehicle in front of the host vehicle, if the vehicle speed difference between the host vehicle and the preceding vehicle is larger, the collision risk between the vehicles is larger, that is, the collision risk between the vehicles is proportional to the vehicle speed difference between the host vehicle and the preceding vehicle. For the vehicle behind the host vehicle, if the vehicle speed difference between the rear vehicle and the host vehicle is larger, the collision risk between the vehicles may be larger, i.e., the collision risk between the vehicles is proportional to the vehicle speed difference between the host vehicle and the rear vehicle. Therefore, according to the vehicle speed v_0,tAnd the speed v of the vehicle ahead of the vehicle_1,t,v_2,t,...,v_n,tCalculating the speed difference between the vehicle and the front vehicle, and recording the speed difference as

According to the speed v of the vehicle_0,tAnd the speed of the rear vehicle

Calculating the speed difference between the rear vehicle and the vehicle, and recording the speed difference as

For the front vehicle of the vehicle, the determination is made

Middle from large to small selection of front k₁For the rear vehicle of the vehicle, the followingDetermined

The first k is selected from large to small₂Satisfy k₁+k₂K is k and k/n is not less than 1-p_traffic. This set of vehicles is referred to as the second set, denoted S₂。

(4) The Internet of vehicles cloud platform determines which vehicles bring significant risks to the vehicle from the perspective of the driving direction.

The cloud platform of the Internet of vehicles according to the position p of the vehicle_0,tPosition p of vehicle within driving safety distance_1,t,p_2,t,...,p_n,tDetermining the included angles (acute angles) between the connecting lines of the vehicle and the vehicles and the lane lines, and respectively recording the included angles as alpha_1,t,α_2,t,...,α_n,tNote that the direction corresponding to the front vehicle of the vehicle is

The direction corresponding to the rear vehicle of the vehicle is

The cloud platform of the Internet of vehicles according to the driving direction theta of the vehicle_0,tAnd the traveling direction of the preceding vehicle

Determine the angle difference between the driving directions of the vehicle and the front vehicle, and respectively record the angle difference

According to the running direction theta of the vehicle_0,tAnd the direction of travel of the rear vehicle

Determine the angle difference between the rear vehicle and the vehicle, and respectively record the angle difference as

For the front car, if

Are respectively connected with

The difference between

The smaller the risk of collision between the preceding vehicle and the host vehicle, and for the following vehicle, if it is

Are respectively connected with

The difference between

The smaller the risk of collision between the rear vehicle and the host vehicle. Thus, from

Selecting k from small to large₃From

Selecting k from small to large₄One, satisfy k₃+_kK is 4 ≧ k and k/n is ≧ 1-p_traffic(ii) a This set of vehicles is referred to as the third set, denoted S₃。

(5) The vehicle networking cloud platform determines vehicles with a large collision risk with the vehicle (the vehicle information is information which should be issued to the vehicle): determining a first set of vehicles S by using an Internet of vehicles cloud platform₁The second vehicle set S₂A third set of vehicles S₃The intersection of (A) and (B), i.e. the set of vehicles which bring about a significant risk of collision to the vehicle from the angles of position, speed and direction of travel, is denoted S_final,1. And transmitting the information of the vehicles to the vehicle. Set of vehicles S_final,1Is the first slice. Then, set S_final,1Is removed from the vehicle set within the driving safety distance of the vehicle (at the moment, n is changed into n- | S_final,1| and return to step 1 to obtainAnd (5) carrying out second vehicle slicing, and the like to obtain each vehicle slice. The vehicle networking cloud platform issues a first vehicle slice to the vehicle, and then issues a second vehicle slice until all vehicle slices are issued.

By applying the scheme of the invention in the simulator, the vehicle can drive into the intersection where the traffic light is positioned in the figure 8 in different driving states. The method comprises the following steps of counting the ratio of the number of vehicles successfully issued to the vehicle, wherein the vehicles with high collision risks with the vehicle are successfully issued to the vehicle, in the process of issuing the information of the surrounding vehicles to the vehicle by adopting the prior art and the method provided by the application. Comparing the performance of the prior art with that of the present invention, it is clear that the performance of the present invention is superior to that of the prior art.

TABLE 1 results of the experiment

The embodiment of the present invention further discloses a driving risk prompting device, as shown in fig. 9, the driving risk prompting device 900 includes: a determining unit 910, configured to determine a neighboring vehicle within a driving safety distance of a target vehicle, and obtain a set of neighboring vehicles; a processing unit 920, configured to perform data processing on the set of nearby vehicles, and screen out a risk vehicle set from the set of nearby vehicles, where a risk vehicle in the risk vehicle set is a vehicle that brings a collision risk to the target vehicle from each preset risk metric angle; an obtaining unit 930, configured to obtain associated data corresponding to the risk vehicle set, where the associated data includes vehicle information and vehicle control information of the risk vehicle; a sending unit 940, configured to send the associated data corresponding to the risk vehicle set to the target vehicle; an updating unit 950, configured to construct a new surrounding vehicle set according to data in the surrounding vehicle set other than the risky vehicle, trigger the data processing unit to perform data processing on the constructed surrounding vehicle set, generate risky vehicle sets corresponding to data processing of each wheel, and issue association data corresponding to each risky vehicle set to the target vehicle in sequence.

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 corresponding 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 located; the screening module is used for screening out a candidate risk vehicle set corresponding to each risk measurement angle from the surrounding vehicle set according to the motion parameters of each surrounding vehicle, the motion parameters of the target vehicle and the historical traffic accident rate; the determining module is used for determining the risk vehicle set according to surrounding vehicles which exist in the candidate risk vehicle sets at the same time.

Further, the screening module specifically includes: a first determining submodule, which is used for 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; the second determining submodule is used for determining the priority ranking of collision risks brought to the target vehicle by the peripheral vehicles from the risk measurement angle according to the motion parameters of the peripheral vehicles and the motion parameters of the target vehicle for any risk measurement angle; and determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles.

In one possible embodiment, the first determining sub-module 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 peripheral vehicles, wherein the ratio of the lower limit of the number of the risk vehicles to the number of the peripheral vehicles is not less 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 risks for each of the nearby vehicles based on a distance between the nearby vehicle and the target vehicle; and constructing a candidate vehicle set corresponding to the vehicle position according to the front k ranked vehicles, 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 target vehicle and the current vehicle position of each peripheral vehicle; for the front vehicle set, performing priority ranking on collision risks of the surrounding vehicles according to a vehicle speed difference between the vehicle speed of the target vehicle and the vehicle speed of each surrounding vehicle in the front vehicle set; selecting the k1 front-ranked surrounding vehicles to form a first candidate vehicle set corresponding to the vehicle speed; for the rear vehicle set, performing priority ranking on collision risks of the surrounding vehicles according to a vehicle speed difference between the vehicle speed of each surrounding vehicle in the rear vehicle set and the vehicle speed of the target vehicle; selecting the k2 front-ranked surrounding vehicles 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 the lower limit of the number of at-risk vehicles and is not more than the total number of nearby vehicles.

In one possible embodiment, the second determining sub-module 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 target vehicle and the current vehicle position of each peripheral vehicle; for the front vehicle set, determining a first direction angle formed between a connecting line of each peripheral vehicle and the target vehicle and a lane line of a road where the target vehicle is located, and a second direction angle formed between a driving direction of the target vehicle and a driving direction of each peripheral vehicle; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, carrying out priority ranking on collision risks of each peripheral vehicle; selecting the k3 front-ranked surrounding vehicles 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 where the target vehicle is located, and a fourth direction angle formed between a driving direction of each peripheral vehicle and a driving 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, carrying out priority ranking on collision risks of each peripheral vehicle; selecting the k4 front-ranked surrounding vehicles to form a fourth candidate vehicle set corresponding to the vehicle speed; constructing a candidate vehicle set corresponding to the vehicle driving direction according to the third candidate vehicle set and the fourth candidate vehicle set; wherein the sum of k3 and k4 satisfies the lower limit of the number of at-risk vehicles and is not more than the total number of nearby vehicles.

In the embodiment of the invention, a peripheral vehicle set is constructed according to peripheral vehicles within the driving safety distance of a target vehicle, the difference of collision risks brought to the target vehicle by different peripheral vehicles is considered, the peripheral vehicles which can bring high collision possibility to the target vehicle at all risk measurement angles in the peripheral vehicle set are taken as risk vehicles, data processing on the peripheral vehicle set is completed, then the risk vehicles are removed from the peripheral vehicle set, an updated peripheral vehicle set is obtained, data processing is further performed on the updated peripheral 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 when 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 risk vehicle set is generated before the time sequence, the risk vehicle set has higher possibility of collision with the target vehicle, the risk vehicle set is generated after the time sequence, and the risk vehicle set has lower possibility of collision with the target vehicle. The technical problem that the early warning accuracy of the existing driving risk early warning scheme is not high is solved.

Specifically, the driving risk prompting device and the driving risk prompting method are based on the same inventive concept. For details, please refer to the method embodiment, which is not described herein.

An embodiment of the present invention provides an electronic device, where the electronic device includes a processor and a memory, where at least one instruction and at least one program are stored in the memory, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the driving risk prompting method corresponding to fig. 3 to 8.

The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, 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 the processor access to the memory.

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 executes the computer instructions, so that the computer device executes the driving risk prompting method provided in the various optional implementation modes. The method comprises at least the following steps:

determining surrounding vehicles within the driving safety distance of the target vehicle to obtain a surrounding vehicle set; performing data processing on the surrounding vehicle set, and screening out 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 comprise vehicle information and vehicle control information of the risk vehicles; issuing the 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 risky vehicle in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

Further, fig. 10 shows a hardware structure diagram of an apparatus for implementing the method provided by the embodiment of the present invention, and the apparatus may participate in forming or containing the device or system provided by the embodiment of the present invention. As shown in fig. 10, the device 10 may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. Besides, the method can also comprise the following steps: 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 source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 10 is merely illustrative and is not intended to limit the structure of the electronic device. For example, 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 circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. 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 the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method described in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the driving risk indication method described above. The 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 device 106 is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by the communication provider of the device 10. In one example, the transmission device 106 includes a network adapter (NIC) that can be connected to other network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

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 precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may 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 may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and server embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.

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 instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A driving risk presentation method is characterized by comprising the following steps:

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

performing data processing on the surrounding vehicle set, and screening out 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 comprise vehicle information and vehicle control information of the risk vehicles;

issuing the 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 risky vehicle in the surrounding vehicle set, returning to the step of executing data processing on the surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

2. The method according to claim 1, wherein the performing data processing on the set of nearby vehicles, screening a set of risky vehicles from the set of nearby vehicles, comprises:

obtaining motion parameters of the target vehicle and the surrounding vehicles corresponding to the risk measurement angles;

acquiring the historical traffic accident rate of the current road section of the target vehicle;

screening out a candidate risk vehicle set corresponding to each risk measurement angle from the surrounding vehicle set according to the motion parameters of each surrounding vehicle, the motion parameters of the target vehicle and the historical traffic accident rate;

and determining the risk vehicle set according to surrounding vehicles simultaneously existing in each candidate risk vehicle set.

3. The method according to claim 2, wherein the screening out a candidate risk vehicle set corresponding to each risk measure angle from the surrounding vehicle set according to the motion parameters of each surrounding vehicle, the motion parameters of the target vehicle and the historical traffic accident rate comprises:

determining a 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;

for any risk measurement angle, determining the priority ranking of the peripheral vehicles for bringing collision risks to the target vehicle from the risk measurement angle according to the motion parameters of the peripheral 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 ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles.

4. The method of claim 3, wherein determining a lower limit on the number of at-risk vehicles that can be fed back to a target vehicle based on the historical traffic accident rate and the number of nearby vehicles comprises:

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 peripheral vehicles, wherein the ratio of the lower limit of the number of the risk vehicles to the number of the peripheral vehicles is not less than the non-road traffic accident rate.

5. The method according to claim 3, wherein when the risk metric angle is a vehicle position, the determining the priority ranking of each nearby vehicle bringing collision risk to the target vehicle from the risk metric angle according to the motion parameter of each nearby vehicle and the motion parameter of the target vehicle; and determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles, wherein the candidate risk vehicle set comprises:

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 risks for each of the nearby vehicles based on a distance between the nearby vehicle and the target vehicle;

and constructing a candidate vehicle set corresponding to the vehicle position according to the front k ranked vehicles, 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.

6. The method according to claim 3, wherein when the risk metric angle is a vehicle speed, the determining of the priority order of each nearby vehicle bringing collision risk to the target vehicle from the risk metric angle according to the motion parameter of each nearby vehicle and the motion parameter of the target vehicle; and determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles, wherein the candidate risk vehicle set comprises:

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

for the front vehicle set, performing priority ranking on collision risks of the surrounding vehicles according to a vehicle speed difference between the vehicle speed of the target vehicle and the vehicle speed of each surrounding vehicle in the front vehicle set; selecting the k1 front-ranked surrounding vehicles to form a first candidate vehicle set corresponding to the vehicle speed;

for the rear vehicle set, performing priority ranking on collision risks of the surrounding vehicles according to a vehicle speed difference between the vehicle speed of each surrounding vehicle in the rear vehicle set and the vehicle speed of the target vehicle; selecting the k2 front-ranked surrounding vehicles 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 the lower limit of the number of at-risk vehicles and is not more than the total number of nearby vehicles.

7. The method according to claim 3, wherein when the risk measure angle term is a vehicle driving direction, the priority ranking of each surrounding vehicle bringing collision risk to the target vehicle from the risk measure angle is determined according to the motion parameter of each surrounding vehicle and the motion parameter of the target vehicle; and determining a candidate risk vehicle set corresponding to each risk measurement angle based on the priority ranking corresponding to each surrounding vehicle and the lower limit of the number of the risk vehicles, wherein the candidate risk vehicle set comprises:

for the front vehicle set, determining a first direction angle formed between a connecting line of each peripheral vehicle and the target vehicle and a lane line of a road where the target vehicle is located, and a second direction angle formed between a driving direction of the target vehicle and a driving direction of each peripheral vehicle; according to the angle difference between the first direction angle and the second direction angle corresponding to each peripheral vehicle, carrying out priority ranking on collision risks of each peripheral vehicle; selecting the k3 front-ranked surrounding vehicles 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 where the target vehicle is located, and a fourth direction angle formed between a driving direction of each peripheral vehicle and a driving 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, carrying out priority ranking on collision risks of each peripheral vehicle; selecting the k4 front-ranked surrounding vehicles to form a fourth candidate vehicle set corresponding to the vehicle speed;

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

wherein the sum of k3 and k4 satisfies the lower limit of the number of at-risk vehicles and is not more than the total number of nearby vehicles.

8. A driving risk presentation device, comprising:

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

the processing unit is used for performing data processing on the surrounding vehicle set and screening out a risk vehicle set from the surrounding vehicle set, wherein the risk vehicles in the risk vehicle set are vehicles which can 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 vehicles;

the sending unit is used for issuing 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 data except the risky vehicle in the surrounding vehicle set, triggering the data processing unit to perform data processing on the constructed surrounding vehicle set, generating risky vehicle sets corresponding to the data processing of each wheel, and issuing the associated data corresponding to the risky vehicle sets to the target vehicle in sequence.

9. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the driving risk notification method according to any one of claims 1 to 7.

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