Method and device for dynamically determining vehicle collision risk

Abstract

The application relates to the technical field of data analysis, and discloses a dynamic judgment method and device for vehicle collision risk, which comprises the following steps: determining a target driving scene corresponding to a vehicle; determining a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene; acquiring traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle; the traffic data comprises road condition data and / or motion data of adjacent objects; judging whether the vehicle has a collision risk according to the traffic data and the real-time driving data to obtain a risk judgment result. It can be seen that the application can judge whether there is a collision risk in real time based on road conditions and vehicle driving data, can improve the timeliness and accuracy of the judgment of the vehicle collision risk, and is thus favorable for taking risk response measures in time and improving driving safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a dynamic determination method and device for vehicle collision risk. BACKGROUND

[0002] In a driving scene (such as a driving test / driving training scene), a vehicle travels in a complex and changeable traffic environment, and it is necessary to determine the collision risk of the vehicle in real time during driving, so that the driver can safely and timely respond to unexpected situations.

[0003] In actual driving, the determination of vehicle collision risk usually depends on manual judgment. However, manual judgment needs to rely on the driving experience and intuitive perception of the driver to evaluate the collision risk. In the case of insufficient driving experience, slow reaction speed, subjective judgment error or sudden change of traffic environment, the result of manual judgment is easily disturbed, and it is difficult to continuously, accurately and comprehensively capture potential risks in the traffic environment, and there are still driving safety hazards.

[0004] Therefore, it is particularly important to propose a technical solution that can improve the timeliness and accuracy of determining vehicle collision risk to improve driving safety. SUMMARY

[0005] The present application provides a dynamic determination method and device for vehicle collision risk, which can improve the timeliness and accuracy of determining vehicle collision risk, and is beneficial to improve driving safety.

[0006] In order to solve the above technical problems, the first aspect of the present application discloses a dynamic determination method for vehicle collision risk, which comprises:

[0007] determining a target driving scene corresponding to a vehicle;

[0008] determining a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene;

[0009] acquiring traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle; the traffic data includes road condition data and / or motion data of adjacent objects;

[0010] determining whether the vehicle has a collision risk according to the traffic data and the real-time driving data, and obtaining a risk determination result.

[0011] As an optional implementation manner, in the first aspect of the present application, the determination of the target monitoring lane based on the positioning technology and the pre-acquired map data according to the target driving scene comprises:

[0012] when the target driving scene comprises a driving test scene, analyzing a test item currently tested in the driving test scene; and determining a first target lane corresponding to the test item based on positioning technology and pre-acquired map data according to the test item; the first target lane comprises at least one lane required to be passed through or required to be entered by the vehicle in the test item;

[0013] when the target driving scene comprises a traffic environment scene, determining a risk factor type corresponding to the traffic environment scene; and determining a second target lane based on the positioning technology and the map data according to the risk factor type; the second target lane comprises at least one adjacent lane corresponding to the vehicle;

[0014] determining a current lane and a target lane in which the vehicle is located as a target monitoring lane corresponding to the vehicle; the target lane comprises the first target lane and / or the second target lane.

[0015] As an optional implementation, in the first aspect of the present application, the judging whether the vehicle has a collision risk according to the traffic data and the real-time driving data to obtain a risk judgment result comprises:

[0016] calculating a traffic flow parameter corresponding to the target monitoring lane according to the traffic data and the real-time driving data; the traffic flow parameter comprises a combination of one or more of an average vehicle speed, a traffic density, a traffic flow and a time index in a target region of the target monitoring lane;

[0017] calculating a risk parameter corresponding to the vehicle based on a pre-determined traffic flow analysis model according to the traffic data and the real-time driving data;

[0018] judging whether a distance between any of the adjacent objects and the vehicle is less than or equal to a preset distance within a first preset time length after a current time according to the traffic flow parameter and the risk parameter based on the determined collision risk evaluation algorithm;

[0019] when it is judged that the distance between any of the adjacent objects and the vehicle is less than or equal to the preset distance within the first preset time length, determining that the vehicle has a collision risk;

[0020] when it is judged that there is no adjacent object within the first preset time length, determining that the vehicle has no collision risk.

[0021] As an optional implementation, in the first aspect of the present application, when the risk judgment result is used to indicate that the vehicle has the collision risk, the method further comprises:

[0022] generating at least one driving adjustment instruction in a form of guidance about the collision risk; the driving adjustment instruction is used to prompt the driver to adjust the driving operation for the vehicle;

[0023] judging whether the driving adjustment operation corresponding to the driving adjustment instruction is detected within a second preset time length and whether a matching degree between the driving adjustment operation and the driving adjustment instruction is higher than or equal to a preset matching degree;

[0024] when it is judged that the driving adjustment operation corresponding to the driving adjustment instruction is not detected within the second preset time length or the matching degree between the driving adjustment operation and the driving adjustment instruction is lower than the preset matching degree, controlling the vehicle to perform an auxiliary driving operation corresponding to the driving adjustment instruction based on an auxiliary driving function corresponding to the vehicle.

[0025] As an optional implementation, in the first aspect of the present application, when the risk judgment result is used to indicate that the vehicle has the collision risk, the method further comprises:

[0026] when the target driving scene comprises a driving test scene and the vehicle is in a test state, extending a preset test time length of the vehicle about a current test item according to the collision risk, the driving adjustment operation and the traffic data to obtain a target test time length;

[0027] detecting a first driving performance of the driver for the vehicle within the target test time length;

[0028] evaluating a first test result of the driver about the current test item according to the target test time length and the first driving performance.

[0029] As an optional implementation, in the first aspect of the present application, the extending the preset test time length of the vehicle about the current test item to obtain the target test time length according to the collision risk, the driving adjustment operation and the traffic data comprises:

[0030] evaluating a risk level of the collision risk and determining a risk level coefficient corresponding to the risk level;

[0031] evaluating a road condition complexity level corresponding to the target monitoring lane according to the traffic data and determining a road condition complexity coefficient corresponding to the road condition complexity level;

[0032] evaluating an emergency reaction coefficient corresponding to the driver according to a reaction time length of the driver about the driving adjustment operation;

[0033] detect a driving intervention corresponding to the assisted driving function, and determine a driving intervention coefficient corresponding to the driving intervention;

[0034] Based on the determined extension formula, the target examination duration is determined according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient and a preset examination duration of a current examination item.

[0035] As an optional implementation, in the first aspect of the present application, based on the determined extension formula, the target examination duration is determined according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient and a preset examination duration of a current examination item, including:

[0036] Based on the determined extension formula, the target examination duration is determined according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient and a preset examination duration of a current examination item.

[0037] It is judged whether the extension examination duration is less than or equal to a preset maximum duration threshold value.

[0038] When it is judged that the extension examination duration is less than or equal to the maximum duration threshold value, the extension examination duration is determined as the target examination duration.

[0039] When it is judged that the extension examination duration is greater than the maximum duration threshold value, the maximum duration threshold value is determined as the target examination duration.

[0040] The second aspect of the present application discloses a dynamic judgment device for vehicle collision risk, the device comprising:

[0041] A determination module is configured to determine a target driving scene corresponding to a vehicle.

[0042] The determination module is further configured to determine a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene.

[0043] An acquisition module is configured to acquire traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle, wherein the traffic data includes road condition data and / or motion data of adjacent objects.

[0044] A judgment module is configured to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data, and obtain a risk judgment result.

[0045] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module determines the target monitoring lane according to the target driving scene, based on the positioning technology and the pre-acquired map data, includes:

[0046] When the target driving scene includes a driving test scene, analyzing a test item currently being tested in the driving test scene; determining a first target lane corresponding to the test item based on the test item, the positioning technology and the pre-acquired map data; the first target lane includes at least one lane that the vehicle needs to pass through / enter in the test item;

[0047] When the target driving scene includes a traffic environment scene, determining a risk factor type corresponding to the traffic environment scene; determining a second target lane based on the risk factor type, the positioning technology and the map data; the second target lane includes at least one adjacent lane corresponding to the vehicle;

[0048] Determining the current lane and the target lane in which the vehicle is located as the target monitoring lane corresponding to the vehicle; the target lane includes the first target lane and / or the second target lane.

[0049] As an optional implementation, in the second aspect of the present application, the specific manner in which the judging module judges whether the vehicle has a collision risk according to the traffic data and the real-time driving data to obtain a risk judgment result includes:

[0050] According to the traffic data and the real-time driving data, calculating a traffic flow parameter corresponding to the target monitoring lane; the traffic flow parameter includes a combination of one or more of an average speed, a traffic density, a traffic flow and a time index in a target area of the target monitoring lane;

[0051] According to the traffic data and the real-time driving data, calculating a risk parameter corresponding to the vehicle based on a pre-determined traffic flow analysis model;

[0052] Based on the determined collision risk evaluation algorithm, judging whether there is any adjacent object whose distance from the vehicle is less than or equal to a preset distance within a first preset time period after the current time according to the traffic flow parameter and the risk parameter;

[0053] When it is judged that there is any adjacent object whose distance from the vehicle is less than or equal to the preset distance within the first preset time period, it is determined that the vehicle has a collision risk;

[0054] When it is judged that there is no any adjacent object whose distance to the vehicle is less than or equal to the preset distance within the first preset time period, it is determined that the vehicle has no collision risk.

[0055] As an optional implementation, in the second aspect of the present application, the device further comprises:

[0056] The generating module is configured to generate a driving adjustment instruction in the form of at least one indication about the collision risk when the risk judgment result indicates that the vehicle has the collision risk, wherein the driving adjustment instruction is used to prompt the driver to adjust the driving operation of the vehicle.

[0057] The judging module is further configured to judge whether the driving adjustment operation corresponding to the driving adjustment instruction is performed by the driver within a second preset time period and whether the matching degree between the driving adjustment operation and the driving adjustment instruction is higher than or equal to a preset matching degree.

[0058] The control module is configured to control the vehicle to perform an auxiliary driving operation corresponding to the driving adjustment instruction based on an auxiliary driving function corresponding to the vehicle when the judging module judges that the driving adjustment operation corresponding to the driving adjustment instruction is not performed by the driver within the second preset time period or the matching degree between the driving adjustment operation and the driving adjustment instruction is lower than the preset matching degree.

[0059] As an optional implementation, in the second aspect of the present application, the device further comprises:

[0060] The time length extension module is configured to extend a preset examination time length of the vehicle about a current examination item to obtain a target examination time length according to the collision risk, the driving adjustment operation and the traffic data when the target driving scene comprises a driving examination scene and the vehicle is in an examination state when the risk judgment result indicates that the vehicle has the collision risk.

[0061] The detecting module is configured to detect a first driving performance of the driver for the vehicle within the target examination time length.

[0062] The evaluating module is configured to evaluate a first examination result of the driver about the current examination item according to the target examination time length and the first driving performance.

[0063] As an optional implementation, in the second aspect of the present application, the specific manner in which the time length extension module extends the preset examination time length of the vehicle about the current examination item to obtain the target examination time length according to the collision risk, the driving adjustment operation and the traffic data comprises:

[0064] evaluate a risk level of the collision risk, and determine a risk level coefficient corresponding to the risk level;

[0065] evaluate a road condition complexity level of the target monitoring lane according to the traffic data, and determine a road condition complexity coefficient corresponding to the road condition complexity level;

[0066] evaluate an emergency reaction coefficient of the driver according to the reaction time length of the driver on the driving adjustment operation;

[0067] detect a driving intervention situation of the auxiliary driving function, and determine a driving intervention coefficient corresponding to the driving intervention situation;

[0068] determine a target examination time length based on the determined extension formula, the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient, and a preset examination time length of a current examination item.

[0069] As an optional implementation, in the second aspect, the specific manner in which the time length extension module determines the target examination time length based on the determined extension formula, the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient, and the preset examination time length of the current examination item includes:

[0070] calculate an extended examination time length corresponding to the current examination item based on the determined extension formula, the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient, and the preset examination time length of the current examination item;

[0071] determine whether the extended examination time length is less than or equal to a preset maximum time length threshold;

[0072] when it is determined that the extended examination time length is less than or equal to the maximum time length threshold, determine the extended examination time length as the target examination time length;

[0073] when it is determined that the extended examination time length is greater than the maximum time length threshold, determine the maximum time length threshold as the target examination time length.

[0074] A third aspect of the present application discloses another dynamic judgment device for vehicle collision risk, which comprises:

[0075] a memory storing executable program codes;

[0076] a processor coupled with the memory;

[0077] The processor invokes the executable program code stored in the memory to execute part or all steps of the dynamic discrimination method of vehicle collision risk disclosed in the first aspect of the application.

[0078] The fourth aspect of the application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are invoked, part or all steps of the dynamic discrimination method of vehicle collision risk disclosed in the first aspect of the application are executed.

[0079] Compared with the prior art, the application has the following beneficial effects:

[0080] In the application, a target driving scene corresponding to the vehicle is determined; a target monitoring lane is determined based on positioning technology and pre-acquired map data according to the target driving scene; traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle are acquired; the traffic data includes road condition data and / or motion data of a neighboring object; and whether the vehicle has a collision risk is judged according to the traffic data and the real-time driving data, and a risk judgment result is obtained. It can be seen that by implementing the application, the target monitoring lane can be determined based on the determined target driving scene corresponding to the vehicle and the positioning technology and the map data, and the traffic data of the target monitoring lane and the real-time driving data of the vehicle are acquired, so as to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data, which can comprehensively and accurately monitor the driving state of the vehicle, thereby being capable of judging in real time whether there is a collision risk at present based on the road condition and the driving data of the vehicle, improving the timeliness and accuracy of the discrimination of the vehicle collision risk, being beneficial to discovering potential collision risks in time and taking risk response measures in time, and further being beneficial to improving driving safety. BRIEF DESCRIPTION OF DRAWINGS

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0082] Figure 1 is a flowchart of a dynamic discrimination method of vehicle collision risk disclosed by an embodiment of the application;

[0083] Figure 2 is a flowchart of another dynamic discrimination method of vehicle collision risk disclosed by an embodiment of the application;

[0084] Figure 3 is a scene diagram of a scene to which a dynamic discrimination method of vehicle collision risk disclosed by an embodiment of the application is applicable;

[0085] Figure 4 is a structural schematic view of a vehicle collision risk dynamic discrimination device according to an embodiment of the present application;

[0086] Figure 5 is a structural schematic view of another vehicle collision risk dynamic discrimination device according to an embodiment of the present application;

[0087] Figure 6 is a structural schematic view of still another vehicle collision risk dynamic discrimination device according to an embodiment of the present application. DETAILED DESCRIPTION

[0088] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0089] The terms "first", "second", and the like in the specification of the present application and the above-mentioned drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or end including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or end.

[0090] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood that the embodiments described herein can be combined with other embodiments.

[0091] The application discloses a dynamic judgment method and device for vehicle collision risk, which can determine a target monitoring lane based on a determined target driving scene of a vehicle and positioning technology and map data, and then acquire traffic data of the target monitoring lane and real-time driving data of the vehicle, so as to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data. The vehicle driving state can be comprehensively and accurately monitored, so that it can be judged in real time whether there is a collision risk at present based on road conditions and vehicle driving data, the timeliness and accuracy of the judgment of the vehicle collision risk are improved, potential collision risks can be found in time, and subsequent risk response measures can be taken in time, thereby improving driving safety. The following will be described in detail.

[0092] Embodiment one

[0093] Please refer to Figure 1 , Figure 1 is a flowchart of a dynamic judgment method for vehicle collision risk disclosed by the embodiment of the application. Wherein, Figure 1 The dynamic judgment method for vehicle collision risk described above can be applied to a dynamic judgment device for vehicle collision risk. The device can include one of a dynamic judgment equipment, a dynamic judgment terminal, a dynamic judgment system and a server, wherein the server includes a local server or a cloud server, and the embodiment of the application does not limit the device. The device can be applied to a vehicle or a control system capable of controlling the driving of the vehicle. Optionally, the vehicle can be a driving training vehicle or a driving test vehicle, or other vehicles, and the embodiment of the application does not limit the vehicle. As shown in Figure 1 The dynamic judgment method for vehicle collision risk can include the following operations:

[0094] 101, determining a target driving scene corresponding to a vehicle.

[0095] In the embodiment of the application, the target driving scene corresponding to the vehicle can include a driving purpose scene and / or a traffic environment scene. Optionally, the driving purpose scene can include one of a driving test / training scene (for example, a driving test scene and a driving training scene), a driving work scene (for example, a freight driver driving scene), a road patrol scene and a road cleaning scene, and the embodiment of the application does not limit the driving purpose scene. Optionally, the traffic environment scene can include a highway scene and / or an urban road scene. For example, the highway scene can include a highway following driving scene, and the urban road scene can include a lane changing scene or a crossroad scene, and the embodiment of the application does not limit the traffic environment scene.

[0096] In the embodiment of the present application, optionally, the target driving scene corresponding to the vehicle can be obtained by analyzing the sensing data (for example, image data collected by a camera) collected by the sensor corresponding to the vehicle, or the target driving scene corresponding to the vehicle can be obtained by analyzing the instructions (for example, driving test instructions) received by the vehicle, and the embodiment of the present application is not limited.

[0097] 102. Determine the target monitoring lane based on the positioning technology and the pre-acquired map data according to the target driving scene.

[0098] In the embodiment of the present application, the positioning technology can be RTK (Real-Time Kinematic, real-time dynamic positioning technology) positioning technology, or other types of positioning technology, and the embodiment of the present application is not limited.

[0099] 103. Obtain the traffic data corresponding to the target monitoring lane and the real-time driving data of the vehicle.

[0100] In the embodiment of the present application, the traffic data corresponding to the target monitoring lane includes road condition data and / or motion data of a nearby object; optionally, the road condition data can include one or a combination of lane width, bend degree, lane accident condition, and lane congestion condition, and the embodiment of the present application is not limited; optionally, the nearby object can include a nearby vehicle and / or a nearby pedestrian, and the embodiment of the present application is not limited; the motion data of the nearby object can include one or a combination of motion direction, motion speed, and distance between the vehicle and the nearby object, and the embodiment of the present application is not limited. Optionally, the road condition data can be collected by the sensor corresponding to the vehicle, or can be determined based on the road condition broadcast data received by the vehicle, and the embodiment of the present application is not limited.

[0101] In the embodiment of the present application, the real-time driving data of the vehicle can include one or a combination of driving direction, driving speed, steering wheel angle, accelerator mileage, brake mileage, and body heading angle, and the embodiment of the present application is not limited.

[0102] 104. Determine whether the vehicle has a collision risk according to the traffic data and the real-time driving data, and obtain a risk determination result.

[0103] It can be seen that the method described in the embodiments of the present application can determine the target monitoring lane based on the determined target driving scene of the vehicle and the positioning technology and the map data, and then obtain the traffic data of the target monitoring lane and the real-time driving data of the vehicle, so as to determine whether the vehicle has a collision risk according to the traffic data and the real-time driving data. The driving state of the vehicle can be comprehensively and accurately monitored, so that it can be determined in real time whether there is a collision risk based on the road conditions and the driving data of the vehicle, the timeliness and accuracy of the collision risk determination of the vehicle are improved, which is conducive to discovering potential collision risks in time and taking timely risk response measures, and thus the driving safety is improved.

[0104] In an optional embodiment, the target monitoring lane can be determined based on the target driving scene, the positioning technology and the pre-acquired map data, and the operation can include the following operations:

[0105] When the target driving scene includes a driving test scene, the current test project in the driving test scene is analyzed; the first target lane corresponding to the test project is determined based on the positioning technology and the pre-acquired map data according to the test project; the first target lane includes at least one lane that the vehicle needs to pass through or needs to enter in the test project.

[0106] When the target driving scene includes a traffic environment scene, the risk factor type corresponding to the traffic environment scene is determined; the second target lane is determined based on the positioning technology and the map data according to the risk factor type; the second target lane includes at least one adjacent lane corresponding to the vehicle.

[0107] The current lane and the target lane where the vehicle is located are determined as the target monitoring lane corresponding to the vehicle; the target lane includes the first target lane and / or the second target lane.

[0108] The test project can be any project in the driving test and / or driving training, which is not limited by the embodiments of the present application.

[0109] The risk factor type can include an environmental factor type (for example, a severe weather factor, a narrow lane factor) and / or a complex scene factor type (for example, a highway scene factor, a crossroad scene factor), which is not limited by the embodiments of the present application. For example, when the risk factor type includes the environmental factor type, the second target lane can include one / two adjacent lanes in the direction of travel of the vehicle; when the risk factor type includes the crossroad scene factor type in the complex scene factor type, the second target lane can include one / two adjacent lanes in the direction of travel of the vehicle and / or a lane in another direction of the crossroad except the direction of travel of the vehicle, which is not limited by the embodiments of the present application.

[0110] It can be seen that the optional embodiment can determine the first target lane corresponding to the test project according to the current tested test project when the target driving scene includes the driving test scene, determine the second target lane according to the risk factor type corresponding to the traffic environment scene when the target driving scene includes the traffic environment scene, and determine the current lane of the vehicle and the target lane as the target monitoring lane corresponding to the vehicle, which can improve the analysis accuracy and analysis flexibility of the target driving scene, and is beneficial to improve the determination flexibility and determination accuracy of the target lane, and further beneficial to improve the determination accuracy of the target monitoring lane.

[0111] In the optional embodiment, optionally, according to the test project, the first target lane corresponding to the test project is determined based on the positioning technology and the pre-acquired map data, which can include the following operations:

[0112] When the test project is the vehicle merging project / vehicle lane changing project, the merging direction / lane changing direction of the vehicle is recognized, and the first lane required to be passed and / or required to be entered by the lane with respect to the merging direction / lane changing direction is determined as the first target lane based on the positioning technology and the pre-acquired map data;

[0113] When the test project is the vehicle merging project, the second lane required to be passed and / or required to be entered by the vehicle U-turn is determined as the first target lane based on the positioning technology and the map data.

[0114] It can be seen that the optional embodiment can further determine the lane required to be passed / entered by the test content of the test project as the first target lane according to different test projects, which can further improve the determination accuracy of the first target lane and is beneficial to further improve the determination accuracy of the target monitoring lane.

[0115] In the optional embodiment, optionally, according to the traffic data and the real-time driving data, it is judged whether the vehicle has a collision risk, and a risk judgment result is obtained, which can include the following operations:

[0116] According to the traffic data and the real-time driving data, the traffic flow parameter corresponding to the target monitoring lane is calculated; the traffic flow parameter includes one or more combinations of the average speed, the traffic density, the traffic flow and the time index in the target area of the target monitoring lane;

[0117] According to the traffic data and the real-time driving data, the risk parameter corresponding to the vehicle is calculated based on the pre-determined traffic flow analysis model;

[0118] Based on the determined collision risk evaluation algorithm, according to the traffic flow parameter and the risk parameter, it is judged whether any adjacent object has a distance less than or equal to the preset distance between the vehicle within a first preset time length after the current time;

[0119] When it is judged that there is any adjacent object whose distance to the vehicle is less than or equal to the preset distance within the first preset time period, it is determined that the vehicle is at risk of collision.

[0120] When it is judged that there is no adjacent object whose distance to the vehicle is less than or equal to the preset distance within the first preset time period, it is determined that the vehicle is not at risk of collision.

[0121] Optionally, the average vehicle speed can be obtained by calculating the average speed of all vehicles in the target area, the traffic density can be obtained by dividing the number of vehicles in the target monitoring lane by the length of the lane, the traffic flow can be obtained by counting the number of vehicles passing a point per unit time, and the time index is used to synchronize the vehicle system time, which is not limited by the embodiments of the present application.

[0122] Optionally, the traffic flow analysis model can include a GM car-following model and / or a safety distance model. The GM car-following model can be used to simulate the influence of the preceding vehicle on the driving state of the following vehicle, especially in high-speed driving and dense traffic. The model includes the relationship between acceleration and the speed difference, distance, etc. of the preceding vehicle. Specifically:

[0123] a n (t)=λΔv n (t)+γΔx n (t)

[0124] wherein a n (t) is the acceleration of the nth vehicle at time t, Δv n (t) is the speed difference between the nth vehicle and the preceding vehicle, Δx n (t) is the distance between the nth vehicle and the preceding vehicle, and λ and γ are model parameters.

[0125] The safety distance model can be used to ensure that there is enough safety distance between vehicles to avoid collision. Specifically:

[0126]

[0127] wherein d safe is the required safety distance, v is the current speed, t react is the reaction time, and a drc is the maximum deceleration.

[0128] The risk parameter can include at least one parameter involved in the above-mentioned models, which is not limited by the embodiments of the present application.

[0129] Optionally, the collision risk assessment algorithm can include a single-target-oriented assessment method and / or a deterministic assessment method; the single-target-oriented assessment method mainly starts from the trajectories of the host vehicle (i.e., the vehicle being assessed) and other traffic participants (such as pedestrians, other vehicles, etc.) and assesses the risk through collision detection; the deterministic assessment method uses a simplified physical model to describe the motion of traffic participants and selects an index to represent the risk, and when the index calculation result exceeds a certain threshold, it is considered that there is a risk. Further optionally, the index used by the deterministic assessment method can include one or more combinations of time-to-collision (TTC), time headway (THW), time to lane crossing (TLC), and minimal safety margin (MSM), which are not limited by the embodiments of the present application; the time-to-collision TTC = distance between two vehicles / (speed of rear vehicle - speed of front vehicle); the time headway is the maximum reaction time of the driver of the rear vehicle in a vehicle queue traveling in the same lane when the current vehicle brakes, which is calculated by measuring the time difference between the front and rear vehicles passing the same point; the time to lane crossing refers to the time taken by the vehicle from the current position to deviate from the lane, which is used to assess whether the vehicle can take timely measures before lane deviation; the minimal safety margin is the minimum safety distance that should be maintained between vehicles, which is calculated according to the braking performance and current speed of the vehicle, and the embodiments of the present application are not limited.

[0130] Further optionally, the collision risk assessment algorithm can be determined by the following method: from all the preset collision risk assessment algorithms, select the preset collision risk assessment algorithm that matches the target driving scene as the above-mentioned collision risk assessment algorithm, which is not limited by the embodiments of the present application. For example, when following a vehicle on a highway, the time headway (THW) and the time-to-collision (TTC) can be more critical; while in complex scenarios such as lane changing or intersections, the time to lane crossing (TLC) and the minimal safety margin (MSM) can be more important, which are not limited by the embodiments of the present application.

[0131] Optionally, the preset distance can be a pre-set safety distance; for example, when it is predicted that any adjacent vehicle may enter an unsafe distance within a few seconds (e.g., 3 seconds) in the future, it is determined that the vehicle has a collision risk, which is not limited by the embodiments of the present application.

[0132] It can be seen that the optional embodiment can also calculate the traffic flow parameter corresponding to the target monitoring lane and the risk parameter corresponding to the vehicle according to the traffic data and the real-time driving data first, and then judge whether there is any adjacent object whose distance with the vehicle is less than or equal to the preset distance within the first preset time period after the current time according to the traffic flow parameter and the risk parameter based on the collision risk evaluation algorithm, if the judgment result is yes, it is determined that the vehicle has a collision risk, otherwise it is determined that the vehicle has no collision risk, which can improve the analysis flexibility and analysis accuracy of the traffic data and the real-time driving data, thereby facilitating to improve the determination accuracy of the traffic flow parameter and the risk parameter, and further facilitating to improve the judgment accuracy of whether the distance between the adjacent object and the vehicle is in the preset range, and improve the precision and reliability of the risk judgment result.

[0133] In the optional embodiment, optionally, the method can further include the following operations:

[0134] According to the traffic flow parameter, the traffic condition corresponding to the target monitoring lane is evaluated; the traffic condition includes traffic flow smoothness and / or road congestion degree.

[0135] Optionally, the average speed can be used to evaluate the traffic flow smoothness of the target monitoring lane, and the traffic density can be used to evaluate the congestion degree of the target monitoring lane, which is not limited in the embodiment of the application.

[0136] Embodiment two

[0137] Please refer to Figure 2 , Figure 2 is a flowchart of a vehicle collision risk dynamic judgment method disclosed by the embodiment of the application. Wherein, Figure 2 The vehicle collision risk dynamic judgment method described can be applied to a vehicle collision risk dynamic judgment device, which can include one of a dynamic judgment equipment, a dynamic judgment terminal, a dynamic judgment system and a server, wherein the server includes a local server or a cloud server, which is not limited in the embodiment of the application; the device can be applied to a vehicle or a control system capable of controlling the driving of the vehicle, optionally, the vehicle can be a driving training vehicle or a driving test vehicle, or other vehicles, which are not limited in the embodiment of the application. As Figure 2 The vehicle collision risk dynamic judgment method can include the following operations:

[0138] 201, determining a target driving scene corresponding to the vehicle.

[0139] 202, determining a target monitoring lane based on the positioning technology and the pre-acquired map data according to the target driving scene.

[0140] 203, acquiring traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle.

[0141] In the embodiment of the present application, the traffic data comprises vehicle road condition data and / or motion data of adjacent objects.

[0142] 204. Determine whether the vehicle is at risk of collision according to the traffic data and the real-time driving data, and obtain a risk determination result.

[0143] 205. When the risk determination result indicates that the vehicle is at risk of collision, generate a driving adjustment guide in at least one guide form about the collision risk.

[0144] In the embodiment of the present application, the driving adjustment guide is used to prompt the driver to adjust the driving operation of the vehicle; optionally, the guide form can include one or a combination of the following forms: sound and light warning form, voice prompt form, visual image / text prompt form, and other guide forms, which are not limited in the embodiment of the present application.

[0145] 206. Determine whether the driver performs the driving adjustment operation corresponding to the driving adjustment guide and whether the matching degree between the driving adjustment operation and the driving adjustment guide is higher than or equal to a preset matching degree within a second preset time period.

[0146] In the embodiment of the present application, when the determination result of step 206 is no, that is, step 206 determines that the driver does not perform the driving adjustment operation corresponding to the driving adjustment guide or the matching degree between the driving adjustment operation and the driving adjustment guide is lower than the preset matching degree within the second preset time period, the operation of step 207 is performed; optionally, when the determination result of step 206 is yes, that is, step 206 determines that the driver performs the driving adjustment operation corresponding to the driving adjustment guide and the matching degree between the driving adjustment operation and the driving adjustment guide is higher than or equal to the preset matching degree within the second preset time period, the operation of step 204 can be re-executed to continue to determine whether the vehicle is at risk of collision in real time, which is not limited in the embodiment of the present application.

[0147] In the embodiment of the present application, optionally, the matching degree between the driving adjustment operation and the driving adjustment guide can be determined by the following way:

[0148] Determine the time difference between the execution time of the driving adjustment operation and the guide time corresponding to the driving adjustment guide; determine the first matching degree between the operation sequence of the driving adjustment operation and the guide sequence corresponding to the driving adjustment guide; determine the second matching degree between the operation parameter (such as one or a combination of the following: steering wheel angle, accelerator mileage, brake mileage, and vehicle body heading angle) of the driving adjustment operation and the guide operation parameter corresponding to the driving adjustment guide; and determine the matching degree between the driving adjustment operation and the driving adjustment guide according to the time difference, the first matching degree and the second matching degree, which is not limited in the embodiment of the present application.

[0149] 207、based on the vehicle corresponding auxiliary driving function, control the vehicle to perform the driving adjustment instruction corresponding auxiliary driving operation.

[0150] In the embodiment of the application, optionally, the vehicle corresponding auxiliary driving function can be any auxiliary driving function in the ADAS (Advanced Driving Assistance System, high-level driving assistance system), or any auxiliary driving function in other auxiliary driving system, which is not limited in the embodiment of the application; for example, the auxiliary driving function can include at least one of the automatic brake function, the automatic speed reduction function, the automatic lane changing function, and the automatic turning function, which is not limited in the embodiment of the application.

[0151] In the embodiment of the application, for other detailed description of steps 201-204, please refer to the detailed description of steps 101-104 in embodiment one, which will not be repeated here.

[0152] It can be seen that the method described in the embodiment of the application can determine the target monitoring lane based on the determined target driving scene of the vehicle and based on the positioning technology and map data, and then obtain the traffic data of the target monitoring lane and the real-time driving data of the vehicle, so as to determine whether the vehicle has a collision risk according to the traffic data and the real-time driving data, which can comprehensively and accurately monitor the driving state of the vehicle, so as to determine in real time whether there is a collision risk based on the road condition and the vehicle driving data, improve the timeliness and accuracy of the vehicle collision risk determination, and be beneficial to timely discovering potential collision risks and timely taking risk response measures, thereby improving the driving safety. In addition, when the vehicle has a collision risk, the driving adjustment instruction can be generated, and it is determined whether the driver performs the corresponding driving adjustment operation within the preset time and whether the operation is performed according to the driving adjustment instruction. If the result is no, the vehicle is controlled to perform the auxiliary driving operation based on the auxiliary driving function, which can improve the driving instruction accuracy of the driver in the case of collision risk, and improve the timeliness and accuracy of the auxiliary driving function in the case of the driver's failure to respond or failure to operate in time, which is beneficial to improve the risk response ability of the vehicle, thereby improving the driving safety.

[0153] In an optional embodiment, when the risk judgment result is used to indicate that the vehicle has a collision risk, the method can further include the following operations:

[0154] When the target driving scene includes a driving test scene and the vehicle is in a test state, the target test duration is obtained by extending the preset test duration of the vehicle for the current test item according to the collision risk, the driving adjustment operation and the traffic data.

[0155] detect a first driving performance of the driver to the vehicle within a target examination duration;

[0156] evaluate a first examination result of the driver on the current examination item according to the target examination duration and the first driving performance.

[0157] The first driving performance can include one or more of a combination of action behavior data of the driver (for example: whether the driver has an observation behavior), vehicle control operation data of the driver (for example: whether the driver correctly shifts gears, whether the driver brakes in time, etc.), and driving data of the vehicle within the target examination duration (for example: driving speed, driving direction, whether the examination content is completed according to the requirements of the examination item, etc.), and the embodiments of the present application are not limited thereto.

[0158] The first examination result can be a score and / or an examination result (for example: pass / fail) of the driver on the current examination item, and the embodiments of the present application are not limited thereto.

[0159] It can be seen that the optional embodiment can extend the preset examination duration of the current examination item according to the collision risk, the driving adjustment operation and the traffic data when the vehicle is in the examination state, obtain the target examination duration, detect the first driving performance of the driver to the vehicle within the target examination duration, and evaluate the first examination result in combination with the target examination duration and the first driving performance. The examination duration of the driving examination and training can be flexibly adjusted in an emergency, thereby improving the adjustment flexibility of the examination standard of the driving examination and training, and further facilitating giving the driver sufficient time to complete the examination in the case of improving the emergency ability of the driver and ensuring the driving safety, thereby facilitating improving the evaluation accuracy and evaluation comprehensiveness of the examination result.

[0160] In the optional embodiment, optionally, generating the at least one driving adjustment instruction in the form of an instruction about the collision risk can include the following operations:

[0161] When the above collision risk is a collision risk on the current lane of the vehicle (for example: emergency braking), it is determined whether the risk level of the adjacent lane is lower than the risk level of the current lane. When it is determined that the risk level of the adjacent lane is lower than the risk level of the current lane, a lane changing / merging suggestion of the vehicle is generated, and at least one driving adjustment instruction in the form of an instruction about the collision risk is generated according to the lane changing / merging suggestion of the vehicle; or,

[0162] The traffic sign of the current lane and the lane type of the current lane are detected, and based on the traffic sign and the lane type, the feasibility of the vehicle U-turn is evaluated based on map data and traffic rules; when the feasibility of the vehicle U-turn is greater than or equal to a preset feasibility, a vehicle U-turn suggestion is generated, and based on the vehicle U-turn suggestion, at least one driving adjustment guidance in the form of guidance about the collision risk is generated.

[0163] Among them, when the lane is a U-turn lane, the feasibility of the vehicle U-turn is higher.

[0164] Optionally, the driving adjustment guidance can be used to guide the driver to perform emergency operations such as braking, deceleration, etc., and the embodiments of the present application are not limited.

[0165] It can be seen that the optional embodiment can also suggest the driver to change lanes / merge lanes if the collision risk is the collision risk of the current lane, and when it is judged that the risk situation of the adjacent lane is better than that of the current lane; or, by detecting the traffic sign of the current lane and the lane type, the feasibility of the vehicle U-turn is evaluated, and if the feasibility is high, the driver is suggested to drive in a U-turn manner, which can improve the generation accuracy of the driving adjustment guidance and improve the matching degree of the driving adjustment guidance and the actual driving situation, which is beneficial to improve the risk handling accuracy.

[0166] In the optional embodiment, optionally, based on the collision risk, the driving adjustment operation, and the traffic data, the preset evaluation time length of the vehicle for the current evaluation item is extended to obtain a target evaluation time length, which can include the following operations:

[0167] The risk level of the collision risk is evaluated, and a risk level coefficient corresponding to the risk level is determined;

[0168] According to the traffic data, the road condition complexity level corresponding to the target monitoring lane is evaluated, and a road condition complexity coefficient corresponding to the road condition complexity level is determined;

[0169] According to the reaction time of the driver to the driving adjustment operation, the emergency reaction coefficient of the driver is evaluated;

[0170] The driving intervention situation corresponding to the auxiliary driving function is detected, and a driving intervention coefficient corresponding to the driving intervention situation is determined;

[0171] Based on the determined extension formula, the target evaluation time length is determined based on the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient, and the preset evaluation time length of the current evaluation item.

[0172] Among them, optionally, the above extension formula can be as follows:

[0173] Target evaluation time length = preset evaluation time length + (risk level coefficient * road condition complexity coefficient + student reaction time coefficient + ADAS intervention degree coefficient) * adjustment factor;

[0174] In the formula, the student reaction time is the emergency response coefficient, and the ADAS intervention degree coefficient is the driving intervention coefficient; further, the adjustment factor in the formula can be determined by iterative optimization, or can be determined according to the driving experience of the driver and / or the driving environment, and the embodiments of the present application are not limited.

[0175] In the formula, the risk level can include one of a low risk level, a medium risk level, and a high risk level; for example, the higher the risk level, the longer the required extension time, that is, the higher the risk level coefficient; the higher the road condition complexity level, the more complex the road condition, and the longer the time required for the driver to handle the emergency, that is, the higher the road condition complexity coefficient; the longer the reaction time, the weaker the ability of the driver to handle the emergency, so more time is given to the driver to give the driver more opportunities, that is, the higher the emergency response coefficient; the higher the intervention degree of the auxiliary driving function, the fewer the emergency measures taken by the driver, and the more the extension time required to evaluate the emergency handling ability of the driver in the emergency, that is, the higher the driving intervention coefficient; for example, if the vehicle does not use the auxiliary driving function, the driving intervention coefficient can be 0, and the embodiments of the present application are not limited.

[0176] It can be seen that the optional embodiment can also evaluate the risk level coefficient corresponding to the risk level of the collision risk, the road condition complexity coefficient corresponding to the target monitoring lane, the emergency response coefficient corresponding to the driver, and the driving intervention coefficient corresponding to the auxiliary driving function, and determine the target examination time based on the extension formula and the preset examination time of the current examination item, which can improve the analysis comprehensiveness of the driving road condition and the driving condition, thereby improving the extension accuracy of the examination time of the current examination item, and is beneficial to improving the determination accuracy of the target examination time.

[0177] In the optional embodiment, based on the determined extension formula, the target examination time can be determined according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and the preset examination time of the current examination item, which can include the following operations:

[0178] Based on the determined extension formula, the extension examination time corresponding to the current examination item is calculated according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and the preset examination time of the current examination item;

[0179] It is judged whether the extension examination time is less than or equal to the preset maximum time threshold;

[0180] When it is judged that the extension examination time is less than or equal to the maximum time threshold, the extension examination time is determined as the target examination time;

[0181] When it is judged that the extended examination duration is greater than the maximum duration threshold, the maximum duration threshold is determined as the target examination duration.

[0182] It can be seen that the optional embodiment can further determine the extended examination duration as the target examination duration if the extended examination duration is less than or equal to the maximum duration threshold, or determine the maximum duration threshold as the target examination duration if the extended examination duration is greater than the maximum duration threshold after calculating the extended examination duration corresponding to the current examination item based on the extension formula, which can further improve the determination accuracy of the target examination duration, and setting the maximum duration threshold for the examination duration can improve the adjustment reliability of the examination duration.

[0183] In yet another optional embodiment, when the risk judgment result is used to indicate that the vehicle does not have a collision risk, the method can further include the following operations:

[0184] Detecting a second driving performance of the driver with respect to the vehicle within a preset examination duration of the current examination item;

[0185] Evaluating a second examination result of the driver with respect to the current examination item according to the preset examination duration and the second driving performance.

[0186] It can be seen that the optional embodiment can directly evaluate the second examination result according to the second driving performance within the original preset examination duration when the vehicle does not have a collision risk, which can improve the examination flexibility and the evaluation flexibility of the examination result.

[0187] In the embodiments of the present application, exemplary, Figure 3 is a scene schematic diagram of a scene to which a vehicle collision risk dynamic discrimination method disclosed by the embodiments of the present application is applicable, wherein, Figure 3 (A) is a vehicle lane changing scene, Figure 3 (B) is a vehicle U-turn scene, which is not limited by the embodiments of the present application. It should be noted that, Figure 3 the scene schematic diagram shown is only used to represent one of the scenes to which the vehicle collision risk dynamic discrimination method is applicable, which is not used to limit other scenes to which the vehicle collision risk dynamic discrimination method and device are applicable, and Figure 1 the scene schematic diagram shown does not limit the specific road conditions, vehicle conditions, etc. in the scene.

[0188] Embodiment three

[0189] Please refer to Figure 4 , Figure 4 is a structure schematic diagram of a vehicle collision risk dynamic discrimination device disclosed by the embodiments of the present application. Wherein, Figure 4The described dynamic discrimination device of vehicle collision risk can include one of a dynamic discrimination device, a dynamic discrimination terminal, a dynamic discrimination system and a server, wherein the server includes a local server or a cloud server, and embodiments of the application are not limited; the device can be applied to a vehicle or a control system capable of controlling the driving of the vehicle, and optionally, the vehicle can be a driving training vehicle or a driving test vehicle, or can be another vehicle, and embodiments of the application are not limited. As shown in the figure, the dynamic discrimination device of vehicle collision risk can include: Figure 4 The dynamic discrimination device of vehicle collision risk can include:

[0190] The determining module 301 is configured to determine a target driving scene corresponding to the vehicle.

[0191] The determining module 301 is further configured to determine a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene.

[0192] The acquiring module 302 is configured to acquire traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle; the traffic data includes road condition data and / or motion data of a neighboring object.

[0193] The judging module 303 is configured to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data, and obtain a risk judgment result.

[0194] It can be seen that the device described in the embodiments of the application can determine a target monitoring lane based on positioning technology and map data according to a determined target driving scene corresponding to the vehicle, and then acquire traffic data of the target monitoring lane and real-time driving data of the vehicle, so as to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data, thereby comprehensively and accurately monitoring the driving state of the vehicle, and thus being capable of judging in real time whether there is a collision risk based on road conditions and vehicle driving data, improving the timeliness and accuracy of the discrimination of the vehicle collision risk, and being beneficial to timely discovering potential collision risks and timely taking risk response measures, and thus being beneficial to improving driving safety.

[0195] In an optional embodiment, the specific manner in which the determining module 301 determines the target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene can include:

[0196] When the target driving scene includes a driving test / training scene, the current test / training project being tested / trained in the driving test / training scene is analyzed; a first target lane corresponding to the test / training project is determined based on positioning technology and pre-acquired map data according to the test / training project; the first target lane includes at least one lane that the vehicle needs to pass through or needs to enter in the test / training project;

[0197] When the target driving scene includes a traffic environment scene, a risk factor type corresponding to the traffic environment scene is determined; and a second target lane is determined based on the risk factor type, the positioning technology and the map data; the second target lane includes at least one adjacent lane corresponding to the vehicle;

[0198] The current lane and the target lane where the vehicle is located are determined as the target monitoring lane corresponding to the vehicle; the target lane includes the first target lane and / or the second target lane.

[0199] It can be seen that the apparatus described in the optional embodiment can determine the first target lane corresponding to the driving test project according to the current driving test project when the target driving scene includes a driving test scene, determine the second target lane according to the risk factor type corresponding to the traffic environment scene when the target driving scene includes a traffic environment scene, and determine the current lane and the target lane where the vehicle is located as the target monitoring lane corresponding to the vehicle, thereby improving the analysis accuracy and flexibility of the target driving scene, improving the determination flexibility and accuracy of the target lane, and further improving the determination accuracy of the target monitoring lane.

[0200] In the optional embodiment, the specific manner in which the judgment module 303 determines whether the vehicle has a collision risk according to the traffic data and the real-time driving data to obtain the risk judgment result can include:

[0201] The traffic flow parameter corresponding to the target monitoring lane is calculated according to the traffic data and the real-time driving data; the traffic flow parameter includes a combination of one or more of the average speed, the traffic density, the traffic flow and the time index in the target region of the target monitoring lane;

[0202] The risk parameter corresponding to the vehicle is calculated according to the traffic data and the real-time driving data based on a pre-determined traffic flow analysis model;

[0203] Based on the determined collision risk evaluation algorithm, it is determined whether there is any adjacent object whose distance from the vehicle is less than or equal to the preset distance within the first preset time length after the current time according to the traffic flow parameter and the risk parameter;

[0204] When it is determined that there is any adjacent object whose distance from the vehicle is less than or equal to the preset distance within the first preset time length, it is determined that the vehicle has a collision risk;

[0205] When it is determined that there is no adjacent object whose distance from the vehicle is less than or equal to the preset distance within the first preset time length, it is determined that the vehicle has no collision risk.

[0206] It can be seen that the device described in the optional embodiment can also calculate the traffic flow parameter corresponding to the target monitoring lane and the risk parameter corresponding to the vehicle according to the traffic data and the real-time driving data first, and then judge whether there is any adjacent object whose distance with the vehicle is less than or equal to the preset distance within the first preset time period after the current time based on the collision risk evaluation algorithm according to the traffic flow parameter and the risk parameter. If the judgment result is yes, it is determined that the vehicle has a collision risk, otherwise it is determined that the vehicle does not have a collision risk. The analysis flexibility and analysis accuracy of the traffic data and the real-time driving data can be improved, thereby facilitating the improvement of the determination accuracy of the traffic flow parameter and the risk parameter, and further facilitating the improvement of the judgment accuracy of whether the distance between the adjacent object and the vehicle is within the preset range, and the precision and reliability of the risk judgment result.

[0207] In an optional embodiment, as shown in Figure 5 The device can further include:

[0208] The generation module 304 is configured to generate a driving adjustment instruction about the collision risk in at least one guidance form when the risk judgment result indicates that the vehicle has a collision risk, and the driving adjustment instruction is used to prompt the driver to adjust the driving operation of the vehicle.

[0209] The judgment module 303 is further configured to judge whether the driving adjustment operation corresponding to the driving adjustment instruction is performed by the driver within the second preset time period and whether the matching degree between the driving adjustment operation and the driving adjustment instruction is higher than or equal to the preset matching degree.

[0210] The control module 305 is configured to control the vehicle to perform the auxiliary driving operation corresponding to the driving adjustment instruction based on the auxiliary driving function corresponding to the vehicle when it is judged that the driving adjustment operation corresponding to the driving adjustment instruction is not performed by the driver within the second preset time period or the matching degree between the driving adjustment operation and the driving adjustment instruction is lower than the preset matching degree.

[0211] It can be seen that the device described in the optional embodiment can generate a driving adjustment instruction when the vehicle has a collision risk, and judge whether the driver performs the corresponding driving adjustment operation within a preset time and whether the operation is performed according to the driving adjustment instruction. If the judgment result is no, the vehicle is controlled to perform the auxiliary driving operation based on the auxiliary driving function, which can improve the driving instruction accuracy of the driver in the case of collision risk, and improve the timeliness of the use of the auxiliary driving function and the accuracy of the vehicle control in the case that the driver fails to react or fails to operate correctly, thereby facilitating the improvement of the risk response ability of the vehicle, and further facilitating the improvement of the driving safety.

[0212] In the optional embodiment, optionally, as shown in Figure 5As shown, the apparatus can further include:

[0213] The time length extension module 306 is configured to, when the risk judgment result indicates that the vehicle is at risk of collision, and when the target driving scene includes a driving test scene and the vehicle is in a test state, extend a preset test time length of the vehicle for a current test item according to the collision risk, the driving adjustment operation, and the traffic data, to obtain a target test time length;

[0214] The detection module 307 is configured to detect a first driving performance of the driving personnel for the vehicle within the target test time length.

[0215] The evaluation module 308 is configured to evaluate a first test result of the driving personnel for the current test item according to the target test time length and the first driving performance.

[0216] It can be seen that the apparatus described in the optional embodiment can also extend the preset test time length of the current test item to obtain the target test time length according to the collision risk, the driving adjustment operation, and the traffic data when the vehicle is in the test state, and detect the first driving performance of the driving personnel for the vehicle within the target test time length, and then evaluate the first test result in combination with the target test time length and the first driving performance, which can flexibly adjust the test time length of the driving test in an emergency, thereby improving the adjustment flexibility of the test standard of the driving test, and further facilitating giving the driving personnel sufficient time to complete the test while improving the emergency response ability of the driving personnel and ensuring driving safety, thereby improving the evaluation accuracy and comprehensiveness of the test result.

[0217] In the optional embodiment, optionally, the specific manner in which the time length extension module 306 extends the preset test time length of the vehicle for the current test item to obtain the target test time length according to the collision risk, the driving adjustment operation, and the traffic data can include:

[0218] evaluating a risk level of the collision risk and determining a risk level coefficient corresponding to the risk level;

[0219] evaluating a road condition complexity level of the target monitoring lane according to the traffic data, and determining a road condition complexity coefficient corresponding to the road condition complexity level;

[0220] evaluating an emergency response coefficient of the driving personnel according to the reaction time length of the driving personnel for the driving adjustment operation;

[0221] detecting a driving intervention situation of the auxiliary driving function and determining a driving intervention coefficient corresponding to the driving intervention situation;

[0222] based on the determined extension formula, determining the target test time length according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and the preset test time length of the current test item.

[0223] It can be seen that the device described in the optional embodiment can also determine the target examination time by evaluating the risk level coefficient corresponding to the risk level of the collision risk, the road condition complexity coefficient corresponding to the target monitoring lane, the emergency response coefficient corresponding to the driver, and the driving intervention coefficient corresponding to the auxiliary driving function, and determining the target examination time based on the extension formula and the preset examination time of the current examination item, which can improve the analysis comprehensiveness of the driving road condition and the driving condition, thereby improving the extension accuracy of the examination time of the current examination item, and is beneficial to improving the determination accuracy of the target examination time.

[0224] In the optional embodiment, optionally, the time extension module 306 can determine the specific manner of determining the target examination time based on the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and the preset examination time of the current examination item according to the determined extension formula, which can include:

[0225] calculating the extension examination time corresponding to the current examination item based on the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and the preset examination time of the current examination item according to the determined extension formula;

[0226] determining whether the extension examination time is less than or equal to a preset maximum time threshold;

[0227] when it is determined that the extension examination time is less than or equal to the maximum time threshold, determining the extension examination time as the target examination time;

[0228] when it is determined that the extension examination time is greater than the maximum time threshold, determining the maximum time threshold as the target examination time.

[0229] It can be seen that the device described in the optional embodiment can further determine the extension examination time as the target examination time when the extension examination time is less than or equal to the maximum time threshold, or determine the maximum time threshold as the target examination time when the extension examination time is greater than the maximum time threshold after calculating the extension examination time corresponding to the current examination item based on the extension formula, which can further improve the determination accuracy of the target examination time, and setting the maximum time threshold for the examination time can improve the adjustment reliability of the examination time.

[0230] Embodiment Four

[0231] Please refer to Figure 6 , Figure 6 is another structure schematic diagram of a vehicle collision risk dynamic discrimination device disclosed by the embodiment of the application. As Figure 6 shown, the vehicle collision risk dynamic discrimination device can include:

[0232] a memory 401 storing executable program codes;

[0233] a processor 402 coupled to the memory 401;

[0234] The processor 402 invokes the executable program code stored in the memory 401 to execute part or all of the steps of the dynamic discrimination method of vehicle collision risk described in the embodiment one or the embodiment two of the present application.

[0235] Embodiment five

[0236] The embodiment of the present application discloses a computer storage medium, which stores computer instructions, and the computer instructions are used to execute part or all of the steps of the dynamic discrimination method of vehicle collision risk described in the embodiment one or the embodiment two of the present application when being invoked.

[0237] Embodiment six

[0238] The embodiment of the present application discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of the dynamic discrimination method of vehicle collision risk described in the embodiment one or the embodiment two.

[0239] The above described device embodiments are only schematic, wherein the modules described as separate components can or can not be physically separate, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0240] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary general hardware platforms through the above specific description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in the sense of contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, which includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM), or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other medium that can be used to carry or store data in a computer readable manner.

[0241] Finally, it should be noted that: the vehicle collision risk dynamic discrimination method and device disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dynamic identification method for vehicle collision risk, characterized in that: The method comprises: determining a target driving scene corresponding to a vehicle; determining a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene; acquiring traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle; the traffic data comprises road condition data and / or motion data of adjacent objects; judging whether the vehicle has a collision risk according to the traffic data and the real-time driving data, to obtain a risk judgment result; wherein the determining of the target monitoring lane based on the positioning technology and the pre-acquired map data according to the target driving scene comprises: when the target driving scene comprises a driving test scene, analyzing a current test item in the driving test scene; determining a first target lane corresponding to the test item based on the positioning technology and the pre-acquired map data according to the test item; the first target lane comprises at least one lane required to be passed through or required to be entered by the vehicle in the test item; when the target driving scene comprises a traffic environment scene, determining a risk factor type corresponding to the traffic environment scene; determining a second target lane based on the positioning technology and the map data according to the risk factor type; the second target lane comprises at least one adjacent lane corresponding to the vehicle; determining a current lane where the vehicle is located and the target lane as the target monitoring lane corresponding to the vehicle; the target lane comprises the first target lane or the second target lane.

2. The method of claim 1, wherein, The judging of whether the vehicle has a collision risk according to the traffic data and the real-time driving data to obtain a risk judgment result comprises: calculating a traffic flow parameter corresponding to the target monitoring lane according to the traffic data and the real-time driving data; the traffic flow parameter comprises a combination of one or more of average speed, traffic density, traffic flow and time index in a target area of the target monitoring lane; calculating a risk parameter corresponding to the vehicle based on a pre-determined traffic flow analysis model according to the traffic data and the real-time driving data; judging whether a distance between any of the adjacent objects and the vehicle is less than or equal to a preset distance within a first preset time period after a current time according to the traffic flow parameter and the risk parameter based on a collision risk evaluation algorithm; the collision risk evaluation algorithm comprises a single-target-oriented evaluation method and / or a deterministic evaluation method; wherein the single-target-oriented evaluation method is to evaluate the risk by collision detection from the trajectories of the host vehicle and other traffic participants; the deterministic evaluation method is to describe the motion of traffic participants by using a simplified physical model and select a certain index to represent the risk, when the index calculation result exceeds a certain threshold, it is considered that there is a risk; when it is judged that the distance between any of the adjacent objects and the vehicle is less than or equal to the preset distance within the first preset time period, it is determined that the vehicle has a collision risk. When it is judged that there is no any adjacent object within the first preset time period and the distance between the vehicle and the adjacent object is less than or equal to the preset distance, it is determined that the vehicle is not in the collision risk.

3. The method of claim 1 or 2, wherein When the risk judgment result indicates that the vehicle is in the collision risk, the method further comprises: generating a driving adjustment instruction in at least one guidance form about the collision risk, the driving adjustment instruction being used to prompt the driver to adjust the driving operation of the vehicle; judging whether the driving adjustment operation corresponding to the driving adjustment instruction is detected within a second preset time period and whether the matching degree between the driving adjustment operation and the driving adjustment instruction is higher than or equal to a preset matching degree; when it is judged that the driving adjustment operation corresponding to the driving adjustment instruction is not detected within the second preset time period or the matching degree between the driving adjustment operation and the driving adjustment instruction is lower than the preset matching degree, controlling the vehicle to perform an auxiliary driving operation corresponding to the driving adjustment instruction based on an auxiliary driving function corresponding to the vehicle.

4. The method of claim 3, wherein the step of determining the risk of collision comprises: When the risk judgment result indicates that the vehicle is in the collision risk, the method further comprises: when the target driving scene includes a driving test scene and the vehicle is in a test state, extending a preset test time period of the vehicle about a current test item to obtain a target test time period according to the collision risk, the driving adjustment operation and the traffic data; detecting a first driving performance of the driver for the vehicle within the target test time period; evaluating a first test result of the driver about the current test item according to the target test time period and the first driving performance.

5. The method of claim 4, wherein the step of determining the risk of collision comprises: The extending of the preset test time period of the vehicle about the current test item to obtain the target test time period according to the collision risk, the driving adjustment operation and the traffic data comprises: evaluating a risk level of the collision risk and determining a risk level coefficient corresponding to the risk level; evaluating a road condition complexity level of the target monitoring lane according to the traffic data and determining a road condition complexity coefficient corresponding to the road condition complexity level; evaluating an emergency reaction coefficient of the driver according to a reaction time of the driver about the driving adjustment operation; detecting a driving intervention situation of the auxiliary driving function and determining a driving intervention coefficient corresponding to the driving intervention situation; determining the target test time period based on the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient and the preset test time period of the current test item according to a determined extension formula; wherein the extension formula is as follows: target test time period = preset test time period + (risk level coefficient * road condition complexity coefficient + emergency reaction coefficient + driving intervention coefficient) * adjustment factor.

6. The method of claim 5, wherein the step of determining the risk of collision comprises: The determining of the target test time period based on the risk level coefficient, the road condition complexity coefficient, the emergency reaction coefficient, the driving intervention coefficient and the preset test time period of the current test item according to the determined extension formula comprises: Based on the determined extension formula, the extension examination duration corresponding to the current examination item is calculated according to the risk level coefficient, the road condition complexity coefficient, the emergency response coefficient, the driving intervention coefficient, and a preset examination duration of the current examination item; It is judged whether the extension examination duration is less than or equal to a preset maximum duration threshold; When it is judged that the extension examination duration is less than or equal to the maximum duration threshold, the extension examination duration is determined as the target examination duration; When it is judged that the extension examination duration is greater than the maximum duration threshold, the maximum duration threshold is determined as the target examination duration.

7. A dynamic determination device for vehicle collision risk, characterized in that: The device comprises: A determination module is configured to determine a target driving scene corresponding to a vehicle; The determination module is further configured to determine a target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene; An acquisition module is configured to acquire traffic data corresponding to the target monitoring lane and real-time driving data of the vehicle; the traffic data comprises road condition data and / or motion data of adjacent objects; A judgment module is configured to judge whether the vehicle has a collision risk according to the traffic data and the real-time driving data, and obtain a risk judgment result; The specific manner in which the determination module determines the target monitoring lane based on positioning technology and pre-acquired map data according to the target driving scene comprises: When the target driving scene comprises a driving examination scene, an examination item currently being examined in the driving examination scene is analyzed; a first target lane corresponding to the examination item is determined based on positioning technology and pre-acquired map data according to the examination item; the first target lane comprises at least one lane that needs to be passed through or entered by the vehicle in the examination item; When the target driving scene comprises a traffic environment scene, a risk factor type corresponding to the traffic environment scene is determined; a second target lane is determined based on the positioning technology and the map data according to the risk factor type; the second target lane comprises at least one adjacent lane corresponding to the vehicle; A current lane in which the vehicle is located and the target lane are determined as the target monitoring lane corresponding to the vehicle; the target lane comprises the first target lane or the second target lane.

8. A dynamic determination device for vehicle collision risk, characterized in that: The device comprises: A memory storing executable program codes; A processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the vehicle collision risk dynamic judgment method according to any one of claims 1-6.

9. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which are invoked to execute the vehicle collision risk dynamic judgment method according to any one of claims 1-6.

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