Road 4D risk occupation assessment method and device based on vehicle-road cloud collaboration framework

Abstract

The invention relates to the technical field of intelligent traffic, in particular to a road 4D risk occupancy assessment method and device based on a vehicle-road cloud collaboration framework, and the method comprises the steps: dynamically calling different sampling point sets; acquiring risk factors influencing the driving safety of the ICV; converting the dynamic and static factors into geometric objects to obtain a plurality of dynamic and static factors in a preset sensing range of each sampling point; and performing risk quantification calculation on the plurality of dynamic and static factors and each sampling point to obtain risk values corresponding to the plurality of dynamic and static factors, accumulating the corresponding risk values, performing normalization to obtain final risk values of all sampling points in the sampling point set, and generating a road 4D risk occupancy map. Therefore, the problems that in the prior art, due to the fact that unified and efficient environment perception and risk assessment standards are lacked, the intelligent networked automobile is difficult to make timely and accurate decisions in the complex and changeable traffic environment, the calculation burden is increased, and the operation efficiency is reduced are solved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a road 4D risk occupancy assessment method and device based on a vehicle-road-cloud collaborative framework. Background Art

[0002] Intelligent connected vehicles (ICVs) can achieve highly automated driving functions by integrating advanced sensors, powerful computing capabilities, real-time communication systems and precise actuators. They can rely on data collected by vehicle sensors to understand the surrounding environment and achieve environmental perception. However, current environmental perception and risk assessment methods have not yet formed a unified standard and usually rely on basic information such as the location and speed of objects.

[0003] In the relevant technologies, multiple factors and spatiotemporal indicators such as vehicles, drivers, road conditions, other traffic participants, traffic flow, potential collision losses and behavior prediction are mainly incorporated into the theoretical framework of risk assessment to construct a new risk assessment method.

[0004] However, in related technologies, the use of parameters that are difficult to measure or subjectively evaluated may increase the complexity and uncertainty of the model and reduce the efficiency and accuracy of practical applications; the inclusion of human psychological and behavioral models may affect the generalization ability and credibility of the model; and the neglect of the impact of the road itself on risk affects the comprehensiveness and effectiveness of the assessment, which urgently needs to be improved. Summary of the invention

[0005] The present application provides a road 4D risk occupancy assessment method and device based on a vehicle-road-cloud collaborative framework to solve the problems in related technologies, such as the lack of unified and efficient environmental perception and risk assessment standards, which makes it difficult for intelligent connected vehicles to make timely and accurate decisions in complex and changeable traffic environments, increases the computing burden, and reduces operating efficiency.

[0006] The first aspect of the present application provides a road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework, comprising the following steps: dynamically retrieving different sampling point sets according to changes in the position and driving direction of an intelligent connected vehicle (ICV); obtaining risk factors that affect the driving safety of the ICV, wherein the risk factors are dynamic and static factors; converting the dynamic and static factors into geometric objects, so as to obtain multiple dynamic and static factors within a preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set; performing risk quantification calculation on the multiple dynamic and static factors and each sampling point to obtain risk values ​​corresponding to the multiple dynamic and static factors, respectively, to accumulate the corresponding risk values, and normalize them to obtain the final risk values ​​of all sampling points in the sampling point set, so as to generate a road 4D risk occupancy map based on the final risk values.

[0007] Optionally, in one embodiment of the present application, obtaining the multiple dynamic and static factors within the preset perception range of each sampling point includes: adopting a preset perception range radius strategy to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than the radius of the dynamic perception range.

[0008] Optionally, in one embodiment of the present application, the risk quantification calculation of the multiple dynamic and static factors and each sampling point includes: using the static factor risk quantification formula to quantify the static factors within the static perception range, and assigning fixed risk constant values ​​and corresponding weights to different types of static factors; using the dynamic factor risk quantification model to quantify the dynamic factors within the dynamic perception range to calculate the risk size of different types of dynamic factors.

[0009] Optionally, in one embodiment of the present application, the static factor risk quantification formula is:

[0010]

[0011] Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point;

[0012] The dynamic factor risk quantification model is:

[0013]

[0014] Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

[0015] The second aspect of the present application provides a road 4D risk occupancy assessment device based on a vehicle-road-cloud collaborative framework, including: a retrieval module, used to dynamically retrieve different sampling point sets according to changes in the position and driving direction of an intelligent connected vehicle ICV; an acquisition module, used to obtain risk factors that affect the driving safety of the ICV, wherein the risk factors are dynamic and static factors; a determination module, used to convert the dynamic and static factors into geometric objects, so as to obtain multiple dynamic and static factors within a preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set; an assessment module, used to perform risk quantification calculations on the multiple dynamic and static factors and each sampling point, to obtain risk values ​​corresponding to the multiple dynamic and static factors respectively, to accumulate the corresponding risk values, and to normalize them to obtain the final risk values ​​of all sampling points in the sampling point set, so as to generate a road 4D risk occupancy map based on the final risk values.

[0016] Optionally, in one embodiment of the present application, the determination module includes: adopting a preset perception range radius strategy to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than the radius of the dynamic perception range.

[0017] Optionally, in one embodiment of the present application, the evaluation module includes: an allocation unit, which is used to quantify the static factors within the static perception range using a static factor risk quantification formula, and assign fixed risk constant values ​​and corresponding weights to different types of static factors; and a calculation unit, which is used to quantify the dynamic factors within the dynamic perception range using a dynamic factor risk quantification model to calculate the risk size of different types of dynamic factors.

[0018] Optionally, in one embodiment of the present application, the static factor risk quantification formula is:

[0019]

[0020] Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point;

[0021] The dynamic factor risk quantification model is:

[0022]

[0023] Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

[0024] The third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework as described in the above embodiment.

[0025] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework.

[0026] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework.

[0027] The embodiment of the present application can dynamically call different sampling point sets according to the changes in the position and driving direction of the ICV, and obtain the risk factors that affect the driving safety of the intelligent networked vehicle ICV, and then convert the dynamic and static factors into geometric objects, so as to obtain multiple dynamic and static factors within the preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set, so as to perform risk quantification calculations on multiple dynamic and static factors and each sampling point, and obtain the risk values ​​corresponding to the multiple dynamic and static factors respectively, and accumulate the corresponding risk values, and normalize to obtain a 4D risk occupancy map composed of the final risk values ​​of all sampling points in the sampling point set, which is helpful to respond to dynamic changes in a timely manner and realize the quantitative assessment of risks. The use of the vehicle-road-cloud collaborative framework is conducive to optimizing resource allocation, improving the accuracy and efficiency of assessment, and can help ICV obtain the beyond-visual-range risk perception capability that a single vehicle does not have, effectively avoid risks in advance, and make more reasonable and long-term plans. Therefore, the problem that the intelligent networked vehicle is difficult to make timely and accurate decisions in a complex and changeable traffic environment, increases the calculation burden, and reduces the operating efficiency is solved in the related technology due to the lack of unified and efficient environmental perception and risk assessment standards.

[0028] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flowchart of a road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework provided according to an embodiment of the present application;

[0031] Figure 2 It is a schematic diagram of the principle of a road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework according to an embodiment of the present application;

[0032] Figure 3 A schematic diagram of an arrangement of road sampling points according to an embodiment of the present application;

[0033] Figure 4 A schematic diagram of a curve showing the relationship between the risk value and the estimated arrival time according to an embodiment of the present application;

[0034] Figure 5 A schematic diagram of a vehicle-road-cloud collaboration framework according to an embodiment of the present application;

[0035] Figure 6 A schematic diagram of the structure of a road 4D risk occupancy assessment device based on a vehicle-road-cloud collaborative framework provided according to an embodiment of the present application;

[0036] Figure 7 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0038] The following describes the road 4D risk occupancy assessment method and device based on the vehicle-road-cloud collaborative framework of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the related technologies mentioned in the above background technology center lack unified and efficient environmental perception and risk assessment standards, which makes it difficult for intelligent connected vehicles to make timely and accurate decisions in complex and changeable traffic environments, increases the computing burden, and reduces operating efficiency, the present application provides a road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework. In this method, different sampling point sets can be dynamically called according to the changes in the position and driving direction of the ICV, and the risk factors affecting the driving safety of the intelligent connected vehicle ICV are obtained. Then, the dynamic and static factors are converted into geometric objects, so as to obtain the risk factors affecting the driving safety of the intelligent connected vehicle ICV based on the geometric objects and each sampling point set. Sample points are obtained, and multiple dynamic and static factors within the preset perception range of each sampling point are obtained, so as to perform risk quantification calculations on multiple dynamic and static factors and each sampling point, and obtain the risk values ​​corresponding to multiple dynamic and static factors respectively, and accumulate the corresponding risk values, and normalize them to obtain a 4D risk occupancy map composed of the final risk values ​​of all sampling points in the sampling point set, which is helpful to respond to dynamic changes in a timely manner and realize quantitative assessment of risks. In addition, the use of the vehicle-road-cloud collaborative framework is conducive to optimizing resource allocation, improving the accuracy and efficiency of assessment, and can help ICV obtain the beyond-visual-range risk perception capability that a single vehicle does not have, effectively avoid risks in advance, and make more reasonable and long-term plans. In this way, the problem of the lack of unified and efficient environmental perception and risk assessment standards in related technologies, which makes it difficult for intelligent connected vehicles to make timely and accurate decisions in complex and changeable traffic environments, increases the computing burden, and reduces operating efficiency, is solved.

[0039] Specifically, Figure 1 A flowchart of a road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework provided in an embodiment of the present application.

[0040] like Figure 1 As shown, the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework includes the following steps:

[0041] In step S101, different sampling point sets are dynamically retrieved according to changes in the position and driving direction of the intelligent connected vehicle ICV.

[0042] During the actual execution process, the embodiment of the present application can obtain the road area where the ICV is located and the driving direction requirements after entering the intersection in the future based on the instructions sent by the ICV from the edge cloud platform, and dynamically retrieve different road sampling point sets stored in advance based on prior information from the edge cloud platform.

[0043] It is worth noting that the road sampling point set is dynamic. When the ICV changes its location to a different road area, the road sampling point set is also dynamically updated. Sampling points can be dynamically selected based on the road sampling point set, which is conducive to improving the timeliness and flexibility of data collection and providing a basis for subsequent calculation and evaluation.

[0044] In step S102, risk factors affecting the driving safety of the ICV are obtained, wherein the risk factors are dynamic and static factors.

[0045] It can be understood that risk factors refer to any factors in the environment that may pose a threat to the driving safety of ICV, including static factors and dynamic factors. Among them, static factors are relatively stationary in space and can be fixed obstacles on the road, such as road markings, railings, curbs, roadblocks, and potholes; dynamic factors are constantly changing in time and space, posing a direct and urgent threat to the driving safety of ICV, including but not limited to human-driven vehicles (HDVs), non-motor vehicles, and pedestrians.

[0046] Specifically, combined Figure 2 As shown, the embodiment of the present application can use the edge cloud platform to store data in advance based on prior information to obtain static factors that affect the driving safety of intelligent connected vehicles (ICVs), including fixed obstacles on the road, such as road markings, railings, curbs, roadblocks, and potholes. Dynamic factors can be sensed by multiple road-side sensing units, including human-driven vehicles (HDVs), non-motor vehicles, pedestrians, etc.

[0047] The embodiment of the present application can obtain the BEV perception results (structured data) sent by the road-side perception unit, and obtain the static and dynamic factors that affect the driving safety of the intelligent connected vehicle ICV, thereby helping to improve the accuracy of risk assessment, carry out targeted protection, and achieve efficient resource allocation, avoid unnecessary computing load, and save energy.

[0048] In step S103, the dynamic and static factors are converted into geometric objects, so as to obtain a plurality of dynamic and static factors within a preset perception range of each sampling point based on the geometric object and each sampling point in the sampling point set.

[0049] It is understandable that geometric objects include four forms of expression: single point, multiple points, line segments and vectors. The preset sensing range refers to the maximum range or area that can effectively detect, identify and track the target at the sampling point, which can be set by those skilled in the art according to actual conditions and is not limited here.

[0050] Specifically, combined Figure 2 and Figure 3As shown, the embodiment of the present application can lay out road risk sampling points in a grid along the road direction according to an adjustable resolution, and present traffic risk factors in the form of geometric objects. Furthermore, the embodiment of the present application can calculate the Euclidean distance between two sampling points, and the point and line segment vectors can calculate the vertical distance from the point to the line, so as to know whether the dynamic and static factors are within the perception range of the sampling point.

[0051] It is worth noting that the embodiments of the present application can lay out road risk sampling points in a grid shape, and use the road space as a perspective to evaluate the risk occupancy of the entire traffic road, achieve comprehensive monitoring of the entire road section, enhance environmental perception capabilities, and convert dynamic and static factors into geometric objects. It can understand the surrounding environment in a more precise geometric form, improve the accuracy and detail of perception, and provide data support for subsequent evaluations.

[0052] Optionally, in one embodiment of the present application, multiple dynamic and static factors within a preset perception range of each sampling point are obtained, including: adopting a preset perception range radius strategy to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than the radius of the dynamic perception range.

[0053] According to the characteristics of dynamic factors and static factors, the embodiment of the present application sets the perception range radius of static factors to be smaller than that of dynamic factors, which can more accurately perceive the specific occupied position of static factor risks. The larger dynamic factor perception range is aimed at the characteristics of large uncertainty in the movement of dynamic factors. Appropriately expanding the perception range of dynamic factors is conducive to improving the safety redundancy of road space. Different perception range radii are used according to the characteristics of different factors to improve the accuracy and safety of risk assessment.

[0054] In step S104, risk quantification calculation is performed on the multiple dynamic and static factors and each sampling point to obtain risk values ​​corresponding to the multiple dynamic and static factors respectively, and the corresponding risk values ​​are accumulated and normalized to obtain the final risk values ​​of all sampling points in the sampling point set, so as to generate a road 4D risk occupancy map based on the final risk values.

[0055] The embodiment of the present application can perform risk quantification calculation on multiple dynamic and static factors and each sampling point of a sampling point set of a partial road area that is called in real time, and obtain risk values ​​corresponding to the multiple dynamic and static factors respectively, and accumulate the corresponding risk values, and normalize them to obtain the final risk value of all sampling points in the sampling point set. Compared with 3D-Occupancy, only the risk value of each road surface unit needs to be calculated, and the amount of calculation will be much lower than traversing the occupancy of voxels in three-dimensional space.

[0056] It is worth noting that this application optimizes the driving risk field modeling in related technologies by removing the reliance on complex and difficult-to-quantify parameters, focusing on directly observable information and key risk factors, and using ETA as a key indicator to quantify the risk of dynamic factors, thereby achieving the quantification of risk. At the same time, this application can combine time prediction with risk valuation, and use perception results to predict the risks of the road environment in the next few seconds, which can support ICV to make plans and decisions in advance in dynamic mixed and dynamic traffic scenarios.

[0057] Optionally, in one embodiment of the present application, a risk quantification calculation is performed on multiple dynamic and static factors and each sampling point, including: using a static factor risk quantification formula to quantify the static factors within the static perception range, and assigning fixed risk constant values ​​and corresponding weights to different types of static factors; using a dynamic factor risk quantification model to quantify the dynamic factors within the dynamic perception range to calculate the risk size of different types of dynamic factors.

[0058] In some embodiments, although static factors are relatively static in space, they may play a key role in traffic accidents. In the embodiments of the present application, the static factors may be quantified using a static factor risk quantification formula. Optionally, in one embodiment of the present application, the static factor risk quantification formula is:

[0059]

[0060] Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point;

[0061] In addition, each type of static obstacle is assigned a fixed risk value and a corresponding weight. The embodiment of the present application can evaluate the risk constants and risk weights of different types of static factors based on the degree of harm to ICV occupants or traffic participants. For example, the weights of railings and curbs will cause collision damage to ICV and occupants, and the risk constant and weight will be higher. The solid line is a traffic rules issue, but will not cause direct harm like the first two, so the risk constant and weight will be smaller.

[0062] In other embodiments, dynamic factors are constantly changing in time and space, posing a direct and urgent threat to the driving safety of ICVs. The embodiments of the present application need to consider key information such as the position, speed, and heading angle of traffic participants, and quantify the risk size of these dynamic factors by introducing the Estimated Time of Arrival (ETA) indicator and a dynamic factor risk quantification model obtained by polynomial regression fitting. Optionally, in one embodiment of the present application, the dynamic factor risk quantification model is:

[0063]

[0064] Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

[0065] Specifically, in the embodiment of the present application, for dynamic factors, the sampling point can calculate the ETA value with the position point of the traffic participant according to formula (1), and calculate the potential collision risk according to the ETA value and formula (2). For static factors, the sampling point can detect whether the vector and point enter the static perception range, and assign a fixed risk value to it according to the type of static factor.

[0066] In the actual execution process, the sampling point can calculate the corresponding ETA value according to formula (1) based on its own position and the position of the dynamic factors, combined with the movement speed and direction. Figure 4 As shown, for example, a vehicle B is traveling at a speed of 4 m / s and is about 2 m away from sampling point A. Using the ETA formula, it can be calculated that the ETA value of the two is about 0.5 seconds. According to the collision risk formula, the potential collision risk caused by this dynamic factor is calculated to be 0.95.

[0067] The embodiment of the present application utilizes a static factor risk quantification formula to quantify static factors, which can achieve a quantitative assessment of risk factors, making risk levels more intuitive and comparable, facilitating the system to perform priority sorting and resource allocation, and utilizes a dynamic factor risk quantification model to quantify dynamic factors, which can respond to changes in real time and help to take timely measures.

[0068] Furthermore, combined with Figure 5 As shown, the embodiment of the present application designs and adopts simple and normalized mathematical modeling, and the data collected from the ICV, multiple road-side sensing units, and edge cloud storage can be integrated and split into independent data vectors, and then the corresponding road section sampling point set to be passed in the future is retrieved on the edge cloud platform and the dynamic and static factors are converted into corresponding geometric objects to realize data collection and processing; road sampling points with risk values ​​lower than the preset threshold, that is, collision-free points, can be screened out and stored in sequence for risk occupancy assessment; the road risk occupancy results can be transmitted back to the ICV from the cloud as reference information to provide real-time support for its decision-making. Using the vehicle-road-cloud collaborative architecture, the algorithm is run on a large computing power server in the cloud, which helps to reduce the computing burden of the vehicle-side hardware and speed up the assessment of road risks.

[0069] According to the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework proposed in the embodiment of the present application, different sampling point sets can be dynamically called according to the changes in the position and driving direction of the ICV, and the risk factors affecting the driving safety of the intelligent connected vehicle ICV are obtained. Then, the dynamic and static factors are converted into geometric objects, so as to obtain multiple dynamic and static factors within the preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set, so as to perform risk quantification calculation on the multiple dynamic and static factors and each sampling point, and obtain the risk values ​​corresponding to the multiple dynamic and static factors respectively, and accumulate the corresponding risk values ​​and normalize them to obtain a 4D risk occupancy map composed of the final risk values ​​of all sampling points in the sampling point set, which is helpful to respond to dynamic changes in a timely manner and realize quantitative assessment of risks. The use of the vehicle-road-cloud collaborative framework is conducive to optimizing resource allocation, improving the accuracy and efficiency of assessment, and can help ICV obtain beyond-visual-range risk perception capabilities that a single vehicle does not have, effectively avoid risks in advance, and make more reasonable and long-term plans. This solves the problem in related technologies that, due to the lack of unified and efficient environmental perception and risk assessment standards, smart connected vehicles find it difficult to make timely and accurate decisions in complex and changeable traffic environments, which increases the computing burden and reduces operating efficiency.

[0070] Secondly, with reference to the accompanying drawings, a road 4D risk occupancy assessment device based on a vehicle-road-cloud collaborative framework proposed according to an embodiment of the present application is described.

[0071] Figure 6 It is a block diagram of a road 4D risk occupancy assessment device based on a vehicle-road-cloud collaborative framework according to an embodiment of the present application.

[0072] like Figure 6 As shown, the road 4D risk occupancy assessment device 10 based on the vehicle-road-cloud collaborative framework includes: a retrieval module 100, an acquisition module 200, a determination module 300 and an assessment module 400.

[0073] Specifically, the retrieval module 100 is used to dynamically retrieve different sampling point sets according to changes in the position and driving direction of the intelligent connected vehicle ICV;

[0074] An acquisition module 200 is used to acquire risk factors that affect the driving safety of the ICV, wherein the risk factors are dynamic and static factors;

[0075] A determination module 300, configured to convert the dynamic and static factors into geometric objects, so as to obtain a plurality of dynamic and static factors within a preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set;

[0076] The evaluation module 400 is used to perform risk quantification calculation on the multiple dynamic and static factors and each sampling point, obtain risk values ​​corresponding to the multiple dynamic and static factors, accumulate the corresponding risk values, normalize them to obtain the final risk values ​​of all sampling points in the sampling point set, and generate a road 4D risk occupancy map based on the final risk values.

[0077] Optionally, in one embodiment of the present application, the determination module 300 includes: a setting unit.

[0078] The setting unit is used to adopt a preset perception range radius strategy to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than the radius of the dynamic perception range.

[0079] Optionally, in one embodiment of the present application, the evaluation module 400 includes: an allocation unit and a calculation unit.

[0080] The allocation unit is used to quantify the static factors within the static perception range using a static factor risk quantification formula, and allocate fixed risk constant values ​​and corresponding weights to different types of static factors;

[0081] The calculation unit quantifies the dynamic factors within the dynamic perception range by using a dynamic factor risk quantification model to calculate the risk sizes of different types of the dynamic factors.

[0082] Optionally, in one embodiment of the present application, the static factor risk quantification formula is:

[0083]

[0084] Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point;

[0085] The dynamic factor risk quantification model is:

[0086]

[0087] Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

[0088] It should be noted that the aforementioned explanation of the embodiment of the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework is also applicable to the road 4D risk occupancy assessment device based on the vehicle-road-cloud collaborative framework of this embodiment, and will not be repeated here.

[0089] According to the road 4D risk occupancy assessment device based on the vehicle-road-cloud collaborative framework proposed in the embodiment of the present application, different sampling point sets can be dynamically called according to the changes in the position and driving direction of the ICV, and the risk factors affecting the driving safety of the intelligent connected vehicle ICV are obtained. Then, the dynamic and static factors are converted into geometric objects, so as to obtain multiple dynamic and static factors within the preset perception range of each sampling point based on the geometric objects and each sampling point in the sampling point set, so as to perform risk quantification calculation on the multiple dynamic and static factors and each sampling point, and obtain the risk values ​​corresponding to the multiple dynamic and static factors respectively, and accumulate the corresponding risk values ​​and normalize them to obtain a 4D risk occupancy map composed of the final risk values ​​of all sampling points in the sampling point set, which is helpful to respond to dynamic changes in a timely manner and realize quantitative assessment of risks. The use of the vehicle-road-cloud collaborative framework is conducive to optimizing resource allocation and improving the accuracy and efficiency of assessment. It can help ICV obtain beyond-visual-range risk perception capabilities that a single vehicle does not have, effectively avoid risks in advance, and make more reasonable and long-term plans.

[0090] Figure 7 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0091] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0092] When the processor 702 executes the program, the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework provided in the above embodiment is implemented.

[0093] Furthermore, the vehicle also includes:

[0094] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0095] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0096] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0097] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The bus is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0098] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0099] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0100] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework.

[0101] An embodiment of the present application also provides a computer program product, which can run computer instructions, and when the computer instructions are executed by a processor, the above-mentioned road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework is implemented.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0103] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0104] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0106] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0109] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework, characterized in that: The following steps are involved: Dynamically retrieve different sampling point sets based on the changes in the position and driving direction of the intelligent connected vehicle (ICV); Acquire risk factors that affect the driving safety of the ICV, wherein the risk factors are dynamic and static factors; Converting the dynamic and static factors into geometric objects, so as to obtain a plurality of dynamic and static factors within a preset perception range of each sampling point based on the geometric object and each sampling point in the sampling point set; A risk quantification calculation is performed on the multiple dynamic and static factors and each sampling point to obtain risk values ​​corresponding to the multiple dynamic and static factors respectively, to accumulate the corresponding risk values, to normalize and obtain a final risk value of all sampling points in the sampling point set, and to generate a road 4D risk occupancy map based on the final risk value.

2. The method according to claim 1, characterized in that The obtaining of multiple dynamic and static factors within a preset sensing range of each sampling point includes: A preset perception range radius strategy is adopted to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than that of the dynamic perception range.

3. The method according to claim 2, characterized in that The risk quantification calculation of the multiple dynamic and static factors and each sampling point includes: Quantifying the static factors within the static perception range using a static factor risk quantification formula, and assigning fixed risk constant values ​​and corresponding weights to different types of static factors; The dynamic factors within the dynamic perception range are quantified using a dynamic factor risk quantification model to calculate the risk sizes of different types of the dynamic factors.

4. The method according to claim 3, characterized in that The static factor risk quantification formula is: Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point; The dynamic factor risk quantification model is: Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

5. A road 4D risk occupancy assessment device based on a vehicle-road-cloud collaborative framework, characterized in that: include: The retrieval module is used to dynamically retrieve different sampling point sets according to the changes in the position and driving direction of the intelligent connected vehicle ICV; An acquisition module, used to acquire risk factors affecting the driving safety of the ICV, wherein the risk factors are dynamic and static factors; A determination module, configured to convert the dynamic and static factors into a geometric object, so as to obtain a plurality of dynamic and static factors within a preset perception range of each sampling point based on the geometric object and each sampling point in the sampling point set; An evaluation module is used to perform risk quantification calculation on the multiple dynamic and static factors and each sampling point to obtain risk values ​​corresponding to the multiple dynamic and static factors respectively, to accumulate the corresponding risk values, to normalize and obtain the final risk values ​​of all sampling points in the sampling point set, and to generate a road 4D risk occupancy map based on the final risk values.

6. The device according to claim 5, characterized in that The determination module comprises: The setting unit is used to adopt a preset perception range radius strategy to set differentiated perception ranges for static factors and dynamic factors, wherein the radius of the static perception range is smaller than the radius of the dynamic perception range.

7. The device according to claim 6, characterized in that The evaluation module includes: an allocating unit, configured to quantify the static factors within the static perception range by using a static factor risk quantification formula, and allocate fixed risk constant values ​​and corresponding weights to different types of the static factors; The calculation unit quantifies the dynamic factors within the dynamic perception range by using a dynamic factor risk quantification model to calculate the risk sizes of different types of the dynamic factors.

8. The device according to claim 7, characterized in that The static factor risk quantification formula is: Among them, Risk sta is the static risk value, Constant is the risk constant value, Distance so is the distance between the factor and the sampling point; The dynamic factor risk quantification model is: Among them, ETA is the predicted arrival time, Distance so is the distance between the factor and the sampling point, Speed ​​is the driving speed of the dynamic factor, Risk dyn is the dynamic risk value.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a road 4D risk occupancy assessment method based on a vehicle-road-cloud collaborative framework as described in any one of claims 1-4.

11. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the road 4D risk occupancy assessment method based on the vehicle-road-cloud collaborative framework as described in any one of claims 1-4.

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