Ghost probe early warning function simulation system and method based on vehicle-road cloud integrated coordination
The simulated system for ghost peeping camera warning function, which integrates vehicle-road-cloud collaboration, utilizes the fusion of multiple roadside sensors and V2X information to achieve accurate warning and proactive intervention for ghost peeping cameras. This solves the problems of perception blind spots, lack of quantitative prediction, and single decision-making in existing technologies, thereby improving the safety and efficiency of autonomous driving systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-03-10
AI Technical Summary
In existing technologies, ghost-protruding early warning solutions suffer from problems such as perception blind spots, lack of quantitative prediction, overly simplistic decision-making, and high risks associated with real-vehicle verification. These issues result in untimely, inaccurate, and uncoordinated early warnings, leading to low verification efficiency.
A simulation system for ghost camera warning based on vehicle-road-cloud integration is adopted. By fusing multiple roadside sensors and V2X information, a full-domain perception network is constructed to perform spatiotemporal correlation analysis and trajectory prediction, generating multi-level collaborative control strategies to achieve accurate early warning and proactive intervention for potential risks.
It improves vehicle safety and stability of auxiliary control while driving, shortens control response time for complex calculations, enhances the accuracy of multi-vehicle collaborative control, and reduces the cost and cycle of real vehicle testing.
Smart Images
Figure CN121634879A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of intelligent vehicle control technology, and more specifically, to a simulation system and method for ghost protrusion warning function based on vehicle-road-cloud integrated collaboration. Background Technology
[0002] "Ghost peek" is an extremely dangerous scenario in road traffic safety, specifically referring to pedestrians or non-motorized vehicles suddenly darting into the motor vehicle lane from blind spots (such as buildings at intersections, parked buses, green belts, etc.), causing collisions due to insufficient reaction time for vehicles traveling normally. Such incidents are characterized by their suddenness, concealment, and high severity, and represent one of the major challenges currently facing autonomous driving and advanced driver assistance systems.
[0003] The inventors discovered in their research that existing technologies for addressing the risk of "ghost peeking out" mainly rely on the following two types of technologies: The first category is single-vehicle intelligent perception and early warning methods. These solutions primarily rely on the vehicle's onboard sensors (such as cameras, millimeter-wave radar, and lidar) for environmental perception. However, in typical "ghost pedestrian" scenarios, due to continuous obstruction by obstacles ahead, vehicle sensors have inherent blind spots, unable to acquire any information before the target appears. When a pedestrian suddenly appears, the reaction time for the vehicle system to identify, make decisions, and control is extremely short, often making it difficult to effectively avoid collisions. Furthermore, the perception range of a single vehicle is limited and susceptible to factors such as inclement weather and lighting conditions, resulting in insufficient reliability and robustness.
[0004] The second category is a simple vehicle-road cooperative early warning method. This type of solution attempts to address the problem of blind spots in single-vehicle perception by deploying roadside sensing devices (such as roadside cameras). The basic idea is that the roadside unit transmits detected potential risk information to the vehicle via V2X communication. However, existing technologies mostly remain at the level of simple information broadcasting, such as one-way transmission of a "pedestrian ahead" warning signal. This approach lacks in-depth risk prediction and collaborative decision-making mechanisms, and cannot quantify the probability, duration, and precise location of risks, making it difficult to support vehicles in making differentiated and optimal response decisions. Furthermore, this simple early warning also fails to coordinate with roadside traffic facilities (such as warning lights) or surrounding vehicles, failing to form a global collaborative risk avoidance strategy.
[0005] Therefore, the verification and optimization of the algorithms in the above methods heavily rely on real-vehicle road testing. However, the high risk of the "ghost peek" scenario makes real-vehicle testing costly, time-consuming, and unrepeatable, and it is difficult to cover all possible complex working conditions, which greatly restricts the research and development iteration and safety verification of related technologies.
[0006] Therefore, the existing technology has the following core defects: there are blind spots at the perception level, making it impossible to detect potential risks in advance; at the prediction level, there is a lack of in-depth analysis of the spatiotemporal correlation of the target, resulting in inaccurate early warning information; at the decision-making level, there is a lack of a collaborative decision-making mechanism integrating vehicles, roads, and the cloud, resulting in a single and inefficient response strategy; and at the verification level, there is a lack of a safe, controllable, and reproducible large-scale simulation testing platform. Summary of the Invention In view of this, this application provides a simulation system and method for ghost camera warning function based on vehicle-road-cloud integrated collaboration to solve the above problems, improve vehicle safety and stability during driving and auxiliary control, shorten the control response time of complex calculations, and improve the accuracy of multi-vehicle collaborative control.
[0007] The purpose of this invention is to solve the problems mentioned in the background art and provide a simulation system and method for ghost peephole warning function based on vehicle-road-cloud integrated collaboration. The technical problem to be solved by this invention is to overcome the fundamental defects of existing "ghost peephole" warning schemes: namely, the problems of untimely, inaccurate, uncoordinated, and low verification efficiency caused by blind spots in perception, lack of quantitative prediction, overly simplistic decision-making, and high risks in real vehicle verification.
[0008] In a first aspect, embodiments of this application provide a simulation system for ghost camera warning function based on vehicle-road-cloud integrated collaboration, which includes a perception layer unit, a prediction layer unit, a decision layer unit, and a control layer unit, wherein: The perception layer unit is configured to collect roadside sensor information, roadside V2X information, and trajectory information of the current simulated vehicle.
[0009] The prediction layer unit is configured to obtain information on multiple spatial density regions based on roadside sensor information and set future consecutive time points. Based on roadside V2X information, it obtains a high-warning driving trajectory based on the trajectory density at future time points being greater than the historical ghost camera collision threshold.
[0010] The decision-making unit is configured to match multiple spatial density area information based on the high-alert driving trajectory and the corresponding time point, and obtain the ghost protrusion warning trajectory from the high-alert driving trajectory.
[0011] The control layer unit is configured to generate driving control information for the current simulated vehicle based on the ghost peek warning trajectory.
[0012] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein the perception layer unit is further configured to include a roadside multi-sensor fusion module and a roadside V2X message module. The roadside multi-sensor fusion module is configured to acquire roadside sensing information through roadside spatial sensing devices. The roadside V2X message module is configured to obtain roadside V2X information and the V2X information of the current simulated vehicle based on the roadside V2X information acquisition device. Based on the V2X information of the current simulated vehicle, the trajectory information of the current simulated vehicle is obtained.
[0013] In conjunction with the first aspect, this application provides a second possible implementation of the first aspect, wherein the prediction layer unit is further configured to obtain, based on roadside sensing information, multiple spatial density region information of static and dynamic obstacles within the warning area at set future consecutive time points. Each spatial density region information includes region area data and region coordinate data.
[0014] In conjunction with the first aspect, this application provides a third possible implementation of the first aspect, wherein the prediction layer unit is further configured to include, among multiple spatial density region information, regional area data and regional coordinate data corresponding to different altitudes, to obtain first altitude spatial density region data and second altitude spatial data.
[0015] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the prediction layer unit is further configured to obtain the trajectory position information of other vehicles within the prediction area at set future consecutive time points based on roadside V2X information. Based on the trajectory position information of other vehicles and the trajectory information of the current simulated vehicle, trajectory density information based on future time points is obtained. Based on the trajectory density information, a high-warning driving trajectory with a trajectory density greater than the historical ghost-probe collision threshold is obtained at the set future consecutive time points.
[0016] In conjunction with the first aspect, this application provides a fifth possible implementation of the first aspect, wherein the decision layer unit is further configured to, Based on the high-altitude warning driving trajectory and the corresponding time point, the first altitude spatial density region data and the second altitude spatial data from multiple spatial density region information are matched, and the first altitude warning driving trajectory and the second altitude warning driving trajectory are obtained based on their overlapping areas.
[0017] Based on the current collision height of the simulated vehicle, the collision overlap point is obtained from the overlap point between the first and second high-alert driving trajectories. Multiple spatial density regions are matched using the collision overlap point, the high-alert driving trajectory, and the corresponding time point to obtain the ghost-protruding warning trajectory from the high-alert driving trajectory.
[0018] In conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein the perception layer unit is further configured to collect change data of historical ghost camera collision thresholds and upload it to the prediction layer unit. The change data of historical ghost camera collision thresholds includes historical data version information of the corresponding historical ghost camera collision thresholds.
[0019] The prediction layer unit is also configured to update the local historical ghost collision threshold based on the historical data version information and the change data of the historical ghost collision threshold.
[0020] In conjunction with the first aspect, this application provides a seventh possible implementation of the first aspect, wherein the decision layer unit is further configured to obtain the roadside warning light illumination time from the roadside V2X information in the prediction layer unit; obtain the vehicle-side emergency braking time information from the V2X information of the current simulated vehicle; and obtain the shortest control time when no collision occurs with other vehicles from the roadside V2X information.
[0021] According to the decision-making unit, it is also configured to mark the first type of mandatory stop time point in the high-alert driving trajectory based on the roadside warning light illumination time and vehicle emergency braking time information, in order to generate the first type of mandatory intersection control information. Based on the roadside warning light illumination time and the shortest control time when no collision occurs with other vehicles, it marks the second type of mandatory stop time point in the high-alert driving trajectory, in order to generate the second type of mandatory intersection control information.
[0022] The mandatory control information for both Type I and Type II intersections is sent to the control layer unit.
[0023] In conjunction with the first aspect, this application provides a seventh possible implementation of the first aspect, wherein the control layer unit is further configured to generate a safe driving trajectory based on the ghost peek warning trajectory. Based on the travel time, first-type intersection mandatory control information and second-type intersection mandatory control information are embedded in the safe driving trajectory to generate an intersection safe driving trajectory. Driving control information for the current simulated vehicle is generated based on the intersection safe driving trajectory.
[0024] Secondly, embodiments of this application provide a simulation method for ghost camera warning function based on vehicle-road-cloud integrated collaboration, including: The system collects roadside sensor information, roadside V2X information, and trajectory information from the current simulated vehicle.
[0025] Based on roadside sensor information, multiple spatial density regions are obtained based on preset future consecutive time points. Based on roadside V2X information, a high-warning driving trajectory is obtained based on the trajectory density at future time points exceeding the historical ghost camera collision threshold.
[0026] Based on the high-alert driving trajectory and the corresponding time point, multiple spatial density area information are matched to obtain the ghost peek warning trajectory from the high-alert driving trajectory.
[0027] The driving control information of the current simulated vehicle is generated based on the ghost peek warning trajectory.
[0028] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0030] Figure 1 A schematic diagram of the composition of the ghost probe early warning function simulation system based on vehicle-road-cloud integrated collaboration provided in the embodiments of this application is shown.
[0031] Figure 2 A flowchart is shown of the simulation method for ghost probe early warning function based on vehicle-road-cloud integrated collaboration provided in the embodiments of this application. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0033] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0034] Figure 1 This illustration shows a schematic diagram of the composition of a simulation system for ghost camera warning function based on vehicle-road-cloud integrated collaboration provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a simulation system for ghost camera warning function based on vehicle-road-cloud integrated collaboration. This system is a hardware system for implementing the ghost camera warning function simulation method based on vehicle-road-cloud integrated collaboration in this invention.
[0035] Reference Figure 1 In one embodiment of the present invention, the vehicle-road-cloud integrated collaborative ghost probe early warning function simulation system is implemented through hardware devices at various levels, including a perception layer unit 101, a prediction layer unit 201, a decision layer unit 301, and a control layer unit 401.
[0036] In the simulation, the sensing layer unit 101 is implemented using local hardware acquisition devices, namely "intelligent roadside terminal and local multi-sensor". Specifically, the local multi-sensor includes surveillance cameras that can be installed at intersections and on the sides of roads to completely cover the monitoring area; microwave radar primarily used to detect pedestrian and vehicle flow speeds; magnetic frequency sensors and loop coils, buried under the road surface, used to detect basic traffic parameters such as vehicle presence, speed, length, and traffic flow; and a combination of surveillance camera and satellite imagery to generate a coordinate map or grid, used to obtain static planar map information (a map containing only buildings) of the intersection to be detected.
[0037] It should be noted that the aforementioned intelligent roadside terminal is a roadside device with antenna and communication functions, and conforms to V2X data transmission standards and standard transmission protocols. In specific implementation schemes, its roadside V2X information can also be realized through the same device.
[0038] The perception layer unit 101 is configured to acquire roadside sensor information, roadside V2X information, and trajectory information of the current simulated vehicle via a local controller or wireless data acquisition unit. The perception layer unit 101 uploads the above information to the prediction layer unit 201 via wireless or wired transmission, typically using the Road-Cloud Protocol for data transmission.
[0039] It should be noted that the aforementioned V2X (Vehicle-to-Everything) information is the core data stream of the intelligent transportation system, and its format is highly standardized. The sensor information and data collected by the aforementioned perception layer unit 101 can be implemented using the Roadside Safety Message (RSM) format.
[0040] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein the perception layer unit 101 is further configured to include a roadside multi-sensor fusion module and a roadside V2X message module. The roadside multi-sensor fusion module is configured to acquire roadside sensing information through roadside spatial sensing devices. The roadside V2X message module is configured to obtain roadside V2X information and the V2X information of the current simulated vehicle based on the roadside V2X information acquisition device. Based on the V2X information of the current simulated vehicle, the trajectory information of the current simulated vehicle is obtained.
[0041] The aforementioned roadside spatial sensing device is a camera, which collects roadside sensing information including three-dimensional information of buildings at various altitudes, pedestrian location and movement information, and bicycle location and movement information.
[0042] During simulation, the prediction layer unit 201 is implemented via a single-configuration server or a cloud-hosted server, receiving data transmitted from the perception layer unit 101. The prediction layer unit 201 is configured to obtain information on multiple spatial density regions based on roadside sensor information and predetermined future consecutive time points. Based on roadside V2X information, it obtains a high-alert driving trajectory based on a trajectory density at future time points that exceeds the historical ghost camera collision threshold.
[0043] In conjunction with the first aspect, this application provides a second possible implementation of the first aspect, wherein the prediction layer unit 201 is further configured to obtain, based on roadside sensing information, multiple spatial density region information of static and dynamic obstacles within the warning area at set future consecutive time points. Each spatial density region information includes region area data and region coordinate data. The aforementioned spatial density region information is a graphic identifier formed within the warning area by the occupation of static and dynamic obstacles within the warning area. Density identification information can also be added to the aforementioned spatial density region information based on the density of overlapping static and dynamic obstacles.
[0044] The aforementioned spatial density region information includes setting future continuous time point information, so that there will be different spatial density region information at different time points. It can be shown in one map or numbered and shown in multiple maps.
[0045] In conjunction with the first aspect, this application provides a third possible implementation of the first aspect, wherein the prediction layer unit 201 is further configured to include, among multiple spatial density region information, regional area data and regional coordinate data corresponding to different altitudes, to obtain first altitude spatial density region data and second altitude spatial data.
[0046] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the prediction layer unit 201 is further configured to obtain the trajectory position information of other vehicles within the prediction area at set future consecutive time points based on roadside V2X information. Based on the trajectory position information of other vehicles and the trajectory information of the current simulated vehicle, trajectory density information based on future time points is obtained. The aforementioned trajectory density information... Based on trajectory density information, high-alert driving trajectories with trajectory density greater than the historical ghost-probe collision threshold are obtained in the set future consecutive time points.
[0047] During simulation, the decision-making unit 301 is implemented through a single-configuration server or a cloud-hosted server. It is configured to receive data from the prediction unit 201, match multiple spatial density area information based on the high-alert driving trajectory and the corresponding time point, and obtain the ghost protrusion warning trajectory from the high-alert driving trajectory.
[0048] In conjunction with the first aspect, this application provides a fifth possible implementation of the first aspect, wherein the decision layer unit 301 is further configured to, Based on the high-altitude warning driving trajectory and the corresponding time point, the first altitude spatial density region data and the second altitude spatial data from multiple spatial density region information are matched, and the first altitude warning driving trajectory and the second altitude warning driving trajectory are obtained based on their overlapping areas.
[0049] Based on the current collision height of the simulated vehicle, the collision overlap point is obtained from the overlap point between the first and second high-alert driving trajectories. Multiple spatial density regions are matched using the collision overlap point, the high-alert driving trajectory, and the corresponding time point to obtain the ghost-protruding warning trajectory from the high-alert driving trajectory.
[0050] The hardware of the control layer unit 401 can be implemented through the vehicle controller of the Carla simulation platform. It is configured to generate driving control information for the current simulated vehicle based on the ghost peep warning trajectory. When simulating through the Carla simulation platform vehicle controller, it can be set up locally along with the hardware simulation platform of the perception layer unit 101. Its control layer unit 401 receives the processed ghost peep warning trajectory from the decision layer unit 301 through wireless and wired communication.
[0051] Furthermore, the current simulated vehicle can generate a driving trajectory by avoiding the aforementioned ghost camera warning trajectory, thereby enabling the generation of a set of control instructions for the simulated vehicle in a specific area.
[0052] Therefore, the ghost camera warning simulation system based on vehicle-road-cloud integrated collaboration in this invention obtains the ghost camera warning trajectory based on the intersection of spatial density area information superimposed with temporal trajectory information. In one embodiment of this application, in conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein the perception layer unit 101 is further configured to collect change data of historical ghost probe collision thresholds and upload it to the prediction layer unit 201. The change data of historical ghost probe collision thresholds includes historical data version information of the corresponding historical ghost probe collision thresholds.
[0053] The prediction layer unit 201 is also configured to update the local historical ghost collision threshold based on the historical data version information and the change data of the historical ghost collision threshold.
[0054] In conjunction with the first aspect, this application provides a seventh possible implementation of the first aspect, wherein the decision layer unit 301 is further configured to obtain the roadside warning light illumination time from the roadside V2X information in the prediction layer unit 201; obtain the vehicle-side emergency braking time information from the V2X information of the current simulated vehicle; and obtain the shortest control time when no collision occurs with other vehicles from the roadside V2X information.
[0055] According to the decision-making unit 301, it is further configured to mark the first type of mandatory stop time point in the high-alert driving trajectory based on the roadside warning light illumination time and vehicle emergency braking time information, for generating first type of intersection mandatory control information. Based on the roadside warning light illumination time and the shortest control time when no collision occurs with other vehicles, it marks the second type of mandatory stop time point in the high-alert driving trajectory, for generating second type of intersection mandatory control information.
[0056] The mandatory control information for the first type of intersection and the mandatory control information for the second type of intersection are sent to the control layer unit 401.
[0057] In conjunction with the first aspect, this application provides a seventh possible implementation of the first aspect, wherein the control layer unit 401 is further configured to generate a safe driving trajectory based on the ghost peek warning trajectory. Based on the travel time, first-type intersection mandatory control information and second-type intersection mandatory control information are embedded into the safe driving trajectory to generate an intersection safe driving trajectory. Driving control information for the current simulated vehicle is generated based on the intersection safe driving trajectory. This achieves simulation of the vehicle passing through the intersection.
[0058] Figure 2 A flowchart illustrating the simulation method for ghost camera warning function based on vehicle-road-cloud integrated collaboration provided in this application is shown. (Refer to...) Figure 2 This application provides a simulation method for ghost camera warning function based on vehicle-road-cloud integrated collaboration, including the following steps: Step S101: Collect roadside sensor information, roadside V2X information and trajectory information of the current simulated vehicle.
[0059] Step S102: Based on roadside sensor information, obtain information on multiple spatial density regions at predetermined future consecutive time points. Based on roadside V2X information, obtain a high-warning driving trajectory based on a trajectory density at future time points that exceeds the historical ghost camera collision threshold.
[0060] Step S103: Based on the high-alert driving trajectory and the corresponding time point, match multiple spatial density area information to obtain the ghost protrusion warning trajectory from the high-alert driving trajectory.
[0061] Step S104: Generate the driving control information of the current simulated vehicle based on the ghost peek warning trajectory.
[0062] This invention achieves significant improvements in core technology performance through a vehicle-road-cloud integrated collaborative architecture. At the perception level, by leveraging the fusion of multiple roadside sensors and the complementarity of V2X vehicle-side dynamic data, a comprehensive perception network is constructed, eliminating blind spots in single-vehicle intelligence and enabling proactive detection of potential risks behind obstructions. At the prediction and decision-making level, through spatiotemporal correlation analysis and trajectory prediction models, the warning information is upgraded from a simple judgment of target existence to a quantitative risk map including probability of occurrence, duration, and conflict location. This allows the decision-making layer to generate a globally optimal control strategy that integrates vehicle braking, roadside warnings, and coordination with surrounding vehicles, thus transforming passive post-event warnings into proactive pre-event intervention, greatly improving the system's warning accuracy and collision avoidance performance.
[0063] This invention has yielded direct benefits in engineering applications and system optimization. By deeply integrating the collaborative early warning logic with the Carla simulation platform, various high-risk "ghost peek" scenarios can be safely and reproducibly constructed in a virtual environment. Key indicators such as minimum safe distance and system response latency are automatically recorded, providing quantitative data for algorithm iteration. This significantly reduces the high risks and costs of real-vehicle testing and shortens the development cycle. Simultaneously, the system employs a distributed data upload and perception range optimization strategy, effectively reducing cloud computing and communication load and improving resource utilization efficiency. The entire system architecture combines high reliability with excellent scalability, providing a generalizable technical paradigm not only for "ghost peek" scenarios but also for solving other complex traffic hazards.
[0064] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0066] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0067] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0068] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0069] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.
Claims
1. A simulation system of ghost head warning function based on vehicle-road-cloud integration and cooperation, characterized in that, It comprises a perception layer unit, a prediction layer unit, a decision layer unit and a control layer unit, wherein: The perception layer unit is configured to collect roadside sensing information, roadside V2X information and current simulation vehicle end trajectory information; The prediction layer unit is configured to obtain a plurality of spatial density region information based on a set of future continuous time points according to the roadside sensing information; obtain a high warning driving trajectory based on the future time points according to the roadside V2X information, whose trajectory density is greater than the historical ghost head collision threshold; The decision layer unit is configured to match the high warning driving trajectory and the corresponding time point with the plurality of spatial density region information, and obtain the ghost head warning trajectory from the high warning driving trajectory; The control layer unit is configured to generate driving control information of the current simulation vehicle end according to the ghost head warning trajectory. 2.The ghost probe early warning function simulation system based on vehicle-road-cloud integration and cooperation according to claim 1, wherein, The perception layer unit is also configured to include a roadside multi-sensor fusion module and a roadside V2X message module; the roadside multi-sensor fusion module is configured to collect roadside sensing information through roadside space sensing equipment; The roadside V2X message module is configured to obtain roadside V2X information and V2X information of the current simulation vehicle end through roadside V2X information collection equipment; According to the V2X information of the current simulation vehicle end, the trajectory information of the current simulation vehicle end is obtained. 3.The ghost car warning function simulation system based on vehicle-road-cloud integration and cooperation according to claim 1, wherein, The prediction layer unit is also configured to obtain a plurality of spatial density region information of static obstacles and dynamic obstacles in the warning area at a set of future continuous time points based on the set of future continuous time points according to the roadside sensing information; each spatial density region information includes area data and coordinate data.
4. The ghost probe early warning function simulation system based on vehicle-road-cloud integration collaboration according to claim 3, characterized in that, The prediction layer unit is also configured to extract the corresponding area data and coordinate data at different altitudes from the plurality of spatial density region information to obtain first altitude spatial density region data and second altitude spatial data. 5.The ghost car warning function simulation system based on vehicle-road-cloud integration and cooperation according to claim 1, wherein, The prediction layer unit is also configured to obtain other vehicle trajectory position information of other vehicles in the prediction area at the set of future continuous time points according to the roadside V2X information; obtain trajectory density information based on the future time points according to the other vehicle trajectory position information and the trajectory information of the current simulation vehicle end; obtain high warning driving trajectories whose trajectory density is greater than the historical ghost head collision threshold in the set of future continuous time points according to the trajectory density information. 6.The ghost car warning function simulation system based on vehicle-road-cloud integration and cooperation according to claim 4, characterized in that, The decision layer unit is also configured to, According to the high warning driving trajectory and the corresponding time point, match the first altitude spatial density region data and the second altitude spatial data in the plurality of spatial density region information, and obtain first high warning driving trajectory and second high warning driving trajectory according to the overlapping region thereof; According to the non-collision height of the current simulation vehicle end, obtain the collision overlapping point in the overlapping point of the first high warning driving trajectory and the second high warning driving trajectory; According to the collision overlapping point and the high warning driving trajectory and the corresponding time point matching the plurality of spatial density region information, obtain the ghost head warning trajectory from the high warning driving trajectory.
7. The ghost probe early warning function simulation system based on vehicle-road cloud integration cooperation according to claim 4, characterized in that, the perception layer unit is further configured to collect historical ghost probe collision threshold change data and upload the historical ghost probe collision threshold change data to the prediction layer unit; the historical ghost probe collision threshold change data includes corresponding historical data version information of the historical ghost probe collision threshold; the prediction layer unit is further configured to update the local historical ghost probe collision threshold according to the historical data version information and the historical ghost probe collision threshold change data. 8.The simulation system of ghost probe early warning function based on vehicle-road-cloud integration and cooperation according to claim 1, wherein, the decision layer unit is further configured to obtain a road side warning light lighting time from the road side V2X information in the prediction layer unit, obtain vehicle end emergency braking time information from the V2X information at the current simulation vehicle end, and obtain a shortest control time when other vehicles do not collide from the road side V2X information; according to the decision layer unit, the decision layer unit is further configured to identify a first type of forced stop time point in the high warning driving trajectory according to the road side warning light lighting time and the vehicle end emergency braking time information, and generate first type of intersection forced control information; and identify a second type of forced stop time point in the high warning driving trajectory according to the road side warning light lighting time and the shortest control time when other vehicles do not collide, and generate second type of intersection forced control information; the first type of intersection forced control information and the second type of intersection forced control information are sent to the control layer unit. 9.The ghost car warning function simulation system based on vehicle-road-cloud integration and cooperation according to claim 8, wherein, the control layer unit is further configured to generate a safe driving trajectory according to the ghost probe early warning trajectory; the first type of intersection forced control information and the second type of intersection forced control information are embedded in the safe driving trajectory based on driving time to generate an intersection safe driving trajectory; the driving control information at the current simulation vehicle end is generated according to the intersection safe driving trajectory.
10. A ghost head early warning function simulation method based on vehicle-road-cloud integration cooperation, characterized in that, comprising: road side sensing information, road side V2X information, and trajectory information at the current simulation vehicle end are collected; a plurality of spatial density area information based on a set of future continuous time points is obtained according to the road side sensing information; a high warning driving trajectory based on the future time points is obtained according to the road side V2X information, in which the trajectory density is greater than the historical ghost probe collision threshold; the ghost probe early warning trajectory is obtained from the high warning driving trajectory according to the high warning driving trajectory and the corresponding time point matching the plurality of spatial density area information; the driving control information at the current simulation vehicle end is generated according to the ghost probe early warning trajectory.