Unmanned driving-based traffic organization optimization method and system, and storage medium

Abstract

The application discloses a traffic organization optimization method and system based on unmanned driving and a storage medium. The method comprises the following steps: acquiring traffic data of a to-be-optimized traffic hub; establishing a simulation data model according to the traffic data; performing traffic organization optimization in the simulation data model; removing data about traffic accidents in the traffic organization optimization to obtain an unmanned traffic organization optimization data model of the to-be-optimized traffic hub; and comparing the unmanned traffic organization optimization data model with real data of the to-be-optimized traffic hub to obtain an unmanned traffic organization optimization feasibility report. The application fundamentally avoids the generation of traffic accident type congestion in traffic organization optimization through the unmanned driving technology, and improves the efficiency of traffic organization optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic planning, in particular to a traffic organization optimization method and system based on unmanned driving and a storage medium. BACKGROUND

[0002] Road traffic organization optimization is a technical method that can effectively alleviate the congestion of existing traffic roads, which scientifically and reasonably divides time, road, vehicle type, and flow direction to use roads, so that road traffic is always in an orderly and efficient state. The traditional road traffic organization optimization scheme is formulated by manually collecting and sorting the vehicle data, road network data, traffic signal data, and other data of the road section to be optimized, then analyzing the collected road data to find the conflict points of road traffic, formulating an optimization scheme to avoid conflict points to alleviate traffic congestion.

[0003] With the development of Internet technology and the continuous progress of Internet of Things technology, the data collection and sorting process in the traffic organization optimization technical scheme can be replaced by various emerging technologies. Through various sensors of Internet of Things technology, various data required for traffic organization optimization can be directly collected, and through big data technology, these data can be better analyzed to improve the traffic organization optimization scheme to avoid and resolve conflict points, and achieve better results. These technical advancements can improve the effectiveness of traffic organization optimization. For example, the existing patent CN113393671A road traffic organization scheme optimization method and device optimizes traffic organization through a mathematical model, and the existing patent CN113628442B traffic organization scheme optimization method based on multi-signal light reinforcement learning optimizes traffic organization through a neural network algorithm. However, for traffic organization congestion caused by traffic accidents, traffic organization optimization can only speed up the processing of accidents and alleviate the congestion caused by accidents, but cannot avoid the occurrence of traffic accidents in advance to fundamentally optimize this type of traffic organization congestion. SUMMARY

[0004] The embodiments of the present application provide a traffic organization optimization method and system based on unmanned driving and a storage medium to solve the problem that the existing technology can only speed up the processing of traffic accident types of congestion and cannot avoid it in advance.

[0005] In one aspect, the embodiments of the present application provide a traffic organization optimization method based on unmanned driving, comprising: acquiring traffic data of a traffic hub to be optimized;

[0006] establishing a simulation data model according to the traffic data;

[0007] performing traffic organization optimization in the simulation data model;

[0008] Eliminate data about traffic accidents in the traffic organization optimization, obtain unmanned traffic organization optimization data model of the traffic hub to be optimized;

[0009] Compare the unmanned traffic organization optimization data model with real data of the traffic hub to be optimized, and obtain an unmanned traffic organization optimization feasibility report.

[0010] In a possible implementation, the traffic data includes road line geometry data, vehicle sensor data, and video image data.

[0011] In a possible implementation, the simulation data model is established according to the traffic data by using UC / win-road software and a driving simulation experiment platform based on AutoSimAS simulation system.

[0012] In a possible implementation, the comparison of the unmanned traffic organization optimization data model and the real data of the traffic hub to be optimized is a comparison of data such as traffic hub main section traffic capacity, signal control intersection delay, vehicle queue length, and vehicle travel time.

[0013] In a possible implementation, the traffic data of the traffic hub to be optimized is obtained by arranging sensors in the traffic hub and by a monitoring device passing through the traffic hub.

[0014] In another aspect, an embodiment of the present application provides an unmanned traffic organization optimization system, comprising: a data acquisition module configured to acquire traffic data of a traffic hub to be optimized;

[0015] a model establishment module configured to establish a simulation data model according to the traffic data, perform traffic organization optimization in the simulation data model, eliminate data about traffic accidents in the traffic organization optimization, and obtain an unmanned traffic organization optimization data model of the traffic hub to be optimized;

[0016] a feasibility demonstration module configured to compare the unmanned traffic organization optimization data model with real data of the traffic hub to be optimized, and obtain an unmanned traffic organization optimization feasibility report.

[0017] In another aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a plurality of computer instructions, and the plurality of computer instructions are used to make a computer implement the above method.

[0018] The unmanned traffic organization optimization method, system, and storage medium have the following advantages:

[0019] The unmanned driving technology fundamentally avoids the generation of traffic accident type congestion in traffic organization optimization, and improves the efficiency of traffic organization optimization. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A schematic diagram of a traffic organization optimization method based on unmanned driving provided by an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a traffic organization optimization system based on unmanned driving provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] Figure 1 A schematic diagram of a traffic organization optimization method based on unmanned driving provided by an embodiment of the present application. The present application provides a traffic organization optimization method based on unmanned driving, which comprises: acquiring traffic data of a traffic hub to be optimized;

[0025] establishing a simulation data model according to the traffic data;

[0026] performing traffic organization optimization in the simulation data model;

[0027] eliminating data about traffic accidents in the traffic organization optimization to obtain an unmanned driving traffic organization optimization data model of the traffic hub to be optimized;

[0028] comparing the unmanned driving traffic organization optimization data model with real data of the traffic hub to be optimized to obtain an unmanned driving traffic organization optimization feasibility report.

[0029] Exemplarily, in the method of the present application, firstly, traffic data of the traffic hub to be optimized is obtained by arranging sensors on site and calling up the monitoring of the surrounding of the traffic hub to be optimized, and then a simulation data model is established in a computer by UC / win-road software and a driving simulator according to the traffic data, the simulation data model has the same terrain road and the same traffic as the traffic hub to be optimized, and then optimization is performed on the simulation data model by a traffic organization optimization method, before the traffic organization optimization, data about traffic accidents need to be removed to simulate the influence brought by unmanned driving, and then a traffic organization optimization result based on unmanned driving technology of the traffic hub to be optimized is obtained, the traffic organization optimization result is compared with a traffic organization optimization result without removing the data about traffic accidents and real non-optimized data to obtain a feasibility report.

[0030] In a possible embodiment, the traffic data includes road line geometry data, vehicle sensor data, and video image data.

[0031] Exemplarily, the road line geometry data is obtained by field surveying and satellite pictures, and is used to construct the terrain road of the simulation data model in the UC / win-road software, the vehicle sensor data is collected by collecting vehicle-mounted sensor information of vehicles passing through the traffic hub to be optimized, and then the vehicle-mounted sensor information is summarized and arranged, and is used to simulate the automatic driving state of different vehicles in the AutoSimAS simulation system, and the video image data is obtained by road traffic monitoring, and is used to simulate the traffic state of the traffic hub to be optimized in the UC / win-road software.

[0032] In a possible embodiment, the simulation data model is established according to the traffic data by the UC / win-road software and the driving simulation experiment platform based on the AutoSimAS simulation system.

[0033] Exemplarily, the simulation data model is first generated by the road line geometry data and traffic hub plan design drawings in the UC / win-road software to generate a road basic model, then a surrounding facility model is imported according to the result of field surveying to establish a simulation driving scene, then an automatic driving module is loaded by the driving simulation experiment platform of the AutoSimAS simulation system, different vehicle automatic driving running states are simulated by the vehicle sensor data and the automatic driving module to obtain vehicle automatic driving running data, and finally simulation traffic data is calculated by the simulation driving scene and the automatic driving running data of different vehicles to construct the simulation data model.

[0034] In one possible embodiment, the comparison between the autonomous driving traffic organization optimization data model and the actual data of the traffic hub to be optimized is to compare the traffic capacity of the main cross-sections of the traffic hub, the delay of signalized intersections, the vehicle queue length, and the vehicle travel time.

[0035] For example, the comparison of the main cross-sectional capacity of the transportation hub is a comparison of the overall operating efficiency of the transportation hub, the system's traffic carrying capacity and throughput capacity; the comparison of the delay of the signal-controlled intersection and the vehicle queue length is a comparison of the operating efficiency of the key nodes, traffic costs and losses; and the comparison of vehicle travel time is a comparison of the system's service capacity to passengers.

[0036] This invention provides a traffic organization optimization system based on autonomous driving, comprising:

[0037] The data acquisition module is used to acquire traffic data from the transportation hub to be optimized.

[0038] The model building module is used to build a simulation data model based on the traffic data, perform traffic organization optimization in the simulation data model, remove data about traffic accidents from the traffic organization optimization, and obtain the unmanned traffic organization optimization data model of the traffic hub to be optimized.

[0039] The feasibility study module is used to compare the autonomous driving traffic organization optimization data model with the actual data of the traffic hub to be optimized, and obtain an autonomous driving traffic organization optimization feasibility report.

[0040] This invention provides a storage medium storing a plurality of computer instructions, which are used to enable a computer to implement the above-described method.

[0041] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A traffic organization optimization method based on autonomous driving, characterized in that, include: Obtain traffic data for the transportation hubs to be optimized; The traffic data includes road alignment geometry data, vehicle sensor data, and video image data; Road alignment geometry data is acquired through field surveys and satellite imagery, and is used to construct the terrain and road model for the simulation data model in the UC / win-road software. Vehicle sensor data is collected by collecting onboard sensor information of vehicles passing through the traffic hub to be optimized, and then the onboard sensor information is summarized and organized to simulate the autonomous driving state of different vehicles in the AutoSimAS simulation system. Video image data is acquired through road traffic monitoring and is used to simulate the traffic status of the traffic hub to be optimized in the UC / win-road software. A simulation data model is established based on the traffic data; Traffic organization optimization is performed in the simulation data model; By removing data related to traffic accidents from the traffic organization optimization, an autonomous driving traffic organization optimization data model for the traffic hub to be optimized is obtained. By comparing the autonomous driving traffic organization optimization data model with the actual data of the traffic hub to be optimized, a feasibility report on autonomous driving traffic organization optimization is obtained. The comparison between the autonomous driving traffic organization optimization data model and the actual data of the traffic hub to be optimized involves comparing the traffic capacity of the main cross-sections of the traffic hub, the delay at signalized intersections, the vehicle queue length, and the vehicle travel time.

2. The traffic organization optimization method based on autonomous driving according to claim 1, characterized in that, The simulation data model established based on the traffic data is carried out using UC / win-road software and a driving simulation experimental platform based on the AutoSimAS simulation system.

3. The traffic organization optimization method based on autonomous driving according to claim 1, characterized in that, The process of acquiring traffic data for the transportation hub to be optimized involves deploying sensors at the transportation hub and using monitoring equipment at the transportation hub to obtain the traffic data.

4. A traffic organization optimization system based on autonomous driving, characterized in that, The aforementioned traffic organization optimization system based on autonomous driving is used to implement the method described in any one of claims 1-3, comprising: The data acquisition module is used to acquire traffic data from the transportation hub to be optimized. The model building module is used to build a simulation data model based on the traffic data, perform traffic organization optimization in the simulation data model, remove data about traffic accidents from the traffic organization optimization, and obtain the unmanned traffic organization optimization data model of the traffic hub to be optimized. The feasibility study module is used to compare the autonomous driving traffic organization optimization data model with the actual data of the traffic hub to be optimized, and obtain an autonomous driving traffic organization optimization feasibility report.

5. A storage medium, characterized in that, The storage medium stores a plurality of computer instructions, which are used to enable the computer to implement the method described in any one of claims 1-3.

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