A vehicle-road simultaneous time-series data acquisition method and system based on a distributed architecture

Abstract

The present invention relates to the technical field of vehicle-road cooperation, and specifically relates to a vehicle-road simultaneous time-series data acquisition method and system based on a distributed architecture. The roadside perception subsystem and the data acquisition vehicle are respectively synchronized according to the received satellite timing signal to obtain the trajectory point sequence of the current acquisition task. According to the task planning result, the data acquisition vehicle is controlled to switch its state, and when in the upload state, the unuploaded data is transmitted to the cloud server; the state and driving trajectory data of the data acquisition vehicle are obtained and parsed in real time, and it is judged whether the current roadside perception subsystem needs to perform data acquisition. Based on the received judgment result, the roadside perception subsystem is controlled to switch its state, and when in the upload state, the collected unuploaded data is transmitted to the cloud server to dynamically trigger the data recording of roadside devices, reduce redundancy, implement a cross-intersection collaborative data acquisition mechanism, and reduce the data acquisition cost.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-road cooperation, and particularly to a vehicle-road simultaneous sequence data acquisition method and system based on a distributed architecture. Background Art

[0002] In a vehicle-road cooperation system, the synchronous acquisition of sequential data of vehicles and roadside devices (such as cameras, lidar, etc.) is a key foundation for realizing vehicle-road cooperation perception and conducting end-to-end model training. In the prior art, the following problems mainly exist in data acquisition:

[0003] 1. Redundant acquisition: Roadside devices continuously record data, resulting in a large amount of redundant data storage and transmission pressure. However, most of the recorded data is invalid data, wasting cloud storage and transmission network resources;

[0004] 2. Low acquisition efficiency: When the vehicle end and the roadside end simultaneously acquire data, it mainly relies on testers to communicate through mobile phones or intercoms. When a vehicle continuously passes through multiple intersections, it is necessary to manually start the data recording program and manually switch the data recording programs for different intersections. There is a lack of an associated cooperation mechanism between intersections, resulting in low efficiency of vehicle-road simultaneous sequence data acquisition and high labor costs;

[0005] 3. Centralized storage bottleneck: Traditional solutions rely on a single data center to centrally store roadside data, resulting in high network bandwidth pressure and high backhaul latency. Moreover, the full-volume acquisition strategy requires a wired network connection to be built between roadside devices and the data center, with high cost.

[0006] Therefore, there is an urgent need for a high-efficiency vehicle-road simultaneous sequence data acquisition scheme to dynamically trigger the data recording of roadside devices, reduce redundancy, implement a cross-intersection collaborative data acquisition mechanism, and reduce data acquisition costs. Summary of the Invention

[0007] The purpose of the present invention is to provide a vehicle-road simultaneous sequence data acquisition method and system based on a distributed architecture to dynamically trigger the data recording of roadside devices, reduce redundancy, implement a cross-intersection collaborative data acquisition mechanism, and reduce data acquisition costs.

[0008] To achieve the above purpose, in a first aspect, the present invention provides a vehicle-road simultaneous sequence data acquisition method based on a distributed architecture, including the following steps:

[0009] Obtain the trajectory point sequence of the current acquisition task, control the data acquisition vehicle to switch its state according to the task planning result, and when in the upload state, transmit the untransmitted data to the cloud server;

[0010] Obtain and parse the state and driving trajectory data of the data acquisition vehicle in real time, and determine whether the current roadside perception subsystem needs to perform data acquisition;

[0011] Based on the received judgment result, control the roadside perception subsystem to switch its state, and when in the upload state, transmit the untransmitted data collected to the cloud server.

[0012] Wherein, the method further includes:

[0013] Time the roadside perception subsystem and the data collection vehicle respectively according to the received satellite time signal.

[0014] Wherein, obtaining and parsing the state and driving trajectory data of the data collection vehicle in real time, and determining whether the current roadside perception subsystem needs to perform data collection includes:

[0015] Parse the sensor data of the camera and lidar in real time and transmit the parsed perception data to the data collection sub-unit;

[0016] Parse the state data received by the roadside communication unit in real time, and calculate whether the current roadside perception subsystem needs to perform data collection according to the state of the data collection vehicle and the driving trajectory obtained by parsing.

[0017] Wherein, calculating whether the current roadside perception subsystem needs to perform data collection according to the state of the data collection vehicle and the driving trajectory obtained by parsing includes:

[0018] Calculate whether the real-time vehicle state of the data collection vehicle is equal to 0 according to the state of the data collection vehicle and the driving trajectory obtained by parsing;

[0019] If it is equal to 0, the data collection vehicle is not in the data collection state and does not need to perform data collection;

[0020] If it is not equal to 0, the data collection vehicle is in the data collection state.

[0021] Wherein, after the data collection vehicle is in the data collection state, the method further includes:

[0022] Traverse and calculate the distance between the perception center point of the current roadside perception subsystem and the vehicle driving trajectory point. If the distance is not less than the perception range radius of the current roadside perception subsystem, no data collection is required;

[0023] If the distance is less than the perception range radius of the current roadside perception subsystem, determine whether the vehicle trajectory point has passed the perception range radius, and continue to calculate whether the current roadside perception subsystem needs to perform data collection based on the passed vehicle trajectory points.

[0024] Wherein, continuing to calculate whether the current roadside perception subsystem needs to perform data collection based on the passed vehicle trajectory points includes:

[0025] Calculate the distance between the center point position of the data collection vehicle and the center point position of the current roadside perception subsystem. If the collection vehicle has currently left the perception range of the current roadside perception subsystem, data collection is not required; if the data collection vehicle is still within the perception range of the current roadside perception subsystem, the current roadside perception subsystem needs to perform data collection.

[0026] Among them, based on the received judgment result, control the roadside perception subsystem to perform state switching, and when in the upload state, transmit the untransmitted data collected to the cloud server, including:

[0027] Based on the received judgment result, control the roadside perception subsystem to switch states among the data collection state, upload state, and standby state;

[0028] When the roadside perception subsystem needs to perform data collection, the system state switches from the standby state to the data collection state;

[0029] When the roadside perception subsystem does not need to perform data collection, the system state switches from the data collection state to the standby state;

[0030] When there is untransmitted data in the edge storage unit of the roadside perception subsystem, the system state switches from the standby state to the upload state; when all data has been uploaded and there is no untransmitted data in the edge storage unit, the system state switches from the upload state to the standby state.

[0031] Among them, obtain the trajectory point sequence of this collection task, control the data collection vehicle to perform state switching according to the task planning result, and when in the upload state, transmit the untransmitted data to the cloud server, including:

[0032] Obtain the trajectory point sequence of this collection task, and control the data collection vehicle to switch states among the data collection state, upload state, and standby state according to the task planning result;

[0033] When the system of the data collection vehicle is in the upload state, perform data collection based on the set trajectory points of the data collection task, and upload the vehicle state and vehicle trajectory data to the cloud server in real time.

[0034] In a second aspect, the present invention provides a vehicle-road simultaneous time-series data collection system based on a distributed architecture. The vehicle-road simultaneous time-series data collection system based on a distributed architecture includes a roadside perception subsystem, a data collection vehicle, and a cloud server. The roadside perception subsystem and the data collection vehicle are respectively connected to the cloud server;

[0035] The roadside perception subsystem includes roadside sensors, a first PTP time server, a first aggregation switch, an edge storage unit, a roadside communication unit, and a first satellite receiving antenna;

[0036] The data collection vehicle includes perception sensors, a second PTP time server, a second aggregation switch, an on-vehicle storage unit, an on-vehicle communication unit, and a second satellite receiving antenna;

[0037] The cloud server includes a data transfer server and a cloud storage server;

[0038] The first satellite receiving antenna receives the satellite timing signal and transmits it to the first PTP time server. The first PTP time server simultaneously provides timing for the roadside sensors, the first aggregation switch, and the edge storage unit. The roadside communication unit receives the status data from the cloud server through the network and sends the perception data in the edge storage unit to the cloud server through the network; the edge storage unit receives the status data of the roadside communication unit and performs the corresponding data collection process;

[0039] The second satellite receiving antenna receives the satellite timing signal and transmits it to the second PTP time server. The second PTP time server simultaneously provides timing for the on-vehicle sensors, the second aggregation switch, and the on-vehicle storage unit. The on-vehicle communication unit transmits the vehicle's real-time position data and collection status data to the cloud server through the network;

[0040] The data transfer server receives the positioning data, perception data, and status data uploaded by the roadside perception subsystem and the data collection vehicle in real time, and forwards the data to the roadside communication unit and the on-vehicle communication unit. The large-volume original perception data uploaded by the roadside perception subsystem is directly stored in the cloud storage server.

[0041] A vehicle-road simultaneous time-sequence data collection method and system based on a distributed architecture according to the present invention includes a roadside perception subsystem, a data collection vehicle, and a cloud server. The roadside perception subsystem and the data collection vehicle are respectively timed according to the received satellite timing signal to obtain the trajectory point sequence of the current collection task. According to the task planning result, the data collection vehicle is controlled to perform state switching, and when in the upload state, the unuploaded data is transmitted to the cloud server; the status and driving trajectory data of the data collection vehicle are obtained and parsed in real time, and it is judged whether the current roadside perception subsystem needs to perform data collection. Based on the received judgment result, the roadside perception subsystem is controlled to perform state switching, and when in the upload state, the uncollected data collected is transmitted to the cloud server to dynamically trigger the data recording of roadside devices, reduce redundancy, implement a cross-intersection collaborative data collection mechanism, and reduce the data collection cost. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0043] Figure 1 It is a schematic diagram of the steps of the vehicle-road simultaneous time-series data acquisition method based on a distributed architecture provided by the present invention.

[0044] Figure 2 It is a hardware architecture diagram of the vehicle-road simultaneous time-series data acquisition system provided by the present invention.

[0045] Figure 3 It is a software architecture diagram of the vehicle-road simultaneous time-series data acquisition system provided by the present invention.

[0046] Figure 4 It is a flowchart of the judgment of the roadside data acquisition requirement provided by the present invention.

[0047] Figure 5 It is a schematic diagram of the method for judging the roadside data acquisition requirement provided by the present invention.

[0048] Figure 6 It is a logic diagram of the state machine switching of the roadside perception subsystem provided by the present invention.

[0049] Figure 7 It is a logic diagram of the state machine switching of the data acquisition vehicle system provided by the present invention. Specific embodiments

[0050] The following will describe in detail the embodiments of the present invention. The examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention and should not be construed as a limitation to the present invention.

[0051] The first embodiment of the present application is as follows:

[0052] Please refer to Figures 1-7 , Figure 1 It is a schematic diagram of the steps of the vehicle-road simultaneous time-series data acquisition method based on a distributed architecture provided by the present invention. Figure 2 It is a hardware architecture diagram of the vehicle-road simultaneous time-series data acquisition system provided by the present invention. Figure 3 It is a software architecture diagram of the vehicle-road simultaneous time-series data acquisition system provided by the present invention. Figure 4 It is a flowchart of the judgment of the roadside data acquisition requirement provided by the present invention. Figure 5 It is a schematic diagram of the method for judging the roadside data acquisition requirement provided by the present invention. Figure 6 It is a logic diagram of the state machine switching of the roadside perception subsystem provided by the present invention. Figure 7 It is a logic diagram of the state machine switching of the data acquisition vehicle system provided by the present invention.

[0053] The present invention provides a vehicle-road simultaneous data acquisition method based on a distributed architecture, comprising the following steps:

[0054] S101. Obtain the trajectory point sequence of the current acquisition task, control the data acquisition vehicle to switch its state according to the task planning result, and when in the upload state, transmit the untransmitted data to the cloud server.

[0055] Specifically, the vehicle tester inputs the trajectory point sequence of the current acquisition task through the data acquisition task planning subunit. The data acquisition vehicle system state machine switches its state according to the task planning result, as Figure 7 shown. When the system is in the data acquisition state, the system starts the data acquisition subunit to record camera image data, lidar point cloud data, and integrated navigation and positioning data in real time; when the system is in the standby state, the system only runs the sensor driver subunit and the in-vehicle communication unit driver subunit; when the system is in the upload state, the system starts the data upload subunit to upload the untransmitted data to the acquisition data receiving service of the cloud server through the wireless network.

[0056] Among them, the detailed process of the data acquisition vehicle system state machine switching its state according to the task planning result is as follows:

[0057] The data acquisition vehicle system has three states, namely the data acquisition state, the upload state, and the standby state. The vehicle acquisition state of the data acquisition vehicle at this time represents the data acquisition state, represents the standby state, represents the upload state. The initial state of the system is the standby state, where represents the vehicle state.

[0058] When the tester manually sets the data acquisition task trajectory points through the data acquisition task planning subunit and clicks the start data acquisition button, the data acquisition task planning subunit outputs , and the data acquisition vehicle system state switches from the standby state to the data acquisition state; when the tester selects to stop the data acquisition action through the data acquisition task planning subunit, the data acquisition task planning subunit outputs , and the data acquisition vehicle system state switches from the data acquisition state to the standby state, where represents whether the vehicle needs to perform data acquisition.

[0059] When and , that is, there is untransmitted data in the in-vehicle storage unit, the system state switches from the standby state to the upload state; when all data has been uploaded and there is no untransmitted data in the in-vehicle storage unit, that is , the system status switches from the upload status to the standby status, where indicates whether the vehicle needs to upload data.

[0060] When the system is in the upload status, after the tester manually sets the data collection task track points through the data collection task planning subunit and clicks the start data collection button, the data collection task planning subunit outputs , then the system immediately switches from the upload status to the data collection status; the system status cannot directly switch from the data collection status to the upload status.

[0061] S102. Real-time obtain and analyze the status and driving trajectory data of the data collection vehicle, and judge whether the current roadside perception subsystem needs to collect data.

[0062] Specifically: The roadside perception subsystem consists of a camera drive subunit, a lidar drive subunit, a data collection subunit, a data upload subunit, a roadside communication unit drive subunit, a roadside data collection requirement judgment subunit, and a roadside perception system status machine. The camera drive subunit and the lidar drive subunit parse the sensor data of the camera and the lidar in real time and transmit the parsed perception data to the data collection subunit; the roadside communication unit drive subunit parses the status data received by the roadside communication unit in real time and sends the status and driving trajectory data of the data collection vehicle to the roadside data collection requirement judgment subunit. The collection requirement judgment subunit calculates whether the current roadside system needs to collect data based on the status of the data collection vehicle and the driving trajectory of the vehicle as Figure 4 shown, and sends the judgment result to the roadside perception system state machine.

[0063] As Figure 4 and Figure 5 shown, the detailed steps of the judgment method include:

[0064] Step 1: The real-time vehicle status of the data collection vehicle is , where and represent the real-time position of the vehicle, represents the real-time driving heading of the vehicle, represents the vehicle length, represents the vehicle width, represents the vehicle height, represents the real-time collection status of the vehicle, judge the vehicle collection status of the data collection vehicle at this time , if , that is, the collection vehicle is not in the data collection status, then roadside data collection is not required, that is ; if , that is, the collection vehicle is in the data collection status, then proceed to step 2;

[0065] Step 2: The vehicle driving trajectory is , where is the coordinate of the vehicle trajectory point, represents whether the vehicle has passed through this trajectory point, represents that the vehicle has not passed through yet, represents that the vehicle has passed through this trajectory point, where, i 1,......, m;

[0066] The state of this roadside perception subsystem is , where , is the coordinate of the perception center point of this roadside perception subsystem, is the perception range radius of this roadside perception subsystem, is the system state of this roadside perception subsystem.

[0067] Traverse and calculate the distance between the perception center point of this roadside perception subsystem and the vehicle driving trajectory point ,

[0068] where

[0069] If , then this roadside perception subsystem does not need to perform data collection, that is , the judgment program ends, indicates non-existence;

[0070] If , it is identified as judgment condition 1, then go to step 3, indicates existence;

[0071] Step 3: If , that is, the th vehicle trajectory point has not been passed through, and this trajectory point is within the perception range of this roadside perception system, then this roadside perception subsystem needs to perform data collection, that is , the judgment program ends, i* represents the actual case object, i represents generality;

[0072] If , that is, the th vehicle trajectory point has been passed through, go to step 4;

[0073] Step 4: Calculate the distance between the position of the center point of the collection vehicle and the position of the perception center point of this roadside perception subsystem ,

[0074]

[0075] If , that is, the acquisition vehicle has currently left the sensing range of this roadside sensing subsystem, so data acquisition is not required, that is , the judgment program ends;

[0076] If , that is, the acquisition vehicle is still within the sensing range of this roadside sensing subsystem, then this roadside sensing subsystem needs to perform data acquisition, that is , the judgment program ends.

[0077] S103. Based on the received judgment result, control the roadside sensing subsystem to switch states, and when in the upload state, transmit the unuploaded data collected to the cloud server.

[0078] Specifically, the roadside sensing system state machine switches among three states: the data acquisition state, the standby state, and the upload state. As Figure 6 shown, when the roadside sensing subsystem is in the data acquisition state, the system starts the data acquisition sub-unit to record camera image data and lidar point cloud data in real time; when the system is in the standby state, the system only runs the camera driver sub-unit, the lidar driver sub-unit, the roadside data acquisition requirement judgment sub-unit, and the roadside communication unit driver sub-unit; when the system is in the upload state, the system starts the data upload sub-unit to transmit the unuploaded data collected to the acquisition data receiving service of the cloud server through a wired or wireless network.

[0079] The roadside sensing subsystem has three states, namely the data acquisition state, the upload state, and the standby state. represents the data acquisition state, represents the standby state, represents the upload state. The initial state of the system is the standby state, where represents the roadside state.

[0080] When the roadside data acquisition requirement judgment sub-unit outputs , that is, the roadside sensing subsystem needs to perform data acquisition, and the system state switches from the standby state to the data acquisition state; when the roadside data acquisition requirement judgment sub-unit outputs , that is, the roadside sensing subsystem does not need to perform data acquisition, and the system state switches from the data acquisition state to the standby state, where represents whether data acquisition is required at the roadside.

[0081] When and , that is, there is unuploaded data in the roadside edge storage unit, then the system state switches from the standby state to the upload state; when all data has been uploaded and there is no unuploaded data in the roadside edge storage unit, that is , the system status switches from the upload status to the standby status, where indicates whether data upload is required at the roadside.

[0082] When the system is in the upload status, the roadside data acquisition requirement judgment subunit outputs , then the system immediately switches from the upload status to the data acquisition status; the system status cannot directly switch from the data acquisition status to the upload status.

[0083] The method further includes:

[0084] Timing the roadside perception subsystem and the data acquisition vehicle respectively according to the received satellite timing signal.

[0085] Please refer to Figure 2 , the present invention provides a vehicle-road synchronous data acquisition system based on a distributed architecture. The vehicle-road synchronous data acquisition system based on the distributed architecture includes a roadside perception subsystem, a data acquisition vehicle, and a cloud server. The roadside perception subsystem and the data acquisition vehicle are respectively connected to the cloud server;

[0086] The roadside perception subsystem includes roadside sensors, a first PTP timekeeper, a first aggregation switch, an edge storage unit, a roadside communication unit, and a first satellite receiving antenna;

[0087] The data acquisition vehicle includes perception sensors, a second PTP timekeeper, a second aggregation switch, an on-vehicle storage unit, an on-vehicle communication unit, and a second satellite receiving antenna;

[0088] The cloud server includes a data transfer server and a cloud storage server;

[0089] The first satellite receiving antenna receives the satellite timing signal and transmits it to the first PTP timekeeper. The first PTP timekeeper simultaneously times the roadside sensors, the first aggregation switch, and the edge storage unit. The roadside communication unit receives the status data from the cloud server through the network and sends the perception data in the edge storage unit to the cloud server through the network; the edge storage unit receives the status data of the roadside communication unit and executes the corresponding data acquisition process;

[0090] The second satellite receiving antenna receives the satellite timing signal and transmits it to the second PTP timekeeper. The second PTP timekeeper simultaneously times the vehicle-mounted sensors, the second aggregation switch, and the on-vehicle storage unit. The on-vehicle communication unit transmits the vehicle real-time position data and the acquisition status data to the cloud server through the network;

[0091] The data transfer server receives in real time the positioning data, sensing data, and status data uploaded by the roadside sensing subsystem and the data collection vehicle, and forwards the data to the roadside communication unit and the vehicle-mounted communication unit. The large-volume original sensing data uploaded by the roadside sensing subsystem is directly stored in the cloud storage server.

[0092] In this embodiment, the system mainly consists of a roadside sensing subsystem, a data collection vehicle, and a cloud server.

[0093] The roadside sensing subsystem includes roadside sensors, a first PTP time server, a first aggregation switch, an edge storage unit, a roadside communication unit, and a first satellite receiving antenna. The first satellite receiving antenna receives the satellite timing signal and transmits it to the first PTP time server. The first PTP time server simultaneously provides timing for the roadside sensors, the first aggregation switch, and the edge storage unit. The roadside communication unit receives the status data from the cloud through the 4G / 5G wireless cellular network and sends the sensing data in the edge storage unit to the cloud through the 4G / 5G wireless cellular network. The edge storage unit receives the status data of the roadside communication unit and executes the corresponding data collection sub-unit.

[0094] The data collection vehicle is equipped with sensing sensors, a second PTP time server, a second aggregation switch, an on-vehicle storage unit, an on-vehicle communication unit, and a second satellite receiving antenna. The second satellite receiving antenna receives the satellite timing signal and transmits it to the second PTP time server. The second PTP time server simultaneously provides timing for the vehicle-mounted sensors, the second aggregation switch, and the on-vehicle storage unit. The on-vehicle communication unit transmits the vehicle's real-time position data and collection status data to the cloud server through the 4G or 5G mobile network.

[0095] The first aggregation switch and the second aggregation switch are normal aggregation switches used to transfer and exchange data.

[0096] The cloud server mainly includes a data transfer server and a cloud storage server. The data transfer server receives in real time the positioning data, sensing data, and status data uploaded by the roadside and the vehicle end, and forwards the data to the roadside communication unit and the vehicle-mounted communication unit. The large-volume original sensing data uploaded by the roadside is directly stored in the cloud storage server.

[0097] As shown in the appendix Figure 3 shown, where Figure 3 in fact, there are multiple edge storage units. To ensure the clarity of the attached drawing, the edge storage units from 1 to n - 1 are omitted, and only the one as shown in the appendix Figure 3In the structure shown, the roadside perception subsystem runs in the edge storage unit, which consists of a camera driving subunit, a lidar driving subunit, a data acquisition subunit, a data upload subunit, a roadside communication unit driving subunit, a roadside data acquisition requirement judgment subunit, and a roadside perception system state machine. The camera driving subunit and the lidar driving subunit parse the sensor data of the camera and lidar in real time and transmit the parsed perception data to the data acquisition subunit; the roadside communication unit driving subunit parses the status data received by the roadside communication unit in real time and sends the status and driving trajectory data of the data acquisition vehicle to the roadside data acquisition requirement judgment subunit. The acquisition requirement judgment subunit calculates whether the current roadside system needs to perform data acquisition based on the status of the data acquisition vehicle and the driving trajectory of the vehicle, as Figure 4 shown, and sends the judgment result to the roadside perception system state machine.

[0098] According to the information input, the roadside perception system state machine switches among three states: data acquisition state, standby state, and upload state, as Figure 6 shown. When the roadside perception subsystem is in the data acquisition state, the system starts the data acquisition subunit to record the camera image data and lidar point cloud data in real time; when the system is in the standby state, the system only runs the camera driving subunit, the lidar driving subunit, the roadside data acquisition requirement judgment subunit, and the roadside communication unit driving subunit; when the system is in the upload state, the system starts the data upload subunit to transmit the untransmitted collected data to the acquisition data receiving service of the cloud server through a wired or wireless network.

[0099] The data acquisition vehicle runs in the on-vehicle storage unit, which includes a camera driving subunit, a lidar driving subunit, an integrated navigation driving subunit, a data acquisition subunit, a data upload subunit, an on-vehicle communication unit driving subunit, a data acquisition task planning subunit, and a data acquisition vehicle system state machine subunit. Among them, the driving subunits always keep running, and the on-vehicle communication unit driving subunit uploads the vehicle status and vehicle trajectory data to the real-time data transmission cloud service of the cloud server in real time.

[0100] The vehicle tester inputs the trajectory point sequence of this acquisition task through the data acquisition task planning subunit. The data acquisition vehicle system state machine switches its state according to the task planning result, as Figure 7As shown in the figure. When the system is in the data acquisition state, the system will start the data acquisition sub-unit to record camera image data, lidar point cloud data, and integrated navigation and positioning data in real time; when the system is in the standby state, the system only runs the sensor driver sub-unit and the vehicle communication unit driver sub-unit; when the system is in the upload state, the system will start the data upload sub-unit to upload the unuploaded data to the acquisition data receiving service of the cloud server through the wireless network.

[0101] In traditional data acquisition solutions, there is no real-time communication between the vehicle end and the roadside end during the acquisition process. The roadside end and the vehicle end do not know each other's acquisition status, or only confirm the acquisition status through telephone communication between the testers at the roadside end and the testers at the vehicle end. This approach has low acquisition efficiency and high labor costs. The architecture of this solution solves this problem. In this solution, the data acquisition vehicle and the roadside perception subsystem communicate in real time through the cloud data transfer server and the mobile cellular network. The vehicle end can obtain the status of the roadside perception system in real time, such as whether the sensors are working properly and whether it is in the acquisition state; the roadside perception system can also obtain the status of the vehicle end in real time, including the real-time positioning of the vehicle and whether the vehicle end is in the acquisition state. The vehicle end and the roadside can dynamically adjust the data acquisition strategy based on the status data of each other obtained, and no longer require the testers at both ends to communicate by phone to confirm the acquisition task. This greatly improves the data acquisition efficiency and reduces the labor cost of data acquisition.

[0102] The beneficial effects of this invention are as follows:

[0103] 1. Precise data acquisition reduces the cost of data post-processing

[0104] Traditional roadside data acquisition methods generally use the method of recording data continuously for 24 hours. This method causes great difficulties in selecting useful data from a large amount of data later.

[0105] This system can accurately identify and judge the roadside acquisition requirements through an intelligent roadside data acquisition requirement judgment algorithm, and realize data acquisition only when it is needed. As long as the data collected is useful data, it directly avoids the later data screening work and greatly reduces the cost of data post-processing.

[0106] 2. Distributed data storage reduces the system construction cost

[0107] Traditional vehicle-road data acquisition systems build a fiber-optic wired data connection between roadside perception devices and the data center, and realize the real-time upload of raw data to the data center server for unified storage through multi-layer switches. This method has the following disadvantages when the number of roadside devices is large:

[0108] The cost of building a wired network is huge. Building network devices such as optical fibers and switches from multiple intersections to the data center requires high construction and equipment procurement costs.

[0109] The cost of building network storage in the data center is huge. Using the traditional method of directly collecting data from roadside sensors requires the data center to build a large enough storage capacity, resulting in high costs.

[0110] In this system, roadside edge storage devices are deployed on the roadside to achieve distributed storage of a large amount of raw data in each roadside edge storage device first. Instead of immediately transmitting the data back to the data center, it is transmitted back to the data center through a wireless network in sequence after the data collection task is completed. This not only greatly reduces the construction cost of the data center's storage capacity but also directly eliminates the construction cost of the wired network, further reducing the cost.

[0111] 3. Collaborative automated collection reduces the labor cost of data collection

[0112] In the traditional vehicle-road data collection process, multiple testers are required to cooperate. For example, 1-2 testers are needed on the data collection vehicle to drive the vehicle, start the vehicle-end data collection, and communicate with other testers; 1-2 testers are needed in the data center to communicate with the vehicle-end testers and dynamically activate the cloud data receiving sub-unit according to the needs of the vehicle-end testers; 1 tester is needed at each roadside edge to communicate with the vehicle-end testers and dynamically activate the data collection sub-unit of the roadside device. It can be seen that when using the traditional method for vehicle-road data collection, the more intersections the data collection vehicle passes through during the collection task, the more testers are needed. This method has too high labor costs for data collection and low efficiency.

[0113] This system enables the vehicle-end, roadside-end, and cloud-end to work collaboratively. By automatically switching the working state through the finite state machines of the roadside-end and vehicle-end, it realizes the automated data collection work of the vehicle-end, roadside-end, and cloud-end. The roadside-end and vehicle-end can automatically switch their states according to the state of the other party, so there is no need to deploy testers at the cloud-end and roadside-end. Only one tester needs to be deployed at the vehicle-end throughout the test process. The tester only needs to set the data collection task and activate the data collection sub-unit before driving the vehicle, and all subsequent collection tasks are fully automated, greatly reducing the labor cost of data collection.

[0114] The above-disclosed are only one or more preferred embodiments of this application, and cannot be used to limit the scope of rights of this application. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of this application still fall within the scope covered by this application.

Claims

1. A method for collecting vehicle-road simultaneous data based on a distributed architecture, characterized in that: The following steps are involved: Obtain the trajectory point sequence for this collection mission, control the data collection vehicle to switch between standby, data collection, and upload states based on the mission planning results, and transmit unuploaded data to the cloud server when in the upload state; Acquire and analyze the status and driving trajectory data of the data collection vehicle in real time, and determine whether the current roadside perception subsystem needs to collect data; If the collection vehicle is not in the data collection state, the roadside perception subsystem does not need to collect data. If the collection vehicle is in the data collection state, the roadside perception subsystem needs to collect data when the trajectory point that the vehicle has not passed is within the perception range of the roadside perception subsystem or when the vehicle has passed the trajectory point but is still within the perception range of the roadside perception subsystem. Based on the received judgment results, the roadside perception subsystem is controlled to switch between the standby state and the data acquisition state, and when in the uploading state, the collected but not uploaded data is transmitted to the cloud server.

2. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 1, characterized in that: The method further comprises: The roadside perception subsystem and data collection vehicle are timed according to the received satellite timing signals.

3. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 1, characterized in that: Acquire and analyze the status and driving trajectory data of the data collection vehicle in real time, and determine whether the current roadside perception subsystem needs to collect data, including: Analyze sensor data from cameras and lidars in real time and transmit the analyzed perception data to the data acquisition subunit; The status data received by the roadside communication unit is analyzed in real time, and the status and driving trajectory of the vehicle collected based on the analyzed data are used to calculate whether the current roadside perception subsystem needs to collect data.

4. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 3, characterized in that: Based on the analyzed data collection vehicle status and driving trajectory, the current roadside perception subsystem calculates whether data collection is required, including: According to the state and driving trajectory of the data collection vehicle obtained by analysis, calculate whether the real-time vehicle state of the data collection vehicle is equal to 0; If it is equal to 0, the data collection vehicle is not in the data collection state and no data collection is required; If it is not equal to 0, the data collection vehicle is in the data collection state.

5. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 4, characterized in that: After the data collection vehicle is in a data collection state, the method further includes: The distance between the perception center point of the current roadside perception subsystem and the vehicle's driving trajectory is calculated. If the distance is not less than the perception range radius of the current roadside perception subsystem, data collection is not required. If the distance is less than the perception range radius of the current roadside perception subsystem, it is determined whether the vehicle trajectory point has passed the perception range radius, and based on the vehicle trajectory points that have passed, it is continued to calculate whether the current roadside perception subsystem needs to collect data.

6. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 5, characterized in that: Based on the vehicle trajectory points that have already passed, the current roadside perception subsystem continues to calculate whether data collection is required, including: Calculate the distance between the center point of the data collection vehicle and the perception center point of the current roadside perception subsystem. If the data collection vehicle has currently left the perception range of the current roadside perception subsystem, data collection is not required; if the data collection vehicle is still within the perception range of the current roadside perception subsystem, the current roadside perception subsystem needs to collect data.

7. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 1, characterized in that: Based on the received judgment results, the roadside perception subsystem is controlled to switch states and, when in the upload state, the collected but not uploaded data is transmitted to the cloud server, including: Based on the received judgment result, the roadside sensing subsystem is controlled to switch between the data acquisition state and the standby state; When the roadside sensing subsystem needs to collect data, the system state switches from the standby state to the data collection state; When the roadside sensing subsystem does not need to collect data, the system state switches from the data collection state to the standby state; When there is unuploaded data in the edge storage unit of the roadside perception subsystem, the system state switches from the standby state to the upload state; when all data are uploaded and there is no unuploaded data in the edge storage unit, the system state switches from the upload state to the standby state.

8. The method for collecting vehicle-road simultaneous data based on a distributed architecture as claimed in claim 1, wherein: Obtain the trajectory point sequence for this collection mission, control the data collection vehicle to switch states according to the mission planning results, and transfer the unuploaded data to the cloud server when in the upload state, including: Obtain the trajectory point sequence for this collection mission, and control the data collection vehicle to switch between data collection state, upload state, and standby state according to the mission planning results; When the system of the data collection vehicle is in the uploading state, data collection is performed based on the set data collection task trajectory points, and the vehicle status and vehicle trajectory data are uploaded to the cloud server in real time.

9. A vehicle-road simultaneous sequential data collection system based on a distributed architecture, using the vehicle-road simultaneous sequential data collection method based on a distributed architecture according to any one of claims 1 to 8, characterized in that: The vehicle-road simultaneous data collection system based on a distributed architecture includes a roadside perception subsystem, a data collection vehicle, and a cloud server, wherein the roadside perception subsystem and the data collection vehicle are respectively connected to the cloud server; The roadside sensing subsystem includes a roadside sensor, a first PTP timer, a first convergence switch, an edge storage unit, a roadside communication unit, and a first satellite receiving antenna; The data collection vehicle includes a perception sensor, a second PTP timer, a second convergence switch, an on-board storage unit, an on-board communication unit, and a second satellite receiving antenna; The cloud server includes a data transfer server and a cloud storage server; The first satellite receiving antenna receives a satellite timing signal and transmits it to the first PTP timing device. The first PTP timing device simultaneously provides time for the roadside sensor, the first convergence switch, and the edge storage unit. The roadside communication unit receives status data from the cloud server via a network and sends the perception data in the edge storage unit to the cloud server via the network. The edge storage unit receives the status data of the roadside communication unit and performs a corresponding data collection process; The second satellite receiving antenna receives the satellite timing signal and transmits it to the second PTP timer, and the second PTP timer simultaneously provides time for the perception sensor, the second convergence switch, and the on-board storage unit. The on-board communication unit transmits the vehicle's real-time position data and collection status data to the cloud server via the network; The data transfer server receives the positioning data, perception data and status data uploaded by the roadside perception subsystem and the data collection vehicle in real time, and forwards the data to the roadside communication unit and the vehicle-mounted communication unit. The original perception data uploaded by the roadside perception subsystem is directly stored in the cloud storage server.

Images