WO2024001356A1 - 车辆的检测方法、控制器、程序和存储介质 - Google Patents

车辆的检测方法、控制器、程序和存储介质 Download PDF

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Publication number
WO2024001356A1
WO2024001356A1 PCT/CN2023/084896 CN2023084896W WO2024001356A1 WO 2024001356 A1 WO2024001356 A1 WO 2024001356A1 CN 2023084896 W CN2023084896 W CN 2023084896W WO 2024001356 A1 WO2024001356 A1 WO 2024001356A1
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Prior art keywords
vehicle
target
target vehicle
detection area
legal
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PCT/CN2023/084896
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English (en)
French (fr)
Inventor
陈美竹
陈楚君
卓开阔
刘伟华
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比亚迪股份有限公司
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Publication of WO2024001356A1 publication Critical patent/WO2024001356A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats

Definitions

  • the present disclosure relates to the field of vehicle technology, and in particular, to a vehicle detection method, controller, program and storage medium.
  • the existing rail transit signaling system arranges a large number of axle counting equipment on the line, and uses the axle counting equipment to determine the section occupancy, clearing, front screening, rear screening and other functions of the train.
  • a detection axle counter is usually installed outside the parking garage line to detect whether there is a non-communication train entering the main line operation, so as to avoid the appearance of non-communication trains on the line where trains communicate, causing safety hazards to the vehicles running on the line.
  • the detection axle counter is susceptible to interference. When disturbed, manual reset is required to resume normal detection, resulting in inaccurate axle counter detection results and affecting the safety of vehicle-to-vehicle communication lines.
  • the present disclosure aims to solve one of the technical problems in the related art, at least to a certain extent.
  • the first purpose of the present disclosure is to propose a vehicle detection method to solve the technical problem of being unable to accurately detect vehicles in the prior art.
  • a second object of the present disclosure is to provide a controller.
  • a third object of the present disclosure is to propose a computer program.
  • a fourth object of the present disclosure is to provide a computer-readable storage medium.
  • the first embodiment of the present disclosure proposes a vehicle detection method, which method includes:
  • comparing the target vehicle type and the target vehicle identification with a legal vehicle information set to determine whether the target vehicle is an illegal vehicle includes:
  • the target vehicle is determined to be a legal vehicle; or, when it is determined that the legal vehicle information set does not include the target vehicle type and the target vehicle identification. If the target vehicle identification is specified, it is determined that the target vehicle is an illegal vehicle.
  • the method further includes:
  • Speed limit information is sent to vehicles located in the control section, where the speed limit information is used to instruct the vehicles in the control section to drive at a speed less than or equal to a specified speed.
  • determining the control section based on the location of the illegal vehicle includes:
  • the section between the target detection area and the designated detection area is regarded as the control section.
  • the designated detection area is the detection area closest to the target detection area along the traveling direction of the illegal vehicle.
  • the method further includes: in the case where the target vehicle is an illegal vehicle and a first turnout change request message sent by a vehicle located in the control section is received, refusing to respond to the first turnout. Change request message.
  • the method further includes:
  • the second switch change request message is executed.
  • the method further includes:
  • alarm information is sent to the automatic train monitoring system, and the alarm information is used to instruct the automatic train monitoring system to issue an alarm prompt.
  • the obtaining point cloud data of the target detection area and the image information of the target detection area include:
  • the image information of the target detection area is obtained through the camera.
  • the method further includes:
  • the target shape matches the preset vehicle shape, it is determined that the target vehicle exists in the target detection area.
  • the first embodiment of the present disclosure proposes a vehicle detection method, which first obtains point cloud data of the target detection area and image information of the target detection area. When it is determined that there is a target vehicle in the target detection area based on the point cloud data, the target vehicle type of the target vehicle is determined based on the point cloud data, and the target vehicle identification of the target vehicle is determined based on the image information, and then the target vehicle type and target vehicle identification are Compare with the legal vehicle information set to determine whether the target vehicle is an illegal vehicle.
  • the legal vehicle information set includes the vehicle type and vehicle identification of the legal vehicle.
  • a legal vehicle is a vehicle with normal communication functions and operating on the main line.
  • the present disclosure determines the target vehicle type and target vehicle identification of the target vehicle based on the obtained point cloud data and image information, thereby determining whether the vehicle is an illegal vehicle based on the target vehicle type and target vehicle identification, and can detect illegal vehicles more accurately.
  • a controller including:
  • a memory having computer readable code stored therein;
  • One or more processors when the computer readable code is executed by the one or more processors, the controller executes the vehicle detection method proposed in the embodiment of the first aspect of the present disclosure.
  • a third embodiment of the present disclosure provides a computer program, including computer readable code.
  • the computer readable code When the computer readable code is run on a controller, it causes the controller to execute the first aspect of the present disclosure.
  • the fourth embodiment of the present disclosure provides a computer-readable storage medium, which stores the computer program proposed by the third embodiment of the present disclosure.
  • Figure 1 is a flow chart of a vehicle detection method according to an exemplary embodiment
  • Figure 2 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 3 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 4 is a schematic diagram of a management and control section according to the embodiment of Figure 3;
  • Figure 5 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 6 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 7 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 8 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • FIG. 9 is a schematic diagram of a lightning scope according to the embodiment of Figure 8.
  • Figure 10 is a flow chart of another vehicle detection method according to an exemplary embodiment
  • Figure 11 is a schematic diagram of a controller according to an exemplary embodiment
  • FIG. 12 is a schematic diagram of a storage unit for program codes for portable or fixed implementation of the method according to the present invention according to an exemplary embodiment.
  • the vehicle in this application scenario can be any vehicle running on the preset track, such as trains, subways, light rails, trams, etc.
  • Vehicles running on the preset track can pass TACS (English: Train Autonomous) Circumambulate System, Chinese: autonomous train operation system based on train-to-train communication) communicates.
  • TACS Train Autonomous Circumambulate System
  • Chinese autonomous train operation system based on train-to-train communication
  • Figure 1 is a flow chart of a vehicle detection method according to an exemplary embodiment. As shown in Figure 1, applied to a controller, the method may include:
  • Step 101 Obtain point cloud data of the target detection area and image information of the target detection area.
  • the execution subject of this disclosure may be an Object Controller (OC), and the location of the conversion rail where the parking garage line and the main line meet can be used as the target detection area, and a detection device is set near the conversion rail.
  • the detection device obtains point cloud data of the target detection area and image information of the target detection area.
  • point cloud data can be obtained through radar and image information can be obtained through a camera.
  • the controller determines the shape of the target object based on the point cloud data of the target detection area, and determines whether the target object is the target vehicle based on the shape of the target object.
  • point cloud data can be input into a pre-trained recognition model to obtain the shape of the target object output by the recognition model, and whether the target object is a target is determined based on the matching degree between the shape of the target object and the preset vehicle shape. vehicle.
  • Step 102 When it is determined based on the point cloud data that there is a target vehicle in the target detection area, determine the target vehicle type of the target vehicle based on the point cloud data.
  • Step 103 Determine the target vehicle identification of the target vehicle based on the image information.
  • the target vehicle type of the target vehicle can be further determined based on the point cloud data.
  • the vehicle type may include, for example: communication trains, ordinary trains, etc.
  • the vehicle type recognition model can be pre-trained, and by inputting point cloud data into the vehicle type recognition model, the target vehicle type output by the vehicle type recognition model can be obtained.
  • the target vehicle identification of the target vehicle in the image information can be obtained through an image recognition method, where the vehicle identification can be a vehicle number.
  • a text recognition model for identifying text information in images can be pre-trained. After obtaining the image information, the image information is input into the text recognition model to obtain the target vehicle identification output by the text recognition model.
  • the image information can be processed through a preset text recognition algorithm to obtain the target vehicle identification in the image information. This disclosure does not specifically limit this.
  • Step 104 Compare the target vehicle type and target vehicle identification with the legal vehicle information set to determine whether the target vehicle is an illegal vehicle.
  • the legal vehicle information set includes the vehicle type and vehicle identification of the legal vehicle.
  • a legal vehicle has normal communication functions and Vehicles operating on the main line.
  • the target vehicle type and target vehicle identification can be compared with the legal vehicle information set, the target vehicle type and the target vehicle identification can be found in the legal vehicle information set, and the target vehicle type and target vehicle identification can be searched according to the legal vehicle information.
  • the legal vehicle information centrally stores the vehicle type and vehicle identification of the legal vehicle.
  • legal vehicles and illegal vehicles may be predetermined, and the vehicle type and vehicle identification of the legal vehicles may be stored in the legal vehicle information set.
  • legal vehicles can be understood as vehicles with normal communication functions and operating on the main line, such as vehicles equipped with TACS equipment and the TACS equipment is normal.
  • Illegal vehicles can be understood as vehicles with abnormal communication functions, such as vehicles that are not equipped with TACS equipment or the TACS equipment malfunctions. Vehicles.
  • the legal vehicle can send the vehicle type and vehicle identification of the vehicle to the controller, and the controller can store the vehicle in the legal vehicle information set. If the target vehicle type and target vehicle identification exist in the preset legal vehicle information set, then the target vehicle can be determined to be a legal vehicle. If the target vehicle type and target vehicle identification do not exist in the preset legal vehicle information set, then the target vehicle can be determined to be an illegal vehicle.
  • the present disclosure first obtains point cloud data of the target detection area and image information of the target detection area.
  • the target vehicle type of the target vehicle is determined based on the point cloud data
  • the target vehicle identification of the target vehicle is determined based on the image information
  • the target vehicle type and target vehicle identification are Compare with the legal vehicle information set to determine whether the target vehicle is an illegal vehicle.
  • the legal vehicle information set includes the vehicle type and vehicle identification of the legal vehicle.
  • a legal vehicle is a vehicle with normal communication functions and operating on the main line.
  • the present disclosure determines the target vehicle type and target vehicle identification of the target vehicle based on the obtained point cloud data and image information, thereby determining whether the vehicle is an illegal vehicle based on the target vehicle type and target vehicle identification, and can detect illegal vehicles more accurately.
  • FIG. 2 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 2, step 104 can be implemented in the following manner:
  • Step 1041 Determine whether the legal vehicle information set includes the target vehicle type and target vehicle identification.
  • Step 1042 When it is determined that the legal vehicle information set includes the target vehicle type and the target vehicle identification, determine that the target vehicle is a legal vehicle. or,
  • Step 1043 If it is determined that the legal vehicle information set does not include the target vehicle type and target vehicle identification, determine that the target vehicle is an illegal vehicle.
  • the vehicle when a vehicle leaves the warehouse and enters the main line from the transfer rail, if the vehicle is a communication train equipped with TACS equipment, the vehicle will send the vehicle type and vehicle identification to the controller, and the controller can add the vehicle type and vehicle identification to the legal Vehicle information is centralized. If the vehicle is an illegal vehicle that is not equipped with TACS equipment or ATP (English: Automatic Train Protection, Chinese: Automatic Train Protection) and other communication function failures, the vehicle will not be able to send the vehicle type and vehicle identification to the controller, so the legal vehicle information The vehicle type and vehicle identification of this vehicle do not exist in the collection. Therefore, after obtaining the target vehicle type and target vehicle identification of the target vehicle, the target vehicle type and target vehicle identification can be searched in the legal vehicle information set.
  • the legal vehicle information set includes the target vehicle type and target vehicle identification, indicating that the communication function of the target vehicle is normal, then the vehicle can be determined to be a legal vehicle. If the target vehicle type and target vehicle identification are not included in the legal vehicle information set, it indicates that the communication function of the target vehicle is faulty, and therefore the target vehicle type and target vehicle identification are not sent to the controller, then the target vehicle can be determined to be an illegal vehicle.
  • the vehicle when the vehicle leaves the warehouse and enters the main line from the transfer rail, the vehicle can also be positioned and the vehicle position and vehicle identification are sent to the controller.
  • the controller can add the vehicle position and vehicle identification to the Centralized legal vehicle information.
  • the legal vehicle information set includes the vehicle location and vehicle identification of legal vehicles. Therefore, the target vehicle identification and target vehicle location of the target vehicle can be obtained, and the target vehicle identification and target vehicle location can be searched in the legal vehicle information set. If the legal vehicle information set includes the target vehicle identification and the target vehicle location, it means that the communication function of the target vehicle is normal, then the vehicle can be determined to be a legal vehicle.
  • the legal vehicle information set does not include the target vehicle identification and the target vehicle location, it indicates that the communication function of the target vehicle is faulty, and therefore the target vehicle identification and the target vehicle location are not sent to the controller, then the target vehicle can be determined to be an illegal vehicle.
  • Figure 3 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 3, the method may further include:
  • Step 105 Determine the control section based on the location of the illegal vehicle.
  • Step 106 Send speed limit information to vehicles located in the controlled section.
  • the speed limit information is used to instruct the vehicles in the controlled section to drive at a speed less than or equal to the specified speed.
  • an implementation method of step 105 can be:
  • the section between the target detection area and the designated detection area is regarded as the control section, and the designated detection area is along the driving direction of the illegal vehicle.
  • the detection area closest to the target detection area is regarded as the control section.
  • the section between the target detection area and the designated detection area can be used as a control section.
  • area A is the target detection area
  • area B is the designated detection area
  • the ab section between area A and area B is the control section.
  • the controller can send speed limit information to all legal vehicles located in the controlled section to instruct the vehicles in the controlled section to drive at a speed less than or equal to the specified speed to ensure that the controlled section safe driving of vehicles inside.
  • the controller can also instruct legal vehicles in the controlled section to turn on the anti-collision mode.
  • the vehicle In the anti-collision mode, the vehicle can detect obstacles in front of the vehicle to control the vehicle to slow down or emergency brake, so that the control section can be avoided. Vehicles in the section collided with illegal vehicles.
  • Figure 5 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 5, the method may further include:
  • Step 107 If the target vehicle is an illegal vehicle and receives the first switch change request message sent by the vehicle located in the controlled section, refuse to respond to the first switch change request message.
  • the target vehicle is an illegal vehicle
  • the vehicle in the controlled section requests to change the switch, because there is an illegal switch in the controlled section at this time
  • a vehicle changes the switch at will it may cause a safety accident, so it can refuse to respond to the first switch change request message sent by the vehicle.
  • Figure 6 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 6, the method may further include:
  • Step 108 If the target vehicle is an illegal vehicle and receives the second switch change request message sent by the automatic train monitoring system, execute the second switch change request message.
  • the automatic train monitoring system when it is determined that the target vehicle is an illegal vehicle, if the second switch change request message sent by the automatic train monitoring system is received, it means that the automatic train monitoring system requests to change the switch, where the automatic train monitoring system can be ATS (English) :Automatic Train Supervision, Chinese: Automatic Train Supervision System). Since the automatic train monitoring system sends the second switch change request message based on the operation conditions of all vehicles running in the controlled section, the purpose is to ensure that the vehicles in the controlled section can operate safely, so the second switch change request message can be executed.
  • Figure 7 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 7, the method may further include:
  • Step 109 When the target vehicle is an illegal vehicle, alarm information is sent to the automatic train monitoring system.
  • the alarm information is used to instruct the automatic train monitoring system to issue an alarm prompt.
  • an alarm message can be sent to the automatic train monitoring system.
  • the automatic train monitoring system can issue an alarm prompt to prompt that an illegal vehicle has entered the main line.
  • the alarm prompt It can be a sound prompt, a light prompt, etc.
  • the controller may also send the target vehicle identification of the target vehicle to the automatic train monitoring system, so that the automatic train monitoring system obtains detailed information of the target vehicle based on the target vehicle representation.
  • FIG. 8 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 8, step 101 can be implemented in the following manner:
  • Step 1011 Obtain point cloud data of the target detection area through lidar.
  • Step 1012 Obtain image information of the target detection area through the camera.
  • a detection device can be set up at the location of the target detection area.
  • the detection device can include a radar and a camera.
  • the radar can be, for example, a laser radar that can acquire point cloud data.
  • the camera can be, for example, a monocular camera or a binocular camera. wait.
  • the detection device can be arranged at the position of the conversion rail, and the detection device can be higher than the track plane.
  • a carrier pole can be set up near the position of the conversion rail, or a device pole of a base station installed near the conversion rail can be used. As shown in Figure 9, the area The accuracy of the device in identifying vehicles. Point cloud data of the target detection area can be obtained through radar, and image information of the target detection area can be obtained through cameras.
  • Figure 10 is a flow chart of another vehicle detection method according to an exemplary embodiment. As shown in Figure 10, the method may further include:
  • Step 110 Input the point cloud data into a pre-trained recognition model, and obtain the target shape through the recognition model.
  • Step 111 When the target shape matches the preset vehicle shape, it is determined that the target vehicle exists in the target detection area.
  • a recognition model that recognizes object shapes based on point cloud data can be pre-trained. After obtaining the point cloud data, the point cloud data can be input into the pre-trained recognition model. The recognition model can process the point cloud data and output the point cloud. The target shape corresponding to the data. After the target shape is obtained, the target shape can be compared with the preset vehicle shape. If the target shape matches the preset vehicle shape, it can be determined that the target vehicle exists in the target detection area. In one implementation, a similarity threshold can be set in advance. If the similarity between the target shape and the preset vehicle shape is greater than or equal to the similarity threshold, then the target shape can be considered to match the preset vehicle shape.
  • the present disclosure first obtains point cloud data of the target detection area and image information of the target detection area.
  • the target vehicle type of the target vehicle is determined based on the point cloud data
  • the target vehicle identification of the target vehicle is determined based on the image information
  • the target vehicle type and target vehicle identification are Compare with the legal vehicle information set to determine whether the target vehicle is an illegal vehicle.
  • the legal vehicle information set includes the vehicle type and vehicle identification of the legal vehicle.
  • a legal vehicle is a vehicle with normal communication functions and operating on the main line.
  • the present disclosure determines the target vehicle type and target vehicle identification of the target vehicle based on the obtained point cloud data and image information, thereby determining whether the vehicle is an illegal vehicle based on the target vehicle type and the target vehicle identification, and can more accurately detect illegal vehicles. .
  • the present disclosure also proposes a controller, including:
  • a memory having computer readable code stored therein;
  • One or more processors when the computer readable code is executed by the one or more processors, the controller performs the aforementioned vehicle detection method.
  • the present disclosure also proposes a computer program, which includes a computer readable code.
  • the computer readable code When the computer readable code is run on a controller, it causes the controller to execute the aforementioned vehicle detection method.
  • the present disclosure also proposes a computer-readable storage medium in which the aforementioned computer program is stored.
  • Figure 11 provides a schematic structural diagram of a controller according to an embodiment of the present disclosure.
  • the controller typically includes a processor 1110 and a computer program product or computer-readable medium in the form of memory 1130 .
  • Memory 1130 may be electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM.
  • the memory 1130 has storage space 1150 for program code 1151 for executing any method steps in the above-described methods.
  • the storage space 1150 for program codes may include individual program codes 1151 respectively used to implement various steps in the above method. These program codes can be read from or written into one or more computer program products.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such computer program products are typically in the form of portable or fixed storage units as shown in Figure 12.
  • the storage unit may have storage segments, storage spaces, etc. arranged similarly to the memory 1130 in the server of FIG. 11 .
  • the program code may, for example, be compressed in a suitable form.
  • the storage unit includes computer readable code 1151', that is, code that can be read by, for example, a processor such as 1110, which when run by a server, causes the server to perform various steps in the method described above.
  • references to the terms “one embodiment,” “some embodiments,” “an example,” “specific examples,” or “some examples” or the like means that specific features are described in connection with the embodiment or example. , structures, materials, or features are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • various parts of the present disclosure may be implemented in hardware, software, firmware, or combinations thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: discrete logic gate circuits with logic functions for implementing data signals; Logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • the program can be stored in a computer-readable storage medium.
  • the program can be stored in a computer-readable storage medium.
  • each functional unit in various embodiments of the present disclosure may be integrated into one processing module, each unit may exist physically alone, or two or more units may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

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Abstract

一种车辆的检测方法、控制器、程序和存储介质，该方法包括：获取目标探测区域的点云数据，以及目标探测区域的图像信息。在根据点云数据确定目标探测区域内存在目标车辆的情况下，根据点云数据确定目标车辆的目标车辆类型。根据图像信息确定目标车辆的目标车辆标识。将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，确定目标车辆是否为非法车辆，其中，合法车辆信息集包括合法车辆的车辆类型和车辆标识，合法车辆为通信功能正常且在正线运营的车辆。

Description

车辆的检测方法、控制器、程序和存储介质
相关申请的交叉引用
本公开要求在2022年06月30日提交中国专利局、申请号为202210771256.7、名称为“车辆的检测方法和控制器”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及车辆技术领域，尤其涉及一种车辆的检测方法、控制器、程序和存储介质。
背景技术
现有的轨道交通信号系统，通过在线路上布置大量的计轴设备，并利用计轴设备来判断列车的区段占用、出清、列车前筛、列车后筛等功能。目前，通常在停车库线的外方设置检测计轴，以检测是否存在非通信列车进入正线运行，从而避免在车车通信的线路上出现非通信列车，给线路上运行的车辆造成安全隐患。但是检测计轴容易受到干扰，在受到干扰时需要人工进行复位才能恢复正常检测，导致计轴的检测结果不准确，影响了车车通信线路的安全性。
发明内容
本公开旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本公开的第一个目的在于提出一种车辆的检测方法，以解决现有技术中存在的，无法准确检测车辆的技术问题。
本公开的第二个目的在于提出一种控制器。
本公开的第三个目的在于提出一种计算机程序。
本公开的第四个目的在于提出一种计算机可读存储介质。
为达上述目的，本公开第一方面实施例提出了一种车辆的检测方法，所述方法包括：
获取目标探测区域的点云数据，以及所述目标探测区域的图像信息；
在根据所述点云数据确定所述目标探测区域内存在目标车辆的情况下，根据所述点云数据确定所述目标车辆的目标车辆类型；
根据所述图像信息确定所述目标车辆的目标车辆标识；
将所述目标车辆类型和所述目标车辆标识与合法车辆信息集进行比较，确定所述目标车辆是否为非法车辆，其中，所述合法车辆信息集包括合法车辆的车辆类型和车辆标识；所述合法车辆为通信功能正常且在正线运营的车辆。
根据本公开的一个实施例，所述将所述目标车辆类型和所述目标车辆标识与合法车辆信息集进行比较，确定所述目标车辆是否为非法车辆包括：
确定所述合法车辆信息集中是否包括所述目标车辆类型和所述目标车辆标识；
在确定所述合法车辆信息集中包括所述目标车辆类型和所述目标车辆标识的情况下，确定所述目标车辆为合法车辆；或者，在确定合法车辆信息集中不包括所述目标车辆类型和所述目标车辆标识的情况下，确定所述目标车辆为非法车辆。
根据本公开的一个实施例，所述方法还包括：
根据所述非法车辆的位置确定管控区段；
向位于所述管控区段内的车辆发送限速信息，所述限速信息用于指示所述管控区段内的车辆按照小于或等于指定车速的车速行驶。
根据本公开的一个实施例，所述根据所述非法车辆的位置确定管控区段包括：
在所述非法车辆不在所述目标探测区域内，且所述非法车辆未经过指定探测区域的情况下，将所述目标探测区域与所述指定探测区域之间的区段作为所述管控区段，所述指定探测区域为沿所述非法车辆的行驶方向，距离所述目标探测区域最近的探测区域。
根据本公开的一个实施例，还包括：在所述目标车辆为非法车辆，且接收到位于所述管控区段的车辆发送的第一道岔变更请求消息的情况下，拒绝响应所述第一道岔变更请求消息。
根据本公开的一个实施例，所述方法还包括：
在所述目标车辆为非法车辆，且接收到列车自动监控系统发送的第二道岔变更请求消息的情况下，执行所述第二道岔变更请求消息。
根据本公开的一个实施例，所述方法还包括：
在所述目标车辆为非法车辆的情况下，向所述列车自动监控系统发送报警信息，所述报警信息用于指示所述列车自动监控系统发出报警提示。
根据本公开的一个实施例，所述获取目标探测区域的点云数据，以及所述目标探测区域的图像信息包括：
通过激光雷达获取所述目标探测区域的点云数据；
通过摄像头获取所述目标探测区域的图像信息。
根据本公开的一个实施例，所述方法还包括：
将所述点云数据输入预先训练的识别模型，通过所述识别模型得到目标形状；
在所述目标形状与预设的车辆形状匹配的情况下，确定所述目标探测区域存在所述目标车辆。
本公开第一方面实施例提出了一种车辆的检测方法，首先获取目标探测区域的点云数据，以及目标探测区域的图像信息。在根据点云数据确定目标探测区域内存在目标车辆的情况下，根据点云数据确定目标车辆的目标车辆类型，并根据图像信息确定目标车辆的目标车辆标识，之后将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，确定目标车辆是否为非法车辆，其中，合法车辆信息集包括合法车辆的车辆类型和车辆标识，合法车辆为通信功能正常且在正线运营的车辆。本公开根据获取到的点云数据和图像信息确定目标车辆的目标车辆类型和目标车辆标识，从而根据目标车辆类型和目标车辆标识来确定车辆是否为非法车辆，能够更加准确地检测出非法车辆。
为达上述目的，本公开第二方面实施例提出了一种控制器，包括：
存储器，其中存储有计算机可读代码；以及
一个或多个处理器，当所述计算机可读代码被所述一个或多个处理器执行时，所述控制器执行本公开第一方面实施例所提出的车辆的检测方法。
为达上述目的，本公开第三方面实施例提出了一种计算机程序，包括计算机可读代码，当所述计算机可读代码在控制器上运行时，导致所述控制器执行本公开第一方面实施例所提出的车辆的检测方法。
为达上述目的，本公开第四方面实施例提出了一种计算机可读存储介质，其中存储了本公开第三方面实施例所提出的计算机程序。
本公开附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。
附图说明
本公开上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1是根据一示例性实施例示出的一种车辆的检测方法的流程图；
图2是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图3是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图4是根据图3实施例示出的一种管控区段的示意图；
图5是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图6是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图7是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图8是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图9是根据图8实施例示出的一种雷视范围的示意图；
图10是根据一示例性实施例示出的另一种车辆的检测方法的流程图；
图11是根据一示例性实施例示出的一种控制器的示意图；
图12是根据一示例性实施例示出的一种用于便携式或者固定实现根据本发明的方法的程序代码的存储单元的示意图。
具体实施方式
以下结合附图对本公开的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本公开，并不用于限制本公开。
在介绍本公开提供的车辆的检测方法、控制器、程序和存储介质之前，首先对本公开各个实施例所涉及的应用场景进行介绍。该应用场景中的车辆可以是任一种按照预设轨道运行的车辆，例如：火车、地铁、轻轨、有轨电车等，在预设轨道上运行的车辆之间可以通过TACS(英文：Train Autonomous Circumambulate System，中文：基于车车通信的列车自主运行系统)进行通信。而当未装备TACS设备或TACS设备发生故障的车辆会进入车车通信的线路运行时，可能会为车车通信的线路带来安全隐患。
图1是根据一示例性实施例示出的一种车辆的检测方法的流程图，如图1所示，应用于控制器，该方法可以包括：
步骤101，获取目标探测区域的点云数据，以及目标探测区域的图像信息。
举例来说，本公开的执行主体可以是目标控制器(Object Controller，OC)，可以将停车库线与正线交接的转换轨位置处作为目标探测区域，并在转换轨附近设置探测装置，通过探测装置获取目标探测区域的点云数据，以及目标探测区域的图像信息，例如可以通过雷达来获取点云数据，通过摄像头来获取图像信息。当有目标物体进入目标探测区域之后，控制器根据目标探测区域的点云数据来确定目标物体的形状，并根据目标物体的形状来确定目标物体是否为目标车辆。在一些实施例中，可以将点云数据输入预先训练的识别模型，得到识别模型输出的目标物体的形状，并根据目标物体的形状与预设的车辆形状的匹配度来确定目标物体是否为目标车辆。
步骤102，在根据点云数据确定目标探测区域内存在目标车辆的情况下，根据点云数据确定目标车辆的目标车辆类型。
步骤103，根据图像信息确定目标车辆的目标车辆标识。
示例的，如果检测到目标探测区域内存在目标车辆，那么可以进一步根据点云数据确定目标车辆的目标车辆类型，车辆类型例如可以包括：通信列车、普通列车等。在一些实施例中，可以预先训练车辆类型识别模型，通过将点云数据输入车辆类型识别模型，可以得到车辆类型识别模型输出的目标车辆类型。并且，可以通过图像识别方法来获取图像信息中目标车辆的目标车辆标识，其中，车辆标识可以是车辆编号。在一种实现方式中，可以预先训练用于识别图像中的文本信息的文本识别模型，在获取到图像信息之后，将图像信息输入文本识别模型，得到文本识别模型输出的目标车辆标识。在另一种实现方式中，可以通过预设的文本识别算法对图像信息进行处理，得到图像信息中的目标车辆标识。本公开对此不作具体限定。
步骤104，将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，确定目标车辆是否为非法车辆，其中，合法车辆信息集包括合法车辆的车辆类型和车辆标识，合法车辆为通信功能正常且在正线运营的车辆。
示例的，在得到目标车辆类型和目标车辆标识之后，可以将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，在合法车辆信息集中查找目标车辆类型和目标车辆标识，并根据合法车辆信息集中是否存在目标车辆类型和目标车辆标识来确定目标车辆是否为非法车辆。其中，合法车辆信息集中存储有合法车辆的车辆类型和车辆标识。在一种实现方式中，可以预先确定合法车辆和非法车辆，并将合法车辆的车辆类型和车辆标识存储在合法车辆信息集中。其中，合法车辆可以理解为通信功能正常且在正线运营的车辆，例如装备TACS设备且TACS设备正常的车辆，非法车辆可以理解为通信功能异常的车辆，例如未装备TACS设备或者TACS设备发生故障的车辆。在另一种实现方式中，合法车辆在进入正线之后可以向控制器发送该车辆的车辆类型和车辆标识，控制器可以将存储在合法车辆信息集中。如果预设的合法车辆信息集中存在目标车辆类型和目标车辆标识，那么可以确定目标车辆为合法车辆。如果预设的合法车辆信息集中不存在目标车辆类型和目标车辆标识，那么可以确定目标车辆为非法车辆。
综上所述，本公开首先获取目标探测区域的点云数据，以及目标探测区域的图像信息。在根据点云数据确定目标探测区域内存在目标车辆的情况下，根据点云数据确定目标车辆的目标车辆类型，并根据图像信息确定目标车辆的目标车辆标识，之后将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，确定目标车辆是否为非法车辆，其中，合法车辆信息集包括合法车辆的车辆类型和车辆标识，合法车辆为通信功能正常且在正线运营的车辆。本公开根据获取到的点云数据和图像信息确定目标车辆的目标车辆类型和目标车辆标识，从而根据目标车辆类型和目标车辆标识来确定车辆是否为非法车辆，能够更加准确地检测出非法车辆。
图2是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图2所示，步骤104可以通过以下方式来实现：
步骤1041，确定合法车辆信息集中是否包括目标车辆类型和目标车辆标识。
步骤1042，在确定合法车辆信息集中包括目标车辆类型和目标车辆标识的情况下，确定目标车辆为合法车辆。或者，
步骤1043，在确定合法车辆信息集中不包括目标车辆类型和目标车辆标识的情况下，确定目标车辆为非法车辆。
示例的，车辆出库从转换轨进入正线运行，如果车辆为装备TACS设备的通信列车，那么车辆会将车辆类型和车辆标识发送给控制器，控制器可以将车辆类型和车辆标识添加在合法车辆信息集中。如果车辆为未装备TACS设备或ATP(英文：Automatic Train Protection，中文：列车自动防护)切除等通信功能故障的非法车辆，那么车辆将无法将车辆类型和车辆标识发送给控制器，因此合法车辆信息集中不存在该车辆的车辆类型和车辆标识。因此在获取到目标车辆的目标车辆类型和目标车辆标识之后，可以在合法车辆信息集中查找目标车辆类型和目标车辆标识。如果合法车辆信息集中包括目标车辆类型和目标车辆标识，表示目标车辆的通信功能正常，那么可以确定车辆为合法车辆。如果合法车辆信息集中不包括目标车辆类型和目标车辆标识，表示目标车辆的通信功能故障，因此没有将目标车辆类型和目标车辆标识发送至控制器，那么可以确定目标车辆为非法车辆。
在另一些实施例中，车辆在出库从转换轨进入正线运行时，还可以对车辆进行定位，并将车辆位置和车辆标识发送给控制器，控制器可以将车辆位置和车辆标识添加在合法车辆信息集中。其中，合法车辆信息集包括合法车辆的车辆位置和车辆标识。因此可以获取到目标车辆的目标车辆标识和目标车辆位置，并在合法车辆信息集中查找目标车辆标识和目标车辆位置。如果合法车辆信息集中包括目标车辆标识和目标车辆位置，表示目标车辆的通信功能正常，那么可以确定车辆为合法车辆。如果合法车辆信息集中不包括目标车辆标识和目标车辆位置，表示目标车辆的通信功能故障，因此没有将目标车辆标识和目标车辆位置发送至控制器，那么可以确定目标车辆为非法车辆。
图3是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图3所示，该方法还可以包括：
步骤105，根据非法车辆的位置确定管控区段。
步骤106，向位于管控区段内的车辆发送限速信息，限速信息用于指示管控区段内的车辆按照小于或等于指定车速的车速行驶。
在一种应用场景中，步骤105的一种实现方式可以为：
在非法车辆不在目标探测区域内，且非法车辆未经过指定探测区域的情况下，将目标探测区域与指定探测区域之间的区段作为管控区段，指定探测区域为沿非法车辆的行驶方向，距离目标探测区域最近的探测区域。
示例的，由于在正线上设置有多个探测区域，因此在检测到非法车辆离开目标探测区域之后，可以进一步确定非法车辆是否经过沿非法车辆行驶方向，距离目标探测区域最近的探测区域，如果非法车辆没有经过指定探测区域，表示非法车辆在目标探测区域与指定探测区域之间的区段运行，因此可以将目标探测区域与指定探测区域之间的区段作为管控区段。如图4所示，区域A为目标探测区域，区域B为指定探测区域，区域A和区域B之间的ab区段为管控区段。为了保证管控区段车辆的行驶安全，控制器可以向位于管控区段的所有合法车辆发送限速信息，以指示管控区段内的车辆按照小于或等于指定车速的车速行驶，以保证管控区段内车辆的安全行驶。在一些实施例中，控制器还可以指示管控区段的合法车辆开启防撞模式，在防撞模式下车辆可以检测车辆前方的障碍物，以控制车辆减速或紧急制动，这样，可以避免管控区段的车辆与非法车辆相撞。
图5是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图5所示，该方法还可以包括：
步骤107，在目标车辆为非法车辆，且接收到位于管控区段的车辆发送的第一道岔变更请求消息的情况下，拒绝响应第一道岔变更请求消息。
示例的，在确定目标车辆为非法车辆的情况下，如果收到位于管控区段的车辆发送的第一道岔变更请求消息，表示管控区段的车辆请求变更道岔，由于此时管控区段存在非法车辆，若随意变更道岔可能会造成安全事故，因此可以拒绝响应车辆发送的第一道岔变更请求消息。
图6是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图6所示，该方法还可以包括：
步骤108，在目标车辆为非法车辆，且接收到列车自动监控系统发送的第二道岔变更请求消息的情况下，执行第二道岔变更请求消息。
示例的，在确定目标车辆为非法车辆的情况下，如果收到列车自动监控系统发送的第二道岔变更请求消息，表示列车自动监控系统请求变更道岔，其中，列车自动监控系统可以是ATS(英文：Automatic Train Supervision，中文：列车自动监控系统)。由于列车自动监控系统是根据管控区段内运行的所有车辆的运行情况发送的第二道岔变更请求消息，目的是保证管控区段内车辆能够安全运行，因此可以执行第二道岔变更请求消息。
图7是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图7所示，该方法还可以包括：
步骤109，在目标车辆为非法车辆的情况下，向列车自动监控系统发送报警信息，报警信息用于指示列车自动监控系统发出报警提示。
示例的，如果确定目标车辆是非法车辆，那么可以向列车自动监控系统发送报警信息，列车自动监控系统在接收到报警信息之后可以发出报警提示，以提示有非法车辆进入正线运行，其中报警提示可以是声音提示、灯光提示等。在一些实施例中，控制器还可以将目标车辆的目标车辆标识发送给列车自动监控系统，以使列车自动监控系统根据目标车辆表示获取目标车辆的详细信息。
图8是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图8所示，步骤101可以通过以下方式来实现：
步骤1011，通过激光雷达获取目标探测区域的点云数据。
步骤1012，通过摄像头获取目标探测区域的图像信息。
示例的，可以在目标探测区域所在的位置设置探测装置，其中探测装置可以包括雷达和摄像头，雷达例如可以是激光雷达等可以获取点云数据的雷达，摄像头例如可以是单目相机、双目相机等。探测装置可以设置在转换轨位置，并且探测装置高于轨道平面，可以在转换轨位置附近设立载体杆，也可以借助设置在转换轨附近的基站的装置杆。如图9所示，X区域为正线所在区域，Y区域为雷达和摄像头的雷视范围，雷达和摄像头覆盖的雷视范围区域内的区段长度应大于一个半的车辆长度，以保证探测装置识别车辆的准确性。可以通过雷达获取目标探测区域的点云数据，并通过摄像头获取目标探测区域的图像信息。
图10是根据一示例性实施例示出的另一种车辆的检测方法的流程图，如图10所示，该方法还可以包括：
步骤110，将点云数据输入预先训练的识别模型，通过识别模型得到目标形状。
步骤111，在目标形状与预设的车辆形状匹配的情况下，确定目标探测区域存在目标车辆。
示例的，可以预先训练根据点云数据识别物体形状的识别模型，在获取到点云数据之后，可以将点云数据输入预先训练的识别模型，识别模型可以对点云数据进行处理，输出点云数据对应的目标形状。在得到目标形状之后，可以将目标形状与预设的车辆形状进行对比，如果目标形状与预设的车辆形状匹配，那么可以确定目标探测区域内存在目标车辆。在一种实现方式中，可以预先设置相似度阈值，如果目标形状与预设的车辆形状的相似度大于或等于相似度阈值，那么可以认为目标形状与预设的车辆形状匹配。
综上所述，本公开首先获取目标探测区域的点云数据，以及目标探测区域的图像信息。在根据点云数据确定目标探测区域内存在目标车辆的情况下，根据点云数据确定目标车辆的目标车辆类型，并根据图像信息确定目标车辆的目标车辆标识，之后将目标车辆类型和目标车辆标识与合法车辆信息集进行比较，确定目标车辆是否为非法车辆，其中，合法车辆信息集包括合法车辆的车辆类型和车辆标识，合法车辆为通信功能正常且在正线运营的车辆。本公开根据获取到的点云数据和图像信息确定目标车辆的目标车辆类型和目标车辆标识，从而根据目标车辆类型和和目标车辆标识来确定车辆是否为非法车辆，能够更加准确地检测出非法车辆。
为了实现上述实施例，本公开还提出了一种控制器，包括：
存储器，其中存储有计算机可读代码；以及
一个或多个处理器，当所述计算机可读代码被所述一个或多个处理器执行时，所述控制器执行前述的车辆的检测方法。
为了实现上述实施例，本公开还提出了一种计算机程序，包括计算机可读代码，当所述计算机可读代码在控制器上运行时，导致所述控制器执行前述的车辆的检测方法。
为了实现上述实施例，本公开还提出了一种计算机可读存储介质，其中存储了前述的计算机程序。
图11为本公开实施例提供了一种控制器的结构示意图。该控制器通常包括处理器1110和以存储器1130形式的计算机程序产品或者计算机可读介质。存储器1130可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。存储器1130具有用于执行上述方法中的任何方法步骤的程序代码1151的存储空间1150。例如，用于程序代码的存储空间1150可以包括分别用于实现上面的方法中的各种步骤的各个程序代码1151。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。这些计算机程序产品包括诸如硬盘，紧致盘(CD)、存储卡或者软盘之类的程序代码载体。这样的计算机程序产品通常为如图12所示的便携式或者固定存储单元。该存储单元可以具有与图11的服务器中的存储器1130类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单元包括计算机可读代码1151’，即可以由例如诸如1110之类的处理器读取的代码，这些代码当由服务器运行时，导致该服务器执行上面所描述的方法中的各个步骤。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本公开的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本公开的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本公开各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。尽管上面已经示出和描述了本公开的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本公开的限制，本领域的普通技术人员在本公开的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (12)

  1. 一种车辆的检测方法，其特征在于，所述方法包括：
    获取目标探测区域的点云数据，以及所述目标探测区域的图像信息；
    在根据所述点云数据确定所述目标探测区域内存在目标车辆的情况下，根据所述点云数据确定所述目标车辆的目标车辆类型；
    根据所述图像信息确定所述目标车辆的目标车辆标识；
    将所述目标车辆类型和所述目标车辆标识与合法车辆信息集进行比较，确定所述目标车辆是否为非法车辆，其中，所述合法车辆信息集包括合法车辆的车辆类型和车辆标识；所述合法车辆为通信功能正常且在正线运营的车辆。
  2. 根据权利要求1所述的方法，其特征在于，所述将所述目标车辆类型和所述目标车辆标识与合法车辆信息集进行比较，确定所述目标车辆是否为非法车辆包括：
    确定所述合法车辆信息集中是否包括所述目标车辆类型和所述目标车辆标识；
    在确定所述合法车辆信息集中包括所述目标车辆类型和所述目标车辆标识的情况下，确定所述目标车辆为合法车辆；或者，在确定合法车辆信息集中不包括所述目标车辆类型和所述目标车辆标识的情况下，确定所述目标车辆为非法车辆。
  3. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    根据所述非法车辆的位置确定管控区段；
    向位于所述管控区段内的车辆发送限速信息，所述限速信息用于指示所述管控区段内的车辆按照小于或等于指定车速的车速行驶。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述非法车辆的位置确定管控区段包括：
    在所述非法车辆不在所述目标探测区域内，且所述非法车辆未经过指定探测区域的情况下，将所述目标探测区域与所述指定探测区域之间的区段作为所述管控区段，所述指定探测区域为沿所述非法车辆的行驶方向，距离所述目标探测区域最近的探测区域。
  5. 根据权利要求3所述的方法，其特征在于，所述方法还包括：
    在所述目标车辆为非法车辆，且接收到位于所述管控区段的车辆发送的第一道岔变更请求消息的情况下，拒绝响应所述第一道岔变更请求消息。
  6. 根据权利要求3所述的方法，其特征在于，所述方法还包括：
    在所述目标车辆为非法车辆，且接收到列车自动监控系统发送的第二道岔变更请求消息的情况下，执行所述第二道岔变更请求消息。
  7. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    在所述目标车辆为非法车辆的情况下，向所述列车自动监控系统发送报警信息，所述报警信息用于指示所述列车自动监控系统发出报警提示。
  8. 根据权利要求7所述的方法，其特征在于，所述获取目标探测区域的点云数据，以及所述目标探测区域的图像信息包括：
    通过激光雷达获取所述目标探测区域的点云数据；
    通过摄像头获取所述目标探测区域的图像信息。
  9. 根据权利要求1至8任一项所述的方法，其特征在于，所述方法还包括：
    将所述点云数据输入预先训练的识别模型，通过所述识别模型得到目标形状；
    在所述目标形状与预设的车辆形状匹配的情况下，确定所述目标探测区域存在所述目标车辆。
  10. 一种控制器，其特征在于，包括：
    存储器，其上存储有计算机程序；
    处理器，用于执行所述存储器中的所述计算机程序，以实现权利要求1-9中任一项所述的车辆的检测方法。
  11. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在控制器上运行时，导致所述控制器执行根据权利要求1-9中任一项所述的车辆的检测方法。
  12. 一种计算机可读存储介质，其中存储了如权利要求11所述的计算机程序。
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