WO2019205296A1 - 一种无人车调度方法、系统、设备及存储介质 - Google Patents
一种无人车调度方法、系统、设备及存储介质 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
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- the present application relates to the field of driverless technology, and in particular, to an unmanned vehicle scheduling method, system, device, and storage medium.
- unmanned vehicles With the development of driverless technology, unmanned vehicles may become the mainstream of the masses in the future, but due to the improvement of the public's living standards, the number of vehicles driven by humans in large cities with large vehicles is relatively large. Therefore, unmanned vehicles may have unpredictable and complex scenes during the travel process, which may lead to traffic accidents. Most of the reasons are caused by the uncertainty of the manually driven motor vehicles when moving. Therefore, how to safely and effectively dispatch unmanned vehicles becomes an urgent problem to be solved.
- the main purpose of the present application is to provide an unmanned vehicle scheduling method, system, device and storage medium, which aims to solve the technical problem that the prior art cannot safely and effectively dispatch an unmanned vehicle.
- the present application provides an unmanned vehicle scheduling method, the method comprising the following steps:
- the present application further provides an unmanned vehicle dispatching system, where the system includes: an information acquiring module, a path prediction module, and a path planning module;
- the information acquiring module is configured to acquire the unmanned vehicle location information and the distance information between the object within the preset range around the unmanned vehicle and the unmanned vehicle;
- the path prediction module is configured to receive driving information uploaded by the plurality of manually driven vehicles in the preset range, and perform driving path prediction on the manually driving vehicle according to the driving information, to obtain a plurality of corresponding predicted paths;
- the path planning module is configured to: according to the predicted path, plan a driving path of the unmanned vehicle according to the unmanned vehicle position information and the distance information, obtain a target driving path, and drive the target driving A route is sent to the unmanned vehicle to enable scheduling of the unmanned vehicle.
- the present application further provides an unmanned vehicle dispatching device, the unmanned vehicle dispatching device comprising: a memory, a processor, and a memory stored on the memory and operable on the processor A human vehicle dispatcher configured to implement the steps of the unmanned vehicle dispatching method as described above.
- the present application further provides a storage medium on which an unmanned vehicle scheduler is stored, and when the unmanned vehicle scheduler is executed by the processor, the unmanned vehicle as described above is implemented. The steps of the scheduling method.
- the unmanned vehicle dispatching method, system, device and storage medium of the present application obtain the distance information between the unmanned vehicle position information and the object within the preset range around the unmanned vehicle and the unmanned vehicle; and then receive the preset range Driving information uploaded by a plurality of manually driven vehicles, and predicting a driving route of the artificially driven vehicle according to the driving information, obtaining a plurality of corresponding predicted paths; and then, based on the predicted path, the unmanned vehicle position information and the distance information for the unmanned vehicle
- the driving route is planned, the target driving path is obtained, and the target driving path is sent to the unmanned vehicle to realize the scheduling of the unmanned vehicle.
- the present invention first predicts the moving path of the artificially driven vehicle around the unmanned vehicle before obtaining the target driving path of the unmanned vehicle, and uses the predicted moving path and the distance information with the unmanned vehicle and the surrounding object as the target path. According to the planning basis, it can effectively avoid the occurrence of traffic accidents caused by the uncertainty of the movement of the artificially driven vehicle, and improve the safety of the unmanned vehicle.
- FIG. 1 is a schematic structural diagram of an unmanned vehicle dispatching device in a hardware operating environment according to an embodiment of the present application
- FIG. 2 is a schematic flow chart of a first embodiment of an unmanned vehicle scheduling method according to the present application
- FIG. 3 is a schematic flow chart of a second embodiment of an unmanned vehicle scheduling method according to the present application.
- FIG. 4 is a schematic flow chart of a third embodiment of an unmanned vehicle scheduling method according to the present application.
- FIG. 5 is a structural block diagram of a first embodiment of an unmanned vehicle dispatching system of the present application.
- FIG. 1 is a schematic structural diagram of an unmanned vehicle dispatching device in a hardware operating environment according to an embodiment of the present application.
- the unmanned vehicle dispatching device may include a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005.
- the communication bus 1002 is used to implement connection communication between these components.
- the user interface 1003 can include a display, an input unit such as a keyboard, and the optional user interface 1003 can also include a standard wired interface, a wireless interface.
- the network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
- the memory 1005 may be a high speed RAM memory or a non-volatile memory such as a disk memory.
- the memory 1005 can also optionally be a storage device independent of the aforementioned processor 1001.
- FIG. 1 does not constitute a limitation on the unmanned vehicle dispatching device, and may include more or less components than those illustrated, or combine some components, or different component arrangements. .
- a memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and an unmanned vehicle scheduler.
- the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 in the unmanned vehicle dispatching device of the present application
- the memory 1005 may be disposed in the unmanned vehicle dispatching device, and the unmanned vehicle dispatching device calls the unmanned vehicle dispatching program stored in the memory 1005 by the processor 1001, and executes the unmanned vehicle dispatching method provided by the embodiment of the present application.
- FIG. 2 is a schematic flowchart of a first embodiment of an unmanned vehicle scheduling method according to the present application.
- the unmanned vehicle scheduling method includes the following steps:
- Step S10 acquiring unmanned vehicle position information and distance information between the object within the preset range around the unmanned vehicle and the unmanned vehicle;
- the execution body of the method in this embodiment may be the above-mentioned unmanned vehicle dispatching device, and the device may be a computing service device (server) or system capable of performing vehicle-wide vehicle scheduling with data acquisition and analysis and calculation functions. It can also be other devices having similar functions, which is not limited in this embodiment.
- server computing service device
- the execution body of the method in this embodiment may be the above-mentioned unmanned vehicle dispatching device, and the device may be a computing service device (server) or system capable of performing vehicle-wide vehicle scheduling with data acquisition and analysis and calculation functions. It can also be other devices having similar functions, which is not limited in this embodiment.
- the vehicle-mounted terminal is a front-end device of the vehicle monitoring and management system, and can also be called a vehicle dispatching monitoring terminal, which integrates multiple functions such as positioning, communication, and vehicle traveling recorder; and has powerful business scheduling functions and data processing capabilities.
- the unmanned vehicle can perform a Global Positioning System (GPS) positioning of the vehicle through the built-in vehicle terminal, and obtain the position information of the unmanned vehicle and the preset range around the unmanned vehicle (eg, 3 meters).
- Distance information for objects eg, pedestrians, flower beds, non-motor vehicles, and/or motor vehicles, etc.
- the vehicle terminal can upload the acquired information to the unmanned vehicle dispatching device.
- the unmanned vehicle dispatching device receives the unmanned vehicle position information and the distance information sent by the unmanned vehicle vehicle terminal.
- Step S20 receiving driving information uploaded by a plurality of manually driven vehicles in the preset range, and performing driving path prediction on the manually driving vehicle according to the driving information, and obtaining a plurality of corresponding predicted paths;
- an unmanned vehicle may include several manually driven vehicles in an object within a predetermined range when traveling on a road.
- the manually driven vehicles may upload their own travel information to the unmanned vehicle dispatching device via the built-in vehicle-mounted terminal in response to the information acquisition request sent by the unmanned vehicle dispatching device.
- the travel information may include vehicle speed information, vehicle position information, historical travel route information, and/or destination information, and the like.
- the unmanned vehicle dispatching device may substantially predict the driving route of the artificial driving vehicle for a period of time according to the driving information, so as to obtain corresponding to different artificial driving vehicles.
- the driving path that is, the predicted path.
- Step S30 planning, according to the predicted route, the travel route of the unmanned vehicle according to the unmanned vehicle position information and the distance information, obtaining a target travel route, and transmitting the target travel route to the The unmanned vehicle realizes the dispatch of the unmanned vehicle.
- the unmanned vehicle dispatching device may not be based on the predicted path and the distance information between the unmanned vehicle and the surrounding object.
- the driving route of the unmanned vehicle is roughly planned, and the next driving path of the unmanned vehicle, that is, the target driving path is obtained.
- the unmanned vehicle dispatching device sends the driving path to the unmanned vehicle, so that the unmanned vehicle runs according to the target driving path, and the unmanned vehicle is safely driven. Sex.
- the unmanned vehicle dispatching device acquires the distance information between the unmanned vehicle position information and the object within the preset range around the unmanned vehicle and the unmanned vehicle; and then receives the driving of the plurality of artificially driven vehicles within the preset range.
- Information, and predicting the driving route of the artificially driven vehicle according to the driving information obtaining a plurality of corresponding predicted paths; then, based on the predicted path, planning the driving route of the unmanned vehicle according to the unmanned vehicle position information and the distance information, obtaining the target driving
- the route is sent to the unmanned vehicle to achieve the dispatch of the unmanned vehicle.
- the unmanned vehicle driving process can be ensured by determining the target driving path of the unmanned vehicle by using the predicted path of the artificially driven vehicle within a certain range around the unmanned vehicle and the distance information between the unmanned vehicle and the surrounding object as the path planning basis. Safety in the field reduces the incidence of traffic accidents.
- FIG. 3 is a schematic flowchart diagram of a second embodiment of an unmanned vehicle scheduling method according to the present application.
- the step S20 includes:
- Step S201 receiving driving information uploaded by a plurality of manually driven vehicles in the preset range
- the unmanned vehicle can be centered with a preset distance as a radius, and a receiving range of the driving information (ie, the preset range) is set, so that the unmanned vehicle dispatching device only receives the preset range.
- Driving information uploaded by a manually driven vehicle may be set according to an actual experience value, and the specific value is not limited in this embodiment.
- Step S202 Acquire a current network delay, and determine a propagation duration corresponding to the travel information according to the current network delay;
- the present embodiment considers the prediction of the driving path of the artificially driven vehicle.
- the unmanned vehicle dispatching device can determine the propagation duration corresponding to the driving information.
- Step S203 predicting a travel route of the artificially-driven vehicle according to the propagation duration and the travel information, and obtaining a plurality of corresponding predicted paths.
- the travel route of the artificially driven vehicle can be predicted according to the vehicle speed information and the vehicle position information included in the travel information, thereby obtaining a predicted path.
- the present embodiment predicts the driving distance of the artificially driven vehicle over a period of time.
- the unmanned vehicle dispatching device may first extract vehicle position information in the travel information and a vehicle travel speed, and then determine, according to the vehicle travel speed and the propagation duration, that the artificially driven vehicle is within a preset time period. a travel distance; predicting a travel route of the artificially-driving vehicle based on the vehicle position information and the travel distance, and obtaining a predicted route.
- T 1 is the propagation duration and T 2 is the preset period.
- the preset time period is a preset prediction time length, for example, 1 minute, 2 minutes, and the like.
- the unmanned vehicle dispatching device obtains the current network delay by receiving the travel information uploaded by the plurality of manually driven vehicles in the preset range, and determines the propagation duration corresponding to the travel information according to the current network delay; according to the propagation duration Determining a driving distance of the artificially-driving vehicle within a preset time period according to a driving speed of the vehicle included in the driving information; and driving the artificially-driving vehicle according to the driving distance and the vehicle position information included in the driving information and the driving direction of the vehicle Path prediction, obtaining the prediction path, so that the obtained prediction path is more accurate and effective, further improving the safety of unmanned vehicle scheduling.
- FIG. 4 is a schematic flowchart diagram of a third embodiment of an unmanned vehicle scheduling method according to the present application.
- the method further includes:
- Step S101 When detecting that there is an object whose distance from the unmanned vehicle is less than the warning distance, send a braking command to the unmanned vehicle to control the unmanned vehicle to reduce the vehicle speed.
- the unmanned vehicle dispatching device can acquire an object (a pedestrian, a flower bed, a non-motor vehicle, and/or a motor vehicle, etc.) between the unmanned vehicle and the unmanned vehicle according to the distance information uploaded by the unmanned vehicle. Current distance.
- an object a pedestrian, a flower bed, a non-motor vehicle, and/or a motor vehicle, etc.
- a safety distance that is, the warning distance
- the specific value of the early warning distance may be set according to the actual situation, for example, 1 meter, 1.5 meters, etc., which is not limited in this embodiment.
- the unmanned vehicle dispatching device detects that the current distance is less than the early warning distance, indicating that the unmanned vehicle is too close to the surrounding object at this time, and the unmanned vehicle dispatching device will The passenger car sends a brake command to cause the unmanned vehicle to reduce the vehicle speed or perform emergency braking in response to the braking command to prevent collision.
- the unmanned vehicle dispatching device acquires the current distance between the object within the preset range around the unmanned vehicle and the unmanned vehicle from the distance information; and transmits the system to the unmanned vehicle when detecting that the current distance is less than the early warning distance
- the command is executed so that the unmanned vehicle can reduce the speed of the vehicle in response to the braking command, effectively keeping the unmanned vehicle at a distance from the surrounding objects, and ensuring driving safety.
- step S30 may specifically include the following steps:
- the unmanned vehicle dispatching device can roughly determine the average traveling speed of the vehicle on the current road according to the driving information uploaded by the manually driven vehicle.
- the unmanned vehicle dispatching device can also obtain the average speed of the vehicle on the road where the unmanned vehicle is currently located according to the location information of the manually driven vehicle or the unmanned vehicle, which is not limited in this embodiment. .
- the unmanned vehicle dispatching device can acquire the current vehicle speed of the unmanned vehicle, compare the current vehicle speed with the average vehicle speed, and detect whether the unmanned vehicle has a too fast speed. .
- the unmanned vehicle dispatching device when the unmanned vehicle dispatching device detects that the current vehicle speed is lower than the average speed of the vehicle, the predicted path of the unmanned vehicle and the unmanned vehicle and the unmanned vehicle may be used according to different predicted paths of different artificially driven vehicles.
- the distance information of the surrounding objects is planned for the traveling path of the unmanned vehicle, thereby obtaining a safe and effective target traveling path.
- the driving path of the human car is planned to obtain the target driving path.
- the unmanned vehicle dispatching device when the unmanned vehicle dispatching device detects that the current vehicle speed is higher than the average vehicle speed, it indicates that the current speed of the unmanned vehicle may be faster, and for safety reasons, the unmanned vehicle dispatching device may Sending a brake command to the unmanned vehicle to cause the unmanned vehicle to reduce the vehicle speed to the vicinity of the average vehicle speed, and then to the unmanned person according to the predicted path, the unmanned vehicle position information, and the distance information
- the driving path of the car is planned to obtain the target driving path.
- a preset threshold for example, 5%, 8%, etc.
- the unmanned vehicle dispatching device determines the average speed of the vehicle on the road where the unmanned vehicle is located according to the received driving information; obtains the current speed of the unmanned vehicle, and detects whether the current vehicle speed is higher than the average speed of the vehicle; When detecting that the current vehicle speed is lower than the average vehicle speed, the driving route of the unmanned vehicle is planned according to the predicted path, the unmanned vehicle position information and the distance information; when the current vehicle speed is detected to be higher than the average vehicle speed, the unmanned vehicle is sent to the unmanned vehicle. Brake commands, and then travel path planning, to ensure the safety of unmanned vehicle dispatch.
- the above-mentioned storage medium may be a read only memory, a magnetic disk or an optical disk or the like.
- FIG. 5 is a structural block diagram of a first embodiment of an unmanned vehicle dispatching system of the present application.
- the unmanned vehicle dispatching system proposed by the embodiment of the present application includes: an information acquiring module 501, a path prediction module 502, and a path planning module 503;
- the information obtaining module 501 is configured to acquire the unmanned vehicle location information and the distance information between the object in the preset range around the unmanned vehicle and the unmanned vehicle;
- the vehicle-mounted terminal is a front-end device of the vehicle monitoring and management system, and can also be called a vehicle dispatching monitoring terminal, which integrates multiple functions such as positioning, communication, and vehicle traveling recorder; and has powerful business scheduling functions and data processing capabilities.
- the unmanned vehicle can perform GPS positioning on the vehicle through the built-in vehicle terminal, and obtain the position information of the unmanned vehicle and the preset range around the unmanned vehicle (for example, 3 meters, 5 meters, 7 meters, etc.) Distance information for objects (eg, pedestrians, flower beds, non-motorized vehicles, and/or motor vehicles, etc.). After acquiring the location information of the user and the distance information, the unmanned vehicle vehicle terminal can upload the acquired information to the information acquisition module 501.
- the path prediction module 502 is configured to receive travel information uploaded by the plurality of manually driven vehicles in the preset range, and predict a travel route of the artificially driven vehicle according to the travel information, and obtain a plurality of corresponding predicted paths. ;
- an unmanned vehicle may include several manually driven vehicles in an object within a predetermined range when traveling on a road.
- the manually driven vehicles can upload their own travel information to the route prediction module 502 through the built-in vehicle terminal in response to the information acquisition request sent by the unmanned vehicle dispatching device.
- the travel information may include vehicle speed information, vehicle position information, historical travel route information, and/or destination information, and the like.
- the path prediction module 502 may substantially predict the driving route of the artificial driving vehicle for a period of time according to the driving information, so as to obtain corresponding to different artificial driving vehicles.
- the travel path that is, the predicted path.
- the path planning module 503 is configured to: according to the predicted path, plan a driving path of the unmanned vehicle according to the unmanned vehicle position information and the distance information, obtain a target driving path, and obtain the target A travel route is sent to the unmanned vehicle to enable scheduling of the unmanned vehicle.
- the path planning module 503 may determine the premise that the unmanned vehicle does not collide with the surrounding object based on the predicted path and the distance information between the unmanned vehicle and the surrounding object. Next, combined with the current location information of the unmanned vehicle, the driving route of the unmanned vehicle is roughly planned, and the next driving path of the unmanned vehicle, that is, the target driving path is obtained. After the driving path corresponding to the unmanned vehicle is obtained, the path planning module 503 sends the driving path to the unmanned vehicle, so that the unmanned vehicle runs according to the target driving path, and the safety of the unmanned vehicle is ensured. .
- the unmanned vehicle dispatching system of the present embodiment acquires the distance information between the unmanned vehicle position information and the object within the preset range around the unmanned vehicle and the unmanned vehicle; and then receives the driving of the plurality of artificially driven vehicles within the preset range. Information, and predicting the driving path of the artificially driven vehicle according to the driving information, obtaining a predicted path; then, based on the predicted path, planning the driving path of the unmanned vehicle according to the unmanned vehicle position information and the distance information, obtaining the target driving path, and The target driving path is sent to the unmanned vehicle to realize the scheduling of the unmanned vehicle.
- the unmanned vehicle driving process can be ensured by determining the target driving path of the unmanned vehicle by using the predicted path of the artificially driven vehicle within a certain range around the unmanned vehicle and the distance information between the unmanned vehicle and the surrounding object as the path planning basis. Safety in the field reduces the incidence of traffic accidents.
- the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
- Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
- the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.
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Abstract
一种无人车调度方法、系统、设备及存储介质。其中，无人车调度设备通过获取无人车位置信息以及无人车周围预设范围内的物体与无人车之间的距离信息；接收预设范围内若干辆人工驾驶车辆上传的行驶信息并根据行驶信息对人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；再基于预测路径根据无人车位置信息及距离信息对无人车的行驶路径进行规划，获得目标行驶路径，并将目标行驶路径发送至无人车，实现对无人车的调度。
Description
本申请要求于2018年04月27日提交中国专利局、申请号为201810396231.7、发明名称为“一种无人车调度方法、系统、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
本申请涉及无人驾驶技术领域，尤其涉及一种无人车调度方法、系统、设备及存储介质。
随着无人驾驶技术的发展，无人车将来可能会成为大众主流的出行方式，但由于大众生活水平的提高，在车辆较多的大城市人工驾驶的车辆基数较为庞大。因此，无人车在行进过程中可能会出现不可预知的复杂场景，从而导致交通事故的发生，究其原因大多是由人工驾驶的机动车在移动时的不确定性引起的。因此，如何对无人车进行安全有效地调度，成为一个亟待解决的问题。
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。
发明内容
本申请的主要目的在于提供了一种无人车调度方法、系统、设备及存储介质，旨在解决现有技术无法对无人车进行安全有效调度的技术问题。
为实现上述目的，本申请提供了一种无人车调度方法，所述方法包括以下步骤:
获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；
接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；
基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
此外，为实现上述目的，本申请还提出一种无人车调度系统，所述系统包括：信息获取模块、路径预测模块和路径规划模块；
所述信息获取模块，用于获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；
所述路径预测模块，用于接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；
所述路径规划模块，用于基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
此外，为实现上述目的，本申请还提出一种无人车调度设备，所述无人车调度设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的无人车调度程序，所述无人车调度程序配置为实现如上文所述的无人车调度方法的步骤。
此外，为实现上述目的，本申请还提出一种存储介质，所述存储介质上存储有无人车调度程序，所述无人车调度程序被处理器执行时实现如上文所述的无人车调度方法的步骤。
本申请的无人车调度方法、系统、设备及存储介质，通过获取无人车位置信息以及无人车周围预设范围内的物体与无人车之间的距离信息；然后接收预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据行驶信息对人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；然后基于预测路径，根据无人车位置信息以及距离信息对无人车的行驶路径进行规划，获得目标行驶路径，并将目标行驶路径发送至无人车，实现对无人车的调度。本申请在获得无人车的目标行驶路径前会先对无人车周围人工驾驶车辆的移动路径进行预测，并将预测获得的移动路径以及与无人车与周围物体的距离信息作为目 标路径的规划依据，因而能够有效避免由人工驾驶车辆移动不确定性导致的交通事故发生的情况，提高了无人车行驶的安全性。
图1是本申请实施例方案涉及的硬件运行环境的无人车调度设备的结构示意图；
图2为本申请无人车调度方法第一实施例的流程示意图；
图3为本申请无人车调度方法第二实施例的流程示意图；
图4为本申请无人车调度方法第三实施例的流程示意图；
图5为本申请无人车调度系统第一实施例的结构框图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
参照图1，图1为本申请实施例方案涉及的硬件运行环境的无人车调度设备结构示意图。
如图1所示，该无人车调度设备可以包括：处理器1001，例如CPU，通信总线1002、用户接口1003，网络接口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)，可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM存储器，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
本领域技术人员可以理解，图1中示出的结构并不构成对无人车调度设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，作为一种存储介质的存储器1005中可以包括操作系统、数据存储模块、网络通信模块、用户接口模块以及无人车调度 程序。
在图1所示的无人车调度设备中，网络接口1004主要用于与网络服务器进行数据通信；用户接口1003主要用于与用户进行数据交互；本申请无人车调度设备中的处理器1001、存储器1005可以设置在无人车调度设备中，所述无人车调度设备通过处理器1001调用存储器1005中存储的无人车调度程序，并执行本申请实施例提供的无人车调度方法。
本申请实施例提供了一种无人车调度方法，参照图2，图2为本申请无人车调度方法第一实施例的流程示意图。
本实施例中，所述无人车调度方法包括以下步骤：
步骤S10：获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；
需要说明的是，本实施例方法的执行主体可以是上述无人车调度设备，该设备可以是具有数据获取以及分析运算功能的、能够进行全城车辆调度的计算服务设备(服务器)或系统，也可以是其它具有类似功能的设备，本实施例对此不作限制。
可理解的是，车载终端是车辆监控管理系统的前端设备，也可以叫做车辆调度监控终端，它集成定位，通信、汽车行驶记录仪等多项功能；具有强大的业务调度功能和数据处理能力。在本实施例中，无人车可通过内置的车载终端对车辆进行全球定位系统(Global Positioning System，GPS)定位，获取无人车的位置信息以及无人车周围预设范围(如，3米、5米、7米等)内的物体(如，行人、花坛、非机动车辆和/或机动车辆等)的距离信息。在获取到自身的位置信息以及上述距离信息后，车载终端可将获取到的信息上传至无人车调度设备。
在具体实现中，所述无人车调度设备接收无人车车载终端发送的所述无人车位置信息以及所述距离信息。
步骤S20：接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；
可以理解的是，实际情况中，无人车在道路上行驶时周围预设范围内的物体中可能包括若干辆人工驾驶的车辆。为了能够保证无人车调度的安全性，这些人工驾驶车辆可响应于无人车调度设备发送的信息获取请求，通过内置的车载终端将自身的行驶信息上传至所述无人车调度设备。
所述行驶信息可包括：车辆速度信息、车辆位置信息、历史行驶路径信息和/或目的地信息等。相应地，所述无人车调度设备在接收到不同人工驾驶车辆发送的行驶信息后，可根据该行驶信息对人工驾驶车辆接下来一段时间的行驶路径进行大致预测，以获取不同人工驾驶车辆对应的行驶路径，即所述预测路径。
步骤S30：基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
在本步骤中，所述无人车调度设备在获取到不同人工驾驶车辆对应的预测路径后，可基于所述预测路径以及无人车与周围物体的距离信息来对在保证无人车不与周围物体发生碰撞的前提下，结合无人车当前所处的位置信息来对无人车的行驶路径进行一个大致规划，获得无人车接下来的行驶路径，即所述目标行驶路径。在得出无人车对应的行驶路径后，所述无人车调度设备将该行驶路径发送至无人车，以使无人车的按照所述目标行驶路径行驶，保证无人车行驶的安全性。
本实施例无人车调度设备通过获取无人车位置信息以及无人车周围预设范围内的物体与无人车之间的距离信息；然后接收预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据行驶信息对人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；然后基于预测路径，根据无人车位置信息以及距离信息对无人车的行驶路径进行规划，获得目标行驶路径，并将目标行驶路径发送至无人车，实现对无人车的调度。由于是以无人车周围一定范围内人工驾驶车辆的预测路径以及无人车与周围物体之间的距离信息作为路径规划依据来确定无人车的目标行驶路径，从而能够保证无人车行驶过程中的安全性， 减少了交通事故的发生率。
参考图3，图3为本申请无人车调度方法第二实施例的流程示意图。
基于上述第一实施例，在本实施例中，所述步骤S20包括：
步骤S201：接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息；
可以理解的是，无人车在道路上行驶时，周围可能存在大量的人工驾驶车辆，为了避免无人车调度设备获取过多的人工驾驶车辆上传的驾驶信息，从而导致无人车调度设备长时间处于高运算量状态，影响其使用寿命。本实施例中可以无人车为中心以预设距离为半径，设定一个行驶信息的接收范围(即所述预设范围)，以便所述无人车调度设备只接收该预设范围内的人工驾驶车辆上传的行驶信息。其中，所述预设距离可根据实际经验值设定，具体数值本实施例不做限制。
步骤S202：获取当前网络延时，根据所述当前网络延时确定所述行驶信息对应的传播时长；
应理解的是，由于网络环境的差异，信息数据的传递会存在延时，因此，为了保证人工驾驶车辆行驶路径预测的准确性，本实施例在对人工驾驶车辆的行驶路径进行预测时，考虑数据传递的网络延时。具体的，所述无人车调度设备根据所述行驶信息离开人工驾驶车辆的时刻(即离开时刻)以及接收到所述行驶信息的时刻(即接收时刻)确定出所述当前网络延时，即传播时长＝当前网络延时＝接收时刻-发送时刻。
在获取到当前网络延时后，所述无人车调度设备即可确定出行驶信息对应的传播时长。
步骤S203：根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
在具体实现中，无人车调度设备确定出行驶信息的传播时长后，便可根据行驶信息中包含的车辆速度信息和车辆位置信息来对人工驾驶车辆的行驶路径进行预测，从而获得预测路径。
进一步地，为了保证人工驾驶车辆行驶路径预测的有效性，本实 施例会对人工驾驶车辆在一段时间内的行驶距离进行预测。具体的，所述无人车调度设备可先提取行驶信息中的车辆位置信息以及车辆行驶速度，然后根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离；根据所述车辆位置信息和所述行驶距离对所述人工驾驶车辆的进行行驶路径预测，获得预测路径。考虑信息传递的延时性，本实施例中所述行驶距离的具体计算方式可以是根据公式S＝V×(2T
1+T
2)获得，其中，S为行驶距离，V为车辆行驶速度，T
1为传播时长，T
2为预设时段。所述预设时段为预先设定的预测时长，例如1分钟，2分钟等。
本实施例无人车调度设备通过接收预设范围内若干辆人工驾驶车辆上传的行驶信息，获取当前网络延时，根据所述当前网络延时确定所述行驶信息对应的传播时长；根据传播时长和行驶信息中包含的车辆行驶速度，确定所述人工驾驶车辆在预设时段内的行驶距离；根据行驶距离和行驶信息中包含的车辆位置信息和车辆行驶方向对所述人工驾驶车辆的进行行驶路径预测，获得预测路径，从而使得获得的预测路径更加准确有效，进一步提升了无人车调度的安全性。
参考图4，图4为本申请无人车调度方法第三实施例的流程示意图。
基于上述各实施例，在本实施例中，所述步骤S10之后，所述方法还包括：
步骤S101：在检测到存在与所述无人车的距离小于预警距离的物体时，向所述无人车发送制动指令，以控制所述无人车降低车速。
可以理解的是，所述无人车调度设备可根据无人车上传的距离信息获取无人车周围的物体(行人、花坛、非机动车辆和/或机动车辆等)与无人车之间的当前距离。
为了保证无人车行驶的安全性，避免无人车与周围物体的距离过近发生碰撞事故。在本实施例中，可预先设定一个安全距离，即所述预警距离，来防止碰撞事故的发生。所述预警距离的具体数值可以根据实际情况设定，例如：1米、1.5米等，本实施例对此不做限制。
在具体实现中，所述无人车调度设备在检测到所述当前距离小于 预警距离时，表明无人车此时与周围物体的距离过近，此时无人车调度设备会向所述无人车发送制动指令，以使所述无人车响应于所述制动指令降低车速或进行紧急刹车，防止碰撞。
本实施例无人车调度设备通过从距离信息中获取无人车周围预设范围内的物体与无人车之间的当前距离；在检测到当前距离小于预警距离时，向无人车发送制动指令，以使无人车响应于制动指令降低车速，有效地让无人车保持与周围物体的距离，保证行车安全。
基于上述各实施例，提出本申请一种无人车调度方法第四实施例。
在本实施例中，所述步骤S30可具体包括以下步骤：
根据接收到的若干辆行驶信息，确定当前时刻所述无人车所在道路上的车辆平均速度；
可理解的是，所述无人车调度设备可根据人工驾驶车辆上传的行驶信息来大致确定当前道路上的车辆的平均行驶速度。当然，所述无人车调度设备也可以根据人工驾驶车辆或无人车所在的位置信息，从交通管制系统来获取当前时刻无人车所在道路上的车辆平均速度，本实施例对此不作限制。
获取所述无人车的当前车速，检测所述当前车速是否高于所述车辆平均速度；
在获取到当前道路上车辆的平均速度后，无人车调度设备可获取无人车的当前车速，并将所述当前车速与车辆平均速度进行比较，检测无人车是否存在速度过快的情况。
在检测到所述当前车速低于所述车辆平均速度时，根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
在具体实现中，当所述无人车调度设备检测到当前车速低于所述车辆平均速度时，即可根据不同人工驾驶车辆各自对应的预测路径，无人车的位置信息以及无人车与周围物体的距离信息对所述无人车的行驶路径进行规划，从而获得安全有效的目标行驶路径。
在检测到所述当前车速高于所述车辆平均速度时，向所述无人车 发送制动指令，并根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
在具体实现中，当所述无人车调度设备检测到当前车速高于所述车辆平均速度时，表明无人车的当前车速可能偏快，出于安全考虑，所述无人车调度设备可向无人车发送制动指令，以使无人车将车速降低到所述车辆平均速度附近，然后再根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
进一步地，为了避免无人车频繁进行制动操作，在对无人车的当前车速进行检测时，可设定一个制动因子来判断此时无人车是否需要进行制动操作，具体的，可令制动因子＝当前车速/车辆平均速度，当检测到所述制动因子大于预设阈值(例如5％、8％等)时，判定无人车当前需要进行制动；反之则不需要进行制动，保持当前车速即可。
本实施例无人车调度设备根据接收到的若干辆行驶信息，确定当前时刻无人车所在道路上的车辆平均速度；获取无人车的当前车速，检测当前车速是否高于车辆平均速度；在检测到当前车速低于车辆平均速度时，根据预测路径、无人车位置信息以及距离信息对无人车的行驶路径进行规划；在检测到当前车速高于车辆平均速度时，向无人车发送制动指令，然后再进行行驶路径规划，保证了无人车调度的安全性。
需要说明的是，本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也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，上述提到的存储介质可以是只读存储器，磁盘或光盘等。
参照图5，图5为本申请无人车调度系统第一实施例的结构框图。
如图5所示，本申请实施例提出的无人车调度系统包括：信息获取模块501、路径预测模块502和路径规划模块503；
所述信息获取模块501，用于获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；
可理解的是，车载终端是车辆监控管理系统的前端设备，也可以 叫做车辆调度监控终端，它集成定位，通信、汽车行驶记录仪等多项功能；具有强大的业务调度功能和数据处理能力。在本实施例中，无人车可通过内置的车载终端对车辆进行GPS定位，获取无人车的位置信息以及无人车周围预设范围(如，3米、5米、7米等)内的物体(如，行人、花坛、非机动车辆和/或机动车辆等)的距离信息。在获取到自身的位置信息以及上述距离信息后，无人车车载终端可将获取到的信息上传至信息获取模块501。
所述路径预测模块502，用于接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；
可以理解的是，实际情况中，无人车在道路上行驶时周围预设范围内的物体中可能包括若干辆人工驾驶的车辆。为了能够保证无人车调度的安全性，这些人工驾驶车辆可响应于无人车调度设备发送的信息获取请求，通过内置的车载终端将自身的行驶信息上传至路径预测模块502。
所述行驶信息可包括：车辆速度信息、车辆位置信息、历史行驶路径信息和/或目的地信息等。相应地，所述路径预测模块502在接收到不同人工驾驶车辆发送的行驶信息后，可根据该行驶信息对人工驾驶车辆接下来一段时间的行驶路径进行大致预测，以获取不同人工驾驶车辆对应的行驶路径，即所述预测路径。
所述路径规划模块503，用于基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
所述路径规划模块503在获取到不同人工驾驶车辆对应的预测路径后，可基于所述预测路径以及无人车与周围物体的距离信息来对在保证无人车不与周围物体发生碰撞的前提下，结合无人车当前所处的位置信息来对无人车的行驶路径进行一个大致规划，获得无人车接下来的行驶路径，即所述目标行驶路径。在得出无人车对应的行驶路径后，所述路径规划模块503将该行驶路径发送至无人车，以使无人 车的按照所述目标行驶路径行驶，保证无人车行驶的安全性。
本实施例无人车调度系统通过获取无人车位置信息以及无人车周围预设范围内的物体与无人车之间的距离信息；然后接收预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据行驶信息对人工驾驶车辆的进行行驶路径预测，获得预测路径；然后基于预测路径，根据无人车位置信息以及距离信息对无人车的行驶路径进行规划，获得目标行驶路径，并将目标行驶路径发送至无人车，实现对无人车的调度。由于是以无人车周围一定范围内人工驾驶车辆的预测路径以及无人车与周围物体之间的距离信息作为路径规划依据来确定无人车的目标行驶路径，从而能够保证无人车行驶过程中的安全性，减少了交通事故的发生率。
本申请无人车调度系统的其他实施例或具体实现方式可参照上述各方法实施例，此处不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范 围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。
Claims (20)
- 一种无人车调度方法，其特征在于，所述方法包括：获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
- 如权利要求1所述的方法，其特征在于，所述接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息；获取当前网络延时，根据所述当前网络延时确定所述行驶信息对应的传播时长；根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求2所述的方法，其特征在于，所述行驶信息包括：车辆位置信息、车辆行驶方向以及车辆行驶速度；所述根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离；根据所述车辆位置信息、所述车辆行驶方向和所述行驶距离对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求3所述的方法，其特征在于，所述根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行 驶距离，包括：根据所述车辆行驶速度和所述传播时长，通过下式计算确定所述人工驾驶车辆在预设时段内的行驶距离，S＝V×(2T 1+T 2)，其中，S为行驶距离，V为车辆行驶速度，T 1为传播时长，T 2为预设时段。
- 如权利要求1所述的方法，其特征在于，所述获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息之后，所述方法还包括：在检测到存在与所述无人车的距离小于预警距离的物体时，向所述无人车发送制动指令，以控制所述无人车降低车速。
- 如权利要求4所述的方法，其特征在于，所述基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，包括：根据接收到的若干辆行驶信息，确定当前时刻所述无人车所在道路上的车辆平均速度；获取所述无人车的当前车速，检测所述当前车速是否高于所述车辆平均速度；在检测到所述当前车速低于所述车辆平均速度时，根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
- 如权利要求6所述的方法，其特征在于，所述获取所述无人车的当前车速，检测所述当前车速是否高于所述车辆平均速度之后，所述方法还包括：在检测到所述当前车速高于所述车辆平均速度时，向所述无人车发送制动指令，并根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
- 一种无人车调度系统，其特征在于，所述系统包括：信息获取模块、路径预测模块和路径规划模块；所述信息获取模块，用于获取无人车位置信息以及所述无人车周 围预设范围内的物体与所述无人车之间的距离信息；所述路径预测模块，用于接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；所述路径规划模块，用于基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
- 一种无人车调度设备，其特征在于，所述无人车调度设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的无人车调度程序，所述无人车调度程序配置为实现如下步骤：获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
- 如权利要求9所述的无人车调度设备，其特征在于，所述接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息；获取当前网络延时，根据所述当前网络延时确定所述行驶信息对应的传播时长；根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求10所述的无人车调度设备，其特征在于，所述行驶信息包括：车辆位置信息、车辆行驶方向以及车辆行驶速度；所述根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离；根据所述车辆位置信息、所述车辆行驶方向和所述行驶距离对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求11所述的无人车调度设备，其特征在于，所述根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离，包括：根据所述车辆行驶速度和所述传播时长，通过下式计算确定所述人工驾驶车辆在预设时段内的行驶距离，S＝V×(2T 1+T 2)，其中，S为行驶距离，V为车辆行驶速度，T 1为传播时长，T 2为预设时段。
- 如权利要求9所述的无人车调度设备，其特征在于，所述获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息之后，所述方法还包括：在检测到存在与所述无人车的距离小于预警距离的物体时，向所述无人车发送制动指令，以控制所述无人车降低车速。
- 如权利要求12所述的无人车调度设备，其特征在于，所述基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，包括：根据接收到的若干辆行驶信息，确定当前时刻所述无人车所在道路上的车辆平均速度；获取所述无人车的当前车速，检测所述当前车速是否高于所述车辆平均速度；在检测到所述当前车速低于所述车辆平均速度时，根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
- 如权利要求14所述的无人车调度设备，其特征在于，所述 获取所述无人车的当前车速，检测所述当前车速是否高于所述车辆平均速度之后，所述方法还包括：在检测到所述当前车速高于所述车辆平均速度时，向所述无人车发送制动指令，并根据所述预测路径、所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径。
- 一种存储介质，其特征在于，所述存储介质上存储有无人车调度程序，所述无人车调度程序被处理器执行时实现如下步骤：获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息；接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径；基于所述预测路径，根据所述无人车位置信息以及所述距离信息对所述无人车的行驶路径进行规划，获得目标行驶路径，并将所述目标行驶路径发送至所述无人车，实现对所述无人车的调度。
- 如权利要求16所述的存储介质，其特征在于，所述接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息，并根据所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：接收所述预设范围内若干辆人工驾驶车辆上传的行驶信息；获取当前网络延时，根据所述当前网络延时确定所述行驶信息对应的传播时长；根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求17所述的存储介质，其特征在于，所述行驶信息包括：车辆位置信息、车辆行驶方向以及车辆行驶速度；所述根据所述传播时长和所述行驶信息对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径，包括：根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离；根据所述车辆位置信息、所述车辆行驶方向和所述行驶距离对所述人工驾驶车辆的进行行驶路径预测，获得若干对应的预测路径。
- 如权利要求18所述的存储介质，其特征在于，所述根据所述车辆行驶速度和所述传播时长，确定所述人工驾驶车辆在预设时段内的行驶距离，包括：根据所述车辆行驶速度和所述传播时长，通过下式计算确定所述人工驾驶车辆在预设时段内的行驶距离，S＝V×(2T 1+T 2)，其中，S为行驶距离，V为车辆行驶速度，T 1为传播时长，T 2为预设时段。
- 如权利要求16所述的存储介质，其特征在于，所述获取无人车位置信息以及所述无人车周围预设范围内的物体与所述无人车之间的距离信息之后，所述方法还包括：在检测到存在与所述无人车的距离小于预警距离的物体时，向所述无人车发送制动指令，以控制所述无人车降低车速。
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| CN111429593A (zh) * | 2020-03-30 | 2020-07-17 | 长安大学 | 一种用于测试无人车自动通过etc收费站能力的系统及方法 |
| CN111982145A (zh) * | 2020-09-25 | 2020-11-24 | 北京百度网讯科技有限公司 | 行驶路径推荐方法、装置、设备及存储介质 |
| CN114355949A (zh) * | 2022-01-22 | 2022-04-15 | 重庆长安汽车股份有限公司 | 共享无人驾驶扫地车的管理方法、系统及可读存储介质 |
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| CN109345829B (zh) * | 2018-10-29 | 2021-11-23 | 百度在线网络技术（北京）有限公司 | 无人车的监控方法、装置、设备及存储介质 |
| CN115097832A (zh) * | 2019-02-25 | 2022-09-23 | 广州文远知行科技有限公司 | 车辆变道方法、装置、设备和存储介质 |
| CN109828583A (zh) * | 2019-02-28 | 2019-05-31 | 北京百度网讯科技有限公司 | 无人驾驶车辆控制方法和装置 |
| CN110275531B (zh) * | 2019-06-21 | 2020-11-27 | 北京三快在线科技有限公司 | 障碍物的轨迹预测方法、装置及无人驾驶设备 |
| CN110794841B (zh) * | 2019-11-12 | 2022-07-12 | 深圳创维数字技术有限公司 | 路径导航方法、装置及计算机可读存储介质 |
| CN111583577B (zh) * | 2020-05-07 | 2022-01-11 | 新石器慧通（北京）科技有限公司 | 远程人员聚集识别与告警系统及方法 |
| CN111861118A (zh) * | 2020-06-17 | 2020-10-30 | 新石器慧通（北京）科技有限公司 | 无人车的调度方法和调度系统 |
| CN113096375B (zh) * | 2021-03-30 | 2022-04-12 | 深圳安途智行科技有限公司 | 车辆调度方法及系统、以及主控制设备 |
| CN113865603B (zh) * | 2021-08-30 | 2024-06-07 | 东风柳州汽车有限公司 | 共享无人车路径规划方法、装置、设备及存储介质 |
| CN113990107A (zh) * | 2021-11-26 | 2022-01-28 | 格林美股份有限公司 | 一种汽车自动驾驶调度系统 |
| CN115675493B (zh) * | 2023-01-04 | 2023-08-11 | 北京易控智驾科技有限公司 | 利用人工驾驶轨迹图层信息进行无人驾驶的方法及装置 |
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