CN111243307A

CN111243307A - System and method for dispatching vehicle with automatic driving function at intersection

Info

Publication number: CN111243307A
Application number: CN201811438924.4A
Authority: CN
Inventors: 徐修信; 赵世杰
Original assignee: Uisee Technologies Beijing Co Ltd
Current assignee: Uisee Technologies Beijing Co Ltd
Priority date: 2018-11-28
Filing date: 2018-11-28
Publication date: 2020-06-05
Anticipated expiration: 2038-11-28
Also published as: CN111243307B

Abstract

The application provides a method, a device, a server and a readable storage medium for dispatching vehicles with automatic driving functions at an intersection, wherein the method comprises the following steps: the server receives a pass-through intersection request sent from an autonomous vehicle at a current time period. The intersection passing request comprises the state of the automatic driving vehicle in the current time period, including information such as position, speed and planned driving path. Vehicles that were entering and not leaving the intersection dispatch area before the current time period are referred to as previous vehicles. The server further acquires the planned driving track of the previous vehicle and generates a speed planning instruction by combining with an intersection passing request sent by the automatic driving vehicle. The server sends the speed planning instruction to the autonomous vehicle. Vehicles in the intersection dispatching area can be uniformly dispatched by the server, so that the safety is guaranteed, and the passing efficiency is increased.

Description

System and method for dispatching vehicle with automatic driving function at intersection

Technical Field

The application relates to the technical field of automatic driving, in particular to a system and a method for dispatching automatic driving vehicles at an intersection.

Background

With the development of vehicle intellectualization and intersection intelligent traffic lights, how to enable intersections to have more efficient traffic efficiency becomes a hotspot in the field of urban traffic research, wherein intelligent scheduling of intersections is an important part. In the prior art, intelligent traffic lights are arranged at intersections of part of cities, the intelligent traffic lights can allocate the time length of traffic lights according to the traffic flow waiting in each lane of the current intersection through calculation, and congestion can be reduced to a certain extent, but the method also has certain defects: vehicles in the red light direction may need to stop for waiting, and the efficiency of the vehicles passing through the intersection is low; the vehicle stops and then starts, certain fuel oil waste is caused, and the intelligent and automatic level is low. The problem to be solved urgently is that how to combine the development of the automatic driving vehicle technology and how to uniformly schedule through the cloud end to enable the automatic driving vehicles to efficiently move together at the intersection and get rid of the constraint of traffic lights.

Disclosure of Invention

Based on the problems, the traffic light restriction method and the traffic light restriction device provide a new technical scheme, and can solve the technical problems that the traffic light restriction is released in an automatic driving environment, and the safety and the efficiency of passing through the intersection are improved.

A first aspect of the present application proposes a method for scheduling a vehicle having an autonomous driving function at an intersection, comprising: the method comprises the steps that in a current time period, a server receives a passing intersection request sent by at least one vehicle, wherein the passing intersection request comprises the state of the at least one vehicle in the current time period, and the state comprises the position, the speed and a planned driving path; the server acquires a planned driving track of previous vehicles in the current time period, wherein the previous vehicles comprise vehicles which are driven in before the current time period and are not driven out of an intersection scheduling area; the server generates a speed planning instruction according to the planned driving track of the previous vehicle and the intersection passing request; and the server sends the speed planning instruction to the at least one vehicle.

In some embodiments, the passing intersection request may be issued by the at least one vehicle upon entering the intersection scheduling area, which includes the intersection area and its extended roads.

In some embodiments, the server generating speed plan instructions may include: according to the planned driving track of the previous vehicle and the intersection passing request, the server determines the driving-in time of the at least one vehicle driving into the intersection area; and according to the state of the at least one vehicle at the current moment and the driving-in moment, the server generates the speed planning instruction.

In some embodiments, the current time period may correspond to a response cycle of the server, and the server may run the scheduling method once for a pass-through intersection request received within one response cycle.

In some embodiments, the current time period may include a preset duration from a first time as a starting time, and the first time may be a time when a first vehicle enters the intersection scheduling region after the end of a time period before the current time period.

In some embodiments, the planned travel trajectory of the previous vehicle may be determined by the previous vehicle prior to a current time period from previous vehicle speed planning instructions received from the server, the previous vehicle speed planning instructions being generated by the server according to the scheduling method.

In some embodiments, the speed plan instructions may further include a speed plan that satisfies a constraint model, the constraint model including a constraint objective: the difference between the total time of the at least one vehicle passing through the intersection scheduling area and the target time is minimum; and at least one of the following constraints:

in the at least one vehicle and the previous vehicles, for the vehicles on the same lane, the vehicle which firstly enters the crossing dispatching area firstly enters the crossing area;

in the at least one vehicle, for any two vehicles with path overlapping points, the time difference value of the two vehicles passing through the path overlapping points is greater than a safety threshold value; and

for any one of the at least one vehicle, for the vehicle and the preceding vehicle having a route overlap point with the vehicle, the preceding vehicle having a route overlap point with the vehicle constitutes a preceding vehicle group, and a time at which the vehicle passes through the route overlap point is later than a time at which any one of the preceding vehicle groups reaches the route overlap point.

In some embodiments, the server generating the speed plan instructions may include: for each of the at least one vehicle, planning a speed change curve of the vehicle on a planned driving path according to the corresponding driving-in time of the vehicle, the current position and the current speed, wherein the speed change curve satisfies the following conditions: when the vehicle runs according to the speed change curve, the vehicle drives into the intersection area at the corresponding driving-in time.

In some embodiments, the extended road may include a first road and a second road, the second road connects the intersection region, and the speed variation curve may further satisfy: and when the vehicle runs according to the speed change curve, each vehicle in the at least one vehicle reaches a first preset speed when running to the tail end of the first section of road, and reaches a second preset speed when running to the tail end of the second section of road.

A second aspect of the present application proposes a server; comprising a memory, a processor and a computer program stored on said memory and executable on said processor, said processor when executing said computer program implementing the steps of the method for intersection scheduling for a vehicle having an autonomous driving function as described hereinbefore.

A third aspect of the present application provides an intersection scheduling apparatus. The intersection scheduling device comprises a data acquisition unit, a data storage unit, an instruction generation unit and an instruction sending unit. The data acquisition unit may be configured to receive a pass-through intersection request sent from at least one vehicle during a current time period. The passing intersection request may include a status of the at least one vehicle at a current time period. The states may include position, speed, and planned travel path. The data storage unit may store planned driving trajectories of previous vehicles for a current period of time. The preceding vehicles may include vehicles that were entering and not leaving the intersection dispatch area prior to the current time period. The data acquisition unit may further acquire the planned travel track of the preceding vehicle from the data storage unit. The instruction generating unit may be configured to generate a speed planning instruction according to the planned travel track of the previous vehicle and the passing intersection request. The instruction transmitting unit may transmit the speed planning instruction to the at least one vehicle.

A fourth aspect of the present application proposes a computer-readable storage medium having a computer program stored thereon. The computer program, when executed by a processor, may implement the steps of the intersection scheduling method as described above.

The device and the method for dispatching the vehicles with the automatic driving function at the intersection are used for reasonably arranging the driving strategy of each vehicle by acquiring the state data of the vehicles needing to pass through the intersection region based on the current passing condition of the intersection and consideration of two aspects of comprehensive safety and efficiency, and can improve the passing efficiency of the whole intersection on the premise of ensuring safety.

Drawings

The following drawings describe in detail exemplary embodiments disclosed in the present application. Wherein like reference numerals represent similar structures throughout the several views of the drawings. Those of ordinary skill in the art will understand that the present embodiments are non-limiting, exemplary embodiments and that the accompanying drawings are for illustrative and descriptive purposes only and are not intended to limit the scope of the present application, as other embodiments may equally fulfill the inventive intent of the present application. Wherein:

FIG. 1 is a schematic view of a scenario of one embodiment of vehicle scheduling at an intersection in the present application;

FIG. 2 is a block diagram of an exemplary vehicle and autonomous driving system with autonomous driving capability according to some embodiments of the present application;

FIG. 3 is a schematic diagram of exemplary hardware and software components of an information processing unit;

FIG. 4 is an exemplary flow chart of one type of dispatch with an autopilot function vehicle at an intersection herein;

FIG. 5 is an exemplary flow chart of the present application for determining speed planning instructions based on planned travel trajectories of previous vehicles and a current time period vehicle's pass-through intersection request;

FIG. 6 is a schematic view of a speed profile in the present application; and

fig. 7 is a schematic diagram of an intersection scheduling apparatus in the present application.

Detailed Description

A system and method for scheduling a vehicle having an autonomous driving function at an intersection is disclosed. The vehicle having the automatic driving function may receive a speed planning instruction and travel according to the instruction request (the following description of the vehicle having the automatic driving function may be replaced with an "automatic driving vehicle" or a "vehicle"). The server can receive a request of vehicles in a certain range near the intersection and needing to pass through the intersection, and uniformly dispatch the vehicles needing to pass through the intersection within a period of time according to the vehicle information contained in the request, so that the overall road crossing passing efficiency is improved, and the vehicles are prevented from colliding. The system and method can also be applied to the dispatching of other traffic participation devices with automatic driving functions. For example, the system and the method can be used for channel scheduling of equipment such as aerial aircraft, surface ships, underwater ships and the like.

In the following detailed description, specific details are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure to those of ordinary skill in the art. However, the disclosure should be understood to be consistent with the scope of the claims and not limited to the specific inventive details. For example, various modifications to the embodiments disclosed herein will be readily apparent to those skilled in the art; and those skilled in the art may now apply the general principles defined herein to other embodiments and applications without departing from the spirit and scope of the present application. For another example, it will be apparent to one skilled in the art that the present application may be practiced without these specific details. In other instances, well known methods, procedures, systems, components, and/or circuits have been described in general terms, but not in detail so as not to unnecessarily obscure aspects of the present application. Accordingly, the disclosure is not limited to the illustrated embodiments, but is consistent with the scope of the claims.

The terminology used in the description presented herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. For example, if a claim element is referred to in the present application as comprising a singular form (e.g., "a," "an," and/or the like), then that claim element can also include plural of that claim element unless the context clearly dictates otherwise. The terms "comprising" and/or "including" as used in this application are intended to be open ended concepts. For example, the inclusion of B in a merely indicates the presence of B in a, but does not exclude the possibility that other elements (such as C) may be present or added to a.

It is to be understood that the terms "system", "unit", "module" and/or "block" as used herein are a way of distinguishing between different components, elements, parts, portions or assemblies at different levels. However, other terms may be used in the present application instead of the above terms if they can achieve the same purpose.

The modules (or units, blocks, units) described in this application may be implemented as software and/or hardware modules. Unless the context clearly indicates otherwise, when a unit or module is described as being "on", "connected to", or "coupled to" another unit or module, the expression may mean that the unit or module is directly on, linked, or coupled to the other unit or module, or that the unit or module is indirectly on, connected, or coupled to the other unit or module. In this application, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In this application, the term "autonomous vehicle" may refer to a vehicle that is capable of sensing its environment and automatically sensing, determining, and making decisions about the external environment without human (e.g., driver, pilot, etc.) input and/or intervention. The terms "autonomous vehicle" and "vehicle" may be used interchangeably. The term "autopilot" may refer to the ability to intelligently judge and navigate the surrounding environment without human (e.g., driver, pilot, etc.) input.

These and other features of the present application, as well as the operation and function of the related elements of structure and the combination of parts and economies of manufacture, may be significantly improved upon consideration of the following description. All of which form a part of this application, with reference to the accompanying drawings. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.

The flow charts used in this application illustrate the operation of system implementations according to some embodiments of the present application. It should be clearly understood that the operations of the flow diagrams may be performed out of order. Rather, the operations may be performed in reverse order or simultaneously. In addition, one or more other operations may be added to the flowchart. One or more operations may be removed from the flowchart.

The positioning techniques used in the present application may be based on the Global Positioning System (GPS), the global navigation satellite system (GLONASS), the COMPASS navigation system (COMPASS), the galileo positioning system, the quasi-zenith satellite system (QZSS), wireless fidelity (WiFi) positioning techniques, etc., or any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

Further, while the systems and methods herein are described primarily with respect to scheduling autonomous vehicles, it should be understood that this is merely an exemplary embodiment. The system or method of the present application may be applied to any other type of transportation system. For example, the systems or methods of the present application may be applied to transportation systems in different environments, including terrestrial, marine, aerospace, etc., or any combination thereof. The autonomous vehicles of the transportation system may include taxis, private cars, trailers, buses, trains, bullet trains, high speed railways, subways, ships, airplanes, space vehicles, hot air balloons, autonomous vehicles, and the like, or any combination thereof. In some embodiments, the system or method may find application in, for example, logistics warehouses, military affairs.

FIG. 1 is a schematic view of a scenario of one embodiment of vehicle scheduling at an intersection in the present application. As shown in fig. 1, the intersection in the figure is a bidirectional six-lane intersection without traffic lights, and it should be noted that the intersection scheduling method provided by the present application can be applied not only to the illustrated intersection situation, but also to other road situations with multiple entrances, such as a t-junction, a roundabout, and the like. The lane situation is not limited to the two-way six-lane shown in the figure, and the situations such as four-lane, one-way lane and the like are also applicable to the scheme provided by the application. In the scenario shown in FIG. 1, vehicles may be driven into each of lanes 1-12. The vehicle may be an autonomous vehicle 130 or may be a vehicle with partial autonomous functionality.

The autonomous vehicle 130 may include some conventional structure not found in autonomous vehicles, such as an engine, wheels, steering wheel, etc., and may also include electronics specific to the autonomous vehicle, which may include a perception module 140, a planning module 150, and a control module 160. The sensing module 140 may obtain environmental information about the autonomous vehicle 130, such as ambient road conditions, obstacle conditions, other vehicle conditions, weather conditions, and the like. The planning module 150 may make a decision of a driving strategy according to the data acquired by the sensing module 140, for example, the sensing module 140 detects that there is an obstacle ahead, and the planning module 150 may make a decision to brake the vehicle, or make a decision to bypass the obstacle. The planning module 150 may communicate the decision made to the control module 160. The control module 160 may control the mechanical structure of the vehicle based on the received decision to achieve the decision effect. The vehicle with partial autopilot functionality may include a perception module 140 and a control module 160 in the autonomous vehicle 130.

The vehicles (including autonomous vehicle 130 and vehicles with partial autonomous functionality) may interact with server 110. For example, the vehicle may transmit vehicle state information to the server 110, and the server 110 may transmit driving strategy information to the vehicle. The server 110 may include a cloud server, and the vehicle may perform data interaction with the server 110 through a wireless network. The server may also include a local server located near the intersection. The signal coverage of the local server may include at least one intersection. The vehicle can establish a data interaction connection with the local server after entering the coverage area of the local server. For example, in some embodiments, the local server may be located in the middle of an intersection for dispatching vehicles through the intersection.

In the scenario shown in fig. 1, each vehicle has its predetermined travel route, and the routes of vehicles traveling in the same lane may be the same or different. The path of travel 120-1 of the vehicle, such as lane 12 in fig. 1, is a right turn at an intersection. In other embodiments, the vehicle traveling on the lane 12 may take a different travel path such as going straight or turning around at an intersection. In some embodiments, the driving path of each vehicle may be notified to the remote server 110 and/or traffic guidance center in advance through wireless communication (e.g., Internet, 4G network, 5G network, etc.), so as to be in the perspective of the server 110 and/or traffic guidance center. In some embodiments, the travel path of each vehicle may be determined by the server 110 and/or traffic directing center. For example, the vehicle sends the starting point and the ending point of the intersection region to the server 110, and the server 110 may determine a driving path for the vehicle according to the starting point and the ending point. Different vehicles may have path overlap points in the intersection area of the figure. If each vehicle continues to travel along its intended travel path, the vehicles may collide at the path overlap point due to the lack of traffic light constraints. The method provided by the application comprises the steps that communication is established between the vehicles and the server 110, and the server plans a driving strategy for the vehicles entering the intersection, so that the passing efficiency and the safety of the whole intersection are improved.

FIG. 2 is a block diagram of an exemplary vehicle and autonomous driving system 200 with autonomous driving capability according to some embodiments of the present application. As shown in FIG. 2, the autonomous vehicle 130 may include a perception module 140, a planning module 150, and a control module 160, a memory 220, a network 230, a gateway module 240, a Controller Area Network (CAN)250, and a vehicle mechanical system 260. The sensing module may further include a sensor group 141 and a sensing and positioning unit 142. The sensor group 141 may be used to sense surrounding environmental information (e.g., third party vehicles, pedestrians, lane information, obstacles, etc.). The sensor group 141 may include a vision sensor (e.g., a monocular camera, a binocular camera, a fisheye camera, a wide-angle camera, etc.), a laser radar, a millimeter-wave radar, and the like. The sensing and positioning unit 142 can realize functions of positioning a vehicle, detecting a driving area, detecting an obstacle, and the like according to data detected by the sensor group 141.

In some embodiments, the perception module 140 may collect environmental information of the road ahead of the vehicle, such as road information, other vehicle information, and traffic signal information, including but not limited to: real-time speed, position, acceleration, etc. of other vehicles, color of traffic lights, and maximum speed limit (e.g., the maximum speed limit for the current road segment, the maximum speed limit for the intersection, etc.). The visual sensors may detect the status of the traffic light 110 (e.g., the color of the traffic light 110), lane markings, signs, and other vehicles, etc., and communicate the detected visual information to the planning module 150. The sensor set 141 may measure the distance of the autonomous vehicle 130 from a target, e.g., another vehicle around the autonomous vehicle 130, etc., and transmit its measurement information to the planning module 150. In some embodiments, the set of sensors 141 may measure the distance between the autonomous vehicle 130 and the target based on the location information of the autonomous vehicle and the position information of the target on the map. In some embodiments, the sensor suite 141 may include a lidar or millimeter wave radar that models the surroundings of the autonomous vehicle 130 in three dimensions. The sensor group 141 may measure the real-time traveling speed of the third party vehicle 110 and transmit its measurement information to the planning module 150. The sensor set 141 may measure the real-time acceleration of the third party vehicle 110 and communicate its measurements to the planning module 150. The perceptual-location unit 142 may locate the autonomous vehicle 130 and the third party vehicle 110 in real time and transmit the location information to the planning module 150. In some embodiments, the perceptual-positioning unit 142 may be included as a high-precision GPS positioning unit.

The planning module 150 may receive the information obtained by the perception module 140 and generate driving decision information. In some embodiments, when the perception module 140 identifies the path overlap point 170, the driving decision information generated by the planning module 150 may be: and issuing a running instruction of passing acceleration, keeping the current speed or decelerating to the vehicle 130.

The control module 160 may process information and/or data related to vehicle driving (e.g., autonomous driving) to perform one or more of the functions described herein. In some embodiments, the control module 160 may receive the decision information and control the autonomous vehicle 130 to execute the decision-making travel command according to the decision information. In some embodiments, the control module 160 may control the vehicle according to the control signal received from the server 110. In some embodiments, the control module 160 may be configured to autonomously drive the vehicle. For example, the control module 160 may output a plurality of control signals to the vehicle mechanical system 260. The plurality of control signals may be configured to be received by a plurality of Electronic Control Units (ECUs) of a mechanical system 260 of the vehicle to control driving of the vehicle. In some embodiments, the control module 160 may determine the travel speed of the vehicle based on environmental information of the vehicle (e.g., travel conditions of surrounding third party vehicles). In some embodiments, the control module 160 may include one or more processing engines (e.g., single core processing engines or multi-core processors). By way of example only, the control module 160 may include a Central Processing Unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a reduced instruction-set computer (RISC), a microprocessor (microprocessor), etc., or any combination thereof.

The memory 220 may store data and/or instructions. In some embodiments, the memory 220 may store data obtained from the autonomous vehicle 130 (e.g., data measured by various sensors in the perception module 140). In some embodiments, the memory 220 may store a high-precision map, which may also include information such as the number of lanes, lane width, road curvature, road grade, maximum speed, and recommended travel speed. In some embodiments, the memory 220 may store data and/or instructions that may be executed or used by the control module 160 to perform the example methods described herein. In some embodiments, the memory 220 may include mass storage, removable storage, volatile read-and-write memory (volatile read-and-write memory), read-only memory (ROM), or the like, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; for example, the removable memory may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape; volatile read and write memory, for example, may include Random Access Memory (RAM); for example, RAM may include Dynamic RAM (DRAM), Double Data Rate Synchronous Dynamic RAM (DDRSDRAM), Static RAM (SRAM), silicon controlled RAM (T-RAM), and zero capacitor RAM (Z-RAM); for example, ROM can include Mask ROM (MROM), Programmable ROM (PROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital versatile disk ROM. In some embodiments, the storage may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.

In some embodiments, the memory 220 may be connected to the network 230 to communicate with one or more components of the autonomous vehicle 130 (e.g., control module 160, sensor group 141). One or more components in the autonomous vehicle 130 may access data or instructions stored in the memory 220 via the network 230. In some embodiments, the memory 220 may be directly connected to or in communication with one or more components in the autonomous vehicle 130 (e.g., control module 160, sensor suite 141). In some embodiments, the memory 220 may be part of the autonomous vehicle 130.

The network 230 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the autonomous vehicle 130 (e.g., perception module 140, control module 160, etc.) may establish data interaction with the server 110 via the network 230. The sensing module 140 sends the sensed information of the own vehicle to the server 110 through the network 230. The control module 160 may receive control instructions from the server 110 over the network 230 to control the autonomous vehicle.

In some embodiments, one or more components (e.g., control module 160, sensor group 141) in the autonomous vehicle 130 may send information and/or data to other components in the autonomous vehicle 130 via the network 230. For example. The control module 160 may obtain/obtain the dynamic condition of the vehicle and/or the environmental information around the vehicle via the network 230. In some embodiments, the network 230 may be any type of wired or wireless network, or combination thereof. By way of example only, the network 230 may include a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof. In some embodiments, the network 230 may include one or more network access points. For example, the network 230 may include wired or wireless network access points, such as base stations and/or internet exchange points 230-1. One or more components of the autonomous vehicle 130 may be connected to the network 230 to exchange data and/or information.

The gateway module 240 may determine command sources for a plurality of ECUs based on the current driving state of the vehicle. The command source may be from a human driver, from the control module 160, etc., or any combination thereof.

The gateway module 240 may determine the current driving state of the vehicle. The driving state of the vehicle may include a manual driving state, a semi-automatic driving state, an error state, or the like, or any combination thereof. For example, the gateway module 240 may determine the current driving state of the vehicle as a manual driving state based on input from a human driver. For another example, when the current road condition is complicated, the gateway module 240 may determine the current driving state of the vehicle as a semi-autonomous driving state. As yet another example, the gateway module 240 may determine the current driving state of the vehicle as an error state when an anomaly (e.g., a signal interrupt, a processor crash) occurs.

In some embodiments, the gateway module 240 may send the human driver's operation to the plurality of ECUs in response to determining that the current driving state of the vehicle is a manual driving state. For example, upon determining that the current driving state of the vehicle is a manual driving state, the gateway module 240 may responsively transmit a pressing operation performed by the human driver on an accelerator of the autonomous vehicle 130 to the vehicle mechanical system 260. Upon determining that the current driving state of the vehicle is an autonomous driving state, the gateway module 240 may transmit a control signal of the control module 160 to the plurality of ECUs in response. For example, upon determining that the current driving state of the vehicle is an autonomous driving state, the gateway module 240 may responsively transmit control signals associated with steering operations to the vehicle mechanical system 260. The gateway module 240 may transmit the operation of the human driver and the control signal of the control module 160 to the plurality of ECUs in response to a conclusion that the current driving state of the vehicle is the semi-autonomous driving state. The gateway module 240 may transmit an error signal to the plurality of ECUs in response when it is determined that the current driving state of the vehicle is an error state.

The controller area network (CAN bus) 250 is a reliable vehicle bus standard (e.g., message-based protocol) that allows a microcontroller (e.g., control module 160) and devices (e.g., vehicle mechanical system 260, etc.) to communicate with each other in an application program without a host computer. The CAN 250 may be configured to connect the control module 160 with a plurality of ECUs of the vehicle mechanical system 260.

Fig. 3 is a schematic diagram of exemplary hardware and software components of information processing unit 300. The information processing unit 300 may carry and implement the scheduling method of the server 110 for the vehicles in the intersection area. For example, the server 110 may include at least one of the information processing units 300, and the information processing unit 300 may process vehicle scheduling work for at least one intersection.

The information processing unit 300 may be a dedicated computer device specifically designed to process a pass-through intersection request from the vehicle, generate vehicle control instructions, and transmit to the vehicle.

For example, the information processing unit 300 may include a COM port 350 connected to a network connected thereto to facilitate data communication. The information processing unit 300 may also include a processor 320, the processor 320 in the form of one or more processors for executing computer instructions. The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform the particular functions described herein. For example, the processor 320 can obtain the position, speed, acceleration of the oncoming traffic. Or the like, or any combination thereof. The processor 320 may further generate and transmit a driving strategy for each vehicle to the vehicle via the I/O component 360.

In some embodiments, the processor 320 may include one or more hardware processors, such as microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASICs), application specific instruction-set processors (ASIPs), Central Processing Units (CPUs), Graphics Processing Units (GPUs), Physical Processing Units (PPUs), microcontroller units, Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGAs), Advanced RISC Machines (ARMs), Programmable Logic Devices (PLDs), any circuit or processor capable of executing one or more functions, or the like, or any combination thereof.

The information handling unit 300 may include an internal communication bus 310, program storage, and various forms of data storage (e.g., disk 370, Read Only Memory (ROM)330, or Random Access Memory (RAM)340) for various data files processed and/or transmitted by the computer. The information processing unit 300 may also include program instructions stored in ROM 330, RAM 340, and/or other types of non-transitory storage media to be executed by the processor 320. The methods and/or processes of the present application may be implemented as program instructions. The information processing unit 300 also includes I/O components 360 that support input/output between the computer and other components (e.g., user interface elements). The information processing unit 300 may also receive programming and data through network communication.

For illustrative purposes only, only one processor is described in the information processing unit 300 in the present application. It should be noted, however, that the information processing unit 300 may also include multiple processors, and thus, the operations and/or method steps disclosed herein may be performed by one processor as described herein, or may be performed jointly by multiple processors. For example, if in the present application processor 320 of information processing unit 300 performs steps a and B, it should be understood that steps a and B may also be performed jointly or separately by two different processors in the information processing (e.g., a first processor performs step a, a second processor performs step B, or both a first and a second processor perform steps a and B together).

FIG. 4 is an exemplary flow chart of scheduling a vehicle with autonomous driving capability at an intersection according to the present application. The method mainly comprises the steps that the server 110 obtains state information of vehicles about to enter a crossing, a driving strategy is generated for each vehicle after unified processing, and a control command containing the driving strategy is issued to the vehicles through the network 230 so as to achieve the purpose of unified scheduling. For illustrative purposes only, the present disclosure will describe the inventive aspects of the present application in terms of an autonomous vehicle, however, one of ordinary skill in the art will recognize that the inventive aspects of the present disclosure may also be applied to a vehicle that is manually driven. For example, the manually driven vehicle has a function of complying with scheduling when the manually driven vehicle is driven to an intersection, that is, the manually driven vehicle is controlled by a control instruction sent by the server 110 when the manually driven vehicle is about to enter the intersection region, so as to achieve the purpose of increasing the intersection passing efficiency by multiple vehicles in a quick cooperation manner. The server 110 may include at least one set of structures shown in fig. 3, and is configured to process a passing intersection request sent by a vehicle near the intersection and issue a control command.

At 410, the server 110 can receive a pass through intersection request sent from at least one vehicle during a current time period. The passing intersection request may include a status of the at least one vehicle at a current time period. The states include position, speed, and planned travel path. The intersection comprises an area where different roads are overlapped, namely an intersection area. In the scene shown in fig. 1, the intersection region is a square region corresponding to a dot-dash line in the figure. The vehicle sends the intersection passing request to the server 110 when entering the intersection dispatching area. The intersection scheduling area comprises the intersection area and a peripheral extended road passing through the intersection area (such as an area where each vehicle is located currently in the figure). In some embodiments, the length of the extended road is a preset value (e.g., 100 meters, 300 meters, 1 kilometer, 5 kilometers, etc.).

In some embodiments, the server 110 may periodically process the passing intersection requests it receives. The current time period corresponds to a time length of one cycle. For example, the server 110 processes the intersection passing request received in 5 seconds every 5 seconds. The current time period may correspond to one of the 5 second time lengths. Three vehicles respectively transmit the passing intersection request to the server 110 in the 1 st, 3 rd and 4 th seconds of the 5 seconds, the passing intersection requests of the three vehicles are identified as belonging to the current time period by the server 110, and the vehicles are also grouped into one group, namely the vehicle group of the current time period. For another example, the server 110 may process the crossing request received in 1 second every 1 second. Accordingly, the current time period may correspond to one of the time lengths of 1 second. If one or more vehicles send a passing intersection request to the server 110 in the 1 second, the passing intersection requests of the vehicles are identified by the server 110 as belonging to the current time period, and the vehicles are also grouped into a group, i.e., a vehicle group of the current time period.

In some embodiments, the server 110 may also responsively process requests for passing intersections over a period of time. For example, no vehicle enters the intersection dispatching area within 20 seconds, the server is in a standby state. If the first vehicle enters the intersection scheduling area between the 21 st and 22 nd seconds and sends the intersection passing request to the server, the server 110 may start at 21 seconds and take a preset time duration (e.g., 5 seconds, 1 second, etc.) as a time period. Similarly, after the time interval is finished, the next time interval is counted from the time when the next vehicle enters the intersection scheduling area and sends the intersection passing request.

The vehicle sends the passing intersection request to the server 110 when entering the intersection dispatching area. The corresponding time when the vehicle enters the intersection dispatching area can be the time when the vehicle head just enters the intersection dispatching area, and also can be the preset time when a part of the vehicle body enters the intersection dispatching area or the preset time when the whole vehicle body completely enters the intersection dispatching area.

The state of the vehicle may be obtained by the vehicle's perception module 140. In some embodiments, the planned travel path of the vehicle may be determined by the planning module 150. For example, prior to the current time period, the vehicle planning module 150 has determined a planned travel path through the intersection dispatch area based on the destination location, the vehicle location, and the high accuracy map.

In some embodiments, the planned travel path of a vehicle traveling in a fixed lane may be predetermined. For example, in the scenario shown in fig. 1, the travel path corresponding to each lane is fixed. Among the three lanes, the left lane corresponds to a left-turn path, the right lane corresponds to a right-turn path, and the middle lane corresponds to a straight path. Also, the trajectory of each path in the intersection area shown in the figure is fixed, e.g., path 120-2 corresponds to the travel path of the incoming vehicle in lane 1. When the vehicle enters the intersection dispatching area, the decision unit 150 may determine its planned driving path according to its corresponding lane or determine whether to merge into the corresponding lane according to the established planned driving path.

The driving path of each vehicle can be notified to the remote server 110 and/or traffic guidance center in advance through wireless communication (such as Internet, 4G network, 5G network, etc.), so that the driving path of each vehicle passing through the intersection is predetermined from the perspective of the server 110 and/or traffic guidance center. The predetermined vehicle travel path may correspond to a fixed lane of travel (e.g., the vehicle travel path may be determined by the server 110 based on information from the time the vehicle sends the request. The server 110 and/or the traffic guidance center will plan the driving speed and position of the vehicles at the intersection as a whole. In some embodiments, the planned driving trajectory may further include position information corresponding to different times when the vehicle is located in the intersection region. Or the planned driving path may further comprise a driving speed of the vehicle along the planned driving path in the intersection area. The travel speed may be a maximum speed limit allowed for the intersection region. In some embodiments, the vehicles may pass through the intersection region at the same preset speed. The preset speed may be a maximum speed limit of the intersection region.

At 420, the server obtains a planned travel trajectory of a previous vehicle for a current time period. The previous vehicles include vehicles that were entering prior to the current time period and did not exit the intersection dispatch area. Before the current time period, a previous vehicle has driven into the intersection scheduling area and has not driven out of the intersection scheduling area at the end time of the current time period. The exit crossing dispatch area may include a head or tail crossing the crossing area. The planned travel trajectory of the previous vehicle includes a travel path of the previous vehicle and a speed on the travel path thereof for a period of time after a current time period. A period of time after the current time period includes a period of time from the current time period to exiting the intersection area in accordance with the previous vehicle's planned travel trajectory. The projected driving trajectory of the previous vehicle may be stored in a storage device (e.g., rom 330, ram 340, disk 370, etc.) of the server 110. The server 110 may retrieve the planned travel trajectory of the previous vehicle from the storage device.

In some embodiments, the planned driving trajectory of the previous vehicle stored in the storage device may be determined for the previous vehicle by the server 110 running the intersection scheduling method described herein before the current time period. For example, in a first time period before the current time period, a first previous vehicle enters the intersection scheduling area, and in a second time period before the first time period, a second previous vehicle enters the intersection scheduling area. The server 110 operates the method described herein to determine a planned travel trajectory of a previous vehicle number one based on the planned travel trajectory of the previous vehicle number two. At the current time period, a portion of the planned travel trajectory of the previous vehicle number one corresponding to after the current time period may be taken as a planned travel trajectory of a previous vehicle with respect to the vehicle entering the intersection scheduling area at the current time period. For example, when T is 0, the planned driving trajectory determined by the server 110 is that the vehicle exits the intersection scheduling area when T is 30. If the current time period corresponds to T-10, the portion of the planned travel track of the vehicle number one from T-10 to T-30 may be taken as the planned travel track of the previous vehicle number one in the current time period. When the server 110 generates a control instruction according to the planned driving track of the previous vehicle number one and issues the previous vehicle number one, the instruction information may be stored in the storage device for being called in the current time period or the later time period.

In some embodiments, the planned driving trajectory of the previous vehicle stored in the storage device may be that the previous vehicle sent its planned driving trajectory to the server 110 before the current time period. For example, when the previous vehicle enters the intersection scheduling area, there is no other vehicle in the intersection scheduling area, and after the previous vehicle includes the planned driving track determined by the planning module 150 into the passing intersection request and sends the passing intersection request to the server 110, the server 110 may store the planned driving track in the storage device and send (or not send) an instruction that the driving strategy adjustment is not needed to the previous vehicle.

At 430, the server 110 can generate a speed planning instruction according to the planned driving trajectory of the previous vehicle and the passing intersection request. The speed planning instruction comprises a control command for controlling the speed of the vehicle, and is used for changing the speed of the vehicle on the planned driving path of the vehicle so as to achieve the purpose of avoiding collision. For example, if the vehicle may collide with a previous vehicle or another vehicle of the current time period in the intersection area according to its current driving state, the control command may change the speed of the vehicle before and/or after entering the intersection area such that the vehicle reaches the path overlapping point before or after the vehicle that may collide with the vehicle. For details of generating the speed planning instruction, please refer to fig. 5 and the related description thereof.

At 440, the server 110 sends the speed planning instruction to the at least one vehicle to instruct the at least one vehicle to adjust the speed of the vehicle according to the speed planning instruction. The server can uniformly process the acquired intersection passing requests of the vehicles entering the intersection dispatching area in the current time period after the current time period is finished, namely, after the method is operated for one time, the server respectively issues the speed planning instruction of each vehicle to each vehicle through the network 230. In addition, the server 110 may also store the speed planning instruction of each vehicle and the planned driving path included in the intersection passing request thereof into the storage device, and call the speed planning instruction as a time period after the current time period when speed planning is performed on subsequent vehicles. The speed planning instructions include a preset data structure. For example, the preset data structure includes an instruction section and a speed planning section. The instruction component may be automatically executed by the at least one vehicle; the speed planning section includes a description of the vehicle speed. When the at least one vehicle receives the speed planning instruction, the instruction part is automatically operated, and the instruction part guides the at least one vehicle to plan the speed according to the speed planning part.

Fig. 5 is an exemplary flow chart of the present application for determining speed planning instructions based on planned travel trajectories of previous vehicles and a passing intersection request of the vehicles during a current time period. The server 110 may determine a number of path overlap points in the intersection region based on the planned travel path contained in the pass intersection request and the planned travel trajectory of the previous vehicle. In the scenario shown in fig. 1, a case where the path corresponding to each lane is fixed is taken as an example (it can also be understood by those skilled in the art that the method described in the present application is also true in a case where the vehicle path corresponding to each lane is not fixed), where whether there is a path overlapping point between the path corresponding to any one lane and the path corresponding to another lane or whether there is a specific position where there is a path overlapping point is fixed, such as 16 path overlapping points shown in the figure.

It should be noted that the flowchart explained by using the scene diagram shown in fig. 1 is only for convenience of description and does not limit the present application. One of ordinary skill in the art will recognize that the methods disclosed herein may be applied to other similar scenarios, such as two-way four-lane, one-way, etc.

The two vehicles which are possibly collided can be prevented from colliding in the intersection area by respectively enabling the time difference value of the corresponding path overlapping point to be larger than a certain threshold value. For the speed plan control instruction, a command to increase or decrease the speed of the vehicle may be included so that the vehicle may stagger vehicles with which a collision may occur at the path overlapping point.

In some embodiments, the speed of the vehicle and the previous vehicle when passing through the intersection region are the same preset value. The time from the entry of the vehicle/preceding vehicle into the intersection area (head entry or tail entry) to the path overlap point is a fixed value. The speed planning instruction may thus comprise a command to increase or decrease the speed of the vehicle so that it is advanced or delayed to enter the intersection area, i.e. vehicles that may collide with it overlapping the path may be staggered.

In 510, the server 110 may determine an entry time of the at least one vehicle into the intersection region according to the planned driving track of the previous vehicle and the intersection passing request. In some embodiments, the speed planning instructions may include commands to slow the vehicle for the purpose of deferring entry into the intersection region. The degree of deceleration may be measured in terms of delay time. The delay time may be a difference between a time (ideal arrival time) at which the vehicle can enter the intersection region at the fastest speed according to the current state and the road network speed limit of the vehicle and an actual time (actual arrival time) at which the vehicle enters the intersection region after being decelerated to avoid a collision. That is, the server 110 may determine the entry time of the vehicle according to the delay time and the vehicle state. In some embodiments of the present application, the delay time satisfies a constraint model that includes at least one of the following constraints:

optimizing the target: the difference between the total time of the at least one vehicle reaching the intersection area and the ideal total time of the at least one vehicle reaching the intersection area is the smallest.

constraint 3. for any one of the at least one vehicle, for that vehicle and the preceding vehicle with which there is a route overlap point, the preceding vehicle with which there is a route overlap point constitutes a preceding vehicle group, the time at which the vehicle passes through the route overlap point being later than the time at which any one of the preceding vehicle group reaches the route overlap point.

For convenience of explanation, one embodiment of the constraint model is given below and should not be taken as limiting the scope of the disclosure of the present application:

optimization objective (objective function):

constraint 1: (OT)_i+D_i)-(OT_j+D_j)≥Hmin(l_iml_jm)........................(2)；

Constraint 2: [ section of (OT)_i+D_i+τ_mn)-(OT_k+D_k+τ_mn)|Δτ(l_iml_knC_mn)..................(3)；

Constraint 3: (OT)_i+D_i+τ_mn)≥max[(OT_f+D_f+τ_mn)，(OT_p+D_p+τ_mn)].........(4)；

Constraint 4:

wherein i, j, k, f and p represent vehicle numbers, m and n represent lane numbers, and Ψ represents a set of all lanes of the intersection region;

omega 1: the vehicles are driven into the intersection scheduling area at the current time period;

omega 0: a set of previous vehicles;

omega: the method comprises the steps that at the current moment, all vehicles in the intersection scheduling area are collected, and omega is omega 0+ omega 1;

D_i: the time (AT) when the vehicle i actually arrives AT the intersection is different from the time (OT) when the vehicle i ideally arrives AT the intersection, i.e. the delay time, i.e.: d_i＝AT_i-OT_i；

H_min: the safety distance to be kept between two vehicles on the same lane;

l_im: when the vehicle with the number i enters the lane with the number m, the value is 1, otherwise, the value is 0;

C_mn: the path overlapping point of the m lanes of incoming vehicles and the n lanes of incoming vehicles is 1 when the path overlapping point exists, otherwise, the value is 0;

τ_mn: the time length from the time when the m-lane coming vehicle enters the intersection area to the time when the m-lane coming vehicle travels to the path overlapping point of the n-lane coming vehicle, or the time length from the time when the n-lane coming vehicle enters the intersection area to the time when the n-lane coming vehicle travels to the path overlapping point of the m-lane coming vehicle is represented. Tau is_mnFor a vehicle traveling to the intersection on m lanes, τ is a function of vehicle and lane_mnRepresents the time length from the time when the m lanes of coming vehicles drive into the intersection area to the time when the m lanes of coming vehicles drive to the path overlapping point of the n lanes of coming vehicles, and for the vehicles driving to the intersection on the n lanes, tau_mnRepresenting the time length from the time when the vehicle coming from the n lanes drives into the intersection area to the time when the vehicle drives to the path overlapping point of the vehicle coming from the m lanes;

Δ τ: the safety time difference represents the minimum value of the difference between the time points when the two vehicles safely pass through the same path overlapping point.

With respect to equation 1, the delay time of each vehicle is summed up for vehicles entering the intersection scheduling area in the current time period, and the value of the summation is required to be minimum, which means that the overall traffic efficiency is highest for the vehicles in the current time period, and the overall time for passing through the intersection scheduling area is minimum.

Regarding formula 2, i car and j car are both the vehicles driving into the m lanes of the intersection scheduling area at the current time period or before the current time period, and i car enters the intersection scheduling area after j car. The formula represents the time when the vehicle enters the crossing dispatching areaAt least H later than the time when any vehicle which drives into the m lanes before the vehicle enters the crossing dispatching area_min(l_iml_jm) The length of time. H_min(l_iml_jm) The value is determined according to the states of the i car and the j car in the current time period. For example, H corresponds to the case when the speed of the vehicle i is higher than that of the vehicle j_min(l_iml_jm) Value H may be slower than i car speed than j car_min(l_iml_jm) The value is large. If the i car does not drive into the m lanes, then l_im＝0，H_min(l_iml_jm) And 0 means that the i vehicle and the j vehicle are not restrained on the m lanes, and the j vehicle has the same reason. The constraint can ensure that vehicles which firstly enter the crossing dispatching area firstly enter the crossing area on the same lane.

Regarding formula 3, i car and k car are both vehicles entering the intersection dispatch area at the current time period, and i car and k car enter from m lane or n lane. (OT)_i+D_i+τ_mn) Indicating the time when the vehicle travels to the path overlapping point corresponding to the m lane and the n lane, (OT)_k+D_k+τ_mn) And the time when the k vehicle runs to the path overlapping point corresponding to the m lane and the n lane is shown. The absolute value of the difference between the two moments being representative of the difference between the moments at which the vehicles pass through the point of overlap of the paths, respectively, this difference being not less than the safety time difference Δ τ (l)_iml_knC_mn). The constraint condition ensures that no collision occurs between any two vehicles driving into the intersection scheduling area at the current time period.

Regarding equation 4, i car is a vehicle that enters m lanes of the intersection scheduling area at the current time period, and f car and p car are previous vehicles that enter m lanes or n lanes of the intersection scheduling area before the current time period. The formula shows that for all the previous vehicles which enter from the m lane or the n lane and pass through the path overlapping point of the m lane and the n lane, the time for the vehicle which enters the m lane of the intersection scheduling area at the current time interval to reach the path overlapping point of the m lane and the n lane is later than all the previous vehicles. The constraint reflects the priority between vehicles, i.e. the priority of the vehicle entering the crossing scheduling area at the current time is lower than that of the previous vehicle.

With respect to equation 5, it may be used to constrain the delay time to be a positive value.

The delay time of each vehicle can be determined according to the constraint model, and the corresponding entry time is further determined according to the current state of each vehicle.

At 520, the server 110 may generate the speed planning instruction according to the state of the at least one vehicle at the current time and the entry time. For example, in some embodiments, the speed planning instruction may include a speed variation curve for a vehicle entering the intersection dispatch area at the current time period, and after the vehicle travels along the planned travel path according to the speed variation curve, the vehicle may enter the intersection area at the corresponding entry time determined in 510.

Fig. 6 is a schematic diagram of a speed profile in the present application. The left extended road of the intersection region includes a section 1 and a section 2. In section 1, the three speed curves (one solid line and two dashed lines) are accelerated from different speeds immediately before entering section 1 to the same first preset speed before the end of section 1. The first preset speed may be the highest speed limit for the extended stretch. In the interval 2, the three speed curves have different degrees of deceleration at the middle end respectively, and then reach the same second preset speed at the end of the interval 2. The second preset speed may be a maximum speed limit that allows driving into the intersection area. The second preset speed may also be the maximum speed limit on the extended road, i.e. equal to the first preset speed. Different degrees of deceleration in interval 2 may satisfy the delay time requirement for each vehicle. For example, if the delay time of a certain vehicle is relatively long, the vehicle speed can be reduced (or even stopped) in the section 2, and the travel time in the section 2 can be made longer.

Fig. 7 is a schematic diagram of an intersection scheduling apparatus 700 in the present application. The intersection scheduling apparatus 700 includes a data acquisition unit 710, a data storage unit 720, an instruction generation unit 730, and an instruction transmission unit 740.

The data acquisition unit 710 may be configured to receive a request for passing through an intersection sent from at least one vehicle during a current time period. The passing intersection request may include a status of the at least one vehicle at a current time period. The states may include position, speed, and planned travel path.

The data storage unit 720 may store the planned driving trajectory of the previous vehicle for the current period. The preceding vehicles may include vehicles that were entering and not leaving the intersection dispatch area prior to the current time period.

The data obtaining unit 710 may further obtain the planned driving trajectory of the previous vehicle from the data storage unit 720.

The instruction generating unit 730 can be configured to generate a speed planning instruction according to the planned driving track of the previous vehicle and the passing intersection request.

The instruction sending unit 740 may send the speed planning instruction to the at least one vehicle.

The application also proposes a server comprising a memory, a processor and a computer program stored on the memory and executable on the processor. The processor, when executing the computer program, may implement the steps of the intersection scheduling method as described above.

The present application also proposes a computer-readable storage medium having stored thereon a computer program. The computer program, when executed by a processor, may implement the steps of the intersection scheduling method as described above.

In conclusion, upon reading the present detailed disclosure, those skilled in the art will appreciate that the foregoing detailed disclosure can be presented by way of example only, and not limitation. Those skilled in the art will appreciate that the present application is intended to cover various reasonable variations, adaptations, and modifications of the embodiments described herein, although not explicitly described herein. Such alterations, improvements, and modifications are intended to be suggested by this application and are within the spirit and scope of the exemplary embodiments of the application.

Furthermore, certain terminology has been used in this application to describe embodiments of the application. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined as suitable in one or more embodiments of the application.

It should be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of such feature. This application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. This is not to be taken as an admission that any of the features of the claims are essential, and it is fully possible for a person skilled in the art to extract some of them as separate embodiments when reading the present application. That is, embodiments in the present application may also be understood as an integration of multiple sub-embodiments. And each sub-embodiment described herein is equally applicable to less than all features of a single foregoing disclosed embodiment.

In some embodiments, numbers expressing quantities or properties useful for describing and claiming certain embodiments of the present application are to be understood as being modified in certain instances by the terms "about", "approximately" or "substantially". For example, "about", "approximately" or "substantially" may mean a ± 20% variation of the value it describes, unless otherwise specified. Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the embodiments of the application are approximations, the numerical values set forth in the specific examples are reported as precisely as possible.

Each patent, patent application, publication of a patent application, and other material, such as articles, books, descriptions, publications, documents, articles, and the like, cited herein is hereby incorporated by reference. All matters hithertofore set forth herein except as related to any prosecution history, may be inconsistent or conflicting with this document or any prosecution history which may have a limiting effect on the broadest scope of the claims. Now or later associated with this document. For example, if there is any inconsistency or conflict in the description, definition, and/or use of terms associated with any of the included materials with respect to the terms, descriptions, definitions, and/or uses associated with this document, the terms in this document are used.

Finally, it should be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the present application. Other modified embodiments are also within the scope of the present application. Accordingly, the disclosed embodiments are presented by way of example only, and not limitation. Those skilled in the art can implement the invention in the present application in alternative configurations according to the embodiments in the present application. Thus, embodiments of the present application are not limited to those embodiments described with accuracy in the application.

Claims

1. A method for scheduling a vehicle having an autonomous driving function at an intersection, comprising:

the method comprises the steps that in a current time period, a server receives a passing intersection request sent by at least one vehicle, wherein the passing intersection request comprises the state of the at least one vehicle in the current time period, and the state comprises the position, the speed and a planned driving path;

the server acquires a planned driving track of previous vehicles in the current time period, wherein the previous vehicles comprise vehicles which are driven in before the current time period and are not driven out of an intersection scheduling area;

the server generates a speed planning instruction according to the planned driving track of the previous vehicle and the intersection passing request; and

and the server sends the speed planning instruction to the at least one vehicle to instruct the at least one vehicle to adjust the vehicle speed according to the speed planning instruction.

2. The method of claim 1, wherein the passing intersection request is issued by the at least one vehicle upon entering the intersection dispatch area, the intersection dispatch area including an intersection area and its extended roads.

3. The method of claim 2, wherein the server generating speed plan instructions comprises:

according to the planned driving track of the previous vehicle and the intersection passing request, the server determines the driving-in time of the at least one vehicle driving into the intersection area; and

and the server generates the speed planning instruction according to the state of the at least one vehicle at the current moment and the driving-in moment.

4. The method of claim 1, wherein the current time period corresponds to a response cycle of the server, and wherein the server runs the scheduling method once for a pass through intersection request received within one response cycle.

5. The method of claim 1, wherein the current time period comprises a preset duration from a first time as a starting time, the first time being a time at which a first vehicle enters the intersection dispatch area after the end of a time period prior to the current time period.

6. The method of claim 1, wherein the planned travel trajectory of the previous vehicle is determined by the previous vehicle prior to a current time period according to previous vehicle speed planning instructions received from the server, the previous vehicle speed planning instructions generated by the server according to the scheduling method.

7. The method of claim 3, wherein the speed plan instructions further comprise a speed plan that satisfies a constraint model, the constraint model comprising a constraint objective: the difference between the total time of the at least one vehicle reaching the intersection area and the total time of the at least one vehicle reaching the intersection area under the ideal condition is minimum; and at least one of the following constraints:

8. The method of claim 7, wherein the server generating the speed plan instructions comprises:

for each of the at least one vehicle, planning a speed change curve of the vehicle on a planned path according to the corresponding entry time of the vehicle, the current position and the current speed, wherein the speed change curve satisfies the following conditions: when the vehicle runs according to the speed change curve, the vehicle drives into the intersection area at the corresponding driving-in time.

9. The method of claim 8, wherein the extended roadway comprises a first section of roadway and a second section of roadway, the second section of roadway connecting the intersection region, the speed profile further satisfying: and when the vehicle runs according to the speed change curve, each vehicle in the at least one vehicle reaches a first preset speed when running to the tail end of the first section of road, and reaches a second preset speed when running to the tail end of the second section of road.

10. A server, characterized by comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor implementing the steps of the intersection scheduling method for a vehicle with an automatic driving function according to any one of claims 1 to 9 when executing the computer program.