Control method, device, equipment and vehicle for vehicle-road cooperative automatic driving

Abstract

The present disclosure provides a control method, device, equipment and vehicle for vehicle-road cooperative automatic driving, which relates to the field of artificial intelligence, and in particular to the field of automatic driving and intelligent transportation technology. The implementation scheme is: in response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from driving along the first lane, obtaining driving information on the second lane adjacent to the first lane through the first roadside equipment; based on the driving information on the second lane, determining the predicted speed at which the vehicle can drive in the second lane after entering the second lane; and in response to determining that the predicted speed is within a preset range, initiating lane change control of the vehicle so that the vehicle can drive from the first lane to the second lane.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to the field of autonomous driving and intelligent transportation technology, and specifically to a control method, device, electronic device, computer-readable storage medium, computer program product, autonomous driving vehicle, and vehicle-road cooperative system. Background Art

[0002] Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing. Artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0003] Autonomous driving currently relies mainly on single-vehicle intelligent autonomous driving (AD), which mainly relies on the vehicle's own vision, millimeter-wave radar, lidar and other sensors, computing units, and wire control systems for environmental perception, computing decisions, and control execution.

[0004] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0005] The present disclosure provides a control method, device, electronic device, computer-readable storage medium, computer program product, autonomous driving vehicle and vehicle-road cooperative system for vehicle-road cooperative autonomous driving.

[0006] According to one aspect of the present disclosure, a control method for vehicle-road cooperative automatic driving is provided, including: in response to determining that there is an obstacle in front of a vehicle that prevents the vehicle from traveling along a first lane, obtaining driving information on a second lane adjacent to the first lane through a first roadside device; based on the driving information on the second lane, determining a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane, wherein the driving direction of the vehicle in the second lane is the same as the driving direction of the vehicle in the first lane; and in response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle so that the vehicle can enter the second lane from the first lane.

[0007] According to another aspect of the present disclosure, a device for vehicle-road cooperative automatic driving is provided, including: a first unit, configured to obtain driving information on a second lane adjacent to the first lane through a first roadside device in response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane; a second unit, configured to determine, based on the driving information on the second lane, a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane, the driving direction of the vehicle in the second lane being the same as the driving direction of the vehicle in the first lane; and a third unit, in response to determining that the predicted vehicle speed is within a preset range, initiates lane change control of the vehicle to enable the vehicle to enter the second lane from the first lane.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above methods.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any one of the above methods.

[0010] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements any one of the above methods when executed by a processor.

[0011] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above methods.

[0012] According to another aspect of the present disclosure, a vehicle-road cooperative system is provided, including roadside equipment and the autonomous driving vehicle as described above.

[0013] According to one or more embodiments of the present disclosure, the driving information of the vehicle about to enter the second lane is sensed by the first roadside device, and lane change control is performed accordingly. Since the sensing range of the first roadside device is wide, the predicted speed that the vehicle can reach after entering the second lane can be predicted more accurately, thereby achieving more accurate lane change decision and control.

[0014] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0016] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0017] Figure 2 A flow chart of a control method for vehicle-road cooperative automatic driving according to an embodiment of the present disclosure is shown;

[0018] Figure 3 A schematic diagram showing a control method for vehicle-road cooperative automatic driving according to an embodiment of the present disclosure is shown;

[0019] Figure 4 A schematic diagram showing another method for controlling vehicle-road cooperative automatic driving according to an embodiment of the present disclosure is shown;

[0020] Figure 5 A structural block diagram of a control device for vehicle-road cooperative automatic driving according to an embodiment of the present disclosure is shown; and

[0021] Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0022] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0023] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0024] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0025] In the related art, when an autonomous vehicle performs lane change control, it often performs lane change control based on the vehicle's own sensing system. Since the sensing range of the vehicle's own sensing system is limited, lane change decisions can only be made based on driving information within the vehicle's detectable range, which often causes inaccurate lane change control.

[0026] Based on this, the present disclosure proposes a control method for vehicle-road cooperative automatic driving. In response to the existence of an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane, the driving information on the second lane is obtained through the first roadside equipment, and the predicted vehicle speed that the vehicle can travel in the second lane after entering the second lane is predicted based on the driving information, thereby determining whether to initiate lane change control of the vehicle.

[0027] Thus, the first roadside device can be used to sense the driving information of the vehicle in the second lane to be entered, and the lane change control can be performed accordingly. Since the first roadside device has a wide sensing range, it can more accurately predict the predicted speed that the vehicle can reach after entering the second lane, thereby achieving more accurate lane change decision and control.

[0028] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes a motor vehicle 110 , a server 120 , and one or more communication networks 130 coupling the motor vehicle 110 to the server 120 .

[0030] In an embodiment of the present disclosure, the motor vehicle 110 may include a computing device according to an embodiment of the present disclosure and / or be configured to perform a method according to an embodiment of the present disclosure.

[0031] The server 120 may run one or more services or software applications that enable the method of autonomous driving. In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. Figure 1In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. A user of the motor vehicle 110 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0032] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0033] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0034] In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of motor vehicle 110.

[0035] The network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including, but not limited to, TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a satellite communications network, a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (including, for example, Bluetooth, WiFi), and / or any combination of these and other networks.

[0036] The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The data repository 150 may reside in various locations. For example, the data repository used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data repository 150 may be of different types. In some embodiments, the data repository used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to a command.

[0037] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0038] The motor vehicle 110 may include a sensor 111 for sensing the surrounding environment. The sensor 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter wave radar, and a laser radar (LiDAR). Different sensors may provide different detection accuracy and range. The camera may be installed at the front, rear, or other location of the vehicle. The visual camera may capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual camera, information such as traffic light indications, intersection conditions, and the operating status of other vehicles may be obtained. The infrared camera may capture objects under night vision conditions. Ultrasonic sensors may be installed around the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of strong ultrasonic directionality. Millimeter wave radars may be installed at the front, rear, or other location of the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of electromagnetic waves. Laser radars may be installed at the front, rear, or other location of the vehicle to detect object edges and shape information, thereby identifying and tracking objects. Due to the Doppler effect, the radar device may also measure the speed change of the vehicle and the moving object.

[0039] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., Beidou, GPS, GLONASS, and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as GSM / GPRS, CDMA, LTE, and other current or evolving wireless communication technologies (e.g., 5G technology). The communication device 112 may also have a vehicle-to-vehicle or vehicle-to-everything (V2X) module, which is configured to implement, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. In addition, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, a tablet computer, or a wearable device such as a watch) by, for example, using a wireless LAN or Bluetooth of the IEEE802.11 standard. Using the communication device 112, the motor vehicle 110 may also access the server 120 via the network 130.

[0040] The motor vehicle 110 may also include a control device 113. The control device 113 may include a processor that communicates with various types of computer-readable storage devices or media, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors. The control device 113 may include an automatic driving system for automatically controlling various actuators in the vehicle. The automatic driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Some processing functions of the control device 113 can be implemented through cloud computing. For example, some processing can be performed using an on-board processor, while other processing can be performed using computing resources in the cloud. The control device 113 can be configured to perform a method according to the present disclosure. In addition, the control device 113 can be implemented as an example of a computing device on the motor vehicle side (client) according to the present disclosure.

[0041] It is understood that the motor vehicle does not necessarily have to include the above-mentioned various vehicle-side sensing devices. According to some embodiments of the present disclosure, safe and reliable automatic driving can still be achieved when the motor vehicle does not have or does not enable these vehicle-side sensing devices.

[0042] The vehicle-road collaboration involved in the present disclosure may refer to the use of technologies such as wireless communication and the Internet to implement dynamic real-time information interaction between vehicles, vehicles and roads, and vehicles and people, and to carry out vehicle active safety control and road collaborative management based on the collection and integration of dynamic traffic information. Vehicle-road collaborative autonomous driving uses vehicle and road perception and positioning equipment (such as cameras, radars, etc.) to perceive and locate the road traffic environment in real time, and interact with data in accordance with agreed protocols to achieve different degrees of information interaction and sharing (network interconnection) between vehicles and vehicles, vehicles and roads, and vehicles and people, and covers different degrees of vehicle automated driving stages (vehicle automation). Through vehicle automation, network interconnection and system integration, a vehicle-road collaborative autonomous driving system is ultimately constructed.

[0043] Figure 1 The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0044] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0045] Figure 2 A flow chart of a control method 200 for vehicle-road cooperative automatic driving according to an embodiment of the present disclosure is shown. The method 200 may include the following steps: step S201, in response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane, obtaining driving information on a second lane adjacent to the first lane through a first roadside device; step S202, based on the driving information on the second lane, determining a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane, wherein the driving direction of the vehicle in the second lane is the same as the driving direction of the vehicle in the first lane; and step S203, in response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle so that the vehicle can travel from the first lane to the second lane.

[0046] Thus, the first roadside device can be used to sense the driving information of the vehicle in the second lane to be entered, and the lane change control can be performed accordingly. Since the first roadside device has a wide sensing range, it can more accurately predict the predicted speed that the vehicle can reach after entering the second lane, thereby achieving more accurate lane change decision and control.

[0047] The obstacle in step S201 may be a traffic accident, illegal road occupation, road maintenance, pedestrian intrusion, natural disaster, slow-moving vehicle ahead, or other objects or events that may affect the normal driving of the vehicle in the first lane.

[0048] Those skilled in the art can understand that the obstacle to the vehicle may have already occurred or may occur in the future. For example, the obstacle is a traffic accident far away from the vehicle in the first lane. The vehicle can still run normally in the first lane at present, but it can be predicted that when the vehicle drives close to the traffic accident, the vehicle will not be able to run normally at the current speed.

[0049] According to some embodiments, the first roadside equipment may include road engineering and supporting ancillary facilities, intelligent sensing facilities, such as cameras, millimeter wave radars, lidars, etc., roadside communication facilities, such as direct wireless communication facilities, cellular mobile communication facilities, etc., computing and control facilities, such as edge computing nodes, MEC or cloud platforms at all levels, high-precision maps and auxiliary positioning facilities, and supporting ancillary equipment such as power functions, etc.

[0050] In one embodiment, the roadside perception device is a combination of a roadside video device and a laser radar. The laser radar provides a point cloud, which is then integrated with the image provided by the camera, so that each vehicle can obtain driving information of adjacent lanes by tracking pixels.

[0051] According to some embodiments, during the driving process of the vehicle, obstacles ahead can be identified by an on-board unit (OBU) provided on the vehicle itself.

[0052] According to some embodiments, the obstacle that prevents the vehicle from traveling along the first lane is detected by the second roadside equipment.

[0053] Due to the limited sensing range of the vehicle itself, it is difficult to detect obstacles that will hinder the normal driving of the vehicle in the future, such as the traffic accident in the first lane far away from the vehicle. This results in the vehicle being unable to change lanes when the vehicle detects the obstacle. For example, when the vehicle detects the obstacle, multiple lanes near the obstacle are congested. For another example, when the vehicle detects the obstacle, the traffic rules at the driving location do not allow the vehicle to change lanes.

[0054] By performing obstacle detection through the second roadside device, the obstacle detection range can be significantly expanded, so that the vehicle can be informed of obstacles that may hinder the normal driving of the vehicle as early as possible, thereby leaving the vehicle with sufficient time to obtain driving information on the second lane and execute lane change decisions and controls.

[0055] After the driving information on the second lane is acquired, a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane can be determined based on the driving information on the second lane.

[0056] Among them, the predicted vehicle speed can be determined based on the driving information on the second lane by one or more of the vehicle, the first roadside device and the cloud server.

[0057] In one driving scenario, the second lane is in the same direction as the first lane.

[0058] According to some embodiments, the second lane is in the same direction as the first lane, and the driving information includes the speed of each of at least one reference vehicle located between the host vehicle and the obstacle in the second lane. Thus, according to the speed of the reference vehicle in the second lane in the same direction, it can be determined whether the host vehicle can drive at a normal speed after changing lanes to the second lane.

[0059] Figure 3 A schematic diagram of a scenario 300 of a control method for applying vehicle-road cooperative autonomous driving according to an embodiment of the present disclosure is shown.

[0060] like Figure 3 As shown, the second lane 305 and the first lane 306 are in the same direction. The host vehicle 301 is traveling on the first lane 306, and the reference vehicle 304 is traveling on the second lane 305. The host vehicle 301 and the reference vehicle 304 are traveling in the same direction.

[0061] In the first lane 306 , the obstacle vehicle 302 illegally occupies the road, causing the vehicle 301 to be unable to travel normally in the first lane 306 . The vehicle 301 obtains the speed of the reference vehicle 304 in the second lane 305 through the first roadside equipment 303 .

[0062] In this example, for ease of description, the reference vehicle 304 is located within the detection range of the host vehicle 301. Those skilled in the art will appreciate that, based on the wide range of driving information provided by the first roadside device 303, the acquired speed of the reference vehicle in the second lane 305 may also include one or more speeds outside the detection range of the host vehicle 301.

[0063] Based on the speed of the reference vehicle 304 in the second lane 305 , it is convenient for the vehicle 301 to determine whether to drive from the first lane 306 into the second lane 305 .

[0064] According to some embodiments, based on the driving information on the second lane, determining the predicted speed at which the vehicle can travel in the second lane after entering the second lane includes: determining the predicted speed based on an average of the speeds of each of at least one reference vehicle.

[0065] In one example, assuming that the current speed of the vehicle is 30km / h, the speed of the reference vehicle A in the second lane is 2km / h, and the speed of the reference vehicle B is 10km / h, the predicted speed is calculated to be the average of 2km / h and 10km / h, which is 6km / h. That is, it is predicted that when the vehicle enters the second lane, the driving speed that can be reached is 6km / h. On this basis, it can be judged that congestion may have occurred in the second lane before the first lane, and it is not appropriate for the vehicle to change lanes to the second lane.

[0066] In another driving scenario, the second lane is in the opposite direction of the first lane.

[0067] According to some embodiments, the second lane is opposite to the first lane, and the driving information includes the speed of a reference vehicle located in front of and closest to the vehicle on the second lane and a first distance between the reference vehicle and the vehicle along the driving direction. Based on the driving information on the second lane, determining the predicted speed at which the vehicle can travel in the second lane after entering the second lane may include: in response to determining that the first distance is greater than the second distance between the obstacle and the vehicle, determining the predicted speed at which the vehicle can travel in the second lane based on the first distance, the second distance and the speed of the reference vehicle, the predicted speed enabling the vehicle to return to the first lane from in front of the obstacle before colliding with the reference vehicle.

[0068] Therefore, based on the speed and distance of the reference vehicle in the opposite second lane, it can be determined whether the vehicle can bypass the obstacle ahead by using the second lane without colliding with the reference vehicle.

[0069] Figure 4 A schematic diagram of a scenario 400 of a control method for applying vehicle-road cooperative autonomous driving according to an embodiment of the present disclosure is shown.

[0070] exist Figure 4 , the second lane 405 and the first lane 406 are in opposite directions. The host vehicle 401 is traveling in the first lane 406, and the reference vehicle 404 is traveling in the second lane 405. The host vehicle 401 and the reference vehicle 404 are traveling in opposite directions, and the reference vehicle 404 is the vehicle closest to the host vehicle 401 traveling in the second lane 405.

[0071] In the first lane 406, the obstacle vehicle 402 illegally occupies the road, causing the vehicle 401 to be unable to travel normally in the first lane 406. The vehicle 401 obtains the speed v' of the reference vehicle 404 in the second lane 405, the first distance L1 between the vehicle 401 and the reference vehicle 404 along the driving direction, and the second distance L2 between the obstacle vehicle 402 and the vehicle 401 through the first roadside equipment 403.

[0072] Those skilled in the art will appreciate that in order to improve the accuracy of the calculation, the lengths of the host vehicle 401, the reference vehicle 404, and the obstacle 402 itself should be considered when determining the first distance L1 and the second distance L2. Figure 4 As shown, the first distance L1 is the distance between the front of the host vehicle 401 and the front of the reference vehicle 404 traveling in the opposite direction along the traveling direction, and the second distance L2 is the distance between the rear of the host vehicle 401 and the front of the obstacle vehicle 402.

[0073] like Figure 4 As shown, the first distance L1 between the host vehicle 401 and the reference vehicle 404 along the travel direction is greater than the second distance L2 between the obstacle vehicle 402 and the host vehicle 401. The minimum value of the predicted vehicle speed v can be determined by the following calculation:

[0074]

[0075] In other words, if the vehicle 401 enters the second lane 405 from the current position of the first lane 406 and drives in the reverse direction in the second lane 405, it needs to drive at a speed higher than Only by driving at a speed of , can it be ensured that the vehicle 401 can return to the first lane from in front of the obstacle vehicle 402 before colliding with the reference vehicle 404.

[0076] According to some embodiments, in response to determining that the first distance is not greater than a second distance between the obstacle and the host vehicle, the predicted vehicle speed is set to a maximum value or a minimum value.

[0077] When it is determined that the first distance is not greater than the second distance between the obstacle and the vehicle, the vehicle does not have the possibility of bypassing the obstacle ahead by using the second lane. Therefore, in order to improve the efficiency of decision-making and control, the predicted vehicle speed can be set to a maximum or minimum value.

[0078] After the predicted vehicle speed is determined, in response to determining that the predicted vehicle speed is within a preset range, lane change control of the host vehicle is initiated to enable the host vehicle to drive from the first lane into the second lane.

[0079] The lower speed limit of the preset range is the minimum driving speed that the vehicle can tolerate. When the vehicle is traveling at a speed lower than the lower speed limit, it can be considered that the vehicle is in a congested state. The upper speed limit of the preset range is the maximum driving speed for the vehicle to travel safely. When the vehicle is traveling at a speed greater than the upper speed limit, it can be considered that the current driving of the vehicle is unsafe.

[0080] According to some embodiments, in response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle includes at least one of the following operations: generating lane change prompt information; and generating a lane change control instruction.

[0081] In one embodiment, lane change prompt information is generated and provided to the driver, so that the driver can refer to the lane change prompt information to make a decision and control whether to execute the lane change. This control method is suitable for assisted automatic driving mode, that is, the vehicle only provides prompt information, and the driver controls the vehicle.

[0082] In another embodiment, a lane change control instruction is generated, and based on the lane change control instruction, the vehicle is directly controlled to change lanes or continue to drive in the first lane. This control method is suitable for an autonomous automatic driving mode, that is, the vehicle automatically performs lane change control without driver operation.

[0083] According to an embodiment of the present disclosure, a control device 500 for vehicle-road cooperative automatic driving is also provided. Figure 5 As shown, the device 500 includes: a first determination unit 501, configured to obtain driving information on a second lane adjacent to the first lane through a first roadside device in response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane; a second determination unit 502, configured to determine a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane based on the driving information on the second lane, wherein the driving direction of the vehicle in the second lane is the same as the driving direction of the vehicle in the first lane; and a control unit 503, configured to initiate lane change control of the vehicle in response to determining that the predicted vehicle speed is within a preset range, so that the vehicle can enter the second lane from the first lane.

[0084] According to some embodiments, the second lane is in the same direction as the first lane, and the driving information includes a vehicle speed of each of at least one reference vehicle located between the host vehicle and the obstacle on the second lane.

[0085] According to some embodiments, the second determination unit includes: a first determination subunit configured to determine the predicted vehicle speed based on an average of the vehicle speeds of each of the at least one reference vehicle.

[0086] According to some embodiments, the second lane is opposite to the first lane, and the driving information includes the speed of a reference vehicle located in front of and closest to the vehicle on the second lane and a first distance between the reference vehicle and the vehicle along the driving direction, and wherein the second determination unit includes: a second determination subunit, configured to determine, in response to determining that the first distance is greater than the second distance between the obstacle and the vehicle, a predicted speed at which the vehicle can travel in the second lane based on the first distance, the second distance and the speed of the reference vehicle, wherein the predicted speed enables the vehicle to return to the first lane from in front of the obstacle before colliding with the reference vehicle.

[0087] According to some embodiments, the second determination unit further includes: a third determination subunit configured to set the predicted vehicle speed to a maximum value or a minimum value in response to determining that the first distance is not greater than a second distance between the obstacle and the vehicle.

[0088] According to some embodiments, in response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle includes at least one of the following operations: generating lane change prompt information; and generating a lane change control instruction.

[0089] According to some embodiments, the obstacle that prevents the vehicle from traveling along the first lane is detected by the second roadside equipment.

[0090] According to an embodiment of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above methods.

[0091] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to enable a computer to execute any one of the above methods.

[0092] According to an embodiment of the present disclosure, a computer program product is also provided, including a computer program, wherein the computer program implements any one of the above methods when executed by a processor.

[0093] According to an embodiment of the present disclosure, there is also provided an autonomous driving vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above methods.

[0094] According to an embodiment of the present disclosure, a vehicle-road cooperative system is also provided, including roadside equipment and the above-mentioned autonomous driving vehicle.

[0095] The vehicle-road cooperative autonomous driving technology adopted in the present disclosure may include one or more of the following technologies: cooperative perception technology, for example, high-precision sensor calibration technology, environmental perception technology, fusion and prediction technology, etc.; high-precision map and high-precision positioning technology, for example, high-precision map, roadside auxiliary positioning technology, etc.; cooperative decision-making and cooperative control technology, for example, intention prediction, game arbitration, guidance and scheduling and other cooperative decision-making, vehicle, facility, human and other cooperative control guidance, etc.; high-reliability and low-latency network communication technology, for example, direct wireless communication technology, cellular mobile communication technology, traffic system integration optimization and wired network transmission technology, etc.; cloud computing technology, for example, edge computing MEC technology, multi-level cloud control platform technology, big data and artificial intelligence platform technology, etc.; functional safety and expected functional safety; Internet of Things IoT technology; network security technology, etc.

[0096] The vehicle-road cooperative autonomous driving realized based on the present disclosure can achieve high-dimensional data, high-dimensional computing power and high-dimensional algorithms.

[0097] Among them, high-dimensional data characteristics can be divided into new categories of high-dimensional data in terms of space, time, type and other dimensional characteristics.

[0098] In terms of space, the main difference is the deployment location and angle selection range of single-vehicle equipment and roadside equipment, which are divided into three subcategories: range, viewing angle, and blind spot. Compared with the data of single-vehicle intelligence, roadside intelligence has another set of spatial data dimensions. In terms of range, the deployment location of the vehicle-mounted equipment is to cover the local range of the single vehicle, and local data can be collected more densely and accurately under the premise of using the same equipment; the deployment location of the roadside equipment is multi-point, so as to carry out global beyond-line-of-sight coverage, and it is possible to invest in more types and quantities of equipment for more local deployment. In terms of viewing angle, the vehicle-mounted equipment is the first perspective, which has certain advantages and is easily affected by the line-of-sight problem; the roadside equipment is installed on the roadside pole, which can have multi-perspective capabilities, and the current deployment form is mostly a bird's-eye view that the vehicle-mounted equipment does not have. In terms of blind spots, the on-board equipment sensors installed on the vehicle have static blind spots and are prone to dynamic occlusion blind spots. Once a vehicle or building causes occlusion and a blind spot occurs, the redundant sensors of the on-board multi-sensor fusion will be blocked at the same time, and the blind spots can only be compensated by reasoning about them through more complex mobile perspectives. On the one hand, roadside sensors are installed higher than traffic participants, and on the other hand, there are conditions for cross-coverage at different angles between multiple points, so they are not easily blocked and cause blind spots. Even if they are blocked, they can be solved by continuous continuous space detection. It is worth mentioning that the vehicle-road collaborative fusion has the advantages of both on-board and roadside equipment. The occlusion is small under cross-detection, and the vehicle-mounted and roadside continuous space detection can also be used to deduce what kind of traffic participants exist within the occlusion range, thereby responding to traffic risks in the occluded area.

[0099] In terms of time, it is mainly the difference between the dynamic and static properties of location and time and the time axis properties of data processing. Compared with the data of single-vehicle intelligence, it has the conditions to become a new high-dimensional data in terms of time. In terms of dynamic and static, the superposition of the location and time attributes of the observation equipment constitutes the difference between dynamic and static observation points. The observation points of the vehicle-mounted equipment are dynamically moving, which has the complexity of the change of observation points, and also has the advantage of measurable changes in the first-person perspective focus point, thus verifying the past and the future in time; the observation points of the roadside equipment are static, and the same position can be observed for a long time to distinguish differences. In terms of time range, the observation and processing of vehicle-mounted equipment are generally based on the real-time data of a single vehicle. Since there are no long-term observation and processing conditions for targets and scenes, historical data may be reflected as a model or as a shorter state information similar to the Markov process; roadside facilities have long-term continuous observation, combined with the data of roadside and cloud infrastructure, and reasoning with longer-term data, and have the ability to predict a period of time in the future in a localized and personalized way.

[0100] In terms of types, it has multi-source and multi-layer data characteristics. The data type of vehicle-side intelligence is single-vehicle sensors, and the advantage is real-time first-hand data. In addition to roadside sensors, roadside intelligence can also widely connect to multi-source and multi-layer data such as traffic control, scene systems, and user systems. It is comprehensive and easy to perform high-level reasoning such as disaster anomalies.

[0101] In terms of equipment capabilities, vehicle-mounted equipment hidden inside the vehicle must be small in size and energy consumption, and resistant to high temperature, vibration and electromagnetics, so its capabilities are limited; roadside equipment is installed on the roadside or in a computing center and can choose different forms and types, so its upper limit is higher.

[0102] From the data characteristics introduced above, we can see that vehicle-road collaboration introduces high-dimensional data with orthogonal attributes to a single vehicle. Based on such data, the differential computing power of the road side and the differential algorithms introduced can benefit both the real-time online processing system and the offline mining and training system. The intelligent system after the integration of vehicle-road collaboration will have new intelligent features and reach a new level of intelligence.

[0103] The high-dimensional computing power includes: in terms of mobility, fixed equipment, multi-layer end-edge-cloud, the end equipment must be shock-proof, heat-proof, electromagnetic-proof, dust-proof, etc., and the cloud equipment has a good IDG environment; in terms of power supply properties, it is powered by the power grid and energy consumption is not limited; in terms of decoupled scheduling, there are various decoupled scheduling forms, such as busy-idle scheduling, multi-point space scheduling, time scheduling, step coordination scheduling, online and offline scheduling; in terms of communication, there is wireless communication between vehicles and roads, and wired transmission between roads and clouds.

[0104] With high-dimensional data, vehicle-road collaboration also brings the possibility of stronger computing power, which also provides a wider space for algorithms and mechanisms. The characteristics of computing power can be divided into several categories: mobility and power properties, decoupled scheduling properties, and communication properties.

[0105] In terms of mobility, the computing power of a single-vehicle intelligence requires on-board mobility, so it has the benefits of mobile front-line computing, but also has limitations on mobile computing power. The computing power of roadside intelligence is fixed, even at the edge of the road. The network is wired, so the conditions of the edge computer room and data center are more favorable, so the upper limit of computing power is high and the degree of freedom is large.

[0106] In terms of power supply properties, the computing power of single-vehicle intelligence uses batteries, which limits energy consumption and thus computing power; roadside intelligence is powered by the power grid, so energy consumption is not limited and it is possible to use powerful computing power.

[0107] In terms of decoupled scheduling, the computing power of a single vehicle's intelligence is focused and bound within the on-board computing resources. It cannot expand even when there are more computing demands during busy times, nor can it shrink or transfer computing demands during idle times. The computing power of roadside intelligence can be decoupled from the computing infrastructure, so it can perform spatial scheduling between resource units according to busy and idle times, time scheduling for peak and trough times, scheduling for online and offline processing, and scheduling for persistence and restoration. Vehicle-road collaboration integrates the capabilities of single vehicle intelligence and roadside intelligence, and can combine the dedicated computing power bound to the vehicle with the MEC and cloud computing power of the roadside plus new infrastructure to provide flexible computing power that takes into account both dedicated and shared computing power.

[0108] In terms of communication, the communication network of the intelligent computing power of a single vehicle is divided into two sections: the vehicle and the vehicle-cloud. The vehicle-inside communication network is based on vehicle-mounted Ethernet and CAN, so the communication between vehicle-mounted devices such as sensors and computing units is simple and direct. The vehicle-cloud communication of the intelligent single vehicle is an ordinary mobile operator network that has not been optimized for vehicle-road communication. It has low cost and high penetration rate, but the network communication quality is not reliable enough, the delay is not guaranteed, the bandwidth is not guaranteed, the number of access devices is not guaranteed, and the coverage is not guaranteed. There are two sections of intelligent communication on the road side. The communication between the vehicle and the road is through vehicle-road collaborative communication methods such as LTE-V 5G NR-V2X, which has relatively high reliability, low latency, high bandwidth, and high concurrency characteristics. The communication between the road and the cloud is generally a wired network, and high-speed communication methods such as optical fiber can be used to connect the end, pipe, and cloud into one, providing a flexible computing platform. This makes the platform have a wide range of functions. On the one hand, it can provide support for online collaboration and deep business of construction, management, maintenance, and operation. On the other hand, it can also provide support for offline mining, training, and simulation, and can also help the system build a big data learning and growth system.

[0109] The high-dimensional algorithms include: scenario-based high-precision, that is, it has the participation of infrastructure design and can be processed dynamically; division of labor and service, that is, transportation operator services; global big data, that is, end-edge-cloud fusion big data analysis and processing; collaborative intelligence, that is, multi-party and multi-level collaboration. The algorithm and collaborative characteristics include scenario-based high-precision, division of labor and service, global big data, and collaborative intelligence. 1) In terms of scenario-based high-precision, the algorithms and mechanisms of single-vehicle intelligence rely on reasoning high-level semantics and maps. Since the scenario strategy reasoning capabilities and high-precision maps of autonomous driving have been determined when the models and maps are released in the R&D stage, the strategy processing capabilities are relatively static, and all scenarios are handled with a set of complex scenario strategies; roadside intelligence has the design and participation of infrastructure, and can directly handle the fine-grained scheduling algorithm and coordination mechanism according to the needs of the scenario; vehicle-road collaboration combined with single-vehicle intelligence and roadside intelligence can select more reasonable algorithms and coordination mechanisms for vehicles and roads. 2) In terms of division of labor and service, the ability of a single vehicle's intelligence will complete a closed loop on a single vehicle, which is dedicated and direct; roadside intelligence can provide distributed algorithms and collaborative services to traffic participants in the form of infrastructure, which is flexible and extensive; vehicle-road collaboration can combine single-vehicle intelligence and roadside intelligence to provide a greater degree of freedom in the division of labor and service capabilities. 3) In terms of global big data, single-vehicle intelligent algorithms are real-time processing, with advantages such as low latency, but in terms of the ability to use massive resources, it only has the ability to use offline resources such as maps and models; roadside intelligence has edge-cloud fusion big data analysis and processing capabilities. On the one hand, it can aggregate massive data on roads, vehicles and the environment into clusters, and can use online and offline mining training simulation mechanisms to enable the system to learn and iterate, and provide services through roadside and new infrastructure as well as OTA empowerment to enhance autonomous driving capabilities. 4) In terms of collaborative intelligence, the collaboration of single-vehicle intelligence is one-way and follows pre-set game reasoning; road-side intelligence is a comprehensive level of collaboration, which can be information status collaboration (such as status, events, perception, positioning), intention prediction collaboration (such as trajectory planning interaction), decision-making planning collaboration (such as guidance, scheduling), and control collaboration (such as formation, escape from difficulties). Especially when the participants on the road have not reached 100% high-level autonomous driving penetration rate, as long as they can follow the guidance of road-side intelligence, they can still form a high-level collaborative network; vehicle-road collaboration combines single-vehicle intelligence and road-side intelligence to provide a flexible collaborative intelligence mechanism.

[0110] refer to Figure 6, a block diagram of an electronic device 600 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0111] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0112] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device that can input information to the electronic device 600. The input unit 606 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 607 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 can include but is not limited to a disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0113] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0119] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0121] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A control method for vehicle-road cooperative automatic driving, comprising: In response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane, obtaining driving information on a second lane adjacent to the first lane through a first roadside device; Determining, based on driving information on the second lane, a predicted vehicle speed at which the host vehicle can travel in the second lane after entering the second lane, wherein a traveling direction of the host vehicle in the second lane is the same as a traveling direction of the host vehicle in the first lane; and In response to determining that the predicted vehicle speed is within a preset range, a lane change control of the host vehicle is initiated to enable the host vehicle to move from the first lane to the second lane.

2. The method according to claim 1, wherein: The second lane is in the same direction as the first lane, and the driving information includes a vehicle speed of each of at least one reference vehicle located between the host vehicle and the obstacle on the second lane.

3. The method according to claim 2, wherein: The determining, based on the driving information on the second lane, the predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane comprises: The predicted vehicle speed is determined based on an average of the vehicle speeds of each of the at least one reference vehicle.

4. The method according to claim 1, wherein: The second lane is opposite to the first lane, and the driving information includes a speed of a reference vehicle located in front of the host vehicle and closest to the host vehicle on the second lane, and a first distance between the reference vehicle and the host vehicle along the driving direction. And wherein, determining the predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane based on the driving information in the second lane includes: In response to determining that the first distance is greater than a second distance between the obstacle and the host vehicle, determining a predicted vehicle speed at which the host vehicle can travel in the second lane based on the first distance, the second distance, and the speed of the reference vehicle, The predicted vehicle speed enables the host vehicle to drive back to the first lane from in front of the obstacle before colliding with the reference vehicle.

5. The method according to claim 4, further comprising: In response to determining that the first distance is not greater than a second distance between the obstacle and the host vehicle, the predicted vehicle speed is set to a maximum value or a minimum value.

6. The method according to claim 1, wherein: In response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle includes at least one of the following operations: generating lane change prompt information; and Generate lane change control instructions.

7. The method according to any one of claims 1 to 6, wherein: The obstacle that prevents the vehicle from traveling along the first lane is detected by a second roadside device.

8. A control device for vehicle-road cooperative automatic driving, comprising: A first determining unit is configured to, in response to determining that there is an obstacle in front of the vehicle that prevents the vehicle from traveling along the first lane, obtain driving information on a second lane adjacent to the first lane through a first roadside device; a second determining unit configured to determine, based on driving information on the second lane, a predicted vehicle speed at which the vehicle can travel in the second lane after entering the second lane, wherein a driving direction of the vehicle in the second lane is the same as a driving direction of the vehicle in the first lane; as well as The control unit is configured to initiate lane change control of the vehicle in response to determining that the predicted vehicle speed is within a preset range, so that the vehicle can move from the first lane to the second lane.

9. The device according to claim 8, wherein: The second lane is in the same direction as the first lane, and the driving information includes a vehicle speed of each of at least one reference vehicle located between the host vehicle and the obstacle on the second lane.

10. The device according to claim 9, wherein: The second determining unit includes: The first determination subunit is configured to determine the predicted vehicle speed based on an average of the vehicle speeds of each of the at least one reference vehicle.

11. The device according to claim 8, wherein: The second lane is opposite to the first lane, the driving information includes a speed of a reference vehicle located in front of the host vehicle and closest to the host vehicle on the second lane and a first distance between the reference vehicle and the host vehicle along the driving direction, and wherein the second determining unit includes: a second determining subunit configured to determine, in response to determining that the first distance is greater than a second distance between the obstacle and the host vehicle, a predicted vehicle speed at which the host vehicle can travel in the second lane based on the first distance, the second distance, and a speed of the reference vehicle; The predicted vehicle speed enables the host vehicle to drive back to the first lane from in front of the obstacle before colliding with the reference vehicle.

12. The device according to claim 11, wherein The second determining unit further includes: The third determining subunit is configured to set the predicted vehicle speed to a maximum value or a minimum value in response to determining that the first distance is not greater than a second distance between the obstacle and the host vehicle.

13. The device according to claim 8, wherein: In response to determining that the predicted vehicle speed is within a preset range, initiating lane change control of the vehicle includes at least one of the following operations: generating lane change prompt information; and Generate lane change control instructions.

14. The device according to any one of claims 8 to 13, wherein: The obstacle that prevents the vehicle from traveling along the first lane is detected by a second roadside device.

15. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

18. An autonomous driving vehicle comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

19. A vehicle-road cooperative system, comprising roadside equipment and an autonomous driving vehicle as described in claim 18.

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