Automatic driving control method, device and equipment based on vehicle-road system and medium

Abstract

The invention provides an automatic driving control method, device and equipment based on a vehicle-road system and a medium, and the method comprises the steps: obtaining the state feedback information of each vehicle in a service range to which road side equipment belongs; wherein the state feedback information is used for indicating the state information of the vehicle; when it is determined that a stagnation object exists in the vehicle based on the state feedback information, a roadside decision group is built; wherein the roadside decision-making group comprises the roadside equipment and the intelligent driving vehicles which normally run within the service range; determining the self-limiting condition of the stagnation object through the roadside decision group, and carrying out auxiliary driving on the stagnation object according to the self-limiting condition; wherein the self-limiting condition is used for indicating the fault processing capability of the stagnation object.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and more specifically, to an autonomous driving control method, device, equipment, and medium based on a vehicle-road system. Background Technology

[0002] Current intelligent driving technology mainly focuses on the driving process. To ensure system availability, multiple independent systems are generally used to work together to control the vehicle, so as to minimize the impact of system failure on the intelligent driving vehicle. Common control systems include: intelligent driving system, dynamic perception system, decision and planning system, safety system, entertainment system, communication system, etc.

[0003] To ensure vehicle safety in intelligent driving systems and prevent serious impacts from system malfunctions, the system first activates a local safety backup plan when any part of the control system fails. This ensures basic vehicle controllability and automatically executes emergency actions, such as deceleration or safe stopping, to mitigate potential risks. Simultaneously, the system can be assisted via a remote control terminal, connecting to a technical support center for real-time diagnostics and guidance to help the driver or technicians extricate themselves from high-risk areas until the fault is resolved. In related technologies, local safety backup systems often only provide safety-related capabilities. After a partial system failure, they only guarantee the safety of the autonomous driving terminal. However, due to the limited capabilities they provide, the intelligent driving capabilities of the vehicle in this state differ significantly from those in normal states, resulting in a poorer intelligent driving experience. Summary of the Invention

[0004] This disclosure provides at least one method, apparatus, device, and medium for autonomous driving control based on a vehicle-road system.

[0005] In a first aspect, embodiments of this disclosure provide an automated driving control method based on a vehicle-to-infrastructure system, the method comprising: Obtain status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein, the status feedback information is used to indicate the status information of the vehicles; When it is determined that there is a stationary object in the vehicle based on the status feedback information, a roadside decision group is established; wherein, the roadside decision group includes the roadside equipment and the intelligent driving vehicles that are driving normally within the service area; The self-limitation status of the stationary object is determined by the roadside decision group, and assisted driving is provided to the stationary object based on the self-limitation status; wherein, the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0006] In one optional implementation, obtaining the status feedback information of each vehicle within the service area of ​​the roadside equipment includes: The roadside equipment sends general broadcasts to each vehicle through the base station and specific broadcasts to each vehicle through the roadside unit. The vehicle acquires status feedback information based on the received broadcast; wherein the received broadcast includes the general broadcast and / or the specific broadcast; when the vehicle is stationary, the vehicle returns status feedback information to the base station and the roadside equipment; when the vehicle is moving, the vehicle returns the status feedback information to the equipment that sent the received broadcast.

[0007] In one optional implementation, each vehicle determines whether it is stationary relative to the roadside equipment using the following formula: ; in, To actively control movement speed, The passive movement speed is represented by ▽t, which is the time increment. , p mm This is the state control threshold. s rb Doppler monitoring speed, when s ms When = 0, it indicates that the vehicle is stationary relative to the roadside equipment. s ms When =1, it indicates that the vehicle is in a moving state relative to the roadside equipment.

[0008] In one optional implementation, the step of establishing a roadside decision group when determining, based on the state feedback information, that there is a stationary object in the vehicle includes: Among all normally driving vehicles connected to the roadside equipment, identify target normally driving vehicles whose coverage includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold. The roadside decision group is established based on the roadside equipment and the target normally moving vehicles for the stationary object.

[0009] In one optional implementation, determining the target normally traveling vehicles among all normally traveling vehicles connected to the roadside equipment whose coverage area includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold includes: Through formula Identify the target vehicle as being in normal driving condition; in, c i Indicating intelligent driving vehicles C aIn the diagram, the i-th vehicle is shown, where θ represents the maximum directional angle and d represents the actively controlled movement speed. direction, d i Indicating intelligent driving vehicles c i Active control of movement speed direction, e x ( y i , p m () indicates retrieving element x from list x. i Greater than the same frequency arrangement coefficient p m corresponding y i Worthwhile projects.

[0010] In one optional implementation, the method further includes: After determining that there is a stationary object in the vehicle based on the status feedback information, the location information is extracted from the status feedback information sent by the stationary object to the roadside equipment, and it is determined whether there is a vehicle at the location corresponding to the location information. When it is determined that a vehicle exists at the location corresponding to the location information, the step of building a roadside decision group is executed.

[0011] In one optional implementation, the step of providing assisted driving to the stationary object based on the self-limiting condition includes: When the automatic safety edge condition is met based on the self-limitation of the stagnant object, the stagnant object is automatically controlled through the local safety backup system of the stagnant object.

[0012] In one optional implementation, the step of performing automatic driving control on the stagnant object through the local secure backup system of the stagnant object includes: Based on the stationary object's own detected driving status, the roadside decision group's observation of the stationary object's driving status, and the roadside decision group's observation of the driving status of vehicles surrounding the stationary object, control commands are generated to perform intelligent driving control on the stationary object until the stationary object is in a safe driving state.

[0013] In one optional implementation, the method further includes: The roadside decision-making group sends takeover information and hedging information to the stationary object; wherein, the hedging information carries the driving status of the stationary object observed by the roadside decision-making group, as well as the driving status of the vehicles around the stationary object observed by the roadside decision-making group. When the stationary object passes the verification of the roadside decision-making group based on the takeover information, it executes the step of generating control commands for intelligent driving control of the stationary object based on its own detected driving status, the driving status of the stationary object observed by the roadside decision-making group, and the driving status of the vehicles around the stationary object observed by the roadside decision-making group.

[0014] In one optional implementation, the roadside decision group includes terminal devices; the step of providing assisted driving to the stationary object based on the self-limiting conditions includes: When the self-limitation condition of the stalled object does not meet the automatic safety edge condition, the stalled object is taken over by a terminal device with the same intelligent driving system account as the stalled object.

[0015] In one optional implementation, the step of taking over the stagnant object via a terminal device with the same intelligent driving system account as the stagnant object includes: The available instruction set of the stalled object is validated to obtain the target instruction set; The target instruction set enables data interaction for assisted driving between the terminal device and the stationary object.

[0016] In one optional implementation, the validity conversion of the available instruction set of the stalled object to obtain the target instruction set includes: The terminal device obtains the available instruction set of the local security backup system of the stagnant object, and performs validity conversion on the available instruction set to obtain the target instruction set.

[0017] In one optional implementation, the step of performing validity conversion on the available instruction set to obtain the target instruction set includes: After establishing a connection between the terminal device and the intelligent driving control center of the stationary object, an available instruction set is obtained from the local security backup system of the stationary object through the connection; The terminal device performs validity conversion on the available instruction set according to its own predefined assisted driving control instruction set to obtain the converted available instruction set. The target instruction set is determined based on the converted available instruction set.

[0018] In one optional implementation, determining the target instruction set based on the converted available instruction set includes: The terminal device is used to obtain the set of identifiers generated by the terminal device and the stagnant object during the data interaction process. The union of the converted set of available instructions and the set of identifiers is used to form a set of available instructions and identifiers. The available instruction and identifier set is re-encoded using Huffman coding to generate the target instruction set.

[0019] In one optional implementation, the method further includes: Through formula Calculate the weight of each available instruction in the available instruction set; wherein, f t,i Used to indicate the frequency of instruction usage t i Indicates the instruction type. No intelligent driving level, p tp This is the conversion coefficient for intelligent driving; When it is determined that the weight is not equal to the preset value, the step of re-encoding the available instruction and identifier set using Huffman coding to generate the target instruction set is performed.

[0020] In one optional implementation, the step of taking over the stagnant object via a terminal device with the same intelligent driving system account as the stagnant object includes: The terminal device is used to obtain target object information of the surrounding objects of the stationary object; Based on the target object information, determine the control command for the stagnant object at the next moment, and send the control command to the stagnant object.

[0021] In one optional implementation, obtaining target object information of surrounding objects of the stationary object through the terminal device includes: The terminal device obtains all objects surrounding the stationary object from the roadside decision group; The system filters out surrounding intelligent driving vehicles whose detection coverage meets the requirements from all objects, and determines the target object information based on these surrounding intelligent driving vehicles whose detection coverage meets the requirements.

[0022] In one optional implementation, the step of filtering surrounding intelligent driving vehicles whose detection coverage meets the requirements from all surrounding intelligent driving vehicles includes: Through formula Select nearby intelligent driving vehicles whose detection coverage meets the requirements; among them, w d For the quality of stationary objects,w i The i-th surrounding intelligent driving vehicle c i quality The velocity of the stationary object: , The i-th surrounding intelligent driving vehicle c i speed, This means retrieving the minimum element in list x. x i corresponding The n items with the smallest value nv d,i The quantity, size, frequency, and volume of the obstructing objects in the middle.

[0023] In one optional implementation, determining the control command for the stagnant object at the next moment based on the target object information and sending the control command to the stagnant object includes: Obtain the basic data of the stagnant object; The terminal device determines the control command for the stagnant object at the next moment based on the target object information and the stagnant object's own basic data.

[0024] In one optional implementation, the method further includes: The roadside decision group sends coordinated hedging messages to vehicles in the same frequency vehicle network of the stationary object, so that the vehicles in the same frequency vehicle network can send their own status information and the status information of obstacles detected by the vehicles in the same frequency vehicle network to the local safety backup system of the stationary object in real time.

[0025] In one optional implementation, the cooperative hedging message includes a feedback frequency, and the vehicles in the same-frequency vehicle network send their own status information and the status information of obstacles detected by the vehicles in the same-frequency vehicle network to the local security backup system of the stationary object according to the feedback frequency.

[0026] In one optional implementation, the method further includes: The status feedback information from the stationary object is acquired in real time until the stationary object is in a safe driving state.

[0027] Secondly, embodiments of this disclosure also provide an automated driving control device based on a vehicle-road system, the device comprising: The acquisition unit is used to acquire status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein the status feedback information is used to indicate the status information of the vehicles. A determining unit is configured to establish a roadside decision group when it is determined, based on the state feedback information, that there is a stationary object in the vehicle; wherein the roadside decision group includes the roadside equipment and the intelligent driving vehicles normally driving within the service area; An assisted driving unit is used to determine the self-limitation status of the stationary object through the roadside decision group, and to provide assisted driving for the stationary object based on the self-limitation status; wherein the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0028] Thirdly, embodiments of this disclosure also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any possible implementation of the first aspect, are performed.

[0029] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the first aspect or any possible implementation of the first aspect.

[0030] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the first aspect, or any possible implementation of the first aspect.

[0031] This disclosure provides an autonomous driving control method, device, equipment, and medium based on a vehicle-to-infrastructure (V2I) system. In this embodiment, status feedback information of each vehicle within the service area of ​​the roadside equipment is acquired; wherein the status feedback information is used to indicate the vehicle's status information; when it is determined, based on the status feedback information, that a stationary object exists among the vehicles, a roadside decision group is established; wherein the roadside decision group includes the roadside equipment and normally operating intelligent driving vehicles within the service area; the self-limitation status of the stationary object is determined through the roadside decision group, and assisted driving is provided to the stationary object based on the self-limitation status; wherein the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0032] The technical solution of this application involves roadside equipment actively monitoring the status of intelligent driving vehicles. When some subsystems of the intelligent driving system of an intelligent driving vehicle malfunction, a takeover method is activated, using surrounding intelligent driving vehicles and roadside equipment to provide auxiliary guidance and help it achieve stable and smooth fault handling. This avoids the emergency measures taken by the intelligent driving vehicle after activating the safety backup system and directly adopting emergency avoidance strategies. It improves the stability of the intelligent driving vehicle when some intelligent driving subsystems malfunction, while also enhancing the smoothness of mode switching, so as to ensure user experience and reduce accidents caused by emergency handling.

[0033] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0035] Figure 1 A flowchart of an automated driving control method based on a vehicle-road system provided in an embodiment of this disclosure is shown; Figure 2 A schematic diagram of an autonomous driving control system based on a vehicle-road system provided in an embodiment of this disclosure is shown; Figure 3 A schematic diagram of an automated driving control device based on a vehicle-road system provided in an embodiment of this disclosure is shown; Figure 4 A schematic diagram of an electronic device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0037] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0038] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0039] Research has found that current intelligent driving technology mainly focuses on the driving process. To ensure system availability, multiple independent systems are generally used to control the vehicle in a collaborative manner to minimize the impact of system failures on the intelligent driving vehicle. Common control systems include: intelligent driving system, dynamic perception system, decision and planning system, safety system, entertainment system, and communication system.

[0040] To ensure vehicle safety in intelligent driving systems and prevent serious impacts from system malfunctions, the system first activates a local safety backup plan when any part of the control system fails. This ensures basic vehicle controllability and automatically executes emergency actions, such as deceleration or safe stopping, to mitigate potential risks. Simultaneously, the system can be assisted via a remote control terminal, connecting to a technical support center for real-time diagnostics and guidance to help the driver or technicians extricate themselves from high-risk areas until the fault is resolved. In related technologies, local safety backup systems often only provide safety-related capabilities. After a partial system failure, they only guarantee the safety of the autonomous driving terminal. However, due to the limited capabilities they provide, the intelligent driving capabilities of the vehicle in this state differ significantly from those in normal states, resulting in a poorer intelligent driving experience.

[0041] Based on the above research, this disclosure provides an autonomous driving control method, device, equipment, and medium based on a vehicle-road system. In this embodiment, status feedback information of each vehicle within the service area of ​​the roadside equipment is obtained; wherein, the status feedback information is used to indicate the vehicle's status information; when it is determined, based on the status feedback information, that a stationary object exists among the vehicles, a roadside decision group is established; wherein, the roadside decision group includes the roadside equipment and normally driving intelligent vehicles within the service area; the self-limitation status of the stationary object is determined through the roadside decision group, and assisted driving is performed on the stationary object according to the self-limitation status; wherein, the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0042] The technical solution of this application involves roadside equipment actively monitoring the status of intelligent driving vehicles. When some subsystems of the intelligent driving system of an intelligent driving vehicle malfunction, a takeover method is activated, using surrounding intelligent driving vehicles and roadside equipment to provide auxiliary guidance and help it achieve stable and smooth fault handling. This avoids the emergency measures taken by the intelligent driving vehicle after activating the safety backup system and directly adopting emergency avoidance strategies. It improves the stability of the intelligent driving vehicle when some intelligent driving subsystems malfunction, while also enhancing the smoothness of mode switching, so as to ensure user experience and reduce accidents caused by emergency handling.

[0043] To facilitate understanding of this embodiment, a detailed description of the autonomous driving control method based on a vehicle-to-infrastructure (V2I) system disclosed in this disclosure will be provided first. The executing entity of the autonomous driving control method based on a V2I system provided in this disclosure is generally an electronic device with a certain computing capability. In some possible implementations, this autonomous driving control method based on a V2I system can be implemented by a processor calling computer-readable instructions stored in memory.

[0044] See Figure 1 The diagram shows a flowchart of an automated driving control method based on a vehicle-road system provided in this disclosure. The method includes steps S101 to S103, wherein: S101: Obtain status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein, the status feedback information is used to indicate the status information of the vehicles.

[0045] The roadside equipment includes base stations and roadside units (RSUs), among which status feedback information... It can be represented as: ;in, This indicates a temporary unique ID for the vehicle. Indicates the instantaneous position of the vehicle. To actively control movement speed, Passive movement speed, The Roadside Unit (RSU) is connected to the current vehicle's intelligent driving system. A base station connected to the current vehicle's intelligent driving system.

[0046] S102: When it is determined that there is a stationary object in the vehicle based on the status feedback information, a roadside decision group is established; wherein, the roadside decision group includes roadside equipment and intelligent driving vehicles that are driving normally within the service range.

[0047] After acquiring vehicle status feedback information, the roadside equipment determines whether there is a stalled vehicle. A stalled vehicle refers to a malfunctioning vehicle that cannot fully complete all autonomous driving tasks due to a partial system failure in the autonomous driving system. If a stalled vehicle is identified based on the status feedback information, a roadside decision group is established for that vehicle.

[0048] The Roadside Decision Group (CS) is an intelligent assistance and guidance system composed of the Roadside Unit (RSU) and surrounding intelligent driving vehicles. This system provides intelligent driving assistance sensor interaction data, decision-making and computing power assistance to the stationary object through its own intelligent driving system hardware and software support, and is a temporary support system that helps the stationary object to steadily get out of the dangerous situation.

[0049] S103: Determine the self-limitation status of the stationary object through the roadside decision group, and provide assisted driving for the stationary object based on the self-limitation status; wherein, the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0050] After establishing a roadside decision-making group, the self-limitation status of a stalled vehicle can be determined through this group. This self-limitation status can then be used to assess the stalled vehicle's ability to handle malfunctions. For example, if a stalled vehicle poses an extremely high driving risk and cannot achieve a smooth transition, its self-limitation status can be determined. For example, if the driving risk of the stationary object is low, a smooth transition can be achieved, and in this case, the self-limitation situation can be determined. .

[0051] In the above embodiments, when some subsystems of the intelligent driving system of an intelligent driving vehicle fail, a takeover method is activated, using surrounding intelligent driving vehicles and roadside equipment to provide auxiliary guidance, helping it to achieve stable and smooth fault handling. This avoids the emergency measures taken by the intelligent driving vehicle after activating the safety backup system and directly adopting emergency avoidance strategies. It improves the stability of the intelligent driving vehicle when some intelligent driving subsystems fail, while also enhancing the smoothness of mode switching, so as to ensure user experience and reduce accidents caused by emergency handling.

[0052] In this embodiment of the application, step S101, which involves obtaining the status feedback information of each vehicle within the service area of ​​the roadside equipment, specifically includes the following steps: Step S11: Send a general broadcast to each vehicle through the base station in the roadside equipment, and send a specific broadcast to each vehicle through the roadside unit in the roadside equipment; Step S12: Obtain the status feedback information sent by the vehicle based on the received broadcast; wherein, the received broadcast includes the general broadcast and / or the specific broadcast, when the vehicle is stationary, the vehicle returns the status feedback information to the base station and the roadside equipment, and when the vehicle is moving, the vehicle returns the status feedback information to the equipment that sent the received broadcast.

[0053] Each vehicle determines whether it is stationary relative to the roadside equipment using the following formula: ; in, To actively control movement speed, The passive movement speed is represented by ▽t, which is the time increment. , p mm This is the state control threshold. s rb Doppler monitoring speed, when s ms When = 0, it indicates that the vehicle is stationary relative to the roadside equipment. s ms When =1, it indicates that the vehicle is in a moving state relative to the roadside equipment.

[0054] After the vehicle's intelligent driving system is activated, it establishes a dynamic link with the base station and roadside unit (RSU), and reports intelligent driving information to the intelligent driving control center in real time through the data network of the base station and RSU.

[0055] During operation, the intelligent driving system communicates with the intelligent driving vehicle through base stations and roadside units (RSUs) to provide network and driving assistance services. During operation, the base stations and RSUs periodically broadcast information about available nearby base stations and RSUs to intelligent driving vehicles within their respective service areas. For example... Figure 2 As shown, the base station sends a general broadcast R1 to vehicles within its service range, and the RSU sends a specific broadcast R2 to vehicles within its service range. When any vehicle's intelligent driving system receives the general broadcast R1 and / or the specific broadcast R2, it adjusts its driving based on the vehicle's stationary state relative to the base station and RSU. s ms Generate status feedback information Ga .

[0056] When the vehicle is relatively stationary (i.e.: s ms When =0), the status feedback information will be sent according to the frequency and receiving target requirements in the standard broadcast R1. G a Transmitted to the roadside unit (RSU) and base station during relative vehicle movement (i.e.: s ms When =1), the device receiving the broadcast from the vehicle can return status feedback information, such as... Figure 2 As shown, if the vehicle receives a specific broadcast R2, it can send status feedback information according to the frequency and receiving target requirements in the specific broadcast R2. G a If the vehicle receives the ubiquitous broadcast R1 sent to the base station, it can then relay the status feedback information according to the ubiquitous broadcast R1, the frequency, and the receiving target requirements. G a Send to the base station.

[0057] Among them, the stationary state of any autonomous driving vehicle relative to the base station and RSU s ms Calculated by the intelligent driving system or local security backup system using the following method: .

[0058] In the above formula, s rb The calculation logic for Doppler velocity monitoring can be expressed by a formula: .

[0059] in, For the actual received CCTV broadcast R 1 wavelength, f r1 Broadcast sent by the base station R The actual transmission frequency of 1 The wavelength of the specific broadcast R2 actually received. f r2 Specific broadcast for Roadside Unit (RSU) broadcast messages R 2's actual transmission frequency, c It is the speed of light.

[0060] In this embodiment of the application, when step S102 determines that there is a stationary object in the vehicle based on the state feedback information, the establishment of a roadside decision group specifically includes the following steps: Step S21: Among all normally driving vehicles connected to the roadside equipment, identify the target normally driving vehicles whose coverage includes the location of the stationary object and whose straight-line distance to the stationary object is less than a preset threshold. Step S22: Based on the roadside equipment and the target normally moving vehicles, establish the roadside decision group for the stationary object.

[0061] The base station and roadside unit (RSU) receive the status feedback message. G a Then, the stalled object is determined by the kernel carried in the status feedback message. This process is repeated when the base station and the Roadside Unit (RSU) parse the status feedback message. G a The stationary state of the vehicle relative to the base station and RSU s ms When it is a relative movement (i.e.: s ms =1), the base station and the roadside unit (RSU) rely on the status feedback message G a The roadside unit connected to the current intelligent driving system in RSU id Base stations connected to current intelligent driving systems NR id They work together, starting from the roadside units. RSU id Among all linked intelligent driving vehicles Ca (excluding stationary vehicles and the currently stationary object itself), select the target normally driving vehicle whose coverage includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold.

[0062] Specifically, it can be done through formulas Identify the target vehicle as being in normal driving condition; in, c i Indicating intelligent driving vehicles C a In the diagram, the i-th vehicle is shown, where θ represents the maximum directional angle and d represents the actively controlled movement speed. direction, d i Indicating intelligent driving vehicles c i Active control of movement speed direction, e x ( y i , p m () indicates retrieving element x from list x. i Greater than the same frequency arrangement coefficient p m correspondingy i Worthwhile projects.

[0063] Then, a roadside decision group can be built based on the roadside equipment and the target normally moving vehicles, for example, such as... Figure 2 The roadside decision-making group shown.

[0064] In this embodiment of the application, the method further includes the following steps: First, after determining that there is a stationary object in the vehicle based on the status feedback information, the location information is extracted from the status feedback information sent by the stationary object to the roadside equipment, and it is determined whether there is a vehicle at the location corresponding to the location information. Secondly, when it is determined that a vehicle exists at the location corresponding to the location information, the step of setting up a roadside decision group is executed.

[0065] After determining the roadside decision group CS, the roadside unit RSU id Send inspection message for stagnant objects to roadside decision group CS M vs = [ Ga The roadside decision-making group (CS) received an inspection message for the stalled object. M vs Then, it invokes its own sensor to check the object's message. M vs The location information is then used to verify the self-limitation status of the suspended entities, thus obtaining the self-limitation status of the entities being verified. When verifying this location information L id When there are no vehicles, A value of -1 indicates that the roadside decision group (CS) is sent to the roadside unit. RSU id Then, exit.

[0066] When location information is determined L id When a vehicle is present and stationary, its intelligent driving risk status is assessed for abnormality. If an abnormality is determined (i.e., the safe driving model judges the intelligent vehicle's driving risk level to be extremely high, making a smooth transition impossible), self-limitation is implemented. If it is 1, otherwise, it is a self-limiting case. A value of 0 indicates that the safe driving model determines the driving risk of the intelligent vehicle to be low, allowing for a smooth transition. Specifically, the safe driving model can determine whether an anomaly is present based on sensor data and motion status.

[0067] In this embodiment of the application, step S103, which provides assisted driving for the stationary object based on the self-limiting condition, specifically includes the following steps: Step S31: When the self-limitation condition of the stagnant object meets the automatic safety edge condition, the stagnant object is automatically controlled through the local safety backup system of the stagnant object.

[0068] like Figure 2 As shown in section 3.1, when the self-limiting condition of a stationary object reaches the self-control safety edge condition, i.e., the self-limiting condition... When the value is 1, autonomous driving can be taken over by combining the roadside decision group with the local safety backup system of the stationary object, so that the local safety backup system of the stationary object can realize autonomous driving control of the stationary object based on the status information observed by the roadside role group.

[0069] In this embodiment of the application, the above steps involve performing automatic driving control on the stalled object through the local security backup system of the stalled object, including: Based on the stationary object's own detected driving status, the roadside decision group's observation of the stationary object's driving status, and the roadside decision group's observation of the driving status of vehicles surrounding the stationary object, control commands are generated to perform intelligent driving control on the stationary object until the stationary object is in a safe driving state.

[0070] Here, if the roadside decision group (CS) takes over, the CS will determine the driving status based on the information sent by the stationary object. The driving status of stationary objects observed by the joint roadside decision group (CS). In addition, the roadside decision group (CS) observes the driving status of vehicles around the stationary object. Using the intelligent driving assistance capabilities of the roadside decision group (CS), the stationary object is guided to assist it in self-limiting.

[0071] At this point, the local safety backup system of the stationary object combines sensor data from the roadside decision group (CS), recommended driving instructions from the roadside decision group (CS), and sensor data collected by its own normal system to generate intelligent driving instructions using the fastest safe disengagement mechanism, thereby realizing autonomous driving control of the stationary object and guiding the stationary object to release its self-limiting state.

[0072] In this embodiment of the application, the method further includes the following steps: The roadside decision-making group sends takeover information and hedging information to the stationary object; wherein, the hedging information carries the driving status of the stationary object observed by the roadside decision-making group, as well as the driving status of the vehicles around the stationary object observed by the roadside decision-making group. When the stationary object passes the verification of the roadside decision-making group based on the takeover information, it executes the step of generating control commands for intelligent driving control of the stationary object based on its own detected driving status, the driving status of the stationary object observed by the roadside decision-making group, and the driving status of the vehicles around the stationary object observed by the roadside decision-making group.

[0073] Here, when the self-limitation condition based on the stagnant object satisfies the automatic safe edge condition, such as Figure 2 As shown, the roadside decision group CS is determined through roadside units. RSU id Send takeover information to the local secure backup system of the stalled object M to Simultaneously, a hedging message is sent to the intelligent driving system of the stationary object. M toh To enable the intelligent driving system of the stationary object to execute the guidance and takeover instructions of the roadside decision group (CS). O toh .

[0074] When the roadside decision group (CS) determines that the self-limiting condition of the stagnant object has reached the self-control safety edge condition, that is: When =1, the roadside decision group CS passes through the roadside unit. RSU id Send takeover information to the local secure backup system of the stalled object: M to =[ t to, r s, v s ].in, t to A specific identifier representing the type of message being received, such as t to =TO, r s This represents a random checksum. v s The identity verification value represented by the random check code is calculated using the following logic: v s =E( r s ,ca p ),in ca p For the CA certificate private key, E(r s ,ca p ) indicates the use ca p right r s Sign the agreement.

[0075] Meanwhile, the roadside decision-making group (CS) attempts to send a hedging message to the local security backup system of the stationary object's intelligent driving system. M toh : M toh =[ t toh ,r s ,v s ,O toh ],in, t toh This indicates the type of hedging message, which is a specific identifier. O toh The local security backup system of the stalled object executes the takeover command for the roadside decision group (CS) after completing the identity check of the CS based on the received information. O toh Among them, the takeover instructions. O toh The system carries the driving status of the stationary object observed by the roadside decision-making group, as well as the driving status of vehicles surrounding the stationary object observed by the roadside decision-making group. The local security backup system then takes over according to the guided takeover command. O toh The provided driving status, combined with normal system detection data, allows the vehicle to complete driving tasks automatically using emergency driving mode until the self-limitation condition of the stationary object no longer reaches the self-control safety edge condition. Then, it enters the subsequent takeover process, that is, the stationary object is taken over by a terminal device with the same intelligent driving system account as the stationary object.

[0076] In this embodiment of the application, the roadside decision-making group further includes a terminal device; the above steps for assisting driving the stationary object based on the self-limitation situation specifically include: When the self-limitation condition of the stalled object does not meet the automatic safety edge condition, the stalled object is taken over by a terminal device with the same intelligent driving system account as the stalled object.

[0077] When the stopped object does not meet the autonomous control safety edge condition, the roadside decision group (CS) sends a takeover command through the mobile operator's driver assistance control center to the terminal device under the same account as the intelligent driving system of the currently stopped object. O ed This allows for the takeover of the stalled object.

[0078] Specifically, such as Figure 2 As shown in section 3.2, when the self-controlled safety edge condition is not met, i.e.: When the value is ≠1, the roadside decision group (CS) sends a takeover command via the 5G network through the mobile operator's driver assistance control center to the terminal device under the same account as the currently stalled intelligent driving system. O ed =[ t ed , r s , v s ], t ed This indicates the misaligned connection message type, which is a specific identifier.

[0079] "Takeover" means that the terminal device will then assist the stopped object based on sensor data, computing power support, and suggested driving instructions provided by the roadside decision group (CS).

[0080] In this embodiment of the application, the above steps involve taking over the stagnant object through a terminal device with the same intelligent driving system account as the stagnant object, specifically including: First, the available instruction set of the stalled object is validated to obtain the target instruction set; Secondly, data interaction for assisted driving is achieved between the terminal device and the stationary object based on the target instruction set.

[0081] If a takeover is initiated, the roadside decision group (CS) receives the takeover instruction from the intelligent driving control center to the terminal devices under the same account. O ed Then, the terminal device uses NFC and RFID to send a pause message to the stationary object. M ed It then performs a replacement of core auxiliary commands (i.e., performs validity conversion on the available command set) to prepare for the replacement of subsequent control commands. The local security backup system receives a pause message. M edAfterwards, a certificate and login account identity check is performed to ensure that the device is issued by a trusted organization and that the login account is consistent. Once the check is passed, the local security backup system of the stalled object establishes a high-speed data transmission connection with the terminal device and turns on the core auxiliary command switch to receive control commands from the HarmonyOS terminal.

[0082] After the connection is established, the terminal device performs validity conversion on the available instruction set of the stationary object to obtain the target instruction set, thereby realizing data interaction for assisted driving between the terminal device and the stationary object through the target instruction set.

[0083] In this embodiment of the application, the above steps perform validity conversion on the available instruction set of the stalled object to obtain the target instruction set, specifically including the following steps: The available instruction set of the local security backup system of the stalled object is obtained through the terminal device, and the validity conversion of the available instruction set is performed to obtain the target instruction set.

[0084] After the connection is established, the terminal device first obtains the available instruction set from the intelligent driving control center's local security backup system for the stationary object. Then, the available instruction set is validated to obtain the target instruction set.

[0085] By performing validity conversion on the available instruction set, it is possible to filter out the instructions that the local security backup system can use for stalled objects.

[0086] In this embodiment of the application, the above steps perform validity conversion on the available instruction set to obtain the target instruction set, specifically including the following steps: First, after establishing a connection between the terminal device and the intelligent driving control center of the stationary object, the available instruction set is obtained from the local security backup system of the stationary object through the connection.

[0087] Secondly, the terminal device performs validity conversion on the available instruction set according to its own predefined assisted driving control instruction set to obtain the converted available instruction set; Next, the target instruction set is determined based on the converted available instruction set.

[0088] After the connection is established, the terminal device first obtains the available instruction set from the intelligent driving control center's local security backup system for the stationary object. Then, based on the predefined set of assisted driving control instructions of the terminal device itself... O h The available instruction set is validated to obtain the validated available instruction set: Then, based on the obtained converted and usable instruction set... Or Determine the target instruction set.

[0089] In this embodiment of the application, the above steps determine the target instruction set based on the converted available instruction set, specifically including the following steps: First, the set of identifiers generated by the terminal device and the stagnant object during the data interaction process is obtained through the terminal device; Secondly, the union of the converted set of available instructions and the set of identifiers is used to form a set of available instructions and identifiers; Next, the available instructions and identifier set are re-encoded using Huffman coding to generate the target instruction set.

[0090] After the connection is established, the terminal device first obtains the available instruction set from the intelligent driving control center's local security backup system for the stationary object. Then, based on the predefined set of assisted driving control instructions of the terminal device itself... O h The available instruction set is validated to obtain the validated available instruction set: Next, obtain the set of identifiers from all data interaction processes. F ex Then, the union of the converted set of available instructions and the set of identifiers is taken to form the set of available instructions and identifiers: E = O r ∪ F ex Then, following the Huffman coding method, the available instructions and identifier set are re-encoded to obtain the target instruction set, i.e., the available instructions and identifier set E'.

[0091] When subsequent terminal devices interact with the local safety backup system of the stationary object to perform assisted driving data, they can use the available set of instructions and identifiers E' to replace the corresponding instructions and identifiers before sending them, and then perform reverse encoding restoration after receiving them, so as to reduce the data transmission pressure and reduce data latency.

[0092] In this embodiment of the application, the method further includes the following steps: Through formula Calculate the weight of each available instruction in the available instruction set; wherein, f t,i Used to indicate the frequency of instruction usage t i Indicates the instruction type. No intelligent driving level, p tp This is the conversion coefficient for intelligent driving; When it is determined that the weight is not equal to the preset value, the step of re-encoding the available instruction and identifier set using Huffman coding to generate the target instruction set is performed.

[0093] Here, the weight of each available instruction can be calculated. When the weight of an available instruction is 0, the available instruction and its corresponding identifier are not re-encoded. When the weight of an available instruction is not 0, the step of re-encoding the available instructions and identifier set using Huffman coding to generate the target instruction set is performed.

[0094] Here, 0 represents a preset value. When the weight is 0, it indicates that the usage frequency of the available instruction is 0; when the weight is not 0, it indicates that the usage frequency of the available instruction is not 0. In other words, the technical solution of this application chooses to re-encode the available instructions with a usage frequency of non-zero and their corresponding identifiers.

[0095] In this embodiment of the application, the above steps involve taking over the stagnant object through a terminal device with the same intelligent driving system account as the stagnant object, specifically including the following steps: First, obtain the target object information of the surrounding objects of the stationary object through the terminal device; Next, based on the target object information, the control command for the stagnant object at the next moment is determined, and the control command is sent to the stagnant object.

[0096] Here, the terminal device acquires the basic data of the stationary object; and based on the target object information and the stationary object's basic data, determines the control command for the stationary object at the next moment. The target object information includes: the external object's orientation, motion, and quantity information.

[0097] In specific implementation, such as Figure 2 As shown in Figure 6, the terminal device plans the next moment's instruction based on the received target object information and the stationary object's own basic data, and sends it back to the stationary object's local safety backup system. The stationary object's local safety backup system combines the sensor data of the roadside decision group CS, the control instruction for the stationary object determined by the terminal device for the next moment, and the sensor data collected by its own normal system, and generates intelligent driving instructions using a steady and safe disengagement mechanism to guide the intelligent driving vehicle to gradually release the self-limitation.

[0098] In this embodiment of the application, the above steps involve obtaining target object information of surrounding objects of the stationary object through a terminal device, specifically including the following steps: First, obtain all objects around the stationary object from the roadside decision group through the terminal device; Next, based on all the objects, surrounding intelligent driving vehicles with detection coverage that meet the requirements are selected, and target object information is determined based on the objects perceived by the surrounding intelligent driving vehicles with detection coverage that meet the requirements; wherein, it can be determined by formula Select nearby intelligent driving vehicles whose detection coverage meets the requirements; among them, w d For the quality of stationary objects, w i The i-th surrounding intelligent driving vehicle c i quality The velocity of the stationary object: , The i-th surrounding intelligent driving vehicle c i speed, This means retrieving the minimum element in list x. x i corresponding The n items with the smallest value nv d,i The quantity, size, frequency, and volume of the obstructing objects in the middle.

[0099] After the terminal equipment has undergone takeover, such as Figure 2 As shown in Figure 5, the terminal device, based on the current state of surrounding objects detected by the vehicle's built-in sensors, combined with the same-frequency vehicle arrangement matrix on the road surface, and in conjunction with the roadside unit... RSU id Provided surrounding intelligent driving vehicles C a With real-time dynamics D C Finally, the target object information and external object are determined. c o and quantity information n o .

[0100] In practice, after the terminal equipment has undergone the takeover, the terminal equipment first connects with the roadside unit. RSU id Establish connections and obtain information about nearby intelligent driving vehicles. C a All real-time dynamics D C and surrounding intelligent driving vehicles C a Establish a temporary link and update the dynamic surrounding intelligent driving vehicles in real time based on the temporary link. C a Real-time dynamics D C At the same time, it relies on surrounding intelligent driving vehicles.C a Acquiring non-intelligent driving vehicles C no Real-time dynamics (including information on various obstacles) D CN The sum of surrounding objects is: C= C a ∪ C no The corresponding real-time dynamic D= D C ∪ D CN Subsequently, the terminal device performs spatial modeling based on the orientation and motion information of all objects, and uses the results of spatial modeling to identify vehicles within the surrounding efficient assistance area as external objects. c o As an external object (selecting surrounding intelligent driving vehicles whose detection coverage meets the requirements), the external object c o The calculation method is as follows: .

[0101] In the above formula, ,in, f d For the frequency of communication signals in the transmission channel, n (d,i) The number of obstructions between the two. v (d,i,j) Let be the volume of the j-th occluder.

[0102] n o The number of external objects is calculated using the following logic: in, For safety reasons, s t The median size of the data to be transmitted. c ac To meet the overall computing power requirements, This represents the idle supporting computing power of the i-th node.

[0103] In this embodiment of the application, the method further includes: The roadside decision group sends coordinated hedging messages to vehicles in the same-frequency vehicle network of the stationary object, enabling the vehicles in the same-frequency vehicle network to send their own status information and the status information of obstacles detected by the vehicles in the same-frequency vehicle network to the local safety backup system of the stationary object in real time. The coordinated hedging message includes a feedback frequency, and the vehicles in the same-frequency vehicle network send their own status information and the status information of obstacles detected by the vehicles in the same-frequency vehicle network to the local safety backup system of the stationary object according to the feedback frequency.

[0104] In the embodiments of this application, such as Figure 2 As shown in Figure 7, after determining the self-limitation status of the stationary object through the roadside decision group, the roadside decision group (CS) can send a cooperative hedging message to the vehicles in the same frequency vehicle network of the stationary object. M a The same frequency vehicle deployment network received a coordinated hedging message. M a =[ F a After that, according to the coordinated hedging message M a The return frequency in F a The system sends real-time updates of the vehicle's status to the local security backup system of the stationary object. D (C,i) This vehicle detected a non-intelligent driving vehicle. C (no,i) Real-time dynamics (including information on various obstacles) D (CN,i) .

[0105] The calculation logic for the feedback frequency is as follows: ,in, For safety reasons, s t This represents the median size of the transmitted data. c ac To meet the overall computing power requirements, This represents the idle supporting computing power of the i-th node. d fm This indicates the reference frequency for the communication protocol.

[0106] In this embodiment of the application, the method further includes: The status feedback information from the stationary object is acquired in real time until the stationary object is in a safe driving state.

[0107] Here, the roadside unit (RSU) continuously monitors specific broadcasts. R The receipt message for item 2 will continue until the stagnant object automatically lifts the restriction.

[0108] As described above, the technical solution of this application utilizes surrounding intelligent driving vehicles, roadside units (RSUs), mobile base stations, and terminal equipment to jointly assist and guide the local safety backup system of the intelligent driving system when the intelligent driving terminal malfunctions, helping it to get out of the fault state. This method uses surrounding intelligent driving vehicles, roadside units (RSUs), and mobile terminal operating systems to assist the stationary object, improving the smoothness of the malfunctioning vehicle's exit from the safe state and achieving a steady switchover.

[0109] This application's technical solution proactively detects faulty vehicles by coordinating with base stations and roadside units in conjunction with roadside decision-making groups. Utilizing nearby devices avoids time-consuming data transmission and the concentration of large amounts of computation, thus improving fault detection efficiency. This application's technical solution uses an instruction validity conversion method to remove invalid and unnecessary instructions and simplifies coding, making the interaction between the HarmonyOS terminal and the local security backup system of the stalled object more efficient, reducing data transmission pressure and lowering data latency. This application's technical solution uses a terminal device-based takeover method, through deep collaboration between the terminal device, roadside units, and base stations, to dynamically assess the surrounding traffic environment and adjust the working mode according to different traffic conditions, leveraging the terminal device's capabilities to assist the local security backup system of the stalled object. This application's technical solution uses a method for determining the number and types of external objects by the terminal device. By determining the number and types of external objects, it selects efficient external objects for assistance, avoiding the increase of invalid data processing pressure from inefficient external object data and efficiently utilizing limited computing power.

[0110] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0111] Based on the same inventive concept, this disclosure also provides an autonomous driving control device based on a vehicle-road system, which corresponds to the autonomous driving control method based on a vehicle-road system. Since the principle of the device in this disclosure is similar to the above-mentioned autonomous driving control method based on a vehicle-road system in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0112] Reference Figure 3 The diagram shown is a schematic of an automated driving control device based on a vehicle-road system according to an embodiment of this disclosure. The device includes: an acquisition unit 10, a determination unit 20, and an assisted driving unit 30; wherein, The acquisition unit 10 is used to acquire status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein the status feedback information is used to indicate the status information of the vehicles. The determining unit 20 is used to establish a roadside decision group when it is determined that there is a stationary object in the vehicle based on the status feedback information; wherein, the roadside decision group includes the roadside equipment and the intelligent driving vehicles that are driving normally within the service area; The assisted driving unit 30 is used to determine the self-limitation status of the stationary object through the roadside decision group, and to provide assisted driving for the stationary object based on the self-limitation status; wherein the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0113] In one possible implementation, the acquiring unit is further configured to: The roadside equipment sends general broadcasts to each vehicle through the base station and specific broadcasts to each vehicle through the roadside unit. The vehicle acquires status feedback information based on the received broadcast; wherein the received broadcast includes the general broadcast and / or the specific broadcast; when the vehicle is stationary, the vehicle returns status feedback information to the base station and the roadside equipment; when the vehicle is moving, the vehicle returns the status feedback information to the equipment that sent the received broadcast.

[0114] In one possible implementation, the acquiring unit is further configured to: The following formula is used to determine whether a device is stationary relative to the roadside equipment: ; in, To actively control movement speed, The passive movement speed is represented by ▽t, which is the time increment. , p mm This is the state control threshold. s rb Doppler monitoring speed, when s ms When = 0, it indicates that the vehicle is stationary relative to the roadside equipment. s ms When =1, it indicates that the vehicle is in a moving state relative to the roadside equipment.

[0115] In one possible implementation, the determining unit is further configured to: Among all normally driving vehicles connected to the roadside equipment, identify target normally driving vehicles whose coverage includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold. The roadside decision group is established based on the roadside equipment and the target normally moving vehicles for the stationary object.

[0116] In one possible implementation, the determining unit is further configured to: Through formula Identify the target vehicle as being in normal driving condition; in, c i Indicating intelligent driving vehicles C a In the diagram, the i-th vehicle is shown, where θ represents the maximum directional angle and d represents the actively controlled movement speed. direction, d i Indicating intelligent driving vehicles c i Active control of movement speed direction, e x ( y i , p m () indicates retrieving element x from list x. i Greater than the same frequency arrangement coefficient p m corresponding y i Worthwhile projects.

[0117] In one possible implementation, the device is further used for: After determining that there is a stationary object in the vehicle based on the status feedback information, the location information is extracted from the status feedback information sent by the stationary object to the roadside equipment, and it is determined whether there is a vehicle at the location corresponding to the location information. When it is determined that a vehicle exists at the location corresponding to the location information, the step of building a roadside decision group is executed.

[0118] In one possible implementation, the driver assistance unit is further configured to: When the automatic safety edge condition is met based on the self-limitation of the stagnant object, the stagnant object is automatically controlled through the local safety backup system of the stagnant object.

[0119] In one possible implementation, the driver assistance unit is further configured to: Based on the stationary object's own detected driving status, the roadside decision group's observation of the stationary object's driving status, and the roadside decision group's observation of the driving status of vehicles surrounding the stationary object, control commands are generated to perform intelligent driving control on the stationary object until the stationary object is in a safe driving state.

[0120] In one possible implementation, the device is further used for: The roadside decision-making group sends takeover information and hedging information to the stationary object; wherein, the hedging information carries the driving status of the stationary object observed by the roadside decision-making group, as well as the driving status of the vehicles around the stationary object observed by the roadside decision-making group. When the stationary object passes the verification of the roadside decision-making group based on the takeover information, it executes the step of generating control commands for intelligent driving control of the stationary object based on its own detected driving status, the driving status of the stationary object observed by the roadside decision-making group, and the driving status of the vehicles around the stationary object observed by the roadside decision-making group.

[0121] In one possible implementation, the roadside decision group includes terminal equipment; the assisted driving unit is further configured to: When the self-limitation condition of the stalled object does not meet the automatic safety edge condition, the stalled object is taken over by a terminal device with the same intelligent driving system account as the stalled object.

[0122] In one possible implementation, the driver assistance unit is further configured to: The available instruction set of the stalled object is validated to obtain the target instruction set; The target instruction set enables data interaction for assisted driving between the terminal device and the stationary object.

[0123] In one possible implementation, the driver assistance unit is further configured to: The terminal device obtains the available instruction set of the local security backup system of the stagnant object, and performs validity conversion on the available instruction set to obtain the target instruction set.

[0124] In one possible implementation, the driver assistance unit is further configured to: After a connection is established between the terminal device and the intelligent driving control center of the stationary object, an available instruction set is obtained from the local security backup system of the stationary object through the connection.

[0125] The terminal device performs validity conversion on the available instruction set according to its own predefined assisted driving control instruction set to obtain the converted available instruction set. The target instruction set is determined based on the converted available instruction set.

[0126] In one possible implementation, the driver assistance unit is further configured to: The terminal device is used to obtain the set of identifiers generated by the terminal device and the stagnant object during the data interaction process. The union of the converted set of available instructions and the set of identifiers is used to form a set of available instructions and identifiers. The available instruction and identifier set is re-encoded using Huffman coding to generate the target instruction set.

[0127] In one possible implementation, the device is further used for: Through formula Calculate the weight of each available instruction in the available instruction set; wherein, f t,i Used to indicate the frequency of instruction usage t i Indicates the instruction type. No intelligent driving level, p tp This is the conversion coefficient for intelligent driving; When it is determined that the weight is not equal to the preset value, the step of re-encoding the available instruction and identifier set using Huffman coding to generate the target instruction set is performed.

[0128] In one possible implementation, the driver assistance unit is further configured to: The terminal device is used to obtain target object information of the surrounding objects of the stationary object; Based on the target object information, determine the control command for the stagnant object at the next moment, and send the control command to the stagnant object.

[0129] In one possible implementation, the driver assistance unit is further configured to: The terminal device obtains all objects surrounding the stationary object from the roadside decision group; The system filters out surrounding intelligent driving vehicles whose detection coverage meets the requirements from all objects, and determines the target object information based on these surrounding intelligent driving vehicles whose detection coverage meets the requirements.

[0130] In one possible implementation, the driver assistance unit is further configured to: Through formula Select nearby intelligent driving vehicles whose detection coverage meets the requirements; among them, w d For the quality of stationary objects, w i The i-th surrounding intelligent driving vehicle c i quality The velocity of the stationary object: , The i-th surrounding intelligent driving vehicle c i speed, This means retrieving the minimum element in list x.x i corresponding The n items with the smallest value nv d,i The quantity, size, frequency, and volume of the obstructing objects in the middle.

[0131] In one possible implementation, the driver assistance unit is further configured to: Obtain the basic data of the stagnant object; The terminal device determines the control command for the stagnant object at the next moment based on the target object information and the stagnant object's own basic data.

[0132] In one possible implementation, the device is further used for: The roadside decision group sends coordinated hedging messages to vehicles in the same frequency vehicle network of the stationary object, so that the vehicles in the same frequency vehicle network can send their own status information and the status information of obstacles detected by the vehicles in the same frequency vehicle network to the local safety backup system of the stationary object in real time.

[0133] In one possible implementation, the cooperative hedging message includes a feedback frequency, and the vehicles in the same-frequency vehicle network send their own status information and the status information of obstacles detected by the vehicles in the same-frequency vehicle network to the local security backup system of the stationary object according to the feedback frequency.

[0134] In one possible implementation, the device is further configured to: acquire the status feedback information from the stationary object in real time until the stationary object is in a safe driving state.

[0135] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0136] Corresponding to Figure 1 In the vehicle-to-infrastructure (V2I) autonomous driving control method, this disclosure also provides an electronic device 400, such as... Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including: The system includes a processor 41, a memory 42, and a bus 43. The memory 42 stores execution instructions and includes main memory 421 and external memory 422. The main memory 421, also called internal memory, temporarily stores the computational data in the processor 41, as well as data exchanged with external memory such as a hard disk. The processor 41 exchanges data with the external memory 422 through the main memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through the bus 43, causing the processor 41 to execute the following instructions: Obtain status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein, the status feedback information is used to indicate the status information of the vehicles; When it is determined that there is a stationary object in the vehicle based on the status feedback information, a roadside decision group is established; wherein, the roadside decision group includes the roadside equipment and the intelligent driving vehicles that are driving normally within the service area; The self-limitation status of the stationary object is determined by the roadside decision group, and assisted driving is provided to the stationary object based on the self-limitation status; wherein, the self-limitation status is used to indicate the stationary object's ability to handle faults.

[0137] This disclosure also provides a computer-readable storage medium storing a computer program. When a processor executes the computer program, it performs the steps of the autonomous driving control method based on a vehicle-to-infrastructure system described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0138] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the vehicle-road system-based autonomous driving control method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0139] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0143] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. An automated driving control method based on a vehicle-road system, characterized in that, The method includes: Obtain status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein, the status feedback information is used to indicate the status information of the vehicles; When it is determined that there is a stationary object in the vehicle based on the status feedback information, a roadside decision group is established; wherein, the roadside decision group includes the roadside equipment and the intelligent driving vehicles that are driving normally within the service area; The self-limitation status of the stationary object is determined by the roadside decision group, and assisted driving is provided to the stationary object based on the self-limitation status; wherein, the self-limitation status is used to indicate the stationary object's ability to handle faults.

2. The method according to claim 1, characterized in that, The acquisition of status feedback information for each vehicle within the service area of ​​the roadside equipment includes: The roadside equipment sends general broadcasts to each vehicle through the base station and specific broadcasts to each vehicle through the roadside unit. The vehicle acquires status feedback information based on the received broadcast; wherein the received broadcast includes the general broadcast and / or the specific broadcast; when the vehicle is stationary, the vehicle returns status feedback information to the base station and the roadside equipment; when the vehicle is moving, the vehicle returns the status feedback information to the equipment that sent the received broadcast.

3. The method according to claim 2, characterized in that, Each vehicle determines whether it is stationary relative to the roadside equipment using the following formula: ； in, To actively control movement speed, The passive movement speed is represented by ▽t, which is the time increment. , p mm This is the state control threshold. s rb Doppler monitoring speed, when s ms When = 0, it indicates that the vehicle is stationary relative to the roadside equipment. s ms When =1, it indicates that the vehicle is in a moving state relative to the roadside equipment.

4. The method according to claim 1, characterized in that, When it is determined, based on the status feedback information, that there is a stationary object in the vehicle, a roadside decision-making group is established, including: Among all normally driving vehicles connected to the roadside equipment, identify target normally driving vehicles whose coverage includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold. The roadside decision group is established based on the roadside equipment and the target normally moving vehicles for the stationary object.

5. The method according to claim 4, characterized in that, The step of determining, among all normally traveling vehicles connected to the roadside equipment, target normally traveling vehicles whose coverage area includes the location of the stationary object and whose straight-line distance from the stationary object is less than a preset threshold includes: Through formula Identify the target vehicle as being in normal driving condition; in, c i Indicating intelligent driving vehicles C a In the diagram, the i-th vehicle is shown, where θ represents the maximum directional angle and d represents the actively controlled movement speed. direction, d i Indicating intelligent driving vehicles c i Active control of movement speed direction, e x ( y i , p m () indicates retrieving element x from list x. i Greater than the same frequency arrangement coefficient p m corresponding y i Worthwhile projects.

6. The method according to claim 1, characterized in that, The method further includes: After determining that there is a stationary object in the vehicle based on the status feedback information, the location information is extracted from the status feedback information sent by the stationary object to the roadside equipment, and it is determined whether there is a vehicle at the location corresponding to the location information. When it is determined that a vehicle exists at the location corresponding to the location information, the step of building a roadside decision group is executed.

7. The method according to claim 1, characterized in that, The step of providing assisted driving for the stationary object based on the self-limiting condition includes: When the automatic safety edge condition is met based on the self-limitation of the stagnant object, the stagnant object is automatically controlled through the local safety backup system of the stagnant object.

8. The method according to claim 7, characterized in that, The step of performing automatic driving control on the stagnant object through the local security backup system of the stagnant object includes: Based on the stationary object's own detected driving status, the roadside decision group's observation of the stationary object's driving status, and the roadside decision group's observation of the driving status of vehicles surrounding the stationary object, control commands are generated to perform intelligent driving control on the stationary object until the stationary object is in a safe driving state.

9. The method according to claim 8, characterized in that, The method further includes: The roadside decision-making group sends takeover information and hedging information to the stationary object; wherein, the hedging information carries the driving status of the stationary object observed by the roadside decision-making group, as well as the driving status of the vehicles around the stationary object observed by the roadside decision-making group. When the stationary object passes the verification of the roadside decision-making group based on the takeover information, it executes the step of generating control commands for intelligent driving control of the stationary object based on its own detected driving status, the driving status of the stationary object observed by the roadside decision-making group, and the driving status of the vehicles around the stationary object observed by the roadside decision-making group.

10. The method according to claim 1, characterized in that, The roadside decision-making group includes terminal devices; the step of providing assisted driving to the stationary object based on the self-limitation situation includes: When the self-limitation condition of the stalled object does not meet the automatic safety edge condition, the stalled object is taken over by a terminal device with the same intelligent driving system account as the stalled object.

11. The method according to claim 10, characterized in that, The step of taking over the stagnant object through a terminal device with the same intelligent driving system account as the stagnant object includes: The available instruction set of the stalled object is validated to obtain the target instruction set; The target instruction set enables data interaction for assisted driving between the terminal device and the stationary object.

12. The method according to claim 11, characterized in that, The validity conversion of the available instruction set for the stalled object to obtain the target instruction set includes: The terminal device obtains the available instruction set of the local security backup system of the stagnant object, and performs validity conversion on the available instruction set to obtain the target instruction set.

13. The method according to claim 12, characterized in that, The step of performing validity conversion on the available instruction set to obtain the target instruction set includes: After establishing a connection between the terminal device and the intelligent driving control center of the stationary object, an available instruction set is obtained from the local security backup system of the stationary object through the connection; The terminal device performs validity conversion on the available instruction set according to its own predefined assisted driving control instruction set to obtain the converted available instruction set. The target instruction set is determined based on the converted available instruction set.

14. The method according to claim 13, characterized in that, Determining the target instruction set based on the converted available instruction set includes: The terminal device is used to obtain the set of identifiers generated by the terminal device and the stagnant object during the data interaction process. The union of the converted set of available instructions and the set of identifiers is used to form a set of available instructions and identifiers. The available instruction and identifier set is re-encoded using Huffman coding to generate the target instruction set.

15. The method according to claim 11, characterized in that, The method further includes: Through formula Calculate the weight of each available instruction in the available instruction set; wherein, f t,i Used to indicate the frequency of instruction usage t i Indicates the instruction type. No intelligent driving level, p tp This is the conversion coefficient for intelligent driving; When it is determined that the weight is not equal to the preset value, the step of re-encoding the available instruction and identifier set using Huffman coding to generate the target instruction set is performed.

16. The method according to claim 10, characterized in that, The step of taking over the stagnant object through a terminal device with the same intelligent driving system account as the stagnant object includes: The terminal device is used to obtain target object information of the surrounding objects of the stationary object; Based on the target object information, determine the control command for the stagnant object at the next moment, and send the control command to the stagnant object.

17. The method according to claim 16, characterized in that, The step of obtaining target object information of surrounding objects of the stationary object through the terminal device includes: The terminal device obtains all objects surrounding the stationary object from the roadside decision group; The system filters out surrounding intelligent driving vehicles whose detection coverage meets the requirements from all objects, and determines the target object information based on these surrounding intelligent driving vehicles whose detection coverage meets the requirements.

18. The method according to claim 17, characterized in that, The step of selecting surrounding intelligent driving vehicles whose detection coverage meets the requirements from all surrounding intelligent driving vehicles includes: Through formula Select nearby intelligent driving vehicles whose detection coverage meets the requirements; among them, w d For the quality of stationary objects, w i The i-th surrounding intelligent driving vehicle c i quality The velocity of the stationary object: , The i-th surrounding intelligent driving vehicle c i speed, This means retrieving the minimum element in list x. x i corresponding The n items with the smallest value nv d,i The quantity, size, frequency, and volume of the obstructing objects in the middle.

19. The method according to claim 16, characterized in that, The step of determining the control command for the stagnant object at the next moment based on the target object information and sending the control command to the stagnant object includes: Obtain the basic data of the stagnant object; The terminal device determines the control command for the stagnant object at the next moment based on the target object information and the stagnant object's own basic data.

20. The method according to claim 1, characterized in that, The method further includes: The roadside decision group sends coordinated hedging messages to vehicles in the same frequency vehicle network of the stationary object, so that the vehicles in the same frequency vehicle network can send their own status information and the status information of obstacles detected by the vehicles in the same frequency vehicle network to the local safety backup system of the stationary object in real time.

21. The method according to claim 20, characterized in that, The coordinated hedging message includes a feedback frequency. The vehicles in the same-frequency vehicle network send their own status information and the status information of the obstacles detected by the vehicles in the same-frequency vehicle network to the local security backup system of the stationary object according to the feedback frequency.

22. The method according to claim 1, characterized in that, The method further includes: The status feedback information from the stationary object is acquired in real time until the stationary object is in a safe driving state.

23. An automated driving control device based on a vehicle-road system, characterized in that, The device includes: The acquisition unit is used to acquire status feedback information of each vehicle within the service area of ​​the roadside equipment; wherein the status feedback information is used to indicate the status information of the vehicles. A determining unit is configured to establish a roadside decision group when it is determined, based on the state feedback information, that there is a stationary object in the vehicle; wherein the roadside decision group includes the roadside equipment and the intelligent driving vehicles normally driving within the service area; An assisted driving unit is used to determine the self-limitation status of the stationary object through the roadside decision group, and to provide assisted driving for the stationary object based on the self-limitation status; wherein the self-limitation status is used to indicate the stationary object's ability to handle faults.

24. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the autonomous driving control method based on a vehicle-to-infrastructure system as described in any one of claims 1 to 22.

25. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the vehicle-to-infrastructure (V2I) autonomous driving control method as described in any one of claims 1 to 22.

26. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the vehicle-to-infrastructure (V2I) autonomous driving control method as described in any one of claims 1 to 22.

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