CN113665588B

CN113665588B - Method for controlling vehicle driving of automatic driving vehicle based on wireless road data

Info

Publication number: CN113665588B
Application number: CN202111062738.7A
Authority: CN
Inventors: 于骞
Original assignee: Suzhou Qingyu Technology Co Ltd
Current assignee: Suzhou Qingyu Technology Co Ltd
Priority date: 2021-09-10
Filing date: 2021-09-10
Publication date: 2022-09-20
Anticipated expiration: 2041-09-10
Also published as: CN113665588A

Abstract

The embodiment of the invention relates to a method for controlling vehicle running of an automatic driving vehicle based on wireless road data, which comprises the following steps: acquiring first real-time speed and first wireless road data; analyzing to generate first analysis data; identifying whether running vehicles exist in the running road section where the self vehicle is located and in front of the adjacent road section according to the front vehicle type mode; setting a first vehicle control state as a non-following state when the state of no front vehicle in the whole road section is identified; when the vehicle state before the current road section is identified, evaluating a first vehicle control state to generate a first vehicle control state; when the vehicle state before the adjacent road section is identified, the doubling trend and the doubling track at the future moment are evaluated, when the vehicle state is evaluated to be the doubling state, the second vehicle control state is evaluated to generate a first vehicle control state, and when the vehicle state is the non-doubling state, the first vehicle control state is set to be the non-following state; corresponding vehicle control processing is performed. The invention improves the vehicle running control capability and the adaptability to different running environments.

Description

Method for controlling vehicle running of automatic driving vehicle based on wireless road data

Technical Field

The invention relates to the technical field of data processing, in particular to a method for controlling vehicle running of an automatic driving vehicle based on wireless road data.

Background

When the automatic driving vehicle carries out vehicle running control, self-adaptive control state evaluation is carried out according to real-time road conditions, an evaluation result which is specifically constant speed, acceleration or deceleration running is obtained, and corresponding running control is carried out according to the evaluation result. Conventionally, when the self-adaptive control state is evaluated, the self-adaptive control state is evaluated by referring to real-time environment information acquired by a radar or a camera more, and the judgment is not performed based on data information (such as different speed limit information of a continuous road section) of a road, so that the experience of acceleration and deceleration is poor. In addition, for vehicles in the same fleet (such as autonomous or unmanned delivery vehicles in large unmanned storage parks, autonomous or unmanned operation vehicles in standard autonomous operation networks, etc.) with coordination capability traveling in the same area, because the fleet vehicles are equipped with wireless communication devices that can communicate with each other, if the adaptive control state evaluation method can be improved by using the interaction information between the vehicles, better control effect can be obtained.

Disclosure of Invention

The invention aims to provide a method, an electronic device and a computer readable storage medium for controlling vehicle running of an automatic driving vehicle based on wireless road data, which are used for acquiring real-time information of each road section from a pre-embedded wireless transmitting device of a running road, adding a same-fleet vehicle type (fleet vehicle type) with cooperative capability on the basis of a conventional private vehicle type (non-fleet vehicle type), and performing adaptive control state evaluation on the non-fleet vehicle type by taking the real-time road section information and real-time environment information acquired by a radar or a camera as a basis; the adaptive control state evaluation of the vehicles is carried out on the types of the vehicles of the fleet according to real-time road section information, real-time environment information acquired by a radar or a camera and real-time cooperative information among the vehicles; and corresponding vehicle running control is carried out according to the final evaluation result. The invention can improve the running control capability of the automatic driving vehicle and can improve the self-adaptability of the vehicle to different running environments.

In order to achieve the above object, a first aspect of the embodiments of the present invention provides a method for controlling vehicle driving by an autonomous vehicle based on wireless road data, the method including:

the method comprises the steps that in the driving process of an automatic driving vehicle, the driving speed of the automatic driving vehicle is obtained in real time to generate corresponding first real-time speed data, and road information sent by a wireless transmitting device of a driving road section where the automatic driving vehicle is located is received in real time to generate corresponding first wireless road data; the first wireless channel data comprises first wireless type data and a first data packet; the first wireless type data at least comprises a magnetic spike type, a dedicated short-range communication DSRC type and a short-range straight-through communication LTE-V-Direct type;

performing data analysis on the first data packet based on a wireless communication protocol corresponding to the first wireless type data to generate corresponding first analysis data;

according to a preset front vehicle type mode, identifying whether a running vehicle exists in a running road section where a self vehicle is located and in front of an adjacent road section or not, and generating first identification state data; the front vehicle type mode comprises a non-motorcade vehicle type and a motorcade vehicle type;

when the first identification state data is in a full road section no-front-vehicle state, setting a first vehicle control state as a non-following vehicle state;

when the first identification state data is the state of the vehicle ahead of the current road section, recording the vehicle ahead of the current vehicle on the driving road section as a first vehicle ahead; estimating the running speed, the acceleration and the relative distance between the first front vehicle and the self vehicle to generate corresponding first front vehicle speed data, first front vehicle acceleration data and first relative distance data; performing first vehicle control state evaluation according to the first preceding vehicle speed data, the first preceding vehicle acceleration data, the first relative distance data and the first real-time speed data to generate a corresponding first vehicle control state;

when the first identification state data is the state of the vehicle ahead of the adjacent road section, recording the vehicle ahead running on the adjacent road section as a second vehicle ahead; acquiring historical track information of the second front vehicle at the current moment to generate corresponding first historical track data; carrying out the evaluation of the doubling trend and the doubling track at the future moment according to the first historical track data to generate a corresponding first doubling trend state and first doubling track data; when the first parallel trend state is a parallel state, performing second vehicle control state evaluation according to the first parallel track data and the first real-time speed data to generate a corresponding first vehicle control state; when the first parallel trend state is a non-parallel state, setting the first vehicle control state as a non-following state;

and carrying out corresponding vehicle control processing according to the first vehicle control state, the first analysis road data and the first real-time speed data.

Preferably, the first analysis road data includes first road segment coded data, first road segment remaining distance data, first road segment highest speed limit data and next road segment highest speed limit data;

the first vehicle control state comprises a non-vehicle following state, a constant speed vehicle following state, a deceleration vehicle following state, an acceleration vehicle following state and an emergency braking state.

Preferably, the identifying whether there is a driving vehicle in the driving road section where the vehicle is located and in front of the adjacent road section according to the preset preceding vehicle type mode to generate the first identification state data specifically includes:

identifying the front vehicle type mode; if the preceding vehicle type mode is a non-fleet vehicle type, calling a vehicle-mounted radar or a vehicle-mounted camera to identify whether a running vehicle exists in a running road section where the own vehicle is located and in front of an adjacent road section, and generating first identification state data; and if the preceding vehicle type mode is the vehicle type of the motorcade, calling a vehicle-mounted wireless communication device of the same motorcade to identify whether running vehicles exist in the running road section where the self vehicle is located and in front of the adjacent road section, and generating the first identification state data.

Further, the calling of the vehicle-mounted radar or the vehicle-mounted camera identifies whether a running vehicle exists in a running road section where the own vehicle is located and in front of an adjacent road section, and generates the first identification state data, which specifically includes:

calling the vehicle-mounted radar or the vehicle-mounted camera to perform corresponding radar scanning processing or scene shooting processing on the driving road section of the vehicle and the front scene of the adjacent road section, and generating corresponding first scene radar point cloud data or first scene image data;

performing corresponding point cloud vehicle target detection processing or image vehicle target detection processing on the first scene radar point cloud data or the first scene image data to generate a plurality of first detected vehicle data; the first detected vehicle data includes at least first detected vehicle segment encoded data;

when the plurality of first detected vehicle data are null, setting the first identification state data as a full-road-section front-vehicle-free state;

when the plurality of first detected vehicle data are not empty, identifying whether the first detected vehicle section coded data matched with the first section coded data exist in all the first detected vehicle section coded data or not; if the identification result is present, setting the first identification state data as the state of the vehicle ahead in the current road section; and if the identification result is that the vehicle does not exist, setting the first identification state data as the state of the vehicle ahead of the adjacent road section.

Further, the invoking of the vehicle-mounted wireless communication device of the same fleet identifies whether a running vehicle exists in the running road section where the own vehicle is located and in front of the adjacent road section, and generates the first identification state data specifically includes:

assembling according to the first road segment coded data and a preset fleet interconnection data assembling format to generate corresponding first fleet interconnection data;

calling the vehicle-mounted wireless communication device of the same fleet to perform wireless broadcast processing on the first fleet interconnection data and receive wireless feedback data returned by other automatic driving vehicles of the same fleet within a preset receiving time limit;

if the wireless feedback data returned by the other automatic driving vehicles are received within the receiving time limit, recording the wireless feedback data corresponding to each other automatic driving vehicle as corresponding first vehicle feedback data; the first vehicle feedback data at least comprises first feedback section coded data and first feedback section remaining distance data;

identifying whether the first vehicle feedback data with the first feedback road section coded data matched with the first road section coded data and the first feedback road section remaining distance data smaller than the first road section remaining distance data exists or not, and generating a corresponding first identification result;

when the first identification result is present, setting the first identification state data as the state of the vehicle ahead of the current road section;

when the first identification result is not present, calculating the relative distance between the other corresponding automatic driving vehicles and the self-vehicle according to the first vehicle feedback data and the first analysis road data to generate corresponding first relative distance data; identifying whether the first relative distance data exceeds a preset first minimum doubling distance threshold or not; if the identification result is that the first identification state data exceeds the second identification state data, setting the first identification state data as the state of the vehicle ahead of the adjacent road section; and if the identification result is not over, setting the first identification state data as a full-road-section no-front-vehicle state.

Further preferably, the method further comprises:

and if the wireless feedback data returned by any other automatic driving vehicle cannot be received within the receiving time limit, setting the first identification state data as a full-road-section no-front-vehicle state.

Preferably, the performing a first vehicle control state evaluation according to the first preceding vehicle speed data, the first preceding vehicle acceleration data, the first relative distance data, and the first real-time speed data to generate a corresponding first vehicle control state specifically includes:

when the first front vehicle speed data is not lower than the first real-time speed data and the first front vehicle acceleration data is 0, setting the first vehicle control state as a constant speed following state;

when the first preceding vehicle speed data is not lower than the first real-time speed data and the first preceding vehicle acceleration data is smaller than 0, setting the first vehicle control state as a deceleration following state;

when the first preceding vehicle speed data is not lower than the first real-time speed data and the first preceding vehicle acceleration data is larger than 0, identifying whether the first relative distance data exceeds a preset safety distance threshold value, if so, setting the first vehicle control state as an acceleration following state, and if not, setting the first vehicle control state as a deceleration following state;

when the first preceding vehicle speed data is lower than the first real-time speed data and the first preceding vehicle acceleration data is greater than or equal to 0, identifying whether the first relative distance data exceeds a preset safety distance threshold value, if so, setting the first vehicle control state as a deceleration following state, and if not, setting the first vehicle control state as an emergency braking state;

and when the first front vehicle speed data is lower than the first real-time speed data and the first front vehicle acceleration data is smaller than 0, setting the first vehicle control state as an emergency braking state.

Preferably, the performing, according to the first historical trajectory data, the evaluation of the merging trend and the merging trajectory at a future time to generate a corresponding first merging trend state and first merging trajectory data specifically includes:

estimating the motion trail of the vehicle in the future appointed time period of the second front vehicle according to the first historical trail data by using a well-trained vehicle motion trail estimation model to generate corresponding first estimated motion trail data; the first estimated motion trajectory data includes a plurality of first estimated motion trajectory points;

calculating the vertical distance from a first estimated motion track point in the first estimated motion track data to a road route of a running road where the self-vehicle is located, and generating initial transverse offset data; calculating the vertical distance from the last first estimated motion track point in the first estimated motion track data to the road route of the running road section where the self-vehicle is located, and generating finished transverse offset data;

if the ending transverse deviation data is smaller than the starting transverse deviation data, setting the first line merging trend state as a parallel line state; if the ending transverse deviation data is larger than or equal to the starting transverse deviation data, setting the first doubling trend state as a non-doubling state;

when the first parallel trend state is a parallel state, estimating the motion trail of the vehicle in a future appointed time period of the second front vehicle according to the first estimated motion trail data by using the vehicle motion trail estimation model to generate corresponding second estimated motion trail data; judging whether the current second estimated motion trail data is intersected with the road route of the driving road section where the self-vehicle is located; if the judgment result is intersection, track merging processing is carried out on the first estimated motion track data and the current second estimated motion track data according to the time sequence to generate first parallel track data, and estimated motion track points intersected with the road route of the driving road section where the self vehicle is located in the first parallel track data are marked as first intersected track points; if the judgment result is non-intersection, continuing to estimate the motion trail of the vehicle in the future specified time period by using the vehicle motion trail estimation model according to the current second estimated motion trail data to generate new second estimated motion trail data until the new second estimated motion trail data is intersected with the road route of the driving road section where the vehicle is located, merging the first parallel track data and all the second estimated motion trail data according to the time sequence to generate first parallel track data, and recording the estimated motion trail points intersected with the road route of the driving road section where the vehicle is located in the first parallel track data as the first intersected track points;

and when the first wiring trend state is a non-parallel state, setting the first wiring track data to be null.

Preferably, when the first parallel line trend state is a parallel line state, performing second vehicle control state evaluation according to the first parallel line trajectory data and the first real-time speed data to generate the corresponding first vehicle control state specifically includes:

when the first parallel trend state is a parallel state, calculating the driving time of the second front vehicle from the current position to the first intersection track point according to the first parallel track data, and generating corresponding first driving time;

calculating the vehicle driving distance according to the first real-time speed data and the first driving time of the automatic driving vehicle to generate a corresponding first driving distance; calculating the position information of the automatic driving vehicle after first driving time according to the first position point and the first driving distance by taking the current position information of the automatic driving vehicle as a first position point, and marking as a second position point; calculating the driving distance from the second position point to the first intersection track point, and generating a corresponding second driving distance;

when the direction of the second driving distance is opposite to the driving direction of the automatic driving vehicle, setting the first vehicle control state as a constant-speed vehicle following state;

and when the direction of the second running distance is the same as the running direction of the automatic driving vehicle, identifying whether the second running distance exceeds a preset safety distance threshold value, if so, setting the first vehicle control state as a deceleration vehicle following state, and if not, setting the first vehicle control state as an emergency braking state.

Preferably, the performing corresponding vehicle control processing according to the first vehicle control state, the first analytic road data, and the first real-time speed data specifically includes:

when the first vehicle control state is a non-following state, if the first real-time speed data is higher than the highest speed limit data of the first road section, or the first real-time speed data is lower than the highest speed limit data of the first road section but higher than the highest speed limit data of the next road section, performing deceleration driving based on a preset deceleration rule, and stopping deceleration and entering constant speed driving until the decelerated real-time speed reaches a preset constant speed threshold; if the first real-time speed data is lower than the first road section highest speed limit data and the next road section highest speed limit data, performing accelerated driving based on the first real-time speed data, and stopping acceleration and entering constant speed driving until the accelerated real-time speed reaches the constant speed threshold;

when the first vehicle control state is a constant-speed vehicle following state, performing constant-speed driving by taking the first real-time speed data as a real-time speed;

when the first vehicle control state is a deceleration following state, performing deceleration running based on a preset deceleration rule, and stopping deceleration and entering constant-speed running until the relative distance between the decelerated automatic driving vehicle and a front vehicle exceeds a preset safety distance threshold;

when the first vehicle control state is an acceleration following state, performing acceleration running based on a preset acceleration rule, and stopping acceleration and entering constant-speed running until the real-time speed after acceleration is not lower than the highest speed limit data of the first road section or the relative distance between the automatic driving vehicle and a front vehicle after acceleration is lower than a preset increased safety distance threshold;

and when the first vehicle control state is an emergency braking state, performing continuous braking operation based on a preset emergency braking rule.

A second aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

the processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps of the first aspect;

the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.

A third aspect of embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.

The embodiment of the invention provides a method for controlling vehicle running of an automatic driving vehicle based on wireless road data, electronic equipment and a computer readable storage medium, wherein real-time information of each road section is obtained from a pre-embedded wireless transmitting device of a running road, the type of vehicles (vehicle types of a fleet) of the same fleet with cooperative capability is added on the basis of the type of conventional private vehicles (non-fleet vehicle type), and the self-adaptive control state evaluation of the vehicle is carried out on the non-fleet vehicle type by taking the real-time road section information and the real-time environment information acquired by a radar or a camera as the basis; the adaptive control state evaluation of the vehicles is carried out on the types of the vehicles of the fleet according to real-time road section information, real-time environment information acquired by a radar or a camera and real-time cooperative information among the vehicles; and corresponding vehicle running control is carried out according to the final evaluation result. The invention can improve the running control capability of the automatic driving vehicle and can improve the self-adaptability of the vehicle to different running environments.

Drawings

FIG. 1 is a schematic diagram of a method for controlling vehicle driving by an autonomous vehicle based on wireless road data according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a schematic diagram of a method for controlling vehicle running by an autonomous vehicle based on wireless road data according to an embodiment of the present invention, where the method mainly includes the following steps:

step 1, acquiring the running speed of an automatic driving vehicle in real time to generate corresponding first real-time speed data in the running process of the automatic driving vehicle, and receiving road information sent by a wireless transmitting device of a running road section where the automatic driving vehicle is located in real time to generate corresponding first wireless road data;

the first wireless channel data comprises first wireless type data and a first data packet; the first wireless type data includes at least a magnetic spike type, a Dedicated Short Range Communication (DSRC) type, and a Short Range Communication LTE-V-Direct (Long Term Evolution-Vehicle-Direct) type.

Here, the first real-time speed data is real speed information of the autonomous vehicle at the present time; the first wireless road data is road data received in real time when an automatic driving vehicle passes through a driving road pre-installed with a road wireless transmitting device; the automatic driving vehicle provided by the embodiment of the invention can identify the sending data of various road wireless emitting devices, wherein the sending data comprises a magnetic vehicle spike type wireless emitting device, a DSRC type wireless emitting device commonly used in the Internet of things and the Internet of vehicles, and an LTE-V-Direct type wireless emitting device special for the 5G Internet of vehicles.

Step 2, performing data analysis on the first data packet based on a wireless communication protocol corresponding to the first wireless type data to generate corresponding first analysis data;

the first analysis road data comprises first road segment coding data, first road segment residual distance data, first road segment highest speed limit data and next road segment highest speed limit data.

When the first wireless type data is a magnetic vehicle spike type, analyzing the data of the first data packet based on an electromagnetic signal analysis protocol agreed with the wireless transmitting device; when the first wireless type data is of a DSRC type, analyzing the data of the first data packet based on a specific DSRC communication protocol; when the first wireless type data is of a DSRC type, carrying out data analysis on the first data packet based on a specific LTE-V-Direct communication protocol; the first analysis data is analyzed real-time road section information of the current driving road; the first road section coded data is sequential coded information of a current road section in a road to which the current road section belongs; the first road section remaining distance data is distance information from a wireless transmitting device corresponding to the current position point of the automatic driving vehicle to the end position of the current road section, and the data can be regarded as the distance information from the current position point of the automatic driving vehicle to the end position of the current road section; the highest speed limit data of the first road section is the highest speed limit information in the current road section; and the highest speed limit data of the next road section is the highest speed limit information of the next road section connected with the current road section.

Step 3, identifying whether running vehicles exist in the running road section where the own vehicle is located and in front of the adjacent road section according to a preset front vehicle type mode, and generating first identification state data;

the front vehicle type mode comprises a non-motorcade vehicle type and a motorcade vehicle type;

the method specifically comprises the following steps: step 31, identifying a front vehicle type mode; if the preceding vehicle type mode is a non-fleet vehicle type, go to step 32; if the preceding vehicle type mode is the vehicle type of the fleet, go to step 33;

the front vehicle type mode is used for identifying the cooperative relationship between the automatic driving vehicles and the front vehicle, when the front vehicle type mode is a non-motorcade vehicle type, the current automatic driving vehicles and the front vehicle are both conventional private vehicles without cooperative ability, and when the front vehicle type mode is a motorcade vehicle type, the current automatic driving vehicles and the front vehicle are the same motorcade and have cooperative ability; corresponding to different cooperative relations, the embodiment of the invention provides different recognition processing mechanisms for the running vehicles of the front vehicles;

step 32, calling a vehicle-mounted radar or a vehicle-mounted camera to identify whether a running vehicle exists in the running road section where the own vehicle is located and in front of the adjacent road section, and generating first identification state data; turning to the step 4;

the first identification state data comprises a full road section front-vehicle-free state, a local road section front-vehicle state and an adjacent road section front-vehicle state;

when the front vehicle type mode is a non-fleet vehicle type, which indicates that the current automatic driving vehicle and the front vehicle are both conventional private vehicles without cooperative ability, a vehicle-mounted radar or a vehicle-mounted camera is adopted to acquire environmental parameters, and front vehicle identification is performed according to the acquired environmental parameters; the first identification state data is used for identifying whether a running vehicle exists in a running road section where a self vehicle is located and a road scene in front of an adjacent road section, namely a state parameter of a preceding vehicle, is a state of no preceding vehicle in the whole road section, which indicates that no preceding vehicle exists in the scene, is a state of a preceding vehicle in the current road section, which indicates that the scene has a preceding vehicle and is on the same road with the automatic driving vehicle, and is an adjacent road section which has a preceding vehicle in the adjacent road section, which indicates that the scene has a preceding vehicle and is located in the road section where the automatic driving vehicle is located;

the method specifically comprises the following steps: step 321, calling a vehicle-mounted radar or a vehicle-mounted camera to perform corresponding radar scanning processing or scene shooting processing on a driving road section where the vehicle is located and a front scene of an adjacent road section, and generating corresponding first scene radar point cloud data or first scene image data;

the vehicle-mounted radar scans a front scene to generate a corresponding radar scanning data frame, and performs point cloud data conversion on the radar scanning data frame to generate corresponding first scene radar point cloud data; the vehicle-mounted camera shoots an image of a front scene to generate corresponding first scene image data;

step 322, performing corresponding point cloud vehicle target detection processing or image vehicle target detection processing on the first scene radar point cloud data or the first scene image data to generate a plurality of first detected vehicle data;

wherein the first detected vehicle data includes at least first detected vehicle segment encoded data;

here, a common Point cloud target detection model (for example, a VoxelRPN model, a Fast Point R-CNN model, etc.) is adopted to perform vehicle target detection on the first scene radar Point cloud data, and the detected Point cloud vehicle target is marked as a first detection vehicle, so that distance information between each first detection vehicle and the autonomous driving vehicle can be correspondingly obtained;

or, a common two-dimensional image target detection model (e.g., a YOLO model) is adopted to perform vehicle target detection on the first scene image data, and the detected point cloud vehicle target is marked as a first detection vehicle, so that distance information between each first detection vehicle and the automatic driving vehicle can be correspondingly obtained;

after the distance information between a plurality of first detection vehicles and the automatic driving vehicle is obtained, on the basis of the known first road section coding data of the automatic driving vehicle, the road section coding information of the first detection vehicles in the same lane or the adjacent lane, namely the first detection vehicle road section coding data, can be calculated according to the known road section coding rule;

step 323, when the plurality of first detected vehicle data are empty, setting the first identification state data as a full-road-section no-front-vehicle state;

here, the plurality of first detected vehicle data being empty means that the result of vehicle object detection for the scene ahead of the travel section where the own vehicle is located and the adjacent section is empty, that is, no preceding vehicle exists in the scene;

step 324, when the plurality of first detected vehicle data are not empty, identifying whether first detected vehicle section coded data matched with the first section coded data exist in all the first detected vehicle section coded data; if the identification result is present, setting the first identification state data as the state of the vehicle ahead of the current road section; if the identification result is that the first identification state data does not exist, setting the first identification state data as the state of the vehicle ahead of the adjacent road section; turning to the step 4;

here, the fact that the plurality of first detected vehicle data are not empty means that the result of detecting the vehicle target in the scene in front of the road segment where the vehicle is traveling and the adjacent road segment indicates that a preceding vehicle exists in the scene, and if the first detected vehicle segment coded data of the preceding vehicle matches the first segment coded data, the preceding vehicle and the automatically driven vehicle are on the same lane; if the coded data of the first road section are not matched, the front vehicle is positioned at the adjacent road section of the road section where the automatic driving vehicle is positioned;

step 33, calling a vehicle-mounted wireless communication device of the same motorcade to identify whether running vehicles exist in the running road section where the self vehicle is located and in front of the adjacent road section, and generating first identification state data;

here, when the preceding vehicle type mode is a vehicle type of a fleet of vehicles, it is described that two current automatically driven vehicles and the preceding vehicle are the same fleet of vehicles and have a cooperative capability, and then the same fleet of vehicles, which are installed in advance in a unified manner, are used as the same fleet of vehicles to perform one-to-one, one-to-many or many-to-one cooperative intercommunication among the vehicles, so as to identify whether the preceding vehicle exists in a driving road section where the own vehicle is located and a scene in front of an adjacent road section;

the method specifically comprises the following steps: step 331, according to the first road segment coded data, assembling according to a preset fleet interconnection data assembling format to generate corresponding first fleet interconnection data;

step 332, calling a vehicle-mounted wireless communication device of the same fleet to perform wireless broadcast processing on the first fleet interconnection data, and receiving wireless feedback data returned by other automatic driving vehicles of the same fleet within a preset receiving time limit;

the method comprises the steps that an automatic driving vehicle firstly broadcasts and group-sends to vehicles of the same motorcade possibly existing around, and information of group-sending is first motorcade interconnection data in a motorcade interconnection data splicing format which can be identified mutually; the data can carry basic information (such as vehicle identification, vehicle brand, vehicle type and the like) of the current automatic driving vehicle and real-time road section information of a road section where the current automatic driving vehicle is located, namely first road section coded data;

according to a preset principle of cooperative and intercommunication of the vehicles in the same fleet, if other vehicles in the same fleet exist around the current automatic driving vehicle, feedback information is sent back to the vehicle specified by the first fleet interconnection data immediately after the first fleet interconnection data is received, wherein the feedback information at least comprises basic information (such as vehicle identification, vehicle brand, vehicle type and the like) of the vehicles in the same fleet, and road section coding information and road section remaining distance information of a road section where the vehicles in the same fleet are located;

step 333, if the wireless feedback data returned by any other automatic driving vehicle cannot be received within the receiving time limit, setting the first identification state data as a full-road-section front-vehicle-free state; turning to the step 4;

here, the wireless feedback data returned by any other automatic driving vehicle cannot be received when the receiving time limit is exceeded, which indicates that no other vehicles in the same fleet exist around the current automatic driving vehicle, that is, no front vehicle exists in the scene;

step 334, if the wireless feedback data returned by the other automatic driving vehicles is received within the receiving time limit, recording the wireless feedback data corresponding to each other automatic driving vehicle as corresponding first vehicle feedback data; the first vehicle feedback data at least comprises first feedback section coded data and first feedback section remaining distance data;

step 335, identifying whether first vehicle feedback data exists, wherein the first feedback road section coded data is matched with the first road section coded data, and the first vehicle feedback data of which the first feedback road section remaining distance data is smaller than the first road section remaining distance data, and generating a corresponding first identification result;

step 336, when the first identification result is present, setting the first identification state data as the state of the vehicle ahead in the current road section;

if first vehicle feedback data exists, wherein the first feedback section coded data are matched with the first section coded data, and the first feedback section surplus distance data are smaller than the first section surplus distance data, the first identification result is existence, and the existence is indicated by the fact that a front vehicle exists in front of the current automatic driving vehicle on the same section;

step 337, when the first identification result is that the first identification result does not exist, calculating the relative distance between the other corresponding automatic driving vehicles and the self-vehicle according to the first vehicle feedback data and the first analysis road data, and generating corresponding first relative distance data; identifying whether the first relative distance data exceeds a preset first minimum doubling distance threshold or not; if the identification result is that the road section is in the past, setting first identification state data as a state of a vehicle ahead of the adjacent road section; and if the identification result is not exceeded, setting the first identification state data as a full-road-section no-front-vehicle state.

If the first feedback section coded data are matched with the first section coded data and the first vehicle feedback data that the first feedback section remaining distance data are smaller than the first section remaining distance data do not exist, the first identification result is that the first vehicle feedback data do not exist, and the fact that a front vehicle exists in the adjacent section of the current automatic driving vehicle is indicated; further judging the relative distance between the front vehicle of the adjacent road section and the current automatic driving vehicle, if the relative distance is smaller than a first minimum merging distance threshold value, the front vehicle cannot be merged to the lane where the current automatic driving vehicle is located in a period of time after the current time, and in this case, the current automatic driving vehicle is considered as a front vehicle, so that the first identification state data is set to be a full-road-section front-vehicle-free state; if the relative distance exceeds the first minimum merging distance threshold, the preceding vehicle can be merged to the lane where the current automatic driving vehicle is located in a period of time after the current time, in this case, the preceding vehicle may influence the driving of the current automatic driving vehicle at the future time, so the first identification state data is set as the preceding vehicle state of the adjacent road section.

And 4, when the first identification state data is in the full-road-section front-vehicle-free state, setting the first vehicle control state as a non-following vehicle state.

The first vehicle control state is a system parameter used for identifying the evaluation result of the vehicle self-adaptive control state, and comprises a non-vehicle following state, a constant-speed vehicle following state, a deceleration vehicle following state, an acceleration vehicle following state and an emergency braking state;

when the first vehicle control state is a non-following state, the situation that no front vehicle exists in the driving road section of the self vehicle and the scene in front of the adjacent road section is shown, and the vehicle is controlled to drive only according to the real-time speed of the current vehicle and the road speed limit information;

when the first vehicle control state is a constant-speed vehicle following state, the situation shows that a front vehicle possibly exists in a driving road section where the self vehicle is located and a scene in front of an adjacent road section, and the constant-speed driving needs to be kept in order to avoid vehicle collision;

when the first vehicle control state is a deceleration and vehicle following state, the situation shows that a front vehicle possibly exists in a driving road section where the self vehicle is located and a scene in front of an adjacent road section, the spacing distance is short, and the vehicle needs to be decelerated to avoid vehicle collision;

when the first vehicle control state is an acceleration vehicle following state, the situation shows that a vehicle in front may exist in a driving road section where the vehicle is located and a scene in front of an adjacent road section, the spacing distance is long, and acceleration is needed to improve the road utilization rate and the vehicle following efficiency;

when the first vehicle control state is the emergency braking state, it is indicated that there may be a front vehicle in the driving road section where the vehicle is located and the scene in front of the adjacent road section, and the collision probability between the two is increased, so that emergency braking is required to avoid vehicle collision.

Here, the first identification state data is the all-road-section leading-vehicle-free state, which indicates that the vehicle is not in front of the road section on which the vehicle is traveling and the adjacent road section, and the first vehicle control state is naturally set to the non-following state.

Step 5, when the first identification state data is the state of the vehicle ahead of the current road section, recording the vehicle ahead which is driving on the driving road section where the current vehicle is located as a first vehicle ahead; estimating the running speed and the acceleration of the first front vehicle and the relative distance between the first front vehicle and the self vehicle to generate corresponding first front vehicle speed data, first front vehicle acceleration data and first relative distance data; performing first vehicle control state evaluation according to the first front vehicle speed data, the first front vehicle acceleration data, the first relative distance data and the first real-time speed data to generate a corresponding first vehicle control state;

here, the first identification state data is a vehicle-ahead state of the current road section, which indicates that a vehicle ahead exists in a scene in front of the road section where the vehicle is traveling, and then the current autonomous vehicle needs to perform adaptive driving state evaluation with reference to the traveling state of the vehicle ahead on the same road;

the method specifically comprises the following steps: step 51, marking the front vehicle which is driving on the driving road section where the self vehicle is positioned as a first front vehicle; estimating the running speed and the acceleration of the first front vehicle and the relative distance between the first front vehicle and the self vehicle to generate corresponding first front vehicle speed data, first front vehicle acceleration data and first relative distance data;

here, when the traveling speed, acceleration and relative distance to the host vehicle of the first preceding vehicle are estimated,

the method comprises the following steps that a vehicle-mounted radar can be adopted to continuously scan a first front vehicle to obtain a plurality of continuous radar scanning frames; performing point cloud data conversion on the radar scanning frame to obtain a plurality of continuous point cloud data sets; multi-target detection and motion trail tracking are carried out on the point cloud data sets, and a corresponding front vehicle motion trail can be obtained and consists of a plurality of front vehicle motion trail points with position and time information;

the vehicle-mounted camera can be used for continuously shooting the first front vehicle to obtain a plurality of continuous first front vehicle images; multi-target detection and motion trail tracking are carried out on the first front vehicle image, and a corresponding front vehicle motion trail can be obtained, wherein the front vehicle motion trail consists of a plurality of front vehicle motion trail points with position and time information;

after the motion track of the front vehicle is obtained, the real-time speed and acceleration of the first front vehicle, namely the first front vehicle speed data and the first front vehicle acceleration data, can be calculated according to the motion track of the front vehicle, and the relative distance between the first front vehicle and the current automatic driving vehicle, namely the first relative distance data, can also be obtained;

step 52, performing first vehicle control state evaluation according to the first preceding vehicle speed data, the first preceding vehicle acceleration data, the first relative distance data and the first real-time speed data to generate a corresponding first vehicle control state;

here, the embodiment of the present invention gives 4 evaluation possibilities to the first vehicle control state when the first identification state data is the state of the vehicle ahead of the current road segment: a constant-speed car following state, a deceleration car following state, an acceleration car following state and an emergency braking state;

the method specifically comprises the following steps: step 521, when the first preceding vehicle speed data is not lower than the first real-time speed data and the first preceding vehicle acceleration data is 0, setting the first vehicle control state as a constant-speed following state;

step 522, when the first preceding vehicle speed data is not lower than the first real-time speed data and the first preceding vehicle acceleration data is less than 0, setting a first vehicle control state as a deceleration following state;

523, when the first preceding vehicle speed data is not lower than the first real-time speed data and the first preceding vehicle acceleration data is greater than 0, identifying whether the first relative distance data exceeds a preset safety distance threshold, if so, setting the first vehicle control state as an acceleration following state, and if not, setting the first vehicle control state as a deceleration following state;

step 524, when the first preceding vehicle speed data is lower than the first real-time speed data and the first preceding vehicle acceleration data is greater than or equal to 0, identifying whether the first relative distance data exceeds a preset safety distance threshold, if so, setting the first vehicle control state as a deceleration following state, and if not, setting the first vehicle control state as an emergency braking state;

and step 525, setting the first vehicle control state as an emergency braking state when the first preceding vehicle speed data is lower than the first real-time speed data and the first preceding vehicle acceleration data is less than 0.

Step 6, when the first identification state data is the state of the vehicle ahead of the adjacent road section, marking the vehicle ahead which is driving on the adjacent road section as a second vehicle ahead; acquiring historical track information of a second front vehicle at the current moment to generate corresponding first historical track data; carrying out the evaluation of the doubling trend and the doubling track at a future moment according to the first historical track data to generate a corresponding first doubling trend state and first doubling track data; when the first parallel trend state is a parallel state, performing second vehicle control state evaluation according to the first parallel track data and the first real-time speed data to generate a corresponding first vehicle control state; when the first parallel trend state is a non-parallel state, setting a first vehicle control state as a non-following state;

here, the first identification state data is a state of a vehicle ahead of an adjacent road section, which indicates that a vehicle ahead exists in a scene in front of the adjacent road section of the road section where the vehicle is traveling, and then the current automatic driving vehicle needs a possible merging state of the vehicles ahead of the adjacent road section to perform self-adaptive traveling state evaluation;

the method specifically comprises the following steps: step 61, marking the front vehicle running on the adjacent road section as a second front vehicle; acquiring historical track information of a second front vehicle at the current moment to generate corresponding first historical track data;

the first historical track data is track data formed by motion track points generated by a second front vehicle before the current moment, and each motion track point is provided with position and time information;

step 62, carrying out the evaluation of the doubling trend and the doubling track at the future moment according to the first historical track data, and generating a corresponding first doubling trend state and first doubling track data;

the first doubling trend state comprises a doubling state and a non-doubling state; when the first merging trend state is a merging state, the first merging trajectory data is an estimated motion trajectory of the second front vehicle from the current moment to the point after the merging is successful; when the first parallel trend state is a non-parallel state, the first parallel track data is null;

the method specifically comprises the following steps: step 621, estimating the motion trail of the vehicle in the future designated time period of the second front vehicle according to the first historical trail data by using a well-trained vehicle motion trail estimation model to generate corresponding first estimated motion trail data;

wherein the first estimated motion trajectory data includes a plurality of first estimated motion trajectory points;

here, a common vehicle motion trajectory estimation model (e.g., a kalman filter model) is adopted to estimate a motion trajectory for a specified period (e.g., 3 seconds) in the future with the first historical trajectory data as initial data;

step 622, calculating the vertical distance from the first estimated motion track point in the first estimated motion track data to the road route of the driving road where the self vehicle is located, and generating initial transverse deviation data; calculating the vertical distance from the last first estimated motion track point in the first estimated motion track data to the road route of the running road section where the self-vehicle is located, and generating transverse deviation finishing data;

step 623, if the ending lateral deviation data is smaller than the starting lateral deviation data, setting a first doubling trend state as a doubling state; if the ending transverse deviation data is larger than or equal to the starting transverse deviation data, setting the first parallel line trend state as a non-parallel line state;

here, the fact that the ending lateral deviation data is smaller than the starting lateral deviation data indicates that the preceding vehicle approaches the lane where the current automatic driving vehicle is located, and the preceding vehicle is considered to have a doubling tendency, so that the first doubling tendency state is a doubling state; on the contrary, the front vehicle is far away from the lane where the current automatic driving vehicle is located, and whether the lane is parallel or not is considered to exist, so that the first parallel trend state is a non-parallel state;

step 624, when the first parallel trend state is the parallel state, estimating the motion trail of the vehicle in the future appointed time period of the second front vehicle according to the first estimated motion trail data by using the vehicle motion trail estimation model, and generating corresponding second estimated motion trail data; judging whether the current second estimated motion trail data is intersected with the road route of the driving road section where the self-vehicle is located; if the judgment result is intersection, track merging processing is carried out on the first estimated motion track data and the current second estimated motion track data according to the time sequence to generate first parallel track data, and estimated motion track points intersected with the road route of the driving road section where the self-vehicle is located in the first parallel track data are marked as first intersected track points; if the judgment result is non-intersection, continuing to estimate the motion trail of the vehicle in a future specified time period of the second front vehicle by using the vehicle motion trail estimation model according to the current second estimated motion trail data to generate new second estimated motion trail data until the new second estimated motion trail data is intersected with the road route of the driving road section where the self vehicle is located, merging the first parallel track data and all the second estimated motion trail data according to the time sequence to generate first parallel track data, and recording the estimated motion trail point intersected with the road route of the driving road section where the self vehicle is located in the first parallel track data as a first intersected trail point;

here, when the first merge tendency state is a merge state, the embodiment of the present invention performs continuous iterative motion trajectory estimation based on the first estimated motion trajectory data until the last estimated motion trajectory intersects with the road route of the driving road section where the host vehicle is located, so that a first intersecting trajectory point, which is the entry of the road of the driving road section where the host vehicle is located when the host vehicle merges, can be obtained;

step 625, when the first doubling trend state is a non-doubling state, setting the first doubling trajectory data to be null;

step 63, when the first parallel trend state is a parallel state, performing second vehicle control state evaluation according to the first parallel track data and the first real-time speed data to generate a corresponding first vehicle control state;

here, the embodiment of the present invention gives 3 evaluation possibilities to the first vehicle control state when the first identification state data is the state of the vehicle ahead of the adjacent link and the first merge tendency state is the merge state: a constant-speed car following state, a deceleration car following state and an emergency braking state;

the method specifically comprises the following steps: step 631, when the first parallel trend state is the parallel state, calculating the driving time of the second front vehicle from the current position to the first intersecting track point according to the first parallel track data, and generating corresponding first driving time;

here, the first travel time is the length of a period of time from the current time to the time when the second preceding vehicle enters the lane where the current autonomous vehicle is located, that is, the time when the second preceding vehicle reaches the first intersection track point, which is estimated;

step 632 of calculating a vehicle travel distance according to the first real-time speed data and the first travel time of the autonomous vehicle to generate a corresponding first travel distance; calculating the position information of the automatic driving vehicle after the first running time according to the first position point and the first running distance by taking the current position information of the automatic driving vehicle as the first position point, and recording the position information as a second position point; calculating the driving distance from the second position point to the first intersection track point, and generating a corresponding second driving distance;

here, the first position point is a position point of the current autonomous vehicle at the current time; if the current automatic driving vehicle runs at a constant speed by taking the first real-time speed data as the running speed from the current moment, the second position point is the position point of the current automatic driving vehicle at the moment when the second front vehicle enters the lane where the current automatic driving vehicle is located, namely the moment when the second front vehicle arrives and the first intersection track point moment;

step 633, when the direction of the second driving distance is opposite to the driving direction of the automatic driving vehicle, setting the first vehicle control state as a constant-speed following state;

if the direction of the second driving distance, that is, the direction from the second position point to the first intersecting track point is opposite to the driving direction of the autonomous vehicle, it means that according to the estimation, when the second front vehicle arrives at the first intersecting track point, the current autonomous vehicle has already driven away from the first intersecting track point at a constant speed, and the probability of collision between the first intersecting track point and the second intersecting track point is very low, so that starting from the current moment, the current speed per hour is kept for driving at a constant speed;

step 634, when the direction of the second driving distance is the same as the driving direction of the automatic driving vehicle, identifying whether the second driving distance exceeds a preset safety distance threshold, if so, setting the first vehicle control state as a deceleration following state, and if not, setting the first vehicle control state as an emergency braking state;

here, if the direction of the second travel distance, that is, the direction from the second position point to the first intersection track point is the same as the travel direction of the autonomous vehicle, it means that, according to the estimation, when the second preceding vehicle arrives at the first intersection track point, the current autonomous vehicle has not yet arrived at the first intersection track point, and there is a probability of collision between the first intersection track point and the second intersection track point; if the second driving distance exceeds the safety distance threshold, the braking distance between the first driving distance and the second driving distance is enough, emergency braking is not needed, and only stable deceleration is needed; if the second driving distance does not exceed the safety distance threshold, the distance between the first driving distance and the second driving distance is short, and the stable and effective safety braking distance cannot be guaranteed, so that emergency braking is required for safety driving;

step 64, when the first parallel trend state is a non-parallel state, setting the first vehicle control state as a non-following state;

here, the first merge tendency state is a non-merge state, and the first vehicle control state is set to a non-following state, similar to step 4, as if there is no preceding vehicle.

Step 7, performing corresponding vehicle control processing according to the first vehicle control state, the first analytic road data and the first real-time speed data;

the method specifically comprises the following steps: step 71, when the first vehicle control state is a non-following vehicle state, if the first real-time speed data is higher than the highest speed limit data of the first road section, or the first real-time speed data is lower than the highest speed limit data of the first road section but higher than the highest speed limit data of the next road section, performing deceleration driving based on a preset deceleration rule, and stopping deceleration and entering constant speed driving until the decelerated real-time speed reaches a preset constant speed threshold; if the first real-time speed data is lower than the first road section highest speed limit data and the next road section highest speed limit data, performing accelerated driving based on the first real-time speed data, stopping acceleration until the accelerated real-time speed reaches a uniform speed threshold value, and entering into uniform speed driving;

step 72, when the first vehicle control state is a constant-speed following state, performing constant-speed driving by taking the first real-time speed data as a real-time speed;

step 73, when the first vehicle control state is a deceleration following state, performing deceleration running based on a preset deceleration rule, and stopping deceleration and entering constant speed running until the relative distance between the decelerated automatically-driven vehicle and the front vehicle exceeds a preset safety distance threshold;

step 74, when the first vehicle control state is an acceleration following state, performing acceleration driving based on a preset acceleration rule, and stopping acceleration and entering constant-speed driving until the real-time speed after acceleration is not lower than the highest speed limit data of the first road section or the relative distance between the automatic driving vehicle after acceleration and the front vehicle is lower than a preset increased safety distance threshold;

and 75, when the first vehicle control state is an emergency braking state, performing continuous braking operation based on a preset emergency braking rule.

Here, the deceleration rule is a self-defined acceleration constraint for stable deceleration; the acceleration rule is a self-defined acceleration constraint condition for stable acceleration; the emergency braking rule is a self-defined multi-level acceleration constraint for continuous braking.

Fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention. The electronic device may be a terminal device or a server for implementing the method of the embodiment of the present invention, or may be a terminal device or a server connected to the terminal device or the server for implementing the method of the embodiment of the present invention. As shown in fig. 2, the electronic device may include: a processor 301 (e.g., a CPU), a memory 302, a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiving operation of the transceiver 303. Various instructions may be stored in memory 302 for performing various processing functions and implementing the processing steps described in the foregoing method embodiments. Preferably, the electronic device according to an embodiment of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to implement communication connections between the elements. The communication port 306 is used for connection communication between the electronic device and other peripherals.

The system bus 305 mentioned in fig. 2 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM) and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Graphics Processing Unit (GPU), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.

It should be noted that the embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method and the processing procedure provided in the above-mentioned embodiment.

The embodiment of the present invention further provides a chip for executing the instructions, where the chip is configured to execute the processing steps described in the foregoing method embodiment.

Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for an autonomous vehicle to control vehicle travel based on wireless road data, the method comprising:

the method comprises the steps that in the driving process of an automatic driving vehicle, the driving speed of the automatic driving vehicle is obtained in real time to generate corresponding first real-time speed data, and road information sent by a wireless transmitting device of a driving road section where the automatic driving vehicle is located is received in real time to generate corresponding first wireless road data; the first wireless channel data comprises first wireless type data and a first data packet; the first wireless type data at least comprises a magnetic spike type, a dedicated short-range communication (DSRC) type and a short-range through communication (LTE-V-Direct) type;

performing data analysis on the first data packet based on a wireless communication protocol corresponding to the first wireless type data to generate corresponding first analysis road data;

when the first identification state data is the state of the vehicle ahead of the adjacent road section, recording the vehicle ahead driving on the adjacent road section as a second vehicle ahead; acquiring historical track information of the second previous vehicle at the current moment to generate corresponding first historical track data; carrying out the evaluation of the doubling trend and the doubling track at the future moment according to the first historical track data to generate a corresponding first doubling trend state and first doubling track data; when the first parallel trend state is a parallel state, performing second vehicle control state evaluation according to the first parallel track data and the first real-time speed data to generate a corresponding first vehicle control state; when the first parallel trend state is a non-parallel state, setting the first vehicle control state as a non-following state;

performing corresponding vehicle control processing according to the first vehicle control state, the first analytical road data and the first real-time speed data;

the first analysis road data comprises first road segment coding data, first road segment residual distance data, first road segment highest speed limit data and next road segment highest speed limit data;

the first vehicle control state comprises a non-vehicle following state, a constant-speed vehicle following state, a deceleration vehicle following state, an acceleration vehicle following state and an emergency braking state;

the method for recognizing whether the running vehicle exists in the running road section where the own vehicle is located and in front of the adjacent road section according to the preset front vehicle type mode to generate first recognition state data specifically comprises the following steps:

identifying the front vehicle type mode; if the preceding vehicle type mode is a non-fleet vehicle type, calling a vehicle-mounted radar or a vehicle-mounted camera to identify whether a running vehicle exists in a running road section where the own vehicle is located and in front of an adjacent road section, and generating first identification state data; if the front vehicle type mode is a motorcade vehicle type, calling a vehicle-mounted same-motorcade wireless communication device to identify whether running vehicles exist in the running road section where the self vehicle is located and in front of the adjacent road section, and generating first identification state data;

the corresponding vehicle control processing is performed according to the first vehicle control state, the first analytic road data and the first real-time speed data, and specifically includes:

when the first vehicle control state is a non-following state, if the first real-time speed data is higher than the highest speed limit data of the first road section, or the first real-time speed data is lower than the highest speed limit data of the first road section but higher than the highest speed limit data of the next road section, performing deceleration driving based on a preset deceleration rule, and stopping deceleration and entering constant speed driving until the decelerated real-time speed reaches a preset constant speed threshold; if the first real-time speed data is lower than the first road section highest speed limit data and the next road section highest speed limit data, performing accelerated driving based on the first real-time speed data, and stopping acceleration and entering constant-speed driving until the accelerated real-time speed reaches the constant-speed threshold;

2. The method for controlling vehicle driving by the automatic driving vehicle based on the wireless road data according to claim 1, wherein the step of calling a vehicle-mounted radar or a vehicle-mounted camera to identify whether a driving vehicle exists in a driving road section where the vehicle is located and in front of an adjacent road section and generating the first identification state data comprises the following steps:

when the plurality of first detected vehicle data are empty, setting the first identification state data as a full-road-section front-vehicle-free state;

when the plurality of first detected vehicle data are not empty, identifying whether the first detected vehicle section coded data matched with the first section coded data exist in all the first detected vehicle section coded data or not; if the identification result is present, setting the first identification state data as the state of the vehicle ahead of the current road section; and if the identification result is that the first identification state data does not exist, setting the first identification state data as the state of the vehicle ahead of the adjacent road section.

3. The method of claim 1, wherein the step of invoking the on-board wireless communication device of the same fleet to identify whether there is a driving vehicle in the driving road section and the adjacent road section of the vehicle to generate the first identification status data comprises:

splicing according to the first section coded data and a preset fleet interconnection data splicing format to generate corresponding first fleet interconnection data;

when the first identification result is not present, calculating the relative distance between the other corresponding automatic driving vehicles and the self-vehicle according to the first vehicle feedback data and the first analysis road data to generate corresponding first relative distance data; identifying whether the first relative distance data exceeds a preset first minimum doubling distance threshold or not; if the identification result is that the vehicle speed exceeds the preset speed threshold, setting the first identification state data as the previous vehicle state of the adjacent road section; and if the identification result is not over, setting the first identification state data as a full-road-section no-front-vehicle state.

4. The method of controlling vehicle travel by an autonomous vehicle based on wireless road data as claimed in claim 3, further comprising:

5. The method of claim 1, wherein the step of performing a first vehicle control state evaluation according to the first vehicle speed data, the first vehicle acceleration data, the first relative distance data, and the first real-time speed data to generate a corresponding first vehicle control state comprises:

6. The method for controlling vehicle driving by an autonomous vehicle based on wireless road data as claimed in claim 1, wherein the estimating of the merging trend and the merging trajectory at a future time according to the first historical trajectory data to generate the corresponding first merging trend state and the corresponding first merging trajectory data specifically comprises:

7. The method according to claim 6, wherein when the first parallel trend state is a parallel state, performing a second vehicle control state evaluation according to the first parallel trajectory data and the first real-time speed data to generate the corresponding first vehicle control state, specifically comprising:

calculating the vehicle driving distance according to the first real-time speed data and the first driving time of the automatic driving vehicle to generate a corresponding first driving distance; calculating position information of the automatic driving vehicle after first driving time according to the first position point and the first driving distance by taking the current position information of the automatic driving vehicle as a first position point, and recording the position information as a second position point; calculating the driving distance from the second position point to the first intersection track point, and generating a corresponding second driving distance;

8. An electronic device, comprising: a memory, a processor, and a transceiver;

the processor is used for being coupled with the memory, reading and executing the instructions in the memory to realize the method steps of any one of claims 1-7;

9. A computer-readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-7.