CN116279469B - Automatic driving vehicle following method and system - Google Patents

Automatic driving vehicle following method and system Download PDF

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Publication number
CN116279469B
CN116279469B CN202310572309.7A CN202310572309A CN116279469B CN 116279469 B CN116279469 B CN 116279469B CN 202310572309 A CN202310572309 A CN 202310572309A CN 116279469 B CN116279469 B CN 116279469B
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vehicle
stability
data
speed
running
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CN116279469A (en
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祝实
施小龙
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Anhui Zhongke Xingchi Automatic Driving Technology Co ltd
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Anhui Zhongke Xingchi Automatic Driving Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/005—Handover processes
    • B60W60/0051—Handover processes from occupants to vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo or light sensitive means, e.g. infrared sensors
    • B60W2420/403—Image sensing, e.g. optical camera
    • B60W2420/408—
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/54—Audio sensitive means, e.g. ultrasound
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics

Abstract

The application is applicable to the technical field of automatic driving, and particularly relates to an automatic driving vehicle following method and system, wherein the method comprises the following steps: receiving an automatic driving instruction, and setting a following speed and a following target according to the automatic driving instruction; collecting information of surrounding vehicles to obtain vehicle running state data; judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles; and selecting the vehicle with the highest comprehensive stability as a target vehicle, changing the road, and following the target vehicle. According to the method and the system, information can be acquired on surrounding vehicles autonomously, the running stability of each vehicle is evaluated according to the acquired information, the vehicle with the highest running stability is selected as the target vehicle, then the target vehicle is followed, and target detection is continuously carried out in the running process, so that the vehicle can be ensured to select the vehicle with the highest stability as the target vehicle, and the running safety is ensured.

Description

Automatic driving vehicle following method and system
Technical Field
The application belongs to the technical field of automatic driving, and particularly relates to an automatic driving vehicle following method and system.
Background
The automatic driving automobile relies on cooperation of artificial intelligence, visual computing, radar, monitoring device and global positioning system, so that the computer can automatically and safely operate the motor vehicle without any active operation of human beings.
In the current automatic driving automobile, the automobile can carry out autonomous speed adjustment along with the front automobile, however, when the front automobile is abnormal, the automobile can only respond according to the parking automobile condition, and the proper automobile following target is difficult to select according to the driving condition of the front automobile.
Disclosure of Invention
The embodiment of the application aims to provide an automatic driving vehicle following method, which aims to solve the problems that when a front vehicle is abnormal, the automatic driving vehicle following method can only respond according to the parking condition and is difficult to select a proper following target according to the driving condition of the front vehicle.
The embodiment of the application is realized in such a way that an automatic driving vehicle follows a method, the method comprises the following steps:
receiving an automatic driving instruction, and setting a following speed and a following target according to the automatic driving instruction;
collecting information of surrounding vehicles to obtain vehicle running state data;
judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles;
and selecting the vehicle with the highest comprehensive stability as a target vehicle, changing the road, and following the target vehicle.
Preferably, the step of acquiring information of surrounding vehicles to obtain vehicle running state data specifically includes:
video acquisition is carried out on surrounding vehicles, and license plate numbers of all vehicles are determined through image recognition;
extracting pictures from the acquired video, and determining basic driving data of each vehicle;
and recording the running speed of each vehicle, and generating vehicle running state data by combining the basic running data.
Preferably, the step of determining the driving stability of each surrounding vehicle according to the driving state data of the vehicle, calculating the comprehensive stability according to the driving stability, and sequencing the surrounding vehicles specifically includes:
extracting vehicle speed data and vehicle light data according to the vehicle running state data;
judging the frequency of occurrence of emergencies in the running process of the vehicle according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability;
and calculating comprehensive stability according to the automatic driving instruction, and sequencing the surrounding vehicles according to the comprehensive stability.
Preferably, the step of selecting the vehicle with the highest comprehensive stability as the target vehicle, performing lane changing, and following the target vehicle specifically includes:
according to the sequencing condition of the comprehensive stability, determining the corresponding vehicle as a target vehicle, and locking the target vehicle;
selecting proper time to change the lane and follow according to the lane in which the target vehicle is positioned;
and detecting surrounding vehicles in real time in the following process, and switching the target vehicle when the vehicle with higher comprehensive stability appears.
Preferably, the basic driving data at least comprises lane data and light data.
Preferably, stability consideration priority is set in the automatic driving instruction.
It is a further object of an embodiment of the present application to provide an autonomous vehicle following system, the system comprising:
the instruction receiving module is used for receiving an automatic driving instruction and setting a following speed and a following target according to the automatic driving instruction;
the data acquisition module is used for acquiring information of surrounding vehicles to obtain vehicle running state data;
the vehicle stability evaluation module is used for judging the running stability of each peripheral vehicle according to the vehicle running state data, calculating the comprehensive stability according to the running stability and sequencing the peripheral vehicles;
and the target vehicle following module is used for selecting the vehicle with the highest comprehensive stability as the target vehicle, changing the road and following the target vehicle.
Preferably, the data acquisition module includes:
the license plate number identification unit is used for carrying out video acquisition on surrounding vehicles and determining license plate numbers of all the vehicles through image identification;
the image recognition unit is used for extracting pictures of the acquired videos and determining basic driving data of each vehicle;
and the driving data aggregation unit is used for recording the driving speeds of all vehicles and generating vehicle driving state data by combining the basic driving data.
Preferably, the vehicle stability evaluation module includes:
the driving data extraction unit is used for extracting vehicle speed data and vehicle light data according to the vehicle driving state data;
the stability evaluation unit is used for judging the frequency of the occurrence of the emergency in the running process of the vehicle according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability;
and the vehicle sequencing unit is used for calculating the comprehensive stability according to the automatic driving instruction and sequencing the surrounding vehicles according to the comprehensive stability.
Preferably, the target vehicle following module includes:
the target vehicle locking unit is used for determining the corresponding vehicle as a target vehicle according to the sequencing situation of the comprehensive stability and locking the corresponding vehicle;
the vehicle following unit is used for selecting proper time to change the lane and follow according to the lane in which the target vehicle is positioned;
and the target switching unit is used for detecting surrounding vehicles in real time in the following process, and switching the target vehicles when the vehicles with higher comprehensive stability appear.
According to the automatic driving vehicle following method provided by the embodiment of the application, information can be acquired automatically for surrounding vehicles, the running stability of each vehicle is evaluated according to the acquired information, the vehicle with the highest running stability is selected as the target vehicle, then the vehicle is followed, and the target detection is continuously carried out in the running process, so that the vehicle can be ensured to select the vehicle with the highest stability as the target vehicle, and the running safety is ensured.
Drawings
FIG. 1 is a flow chart of an autonomous vehicle following method according to an embodiment of the present application;
FIG. 2 is a flowchart of steps for acquiring information of surrounding vehicles to obtain vehicle driving status data according to an embodiment of the present application;
FIG. 3 is a flowchart showing steps for determining the driving stability of each peripheral vehicle according to the driving state data of the vehicle, calculating the comprehensive stability according to the driving stability, and sorting the peripheral vehicles according to the embodiment of the present application;
FIG. 4 is a flowchart of steps for selecting a vehicle with highest comprehensive stability as a target vehicle, performing lane changing, and following the target vehicle according to the embodiment of the present application;
FIG. 5 is a block diagram of an autonomous vehicle following system according to an embodiment of the present application;
FIG. 6 is a block diagram of a data acquisition module according to an embodiment of the present application;
fig. 7 is a schematic diagram of a vehicle stability evaluation module according to an embodiment of the present application;
fig. 8 is a schematic diagram of a target vehicle following module according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, a flowchart of an automatic driving vehicle following method according to an embodiment of the present application is provided, where the method includes:
s100, receiving an automatic driving instruction, and setting a following speed and a following target according to the automatic driving instruction.
In this step, an automatic driving instruction is received, the driver selects to enter an automatic driving mode during driving, the automatic driving instruction is set, the automatic driving instruction at least includes an automatic driving speed, a following target and a stability consideration priority, the automatic driving speed is the maximum speed that the automatic driving vehicle should keep during driving, the following target is the 1 st vehicle that follows when entering the automatic driving mode, the policy target is the autonomous selection of the driver, the stability consideration priority is to prioritize speed stability or vehicle control stability when determining the target vehicle, the speed stability is whether the speed of the preceding vehicle is suddenly changed during driving, the stability is inspected according to the speed change, and the vehicle control stability is whether frequent lane change occurs or the vehicle does not drive according to the regulation system during lane change or normal driving.
S200, collecting information of surrounding vehicles to obtain vehicle running state data.
In this step, information acquisition is performed on surrounding vehicles, factors influencing vehicle safety in the vehicle driving process are very many, such as the speed of the vehicle, whether frequent rapid acceleration or rapid deceleration occurs in the vehicle in the driving process, whether frequent lane changing occurs in the vehicle in the driving process, whether corresponding indicator lamps are turned on in the lane changing process, and when information acquisition is performed, video data acquisition is performed, radar data acquisition is also performed, video data is derived from a camera installed on the vehicle, radar data is derived from an ultrasonic sensor arranged on the vehicle, a laser radar sensor and an ultrasonic radar sensor, and the radar data and the video data are collected and stored as vehicle driving state data.
S300, judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles.
In the step, the running stability of each peripheral vehicle is judged according to the running state data of the vehicle, the running data of the nearby vehicle is acquired in the last step, the situation of whether the vehicle has rapid acceleration or rapid deceleration is judged according to the acquired video data and radar data, the number of times of rapid acceleration or rapid deceleration of each vehicle is counted, the number of times of vehicle lane changing is recognized in the process, the corresponding steering lamp is started or not in the lane changing process, the running stability of the vehicle is judged according to the data, the normal stability comprises two aspects, namely, the speed stability is on one hand, the vehicle control stability is on the other hand, the comprehensive stability is calculated according to the automatic driving instruction, the speed stability and the vehicle control stability, and the peripheral vehicles are ordered according to the comprehensive stability.
S400, selecting the vehicle with the highest comprehensive stability as a target vehicle, changing the road, and following the target vehicle.
In the step, the vehicle with the highest comprehensive stability is selected as the target vehicle, the following vehicle is autonomously selected by a driver when the vehicle initially enters an automatic driving mode, the driving stability of the following vehicle is not checked, the surrounding vehicles are subjected to data acquisition in the driving process, the stability of the surrounding vehicles is determined through analysis, the vehicle with the highest numerical value is selected as the target vehicle of the surrounding vehicles according to the comprehensive stability, the current target vehicle is controlled to change the lane, so that the following vehicle is driven, the vehicle with the higher comprehensive stability appears in the driving process, the lane is switched to the target vehicle, the newly appearing target vehicle is driven, namely, the dynamic selection process exists in the whole process, and the vehicle with the highest stability is selected as the carsickness target each time.
As shown in fig. 2, as a preferred embodiment of the present application, the step of acquiring information of the surrounding vehicles to obtain vehicle driving status data specifically includes:
s201, video acquisition is carried out on surrounding vehicles, and license plate numbers of all vehicles are determined through image recognition.
In the step, video acquisition is carried out on surrounding vehicles, all cameras of the current vehicle are started when the video acquisition is carried out, video data are acquired through the cameras, license plate numbers of nearby vehicles are recorded in the video, license plate numbers of all vehicles are extracted by utilizing an image recognition technology, and then the license plate numbers are stored in units of license plate numbers when the data are stored.
S202, extracting pictures of the acquired videos, and determining basic driving data of each vehicle.
In this step, the collected video is extracted at a high frame rate, and part of the pictures are extracted from the preset collecting interval layer, and by comparing the pictures, whether the lane change condition occurs in each vehicle is determined, whether the corresponding turn signal lamp is turned on when the vehicle changes lanes, lane change is performed on the vehicle, and the number of times of turning on the turn signal lamp is recorded, so that basic driving data are obtained.
S203, recording the running speeds of the vehicles, and generating vehicle running state data by combining the basic running data.
In this step, the driving speed of each vehicle is calculated by an image recognition technique, specifically, by setting a marker in a screen, determining the time when other vehicle bodies pass the marker, estimating the average relative speed of the vehicle according to the information, adding the average relative speed of the vehicle to the running speed of the vehicle, and obtaining the running speed of the vehicle, wherein the running speed is an estimated value, and combining the running speed with basic running data to obtain the running state data of the vehicle.
As shown in fig. 3, as a preferred embodiment of the present application, the steps of determining the driving stability of each surrounding vehicle according to the driving state data of the vehicle, calculating the comprehensive stability according to the driving stability, and sorting the surrounding vehicles specifically include:
s301, extracting vehicle speed data and vehicle light data according to vehicle running state data.
In the step, data are carried out according to the running state data of the vehicle, and the license plate number is identified, so that when the vehicle is extracted, only one piece of data corresponding to the license plate number is extracted at a time, and the vehicle digital data and the light data of the vehicle corresponding to the vehicle scheme can be determined.
S302, judging the frequency of occurrence of emergencies of the vehicle in the running process according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability.
In the step, vehicle control judgment is carried out according to vehicle speed data and vehicle light data, a vehicle speed curve is specifically constructed according to the vehicle speed data, the curvature of each point in the vehicle speed curve is identified, the water ratio exceeds the preset value, the speed mutation exists once, the occurrence times of the speed are recorded, the lane change condition of each vehicle is determined, when the vehicle changes lanes, whether a turn light of the vehicle is started or not is judged, if the turn light is not started, the number of times of illegal driving of the vehicle is regarded as being recorded, the distance travelled by the vehicle, the speed mutation times and the vehicle illegal driving test are imported into an evaluation model, and the speed stability and the vehicle control stability are obtained, and are scored by numbers.
S303, calculating comprehensive stability according to the automatic driving instruction, and sequencing the surrounding vehicles according to the comprehensive stability.
In this step, the comprehensive stability is calculated according to the automatic driving instruction, and weights of two types of stability are set in the automatic driving instruction, for example, the weight of the speed stability is 0.7, the weight of the vehicle control stability is 0.3, the weights of the speed stability and the vehicle control stability are multiplied by corresponding scores and summed, so that the comprehensive stability can be obtained, and the vehicles are ranked according to the calculated comprehensive stability.
As shown in fig. 4, as a preferred embodiment of the present application, the step of selecting the vehicle with the highest comprehensive stability as the target vehicle, performing lane change, and following the target vehicle specifically includes:
s401, determining the corresponding vehicle as a target vehicle according to the ordering condition of the comprehensive stability, and locking the target vehicle.
In the step, vehicle selection is performed according to the sequencing situation of the comprehensive stability, and the higher the comprehensive stability is, the less driving fluctuation of the vehicle in the driving process is indicated, in other words, the sudden braking or sudden acceleration of the front vehicle in the driving process is indicated, the fewer the situation of illegal lane change is indicated, the vehicle is used as an automatic driving vehicle, the less the situation of the change of the current vehicle is indicated, the better the automatic driving condition is, and the automatic driving vehicle is facilitated to control.
S402, selecting proper time to change the lane and follow according to the lane in which the target vehicle is located.
In this step, road recognition is performed according to the lane in which the target vehicle is located, and when a suitable lane change condition is detected, lane change is actively performed to travel to the rear of the vehicle, and then a following operation is performed to control the vehicle speed so as to stably travel in the rear of the front vehicle.
S403, detecting surrounding vehicles in real time in the following process, and switching the target vehicle when a vehicle with higher comprehensive stability appears.
In the step, not only the comprehensive stability calculation is performed on the current vehicle in the driving process, but also the comprehensive stability calculation is performed on the surrounding vehicles at the same time, and the two are compared, if the vehicles with higher comprehensive stability exist on the surrounding vehicles, the target vehicle is switched, and lane changing is performed again.
As shown in fig. 5, an automatic driving vehicle following system according to an embodiment of the present application includes:
the command receiving module 100 is configured to receive an autopilot command, and set a following speed and a following target according to the autopilot command.
In the system, the command receiving module 100 receives an automatic driving command, a driver selects to enter an automatic driving mode during driving, the automatic driving command is set, the automatic driving command at least comprises an automatic driving speed, a following target and a stability consideration priority, the automatic driving speed is the maximum speed that an automatic driving vehicle should keep during driving, the following target is the 1 st vehicle that starts to enter the automatic driving mode, the policy target is selected by the driver, the stability consideration priority is whether to prioritize speed stability or vehicle control stability when determining the target vehicle, the speed stability is whether the speed of a preceding vehicle is suddenly changed during driving, the stability is inspected according to the speed change of the preceding vehicle, and the vehicle control stability is whether frequent lane changes or driving is not performed according to regulations during lane changes or normal driving of the vehicle.
The data acquisition module 200 is configured to acquire information of surrounding vehicles, and obtain vehicle driving status data.
In the system, the data acquisition module 200 performs information acquisition on surrounding vehicles, and has a very large number of factors affecting the safety of the vehicles in the driving process of the vehicles, such as the speed of the vehicles, whether the vehicles frequently accelerate or decelerate suddenly in the driving process, whether the vehicles frequently change lanes in the driving process, whether the vehicles turn on corresponding indicator lamps in the lane changing process, and when the information acquisition is performed, the video data acquisition is performed, the radar data acquisition is also performed, the video data is derived from a camera installed on the vehicles, the radar data is derived from an ultrasonic sensor, a laser radar sensor and an ultrasonic radar sensor which are arranged on the vehicles, and the radar data and the video data are collected and stored as vehicle driving state data.
The vehicle stability evaluation module 300 is configured to determine the driving stability of each surrounding vehicle according to the vehicle driving state data, calculate the comprehensive stability according to the driving stability, and rank the surrounding vehicles.
In the system, the vehicle stability evaluation module 300 determines the running stability of each surrounding vehicle according to the running state data of the vehicle, the running data of the nearby vehicle is acquired in the last step, analysis is carried out according to the acquired video data and radar data, whether the vehicle has rapid acceleration or rapid deceleration is determined, the number of times of rapid acceleration or rapid deceleration of each vehicle is counted, the number of times of lane changing of the vehicle is identified in the process, whether corresponding turn lamps are started during lane changing, the running stability of the vehicle is determined according to the data, and the normal stability comprises two aspects, namely speed stability on one hand and vehicle control stability on the other hand, comprehensive stability is calculated according to automatic driving instructions, speed stability and vehicle control stability, and the surrounding vehicles are ordered according to the comprehensive stability.
The target vehicle following module 400 is configured to select a vehicle with the highest comprehensive stability as a target vehicle, perform lane changing, and follow the target vehicle.
In the system, the target vehicle following module 400 selects the vehicle with the highest comprehensive stability as the target vehicle, when the vehicle initially enters the automatic driving mode, the following vehicle is autonomously selected by the driver and is not checked for driving stability, because the surrounding vehicles are subjected to data acquisition in the driving process and the stability of the surrounding vehicles is determined through analysis, the vehicle with the highest numerical value is selected as the target vehicle of the surrounding vehicles, the current target vehicle is controlled to change the lane according to the comprehensive stability, so that the vehicle with the higher comprehensive stability appears in the driving process and the target vehicle is switched to change the lane, and the newly appearing target vehicle is followed, namely, the dynamic selection process exists in the whole process, and the vehicle with the highest stability is selected as the carsickness target each time, so that safer driving conditions are provided for the automatic driving vehicle due to the stability of the preceding vehicle in the driving process.
As shown in fig. 6, as a preferred embodiment of the present application, the data acquisition module 200 includes:
the license plate number recognition unit 201 is configured to perform video acquisition on surrounding vehicles, and determine license plate numbers of the respective vehicles through image recognition.
In this module, the license plate number recognition unit 201 performs video collection on surrounding vehicles, and when video collection is performed, all cameras of the current vehicle are turned on, video data are collected through the cameras, license plate numbers of nearby vehicles are recorded in the video, license plate numbers of the respective vehicles are extracted by using an image recognition technology, and then when data are stored, the license plate numbers are stored as units.
The image recognition unit 202 is configured to perform image extraction on the acquired video, and determine basic driving data of each vehicle.
In this module, the image recognition unit 202 performs image extraction on the acquired video, where the acquired video is generally of a high frame rate, extracts part of the images from the preset acquisition spacer layer, determines whether each vehicle has a lane change condition by comparing the images, turns on the corresponding turn signal lamp when the vehicle changes lanes, performs lane change on the vehicle, and records the number of times the turn signal lamp is turned on to obtain basic driving data.
And a driving data aggregation unit 203, configured to record the driving speeds of the vehicles, and generate vehicle driving status data in combination with the basic driving data.
In this module, the driving data aggregation unit 203 calculates the driving speed of each vehicle through the image recognition technology, specifically, sets a marking in a picture, determines the time when other vehicle bodies pass the marking, estimates the average relative speed of the vehicle according to the information, adds the average relative speed of the vehicle to the driving speed of the vehicle, and obtains the driving speed of the vehicle, wherein the driving speed is the estimated value, and combines the driving speed with the basic driving data to obtain the driving state data of the vehicle.
As shown in fig. 7, as a preferred embodiment of the present application, the vehicle stability evaluation module 300 includes:
the driving data extracting unit 301 is configured to extract vehicle speed data and vehicle light data according to vehicle driving state data.
In this module, the driving data extracting unit 301 performs data according to the driving state data of the vehicle, and since license plate number identification has already been performed, only one piece of data corresponding to a license plate number is extracted at a time during extraction, and the vehicle digital data and the light data of the vehicle corresponding to the driving scheme can be determined.
And the stability evaluation unit 302 is configured to determine, according to the vehicle speed data and the vehicle light data, the frequency of occurrence of an emergency in the running process of the vehicle, and evaluate the running stability of each vehicle according to a preset evaluation model, where the running stability includes speed stability and vehicle control stability.
In this module, the stability evaluation unit 302 performs vehicle control determination according to the vehicle speed data and the vehicle light data, specifically constructs a vehicle speed curve according to the vehicle speed data, identifies the curvature of each point in the vehicle speed curve, indicates that the water ratio exceeds a preset value, has a speed mutation, records the occurrence times of the speed, determines the lane change condition of each vehicle, determines whether the turn signal lamp is turned on when the vehicle changes lanes, considers that the turn signal lamp is turned on, if the turn signal lamp is not turned on, considers that the vehicle is in illegal driving, records the number of times of illegal driving of the vehicle, and introduces the distance travelled by the vehicle, the speed mutation times and the vehicle illegal driving test into the evaluation model to obtain the speed stability and the vehicle control stability, and scores the speed stability and the vehicle control stability by numbers.
And a vehicle ranking unit 303 for calculating the comprehensive stability according to the automatic driving instruction and ranking the surrounding vehicles according to the comprehensive stability.
In this module, the vehicle ranking unit 303 calculates the comprehensive stability according to the automatic driving instruction, sets weights of two types of stability in the automatic driving instruction, for example, the weight of the speed stability is 0.7, the weight of the vehicle control stability is 0.3, multiplies the weights of the speed stability and the vehicle control stability by the corresponding scores and sums up to obtain the comprehensive stability, and ranks the vehicles according to the calculated comprehensive stability.
As shown in fig. 8, as a preferred embodiment of the present application, the target vehicle following module 400 includes:
the target vehicle locking unit 401 is configured to determine that the corresponding vehicle is a target vehicle according to the ranking condition of the integrated stability, and lock the target vehicle.
In this module, the target vehicle locking unit 401 performs vehicle selection according to the sorting condition of the comprehensive stability, and the higher the comprehensive stability, the less driving fluctuation of the vehicle occurs in the driving process, in other words, the sudden braking or sudden acceleration of the front vehicle occurs in the driving process, and the fewer the situation of illegal lane change is, as an automatic driving vehicle, the less the situation of the change of the current vehicle is, and then the better the automatic driving condition is, so as to be beneficial to the control of the automatic driving vehicle.
The vehicle following unit 402 is configured to select an appropriate time to change lanes and follow the lane in which the target vehicle is located.
In this module, the vehicle following unit 402 performs road recognition according to the lane in which the target vehicle is located, and when a suitable lane change condition is detected, actively performs lane change to travel to the rear of the vehicle, and then performs a following operation to control the vehicle speed so as to stably travel in the rear of the front vehicle.
And a target switching unit 403, configured to detect the surrounding vehicles in real time during the following process, and switch the target vehicles when the vehicle with higher overall stability appears.
In this module, the target switching unit 403 not only calculates the comprehensive stability of the current vehicle but also calculates the comprehensive stability of surrounding vehicles during traveling, compares the two, and switches the target vehicle if there is a vehicle with higher comprehensive stability in the surrounding, and changes the lane again.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.

Claims (9)

1. An autonomous vehicle following method, the method comprising:
receiving an automatic driving instruction, and setting a following speed and a following target according to the automatic driving instruction;
collecting information of surrounding vehicles to obtain vehicle running state data;
judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles;
selecting a vehicle with highest comprehensive stability as a target vehicle, changing lanes, and following the target vehicle;
the step of judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles specifically comprises the following steps:
extracting vehicle speed data and vehicle light data according to the vehicle running state data;
judging the frequency of occurrence of emergencies in the running process of the vehicle according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability;
calculating comprehensive stability according to the automatic driving instruction, and sequencing surrounding vehicles according to the comprehensive stability;
and carrying out vehicle control judgment according to the vehicle speed data and the vehicle light data, specifically, constructing a vehicle speed curve according to the vehicle speed data, identifying the curvature of each point in the vehicle speed curve, indicating that a speed mutation exists when the curvature exceeds a preset value, recording the frequency of occurrence of the speed mutation, determining the lane change condition of each vehicle, judging whether a turn light of the vehicle is turned on or not when the vehicle changes lanes, if the turn light is not turned on, considering illegal driving, recording the frequency of illegal driving of the vehicle, and importing the distance travelled by the vehicle, the frequency of the speed mutation and the frequency of illegal driving of the vehicle into an evaluation model to obtain speed stability and vehicle control stability, wherein the speed stability and the vehicle control stability are scored by numbers.
2. The method for automatically following a vehicle according to claim 1, wherein the step of acquiring information of surrounding vehicles to obtain vehicle running state data comprises the steps of:
video acquisition is carried out on surrounding vehicles, and license plate numbers of all vehicles are determined through image recognition;
extracting pictures from the acquired video, and determining basic driving data of each vehicle;
and recording the running speed of each vehicle, and generating vehicle running state data by combining the basic running data.
3. The method for automatically following a vehicle according to claim 1, wherein the step of selecting the vehicle with the highest overall stability as the target vehicle, performing lane change, and following the target vehicle specifically comprises:
according to the sequencing condition of the comprehensive stability, determining the corresponding vehicle as a target vehicle, and locking the target vehicle;
selecting proper time to change the lane and follow according to the lane in which the target vehicle is positioned;
and detecting surrounding vehicles in real time in the following process, and switching the target vehicle when the vehicle with higher comprehensive stability appears.
4. The autonomous vehicle following method of claim 2, wherein the base drive data comprises at least lane data and light data.
5. The automated driving vehicle following method according to claim 2, wherein a stability consideration priority is set in the automated driving instructions.
6. An autonomous vehicle following system, the system comprising:
the instruction receiving module is used for receiving an automatic driving instruction and setting a following speed and a following target according to the automatic driving instruction;
the data acquisition module is used for acquiring information of surrounding vehicles to obtain vehicle running state data;
a vehicle stability evaluation module for determining the running stability of each of the surrounding vehicles based on the vehicle running state data,
calculating comprehensive stability according to the driving stability, and sequencing surrounding vehicles;
the target vehicle following module is used for selecting the vehicle with the highest comprehensive stability as a target vehicle, changing the lane and following the target vehicle;
the step of judging the running stability of each peripheral vehicle according to the running state data of the vehicles, calculating the comprehensive stability according to the running stability, and sequencing the peripheral vehicles specifically comprises the following steps:
extracting vehicle speed data and vehicle light data according to the vehicle running state data;
judging the frequency of occurrence of emergencies in the running process of the vehicle according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability;
calculating comprehensive stability according to the automatic driving instruction, and sequencing surrounding vehicles according to the comprehensive stability;
and carrying out vehicle control judgment according to the vehicle speed data and the vehicle light data, specifically, constructing a vehicle speed curve according to the vehicle speed data, identifying the curvature of each point in the vehicle speed curve, indicating that a speed mutation exists when the curvature exceeds a preset value, recording the frequency of occurrence of the speed mutation, determining the lane change condition of each vehicle, judging whether a turn light of the vehicle is turned on or not when the vehicle changes lanes, if the turn light is not turned on, considering illegal driving, recording the frequency of illegal driving of the vehicle, and importing the distance travelled by the vehicle, the frequency of the speed mutation and the frequency of illegal driving of the vehicle into an evaluation model to obtain speed stability and vehicle control stability, wherein the speed stability and the vehicle control stability are scored by numbers.
7. The autonomous vehicle following system of claim 6, wherein the data acquisition module comprises:
the license plate number identification unit is used for carrying out video acquisition on surrounding vehicles and determining license plate numbers of all the vehicles through image identification;
the image recognition unit is used for extracting pictures of the acquired videos and determining basic driving data of each vehicle;
and the driving data aggregation unit is used for recording the driving speeds of all vehicles and generating vehicle driving state data by combining the basic driving data.
8. The autonomous vehicle following system of claim 6, wherein the vehicle stability evaluation module comprises:
the driving data extraction unit is used for extracting vehicle speed data and vehicle light data according to the vehicle driving state data;
the stability evaluation unit is used for judging the frequency of the occurrence of the emergency in the running process of the vehicle according to the vehicle speed data and the vehicle light data, and evaluating the running stability of each vehicle according to a preset evaluation model, wherein the running stability comprises speed stability and vehicle control stability;
and the vehicle sequencing unit is used for calculating the comprehensive stability according to the automatic driving instruction and sequencing the surrounding vehicles according to the comprehensive stability.
9. The autonomous vehicle following system of claim 6, wherein the target vehicle following module comprises:
the target vehicle locking unit is used for determining the corresponding vehicle as a target vehicle according to the sequencing situation of the comprehensive stability and locking the corresponding vehicle;
the vehicle following unit is used for selecting proper time to change the lane and follow according to the lane in which the target vehicle is positioned;
and the target switching unit is used for detecting surrounding vehicles in real time in the following process, and switching the target vehicles when the vehicles with higher comprehensive stability appear.
CN202310572309.7A 2023-05-22 2023-05-22 Automatic driving vehicle following method and system Active CN116279469B (en)

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