WO2023005681A1 - 纵向跟车控制方法、装置、电子设备及存储介质 - Google Patents

纵向跟车控制方法、装置、电子设备及存储介质 Download PDF

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WO2023005681A1
WO2023005681A1 PCT/CN2022/105798 CN2022105798W WO2023005681A1 WO 2023005681 A1 WO2023005681 A1 WO 2023005681A1 CN 2022105798 W CN2022105798 W CN 2022105798W WO 2023005681 A1 WO2023005681 A1 WO 2023005681A1
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vehicle
target vehicle
level
data
driving
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English (en)
French (fr)
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李伟男
刘斌
吴杭哲
陈博
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FAW Group Corp
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FAW Group Corp
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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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding 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
    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/162—Speed limiting therefor
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed

Definitions

  • the embodiments of the present application relate to the field of computer technology, for example, to a longitudinal vehicle following control method, device, electronic equipment, and storage medium.
  • the longitudinal control of the vehicle can be understood as the automatic acceleration and deceleration control of the vehicle longitudinal direction.
  • related technologies usually use a neural network to dynamically predict the acceleration of the vehicle.
  • the related technologies do not take into account the driving habits of drivers, and cannot meet the differentiated driving demands of drivers with different driving habits.
  • Embodiments of the present application provide a longitudinal car following control method, device, electronic device, and storage medium, so as to realize longitudinal car following control based on the driving habits of the user and improve the driving experience of the user.
  • the embodiment of the present application provides a longitudinal vehicle following control method, including:
  • the driving condition of the target vehicle is a congestion condition, determining the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle, and determining the vehicle response of the target vehicle based on the vehicle response data of the target vehicle characteristic level;
  • the embodiment of the present application also provides a longitudinal vehicle following control device, including:
  • a characteristic determination module configured to determine the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle if the driving condition of the target vehicle is a congested condition, and determine the level of the user operation characteristics of the target vehicle based on the vehicle response data of the target vehicle. the vehicle response characteristic level of the target vehicle;
  • a driving habit determination module configured to determine a user driving habit level of the target vehicle based on the user operating characteristic level and the vehicle response characteristic level;
  • the acceleration determination module is configured to determine the current following mode of the target vehicle, and determine the vehicle acceleration of the target vehicle in the current following mode based on the user's driving habit level.
  • the embodiment of the present application further provides an electronic device, the electronic device comprising:
  • storage means configured to store at least one program
  • the at least one processor When the at least one program is executed by the at least one processor, the at least one processor is made to implement the longitudinal vehicle following control method provided in any embodiment of the present application.
  • the embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the longitudinal vehicle following control method as provided in any embodiment of the present application is implemented.
  • FIG. 1 is a schematic flowchart of a longitudinal vehicle following control method provided in Embodiment 1 of the present application;
  • FIG. 2 is a schematic flowchart of a longitudinal vehicle following control method provided in Embodiment 2 of the present application;
  • FIG. 3 is a schematic flowchart of a longitudinal vehicle following control method provided in Embodiment 3 of the present application;
  • FIG. 4 is a schematic structural diagram of a longitudinal vehicle following control device provided in Embodiment 4 of the present application.
  • FIG. 5 is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present application.
  • Fig. 1 is a schematic flow chart of a longitudinal car following control method provided in Embodiment 1 of the present application.
  • This embodiment is applicable to controlling the longitudinal car following of the vehicle according to the user's driving habit level when the driving condition of the vehicle is a congested condition.
  • the method can be performed by a longitudinal vehicle following control device, which can be implemented by hardware and/or software, and the method includes the following steps:
  • the driving condition of the target vehicle is a congestion condition, determine the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle, and determine the vehicle response characteristic level of the target vehicle based on the vehicle response data of the target vehicle.
  • the driving condition of the target vehicle may be determined according to current vehicle driving data of the target vehicle.
  • the driving condition of the target vehicle is determined based on data such as average vehicle speed, average acceleration, average vehicle speed of the preceding vehicle in the lane where the target vehicle is located, and average vehicle speed of the preceding vehicle in the adjacent lane of the lane where the target vehicle is located.
  • the driving condition of the target vehicle can also be determined according to the working condition image or working condition video collected by the image acquisition device on the target vehicle.
  • the driving condition of the target vehicle is a congestion working condition; or, according to the working condition image or working condition
  • the video analysis shows that the lane where the target vehicle is located and the number of lane changes of the preceding vehicle in the adjacent lane exceeds the preset number threshold, it is determined that the driving condition of the target vehicle is a congestion condition, etc.
  • the user operation characteristic level of the target vehicle is determined according to the user operation data of the target vehicle
  • the vehicle response of the target vehicle is determined according to the vehicle response data of the target vehicle feature level.
  • the user operation data may be the user's operation data on the target vehicle, for example, data on the user's operation of the accelerator pedal, brake pedal, and vehicle lane change of the target vehicle.
  • the accelerator pedal data of the target vehicle operated by the user may be the displacement of the accelerator pedal
  • the data of the brake pedal may be the displacement of the brake pedal
  • the data of the vehicle lane change may be the lateral displacement of the vehicle.
  • the vehicle response data can be the driving-related data of the target vehicle during the data collection period, such as the reciprocal of the average headway during the data collection period, the variance value of the vehicle speed during the data collection period, and so on.
  • the user's operating characteristic level may be a level representing the driver's operating characteristic.
  • the user operation characteristic level may be determined according to the user operation data.
  • the user's operation reference value may be calculated based on the user's accelerator pedal displacement and/or brake pedal displacement, and based on comparing the calculated operation reference value with standard values corresponding to a plurality of preset operation characteristic levels, determine The user operating characteristic level of the target vehicle is obtained.
  • the user's accelerator pedal displacement can also be compared with standard accelerator pedal displacements corresponding to multiple preset operating characteristic levels, and/or, the user's brake pedal displacement can be compared with preset multiple The standard brake pedal displacement corresponding to each operating characteristic level is compared to determine the user operating characteristic level of the target vehicle.
  • the vehicle response characteristic level may be a level representing the target vehicle response characteristic.
  • the vehicle response characteristic level may be determined according to the vehicle response data.
  • the response reference value of the target vehicle can be calculated based on the reciprocal of the average headway time in the data collection period and/or the variance value of the vehicle speed in the data collection period, based on the calculated response reference value and the preset Standard values corresponding to multiple vehicle response characteristic levels are compared to determine the vehicle response characteristic level of the target vehicle.
  • the user driving habit level of the target vehicle may be determined according to the user operation characteristic level and the vehicle response characteristic level.
  • the user's driving habit level may be a level used to characterize the user's driving habit.
  • the user operation characteristic level and the vehicle response characteristic level and the preset level mapping relationship can be The operating characteristic level is compared with the preset response characteristic level, and then the corresponding user's driving habit level is determined.
  • the user's driving habit level includes extremely conservative, conservative, normal, aggressive and extremely aggressive.
  • the current following mode of the target vehicle includes but not limited to a stable following mode, an acceleration mode without a vehicle in front, and a rapid deceleration mode.
  • the current following mode of the target vehicle may be determined according to the driving information of the vehicle ahead of the target vehicle.
  • the target vehicle when the vehicle in front of the target vehicle is running stably, the target vehicle is longitudinally controlled to enter the stable follow-up mode; Control the target vehicle to accelerate without the vehicle in front; when the vehicle in front of the target vehicle brakes suddenly or the vehicle in the adjacent lane of the target vehicle's lane forcibly merges into the line, the target vehicle is longitudinally controlled to enter the rapid deceleration mode.
  • the vehicle acceleration of the target vehicle in the current car-following mode may be determined according to the driving habit level of the user.
  • determining the current car-following mode of the target vehicle, and determining the vehicle acceleration of the target vehicle in the current car-following mode based on the user's driving habit level include at least one of the following:
  • the target vehicle is in the stable following mode.
  • Vehicle acceleration in car mode based on the user's driving habit level, the current distance between the target vehicle and the vehicle in front, the current speed of the vehicle in front, and the current speed of the target vehicle, it is determined that the target vehicle is in the stable following mode.
  • the current following mode of the target vehicle is the no-front-vehicle acceleration mode
  • the current speed of the target vehicle and the backup acceleration of the target vehicle determine the vehicle acceleration of the target vehicle in the no-front-vehicle acceleration mode
  • the current following mode of the target vehicle is the rapid deceleration mode
  • the backup deceleration of the target vehicle is the full deceleration of the target vehicle, the current speed of the preceding vehicle, the current speed of the target vehicle and the target vehicle
  • the current distance from the vehicle in front determines the vehicle acceleration of the target vehicle in rapid deceleration mode.
  • the vehicle acceleration in the mode can satisfy the following formula:
  • a 0 is the vehicle acceleration in the stable following mode
  • v 1 is the current vehicle speed (real-time vehicle speed) of the preceding vehicle
  • v 0 is the current vehicle speed (real-time vehicle speed) of the target vehicle
  • d r is the current speed of the target vehicle and the preceding vehicle Distance (the real-time distance between the front of the target vehicle and the rear of the vehicle in front)
  • d 0 5m
  • k s represents the user's driving habit coefficient
  • the user's driving habit coefficient can be determined according to the user's driving habit level Sure.
  • the vehicle acceleration of the target vehicle in the acceleration mode without the vehicle in front can be determined, which can satisfy the following formula:
  • a 0 is the vehicle acceleration in the no-front vehicle acceleration mode
  • a cc 1m/s 2
  • v cc 40km/h
  • v 0 indicates the target vehicle
  • k s represents the user's driving habit coefficient.
  • the target vehicle is determined based on the user's driving habit level, the backup deceleration of the target vehicle, the full deceleration of the target vehicle, the current speed of the preceding vehicle, the current speed of the target vehicle, and the current distance between the target vehicle and the preceding vehicle
  • the vehicle acceleration in the rapid deceleration mode can satisfy the following formula:
  • a 0 is the vehicle acceleration in the rapid deceleration mode
  • v 1 indicates the current vehicle speed of the vehicle in front ( real-time vehicle speed)
  • v 0 represents the current vehicle speed (real-time vehicle speed) of the target vehicle
  • d r represents the current distance between the target vehicle and the vehicle in front (the real-time distance between the front of the target vehicle and the rear of the vehicle in front)
  • k s represents the user's driving habit coefficient.
  • the current following mode of the target vehicle is divided into stable following mode, no-vehicle acceleration mode, and rapid deceleration mode, and a corresponding vehicle acceleration determination method is provided in each current following mode to realize
  • adaptive longitudinal car-following control according to driving habits improves the user's driving experience, thereby improving the applicability and safety of the vehicle system.
  • the longitudinal vehicle following control method provided in this embodiment further includes: adjusting the current acceleration of the target vehicle based on the vehicle acceleration of the target vehicle in the current vehicle following mode.
  • the user operation characteristic level is determined through the user operation data of the target vehicle
  • the vehicle response characteristic level is determined through the vehicle response data
  • the user driving habit level of the target vehicle is determined based on the user operation characteristic level and the vehicle response characteristic level
  • the driving habit characteristics of the driver of the target vehicle are obtained, so that in the current following mode, the vehicle acceleration of the target vehicle is determined based on the determined user driving habit level, and the adaptive longitudinal car following control according to the driving habit of the driver is realized.
  • the driving experience of the user is improved, thereby improving the applicability and safety of the vehicle system.
  • Fig. 2 is a schematic flow chart of a longitudinal car following control method provided in Embodiment 2 of the present application.
  • the user operation data includes accelerator pedal displacement data and brake pedal displacement data.
  • Data based on the user operation data of the target vehicle to determine the user operation characteristic level of the target vehicle, including: based on the accelerator pedal displacement data, brake pedal displacement data of the target vehicle and a pre-built first fuzzy controller, in the first fuzzy controller The user operating characteristic level of the target vehicle is determined in the corresponding level fuzzy set.
  • the longitudinal vehicle following control method includes the following steps:
  • the driving condition of the target vehicle is a congested condition, based on the accelerator pedal displacement data, brake pedal displacement data and the pre-built first fuzzy controller of the target vehicle, in the level fuzzy set corresponding to the first fuzzy controller
  • the user-operating characteristic level of the target vehicle is determined in .
  • the user operation data in the above embodiment includes accelerator pedal displacement data and brake pedal displacement data.
  • the pre-built first fuzzy controller may include a first input terminal, a second input terminal and an output terminal, wherein the first input terminal is input with the first input variable, and the second input terminal is input with the second input variable.
  • the first input variable is accelerator pedal displacement data
  • the second input variable is brake pedal displacement data.
  • the value range of the first input terminal can also be set to [0,8].
  • the fuzzy set corresponding to the first input terminal includes three levels: S (small), M (medium), L (large ); set the value range of the second input terminal to [0,12], correspondingly, the fuzzy set corresponding to the second input terminal includes three levels: S (small), M (medium), L (large).
  • the first input variable is determined according to the accelerator pedal displacement data and the fuzzy set corresponding to the first input end;
  • the second input variable is determined according to the brake pedal displacement data and the fuzzy set corresponding to the second input end.
  • the accelerator pedal displacement data is 2
  • the first input variable is S
  • the brake pedal displacement data is 12
  • the second input variable is L.
  • the hierarchical fuzzy set corresponding to the first fuzzy controller is the hierarchical fuzzy set corresponding to the output terminal of the first fuzzy controller.
  • the level fuzzy set corresponding to the first fuzzy controller includes five levels: ES (extremely small), S (small), M (medium), L (large), and EL (extremely large).
  • the user operation characteristics of the target vehicle are determined in the level fuzzy set corresponding to the first fuzzy controller
  • the level may be: based on the membership function of the first fuzzy controller, the first input variable and the second input variable, determine the user operating characteristic level of the target vehicle in the level fuzzy set corresponding to the first fuzzy controller, wherein the first One input variable is the fuzzy level corresponding to the accelerator pedal displacement data, and the second input variable is the fuzzy level corresponding to the brake pedal displacement data.
  • the membership function may be a triangular membership function to ensure the sensitivity of the output user operation characteristic level to changes of the two input variables.
  • the user operation of the target vehicle is determined in the level fuzzy set corresponding to the first fuzzy controller
  • the characteristic level can also be: based on the preset fuzzy rules of the first fuzzy controller, the first input variable and the second input variable, determine the user operation characteristic level of the target vehicle in the level fuzzy set corresponding to the first fuzzy controller .
  • the vehicle response data includes headway data and driving speed data
  • the vehicle response characteristic level of the target vehicle is determined based on the vehicle response data of the target vehicle, including: based on the headway data of the target vehicle, driving speed data and pre-built
  • the second fuzzy controller is used to determine the vehicle response characteristic level of the target vehicle in the level fuzzy set corresponding to the second fuzzy controller.
  • the headway data can be the reciprocal of the average value of the headway within the data collection period, wherein the headway represents the time difference between the front ends of two vehicles passing the same place; Calculated by dividing the distance of the head of the vehicle by the speed of the target vehicle; the driving speed data can be the variance value of the driving speed of the target vehicle within the data collection period.
  • the data acquisition cycle can be determined according to the real-time speed of the target vehicle, such as:
  • K T , C T , and CC are the real-time speed coefficients of the target vehicle, respectively.
  • the pre-built second fuzzy controller may include a first input terminal, a second input terminal and an output terminal, wherein the first input terminal inputs the first input variable, and the second input terminal inputs the second input variable.
  • the first input variable is headway data
  • the second input variable is driving speed data.
  • the value range of the first input terminal can also be set to [0,100], correspondingly, the fuzzy set corresponding to the first input terminal includes 3 levels: S (small), M (medium), L (large); Set the value range of the second input terminal to [0,120].
  • the fuzzy set corresponding to the second input terminal includes five grades ES (extremely small), S (small), M (medium), L (large), EL (extremely large).
  • the first input variable is determined according to the headway data and the fuzzy set corresponding to the first input terminal;
  • the second input variable is determined according to the driving speed data and the fuzzy set corresponding to the second input terminal.
  • the headway data is 10
  • the second input variable is EL.
  • the hierarchical fuzzy set corresponding to the second fuzzy controller is the hierarchical fuzzy set corresponding to the output terminal of the second fuzzy controller.
  • the class fuzzy set corresponding to the second fuzzy controller includes 5 classes: ES (extremely small), S (small), M (medium), L (large), and EL (extremely large).
  • determining the vehicle response characteristic level of the target vehicle in the level fuzzy set corresponding to the second fuzzy controller may be: Based on the membership function of the second fuzzy controller, the first input variable and the second input variable, the vehicle response characteristic level of the target vehicle is determined in the level fuzzy set corresponding to the second fuzzy controller, wherein the first input variable is the vehicle head The fuzzy level corresponding to the time distance data, and the second input variable is the fuzzy level corresponding to the driving speed data.
  • the membership function may be a steeper triangular membership function to ensure the sensitivity of the output vehicle response characteristic level to changes of the two input variables.
  • the vehicle response characteristic level of the target vehicle is determined in the level fuzzy set corresponding to the second fuzzy controller, and it can also be : Based on the preset fuzzy rules of the second fuzzy controller, the first input variable and the second input variable, determine the vehicle response characteristic level of the target vehicle in the level fuzzy set corresponding to the second fuzzy controller.
  • the vehicle response characteristic level of the target vehicle is determined through the pre-built second fuzzy controller, which improves the accuracy of the determined vehicle response characteristic level, thereby improving the accuracy of the user's driving habit level , which improves the technical effect of adaptive longitudinal follow-up control according to driving habits.
  • determining the user driving habit level of the target vehicle based on the user operation characteristic level and the vehicle response characteristic level includes: based on the user operation characteristic level, the vehicle response characteristic level and a pre-built third fuzzy controller, in the third fuzzy control Determine the user's driving habit level of the target vehicle in the level fuzzy set corresponding to the driver.
  • the pre-built third fuzzy controller may include a first input terminal, a second input terminal and an output terminal, wherein the first input terminal inputs the first input variable, and the second input terminal inputs the second input variable.
  • the first input variable is the user operation characteristic level
  • the second input variable is the vehicle response characteristic level.
  • the fuzzy set corresponding to the first input terminal includes 5 grades: ES (extremely small), S (small), M (medium), L (large), EL (extremely large); the fuzzy set corresponding to the second input terminal includes 5 levels There are three grades: ES (extremely small), S (small), M (medium), L (large), EL (extremely large).
  • the class fuzzy set corresponding to the third fuzzy controller includes 5 classes: EG (extremely conservative), G (conservative), M (general), A (aggressive), and EA (extremely aggressive).
  • the user's driving habit level of the target vehicle can be determined in the level fuzzy set corresponding to the third fuzzy controller.
  • the membership function may be a relatively gentle trapezoidal membership function, so as to ensure the stability of the output user's driving habit level.
  • the user's driving habit level of the target vehicle can be determined in the level fuzzy set corresponding to the third fuzzy controller.
  • the user's driving habit level of the target vehicle is determined through the pre-built third fuzzy controller, which improves the accuracy of the determined user's driving habit level and improves the adaptive longitudinal tracking according to the driving habit.
  • the pre-built third fuzzy controller improves the accuracy of the determined user's driving habit level and improves the adaptive longitudinal tracking according to the driving habit.
  • S240 Determine the current car-following mode of the target vehicle, and determine the vehicle acceleration of the target vehicle in the current car-following mode based on the user's driving habit level.
  • the user operating characteristic level of the target vehicle is determined through the pre-built first fuzzy controller, the acceleration pedal displacement data and the brake pedal displacement data, and the accuracy of the determined user operating characteristic level is improved. Furthermore, the accuracy of the user's driving habit level is improved, and the technical effect of the adaptive longitudinal car following control according to the driving habit is improved.
  • Fig. 3 is a schematic flowchart of a longitudinal vehicle following control method provided in Embodiment 3 of the present application.
  • this embodiment may optionally further include: acquiring vehicle driving data of the target vehicle; The driving data and the pre-trained driving condition discrimination model determine the driving condition of the target vehicle, wherein the driving condition includes a congestion condition.
  • the longitudinal vehicle following control method includes the following steps:
  • the driving conditions include congestion conditions.
  • the driving conditions also include non-congested conditions.
  • the vehicle driving data in this embodiment includes but not limited to the following 12 characteristic parameters:
  • the average speed of the target vehicle the arithmetic mean value of the target vehicle speed within the data collection period, excluding the idle state of the vehicle;
  • the average acceleration of the target vehicle the arithmetic mean value of the acceleration per unit time (second) of the target vehicle in the accelerated state within the data collection period;
  • the average deceleration of the target vehicle the arithmetic mean value of the deceleration per unit time (second) of the target vehicle in the deceleration state within the data collection period;
  • the average speed of the vehicle ahead in the lane where the target vehicle is located within the data collection period, the arithmetic mean of the speed of the vehicle ahead in this lane, excluding the idling state of the vehicle;
  • the average speed of the vehicle ahead in the left lane of the lane where the target vehicle is located the arithmetic mean of the speed of the vehicle ahead in the left lane within the data collection period, excluding the vehicle idling state;
  • the average speed of the vehicle ahead in the right lane of the lane where the target vehicle is located the arithmetic mean of the speed of the vehicle ahead in the right lane during the data collection period, excluding the idling state of the vehicle;
  • the average acceleration of the vehicle in front of the lane where the target vehicle is located the arithmetic mean value of the acceleration per unit time (second) of the vehicle in front of the lane in the accelerated state within the data collection period;
  • the average acceleration of the vehicle in front of the left lane of the lane where the target vehicle is located the arithmetic mean value of the acceleration per unit time (second) of the vehicle ahead in the left lane in the accelerated state within the data collection period;
  • the average acceleration of the front vehicle in the right lane of the lane where the target vehicle is located the arithmetic mean value of the acceleration per unit time (second) of the front vehicle in the right lane in the accelerated state within the data collection period;
  • the data collection period may be determined based on the calculation formula of the data collection period in the foregoing embodiments.
  • a characteristic parameter library for identification of driving conditions can be constructed, wherein the characteristic parameter library includes a plurality of vehicle driving data, and a driving condition discrimination model is trained based on the constructed characteristic parameter library.
  • the pre-trained driving condition discrimination model may be a pre-trained operating condition classification model, and the vehicle driving data is input into the pre-trained operating condition classification model to obtain the operating condition classification result of the target vehicle.
  • the vehicle driving data of the target vehicle is obtained, and the driving condition of the target vehicle is determined based on the vehicle driving data and the pre-trained driving condition discrimination model.
  • the vehicle driving data may be input into a pre-trained driving condition discrimination model, and the driving condition discrimination model determines the driving condition of the target vehicle according to the condition mean value and the estimated density function.
  • conditional mean value can be the regression of the driving condition s relative to Z, and Z is the characteristic parameter obtained according to the vehicle driving data, and the conditional mean value can satisfy the following formula:
  • the joint probability density function of the feature parameter library X and the output driving condition s is f(X,s), and f(Z,s) is the joint probability density of the vehicle driving data (actual observation value) and the driving condition s function.
  • Parzen nonparametric estimation can be applied, from the sample dataset
  • the estimated density function is obtained as:
  • the predicted value corresponding to the vehicle driving data is the predicted value corresponding to the vehicle driving data. It should be noted, Possible values are 0 or 1. Exemplarily, if the predicted value is 1, the driving condition of the target vehicle is a congested condition, and if the predicted value is 0, the driving condition of the target vehicle is a non-congested condition.
  • the driving condition of the target vehicle is a congestion condition, determine the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle, and determine the vehicle response characteristic level of the target vehicle based on the vehicle response data of the target vehicle.
  • S340 Determine the current car-following mode of the target vehicle, and determine the vehicle acceleration of the target vehicle in the current car-following mode based on the user's driving habit level.
  • the driving condition is determined according to the driving data of the vehicle and the pre-trained driving condition discrimination model, which improves the accuracy of the driving condition , thereby improving the applicability and safety of the vehicle system.
  • Fig. 4 is a schematic structural diagram of a longitudinal car following control device provided in Embodiment 4 of the present application. This embodiment is applicable to controlling the longitudinal following of the vehicle according to the user's driving habit level when the driving condition of the vehicle is a congested condition.
  • the device includes: a characteristic determination module 410 , a driving habit determination module 420 and an acceleration determination module 430 .
  • the characteristic determination module 410 is configured to determine the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle if the driving condition of the target vehicle is a congestion condition, and determine a vehicle response characteristic level of the target vehicle;
  • the driving habit determination module 420 is configured to determine the user driving habit level of the target vehicle based on the user operation characteristic level and the vehicle response characteristic level;
  • the acceleration determination module 430 is configured to determine the current car following mode of the target vehicle, and determine the vehicle acceleration of the target vehicle in the current car following mode based on the user's driving habit level.
  • the user operation data includes accelerator pedal displacement data and brake pedal displacement data
  • the characteristic determining module 410 includes a first characteristic determining unit configured to The displacement data and the pre-built first fuzzy controller are used to determine the user operating characteristic level of the target vehicle in the level fuzzy set corresponding to the first fuzzy controller.
  • the vehicle response data includes headway data and driving speed data
  • the characteristic determining module 410 includes a second characteristic determining unit, configured to be based on the headway data, driving speed data and pre-determined characteristics of the target vehicle.
  • a second fuzzy controller is constructed, and the vehicle response characteristic level of the target vehicle is determined in the level fuzzy set corresponding to the second fuzzy controller.
  • the driving habit determination module 420 is configured to determine, based on the user operation characteristic level, the vehicle response characteristic level and the pre-built third fuzzy controller, in the level fuzzy set corresponding to the third fuzzy controller The user's driving habit level of the target vehicle.
  • the acceleration determination module 430 is configured to perform at least one of the following operations:
  • the current following mode of the target vehicle is a stable following mode, based on the user's driving habit level, the current distance between the target vehicle and the preceding vehicle, the current speed of the preceding vehicle, and the current speed of the target vehicle, Vehicle speed, determining the vehicle acceleration of the target vehicle in the stable following mode;
  • the target vehicle is The vehicle acceleration under the no-front vehicle acceleration mode
  • the current following mode of the target vehicle is a rapid deceleration mode
  • the backup deceleration of the target vehicle the full deceleration of the target vehicle, the current vehicle speed of the preceding vehicle,
  • the current speed of the target vehicle and the current distance between the target vehicle and the preceding vehicle determine the vehicle acceleration of the target vehicle in the rapid deceleration mode.
  • the longitudinal vehicle following control device further includes a working condition determination module configured to acquire vehicle driving data of the target vehicle; determine the driving working condition of the target vehicle based on the vehicle driving data and a pre-trained driving condition discrimination model , wherein the driving conditions include congestion conditions.
  • a working condition determination module configured to acquire vehicle driving data of the target vehicle; determine the driving working condition of the target vehicle based on the vehicle driving data and a pre-trained driving condition discrimination model , wherein the driving conditions include congestion conditions.
  • the working condition determination module is configured to determine the data collection period based on the current vehicle speed of the target vehicle, and acquire the vehicle driving data of the target vehicle in the data collection;
  • the conditional mean function and the estimated density function determine the driving conditions corresponding to the vehicle driving data.
  • the user operation characteristic level and the vehicle response characteristic level are determined by the characteristic determination module, and the user driving habit level of the target vehicle is determined based on the user operation characteristic level and the vehicle response characteristic level by the driving habit determination module, and the target vehicle is obtained.
  • Vehicle control improves the user's driving experience, thereby improving the applicability and safety of the vehicle system.
  • the longitudinal vehicle following control device provided in the embodiment of the present application can execute the longitudinal vehicle following control method provided in any embodiment of the present application, and has corresponding functional modules for executing the method.
  • FIG. 5 is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present application.
  • FIG. 5 shows a block diagram of an exemplary electronic device 12 suitable for implementing embodiments of the present application.
  • the electronic device 12 shown in FIG. 5 is only an example, and should not limit the functions and scope of use of the embodiment of the present application.
  • the device 12 is typically an electronic device that undertakes the vehicle longitudinal following control function.
  • electronic device 12 takes the form of a general-purpose computing device.
  • Components of the electronic device 12 may include, but are not limited to, at least one processor or processing unit 16, a memory 28, and a bus 18 connecting the various components including the memory 28 and the processing unit 16.
  • Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures.
  • these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Micro Channel Architecture (Micro Channel Architecture, MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards Association, VESA) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
  • Electronic device 12 typically includes a variety of computer-readable media. These media can be any available media that can be accessed by electronic device 12 and include both volatile and nonvolatile media, removable and non-removable media.
  • Memory 28 may include computer device-readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32 .
  • Electronic device 12 may include other removable/non-removable, volatile/nonvolatile computer storage media.
  • storage device 34 may be used to read from and write to non-removable, non-volatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive").
  • a disk drive for reading and writing to a removable non-volatile disk may be provided, as well as a removable non-volatile disk (such as a Compact Disc- Read Only Memory, CD-ROM), Digital Video Disc (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) CD-ROM drive.
  • each drive can be connected to bus 18 via at least one data medium interface.
  • Memory 28 may include at least one program product 40 having a set of program modules 42 configured to perform the functions of various embodiments of the present application.
  • Program product 40 which may be stored, for example, in memory 28.
  • Such program modules 42 include, but are not limited to, at least one application program, other program modules, and program data, each or some combination of which may include the implementation of a network environment .
  • the program modules 42 generally perform the functions and/or methods of the embodiments described herein.
  • the electronic device 12 can also communicate with at least one external device 14 (such as a keyboard, mouse, camera, etc., and a display), and can also communicate with at least one device that enables the user to interact with the electronic device 12, and/or communicate with the electronic device 12 to allow the user to interact with the electronic device 12. 12. Any device capable of communicating with at least one other computing device (eg, network card, modem, etc.). This communication can be performed through an input/output (Input/Output, I/O) interface 22 . Moreover, the electronic device 12 can also communicate with at least one network (such as a local area network (Local Area Network, LAN), wide area network, Wide Area Network, WAN) and/or a public network, such as the Internet, through the network adapter 20.
  • LAN Local Area Network
  • WAN Wide Area Network
  • public network such as the Internet
  • network adapter 20 communicates with other modules of electronic device 12 via bus 18 .
  • other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays) of Independent Disks, RAID) devices, tape drives, and data backup storage devices.
  • the driving condition of the target vehicle is a congestion condition, determining the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle, and determining the vehicle response of the target vehicle based on the vehicle response data of the target vehicle characteristic level;
  • Determining the current car-following mode of the target vehicle and determining the vehicle acceleration of the target vehicle in the current car-following mode based on the user's driving habit level.
  • processor can also implement the technical solution of the longitudinal vehicle following control method provided in any embodiment of the present application.
  • Embodiment 6 of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method for longitudinal vehicle following control provided in any embodiment of the present application are implemented.
  • the method includes:
  • the driving condition of the target vehicle is a congestion condition, determining the user operation characteristic level of the target vehicle based on the user operation data of the target vehicle, and determining the vehicle response of the target vehicle based on the vehicle response data of the target vehicle characteristic level;
  • Determining the current car-following mode of the target vehicle and determining the vehicle acceleration of the target vehicle in the current car-following mode based on the user's driving habit level.
  • the computer storage medium in the embodiments of the present application may use any combination of at least one computer-readable medium.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections having at least one lead, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • any appropriate medium including but not limited to wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • Computer program codes for performing the operations of the embodiments of the present application may be written in one or more programming languages or combinations thereof, the programming languages including object-oriented programming languages—such as Java, Smalltalk, C++, including A conventional procedural programming language - such as "C" or a similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (such as through an Internet Service Provider). Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider such as AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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Abstract

一种纵向跟车控制方法，通过目标车辆的用户操作数据确定用户操作特性等级，通过车辆响应数据确定车辆响应特性等级（S110），并基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级，得到目标车辆的驾驶员的驾驶习性特征（S120），从而在当前跟车模式下，基于确定出的用户驾驶习性等级确定目标车辆的车辆加速度（S130）。还公开了一种纵向跟车控制装置、一种电子设备及一种计算机可读存储介质。

Description

纵向跟车控制方法、装置、电子设备及存储介质
本申请要求在2021年7月29日提交中国专利局、申请号为202110862293.4的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及计算机技术领域，例如涉及一种纵向跟车控制方法、装置、电子设备及存储介质。
背景技术
近年来，随着智能汽车的不断发展，越来越多的研究人员开始投身于智能汽车的纵向控制的研究。其中，车辆的纵向控制可以理解为车辆纵向的自动加速和自动减速控制。
在智能汽车的纵向控制系统中，相关技术通常采用神经网络动态预测出车辆的加速度。然而，相关技术没有考虑到驾驶人的驾驶习性，无法满足不同驾驶习性的驾驶人的差异化驾驶需求。
发明内容
本申请实施例提供了一种纵向跟车控制方法、装置、电子设备及存储介质，以实现基于用户驾驶习性的纵向跟车控制，提高用户的驾驶体验。
第一方面，本申请实施例提供了一种纵向跟车控制方法，包括：
如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述 目标车辆在所述当前跟车模式下的车辆加速度。
第二方面，本申请实施例还提供了一种纵向跟车控制装置，包括：
特性确定模块，设置为如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
驾驶习性确定模块，设置为基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
加速度确定模块，设置为确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
第三方面，本申请实施例还提供了一种电子设备，所述电子设备包括：
至少一个处理器；
存储装置，设置为存储至少一个程序，
当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如本申请任意实施例提供的纵向跟车控制方法。
第四方面，本申请实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请任意实施例提供的纵向跟车控制方法。
附图说明
图1为本申请实施例一所提供的一种纵向跟车控制方法的流程示意图；
图2为本申请实施例二所提供的一种纵向跟车控制方法的流程示意图；
图3为本申请实施例三所提供的一种纵向跟车控制方法的流程示意图；
图4为本申请实施例四所提供的一种纵向跟车控制装置的结构示意图；
图5为本申请实施例五所提供的一种电子设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作详细说明。
实施例一
图1为本申请实施例一提供的一种纵向跟车控制方法的流程示意图，本实施例可适用于在车辆的驾驶工况为拥堵工况时，根据用户驾驶习性等级控制车辆纵向跟车的情况，该方法可以由纵向跟车控制装置来执行，该装置可以由硬件和/或软件来实现，该方法包括如下步骤：
S110、如果目标车辆的驾驶工况为拥堵工况，则基于目标车辆的用户操作数据确定目标车辆的用户操作特性等级，基于目标车辆的车辆响应数据确定目标车辆的车辆响应特性等级。
其中，目标车辆的驾驶工况可以根据目标车辆当前的车辆行驶数据确定。例如，基于平均车速、平均加速度、目标车辆所处车道的前车平均车速、目标车辆所处车道的相邻车道的前车平均车速等数据，确定目标车辆的驾驶工况。或者，还可以根据目标车辆上的图像采集装置采集到的工况图像或工况视频，确定目标车辆的驾驶工况。示例性的，根据工况图像或工况视频确定出目标车辆视野中的车辆的数量大于预设数量阈值，则确定目标车辆的驾驶工况为拥堵工况；或者，根据工况图像或工况视频分析出目标车辆所处车道以及相邻车道的前车变道次数超过预设次数阈值时，确定目标车辆的驾驶工况为拥堵工况，等。
示例性的，本实施例在目标车辆的驾驶工况为拥堵工况时，根据目标车辆的用户操作数据确定目标车辆的用户操作特性等级，并根据目标车辆的车辆响应数据确定目标车辆的车辆响应特性等级。
其中，用户操作数据可以是用户对目标车辆的操作数据，如，用户操作目标车辆的加速踏板、制动踏板、车辆变道的数据。示例性的，用户操作目标车辆的加速踏板数据可以是加速踏板位移，制动踏板数据可以是制动踏板位移，车辆变道数据可以是车辆横向位移。车辆响应数据可以是目标车辆在数据采集周期内的行驶相关数据，如，数据采集周期内车头时距平均值的倒数、数据采 集周期内车辆行驶速度的方差值，等。
在本实施例中，用户操作特性等级可以是表征驾驶员操作特性的等级。可选的，可以根据用户操作数据，确定出用户操作特性等级。示例性的，可以基于用户的加速踏板位移和/或制动踏板位移计算用户的操作参考值，基于计算得到的操作参考值与预设的多个操作特性等级对应的标准值进行比对，确定出目标车辆的用户操作特性等级。在一种实施方式中，还可以将用户的加速踏板位移与预设的多个操作特性等级对应的标准加速踏板位移进行比对，和/或，将用户的制动踏板位移与预设的多个操作特性等级对应的标准制动踏板位移进行比对，确定目标车辆的用户操作特性等级。
在本实施例中，车辆响应特性等级可以是表征目标车辆响应特性的等级。可选的，可以根据车辆响应数据确定出车辆响应特性等级。示例性的，可以基于数据采集周期内车头时距平均值的倒数和/或数据采集周期内车辆行驶速度的方差值计算目标车辆的响应参考值，基于计算得到的响应参考值与预设的多个车辆响应特性等级对应的标准值进行比对，确定出目标车辆的车辆响应特性等级。还可以是，将数据采集周期内车头时距平均值的倒数与预设的多个响应特性等级对应的标准车头时距平均值的倒数进行比对，和/或，将数据采集周期内车辆行驶速度的方差值与预设的多个响应特性等级对应的标准方差值进行比对，确定目标车辆的车辆响应特性等级。
S120、基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级。
可选的，在确定出用户操作特性等级和车辆响应特性等级之后，可以根据用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级。其中，用户驾驶习性等级可以是用于表征用户的驾驶习性的等级。
示例性的，可以根据预先设置的用户操作特性等级、车辆响应特性等级与用户驾驶习性等级之间的等级映射关系，将用户操作特性等级和车辆响应特性等级与预先设置的等级映射关系中的预设操作特性等级和预设响应特性等级进 行比对，进而确定出对应的用户驾驶习性等级。如，用户驾驶习性等级包括极保守、保守、一般、激进和极激进。
S130、确定目标车辆的当前跟车模式，基于用户驾驶习性等级确定目标车辆在当前跟车模式下的车辆加速度。
其中，目标车辆的当前跟车模式包括但不限于稳定跟车模式、无前车加速模式以及急减速模式。在本实施例中，可以根据目标车辆的前车行驶信息确定目标车辆的当前跟车模式。示例性的，目标车辆的前方车辆稳定行驶时，纵向控制目标车辆进入稳定跟车模式；目标车辆的前方低速行驶(车速低于40km/h)的车辆换道离开目标车辆所处车道时，纵向控制目标车辆进行无前车加速模式；目标车辆的前方车辆紧急制动或者目标车辆所处车道的相邻车道中的车辆强行并线切入时，纵向控制目标车辆进入急减速模式。
可选的，在确定出目标车辆的当前跟车模式后，可以根据用户驾驶习性等级确定当前跟车模式下目标车辆的车辆加速度。
在一种实施方式中，确定所述目标车辆的当前跟车模式，基于用户驾驶习性等级确定目标车辆在所述当前跟车模式下的车辆加速度，包括以下中的至少一种：
若确定出目标车辆的当前跟车模式为稳定跟车模式，则基于用户驾驶习性等级、目标车辆与前车的当前距离、前车的当前车速以及目标车辆的当前车速，确定目标车辆在稳定跟车模式下的车辆加速度；
若确定出目标车辆的当前跟车模式为无前车加速模式，则基于用户驾驶习性等级、目标车辆的当前车速以及目标车辆的备份加速度，确定目标车辆在无前车加速模式下的车辆加速度；
若确定出目标车辆的当前跟车模式为急减速模式，则基于用户驾驶习性等级、目标车辆的备份减速度、目标车辆的完全减速度、前车的当前车速、目标车辆的当前车速以及目标车辆与前车的当前距离，确定目标车辆在急减速模式下的车辆加速度。
在该可选的实施方式中，若为稳定跟车模式，基于用户驾驶习性等级、目标车辆与前车的当前距离、前车的当前车速以及目标车辆的当前车速，确定目标车辆在稳定跟车模式下的车辆加速度，可以满足如下公式：
Figure PCTCN2022105798-appb-000001
其中，a 0为稳定跟车模式下的车辆加速度，v 1表示前车的当前车速(实时车速)，v 0表示目标车辆的当前车速(实时车速)，d r表示目标车辆与前车的当前距离(目标车辆的车头与前车的车尾之间的实时距离)，d 0＝5m，表示最小安全距离的默认值，k s表示用户驾驶习性系数，用户驾驶习性系数可以根据用户驾驶习性等级确定。
此外，若为无前车加速模式，基于用户驾驶习性等级、目标车辆的当前车速以及目标车辆的备份加速度，确定目标车辆在无前车加速模式下的车辆加速度，可以满足如下公式：
Figure PCTCN2022105798-appb-000002
其中，a 0为无前车加速模式下的车辆加速度，a cc＝1m/s 2，表示备份加速度，v cc＝40km/h，表示设定的默认的车辆纵向巡航速度，v 0表示目标车辆的当前车速(实时车速)，k s表示用户驾驶习性系数。
若为急减速模式，基于用户驾驶习性等级、目标车辆的备份减速度、目标车辆的完全减速度、前车的当前车速、目标车辆的当前车速以及目标车辆与前车的当前距离，确定目标车辆在急减速模式下的车辆加速度，可以满足如下公式：
Figure PCTCN2022105798-appb-000003
其中，a 0为急减速模式下的车辆加速度，a FOU＝-1m/s 2，表示备份减速度， a FULL＝-7m/s 2，表示完全减速度，v 1表示前车的当前车速(实时车速)，v 0表示目标车辆的当前车速(实时车速)，d r表示目标车辆与前车的当前距离(目标车辆的车头与前车的车尾之间的实时距离)，d 0＝5m，表示最小安全距离的默认值，k s表示用户驾驶习性系数。
可选的，在上述三种当前跟车模式下，k s可以基于如下方法确定：用户驾驶习性等级为极保守型时k s＝0.2，用户驾驶习性等级为保守型时k s＝0.1，用户驾驶习性等级为一般型时k s＝0，用户驾驶习性等级为激进型时k s＝-0.1，驾驶习性为极激进型时k s＝-0.2。
该可选的实施方式，将目标车辆的当前跟车模式分为稳定跟车模式、无前车加速模式以及急减速模式，并在每种当前跟车模式下具备对应的车辆加速度确定方法，实现了在多种当前跟车模式下，根据驾驶习性的自适应纵向跟车控制，提高了用户的驾驶体验，进而提高了车辆系统的适用性以及安全性。
当然，本实施例提供的纵向跟车控制方法还包括：基于目标车辆在当前跟车模式下的车辆加速度，调整所述目标车辆的当前加速度。
本实施例的技术方案，通过目标车辆的用户操作数据确定用户操作特性等级，通过车辆响应数据确定车辆响应特性等级，并基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级，得到目标车辆的驾驶员的驾驶习性特征，从而在当前跟车模式下，基于确定出的用户驾驶习性等级确定目标车辆的车辆加速度，实现了根据驾驶人的驾驶习性的自适应纵向跟车控制，提高了用户的驾驶体验，进而提高了车辆系统的适用性以及安全性。
实施例二
图2为本申请实施例二提供的一种纵向跟车控制方法的流程示意图，本实施例在上述各实施例的基础上，可选的，用户操作数据包括加速踏板位移数据和制动踏板位移数据，基于目标车辆的用户操作数据确定目标车辆的用户操作特性等级，包括：基于目标车辆的加速踏板位移数据、制动踏板位移数据以及 预先构建的第一模糊控制器，在第一模糊控制器对应的等级模糊集合中确定目标车辆的用户操作特性等级。
其中与上述各实施例相同或相应的术语的解释在此不再赘述。参见图2，本实施例提供的纵向跟车控制方法包括以下步骤：
S210、如果目标车辆的驾驶工况为拥堵工况，则基于目标车辆的加速踏板位移数据、制动踏板位移数据以及预先构建的第一模糊控制器，在第一模糊控制器对应的等级模糊集合中确定目标车辆的用户操作特性等级。
其中，上述实施例中的用户操作数据包括加速踏板位移数据和制动踏板位移数据。预先构建的第一模糊控制器可以包括第一输入端、第二输入端和一个输出端，其中，第一输入端输入第一输入变量，第二输入端输入第二输入变量。示例性的，第一输入变量为加速踏板位移数据，第二输入变量为制动踏板位移数据。
又或者，还可以将第一输入端的值域设定为[0,8]，相应的，第一输入端对应的模糊集合包括3个等级：S(小)、M(中)、L(大)；将第二输入端的值域设定为[0,12]，相应的，第二输入端对应的模糊集合包括3个等级：S(小)、M(中)、L(大)。根据加速踏板位移数据以及第一输入端对应的模糊集合，确定第一输入变量；根据制动踏板位移数据以及第二输入端对应的模糊集合，确定第二输入变量。示例性的，加速踏板位移数据为2时，第一输入变量为S；制动踏板位移数据为12时，第二输入变量为L。
在本实施例中，第一模糊控制器对应的等级模糊集合为第一模糊控制器的输出端对应的等级模糊集合。示例性的，第一模糊控制器对应的等级模糊集合包括5个等级：ES(极小)、S(小)、M(中)、L(大)、EL(极大)。
在一种实施方式中，基于目标车辆的加速踏板位移数据、制动踏板位移数据以及预先构建的第一模糊控制器，在第一模糊控制器对应的等级模糊集合中确定目标车辆的用户操作特性等级，可以是：基于第一模糊控制器的隶属度函数、第一输入变量和第二输入变量，在第一模糊控制器对应的等级模糊集合中 确定目标车辆的用户操作特性等级，其中，第一输入变量为加速踏板位移数据对应的模糊等级，第二输入变量为制动踏板位移数据对应的模糊等级。可选的，隶属度函数可以是三角形隶属度函数，以保证输出的用户操作特性等级对两个输入变量的变化的敏感性。
在另一种实施方式中，基于目标车辆的加速踏板位移数据、制动踏板位移数据以及预先构建的第一模糊控制器，在第一模糊控制器对应的等级模糊集合中确定目标车辆的用户操作特性等级，还可以是：基于预设的第一模糊控制器的模糊规则、第一输入变量以及第二输入变量，在第一模糊控制器对应的等级模糊集合中确定目标车辆的用户操作特性等级。
示例性的，如表1所示，展示了一种预设的第一模糊控制器的模糊规则。
表1第一模糊控制器的模糊规则
Figure PCTCN2022105798-appb-000004
S220、基于目标车辆的车辆响应数据确定目标车辆的车辆响应特性等级。
可选的，车辆响应数据包括车头时距数据和行驶速度数据，基于目标车辆的车辆响应数据确定目标车辆的车辆响应特性等级，包括：基于目标车辆的车头时距数据、行驶速度数据以及预先构建的第二模糊控制器，在第二模糊控制器对应的等级模糊集合中确定目标车辆的车辆响应特性等级。
示例性的，车头时距数据可以是数据采集周期内车头时距平均值的倒数，其中，车头时距表示两辆车辆的前端通过同一地点的时间差；车头时距可以使用前车车头和目标车辆的车头的距离除以目标车辆的速度来计算；行驶速度数据可以是数据采集周期内目标车辆行驶速度的方差值。其中，数据采集周期可以根据目标车辆的实时车速确定，如：
Figure PCTCN2022105798-appb-000005
式中，K T、C T、CC分别为目标车辆的实时车速系数。示例性的，当目标车辆的实时车速大于80km/h时，K T＝1.3，C T＝20，CC＝1.2；当目标车辆的实时车速大于40km/h，且小于或等于80km/h时，K T＝1.8，C T＝15，CC＝1.5；当目标车辆的实时车速小于或等于40km/h时，K T＝2.2，C T＝8，CC＝1.8。
其中，预先构建的第二模糊控制器可以包括第一输入端、第二输入端和一个输出端，其中，第一输入端输入第一输入变量，第二输入端输入第二输入变量。示例性的，第一输入变量为车头时距数据，第二输入变量为行驶速度数据。
又或者，还可以将第一输入端的值域设定为[0,100]，相应的，第一输入端对应的模糊集合包括3个等级：S(小)、M(中)、L(大)；将第二输入端的值域设定为[0,120]，相应的，第二输入端对应的模糊集合包括5个等级ES(极小)、S(小)、M(中)、L(大)、EL(极大)。根据车头时距数据以及第一输入端对应的模糊集合，确定第一输入变量；根据行驶速度数据以及第二输入端对应的模糊集合，确定第二输入变量。示例性的，车头时距数据为10时，第一输入变量为S；制动踏板位移数据为120时，第二输入变量为EL。
在该可选的实施方式中，第二模糊控制器对应的等级模糊集合为第二模糊控制器的输出端对应的等级模糊集合。示例性的，第二模糊控制器对应的等级模糊集合包括5个等级：ES(极小)、S(小)、M(中)、L(大)、EL(极大)。
示例性的，基于目标车辆的车头时距数据、行驶速度数据以及预先构建的第二模糊控制器，在第二模糊控制器对应的等级模糊集合中确定目标车辆的车辆响应特性等级，可以是：基于第二模糊控制器的隶属度函数、第一输入变量和第二输入变量，在第二模糊控制器对应的等级模糊集合中确定目标车辆的车辆响应特性等级，其中，第一输入变量为车头时距数据对应的模糊等级，第二输入变量为行驶速度数据对应的模糊等级。可选的，隶属度函数可以是较陡三角形隶属度函数，以保证输出的车辆响应特性等级对两个输入变量的变化的敏感性。
示例性的，基于目标车辆的车头时距数据、行驶速度数据以及预先构建的第二模糊控制器，在第二模糊控制器对应的等级模糊集合中确定目标车辆的车辆响应特性等级，还可以是：基于预设的第二模糊控制器的模糊规则、第一输入变量以及第二输入变量，在第二模糊控制器对应的等级模糊集合中确定目标车辆的车辆响应特性等级。
示例性的，如表2所示，展示了一种预设的第二模糊控制器的模糊规则。
表2第二模糊控制器的模糊规则
Figure PCTCN2022105798-appb-000006
在该可选的实施方式中，通过预先构建的第二模糊控制器确定目标车辆的车辆响应特性等级，提高了确定出的车辆响应特性等级的准确性，进而提高了用户驾驶习性等级的准确性，提高了根据驾驶习性的自适应纵向跟车控制的技术效果。
S230、基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级。
可选的，基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级，包括：基于用户操作特性等级、车辆响应特性等级以及预先构建的第三模糊控制器，在第三模糊控制器对应的等级模糊集合中确定目标车辆的用户驾驶习性等级。
其中，预先构建的第三模糊控制器可以包括第一输入端、第二输入端和一个输出端，其中，第一输入端输入第一输入变量，第二输入端输入第二输入变 量。示例性的，第一输入变量为用户操作特性等级，第二输入变量为车辆响应特性等级。第一输入端对应的模糊集合包括5个等级：ES(极小)、S(小)、M(中)、L(大)、EL(极大)；第二输入端对应的模糊集合包括5个等级：ES(极小)、S(小)、M(中)、L(大)、EL(极大)。第三模糊控制器对应的等级模糊集合包括5个等级：EG(极保守)、G(保守)、M(一般)、A(激进)、EA(极激进)。
示例性的，可以基于第三模糊控制器的隶属度函数、第一输入变量和第二输入变量，在第三模糊控制器对应的等级模糊集合中确定目标车辆的用户驾驶习性等级。其中，隶属度函数可以是较平缓的梯形隶属度函数，以保证输出的用户驾驶习性等级的平稳性。
或者，还可以基于预设的第三模糊控制器的模糊规则、第一输入变量以及第二输入变量，在第三模糊控制器对应的等级模糊集合中确定目标车辆的用户驾驶习性等级。
示例性的，如表3所示，展示了一种预设的第三模糊控制器的模糊规则。
表3第三模糊控制器的模糊规则
Figure PCTCN2022105798-appb-000007
在该可选的实施方式中，通过预先构建的第三模糊控制器确定目标车辆的用户驾驶习性等级，提高了确定出的用户驾驶习性等级的准确性，提高了根据驾驶习性的自适应纵向跟车控制的技术效果。
S240、确定目标车辆的当前跟车模式，基于用户驾驶习性等级确定目标车 辆在当前跟车模式下的车辆加速度。
本实施例的技术方案，通过预先构建的第一模糊控制器，加速踏板位移数据和制动踏板位移数据，确定目标车辆的用户操作特性等级，提高了确定出的用户操作特性等级的准确性，进而提高了用户驾驶习性等级的准确性，提高了根据驾驶习性的自适应纵向跟车控制的技术效果。
实施例三
图3为本申请实施例三提供的一种纵向跟车控制方法的流程示意图，本实施例在上述各实施例的基础上，可选的，还包括：获取目标车辆的车辆行驶数据；基于车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况，其中，驾驶工况包括拥堵工况。
其中与上述各实施例相同或相应的术语的解释在此不再赘述。参见图3，本实施例提供的纵向跟车控制方法包括以下步骤：
S310、获取目标车辆的车辆行驶数据，基于车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况。
其中，驾驶工况包括拥堵工况。可选的，驾驶工况还包括非拥堵工况。示例性的，本实施例中的车辆行驶数据包括但不限于如下12个特征参数：
目标车辆平均速度：数据采集周期内，目标车辆速度的算术平均值，不包含车辆怠速状态；
目标车辆平均加速度：数据采集周期内，目标车辆在加速状态下各单位时间(秒)加速度的算术平均值；
目标车辆平均减速度：数据采集周期内，目标车辆在减速状态下各单位时间(秒)减速度的算术平均值；
目标车辆所处车道的前车平均速度：数据采集周期内，本车道内前方车辆速度的算术平均值，不包含车辆怠速状态；
目标车辆所处车道的左侧车道前车平均速度：数据采集周期内，左侧车道 内前方车辆速度的算术平均值，不包含车辆怠速状态；
目标车辆所处车道的右侧车道前车平均速度：数据采集周期内，右侧车道内前方车辆速度的算术平均值，不包含车辆怠速状态；
目标车辆所处车道的前车平均加速度：数据采集周期内，本车道内前方车辆在加速状态下各单位时间(秒)加速度的算术平均值；
目标车辆所处车道的左侧车道前车平均加速度：数据采集周期内，左侧车道内前方车辆在加速状态下各单位时间(秒)加速度的算术平均值；
目标车辆所处车道的右侧车道前车平均加速度：数据采集周期内，右侧车道内前方车辆在加速状态下各单位时间(秒)加速度的算术平均值；
目标车辆的前车低速时间比：数据采集周期内，本车道内前方车辆行驶速度小于40km/h的累计时间长度占总时间长度的百分比；
目标车辆的前车中速时间比：数据采集周期内，本车道内前方车辆行驶速度处于40-70km/h的累计时间长度占该时间周期总时间长度的百分比；
目标车辆的前车高速时间比：数据采集周期内，本车道内前方车辆行驶速度大于70km/h的累计时间长度占该时间周期总时间长度的百分比。
其中，数据采集周期可以基于上述实施例中数据采集周期的计算公式来确定。
示例性的，本实施例可以构建用于驾驶工况辨识的特征参数库，其中，特征参数库包括多个车辆行驶数据，基于构建的特征参数库，训练驾驶工况判别模型。
示例性的，预先训练的驾驶工况判别模型可以是预先训练的工况分类模型，将车辆行驶数据输入至预先训练的工况分类模型，得到目标车辆的工况分类结果。
或者，获取目标车辆的车辆行驶数据，基于车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况，还可以是：基于目标车辆的当前车速确定数据采集周期，获取目标车辆在数据采集内的车辆行驶数据；基于预 先训练的驾驶工况判别模型中的条件均值函数以及估算密度函数，确定车辆行驶数据对应的驾驶工况。
示例性的，可以将车辆行驶数据输入至预先训练的驾驶工况判别模型，驾驶工况判别模型根据条件均值和估算密度函数，确定目标车辆的驾驶工况。
其中，条件均值可以是驾驶工况s相对于Z的回归，Z为根据车辆行驶数据得到的特征参数，条件均值可以满足如下公式：
Figure PCTCN2022105798-appb-000008
其中，特征参数库X和输出的驾驶工况s的联合概率密度函数为f(X,s)，f(Z,s)为车辆行驶数据(实际观测值)与驾驶工况s的联合概率密度函数。
可选的，可以应用Parzen非参数估计，由样本数据集
Figure PCTCN2022105798-appb-000009
得到估算密度函数为：
Figure PCTCN2022105798-appb-000010
式中，m表示特征参数库样本容量，m＝1000；n＝12表示特征参数库X的维度；σ＝0.1表示光滑因子。基于上述条件均值公式和估算密度函数，驾驶工况判别模型的驾驶工况的预测值为：
Figure PCTCN2022105798-appb-000011
其中，
Figure PCTCN2022105798-appb-000012
为车辆行驶数据对应的预测值。需要说明的是，
Figure PCTCN2022105798-appb-000013
可以的取值为0或1。示例性的，若预测值为1，则目标车辆的驾驶工况为拥堵工况，若预测值为0，则目标车辆的驾驶工况为非拥堵工况。
S320、如果目标车辆的驾驶工况为拥堵工况，则基于目标车辆的用户操作数据确定目标车辆的用户操作特性等级，基于目标车辆的车辆响应数据确定目标车辆的车辆响应特性等级。
S330、基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级。
S340、确定目标车辆的当前跟车模式，基于用户驾驶习性等级确定目标车辆在当前跟车模式下的车辆加速度。
本实施例的技术方案，在根据驾驶人的驾驶习性进行自适应纵向跟车控制之前，根据车辆的行驶数据以及预先训练的驾驶工况判别模型确定驾驶工况，提高了驾驶工况的准确性，进而提高了车辆系统的适用性以及安全性。
实施例四
图4为本申请实施例四提供的一种纵向跟车控制装置的结构示意图，本实施例可适用于在车辆的驾驶工况为拥堵工况时，根据用户驾驶习性等级控制车辆纵向跟车的情况，该装置包括：特性确定模块410、驾驶习性确定模块420以及加速度确定模块430。
特性确定模块410，设置为如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
驾驶习性确定模块420，设置为基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
加速度确定模块430，设置为确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
可选的，所述用户操作数据包括加速踏板位移数据和制动踏板位移数据，所述特性确定模块410包括第一特性确定单元，设置为基于所述目标车辆的加速踏板位移数据、制动踏板位移数据以及预先构建的第一模糊控制器，在所述第一模糊控制器对应的等级模糊集合中确定所述目标车辆的用户操作特性等级。
可选的，所述车辆响应数据包括车头时距数据和行驶速度数据，所述特性确定模块410包括第二特性确定单元，设置为基于所述目标车辆的车头时距数 据、行驶速度数据以及预先构建的第二模糊控制器，在所述第二模糊控制器对应的等级模糊集合中确定所述目标车辆的车辆响应特性等级。
可选的，驾驶习性确定模块420设置为基于所述用户操作特性等级、所述车辆响应特性等级以及预先构建的第三模糊控制器，在所述第三模糊控制器对应的等级模糊集合中确定所述目标车辆的用户驾驶习性等级。
可选的，加速度确定模块430设置为执行下述操作中的至少一种：
若确定出所述目标车辆的当前跟车模式为稳定跟车模式，则基于所述用户驾驶习性等级、所述目标车辆与前车的当前距离、前车的当前车速以及所述目标车辆的当前车速，确定所述目标车辆在所述稳定跟车模式下的车辆加速度；
若确定出所述目标车辆的当前跟车模式为无前车加速模式，则基于所述用户驾驶习性等级、所述目标车辆的当前车速以及所述目标车辆的备份加速度，确定所述目标车辆在所述无前车加速模式下的车辆加速度；
若确定出所述目标车辆的当前跟车模式为急减速模式，则基于所述用户驾驶习性等级、所述目标车辆的备份减速度、所述目标车辆的完全减速度、前车的当前车速、所述目标车辆的当前车速以及所述目标车辆与前车的当前距离，确定所述目标车辆在所述急减速模式下的车辆加速度。
可选的，所述纵向跟车控制装置还包括工况确定模块，设置为获取目标车辆的车辆行驶数据；基于所述车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况，其中，所述驾驶工况包括拥堵工况。
可选的，所述工况确定模块设置为基于目标车辆的当前车速确定数据采集周期，获取所述目标车辆在所述数据采集内的车辆行驶数据；基于预先训练的驾驶工况判别模型中的条件均值函数以及估算密度函数，确定所述车辆行驶数据对应的驾驶工况。
在本实施例中，通过特性确定模块，确定用户操作特性等级和车辆响应特性等级，并通过驾驶习性确定模块，基于用户操作特性等级和车辆响应特性等级确定目标车辆的用户驾驶习性等级，得到目标车辆的驾驶员的驾驶习性特征， 从而在当前跟车模式下，通过加速度确定模块，基于确定出的用户驾驶习性等级确定目标车辆的车辆加速度，实现了根据驾驶人的驾驶习性的自适应纵向跟车控制，提高了用户的驾驶体验，进而提高了车辆系统的适用性以及安全性。
本申请实施例所提供的纵向跟车控制装置可执行本申请任意实施例所提供的纵向跟车控制方法，具备执行方法相应的功能模块。
值得注意的是，上述系统所包括的各个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本申请实施例的保护范围。
实施例五
图5是本申请实施例五提供的一种电子设备的结构示意图。图5示出了适于用来实现本申请实施方式的示例性电子设备12的框图。图5显示的电子设备12仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。设备12典型的是承担车辆纵向跟车控制功能的电子设备。
如图5所示，电子设备12以通用计算设备的形式表现。电子设备12的组件可以包括但不限于：至少一个处理器或者处理单元16，存储器28，连接不同组件(包括存储器28和处理单元16)的总线18。
总线18表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(Industry Standard Architecture，ISA)总线，微通道体系结构(Micro Channel Architecture，MCA)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component Interconnect，PCI)总线。
电子设备12典型地包括多种计算机可读介质。这些介质可以是任何能够被 电子设备12访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
存储器28可以包括易失性存储器形式的计算机装置可读介质，例如随机存取存储器(Random Access Memory，RAM)30和/或高速缓存存储器32。电子设备12可以包括其它可移动/不可移动的、易失性/非易失性计算机存储介质。仅作为举例，存储装置34可以用于读写不可移动的、非易失性磁介质(图5未显示，通常称为“硬盘驱动器”)。尽管图5中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如只读光盘(Compact Disc-Read Only Memory，CD-ROM)、数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过至少一个数据介质接口与总线18相连。存储器28可以包括至少一个程序产品40，该程序产品40具有一组程序模块42，这些程序模块被配置以执行本申请各实施例的功能。程序产品40，可以存储在例如存储器28中，这样的程序模块42包括但不限于至少一个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块42通常执行本申请所描述的实施例中的功能和/或方法。
电子设备12也可以与至少一个外部设备14(例如键盘、鼠标、摄像头等和显示器)通信，还可与至少一个使得用户能与该电子设备12交互的设备通信，和/或与使得该电子设备12能与至少一个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(Input/Output，I/O)接口22进行。并且，电子设备12还可以通过网络适配器20与至少一个网络(例如局域网(Local Area Network，LAN)，广域网Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器20通过总线18与电子设备12的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备12使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks， RAID)装置、磁带驱动器以及数据备份存储装置等。
处理器16通过运行存储在存储器28中的程序，从而执行各种功能应用以及数据处理，例如实现本申请上述实施例所提供的纵向跟车控制方法，包括：
如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
当然，本领域技术人员可以理解，处理器还可以实现本申请任意实施例所提供的纵向跟车控制方法的技术方案。
实施例六
本申请实施例六还提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请任意实施例所提供的纵向跟车控制方法步骤，该方法包括：
如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
本申请实施例的计算机存储介质，可以采用至少一个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介 质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于无线、电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请实施例操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言——诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。

Claims (10)

  1. 一种纵向跟车控制方法，包括：
    响应于目标车辆的驾驶工况为拥堵工况，基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
    基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
    确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
  2. 根据权利要求1所述的方法，其中，所述用户操作数据包括加速踏板位移数据和制动踏板位移数据，所述基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，包括：
    基于所述目标车辆的加速踏板位移数据、制动踏板位移数据以及预先构建的第一模糊控制器，在所述第一模糊控制器对应的等级模糊集合中确定所述目标车辆的用户操作特性等级。
  3. 根据权利要求2所述的方法，其中，所述车辆响应数据包括车头时距数据和行驶速度数据，所述基于所述目标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级，包括：
    基于所述目标车辆的车头时距数据、行驶速度数据以及预先构建的第二模糊控制器，在所述第二模糊控制器对应的等级模糊集合中确定所述目标车辆的车辆响应特性等级。
  4. 根据权利要求3所述的方法，其中，所述基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级，包括：
    基于所述用户操作特性等级、所述车辆响应特性等级以及预先构建的第三模糊控制器，在所述第三模糊控制器对应的等级模糊集合中确定所述目标车辆的用户驾驶习性等级。
  5. 根据权利要求1所述的方法，其中，所述确定所述目标车辆的当前跟车 模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度，包括下述中的至少一种：
    响应于确定出所述目标车辆的当前跟车模式为稳定跟车模式，基于所述用户驾驶习性等级、所述目标车辆与前车的当前距离、前车的当前车速以及所述目标车辆的当前车速，确定所述目标车辆在所述稳定跟车模式下的车辆加速度；
    响应于确定出所述目标车辆的当前跟车模式为无前车加速模式，基于所述用户驾驶习性等级、所述目标车辆的当前车速以及所述目标车辆的备份加速度，确定所述目标车辆在所述无前车加速模式下的车辆加速度；
    响应于确定出所述目标车辆的当前跟车模式为急减速模式，基于所述用户驾驶习性等级、所述目标车辆的备份减速度、所述目标车辆的完全减速度、前车的当前车速、所述目标车辆的当前车速以及所述目标车辆与前车的当前距离，确定所述目标车辆在所述急减速模式下的车辆加速度。
  6. 根据权利要求1所述的方法，还包括：
    获取目标车辆的车辆行驶数据；
    基于所述车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况，其中，所述驾驶工况包括拥堵工况。
  7. 根据权利要求6所述的方法，其中，所述获取目标车辆的车辆行驶数据，基于所述车辆行驶数据以及预先训练的驾驶工况判别模型确定目标车辆的驾驶工况，包括：
    基于目标车辆的当前车速确定数据采集周期，获取所述目标车辆在所述数据采集内的车辆行驶数据；
    基于预先训练的驾驶工况判别模型中的条件均值函数以及估算密度函数，确定所述车辆行驶数据对应的驾驶工况。
  8. 一种纵向跟车控制装置，包括：
    特性确定模块，设置为如果目标车辆的驾驶工况为拥堵工况，则基于所述目标车辆的用户操作数据确定所述目标车辆的用户操作特性等级，基于所述目 标车辆的车辆响应数据确定所述目标车辆的车辆响应特性等级；
    驾驶习性确定模块，设置为基于所述用户操作特性等级和所述车辆响应特性等级确定所述目标车辆的用户驾驶习性等级；
    加速度确定模块，设置为确定所述目标车辆的当前跟车模式，基于所述用户驾驶习性等级确定所述目标车辆在所述当前跟车模式下的车辆加速度。
  9. 一种电子设备，包括：
    至少一个处理器；
    存储装置，设置为存储至少一个程序，
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-7中任一所述的纵向跟车控制方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-7中任一所述的纵向跟车控制方法。
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