WO2022237418A1 - 一种纵向跟踪控制方法、装置、设备及存储介质 - Google Patents

一种纵向跟踪控制方法、装置、设备及存储介质 Download PDF

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Publication number
WO2022237418A1
WO2022237418A1 PCT/CN2022/086059 CN2022086059W WO2022237418A1 WO 2022237418 A1 WO2022237418 A1 WO 2022237418A1 CN 2022086059 W CN2022086059 W CN 2022086059W WO 2022237418 A1 WO2022237418 A1 WO 2022237418A1
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Prior art keywords
vehicle
driving data
style
distance
acceleration
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PCT/CN2022/086059
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English (en)
French (fr)
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李伟男
刘斌
吴杭哲
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中国第一汽车股份有限公司
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Publication of WO2022237418A1 publication Critical patent/WO2022237418A1/zh

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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
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/18—Distance travelled
    • 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
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/802—Longitudinal distance

Definitions

  • the embodiments of the present application relate to the technical field of vehicles, for example, to a longitudinal tracking control method, device, device, and storage medium.
  • Vehicle longitudinal motion control systems are usually designed in a hierarchical structure: the upper layer controller outputs the desired acceleration according to the relative vehicle distance and vehicle speed, and the design mainly considers driver characteristics, queue stability, and traffic flow; the lower layer acceleration tracking controller implements The control of the mechanism makes the actual acceleration of the car track the expected value, and the vehicle dynamics problem is mainly considered in the design.
  • Vehicle longitudinal acceleration tracking control is one of the key technologies of vehicle longitudinal motion control.
  • Embodiments of the present application provide a longitudinal tracking control method, device, equipment, and storage medium to meet the differentiated needs of drivers with different habits and characteristics, greatly improve driver satisfaction and comfort, and provide drivers with more A good driving experience is of great significance to improving system applicability, ensuring vehicle safety, and reducing traffic accidents.
  • the embodiment of the present application provides a longitudinal tracking control method, including:
  • the embodiment of the present application also provides a longitudinal tracking control device, which includes:
  • the acquisition module is set to acquire current driving data
  • a first determining module configured to determine a driving style according to the current driving data
  • the second determining module is configured to determine the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
  • the embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, it implements the The longitudinal tracking control method described in any one of the embodiments.
  • the embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the longitudinal tracking control method as described in any one of the embodiments of the present application is implemented.
  • FIG. 1 is a flow chart of a longitudinal tracking control method in Embodiment 1 of the present application.
  • Fig. 2 is a schematic structural diagram of a longitudinal tracking control device in Embodiment 2 of the present application.
  • FIG. 3 is a schematic structural diagram of a computer device in Embodiment 3 of the present application.
  • Figure 1 is a flow chart of a longitudinal tracking control method provided in Embodiment 1 of the present application. This embodiment is applicable to the situation of vehicle longitudinal tracking control, and the method can be executed by the longitudinal tracking control device in the embodiment of the present application.
  • the device can be implemented in the form of software and/or hardware, as shown in Figure 1, the method includes the following steps:
  • the current driving data includes: at least one of the current vehicle speed, current acceleration, current driving distance, current vehicle state, distance between the vehicle and the vehicle in front, current deceleration, accumulated travel time of the vehicle and the speed of the vehicle in front. kind.
  • the acquisition method of the current driving data may be the current driving data acquired through the controller area network (Controller Area Network, CAN) bus, or the current driving data acquired through the vehicle sensor. No restrictions are imposed.
  • controller area network Controller Area Network, CAN
  • the driving style may be any one of conservative, general, and aggressive.
  • the driving style can also be set to other types according to user requirements, which is not limited in this embodiment of the present application.
  • the way of determining the driving style according to the current driving data may be to obtain a driving data sample; establish a decision tree module to be trained; train the decision tree model to be trained according to the driving data sample to obtain a target decision tree model; The current driving data is input into the target decision tree model to obtain the driving style corresponding to the current driving data.
  • S130 Determine the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
  • the method of determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data may also be: determining the first distance according to the driving style; when the distance between the vehicle and the vehicle in front is less than or When it is equal to the first distance and greater than the distance threshold, it is determined that the vehicle enters the following state; the second expected acceleration of the vehicle is determined according to the following formula: Among them, b h is the second expected acceleration, K CIPV is the first calibration constant, D call is the calibration distance, D real is the distance between the vehicle in front and the vehicle in front, and D style is the first distance; when the vehicle in front and the vehicle in front When the distance between is less than or equal to the distance threshold, it is determined that the vehicle enters the emergency braking state; the third expected acceleration of the vehicle is determined according to the following formula: Wherein, ch is the third expected acceleration, and d cali is the second calibration constant.
  • determining the driving style according to the current driving data includes:
  • obtaining driving data samples includes:
  • the data between the first driving data sampling intervals are supplemented according to the fitting function to obtain driving data samples.
  • training the decision tree model to be trained according to the driving data samples to obtain a target decision tree model including:
  • a feature database is established according to the driving data samples, wherein the feature database includes: average speed, average acceleration, average deceleration, speed standard deviation, acceleration standard deviation, mileage, maximum acceleration, maximum deceleration, maximum speed, idle speed At least one of time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, low speed time ratio, medium speed time ratio and high speed time ratio;
  • determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data including:
  • a h is the first expected acceleration
  • ⁇ style is the individualization index
  • ⁇ style 0.8
  • ⁇ style 0.8
  • ⁇ style 0.8
  • ⁇ style 1
  • ⁇ style 1.2
  • v CIPV is the speed of the vehicle in front
  • v 1 is the speed of the own vehicle
  • v cali is the calibrated speed.
  • determining the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data including:
  • b h is the second expected acceleration
  • K CIPV is the first calibration constant
  • D cali is the calibration distance
  • D real is the distance between the vehicle and the vehicle in front
  • D style is the first distance
  • ch is the third expected acceleration
  • d cali is the second calibration constant
  • the driving data includes: speed, acceleration, travel distance, idling state, acceleration state, deceleration state, distance between the vehicle and the vehicle in front, deceleration, accumulated travel time of the vehicle and speed of the vehicle in front. at least one.
  • a personalized vehicle longitudinal control method includes the following steps:
  • Step 1 Data preprocessing: The historical driving data x and the subsequent main data are obtained from the test collection, as shown in Table 1:
  • Historical driving data x is the data collected through real vehicle tests, but due to the limitations of data collection equipment, the collected data is often in discrete form, and the data interval period is the sampling step. In order to obtain a more comprehensive and continuous The driving data is to supplement the historical driving data x.
  • the historical driving data x is superimposed to obtain the first driving data, so as to increase the linear relationship of the data and facilitate subsequent fitting operations.
  • the first driving data X i.e. the overlay data X in Table 1
  • the first driving data X is obtained by overlaying the historical driving data x:
  • a i is the coefficient of the corresponding x ni , so that the fitting function can be obtained:
  • n is the order of the fitting equation, based on the fitting relationship between the sampling time t and the first driving data X, through the fitting function Supplement the data between the sampling intervals of the first driving data X, and supplement a superposition data X f between every two adjacent superposition data X e , X g :
  • the driving style analysis is performed using the driving data samples.
  • a feature database for driving style judgment is constructed based on driving data samples.
  • the feature database contains the following 16 feature parameters:
  • Average speed the arithmetic mean value of the vehicle speed within T seconds, excluding the idling state of the vehicle;
  • Acceleration standard deviation the standard deviation of the acceleration of the vehicle in the accelerated state within T seconds
  • Acceleration time ratio within T seconds, the percentage of the accumulative time length in the acceleration state to the total time length;
  • Deceleration time ratio within T seconds, the percentage of the accumulated time length in the deceleration state to the total time length;
  • Constant speed time ratio within T seconds, the percentage of the cumulative time length in the state of constant speed (the absolute value of the vehicle acceleration is less than 0.1m/s2 non-idling continuous process) to the total time length;
  • T represents the time period
  • the calculation formula of T is as follows:
  • K T is the first speed coefficient
  • CT is the first speed base
  • CC is the first speed index
  • K 4 ⁇ K N ⁇ C N DD ;
  • K N is the second speed coefficient
  • C N is the second speed base
  • DD b(1); % Randomly generate the value of DD.
  • the decision tree is used to realize the training and generation of the driving style identification decision tree (ie, the target decision tree model), which is implemented under MATLAB as an example.
  • the language program is as follows:
  • T_train Train(:,K);
  • T_test Test(:,K);
  • model classRF_train(P_train, T_train);
  • each of the 50 driving style identification decision trees can output a driving style (conservative, general, aggressive). ) identification results, vote on the output results of 50 decision trees, and the driving style (conservative, general, aggressive) with the most votes is determined as the final driving style judgment result.
  • Step 3 Personalized longitudinal follow-up control:
  • a h is the first expected acceleration
  • ⁇ style is the individualization index
  • ⁇ style 1.2
  • v CIPV is the speed of the vehicle in front
  • v 1 is the speed of the own vehicle
  • v cali is the calibrated speed.
  • the value of the calibrated speed can be 15, or it can be set according to user needs.
  • the vehicle When the distance D real between the vehicle and the vehicle in front is less than D style and greater than the distance threshold, where the distance threshold can be 5m, the vehicle enters the following state, and the expected acceleration of the vehicle is:
  • K CIPV is the first calibration constant
  • D cali is the calibration distance
  • D real is the distance between the vehicle and the vehicle in front
  • D style is the first distance
  • the value of K CIPV can be 36.6
  • the value of D cali can be 8.
  • ch is the third expected acceleration
  • d cali is the second calibration constant, which may be 2.
  • the personalized vehicle longitudinal control method provided by the application brings convenience to the use of the vehicle controller.
  • the embodiment of the application uses data mining and machine learning theory to explore the characteristics and rules of the driver's driving habits, establishes a driving habit identification and characterization scheme, and then Considering the driver's driving habits design, realize the personalized vehicle longitudinal control method, let the design of the car return to people-oriented, meet the differentiated needs of drivers with different habits and characteristics, greatly improve the driver's satisfaction and comfort, and serve the driver Provide a better driving experience, which is of great significance to improve system applicability, ensure vehicle safety, and reduce traffic accidents.
  • FIG. 2 is a schematic structural diagram of a longitudinal tracking control device provided in Embodiment 2 of the present application. This embodiment can be applied to the situation of longitudinal tracking control, the device can be realized by software and/or hardware, and the device can be integrated in any equipment that provides the function of longitudinal tracking control, as shown in Figure 2, the longitudinal tracking
  • the control device includes: an acquisition module 210 , a first determination module 220 and a second determination module 230 .
  • the obtaining module 210 is configured to obtain current driving data
  • the first determination module 220 is configured to determine a driving style according to the current driving data
  • the second determining module 230 is configured to determine the current state of the vehicle and the expected acceleration of the vehicle according to the driving style and the current driving data.
  • the above-mentioned products can execute the method provided by any embodiment of the present application, and have corresponding functional modules for executing the method.
  • FIG. 3 is a schematic structural diagram of a computer device in Embodiment 3 of the present application.
  • FIG. 3 shows a block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present application.
  • the computer device 12 shown in FIG. 3 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.
  • computer device 12 takes the form of a general-purpose computing device.
  • Components of computer device 12 may include, but are not limited to, at least one processor or processing unit 16 , system memory 28 , bus 18 connecting various system components including system memory 28 and processing unit 16 .
  • Bus 18 represents at least one 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.
  • Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12 and include both volatile and nonvolatile media, removable and non-removable media.
  • System memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32 .
  • Computer device 12 may include other removable/non-removable, volatile/nonvolatile computer system storage media.
  • storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard drive").
  • a disk drive for reading and writing to a removable nonvolatile disk may be provided, as well as a disk drive for a removable nonvolatile disk (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 may be connected to bus 18 via at least one data medium interface.
  • System memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present application.
  • a program/utility 40 having a set (at least one) of program modules 42, such as may be stored in system memory 28, such as but not limited to an operating system, at least one application program, other program modules, and program data, Implementations of networked environments may be included in each or some combination of these examples.
  • the program modules 42 generally perform the functions and/or methods of the embodiments described herein.
  • Computer device 12 may also communicate with at least one external device 14 (e.g., a keyboard, pointing device, display 24, etc.), and at least one device that enables a user to interact with 12. Any device capable of communicating with at least one other computing device (eg, network card, modem, etc.). Such communication may occur through input/output (I/O) interface 22 .
  • the display 24 does not exist as an independent entity, but is embedded in the mirror surface. When the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface are visually integrated.
  • the computer device 12 can also communicate with at least one network (such as a local area network (Local Area Network, LAN), a wide area network, Wide Area Network, WAN) and/or a public network, such as the Internet, through the network adapter 20.
  • network adapter 20 communicates with other modules of computer device 12 via bus 18 .
  • other hardware and/or software modules may be used in conjunction with computer 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) systems, tape drives, and data backup storage systems.
  • the processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the longitudinal tracking control method provided by the embodiment of the present application:
  • processing unit 16 may also implement the longitudinal tracking control method provided in any embodiment of the present application by running the program stored in the system memory 28 .
  • Embodiment 4 of the present application provides a computer-readable storage medium, on which a computer program is stored.
  • the program is executed by a processor, the longitudinal tracking control method provided in all the embodiments of the present application is implemented:
  • the longitudinal tracking control method provided in any embodiment of the present application may also be implemented.
  • a computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two.
  • a computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof.
  • 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. .
  • the program code contained on the computer readable medium can be transmitted by any appropriate medium, including but not limited to wireless, electric 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, electric wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • the client and the server can communicate using any currently known or future-developed network protocols such as HTTP (Hyper Text Transfer Protocol, Hypertext Transfer Protocol), and can communicate with any form or medium of digital Data communication (eg, communication network) interconnections.
  • HTTP Hyper Text Transfer Protocol
  • Examples of communication networks include local area networks ("LANs”), wide area networks ("WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network of.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.
  • Computer program code for carrying out the operations of the present application may be written in one or more programming languages or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional process 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 (e.g. via the Internet using an Internet Service Provider). .
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider e.g. via the Internet using an Internet Service Provider.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code that contains at least one programmable logic function for implementing the specified logical function.
  • Execute instructions may also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Wherein, the name of a unit does not constitute a limitation of the unit itself under certain circumstances.
  • exemplary types of hardware logic components include: Field Programmable Gate Arrays (Field Programmable Gate Arrays, FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (Application Specific Standard Parts, ASSP), System on Chip (System on Chip, SOC), Complex Programmable Logic Device (Complex Programmable Logic Device, CPLD) and so on.
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing.
  • machine-readable storage media would include at least one wire-based electrical connection, a portable computer disk, a hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM), or flash memory), optical fiber, compact disc read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory flash memory
  • optical fiber compact disc read only memory
  • CD-ROM compact disc read only memory
  • magnetic storage or any suitable combination of the foregoing.

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Abstract

一种纵向跟踪控制方法、装置、设备及存储介质，其中纵向跟踪控制方法包括：获取当前驾驶数据；根据当前驾驶数据确定驾驶风格；根据驾驶风格和当前驾驶数据确定本车的当前状态和本车期望加速度。

Description

一种纵向跟踪控制方法、装置、设备及存储介质
本申请要求在2021年5月12日提交中国专利局、申请号为202110519112.8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
本申请实施例涉及车辆技术领域，例如涉及一种纵向跟踪控制方法、装置、设备及存储介质。
背景技术
汽车纵向运动控制系统通常被设计成分层结构：上层控制器根据相对车距和车速输出期望加速度，设计时主要考虑驾驶员特性、队列稳定性和交通流等问题；下层加速度跟踪控制器通过对执行机构的控制使汽车实际加速度跟踪期望值，设计时主要考虑车辆动力学问题。汽车纵向加速度跟踪控制是汽车纵向运动控制的关键技术之一。
近年来，随着智能汽车的不断发展，着眼于驾驶人驾驶技能和驾驶风格的研究也不断深入，越来越多的研究人员开始投身于驾驶人驾驶技能辨识方面的研究。考虑到真车存在损耗、浪费能源以及存在危险性等因素，亟需研发一种基于驾驶风格进行纵向跟踪控制的方法。
发明内容
本申请实施例提供一种纵向跟踪控制方法、装置、设备及存储介质，以实现能够满足不同习惯特性的驾驶人的差异化需求，大幅度提高驾驶人满意度和舒适度，为驾驶人提供更好的驾驶体验，这对提高系统适用性、保障汽车安全、减少交通事故具有重大意义。
第一方面，本申请实施例提供了一种纵向跟踪控制方法，包括：
获取当前驾驶数据；
根据所述当前驾驶数据确定驾驶风格；
根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
第二方面，本申请实施例还提供了一种纵向跟踪控制装置，该装置包括：
获取模块，设置为获取当前驾驶数据；
第一确定模块，设置为根据所述当前驾驶数据确定驾驶风格；
第二确定模块，设置为根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
第三方面，本申请实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如本申请实施例中任一所述的纵向跟踪控制方法。
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实施例中任一所述的纵向跟踪控制方法。
附图说明
图1是本申请实施例一中的一种纵向跟踪控制方法的流程图；
图2是本申请实施例二中的一种纵向跟踪控制装置的结构示意图；
图3是本申请实施例三中的一种计算机设备的结构示意图。
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。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行定义和解释。同时，在本申请的描述中，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
实施例一
图1为本申请实施例一提供的一种纵向跟踪控制方法的流程图，本实施例可适用于车辆纵向跟踪控制的情况，该方法可以由本申请实施例中的纵向跟踪控制装置来执行，该装置可采用软件和/或硬件的方式实现，如图1所示，该方法包括如下步骤：
S110，获取当前驾驶数据。
其中，所述当前驾驶数据包括：当前车速、当前加速度、当前行驶距离、当前车辆状态、本车和前车之间的距离、当前减速度、本车累计行驶时间以及前车车速中的至少一种。
其中，所述当前驾驶数据的获取方式可以为通过控制器局域网络(Controller Area Network，CAN)总线获取的当前驾驶数据，也可以为通过车载传感器获取到的当前驾驶数据，本申请实施例对此不进行限制。
S120，根据所述当前驾驶数据确定驾驶风格。
其中，所述驾驶风格可以为保守型、一般型、激进型中的任一种。所述驾驶风格还可以根据用户需求设置其他类型，本申请实施例对此不进行限制。
示例性的，根据当前驾驶数据确定驾驶风格的方式可以为获取驾驶数据样本；建立待训练决策树模块；根据所述驾驶数据样本训练所述待训练决策树模型，得到目标决策树模型；将所述当前驾驶数据输入所述目标决策树模型得到所述当前驾驶数据对应的驾驶风格。
S130，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
示例性的，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度的方式可以为：根据所述驾驶风格确定第一距离；当本车与前车之间的距离大于第一距离时，确定本车进入加速跟进状态；根据如下公式确定本车第一期望加速度：
Figure PCTCN2022086059-appb-000001
其中，a h为第一期望加速度，ε style为个性化指数，驾驶风格为保守型时ε style＝0.8，驾驶风格为一般型 时ε style＝1，驾驶风格为激进型时ε style＝1.2，v CIPV为前车车速，v 1为本车车速，v cali为标定车速。根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度的方式还可以为：根据所述驾驶风格确定第一距离；当本车与前车之间的距离小于或等于第一距离，且大于距离阈值时，确定本车进入跟随状态；根据如下公式确定本车第二期望加速度：
Figure PCTCN2022086059-appb-000002
Figure PCTCN2022086059-appb-000003
其中，b h为第二期望加速度，K CIPV为第一标定常数，D call为标定距离，D real为本车与前车之间距离，D style为第一距离；当本车与前车之间距离小于或等于距离阈值时，确定本车进入紧急制动状态；根据如下公式确定本车第三期望加速度：
Figure PCTCN2022086059-appb-000004
其中，c h为第三期望加速度，d cali为第二标定常数。
可选的，根据所述当前驾驶数据确定驾驶风格包括：
获取驾驶数据样本；
建立待训练决策树模块；
根据所述驾驶数据样本训练所述待训练决策树模型，得到目标决策树模型；
将所述当前驾驶数据输入所述目标决策树模型得到所述当前驾驶数据对应的驾驶风格。
可选的，获取驾驶数据样本包括：
获取历史驾驶数据；
对所述历史驾驶数据进行叠加，得到第一驾驶数据；
对所述第一驾驶数据进行关于采样时间的拟合，得到拟合函数；
根据所述拟合函数对所述第一驾驶数据采样间隔之间的数据进行补充，得到驾驶数据样本。
可选的，根据所述驾驶数据样本训练所述待训练决策树模型，得到目标决策树模型，包括：
根据所述驾驶数据样本建立特征数据库，其中，所述特征数据库包括：平 均速度、平均加速度、平均减速度、速度标准差、加速度标准差、行驶里程、最大加速度、最大减速度、最大速度、怠速时间比、加速时间比、减速时间比、匀速时间比、低速时间比、中速时间比以及高速时间比中的至少一种；
基于所述特征数据库训练至少一个待训练决策树模型，得到至少一个目标决策树模型。
可选的，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，包括：
根据所述驾驶风格确定第一距离；
当本车与前车之间的距离大于第一距离时，确定本车进入加速跟进状态；
根据如下公式确定本车第一期望加速度：
Figure PCTCN2022086059-appb-000005
其中，a h为第一期望加速度，ε style为个性化指数，驾驶风格为保守型时ε style＝0.8，驾驶风格为一般型时ε style＝1，驾驶风格为激进型时ε style＝1.2，v CIPV为前车车速，v 1为本车车速，v cali为标定车速。
可选的，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，包括：
根据所述驾驶风格确定第一距离；
当本车与前车之间的距离小于或等于第一距离，且大于距离阈值时，确定本车进入跟随状态；
根据如下公式确定本车第二期望加速度：
Figure PCTCN2022086059-appb-000006
其中，b h为第二期望加速度，K CIPV为第一标定常数，D cali为标定距离，D real为本车与前车之间距离，D style为第一距离；
当本车与前车之间距离小于或等于距离阈值时，确定本车进入紧急制动状态；
根据如下公式确定本车第三期望加速度：
Figure PCTCN2022086059-appb-000007
其中，c h为第三期望加速度，d cali为第二标定常数。
可选的，所述驾驶数据包括：速度、加速度、行驶距离、怠速状态、加速状态、减速状态、本车和前车之间的距离、减速度、本车累计行驶时间以及前车车速中的至少一种。
在一个例子中，本申请提供的一种个性化的车辆纵向控制方法包括有如下几个步骤：
步骤1、数据预处理：试验采集获得历史驾驶数据x及后续主要数据，如表1所示：
表1
序号k 1 2 … m
时间点t t 1 t 2 … t m
驾驶数据x x 1 x 2 … x m
叠加数据X X 1 X 2 … X m
历史驾驶数据x为通过实车试验采集到的数据，但是受到数据采集设备的局限，采集的到的数据往往是离散形式的，数据间隔周期即为采样步长，为了得到更加全面、更加连续的驾驶数据，对历史驾驶数据x进行补充，首先对历史驾驶数据x进行叠加，得到第一驾驶数据，以增加数据的线性关系，方便进行后续的拟合操作，示例性的，第一驾驶数据X(即表1中的叠加数据X)由历史驾驶数据x通过叠加得到：
X 1＝x 1
X 2＝x 1+x 2
X 3＝x 1+x 2+x 3
……
X m＝x 1+x 2+x 3+……+x m
对具备更佳线性关系的第一驾驶数据X进行关于采样时间t的拟合，在软件matlab中构建自变量向量：m个采样时间t＝[t 1、t 2…t m]，构建因变量向量：m个叠加数据X＝[X 1、X 2…X m]。
通过以下MATLAB程序确定拟合方程的阶数n：
for i＝1:6
xx 1＝polyfit(t,X,i)；
XX＝polyval(xxy 1,t)；
if sum(XX-X) 2<0.05
c＝i
break；
end
end
进而能够得到在误差值平方和小于0.05时的拟合方程阶数n。
接下来在MATLAB窗口中输入函数：
yy 2＝polyfit(t,X,n)
按下回车键即可获得多项式拟合函数系数：
a 0、a 1……、a n；
a i是对应的x n-i的系数，如此即可获得拟合函数：
Figure PCTCN2022086059-appb-000008
其中，n为拟合方程阶数，基于采样时间t与第一驾驶数据X之间的拟合关系，通过拟合函数
Figure PCTCN2022086059-appb-000009
对第一驾驶数据X采样间隔之间的数据进行补充，每两个相邻的叠加数据X e、X g之间补充一个叠加数据X f：
Figure PCTCN2022086059-appb-000010
其中，X e、X g的采样序列号分别为t＝e，t＝g，g＝e+1，
Figure PCTCN2022086059-appb-000011
补充后的叠加数据X如表2所示：
表2
时间点t t 1 t 1.5 t 2 … t e t f t g … t m
叠加数据X X 1 X 1.5 X 2 … X e X f X g … X m
示例性的，利用第一驾驶数据X与历史驾驶数据x之间的关系，反求扩充后的驾驶数据样本：
x 1＝X 1
x 1.5＝X 1.5-X 1
x 2＝X 2-X 1.5
……
x m＝X m-X m-1
驾驶数据样本如表3所示：
表3
Figure PCTCN2022086059-appb-000012
定义驾驶数据样本：
Y 1＝x 1
Y 2＝x 1.5
Y 3＝x 2
……
Y 2m-1＝x m
驾驶数据样本如表4所示：
表4
Figure PCTCN2022086059-appb-000013
Figure PCTCN2022086059-appb-000014
后续步骤中，利用驾驶数据样本进行驾驶风格分析。
步骤2、驾驶风格分析
基于驾驶数据样本构建用于驾驶风格判断的特征数据库，特征数据库包含如下16个特征参数：
(1)平均速度：T秒内车辆速度的算术平均值，不包含车辆怠速状态；
(2)平均加速度：T秒内，车辆在加速状态下各单位时间(秒)加速度的算术平均值；
(3)平均减速度：T秒内，车辆在减速状态下各单位时间(秒)减速度的算术平均值；
(4)速度标准差：T秒内车辆速度的标准差，即包括怠速状态；
(5)加速度标准差：T秒内处在加速状态的车辆加速度的标准差；
(6)行驶里程：T秒内车辆的行驶距离；
(7)最大加速度：T秒内车辆在加速状态下加速度的最大值；
(8)最大减速度：T秒内车辆在减速状态下减速度的最大值；
(9)最大速度：T秒内车辆的速度的最大值；
(10)怠速时间比：T秒内，怠速状态的累计时间长度占总时间长度的百分比；
(11)加速时间比：T秒内，处在加速状态的累计时间长度占总时间长度的百分比；
(12)减速时间比：T秒内，处在减速状态的累计时间长度占总时间长度的百分比；
(13)匀速时间比：T秒内，处在匀速(车辆加速度的绝对值小于0.1m/s2非怠速的连续过程)状态的累计时间长度占总时间长度的百分比；
(14)低速时间比：T秒内，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；
(15)中速时间比：T秒内，车辆行驶速度介于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；
(16)高速时间比：T秒内，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，T表示时间周期，T的计算公式如下：
Figure PCTCN2022086059-appb-000015
其中，K T为第一速度系数，C T为第一速度基数，CC为第一速度指数。
当实时车速大于或等于100km/h时，K T＝1.3，C T＝20，CC＝1.2；
当实时车速大于或等于60km/h时且小于100km/h时，K T＝1.8，C T＝15，CC＝1.5；
当实时车速小于60km/h时，K T＝2.2，C T＝8，CC＝1.8；
基于训练用驾驶数据Y构建的特征数据库，训练N个决策树分类器(即待训练决策树模型)，每次训练时，从16个特征参数中有放回的随机选择K个参数作为决策树分类器的输入参数，K的计算公式如下：
K＝4×K N×C N DD；
其中，K N为第二速度系数，C N为第二速度基数，
当实时车速大于或等于100km/h时，K N＝1.3，C N＝20；
当实时车速大于或等于60km/h时且小于100km/h时，K N＝1.8，C N＝15；
当实时车速小于60km/h时，K N＝2.2，C N＝8；
其中，DD的数值在MATLAB下随机生成：
clear all
clc
warning off
b＝randperm(8)；
DD＝b(1)；％随机生成DD的数值。
然后，采用决策树实现驾驶风格辨识决策树(即目标决策树模型)的训练生成，示例性的在MATLAB下实现，语言程序如下所示：
load data.mat％加载数据
a＝randperm(50)；
Train＝data(a(1:25),:)；
Test＝data(a(6:end),:)；
P_train＝Train(:,K+1:end)；
T_train＝Train(:,K)；
P_test＝Test(:,K+1:end)；
T_test＝Test(:,K)；
model＝classRF_train(P_train,T_train)；
重复运行50次上述程序，及共计生成50个驾驶风格辨识决策树，输入驾驶数据后，50个驾驶风格辨识决策树中每个决策树都能输出一个驾驶风格(保守型、一般型、激进型)辨识结果，对50个决策树输出的结果进行投票，得票数最多的驾驶风格(保守型、一般型、激进型)即认定为最终的驾驶风格判断结果。
步骤3、个性化纵向跟车控制：
当本车与前车之间距离D real大于第一距离D style时(驾驶风格为保守型时D style＝28，驾驶风格为一般型时D style＝20，驾驶风格为激进型时D style＝16)，本车进入加速跟进状态，本车第一期望加速度为：
Figure PCTCN2022086059-appb-000016
其中，a h为第一期望加速度，ε style为个性化指数，驾驶风格为保守型时ε style＝0.8，驾驶风格为一般型时ε style＝1，驾驶风格为激进型时ε style＝1.2，v CIPV为前车车速，v 1为本车车速，v cali为标定车速，标定车速取值可以为15，也可以为根据用户需求进行设定。
当本车与前车之间距离D real小于D style且大于距离阈值时，其中，距离阈值可以为5m，本车进入跟随状态，本车期望加速度为：
当本车与前车之间的距离小于或等于第一距离，且大于距离阈值时，确定本车进入跟随状态；
根据如下公式确定本车第二期望加速度：
Figure PCTCN2022086059-appb-000017
其中，b h为第二期望加速度，K CIPV为第一标定常数，D cali为标定距离，D real为本车与前车之间距离，D style为第一距离；K CIPV取值可以为36.6，D cali取值可以为8。
当本车与前车之间距离小于或等于距离阈值时，确定本车进入紧急制动状态；
根据如下公式确定本车第三期望加速度：
Figure PCTCN2022086059-appb-000018
其中，c h为第三期望加速度，d cali为第二标定常数，取值可以为2。
本申请提供的个性化的车辆纵向控制方法给车辆控制器的使用带来了便利，本申请实施例通过数据挖掘和机器学习理论探究驾驶人驾驶习性特征规律，建立驾驶习性辨识和表征方案，进而考虑驾驶人驾驶习性设计、实现个性化的车辆纵向控制方法，让汽车的设计回归到以人为本，满足不同习惯特性的驾驶人的差异化需求，大幅度提高驾驶人满意度和舒适度，为驾驶人提供更好的驾驶体验，这对提高系统适用性、保障汽车安全、减少交通事故具有重大意义。
本实施例的技术方案，通过获取当前驾驶数据；根据所述当前驾驶数据确定驾驶风格；根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，以实现能够满足不同习惯特性的驾驶人的差异化需求，大幅度提高驾驶人满意度和舒适度，为驾驶人提供更好的驾驶体验，这对提高系统适用性、保障汽车安全、减少交通事故具有重大意义。
实施例二
图2为本申请实施例二提供的一种纵向跟踪控制装置的结构示意图。本实施例可适用于纵向跟踪控制的情况，该装置可采用软件和/或硬件的方式实现，该装置可集成在任何提供纵向跟踪控制功能的设备中，如图2所示，所述纵向跟踪控制装置包括：获取模块210、第一确定模块220和第二确定模块230。
其中，获取模块210，设置为获取当前驾驶数据；
第一确定模块220，设置为根据所述当前驾驶数据确定驾驶风格；
第二确定模块230，设置为根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
上述产品可执行本申请任意实施例所提供的方法，具备执行方法相应的功能模块。
本实施例的技术方案，通过获取当前驾驶数据；根据所述当前驾驶数据确定驾驶风格；根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，以实现能够满足不同习惯特性的驾驶人的差异化需求，大幅度提高驾驶人满意度和舒适度，为驾驶人提供更好的驾驶体验，这对提高系统适用性、保障汽车安全、减少交通事故具有重大意义。
实施例三
图3为本申请实施例三中的一种计算机设备的结构示意图。图3示出了适于用来实现本申请实施方式的示例性计算机设备12的框图。图3显示的计算机设备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。
如图3所示，计算机设备12以通用计算设备的形式表现。计算机设备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，系统存储器28，连接不同系统组件(包括系统存储器28和处理单元16)的总线18。
总线18表示几类总线结构中的至少一种，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。
计算机设备12典型地包括多种计算机系统可读介质。这些介质可以是任何能够被计算机设备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。
系统存储器28可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(Random Access Memory，RAM)30和/或高速缓存存储器32。计算机设备12可以包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统34可以设置为读写不可移动的、非易失性磁介质(图3未显示，通常称为“硬盘驱动器”)。尽管图3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(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。系统存储器28可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请各实施例的功能。
具有一组(至少一个)程序模块42的程序/实用工具40，可以存储在例如系统存储器28中，这样的程序模块42包括但不限于操作系统、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。
计算机设备12也可以与至少一个外部设备14(例如键盘、指向设备、显示器24等)通信，还可与至少一个使得用户能与该计算机设备12交互的设备通 信，和/或与使得该计算机设备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(I/O)接口22进行。另外，本实施例中的计算机设备12，显示器24不是作为独立个体存在，而是嵌入镜面中，在显示器24的显示面不予显示时，显示器24的显示面与镜面从视觉上融为一体。并且，计算机设备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与计算机设备12的其它模块通信。应当明白，尽管图中未示出，可以结合计算机设备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)系统、磁带驱动器以及数据备份存储系统等。
处理单元16通过运行存储在系统存储器28中的程序，从而执行各种功能应用以及数据处理，例如实现本申请实施例所提供的纵向跟踪控制方法：
获取当前驾驶数据；
根据所述当前驾驶数据确定驾驶风格；
根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
可选的，处理单元16通过运行存储在系统存储器28中的程序，还可以实现本申请任意实施例中所提供的纵向跟踪控制方法。
实施例四
本申请实施例四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所有申请实施例提供的纵向跟踪控制方法：
获取当前驾驶数据；
根据所述当前驾驶数据确定驾驶风格；
根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加 速度。
可选的，该程序被处理器执行时，还可以实现本申请任意实施例中所提供的纵向跟踪控制方法。
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或者是上述两者的任意组合。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((Erasable Programmable Read-Only Memory，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。
在一些实施方式中，客户端、服务器可以利用诸如HTTP(Hyper Text Transfer Protocol，超文本传输协议)之类的任何当前已知或未来研发的网络协议进行通信，并且可以与任意形式或介质的数字数据通信(例如，通信网络)互连。通信网络的示例包括局域网(“LAN”)，广域网(“WAN”)，网际网(例如，互联 网)以及端对端网络(例如，ad hoc端对端网络)，以及任何当前已知或未来研发的网络。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
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)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含至少一个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，单元的名称在某种情况下并不构成对该单元本身的限定。
本文中以上描述的功能可以至少部分地由至少一个硬件逻辑部件来执行。例如，非限制性地，可以使用的示范类型的硬件逻辑部件包括：现场可编程门阵列(Field Programmable Gate Array，FPGA)、专用集成电路(Application Specific Integrated Circuit，ASIC)、专用标准产品(Application Specific Standard Parts，ASSP)、片上系统(System on Chip，SOC)、复杂可编程逻辑设备(Complex Programmable Logic Device，CPLD)等等。
在本公开的上下文中，机器可读介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。机器可读介质可以是机器可读信号介质或机器可读储存介质。机器可读介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备，或者上述内容的任何合适组合。机器可读存储介质的更具体示例会包括基于至少一个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。

Claims (10)

  1. 一种纵向跟踪控制方法，包括：
    获取当前驾驶数据；
    根据所述当前驾驶数据确定驾驶风格；
    根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
  2. 根据权利要求1所述的方法，其中，根据所述当前驾驶数据确定驾驶风格，包括：
    获取驾驶数据样本；
    建立待训练决策树模块；
    根据所述驾驶数据样本训练所述待训练决策树模型，得到目标决策树模型；
    将所述当前驾驶数据输入所述目标决策树模型得到所述当前驾驶数据对应的驾驶风格。
  3. 根据权利要求2所述的方法，其中，获取驾驶数据样本，包括：
    获取历史驾驶数据；
    对所述历史驾驶数据进行叠加，得到第一驾驶数据；
    对所述第一驾驶数据进行关于采样时间的拟合，得到拟合函数；
    根据所述拟合函数对所述第一驾驶数据采样间隔之间的数据进行补充，得到驾驶数据样本。
  4. 根据权利要求2所述的方法，其中，根据所述驾驶数据样本训练所述待训练决策树模型，得到目标决策树模型，包括：
    根据所述驾驶数据样本建立特征数据库，其中，所述特征数据库包括：平均速度、平均加速度、平均减速度、速度标准差、加速度标准差、行驶里程、最大加速度、最大减速度、最大速度、怠速时间比、加速时间比、减速时间比、匀速时间比、低速时间比、中速时间比以及高速时间比中的至少一种；
    基于所述特征数据库训练至少一个待训练决策树模型，得到至少一个目标决策树模型。
  5. 根据权利要求1所述的方法，其中，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，包括：
    根据所述驾驶风格确定第一距离；
    响应于本车与前车之间的距离大于第一距离，确定本车进入加速跟进状态；
    根据如下公式确定本车第一期望加速度：
    Figure PCTCN2022086059-appb-100001
    其中，a h为第一期望加速度，ε style为个性化指数，在驾驶风格为保守型的情况下，ε style＝0.8，在驾驶风格为一般型的情况下，ε style＝1，在驾驶风格为激进型的情况下，ε style＝1.2，v CIPV为前车车速，v 1为本车车速，v cali为标定车速。
  6. 根据权利要求5所述的方法，其中，根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度，包括：
    根据所述驾驶风格确定第一距离；
    响应于本车与前车之间的距离小于或等于第一距离，且大于距离阈值时，确定本车进入跟随状态；
    根据如下公式确定本车第二期望加速度：
    Figure PCTCN2022086059-appb-100002
    其中，b h为第二期望加速度，K CIPV为第一标定常数，D cali为标定距离，D real为本车与前车之间距离，D style为第一距离；
    响应于本车与前车之间距离小于或等于距离阈值，确定本车进入紧急制动状态；
    根据如下公式确定本车第三期望加速度：
    Figure PCTCN2022086059-appb-100003
    其中，c h为第三期望加速度，d cali为第二标定常数。
  7. 根据权利要求1所述的方法，其中，所述驾驶数据包括：车速、加速度、 行驶距离、怠速状态、加速状态、减速状态、本车和前车之间的距离、减速度、本车累计行驶时间以及前车车速中的至少一种。
  8. 一种纵向跟踪控制装置，包括：
    获取模块，设置为获取当前驾驶数据；
    第一确定模块，设置为根据所述当前驾驶数据确定驾驶风格；
    第二确定模块，设置为根据所述驾驶风格和所述当前驾驶数据确定本车的当前状态和本车期望加速度。
  9. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1-7中任一所述的纵向跟踪控制方法。
  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踪控制方法。
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