A longitudinal control method and system for an autonomous commercial vehicle

Abstract

The application provides a kind of longitudinal control method and system of automatic driving commercial vehicle, including collecting vehicle motion state parameters of different types of sources;Anti-interference dynamic performance stable architecture is constructed;Vehicle driving Luenberger observer is respectively established to observe the disturbance suffered by vehicle, and vehicle braking Luenberger observer is observed to observe the disturbance suffered by vehicle;In the dynamic performance stable architecture, the disturbance suffered by vehicle is combined with vehicle driving Luenberger observer, and vehicle acceleration control strategy is designed;In the dynamic performance stable architecture, the disturbance suffered by vehicle is combined with vehicle braking Luenberger observer, and vehicle deceleration control strategy is designed;According to vehicle acceleration control strategy, drive torque is calculated, according to vehicle deceleration control strategy, brake pressure is calculated, and longitudinal control mode arbitration strategy is designed based on drive torque and brake pressure.The application is helpful for vehicle longitudinal speed stable control, and can overcome the disturbance caused by vehicle load change and slope change.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile control, in particular to a longitudinal control method and system for an automatic driving commercial vehicle. BACKGROUND

[0002] The importance of automatic driving vehicle technology lies in its potential to completely change the way of transportation, significantly improving safety, efficiency and sustainability. According to statistics, human error is the main cause of traffic accidents, and automatic driving technology can greatly reduce the accident rate and save lives through precise environmental perception and decision control. At the same time, with the help of intelligent path planning and traffic flow optimization, this technology can effectively alleviate traffic congestion, reduce fuel consumption and carbon emissions, and thus promote green travel. At present, although automatic driving technology has made significant breakthroughs in the field of passenger cars and has shown broad application prospects, research in the field of commercial vehicles is still relatively insufficient, and commercial vehicles also need technological breakthroughs due to their complex operating scenarios and significant social benefits. Therefore, in the future, the development of commercial vehicle automatic driving needs to be strengthened to promote the intelligent upgrading of the entire transportation system and achieve more extensive economic, environmental and social benefits.

[0003] The development of automatic driving commercial vehicles faces unique challenges, and the characteristics of large load variation and strong dynamic interference significantly increase the difficulty of vehicle control. Unlike passenger cars, the load fluctuation of commercial vehicles directly affects the vehicle dynamics, and precise control is highly dependent on real-time motion state parameters. However, the acquisition of these key parameters usually requires high-precision sensors, resulting in a significant increase in cost, which contradicts the cost control goal required for the large-scale promotion of automatic driving technology.

[0004] Therefore, how to reduce the dependence on sensors while ensuring control accuracy has become one of the core problems that need to be broken through in the field of automatic driving commercial vehicles.

[0005] In summary, in the prior art, how to ensure the stability of the longitudinal control of automatic driving commercial vehicles under complex working conditions such as load variation without relying on high-cost sensors has become a problem that needs to be solved by technicians in this field. SUMMARY

[0006] Therefore, the purpose of the present application is to provide a longitudinal control method and system for an automatic driving commercial vehicle to solve the above-mentioned problems in the prior art.

[0007] In a first aspect, the present application provides a longitudinal control method for an automatic driving commercial vehicle, which comprises:

[0008] Collecting vehicle motion state parameters from different types of sources;

[0009] Constructing an anti-interference dynamic performance stability architecture;

[0010] establishing a vehicle driving Luenberger observer observing the disturbance suffered by the vehicle based on the vehicle motion state parameters and in the dynamic performance stability architecture respectively;

[0011] combining the vehicle driving Luenberger observer observing the disturbance suffered by the vehicle in the dynamic performance stability architecture, and designing a vehicle acceleration control strategy based on a driving sliding mode controller;

[0012] combining the vehicle braking Luenberger observer observing the disturbance suffered by the vehicle in the dynamic performance stability architecture, and designing a vehicle deceleration control strategy based on a braking sliding mode controller;

[0013] calculating a driving torque according to the vehicle acceleration control strategy, calculating a braking pressure according to the vehicle deceleration control strategy, and designing a longitudinal control mode arbitration strategy based on the driving torque and the braking pressure.

[0014] Compared with the prior art, the beneficial effects of the present application are: the longitudinal control mode arbitration strategy is designed by the vehicle acceleration control strategy and the vehicle deceleration control strategy, the vehicle acceleration control strategy is designed by combining the vehicle driving Luenberger observer observing the disturbance suffered by the vehicle, and the vehicle deceleration control strategy is designed by combining the vehicle braking Luenberger observer observing the disturbance suffered by the vehicle, which not only considers that the driving control and the braking control cannot be triggered at the same time to avoid the conflict of the actuator, but also considers limiting the output range of the driving torque and the braking pressure to prevent the actuator from working beyond the limit, which is helpful for the longitudinal speed stability control of the automatic driving commercial vehicle, and the sliding mode speed control method based on the Luenberger observer can effectively overcome the disturbance caused by the change of the vehicle load and the change of the slope, thereby solving the problem of the longitudinal speed control of the vehicle.

[0015] Further, the vehicle motion state parameters include the vehicle speed, the target vehicle speed, the actual driving torque and the actual braking pressure.

[0016] Further, the step of establishing a vehicle driving Luenberger observer observing the disturbance suffered by the vehicle based on the vehicle motion state parameters and in the dynamic performance stability architecture respectively includes:

[0017] establishing a flat and uphill road working condition longitudinal dynamics model, converting the flat and uphill road working condition longitudinal dynamics model into a first state space equation, and establishing a driving Luenberger observer observing the disturbance suffered by the vehicle on flat and uphill roads based on the first state space equation;

[0018] establishing a downhill road working condition longitudinal dynamics model, converting the downhill road working condition longitudinal dynamics model into a second state space equation, and establishing a brake Lyapunov observer to observe the disturbance to the vehicle on a downhill road based on the second state space equation.

[0019] Further, before the step of observing the disturbance to the vehicle in the dynamic performance stabilizing architecture in combination with the vehicle driving Lyapunov observer, the method further comprises:

[0020] Designing a driving sliding mode speed controller in combination with the observed disturbance by the driving Lyapunov observer.

[0021] Further, before the step of observing the disturbance to the vehicle in the dynamic performance stabilizing architecture in combination with the vehicle brake Lyapunov observer, the method further comprises:

[0022] Designing a brake sliding mode speed controller in combination with the observed disturbance by the brake Lyapunov observer.

[0023] Further, the longitudinal control mode arbitration strategy comprises a first level execution and a second level execution.

[0024] The first level execution is torque arbitration and pressure arbitration, and the second level execution is overrun arbitration.

[0025] In a second aspect, the present application also provides a longitudinal control system for an automatic driving commercial vehicle, which comprises:

[0026] A collection module for collecting vehicle motion state parameters of different types of sources;

[0027] A construction module for constructing an anti-interference dynamic performance stabilizing architecture;

[0028] An establishment module for establishing a vehicle driving Lyapunov observer to observe the disturbance to the vehicle and a vehicle brake Lyapunov observer to observe the disturbance to the vehicle in the dynamic performance stabilizing architecture based on the vehicle motion state parameters;

[0029] A first design module for observing the disturbance to the vehicle in the dynamic performance stabilizing architecture in combination with the vehicle driving Lyapunov observer, and designing a vehicle acceleration control strategy based on a driving sliding mode controller;

[0030] A second design module for observing the disturbance to the vehicle in the dynamic performance stabilizing architecture in combination with the vehicle brake Lyapunov observer, and designing a vehicle deceleration control strategy based on a brake sliding mode controller;

[0031] The computing design module is configured to calculate a driving torque according to the vehicle acceleration control strategy, calculate a brake pressure according to the vehicle deceleration control strategy, and design a longitudinal control mode arbitration strategy based on the driving torque and the brake pressure.

[0032] Further, the establishing module comprises:

[0033] The first establishing unit is configured to establish a flat and uphill road working condition longitudinal dynamics model, convert the flat and uphill road working condition longitudinal dynamics model into a first state space equation, and establish a driving Luenberger observer to observe the disturbance to the vehicle on a flat and uphill road based on the first state space equation.

[0034] The second establishing unit is configured to establish a downhill road working condition longitudinal dynamics model, convert the downhill road working condition longitudinal dynamics model into a second state space equation, and establish a brake Luenberger observer to observe the disturbance to the vehicle on a downhill road based on the second state space equation.

[0035] In a third aspect, the present application further provides a readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the automatic driving commercial vehicle longitudinal control method.

[0036] In a third aspect, the present application further provides a vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the automatic driving commercial vehicle longitudinal control method when executing the program. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flow chart of the automatic driving commercial vehicle longitudinal control method in the first embodiment of the present application;

[0038] Figure 2 A schematic diagram of an exemplary vehicle speed test of different masses;

[0039] Figure 3 A schematic diagram of another exemplary vehicle speed test of different masses;

[0040] Figure 4 A structural block diagram of the automatic driving commercial vehicle longitudinal control system in the second embodiment of the present application;

[0041] Figure 5 A structural block diagram of the vehicle in the third embodiment of the present application.

[0042] Explanation of main element symbols:

[0043] 10, memory; 20, processor; 30, computer program;

[0044] 11, acquisition module; 12, construction module; 13, establishment module; 14, first design module; 15, second design module; 16, calculation design module.

[0045] The following detailed description will further describe the present application with reference to the above mentioned figures. DETAILED DESCRIPTION

[0046] For the purpose of promoting the understanding of the present application, the present application will be described in further detail with reference to the attached drawings. Several embodiments of the present application are shown in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present application will be more thorough and complete.

[0047] It should be noted that when an element is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements can also be present. As used herein the terms "vertical", "horizontal", "left", "right" and similar expressions are used for illustrative purposes only.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0049] Embodiment one

[0050] Referring to Figure 1 , a longitudinal control method for an autonomous commercial vehicle is shown, which includes steps S1 to S6:

[0051] S1, acquiring vehicle motion state parameters of different types of sources;

[0052] It should be noted that in this embodiment, the vehicle motion state parameters include vehicle speed V, target vehicle speed Vd, actual driving torque T and actual brake pressure Fb.

[0053] S2, constructing an anti-interference dynamic performance stability architecture;

[0054] It can be understood that, in order to keep the dynamic performance of the vehicle speed stable under the load and slope interference, it is necessary to build an anti-interference dynamic performance stabilization architecture. The core of the architecture is to use the driving Luenberger observer and the braking Luenberger observer to observe the interference received by the vehicle. Specifically, the driving Luenberger observer and the braking Luenberger observer can monitor and analyze various interference factors received by the vehicle during driving in real time, including load changes and the influence of slopes on vehicle driving, etc. These observed interference information will then be added as compensation to the calculation process of the sliding mode controller. The sliding mode controller accurately calculates the power torque required by the vehicle during acceleration and the brake pressure required during deceleration according to these compensated data, thereby effectively dealing with the interference caused by the load and the slope and ensuring the dynamic performance stability of the vehicle speed.

[0055] S3, based on the vehicle motion state parameters and in the dynamic performance stabilization architecture, respectively establishing a vehicle driving Luenberger observer to observe the interference received by the vehicle and a vehicle braking Luenberger observer to observe the interference received by the vehicle;

[0056] Specifically, the step S3 includes steps S31 to S32:

[0057] S31, establishing a flat and uphill road working condition longitudinal dynamics model, converting the flat and uphill road working condition longitudinal dynamics model into a first state space equation, and based on the first state space equation, establishing a driving Luenberger observer to observe the interference received by the vehicle on flat and uphill roads;

[0058] S32, establishing a downhill road working condition longitudinal dynamics model, converting the downhill road working condition longitudinal dynamics model into a second state space equation, and based on the second state space equation, establishing a braking Luenberger observer to observe the interference received by the vehicle on downhill roads.

[0059] S4, in the dynamic performance stabilization architecture, combining the interference observed by the vehicle driving Luenberger observer, and based on the driving sliding mode controller, designing a vehicle acceleration control strategy;

[0060] It should be noted that before step S4, there is also step S041:

[0061] S041, designing a driving sliding mode speed controller in combination with the interference observed by the driving Luenberger observer;

[0062] It needs to be explained that the vehicle acceleration control strategy is that when the vehicle target speed is higher than the actual speed, the disturbance is observed by the observer first, and the observed disturbance is taken as a compensation term. Then, the driving sliding mode controller designs a sliding mode surface according to the longitudinal dynamics model, the sliding mode surface is the trajectory of the system state motion, and then the driving sliding mode control law is designed to make the system state stably move on the sliding mode surface. At this time, the control input obtained by the driving sliding mode control law is added to the disturbance compensation term, and the result obtained is the final control input of the vehicle acceleration control strategy, that is, the driving torque; the control input can make the vehicle effectively track the target speed, so as to realize the goal of vehicle acceleration control.

[0063] S5, in the dynamic performance stability architecture, the vehicle brake Lyapunov observer is combined to observe the disturbance suffered by the vehicle, and a vehicle deceleration control strategy is designed based on a brake sliding mode controller;

[0064] It needs to be pointed out that before step S5, there is also step S051:

[0065] S051, a brake sliding mode speed controller is designed in combination with the disturbance observed by the brake Lyapunov observer;

[0066] It needs to be explained that when the vehicle target speed is lower than the actual speed, the vehicle needs to decelerate, and the brake pressure calculation process at this time is as follows: first, a brake observer is introduced to observe the disturbance, and the observed disturbance is taken as a compensation term. Then, the brake sliding mode controller designs a sliding mode surface based on the longitudinal dynamics model, and the sliding mode surface is the trajectory of the system state motion. Then, a brake sliding mode control law is designed to make the system state stably move on the sliding mode surface. At this time, the control input obtained by the brake sliding mode control law is added to the disturbance compensation term, and the total sum obtained is the control input of the vehicle deceleration control strategy, that is, the required brake pressure. The brake pressure can make the vehicle effectively track the target speed, so as to realize the goal of deceleration control.

[0067] S6, the driving torque is calculated according to the vehicle acceleration control strategy, the brake pressure is calculated according to the vehicle deceleration control strategy, and a longitudinal control mode arbitration strategy is designed based on the driving torque and the brake pressure;

[0068] It is worth mentioning that the longitudinal control mode arbitration strategy includes first-level execution and second-level execution, wherein the first-level execution is torque arbitration and pressure arbitration, and the second-level execution is overrun arbitration;

[0069] It should be noted that the torque arbitration logic is as follows: when the vehicle speed error is greater than 0, the torque input is the torque calculated by the acceleration controller; otherwise, the torque input is 0. Specifically, a logical threshold ΔVerr for vehicle speed error is set. When Vd-V>0, the arbitration torque Tt=T is output; when Vd-V>ΔVerr and Vd-V<0 occur simultaneously, the arbitration torque Tt=0 is output; when Vd-V<ΔVerr, the arbitration torque Tt=0 is output.

[0070] The pressure arbitration logic is as follows: when the vehicle speed error is greater than the switching logic threshold ΔVerr and less than 0, the pressure input is the pressure calculated by the deceleration controller; otherwise, the pressure input is 0. Specifically, the logic threshold ΔVerr in torque arbitration is used. When Vd-V>0, the output arbitration pressure Fbt=0; when Vd-V>ΔVerr and Vd-V<0 occur simultaneously, the output arbitration pressure Fbt=0; when Vd-V<ΔVerr, the output arbitration pressure Fbt=Fb.

[0071] The over-limit arbitration logic is as follows: When the torque input exceeds the maximum torque threshold ΔTmax, the vehicle torque input is limited to ΔTmax; when the pressure input exceeds the maximum pressure threshold ΔFbmax, the vehicle braking pressure input is limited to ΔFbmax. Specifically, a logical threshold ΔTmax for drive torque and a logical threshold ΔFbmax for braking pressure are set. When Tt < ΔTmax and Vd - V > 0 simultaneously, the drive torque Tarb = Tt is requested, and the braking pressure Farb = 0 is requested. When Tt > ΔTmax and Vd - V > 0 simultaneously, the vehicle brake pressure is requested to be equal to ΔFbmax. Find the driving torque Tarb = ΔTmax and request the braking pressure Farb = 0. When Vd-V > ΔVerr and Vd-V < 0 occur simultaneously, request the driving torque Tarb = 0 and the braking pressure Farb = 0. When Fbt < ΔFbmax and Vd-V < ΔVerr occur simultaneously, request the driving torque Tarb = 0 and the braking pressure Farb = Fbt. When Fbt > ΔFbmax and Vd-V < ΔVerr occur simultaneously, request the driving torque Tarb = 0 and the braking pressure Farb = ΔFbmax.

[0072] Validation cases, such as Figure 2 The diagram illustrates an exemplary scenario of mass-disrupted vehicle testing. Starting from 0, the vehicle speed stabilizes after 6 seconds using traditional PID control methods when the vehicle load varies, with the speed error increasing as the load increases. However, the method proposed in this invention allows vehicles of different weights to track the target speed within 1.5 seconds, and the speed error for vehicles of different weights does not show a significant increasing trend.

[0073] like Figure 3As shown, another example of a ramp interference vehicle test scene is described, where the vehicle speed starts from 0, when the vehicle is interfered by the ramp, the traditional PID control method has failed to stabilize the tracking of the vehicle target speed, and the speed error is getting bigger and bigger, while the automatic driving commercial vehicle longitudinal control method in the application has no obvious trend of getting bigger after the implementation of the vehicle speed error.

[0074] The above results show that the automatic driving commercial vehicle longitudinal control method of the application can overcome the load and ramp interference and stably track the target speed.

[0075] In summary, the automatic driving commercial vehicle longitudinal control method in the above embodiments of the application not only considers the change of vehicle load, but also takes into account the ramp change scene of the vehicle, and can vividly describe the interference problem in the vehicle driving process; the longitudinal control mode arbitration designed by the application not only considers that the drive control and brake control cannot be triggered at the same time to avoid the conflict of the actuator; but also considers limiting the output range of the driving torque and brake pressure to prevent the actuator from working beyond the limit; which is helpful for the longitudinal speed stable control of the automatic driving commercial vehicle; the sliding mode speed control method based on the Luenberger observer designed by the application can effectively overcome the interference caused by the change of vehicle load and ramp, thereby solving the problem of longitudinal speed control of the vehicle.

[0076] Example two

[0077] The application also provides an automatic driving commercial vehicle longitudinal control system, please refer to Figure 4 , which is an automatic driving commercial vehicle longitudinal control system in the second embodiment of the application, the system comprises:

[0078] The acquisition module 11 is used for acquiring vehicle motion state parameters of different types of sources;

[0079] The construction module 12 is used for constructing an anti-interference dynamic performance stability architecture;

[0080] The establishment module 13 is used for establishing a vehicle driving Luenberger observer to observe the interference on the vehicle and a vehicle braking Luenberger observer to observe the interference on the vehicle based on the vehicle motion state parameters and in the dynamic performance stability architecture;

[0081] The first design module 14 is used for combining the vehicle driving Luenberger observer to observe the interference on the vehicle in the dynamic performance stability architecture, and designing a vehicle acceleration control strategy based on a driving sliding mode controller;

[0082] The second design module 15 is used for combining the vehicle braking Luenberger observer to observe the interference on the vehicle in the dynamic performance stability architecture, and designing a vehicle deceleration control strategy based on a braking sliding mode controller;

[0083] The computing design module 16 is configured to calculate the driving torque according to the vehicle acceleration control strategy, calculate the brake pressure according to the vehicle deceleration control strategy, and design a longitudinal control mode arbitration strategy based on the driving torque and the brake pressure.

[0084] In some optional embodiments, the establishing module 13 comprises:

[0085] The first establishing unit is configured to establish a flat and uphill road working condition longitudinal dynamics model, convert the flat and uphill road working condition longitudinal dynamics model into a first state space equation, and establish a driving Luenberger observer to observe the disturbance to the vehicle on a flat and uphill road based on the first state space equation.

[0086] The second establishing unit is configured to establish a downhill road working condition longitudinal dynamics model, convert the downhill road working condition longitudinal dynamics model into a second state space equation, and establish a brake Luenberger observer to observe the disturbance to the vehicle on a downhill road based on the second state space equation.

[0087] In some optional embodiments, the system further comprises:

[0088] The third design module is configured to design a driving sliding mode speed controller in combination with the disturbance observed by the driving Luenberger observer.

[0089] The fourth design module is configured to design a brake sliding mode speed controller in combination with the disturbance observed by the brake Luenberger observer.

[0090] The automatic driving commercial vehicle longitudinal control system provided by the second embodiment of the present application has the same implementation principle and technical effects as the foregoing method embodiments, and for brevity of description, the part not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments.

[0091] Embodiment three

[0092] The present application also proposes a vehicle, please refer to Figure 5 , which is a vehicle in the third embodiment of the present application, comprising a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, wherein the processor 20 implements the automatic driving commercial vehicle longitudinal control method as described above when executing the computer program 30.

[0093] In specific implementation, the processor 20 collects vehicle motion state parameters of different types of sources;

[0094] The processor 20 constructs an anti-interference dynamic performance stable architecture;

[0095] The processor 20 establishes a vehicle drive Luenberger observer observing disturbance to the vehicle and a vehicle brake Luenberger observer observing disturbance to the vehicle in the dynamic performance stabilization framework based on the vehicle motion state parameters;

[0096] The processor 20 combines the vehicle drive Luenberger observer observing disturbance to the vehicle in the dynamic performance stabilization framework and designs a vehicle acceleration control strategy based on a drive sliding mode controller;

[0097] The processor 20 combines the vehicle brake Luenberger observer observing disturbance to the vehicle in the dynamic performance stabilization framework and designs a vehicle deceleration control strategy based on a brake sliding mode controller;

[0098] The processor 20 calculates a drive torque according to the vehicle acceleration control strategy, calculates a brake pressure according to the vehicle deceleration control strategy, and designs a longitudinal control mode arbitration strategy based on the drive torque and the brake pressure.

[0099] In some embodiments, the processor 20 can be an Electronic Control Unit (ECU), a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, which is configured to run program codes or process data stored in the memory 10, such as executing an access restriction program.

[0100] In some embodiments, the memory 10 can be an internal storage unit of the vehicle, such as a hard disk of the vehicle. In other embodiments, the memory 10 can also be an external storage device of the vehicle, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, or the like. Further, the memory 10 can include both an internal storage unit and an external storage device of the vehicle. The memory 10 can be used to store application software and various data installed in the vehicle, and to temporarily store data that has been output or will be output.

[0101] It should be noted that, Figure 5 The structures shown do not constitute a limitation on the vehicle, and in other embodiments, the vehicle can include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0102] The embodiment of the present application also provides a readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the automatic driving commercial vehicle longitudinal control method.

[0103] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be embodied in any computer readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution system, apparatus or device. For the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in connection with an instruction execution system, apparatus or device, or in conjunction with these instruction execution system, apparatus or device.

[0104] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program is printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic means for editing, interpreting or otherwise processing the program to be stored in a computer memory.

[0105] It should be understood that parts of the present application can be realized in hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if realized in hardware, and as in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0106] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0107] The above-described embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the patent scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A longitudinal control method for an autonomous commercial vehicle, characterized in that, The method includes: Collect vehicle motion state parameters from different sources; Build a dynamic, stable architecture that is resistant to interference; Based on the vehicle motion state parameters, and by establishing a vehicle driving Luenberger observer to observe the disturbances experienced by the vehicle and a vehicle braking Luenberger observer to observe the disturbances experienced by the vehicle within the dynamic performance stabilization architecture, this step specifically includes: A longitudinal dynamic model for flat and uphill road conditions is established, and the longitudinal dynamic model for flat and uphill road conditions is converted into a first state space equation. Based on the first state space equation, the disturbances experienced by the vehicle on flat and uphill roads are established to drive the Luneburg observer. A longitudinal dynamic model for downhill road conditions is established, and the longitudinal dynamic model for downhill road conditions is converted into a second state space equation. Based on the second state space equation, a braking Lumberjack observer is established to observe the disturbances experienced by the vehicle on the downhill road. A driving sliding mode speed controller is designed based on the disturbances observed by the driving Lumberjack observer. A driving sliding mode control law is designed based on the driving sliding mode speed controller. The final control input of the vehicle acceleration control strategy is obtained by adding the control input obtained from the driving sliding mode control law with the disturbance compensation term. In the dynamic performance stabilization architecture, the vehicle drive Luenberger is used to observe the disturbances to the vehicle, and a vehicle acceleration control strategy is designed based on the drive sliding mode controller. A braking sliding mode speed controller is designed based on the disturbances observed by the braking Lumberjack observer. A braking sliding mode control law is designed based on the braking sliding mode speed controller. The control input obtained by the braking sliding mode control law is added to the disturbance compensation term to obtain the control input of the vehicle deceleration control strategy. In the dynamic performance stabilization architecture, the vehicle braking Lumberjack observer is used to observe the disturbances to the vehicle, and a vehicle deceleration control strategy is designed based on the braking sliding mode controller. The driving torque is calculated based on the vehicle acceleration control strategy, the braking pressure is calculated based on the vehicle deceleration control strategy, and an arbitration strategy for longitudinal control mode is designed based on the driving torque and the braking pressure.

2. The longitudinal control method for autonomous commercial vehicles according to claim 1, characterized in that, The vehicle motion parameters include vehicle speed, target vehicle speed, actual driving torque, and actual braking pressure.

3. The longitudinal control method for autonomous commercial vehicles according to claim 1, characterized in that, The arbitration strategy of the vertical control mode includes first-level execution and second-level execution; The first level of execution involves torque arbitration and pressure arbitration, while the second level of execution involves over-limit arbitration.

4. A longitudinal control system for an autonomous commercial vehicle, characterized in that, The system includes: The data acquisition module is used to collect vehicle motion state parameters from different sources. Modules for building dynamic, stable architectures that are resistant to interference; A module is established to establish, based on the vehicle motion state parameters and in the dynamic performance stabilization architecture, a vehicle driving Luenberger observer to observe the disturbances experienced by the vehicle and a vehicle braking Luenberger observer to observe the disturbances experienced by the vehicle. The establishment module includes: The first establishment unit is used to establish a longitudinal dynamic model for flat and uphill road conditions, convert the longitudinal dynamic model for flat and uphill road conditions into a first state space equation, and establish the disturbances experienced by the vehicle on flat and uphill roads based on the first state space equation to drive the Lumberjack observer. The second establishment unit is used to establish a longitudinal dynamic model of the downhill road condition, convert the longitudinal dynamic model of the downhill road condition into a second state space equation, and establish a braking Lumberjack observer based on the second state space equation to observe the disturbances experienced by the vehicle on the downhill road. The first design module is used to combine the vehicle drive Lumberjack observer to observe the disturbances to the vehicle in the dynamic performance stabilization architecture, and to design a vehicle acceleration control strategy based on the drive sliding mode controller. The first design module is specifically used to design a braking sliding mode speed controller based on the disturbances observed by the braking Lumberjack observer. Specifically, a braking sliding mode control law is designed based on the braking sliding mode speed controller, and the control input obtained by the braking sliding mode control law is added to the disturbance compensation term to obtain the control input of the vehicle deceleration control strategy. The second design module is used to combine the vehicle braking Lumberjack observer to observe the disturbances to the vehicle in the dynamic performance stabilization architecture, and to design a vehicle deceleration control strategy based on the braking sliding mode controller. The calculation and design module is used to calculate the driving torque according to the vehicle acceleration control strategy, calculate the braking pressure according to the vehicle deceleration control strategy, and design a longitudinal control mode arbitration strategy based on the driving torque and the braking pressure.

5. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the longitudinal control method for autonomous commercial vehicles as described in any one of claims 1 to 3.

6. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the longitudinal control method for autonomous commercial vehicles as described in any one of claims 1 to 3.

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