CN109398479B - A server, intelligent vehicle steering control method, device, medium and intelligent vehicle - Google Patents
A server, intelligent vehicle steering control method, device, medium and intelligent vehicle Download PDFInfo
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Abstract
本申请公开了一种服务器、智能车转向控制方法、装置、介质及一种智能车，包括：获取基准数据以及目标智能车的运动状态参数；其中，基准数据包括基准的传感数据和控制量数据；确定基础转向控制算法和期望行进轨迹，并根据所述期望行进轨迹、所述基准数据和所述运动状态参数优化基础转向控制算法，得到目标智能车在目标车速下对应的优化后转向控制算法；利用优化后转向控制算法控制目标智能车转向。也即，本发明充分利用云端的计算能力自动实现转向控制算法的优化，避免了人工整定算法参数的过程，减少整定算法参数的时间与人力成本，进而提高了智能车提速的效率，并且提高智能车的行进速度，使智能车更加符合实际场景的需求，更快更准的执行任务。
The present application discloses a server, a smart car steering control method, device, medium, and a smart car, including: acquiring reference data and motion state parameters of a target smart car; wherein the reference data includes reference sensing data and control quantities data; determine the basic steering control algorithm and the expected travel trajectory, and optimize the basic steering control algorithm according to the expected travel trajectory, the reference data and the motion state parameters, and obtain the optimized steering control corresponding to the target smart car at the target speed Algorithm; use the optimized steering control algorithm to control the steering of the target smart car. That is, the present invention makes full use of the computing power of the cloud to automatically realize the optimization of the steering control algorithm, avoids the process of manually adjusting the algorithm parameters, reduces the time and labor costs for adjusting the algorithm parameters, thereby improving the speed-up efficiency of the intelligent vehicle and improving the intelligence of the vehicle. The traveling speed of the car makes the smart car more in line with the needs of the actual scene and perform tasks faster and more accurately.
Description
Technical Field
The invention relates to the technical field of intelligent vehicles, in particular to a cloud server, an intelligent vehicle steering control method, an intelligent vehicle steering control device, a storage medium and an intelligent vehicle.
Background
Under the current era, researches on intelligent vehicles and products thereof are endless, such as electromagnetic tracking vehicles, photoelectric tracking vehicles, intelligent vehicle management systems based on the internet of things, intelligent vehicle steering models and the like. Generally, the cloud-based intelligent vehicle has strong associativity with a cloud end and weak associativity with an actual intelligent vehicle; the combination with the actual intelligent vehicle is strong, and the combination with the cloud is weak. In addition, research on the manufacturing of intelligent vehicles has been carried out on intelligent vehicles with various additional functions, such as electromagnetic tracking vehicles for automatically spraying pesticides, photoelectric tracking vehicles for automatically extinguishing fire, and the like, and most of the intelligent vehicles are slow.
In the prior art, the control steering of the intelligent vehicle is generally optimized on the premise of low speed, and the task execution speed is low and the efficiency is low. Under the condition of high speed, a steering control algorithm is easy to generate larger errors, the problem that the moving track of the intelligent vehicle drifts or even completely deviates from the route can be caused by the excessively high speed of the intelligent vehicle in the process of executing a task, and the speed of the intelligent vehicle needs complicated work to set various control parameters. In view of this, how to solve the above problems is a major concern for those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a cloud server, a method and an apparatus for controlling steering of an intelligent vehicle, a storage medium, and an intelligent vehicle, which can automatically complete optimization of a steering control algorithm of the intelligent vehicle by using a computing function of the cloud, and improve efficiency of speed acceleration of the intelligent vehicle during traveling. The specific scheme is as follows:
in a first aspect, the invention discloses an intelligent vehicle steering control method, which comprises the following steps:
acquiring reference data and motion state parameters of a target intelligent vehicle; wherein the reference data includes reference sensing data and control quantity data;
determining a basic steering control algorithm and an expected traveling track, and optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed;
and controlling the target intelligent vehicle to steer by using the optimized steering control algorithm.
Optionally, the optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameter to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at the target vehicle speed includes:
s101: determining a preset dynamic model and a basic steering control algorithm under the current vehicle speed, and optimizing the preset dynamic model by using an optimization method according to the basic steering control algorithm, the reference data and the motion state parameters;
s102: optimizing the basic steering control algorithm using the optimized kinetic model and the expected travel trajectory;
s103: judging whether the vehicle speed corresponding to the optimized steering control algorithm reaches the target vehicle speed or not;
s104: if so, obtaining an optimized steering control algorithm corresponding to the target intelligent vehicle at the target speed;
s105: if not, the vehicle speed corresponding to the optimized steering control algorithm is taken as the current vehicle speed, and the process goes to S101.
Optionally, the acquiring the reference data includes:
acquiring sensing data and control quantity data of a preset intelligent vehicle during running on a runway, wherein the sensing data and the control quantity data are acquired by a sensor on the preset intelligent vehicle; the sensing data comprise the speed of the preset intelligent vehicle and the contact ratio of the advancing track and the runway, and the control quantity data comprise speed control data for controlling the speed of the preset intelligent vehicle and steering control data for controlling the steering of the preset intelligent vehicle.
Optionally, the acquisition predetermines that sensor on the intelligent car gathers sensing data and controlled quantity data when predetermineeing the intelligent car and marcing on the runway include:
acquiring the contact ratio of the running track of the preset intelligent vehicle and the runway, and judging whether the contact ratio is greater than a preset threshold value;
and if so, acquiring the sensing data and the control quantity data of the preset intelligent vehicle during running on the runway, which are acquired by the sensor on the preset intelligent vehicle.
Optionally, after optimizing the basic steering control algorithm by using the preset dynamic model and the expected travel track, the method further includes:
determining a vehicle speed corresponding to the optimized steering control algorithm;
acquiring target sensing data and target control quantity data when the preset intelligent vehicle travels at a speed corresponding to the optimized steering control algorithm, and analyzing to obtain a control relation between steering control data in the target control quantity data and a deflection angle in the target sensing data;
and calibrating the optimized steering control algorithm by using the control relation.
In a second aspect, the invention discloses an intelligent vehicle steering control device, which comprises:
the data acquisition module is used for acquiring reference data and motion state parameters of the target intelligent vehicle; the reference data comprises reference sensing data and control quantity data, and the motion state parameters comprise any one or combination of any several of wheel friction, vehicle gravity center, steering engine steering speed and vehicle speed;
the algorithm optimization module is used for determining a basic steering control algorithm and an expected advancing track, and optimizing the basic steering control algorithm according to the expected advancing track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed;
and the steering control module is used for controlling the steering of the target intelligent vehicle by utilizing the optimized steering control algorithm.
Optionally, the data obtaining module includes:
the judging unit is used for acquiring the contact ratio of the running track of the preset intelligent vehicle and the runway, and judging whether the contact ratio is greater than a preset threshold value;
and the acquisition unit is used for acquiring the sensing data and the control quantity data of the preset intelligent vehicle when the preset intelligent vehicle travels on the runway, wherein the sensing data and the control quantity data are acquired by the sensor on the preset intelligent vehicle if the contact ratio is greater than a preset threshold value.
In a third aspect, the invention discloses an intelligent vehicle which comprises the intelligent vehicle steering control device.
In a fourth aspect, the present invention discloses a cloud server, including:
a memory for storing a computer program;
and the processor is used for realizing the intelligent vehicle steering control algorithm disclosed in the foregoing when executing the computer program.
In a fifth aspect, the present invention discloses a computer readable storage medium for storing a computer program, which when executed by a processor implements the intelligent vehicle steering control method disclosed above.
Therefore, the method obtains the reference data and the motion state parameters of the target intelligent vehicle; wherein the reference data includes reference sensing data and control quantity data; determining a basic steering control algorithm and an expected traveling track, and optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed; and controlling the target intelligent vehicle to steer by using the optimized steering control algorithm. That is, the basic steering control algorithm is optimized according to the expected traveling track, the reference data and the motion state parameters to obtain the steering control algorithm of the target intelligent vehicle at the target speed, the optimization of the steering control algorithm is automatically realized by fully utilizing the computing capability of the cloud, the process of manually setting the parameters of the steering control algorithm is avoided, the time and the labor cost for setting the parameters of the algorithm are reduced, the speed-up efficiency of the intelligent vehicle is improved, the traveling speed of the intelligent vehicle is improved, the intelligent vehicle is enabled to better meet the requirements of actual scenes, and tasks are executed more quickly and accurately. In addition, the cloud terminal is used for carrying out multithreading work, so that simultaneous operation of multiple intelligent vehicles can be supported, and convenience is brought to batch manufacturing and production of the intelligent vehicles.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of a method for controlling the steering of an intelligent vehicle according to the present invention;
FIG. 2 is a flow chart illustrating optimization of a steering control algorithm in an embodiment of the method for controlling a steering of an intelligent vehicle according to the present invention;
FIG. 3 is a flowchart of an embodiment of a method for controlling the steering of an intelligent vehicle according to the present invention;
fig. 4 is a block diagram of the structure of the intelligent vehicle steering control device provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the prior art, the control steering of the intelligent vehicle is generally optimized on the premise of low speed, and the task execution speed is low and the efficiency is low. Under the condition of high speed, a steering control algorithm is easy to generate larger errors, the problem that the moving track of the intelligent vehicle drifts or even completely deviates from the route can be caused by the excessively high speed of the intelligent vehicle in the process of executing a task, and the speed of the intelligent vehicle needs complicated work to set various control parameters.
The embodiment of the invention discloses an intelligent vehicle steering control method, which is shown in figure 1 and comprises the following steps:
step S11: acquiring reference data and motion state parameters of a target intelligent vehicle; wherein the reference data includes reference sensing data and control quantity data;
in this embodiment, reference data is acquired, where the reference data includes reference sensing data and control amount data as expected template data; and acquiring motion state parameters of the target intelligent vehicle, wherein the motion state parameters comprise wheel friction, vehicle gravity center, steering engine steering speed, vehicle speed and the like.
Step S12: determining a basic steering control algorithm and an expected traveling track, and optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed;
specifically, the present embodiment determines a basic steering control algorithm as a basis for optimization, determines an expected travel trajectory, and further optimizes the basic steering control algorithm according to the expected travel trajectory, the reference data, and the motion state parameter to obtain an optimized steering control algorithm at a target vehicle speed.
It can be understood that, in the prior art, if the control steering of the intelligent vehicle is optimized under the condition of high speed, a steering control algorithm is easy to have a large error, and the speed increase of the intelligent vehicle needs complicated work to set various control parameters. In the embodiment, the basic steering control algorithm is automatically optimized through the cloud server based on the expected travelling track according to the reference data and the motion state parameters, the optimal parameters of the steering control algorithm are determined, the steering control algorithm at the target speed is obtained, the steering control is optimized on the premise that the speed of the intelligent vehicle is increased, and the parameter setting efficiency is improved.
Step S13: and controlling the target intelligent vehicle to steer by using the optimized steering control algorithm.
In the embodiment, the optimized steering control algorithm at the target speed is used for steering control over the target intelligent vehicle, so that the task execution speed of the target intelligent vehicle is increased.
Therefore, the method obtains the reference data and the motion state parameters of the target intelligent vehicle; wherein the reference data includes reference sensing data and control quantity data; determining a basic steering control algorithm and an expected traveling track, and optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed; and controlling the target intelligent vehicle to steer by using the optimized steering control algorithm. That is, the basic steering control algorithm is optimized according to the expected traveling track, the reference data and the motion state parameters to obtain the steering control algorithm of the target intelligent vehicle at the target speed, the optimization of the steering control algorithm is automatically realized by fully utilizing the computing capability of the cloud, the process of manually setting the parameters of the steering control algorithm is avoided, the time and the labor cost for setting the parameters of the algorithm are reduced, the speed-up efficiency of the intelligent vehicle is improved, the traveling speed of the intelligent vehicle is improved, the intelligent vehicle is enabled to better meet the requirements of actual scenes, and tasks are executed more quickly and accurately. In addition, the cloud terminal is used for carrying out multithreading work, so that simultaneous operation of multiple intelligent vehicles can be supported, and convenience is brought to batch manufacturing and production of the intelligent vehicles.
In a specific embodiment of the intelligent vehicle steering control method provided by the present invention, a process of optimizing the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameter to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at the target vehicle speed is further described, as shown in fig. 2, the process specifically includes:
s101: determining a preset dynamic model and a basic steering control algorithm under the current vehicle speed, and optimizing the preset dynamic model by using an optimization method according to the basic steering control algorithm, the reference data and the motion state parameters;
in this embodiment, a preset dynamic model and a basic steering control algorithm are determined first. It can be understood that the preset dynamic model and the basic steering algorithm are both better models and algorithms at the current vehicle speed, and the preset dynamic model can be derived from vehicle mechanics.
In the steering control, the parameters such as the control quantity and the vehicle speed are not in a linear relationship, so that a steering control algorithm corresponding to a higher target speed cannot be obtained by directly utilizing a better preset dynamic model and a basic steering algorithm under the current vehicle speed. In this embodiment, based on the current parameters of the basic steering control algorithm, the reference data and the motion state parameters, the preset dynamic model is optimized by using an optimization method, and the parameters of the vehicle dynamic model most fitting the target intelligent vehicle are determined to obtain the optimized dynamic model.
S102: optimizing the basic steering control algorithm using the optimized kinetic model and the expected travel trajectory;
further, the optimal parameter value of the steering control algorithm is determined by an optimization method through the optimized dynamic model based on the expected traveling track.
S103: judging whether the vehicle speed corresponding to the optimized steering control algorithm reaches the target vehicle speed or not;
s104: if so, obtaining an optimized steering control algorithm corresponding to the target intelligent vehicle at the target speed;
s105: if not, the vehicle speed corresponding to the optimized steering control algorithm is taken as the current vehicle speed, and the process goes to S101.
It can be understood that, in the embodiment, the preset dynamic model and the basic steering control algorithm corresponding to the low-speed traveling are optimized, and the optimized steering control algorithm with a higher speed relative to the current speed can be obtained after each optimization. Therefore, the embodiment judges whether the vehicle speed corresponding to the optimized steering control algorithm reaches the target vehicle speed, if so, the optimization process is finished, and the optimized steering control algorithm corresponding to the target intelligent vehicle at the target vehicle speed is obtained; if not, the current speed does not reach the target speed, and further iteration optimization steps are needed.
For example, in the embodiment, a preset dynamic model and a basic steering control algorithm with a current vehicle speed of 1m/s are obtained, and an optimized dynamic model and an optimized steering control algorithm corresponding to a vehicle speed of 2m/s are obtained through one optimization process. If the preset target speed is 5m/s, judging that the vehicle speed corresponding to the current steering control algorithm reaches the target vehicle speed, and continuously iterating the optimization steps until the optimized steering control algorithm corresponding to the target speed is obtained.
In a specific embodiment of the steering control method for the intelligent vehicle provided by the present invention, a process for acquiring reference data is further described, and the process specifically includes:
acquiring sensing data and control quantity data of a preset intelligent vehicle during running on a runway, wherein the sensing data and the control quantity data are acquired by a sensor on the preset intelligent vehicle; the sensing data comprise the speed of the preset intelligent vehicle and the contact ratio of the advancing track and the runway, and the control quantity data comprise speed control data for controlling the speed of the preset intelligent vehicle and steering control data for controlling the steering of the preset intelligent vehicle.
It should be pointed out that predetermine in this embodiment intelligent car selects general type electromagnetic tracking intelligent car for use, and is concrete, general type electromagnetic tracking intelligent car's main component includes: the electromagnetic induction type steering engine comprises an electromagnetic induction coil, an inductance fixing rod, a steering control steering engine, a steering shaft, a chassis, wheels, a central control main board, an electromagnetic filter board, a motor driving board, a 7.2V rechargeable lithium battery and a 12V direct current motor.
Further, this embodiment builds the alternating magnetic field runway in advance, and the alternating circuit who lays under the runway connects the signal source to produce, and the signal source parameter includes: the alternating frequency of the current is 20kHz, the current is 100mA, and the current signal type is square wave. In addition, the alternating magnetic field runway contains road condition elements such as crossroads, u-turns, s-turns and right-angle turns.
It can be understood that, in the embodiment, a WiFi (wireless fidelity) device is also installed on the preset smart car, so as to connect to the Internet by using the WiFi device, and further communicate with the server. Preferably, the isp 8266 module is selected as the WiFi device.
Specifically, the system power supply module in this embodiment is powered and input by a 7.2V lithium battery, and performs voltage reduction or voltage boosting to 3.3V, 5V, and 12V by using an integrated IC on-off power supply technology. Wherein, 3.3V is used for the power supply of master control singlechip, and 5V is used for the wiFi device, and 12V is used for driving direct current motor.
It should be noted that the main control chip and the peripheral circuit module include a main control single chip, a minimum system circuit, a simple man-machine interaction circuit, and an LCD display circuit. The motor driving circuit module is composed of two pieces of IR2184S and four MOSFETs to form a motor driving circuit, and adopts a unipolar control mode to control the rotating speed and the vehicle speed by adjusting the PWM duty ratio. The esp8266 module is used for real-time transmission of electromagnetic inductance data. The alternating electric field induction module consists of 4 crossed inductors and is used for inducing an alternating electromagnetic field to generate an electric field signal and induce electromotive force. The voltage signal detection circuit module consists of an LC parallel resonance circuit, an integrated operational amplifier circuit and a diode voltage-multiplying detection circuit and is used for amplifying and filtering the acquired voltage signals. The alternating electric field signal generator consists of an alternating current coil and is used for generating an alternating electromagnetic field and guiding the vertical trolley.
In this embodiment, advance on the runway of having built under the low-speed of general type electromagnetism tracking smart car is predetermine to the utilization, upload the high in the clouds with the sensing data and the controlled quantity data that the sensor gathered. Preferably, before the data are uploaded to the cloud server, the coincidence degree of the running track of the preset intelligent vehicle and the runway is further obtained, and whether the coincidence degree is greater than a preset threshold value is judged; and if so, acquiring the sensing data and the control quantity data of the preset intelligent vehicle during running on the runway, which are acquired by the sensor on the preset intelligent vehicle. For example, if the contact ratio between the running track of the preset intelligent vehicle and the runway is greater than 97%, uploading sensing data and control data, which are acquired by a sensor when the preset intelligent vehicle runs on the runway, to a cloud server.
The embodiment of the invention discloses a specific intelligent vehicle steering control method, and compared with the previous embodiment, the embodiment further explains and optimizes the technical scheme. Specifically, as shown in fig. 3, the process includes:
s201: determining a preset dynamic model and a basic steering control algorithm under the current vehicle speed, and optimizing the preset dynamic model by using an optimization method according to the basic steering control algorithm, the reference data and the motion state parameters;
s202: optimizing the basic steering control algorithm using the optimized kinetic model and the expected travel trajectory;
s203: determining a vehicle speed corresponding to the optimized steering control algorithm;
in this embodiment, after the basic steering control algorithm is optimized, the vehicle speed corresponding to the optimized current steering control algorithm is determined.
S204: acquiring target sensing data and target control quantity data when the preset intelligent vehicle travels at a speed corresponding to the optimized steering control algorithm, and analyzing to obtain a control relation between steering control data in the target control quantity data and a deflection angle in the target sensing data;
specifically, in the embodiment, target sensing data and target control quantity data acquired by the intelligent vehicle are preset as calibration standards at a vehicle speed corresponding to the current steering control algorithm. And analyzing the steering control data in the target control quantity data and the deflection angle in the target sensing data to obtain the common control relation. Of course, the control relationship between the steering control data and other physical quantities may also be analyzed, and is not limited herein.
S205: and calibrating the optimized steering control algorithm by using the control relation.
Further, the optimized steering control algorithm is further calibrated by utilizing the control relation, so that the optimized steering control algorithm is more accurate.
S206: judging whether the vehicle speed corresponding to the optimized steering control algorithm reaches the target vehicle speed or not;
s207: if so, obtaining an optimized steering control algorithm corresponding to the target intelligent vehicle at the target speed;
s208: if not, the vehicle speed corresponding to the optimized steering control algorithm is taken as the current vehicle speed, and the process goes to S201.
For details of the steps S206 and S208, reference may be made to the detailed process of the foregoing embodiment, and details are not repeated here.
In the following, the intelligent vehicle steering control device provided by the embodiment of the invention is introduced, and the intelligent vehicle steering control device described below and the intelligent vehicle steering control method described above can be referred to correspondingly.
Fig. 4 is a block diagram of a structure of an intelligent vehicle steering control device according to an embodiment of the present invention, and referring to fig. 4, the intelligent vehicle steering control device may include:
the data acquisition module 100 is used for acquiring reference data and motion state parameters of the target intelligent vehicle; the reference data comprises reference sensing data and control quantity data, and the motion state parameters comprise any one or combination of any several of wheel friction, vehicle gravity center, steering engine steering speed and vehicle speed;
the algorithm optimization module 200 is configured to determine a basic steering control algorithm and an expected traveling track, and optimize the basic steering control algorithm according to the expected traveling track, the reference data and the motion state parameters to obtain an optimized steering control algorithm corresponding to the target intelligent vehicle at a target vehicle speed;
and the steering control module 300 is used for controlling the steering of the target intelligent vehicle by utilizing the optimized steering control algorithm.
Further, the data acquisition module comprises:
the judging unit is used for acquiring the contact ratio of the running track of the preset intelligent vehicle and the runway, and judging whether the contact ratio is greater than a preset threshold value;
and the acquisition unit is used for acquiring the sensing data and the control quantity data of the preset intelligent vehicle when the preset intelligent vehicle travels on the runway, wherein the sensing data and the control quantity data are acquired by the sensor on the preset intelligent vehicle if the contact ratio is greater than a preset threshold value.
The intelligent vehicle steering control device in this embodiment is used to implement the foregoing intelligent vehicle steering control method, so that the specific implementation manner of the intelligent vehicle steering control device can be seen in the implementation portion of the foregoing intelligent vehicle steering control method, and details are not described here.
Further, the embodiment of the invention also discloses an intelligent vehicle which comprises the intelligent vehicle steering control device.
Further, the embodiment of the invention also discloses a cloud server which comprises a memory and a processor, wherein the memory is used for storing the computer program, and the processor is used for realizing the intelligent vehicle steering control method when executing the computer program.
Further, the embodiment of the invention also discloses a computer readable storage medium for storing a computer program, and the computer program is executed by a processor to realize the intelligent vehicle steering control method disclosed in the foregoing.
According to the method, the basic steering control algorithm is optimized according to the expected advancing track, the reference data and the motion state parameters, the steering control algorithm of the target intelligent vehicle at the target speed is obtained, the optimization of the steering control algorithm is automatically realized by fully utilizing the computing capability of the cloud, the process of manually setting the parameters of the steering control algorithm is avoided, the time and the labor cost for setting the parameters of the algorithm are reduced, the speed-up efficiency of the intelligent vehicle is further improved, the advancing speed of the intelligent vehicle is improved, the intelligent vehicle can better meet the requirements of actual scenes, and tasks can be executed more quickly and accurately. In addition, the cloud terminal is used for carrying out multithreading work, so that simultaneous operation of multiple intelligent vehicles can be supported, and convenience is brought to batch manufacturing and production of the intelligent vehicles.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The server, the intelligent vehicle steering control method, the intelligent vehicle steering control device, the storage medium and the intelligent vehicle provided by the invention are described in detail, specific examples are applied in the text to explain the principle and the implementation mode of the invention, and the description of the above embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
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| CN107757618A (en) * | 2016-08-18 | 2018-03-06 | 通用汽车环球科技运作有限责任公司 | Track integrality collaboration guiding for autonomous vehicle |
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| CN107200020B (en) * | 2017-05-11 | 2019-05-31 | 江苏大学 | It is a kind of based on mixing theoretical pilotless automobile self-steering control system and method |
| CN107092256B (en) * | 2017-05-27 | 2020-03-17 | 中国科学院自动化研究所 | Steering control method for unmanned vehicle |
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