CN115593510A - Vehicle control method and device, storage medium, and electronic device - Google Patents
Vehicle control method and device, storage medium, and electronic device Download PDFInfo
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Abstract
The application discloses a vehicle control method and device, a storage medium and an electronic device. The control method of the vehicle includes: under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at the current position; determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined by a yaw rate and a lateral acceleration of the target vehicle; determining a first torque of the steering wheel based on the feedforward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for controlling a vehicle, a storage medium, and an electronic apparatus.
Background
In recent years, with the development of artificial intelligence technology, driving assistance technology has also been rapidly developed. In the field of driving assistance, a driver does not need to operate a vehicle, but the vehicle automatically acquires environmental information and performs driving assistance according to the environmental information, and the vehicle automatically drives as shown in fig. 3. The user experience of the driver assistance system is directly affected by the performance of the vehicle sensors. When a sensor fails, abrupt changes in the measurement signal can cause the vehicle lateral control algorithm to degrade dramatically, which greatly impacts the user experience of the assisted driving system.
In the related art, a transverse control algorithm does not consider the problem of how to control a vehicle under the extreme condition that a sensor of the vehicle breaks down, and an effective solution is not provided at present.
Accordingly, there is a need for improvement in the related art to overcome the disadvantages of the related art.
Disclosure of Invention
The embodiment of the invention provides a vehicle control method and device, a storage medium and an electronic device, and aims to at least solve the problem that a transverse control algorithm does not consider how to control a vehicle under the extreme condition that a sensor of the vehicle breaks down.
According to an aspect of an embodiment of the present invention, there is provided a control method of a vehicle, including: under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at the current position; determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined by a yaw rate and a lateral acceleration of the target vehicle; determining a first torque of the steering wheel according to the feedforward torque and the feedback torque so that the target vehicle turns the steering wheel to the desired steering wheel angle according to the first torque.
In one exemplary embodiment, before obtaining the desired steering wheel angle of the target vehicle and the feed-forward torque corresponding to the target vehicle at the home position, the method further comprises: acquiring the measured value and the estimated value of the steering wheel angle measured by the steering wheel sensor; determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value.
In one exemplary embodiment, determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value includes: determining that the steering wheel sensor has a fault when the difference is greater than a preset threshold; and determining that the steering wheel sensor has no fault when the difference value is less than or equal to a preset threshold value.
In one exemplary embodiment, determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value includes: determining an error value for the desired steering wheel angle value and the estimate value; performing integral operation on the error value to obtain an integral value of the error value; and determining the feedback torque according to a first gain corresponding to the error value, a second gain corresponding to the integral value, the error value and the integral value.
In one exemplary embodiment, before determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value, the method further comprises: analyzing the yaw rate and the lateral acceleration by using a preset model to determine an estimated value of a steering wheel angle corresponding to the yaw rate and the lateral acceleration, wherein the preset model is obtained by using a plurality of groups of data through machine learning training, and each group of the plurality of groups of data comprises: yaw rate of the vehicle, lateral acceleration, and steering angle value of the steering wheel angle of the vehicle.
In one exemplary embodiment, determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value includes: determining a target estimation value according to the estimation value and the measurement value; determining a feedback torque of the steering wheel based on the target estimate and the desired steering wheel angle value.
In one exemplary embodiment, determining a target estimate based on the estimate and the measurement comprises: determining the target estimate by:
According to another aspect of the embodiments of the present invention, there is also provided a control apparatus of a vehicle, including: the acquisition module is used for acquiring an expected steering wheel angle of a target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at a current position under the condition that a fault of a steering wheel sensor of the target vehicle is determined; a determination module for determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined from a yaw rate and a lateral acceleration of the target vehicle; a control module to determine a first torque of the steering wheel based on the feedforward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to execute the control method of the vehicle described above when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the control method of the vehicle through the computer program.
With the present invention, since the feedback torque of the steering wheel is determined based on the estimated value of the target vehicle and the desired steering wheel angle value in the case where it is determined that the steering wheel sensor of the target vehicle is out of order, the first torque of the steering wheel is determined based on the feedforward torque and the feedback torque so that the target vehicle turns the steering wheel to the desired steering wheel angle based on the first torque. The problem that how to control the vehicle under the extreme condition that a sensor of the vehicle breaks down is not considered in a transverse control algorithm is solved, and therefore even under the condition that the sensor of the vehicle breaks down, the torque of a steering wheel of the vehicle can be accurately determined.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of an application environment of an alternative vehicle control method according to an embodiment of the application;
FIG. 2 is a flow chart of an alternative method of controlling a vehicle according to an embodiment of the present invention;
FIG. 3 is an overall architecture diagram of an alternative vehicle assistance/autopilot system in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of an alternative method of controlling a vehicle according to an embodiment of the present invention;
fig. 5 is a flowchart of a method of determining an estimated value of a steering wheel angle according to an embodiment of the present invention;
FIG. 6 is a flow chart of an alternative method of determining a failure of a steering wheel sensor of a vehicle according to an embodiment of the present invention;
FIG. 7 is a flowchart (III) of an alternative control method for a vehicle according to an embodiment of the present invention;
FIG. 8 is a schematic view of a two degree-of-freedom "bicycle" model in accordance with an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of an alternative vehicle control apparatus according to an embodiment of the present application;
FIG. 10 is a block diagram of a computer system architecture for an alternative electronic device according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The present application is illustrated below with reference to examples:
according to an aspect of an embodiment of the present application, a control method of a vehicle is provided, and optionally, in the present embodiment, the control method of a vehicle may be applied to a hardware environment formed by a server 101 and an autonomous vehicle 103 as shown in fig. 1. As shown in fig. 1, a server 101 connected to an autonomous vehicle 103 via a network may be used to provide services to the autonomous vehicle 103 or to an application 107 installed on the autonomous vehicle 103, where the application 107 may be a vehicle control application or the like. A database 105 may be provided on the server 101 or separate from the server 101 for providing data storage services for the server 101, such as a vehicle data storage server, an environmental data storage server, and the network may include, but is not limited to: a wired network, a wireless network, wherein the wired network comprises: a local area network, a metropolitan area network, and a wide area network, the wireless network comprising: bluetooth, WIFI, and other networks for implementing wireless communication, the autonomous vehicle 103 may be a terminal configured with an application program, and may include but is not limited to a vehicle-mounted terminal, the server 101 may be a single server, or a server cluster composed of multiple servers, or a cloud server, and the application program 107 using the control method of the autonomous vehicle displays through the autonomous vehicle 103 or other connected display devices.
As shown in fig. 1, the control method of the vehicle described above can be implemented in the autonomous vehicle 103 by the following steps S202 to S206 in fig. 2:
optionally, in this embodiment, the control method of the vehicle may also be implemented by a server, for example, implemented in the server 101 shown in fig. 1; or by both the autonomous vehicle and the server.
The above is merely an example, and the present embodiment is not particularly limited.
Optionally, as an alternative implementation, as shown in fig. 2, the control method of the vehicle includes the following steps S202 to S206:
step S202: under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at the current position;
as an alternative example, the target vehicle is a vehicle having an automatic driving function.
In one exemplary embodiment, it may be determined whether the steering wheel sensor of the target vehicle is malfunctioning by the following steps S11-S12:
step S11: acquiring the measured value and the estimated value of the steering wheel angle measured by the steering wheel sensor;
as an alternative example, the difference between the measured value and the estimated value is determined by the following formula:
wherein, in the step (A),
in order to be the difference value,
for the purpose of the estimation of the value,
is the measurement of the steering wheel sensor.
Step S12: determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value.
As an optional example, in the case that the difference value is greater than a preset threshold value, determining that the steering wheel sensor is faulty; and determining that the steering wheel sensor has no fault when the difference value is less than or equal to a preset threshold value. Namely, when the difference value between the estimated value and the measured value is within a preset range, the steering wheel sensor is considered to have no fault, otherwise, the steering wheel sensor is considered to have fault.
As an alternative example, the feedforward torque corresponding to the target point of the target vehicle at the current position may be determined through the following steps S13 to S15:
step S13, determining a preset driving path and a current position of the target vehicle;
step S14, determining a pre-aiming point of the target vehicle on the preset driving path according to the current position, wherein the distance between the pre-aiming point and the current position is a preset distance;
and S15, determining the road curvature radius of the pre-aiming point according to the tangential direction angle of the pre-aiming point on the preset driving path, and determining the feedforward torque corresponding to the road curvature according to a vehicle transverse motion model.
Step S204: determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined by a yaw rate and a lateral acceleration of the target vehicle;
in an exemplary embodiment, step S204, which may be described above, may be implemented by steps S21-S23, which are as follows:
step S21, determining the desired steering wheel angle value and the error value of the estimated value;
step S22, carrying out integral operation on the error value to obtain an integral value of the error value;
and step S23, determining the feedback torque according to the first gain corresponding to the error value, the second gain corresponding to the integral value, the error value and the integral value.
The feedback torque is determined according to the expected steering wheel rotation angle value and the error value of the estimated value, the first torque of the target vehicle is further determined according to the feedback torque and the feedforward torque, the technical effect that the torque of the steering wheel of the vehicle can be still accurately determined even if the sensor of the vehicle breaks down is achieved, and the problem that how to control the vehicle under the extreme condition that the sensor of the vehicle breaks down is not considered in a transverse control algorithm is solved.
As an alternative example, the error value for the desired steering wheel angle value and the estimated value is determined by the following equation:
wherein, in the process,
in order to be an error value,
in order to expect a steering wheel angle value,
are estimated values.
As an alternative example, the feedback torque is determined by the following equation:
wherein, in the step (A),
in order to feed back the torque, the torque feedback device,
in order to achieve the first gain, the gain is,
is the second gain.
It should be noted that the first gain and the second gain need to be determined by trial-and-run debugging and matching.
In an exemplary embodiment, the step S204 may be further implemented by the following steps S24 to S26:
step S24, determining a target estimation value according to the estimation value and the measurement value;
in particular, the method comprises the following steps of,
wherein, in the step (A),
in order to be a target estimation value,
is an estimated value of the steering wheel angle,
is a measure of the steering wheel angle,
in order to be a smoothing factor, the data is,
the change is made according to a preset rule. There is an error between the estimated value of the steering wheel angle and the measured value, and if the estimated value is directly used in place of the measured value as the system output after the sensor failure, the system may be unstable due to disturbance caused by the step of the error between the estimated value and the measured value. Therefore, in order to reduce the disturbance caused by the error, the error-tolerant compensation output needs to be smoothed, and the stability of the system is further improved.
As an alternative embodiment, the smoothing factor is changed according to a preset rule, including: inputting the measured value of the steering wheel angle into a fault type model, so that the fault type model determines the fault type of the direction sensor according to the measured value, wherein the fault type comprises one of the following: constant fault, multiplicative fault, additive fault; and determining a preset rule corresponding to the fault type according to the fault type so as to change the smoothing factor according to the preset rule.
It should be noted that, under the condition of different types of faults, the smoothing factor selects different adjustment rules, so that the fault-tolerant compensation output can be more intelligently smoothed, and the stability of the system is further improved.
It should be noted that the constant value fault is a steering wheel sensor output of oneThe number of the fixed value is determined,
(ii) a Multiplicative fault, which is the steering wheel sensor output as the actual steering wheel angle multiplied by a fixed gain,
(ii) a Additive faults, which are the steering wheel sensor output as the actual steering wheel angle plus a fixed offset,
。
step S25, determining a target error value of the desired steering wheel angle value and the target estimation value;
in particular, the method comprises the following steps of,
wherein, in the step (A),
in order to be the target error value,
in order to obtain the desired steering wheel angle value,
is a target estimate.
Step S26, performing an integration operation on the target error value to obtain a target integration value of the target error value.
And S27, determining the feedback torque according to the first gain corresponding to the target error value, the second gain corresponding to the target integral value, the target error value and the target integral value.
In one exemplary embodiment, the estimated value of the steering wheel angle is determined by:
analyzing the yaw rate and the lateral acceleration by using a preset model to determine an estimated value of a steering wheel angle corresponding to the yaw rate and the lateral acceleration, wherein the preset model is obtained by using a plurality of groups of data through machine learning training, and each group of the plurality of groups of data comprises: yaw rate of the vehicle, lateral acceleration, and a steering angle value of a steering wheel angle of the vehicle.
That is to say, collect the whole car data that the vehicle corresponds, wherein, whole car data includes: the measured values of the yaw velocity, the lateral acceleration and the steering wheel angle of the vehicle are trained through a deep neural network, the trained network is used as a preset model, and the mapping relation between the yaw velocity, the lateral acceleration and the steering wheel angle of the vehicle is established. In the preset model, the yaw velocity and the lateral acceleration are used as input, the measured value of the steering wheel angle is used as output, the network parameters are continuously optimized in the training process, and the estimated value of the steering wheel angle can be accurately determined under the condition that training data are enough.
As an alternative example, the desired steering wheel angle value for the steering wheel angle of the target vehicle is determined by: determining an expected front wheel steering angle of the target vehicle according to a preset algorithm, wherein the preset algorithm is as follows:
，
is the course angle error of the target vehicle,
in order to be the lateral error of the target vehicle,
is the longitudinal speed of the target vehicle,
a desired front wheel steering angle for the target vehicle; the desired steering wheel angle value is determined based on a steering wheel angle and a conversion ratio of a front wheel angle and the desired front wheel steering angle.
As an alternative example, the desired steering wheel angle value is determined by:
wherein, in the step (A),
in order to obtain the desired steering wheel angle value,
the conversion ratio of the steering wheel angle and the front wheel angle,
the desired front wheel turning angle.
Step S206: determining a first torque of the steering wheel based on the feedforward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
As an alternative example, determining a first torque of the steering wheel based on the feed-forward torque and the feedback torque comprises:
wherein, in the step (A),
in order to feed forward the torque,
is feedback torque.
In an exemplary embodiment, the desired steering wheel angle value is determined according to a preset algorithm in case of no failure of the steering wheel sensor; determining a feedforward moment of the steering wheel according to the road curvature of a preview point corresponding to the current position of the target vehicle, and determining a first feedback moment of the steering wheel of the target vehicle according to the measured value and the expected steering wheel turning angle value; and determining a second torque of the steering wheel according to the feedforward torque and the first feedback torque so that the target vehicle rotates the steering wheel angle to the expected steering wheel angle value according to the second torque.
In the case where the steering wheel sensor has a failure, determining the feedback torque of the steering wheel of the target vehicle based on the estimated value of the steering wheel angle and the desired steering wheel angle value; and when the steering wheel sensor has no fault, determining the first feedback torque of the steering wheel of the target vehicle according to the measured value of the steering wheel angle and the expected steering wheel angle value, and even if the sensor of the vehicle has a fault, accurately determining the torque of the steering wheel of the vehicle, thereby solving the problem that how to control the vehicle under the extreme condition that the sensor of the vehicle has a fault is not considered by a transverse control algorithm.
It is to be understood that the above-described embodiments are only a few, but not all, embodiments of the present invention. In order to better understand the above method, the following describes the above process with reference to an embodiment, but the method is not limited to the technical solution of the embodiment of the present invention, and specifically:
specifically, fig. 4 is a flowchart (ii) of an alternative control method of a vehicle according to an embodiment of the present invention, as shown in fig. 4,
step S401, current vehicle data of a target vehicle are obtained, wherein the current vehicle data comprise: measurements of the current yaw rate, the current lateral acceleration, and the current steering wheel angle of the target vehicle, which measurements are obtained by a steering wheel sensor.
Step S402, inputting the current yaw rate and the current lateral acceleration of the target vehicle into a failure estimator (corresponding to the preset model in the above embodiment) to obtain an estimated value of the steering wheel angle output by the failure estimator;
step S403, inputting the measured value of the current steering wheel angle and the estimated value of the steering wheel angle into a fault detector;
step S404, determining whether the direction sensor has a fault, executing step S405 under the condition that the direction sensor has the fault, otherwise executing step S406;
step S405, determining a transverse control algorithm according to the estimated value of the steering wheel angle;
in step S406, a lateral control algorithm is determined based on the measured value of the steering wheel angle.
It should be noted that, modeling is performed on faults, and typical sensor faults are classified into the following three types:
1. a constant fault, i.e. the steering wheel sensor output is a constant value,
；
2. multiplicative faults, i.e., the steering wheel sensor output is the actual steering wheel angle multiplied by a fixed gain,
；
3. additive faults, i.e. the steering wheel sensor output is the actual steering wheel angle plus a fixed offset,
。
alternatively, a method for determining an estimated value of a steering wheel angle is provided in an embodiment of the present application, and fig. 5 is a flowchart of a method for determining an estimated value of a steering wheel angle according to an embodiment of the present invention, as shown in fig. 5,
step S501, collecting vehicle data (corresponding to multiple sets of data in the above embodiment) of the target vehicle, where the vehicle data includes: yaw rate, lateral acceleration, and steering angle value of the steering wheel angle of the target vehicle;
step S502, training a deep neural network through the data of the whole vehicle;
step S503, using the trained network as a steering wheel angle estimator (equivalent to the preset model in the above embodiment), and establishing a mapping relation between yaw angular velocity, lateral acceleration and a steering angle value of a steering wheel;
step S504, acquiring current vehicle data of the target vehicle;
step S505, inputting the current yaw velocity and the current lateral acceleration into a steering wheel angle estimator;
in step S506, the steering wheel angle estimator outputs an estimated value of the steering wheel angle.
Alternatively, after determining the estimated value of the steering wheel angle, the fault state of the steering wheel sensor at that time is detected, and fig. 6 is a flow chart of an alternative fault determination method for the steering wheel sensor of the vehicle according to the embodiment of the invention, as shown in fig. 6:
step S601, determining a residual error value of a measured value and an estimated value of a steering wheel angle;
specifically, the method comprises the following steps:
wherein, in the step (A),
in order to be the value of the residual error,
as an estimate of the value of the current,
are measured values.
Step S602, determining whether the residual error value is greater than a preset threshold value;
step S603, determining that the steering wheel sensor has a fault under the condition that the residual error value is greater than a preset threshold value;
and step S604, determining that the steering wheel sensor does not have a fault under the condition that the residual value is less than or equal to the preset threshold value.
It should be noted that there is an error between the estimated value and the measured value of the steering wheel angle, and if the estimated value is directly used as the system output instead of the measured value after the sensor failure, the disturbance caused by the step of the error between the estimated value and the measured value may cause the system to lose stability. Therefore, to reduce the disturbance caused by the error, the error-tolerant compensation output needs to be smoothed. The processed compensation output is:
wherein the content of the first and second substances,
as the finally output estimated value of the steering wheel angle,
is an estimated value of the steering wheel angle,
is a measure of the angle of rotation of the steering wheel,
is a smoothing factor. When the control amount is switched from a to a
When the temperature of the water is higher than the set temperature,
slowly varying from 0 to 1 and then slowly changing,
by
Slowly change into
And further achieves the effect of smoothing.
In an exemplary embodiment, a lateral control algorithm using dual loop control is designed using the steering wheel angle failure state transmitted by the failure detection module, fig. 7 is a flowchart (iii) of an alternative control method of a vehicle according to an embodiment of the present invention, fig. 7,
step S701, firstly, a vehicle lateral kinematics model is established:
wherein v is a vehicle speed of a target vehicle, R is a road curvature radius corresponding to a closest point of the target vehicle on a formal path,
the front wheel angle of the target vehicle. Since only lateral control is considered, v is constant. As shown in FIG. 8, FIG. 8 is a schematic view of a two-degree-of-freedom "bicycle" model according to an embodiment of the present invention.
Step S702, acquiring a vehicle transverse kinematics model to determine a course angle error and a transverse error of a target vehicle, and calculating an expected front wheel rotation angle through a stanley algorithm as follows:
wherein, in the step (A),
in order to be the error of the course angle,
in order to be a lateral error,
is the speed of the machine in the longitudinal direction,
the desired front wheel turning angle.
Step S703, determining the conversion ratio between the steering wheel angle and the front wheel angle, and obtaining the desired steering wheel angle value
；
It should be noted that, for convenience of calculation, the embodiment of the present invention considers the conversion ratio of the steering wheel angle and the front wheel angle as a fixed value b, regardless of ackermann steering.
Step S704, determining the feedforward moment of the target vehicle according to the curvature of the road at the preview point
；
It should be noted that the feedforward torque is mainly used for solving the problem of the aligning torque of the road surface at the bend to the tire
Step S705, determining a feedback torque according to an error value between a desired steering wheel angle value and a measured or estimated value
；
It should be noted that the feedback torque is mainly used to solve the problem of steering wheel angle error, so that the steering wheel can be quickly turned to a desired steering wheel turning angle value.
Determining an error value by the following equation:
wherein the content of the first and second substances,
is an error value of the steering wheel angle,
in order to expect a steering wheel angle value,
is a measure of the angle of rotation of the steering wheel,
is an estimated value of the steering wheel angle that is finally output.
The feedback torque is determined by the following equation:
wherein, in the step (A),
、
to control the gain.
And step S706, determining the actual torque according to the feedback torque and the feedforward torque.
The embodiment of the invention provides a vehicle control method, which utilizes a deep neural network to carry out learning training on real vehicle data to obtain a fault estimator, then utilizes an estimated value of a steering wheel corner and a residual error of a measured value of a steering wheel sensor to design a fault detector, and finally utilizes the output of the fault detector to design a vehicle transverse control law, thereby solving the problem of vehicle transverse control under the condition of the fault of the steering wheel sensor.
It is understood that in the specific implementation of the present application, related data such as user information is involved, when the above embodiments of the present application are applied to specific products or technologies, user permission or consent needs to be obtained, and the collection, use and processing of related data need to comply with relevant laws and regulations and standards in relevant countries and regions.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on this understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for causing an autonomous vehicle to perform the method of the embodiments of the present invention.
In this embodiment, a control device for a vehicle is also provided, which is used to implement the above embodiments and preferred embodiments, and the description of the device is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated.
Fig. 9 is a block diagram of a control apparatus of a vehicle according to an embodiment of the present invention, the apparatus including:
the acquiring module 92 is configured to acquire a desired steering wheel angle of a target vehicle and a feed-forward torque corresponding to a pre-aiming point of the target vehicle at a current position when it is determined that a steering wheel sensor of the target vehicle is in a fault;
a determination module 94 for determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined from a yaw rate and a lateral acceleration of the target vehicle;
a control module 96 configured to determine a first torque of the steering wheel based on the feed forward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
With the above arrangement, since the feedback torque of the steering wheel is determined based on the estimated value of the target vehicle and the desired steering wheel angle value in the case where it is determined that the steering wheel sensor of the target vehicle is out of order, the first torque of the steering wheel is determined based on the feedforward torque and the feedback torque, so that the target vehicle turns the steering wheel to the desired steering wheel angle based on the first torque. The problem that how to control the vehicle under the extreme condition that the sensor of the vehicle breaks down is not considered in the transverse control algorithm is solved, and further, the torque of the steering wheel of the vehicle can be accurately determined even under the condition that the sensor of the vehicle breaks down.
In an exemplary embodiment, an obtaining module 92 is configured to obtain the measured value and the estimated value of the steering wheel angle measured by the steering wheel sensor; determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value.
In an exemplary embodiment, the determining module 94 is configured to determine that the steering wheel sensor is faulty if the difference is greater than a preset threshold; and determining that the steering wheel sensor has no fault when the difference value is less than or equal to a preset threshold value.
In an exemplary embodiment, the determination module 94 is configured to determine an error value between the desired steering wheel angle value and the estimated value; performing integral operation on the error value to obtain an integral value of the error value; and determining the feedback torque according to a first gain corresponding to the error value, a second gain corresponding to the integral value, the error value and the integral value.
In an exemplary embodiment, the determining module 94 is configured to analyze the yaw rate and the lateral acceleration using a preset model to determine the estimated values of the steering wheel angles corresponding to the yaw rate and the lateral acceleration, wherein the preset model is obtained by machine learning training using a plurality of sets of data, and each set of data in the plurality of sets of data includes: yaw rate of the vehicle, lateral acceleration, and steering angle value of the steering wheel angle of the vehicle.
In an exemplary embodiment, a determination module 94 for determining a target estimate based on the estimate and the measurement; and determining the feedback torque of the steering wheel according to the target estimation value and the expected steering wheel rotation angle value.
In an exemplary embodiment, the determination module 94 is configured to determine the target estimate by the following equation:
According to an aspect of the application, there is provided a computer program product comprising a computer program/instructions containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication part 1009 and/or installed from the removable medium 1011. When executed by the cpu 1001, the computer program performs various functions provided by the embodiments of the present application.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Fig. 10 schematically shows a computer system configuration block diagram of an electronic device for implementing an embodiment of the present application.
It should be noted that the computer system 1000 of the electronic device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.
As shown in fig. 10, the computer system 1000 includes a Central Processing Unit (CPU) 1001 that can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. In the random access memory 1003, various programs and data necessary for system operation are also stored. The cpu 1001, the rom 1002, and the ram 1003 are connected to each other via a bus 1004. An Input/Output interface 1005 (Input/Output interface, i.e., I/O interface) is also connected to the bus 1004.
The following components are connected to the input/output interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including a Display panel such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 1008 including a hard disk and the like; and a communications portion 1009 including a network interface card such as a local area network card, modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. A driver 1010 is also connected to the input/output interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1010 as necessary, so that a computer program read out therefrom is mounted into the storage section 1008 as necessary.
In particular, according to embodiments of the present application, the processes described in the various method flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from the network through the communication part 1009 and/or installed from the removable medium 1011. When the computer program is executed by the cpu 1001, various functions defined in the system of the present application are executed.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the control method of the vehicle described above, which may be the autonomous vehicle or the server shown in fig. 1. The present embodiment will be described by taking the electronic device as an autonomous vehicle as an example. As shown in fig. 11, the electronic device comprises a memory 1102 and a processor 1104, wherein the memory 1102 stores a computer program and the processor 1104 is arranged to execute the steps of any of the above method embodiments by means of the computer program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at the current position;
s2, determining the feedback torque of the steering wheel according to the estimated value of the steering wheel angle of the target vehicle and the expected steering wheel angle value, wherein the estimated value is determined by the yaw rate and the lateral acceleration of the target vehicle;
and S3, determining a first torque of the steering wheel according to the feedforward torque and the feedback torque so that the target vehicle rotates the steering wheel to the expected steering wheel angle according to the first torque.
Alternatively, those skilled in the art will appreciate that the configuration shown in FIG. 11 is merely illustrative and that the electronics may also be an autonomous vehicle. Fig. 11 is a diagram illustrating a structure of the electronic device. For example, the electronics may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 11, or have a different configuration than shown in FIG. 11.
The memory 1102 may be used to store software programs and modules, such as program instructions/modules corresponding to the control method and apparatus for a vehicle in the embodiment of the present application, and the processor 1104 executes various functional applications and data processing by running the software programs and modules stored in the memory 1102, so as to implement the control method for a vehicle described above. The memory 1102 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1102 can further include memory located remotely from the processor 1104 and such remote memory can be coupled to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 1102 may be used for storing information such as logs containing sensitive data, but is not limited thereto. As an example, as shown in fig. 11, the memory 1102 may include, but is not limited to, the obtaining module 92, the determining module 94, and the control module 96 of the control device of the vehicle. In addition, other module units in the control device of the vehicle may also be included, but are not limited to these, and are not described in detail in this example.
Optionally, the transmitting device 1106 is used for receiving or transmitting data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 1106 includes a Network adapter (NIC) that can be connected to a router via a Network cable to communicate with the internet or a local area Network. In one example, the transmission device 1106 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In addition, the electronic device further includes: a display 1108; and a connection bus 1110 for connecting the respective module components in the above-described electronic apparatus.
In other embodiments, the autonomous vehicle or the server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the plurality of nodes through a network communication. The nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, and other electronic devices, may become a node in the blockchain system by joining the Peer-To-Peer network.
According to an aspect of the present application, there is provided a computer-readable storage medium, from which a processor of a computer device reads computer instructions, the processor executing the computer instructions to cause the computer device to execute a control method of a vehicle provided in the above-mentioned various alternative implementations.
Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:
s1, under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a preview point of the target vehicle at the current position;
s2, determining the feedback torque of the steering wheel according to the estimated value of the steering wheel angle of the target vehicle and the expected steering wheel angle value, wherein the estimated value is determined by the yaw rate and the lateral acceleration of the target vehicle;
and S3, determining a first torque of the steering wheel according to the feedforward torque and the feedback torque so that the target vehicle rotates the steering wheel to the expected steering wheel angle according to the first torque.
Alternatively, in the present embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware related to the automatic driving vehicle, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (10)
1. A control method of a vehicle, characterized by comprising:
under the condition that a steering wheel sensor of a target vehicle is determined to be in fault, acquiring an expected steering wheel angle of the target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at the current position;
determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined by a yaw rate and a lateral acceleration of the target vehicle;
determining a first torque of the steering wheel based on the feedforward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
2. The method of claim 1, wherein prior to obtaining a desired steering wheel angle of the target vehicle and a feed-forward torque corresponding to the target vehicle at a pre-target point of a current location, the method further comprises:
acquiring the measured value and the estimated value of the steering wheel angle measured by the steering wheel sensor;
determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value.
3. The method of claim 2, wherein determining whether the steering wheel sensor is malfunctioning based on a difference between the measured value and the estimated value comprises:
determining that the steering wheel sensor has a fault when the difference is greater than a preset threshold;
and determining that the steering wheel sensor has no fault when the difference value is less than or equal to a preset threshold value.
4. The method of claim 1, wherein determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value comprises:
determining an error value for the desired steering wheel angle value and the estimate value;
performing integral operation on the error value to obtain an integral value of the error value;
and determining the feedback torque according to a first gain corresponding to the error value, a second gain corresponding to the integral value, the error value and the integral value.
5. The method of claim 1, wherein prior to determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value, the method further comprises:
analyzing the yaw rate and the lateral acceleration by using a preset model to determine an estimated value of a steering wheel angle corresponding to the yaw rate and the lateral acceleration, wherein the preset model is obtained by using a plurality of groups of data through machine learning training, and each group of the plurality of groups of data comprises: yaw rate of the vehicle, lateral acceleration, and steering angle value of the steering wheel angle of the vehicle.
6. The method of claim 1, wherein determining the feedback torque of the steering wheel based on the estimated value of the steering wheel angle of the target vehicle and the desired steering wheel angle value comprises:
determining a target estimation value according to the estimation value and the measured value of the steering wheel angle;
determining a feedback torque of the steering wheel based on the target estimate and the desired steering wheel angle value.
7. The method of claim 6, wherein determining a target estimate based on the estimate and the measure comprises:
determining the target estimate by:
wherein the content of the first and second substances,
is a target value of the estimated value of the target,
is an estimated value of the steering wheel angle,
is a measure of the angle of rotation of the steering wheel,
in order to be a smoothing factor, the method,
the change is made according to preset rules.
8. A control apparatus of a vehicle, characterized by comprising:
the acquisition module is used for acquiring an expected steering wheel angle of a target vehicle and a feedforward torque corresponding to a pre-aiming point of the target vehicle at a current position under the condition that a fault of a steering wheel sensor of the target vehicle is determined;
a determination module for determining a feedback torque of the steering wheel based on an estimated value of a steering wheel angle of the target vehicle and the desired steering wheel angle value, wherein the estimated value is determined from a yaw rate and a lateral acceleration of the target vehicle;
a control module to determine a first torque of the steering wheel based on the feedforward torque and the feedback torque to cause the target vehicle to turn the steering wheel to the desired steering wheel angle based on the first torque.
9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
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