CN116749960A

CN116749960A - Control method, device, equipment and storage medium for automatic driving vehicle

Info

Publication number: CN116749960A
Application number: CN202311040455.1A
Authority: CN
Inventors: 吕杨; 李勇强; 吕强; 苗乾坤
Original assignee: Neolix Technologies Co Ltd
Current assignee: Neolix Technologies Co Ltd
Priority date: 2023-08-18
Filing date: 2023-08-18
Publication date: 2023-09-15
Anticipated expiration: 2043-08-18
Also published as: CN116749960B

Abstract

The application discloses a control method, a device, equipment and a storage medium for an automatic driving vehicle, and belongs to the technical field of computers. The method comprises the following steps: acquiring running data of a first vehicle, running data of the automatic driving vehicle, first longitudinal distances of the first vehicle and the automatic driving vehicle, and acquiring the maximum braking deceleration of the first vehicle by using a preset safe distance algorithm based on the first longitudinal distance, the first vehicle running data and the automatic driving vehicle running data in response to the first longitudinal distance reaching a preset distance threshold; utilizing a preset braking deceleration adjustment strategy to adjust the maximum braking deceleration of the first vehicle; based on the adjusted maximum braking deceleration of the first vehicle, a preset braking algorithm is utilized to obtain the first braking deceleration; the first braking deceleration is optimized, and a target braking deceleration is determined to control the vehicle running based on the target braking deceleration. The application ensures the stability of the vehicle in the running process.

Description

Control method, device, equipment and storage medium for automatic driving vehicle

Technical Field

The application relates to the technical field of computers, in particular to the technical fields of intelligent transportation, automatic driving and the like, and particularly relates to a control method, a device, equipment and a storage medium of an automatic driving vehicle.

Background

In some situations of automatic driving vehicle operation, for example, low-speed driving logistics situations, there are a large number of non-motor vehicles with flexible driving behaviors, such as bicycles, battery cars, tricycles and the like. During heavy duty hours, the above-mentioned traffic participants easily take some aggressive driving strategies, such as quickly cutting into the front of the autonomous vehicle and then performing emergency braking.

At present, when the situation of the above-mentioned scene is dealt with, the automatic driving vehicle adopts the strategy of emergency braking or direct braking in many cases, resulting in poor stability of the driving process of the vehicle.

Disclosure of Invention

The application provides a control method, a device, equipment and a storage medium for an automatic driving vehicle, which ensure the safety and stability of the running process of the automatic driving vehicle under the condition that a cut-in vehicle exists in front of the running, and the technical scheme is as follows:

in a first aspect, there is provided a control method of an autonomous vehicle, the method comprising:

Acquiring driving data of a first vehicle, driving data of the autonomous vehicle, and a current first longitudinal distance between the first vehicle and the autonomous vehicle; the first vehicle is a front vehicle cut into a traveling direction of the autonomous vehicle;

responding to the first longitudinal distance reaching a preset interval threshold value, and acquiring the maximum braking deceleration of the first vehicle by utilizing a preset safe distance algorithm based on the first longitudinal distance, the running data of the first vehicle and the running data of the automatic driving vehicle;

adjusting the maximum braking deceleration of the first vehicle by utilizing a preset braking deceleration adjusting strategy;

obtaining a first braking deceleration by using a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle;

and optimizing the first braking deceleration, and determining a target braking deceleration so as to control the running of the automatic driving vehicle based on the target braking deceleration.

In one possible implementation, the driving data of the first vehicle includes a speed of the first vehicle, the driving data of the autonomous vehicle includes a speed of the autonomous vehicle, and the obtaining, based on the first longitudinal distance, the driving data of the first vehicle, and the driving data of the autonomous vehicle, the maximum braking deceleration of the first vehicle using a preset safe distance algorithm includes:

Deducing the first longitudinal distance, the speed of the first vehicle and the speed of the automatic driving vehicle by using the preset safe distance algorithm;

based on the result of the derivation process, a maximum braking deceleration of the first vehicle is obtained.

In one possible implementation manner, the adjusting the maximum braking deceleration of the first vehicle by using a preset braking deceleration adjustment strategy includes:

acquiring preset multi-stage braking deceleration; the multi-stage braking deceleration comprises a plurality of braking decelerations which are ordered from small to large;

and when the maximum braking deceleration of the first vehicle is smaller than the minimum value in the multi-stage braking deceleration, adjusting the maximum braking deceleration of the first vehicle to the minimum value.

In one possible implementation, the method further includes:

when the maximum braking deceleration of the first vehicle is between the minimum and maximum values of the multi-stage braking decelerations, acquiring a braking deceleration value adjacent to and less than the maximum braking deceleration of the first vehicle from the multi-stage braking decelerations;

And adjusting the maximum braking deceleration of the first vehicle to the braking deceleration value.

In one possible implementation manner, the obtaining the first braking deceleration based on the adjusted maximum braking deceleration of the first vehicle by using a preset braking algorithm includes:

obtaining a longitudinal safe distance between the first vehicle and the autonomous vehicle based on the running data of the first vehicle, the running data of the autonomous vehicle, and the adjusted maximum braking deceleration of the first vehicle;

and obtaining a first braking deceleration by using a preset braking algorithm based on the running data of the first vehicle, the running data of the automatic driving vehicle and the longitudinal safety distance.

In one possible implementation manner, the optimizing the first braking deceleration to determine the target braking deceleration includes:

inputting the first braking deceleration into a preset optimization model;

calculating an optimal Lagrangian multiplier based on the input first braking deceleration;

when the optimal Lagrangian multiplier is less than or equal to zero, performing halving on the first braking deceleration;

Iteratively optimizing the first braking deceleration after the bipartite processing based on the optimal Lagrangian multiplier until a preset optimization termination condition is met, so as to obtain an optimization processing result;

based on the result of the optimization process, a target braking deceleration is obtained.

In one possible implementation, the method further includes:

acquiring driving data of a second vehicle; the second vehicle is a rear vehicle that runs behind the autonomous vehicle;

obtaining a second braking deceleration based on the travel data of the autonomous vehicle and the travel data of the second vehicle;

the target braking deceleration is obtained based on a preset condition, the first braking deceleration, and the second braking deceleration.

In one possible implementation manner, the obtaining the target braking deceleration based on the preset condition, the first braking deceleration, and the second braking deceleration includes:

obtaining the target braking deceleration based on the first braking deceleration in response to the first braking deceleration and the second braking deceleration meeting a preset condition; or alternatively, the first and second heat exchangers may be,

and in response to the first braking deceleration and the second braking deceleration not meeting preset conditions, optimizing the second braking deceleration to obtain the target braking deceleration.

In one possible implementation manner, the optimizing the second braking deceleration to obtain the target braking deceleration includes:

optimizing the second braking deceleration by using a preset optimizing model;

the target braking deceleration is obtained based on the result of the optimization process.

In a second aspect, there is provided a control apparatus for an autonomous vehicle, the apparatus comprising:

an acquisition unit configured to acquire travel data of a first vehicle, travel data of the autonomous vehicle, and a current first longitudinal distance between the first vehicle and the autonomous vehicle; the first vehicle is a front vehicle cut into a traveling direction of the autonomous vehicle;

an obtaining unit configured to obtain a maximum braking deceleration of the first vehicle using a preset safe distance algorithm based on the first longitudinal distance, the running data of the first vehicle, and the running data of the autonomous vehicle in response to the first longitudinal distance reaching a preset distance threshold;

the adjusting unit is used for adjusting the maximum braking deceleration of the first vehicle by utilizing a preset braking deceleration adjusting strategy;

The braking unit is used for obtaining the first braking deceleration by utilizing a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle;

and the control unit is used for carrying out optimization processing on the first braking deceleration and determining a target braking deceleration so as to control the running of the automatic driving vehicle based on the target braking deceleration.

In a third aspect, there is provided a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the aspects and any one possible implementation as described above.

In a fourth aspect, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aspects and methods of any one of the possible implementations described above.

In a fifth aspect, there is provided an autonomous vehicle comprising an electronic device as described above.

The technical scheme provided by the application has the beneficial effects that at least:

as can be seen from the above technical solution, in the embodiment of the present application, by acquiring the running data of the first vehicle, the running data of the autonomous vehicle, and the first longitudinal distance between the current first vehicle and the autonomous vehicle, the maximum braking deceleration of the first vehicle may be obtained by using a preset safe distance algorithm based on the first longitudinal distance, the running data of the first vehicle, and the running data of the autonomous vehicle, adjusting the maximum braking deceleration of the first vehicle using a preset braking deceleration adjustment policy, the maximum braking deceleration of the first vehicle may be obtained by using a preset braking deceleration adjustment policy, the first braking deceleration may be obtained by using a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle, so that the first braking deceleration may be optimized, the running deceleration of the autonomous vehicle may be controlled based on the target braking deceleration, the running deceleration of the autonomous vehicle may be controlled, the first vehicle may be prevented from being cut into the running deceleration, the first vehicle may be prevented from being driven by using a preset braking deceleration adjustment policy, thereby effectively ensuring the safety and stability of the running process of the automatic driving vehicle.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for controlling an autonomous vehicle according to one embodiment of the present application;

FIG. 2 is a flow chart of a method for controlling an autonomous vehicle according to another embodiment of the present application;

FIG. 3 is a schematic illustration of a strategy for selecting a multi-level deceleration in a method for controlling an autonomous vehicle according to another embodiment of the present application;

FIG. 4A is a schematic illustration of speed changes and braking deceleration changes of three types of vehicles based on a prior art method of controlling an autonomous vehicle;

FIG. 4B is a schematic illustration of speed changes and braking deceleration changes of three types of vehicles in a control method of an autonomous vehicle according to one embodiment of the present application;

fig. 5 is a block diagram showing a control apparatus for an autonomous vehicle according to still another embodiment of the present application;

fig. 6 is a block diagram of an electronic device for implementing a control method of an autonomous vehicle according to an embodiment of the present application.

Detailed Description

Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that, the terminal device in the embodiment of the present application may include, but is not limited to, smart devices such as a mobile phone, a personal digital assistant (Personal Digital Assistant, PDA), a wireless handheld device, and a tablet computer (tablet computer); the display device may include, but is not limited to, a personal computer, a television, or the like having a display function.

In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

At present, on a low-speed driving road, such as a bicycle lane or a common lane of a vehicle and pedestrians, the number of traffic participants is relatively large, and under the condition that the vehicle or other traffic participants suddenly cut into the front of the driving, the automatic driving vehicle adopts a common emergency braking or braking strategy, so that the driving safety and stability of the vehicle are poor, and rear-end collision of the rear vehicle is easily caused.

Accordingly, there is a need to provide a control method for an autonomous vehicle, which can ensure the safety of the autonomous vehicle during running when a cut-in vehicle is present in front of the running vehicle.

Referring to fig. 1, a flow chart of a control method of an automatic driving vehicle according to an embodiment of the application is shown. The control method of the automatic driving vehicle specifically may include:

step 101, acquiring running data of a first vehicle, running data of the automatic driving vehicle and a current first longitudinal distance between the first vehicle and the automatic driving vehicle; the first vehicle is a front vehicle cut into a traveling direction of the autonomous vehicle.

Step 102, obtaining the maximum braking deceleration of the first vehicle by using a preset safe distance algorithm based on the first longitudinal distance, the running data of the first vehicle and the running data of the automatic driving vehicle in response to the first longitudinal distance reaching a preset interval threshold.

And 103, utilizing a preset braking deceleration adjustment strategy to adjust the maximum braking deceleration of the first vehicle.

And 104, obtaining the first braking deceleration by utilizing a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle.

And 105, optimizing the first braking deceleration, and determining a target braking deceleration so as to control the running of the automatic driving vehicle based on the target braking deceleration.

The autonomous vehicle may be a host vehicle or a self vehicle. The first vehicle is a cut-in vehicle cut into the direction of travel of the autonomous vehicle, i.e. the first vehicle is the front vehicle of the autonomous vehicle.

It should be noted that the autonomous vehicle may include, but is not limited to, an unmanned logistics vehicle, an autonomous delivery vehicle, an autonomous car, etc. The first vehicle and the second vehicle may each include, but are not limited to, a non-motor vehicle, a motor vehicle, and the like.

The preset distance threshold may be preset according to a driving safety condition when the preceding vehicle just cuts into the front of the vehicle. Specifically, the preset distance threshold may be a distance between the first vehicle and the automatically driven vehicle corresponding to a preset maximum braking deceleration of the first vehicle, that is, a distance between the first vehicle and the automatically driven vehicle when the maximum braking deceleration of the first vehicle is the preset maximum braking deceleration.

It is understood that the preset maximum deceleration of the first vehicle may be an empirical value of the maximum deceleration set in the case of cutting into the front of the autonomous vehicle. For example, the preset distance threshold may be a maximum braking deceleration of the first vehicle of-1 meter per square second (m/s) ² ) At that time, the distance between the corresponding first vehicle and the autonomous vehicle.

It is understood that the target braking deceleration may be an acceleration opposite to the direction of travel of the autonomous vehicle. The target braking deceleration may be negative, for example, -1 meter per square second (m/s) ² ) Wherein the negative sign (-) may indicate the opposite direction of travel of the autonomous vehicle, and the magnitude may be indicated based on the absolute value of the braking deceleration.

It should be noted that, part or all of the execution body in steps 101 to 105 may be an application located in the local terminal, or may be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) disposed in the application located in the local terminal, or may be a processing engine located in a server on the network side, or may be a distributed system located on the network side, for example, a processing engine or a distributed system in an autopilot platform on the network side, which is not limited in this embodiment.

It will be appreciated that the application may be a native program (native app) installed on the native terminal, or may also be a web page program (webApp) of a browser on the native terminal, which is not limited in this embodiment.

According to the technical scheme, the maximum braking deceleration of the first vehicle is adjusted according to the braking deceleration adjustment strategy, so that the maximum braking deceleration of the first vehicle is obtained more reasonably, the first braking deceleration is obtained based on the maximum braking deceleration of the first vehicle, the first braking deceleration is optimized, the more reasonable target braking deceleration is determined, the running of the automatic driving vehicle is controlled, the braking deceleration of the automatic driving vehicle is controlled within the reasonable braking amount, and the automatic driving vehicle is prevented from colliding with the cut-in vehicle under the condition that the cut-in vehicle exists in front of the running, so that the safety and the stability of the running process of the automatic driving vehicle are effectively ensured.

Optionally, in one possible implementation manner of this embodiment, the driving data of the first vehicle includes a speed of the first vehicle, the driving data of the autopilot vehicle includes a speed of the autopilot vehicle, in step 102, the first longitudinal distance, the speed of the first vehicle, and the speed of the autopilot vehicle may be specifically deduced by using the preset safe distance algorithm, and a maximum braking deceleration of the first vehicle may be obtained based on a result of the deducing process.

In this implementation, the preset safe distance algorithm may be a distance algorithm based on the RSS model that derives the maximum braking deceleration.

In this particular implementation, the real-time distance between the autonomous vehicle and the first vehicle, i.e., the first longitudinal distance, may be sensed based on the autonomous vehicle-mounted sensor device.

Specifically, the maximum braking deceleration of the first vehicle may be derived by using the following formula (1)：

Wherein, the liquid crystal display device comprises a liquid crystal display device,for a first longitudinal distance between the autonomous vehicle and the first vehicle. Here, the autonomous vehicle is a rear vehicle, the first vehicle is a front vehicle, relative to the first vehicle, the first vehicle is a front vehicle>For the current speed of the autonomous vehicle, i.e. the current speed of the rear vehicle>For the current speed of the first vehicle, i.e. the current speed of the preceding vehicle +.>Reaction time for an autonomous vehicle, +.>For maximum acceleration of an autonomous vehicle, +.>For maximum braking deceleration of an autonomous vehicle, < >>Is the maximum braking deceleration of the first vehicle.

Alternatively, in one possible implementation of the present embodiment, in step 103, a preset multi-stage braking deceleration may be specifically obtained; the multi-stage braking deceleration includes a plurality of braking decelerations that are ordered from small to large, and when the maximum braking deceleration of the first vehicle is smaller than the minimum value of the multi-stage braking decelerations, the maximum braking deceleration of the first vehicle is adjusted to the minimum value.

It is understood that the maximum braking deceleration of the first vehicle is smaller than the minimum value of the multi-stage braking decelerations, i.e., the absolute value of the maximum braking deceleration of the first vehicle is larger than the absolute value of the minimum value of the multi-stage braking decelerations.

In this implementation manner, the preset braking deceleration adjustment strategy may include adjusting the maximum braking deceleration of the first vehicle according to a correlation between the preset multi-stage braking deceleration and the maximum braking deceleration of the first vehicle.

In the present implementation, the multi-stage braking deceleration includes a plurality of braking decelerations ordered from small to large. The plurality of braking decelerations may be selected from a predetermined deceleration range at predetermined intervals.

In one specific implementation of this implementation, when the maximum braking deceleration of the first vehicle is between the minimum value and the maximum value of the multi-stage braking decelerations, a braking deceleration value adjacent to and smaller than the maximum braking deceleration of the first vehicle is obtained from the multi-stage braking decelerations, and the maximum braking deceleration of the first vehicle may be adjusted to the braking deceleration value.

It is understood that the braking deceleration value adjacent to the maximum braking deceleration of the first vehicle and smaller than the maximum braking deceleration of the first vehicle, i.e. the absolute value of the braking deceleration value adjacent to the maximum braking deceleration of the first vehicle and larger than the absolute value of the braking deceleration value in the multi-stage braking deceleration.

The predetermined interval may be, for example, 0.5m/s ² The predetermined deceleration range may be [ -5m/s ² ，-1m/s ² ]Then the braking deceleration can be selected to be respectively-1 m/s ² ，-1.5m/s ² ，……，-4.5 m/s ² ，-5m/s ² 。

For example, when the maximum braking deceleration of the first vehicle is-6 m/s ² The absolute value of the maximum braking deceleration of the first vehicle is greater than-5 m/s ² I.e. the maximum braking deceleration of the first vehicle is less than-5 m/s ² When the maximum braking deceleration of the first vehicle is adjusted to-5 m/s ² 。

For another example, when the maximum braking deceleration of the first vehicle is-1.7 m/s ² The maximum braking deceleration of the first vehicle is-1 m/s ² To-5 m/s ² In between, a braking deceleration value adjacent to and smaller than the maximum braking deceleration of the first vehicle, i.e., -2m/s, may be obtained from the multi-stage braking deceleration ² The maximum braking deceleration of the first vehicle can be adjusted to-2 m/s ² 。

Here, by adjusting the calculated maximum braking deceleration of the first vehicle by using the preset multi-stage braking deceleration, the rationality and reliability of the maximum braking deceleration of the first vehicle can be improved, so that the braking deceleration of the automatically driven vehicle can be obtained more accurately and effectively based on the data later.

Alternatively, in one possible implementation manner of the present embodiment, in step 104, a longitudinal safe distance between the first vehicle and the autonomous vehicle may be obtained specifically based on the running data of the first vehicle, the running data of the autonomous vehicle, and the adjusted maximum braking deceleration of the first vehicle; and obtaining a first braking deceleration by using a preset braking algorithm based on the running data of the first vehicle, the running data of the automatic driving vehicle and the longitudinal safety distance.

In a specific implementation process of this implementation manner, a preset braking algorithm may be specifically used to perform calculation processing on the maximum braking deceleration of the first vehicle, the running data of the autonomous vehicle, the running data of the first vehicle, and the longitudinal safety distance, so as to obtain the first braking deceleration.

In this particular implementation, the travel data of the autonomous vehicle may include position information of the autonomous vehicle, a speed of the autonomous vehicle, a reaction time of the autonomous vehicle, a maximum acceleration of the autonomous vehicle, a minimum braking deceleration of the autonomous vehicle, and a maximum braking deceleration of the autonomous vehicle.

Specifically, the position information of the autonomous vehicle may be the acquired coordinate information of the current autonomous vehicle. The speed of the autonomous vehicle is the current travel speed obtained.

It is to be understood that in calculating the first braking deceleration, the maximum acceleration of the autonomous vehicle, the minimum braking deceleration of the autonomous vehicle, and the maximum braking deceleration of the autonomous vehicle may be preconfigured according to the type of the autonomous vehicle.

In this particular implementation, the first vehicle is a cut-in vehicle cut into the direction of travel of the autonomous vehicle. The travel data of the first vehicle may include a speed of the first vehicle, position information of the first vehicle, and the like.

In this specific implementation, the preset braking algorithm may include a braking algorithm based on a combination of a Responsibility-sensitivity Safety (RSS) model and an intelligent driver model (Intelligent Driver Model, IDM), i.e., a Safe IDM braking algorithm, and a braking algorithm based on an IDM model.

It can be understood that, since the conventional IDM model cannot well meet the constraint of the safety model, and the problem that the braking deceleration may be too safe is calculated, when the braking algorithm based on the RSS model and the IDM model is used, the RSS model can be used to avoid the shortage of the IDM model as much as possible, so that the braking deceleration can be calculated more reasonably.

Specifically, the first braking deceleration may be calculated based on the maximum braking deceleration of the first vehicle, the running data of the autonomous vehicle, the running data of the first vehicle, and the longitudinal safe distance using the following formula (2)。

Wherein, the liquid crystal display device comprises a liquid crystal display device,for the current speed of the autonomous vehicle, i.e. the current speed of the own vehicle +.>For the desired speed of an autonomous vehicle, +.>For the speed difference between the first vehicle and the autonomous vehicle,/-for the speed difference between the first vehicle and the autonomous vehicle>For the desired acceleration of an autonomous vehicle, +.>For the desired deceleration of an autonomous vehicle, +.>For acceleration index>For the co-running longitudinal safety distance based on the RSS model, i.e. longitudinal safety distance, +.>For the safety factor, it is preferable that the safety factor may be 1.1.

Wherein the longitudinal safety distanceCan be calculated using the following formula (3).

Here, the autonomous vehicle is a rear vehicle, with respect to the first vehicle, the first vehicle is a front vehicle,for the current speed of the autonomous vehicle, i.e. the current speed of the rear vehicle>For the current speed of the first vehicle, i.e. the current speed of the preceding vehicle +.>Reaction time for an autonomous vehicle, +.>For maximum acceleration of an autonomous vehicle, +. >For maximum braking deceleration of an autonomous vehicle, < >>Is the maximum braking deceleration of the first vehicle.

In this way, the maximum braking deceleration of the first vehicle, the running data of the automatic driving vehicle, the running data of the first vehicle and the longitudinal safety distance can be calculated by utilizing a preset braking algorithm, so that the more accurate first braking deceleration is obtained, the reliability of the first braking deceleration is improved, the rationality and the reliability of the target braking deceleration can be further improved, and the stability of the running process of the automatic driving vehicle is further ensured.

Optionally, in one possible implementation manner of this embodiment, in step 105, the first braking deceleration may be specifically input into a preset optimization model, an optimal lagrangian multiplier is calculated based on the input first braking deceleration, when the optimal lagrangian multiplier is less than or equal to zero, a binary process is performed on the first braking deceleration, and based on the optimal lagrangian multiplier, iterative optimization is performed on the binary processed first braking deceleration until a preset optimization termination condition is met, so that a result of the optimization process is obtained, so that a target braking deceleration can be obtained based on the result of the optimization process.

In this implementation, the objective function of the preset optimization model may be expressed as:||a-acc|| ² the constraint conditions of the preset optimization model can be expressed as: />。

Wherein, the liquid crystal display device comprises a liquid crystal display device,accmay be a braking deceleration, such as a first braking deceleration,ait is possible to provide an optimized braking deceleration,cit may be a safety signal that is a function of the safety signal,Cthe threshold may be constrained for the security signal.

In the planning period for controlling the running of the autonomous vehicle, the safety signal of the next running state is providedSafety signal to the current driving state->The relationship of (2) may be as shown in the following formula (4):

wherein, the liquid crystal display device comprises a liquid crystal display device,for a safety signal of the next driving state, +.>For a safety signal of the current driving state, +.>Is a permutation matrix of the coefficients and,athe action of the current running state, i.e., the braking deceleration, may be indicated. Here, rough maximum limits can be set for the safety signal and the braking deceleration, respectively, and one-dimensional +.>1.0.

Specifically, the best defined in the preset optimization modelLagrangian multiplierλ ^* The following formula (5) shows:

wherein, the liquid crystal display device comprises a liquid crystal display device,is a coefficient of->Is a permutation matrix of coefficients>For braking deceleration +.>Is a safety signal of the current state,Ca safety signal constraint threshold may be represented.

In particular, an optimised braking deceleration, i.e. a modified actiona ^* The following formula (6) shows:

wherein, the liquid crystal display device comprises a liquid crystal display device,in order to brake the deceleration rate,λ ^* is the optimal Lagrangian multiplier, +.>Is a coefficient.

In this implementation manner, the preset optimization termination condition may include that the number of times of the iterative process reaches a preset iteration number threshold, or that the optimal lagrangian multiplier is equal to or less than zero.

One case of this embodiment is that first, the first braking deceleration is input into a preset optimization model. Next, an optimal lagrangian multiplier is calculated based on the input first braking deceleration. If the optimal Lagrangian multiplier is greater than zero, then for the firstAnd (5) optimizing a braking deceleration. On the other hand, if the optimal Lagrangian multiplier is larger than zero, the first braking deceleration is subjected to re-optimization processing, and a new optimal Lagrangian multiplier is calculated, and if the new optimal Lagrangian multiplier is larger than zero, the first braking deceleration can be subjected to iterative optimization processing until the number of iterative processing reaches a preset iteration number threshold, and the output first braking deceleration is 0 m/s ² The method comprises the steps of carrying out a first treatment on the surface of the On the other hand, if the optimal lagrangian multiplier is equal to or less than zero, the optimization may be terminated, and the corresponding optimized first braking deceleration may be output when the optimal lagrangian multiplier is equal to or less than zero.

Another case of this embodiment is that first, the first braking deceleration is input to a preset optimization model. Next, an optimal lagrangian multiplier is calculated based on the input first braking deceleration. And if the optimal Lagrangian multiplier is smaller than or equal to zero, performing halving on the input first braking deceleration. Again, based on the first braking deceleration after the bipartite processing, the optimal lagrangian multiplier is calculated again. If the optimal Lagrangian multiplier is greater than zero, performing optimization processing on the first braking deceleration, and based on the optimized first braking deceleration, re-calculating a new optimal Lagrangian multiplier, if the new optimal Lagrangian multiplier is greater than zero, performing iterative optimization processing on the first braking deceleration until the number of iterative processing reaches a preset iteration number threshold, wherein the output first braking deceleration is 0m/s ² The method comprises the steps of carrying out a first treatment on the surface of the On the other hand, if the new optimal lagrangian multiplier is equal to or less than zero, the optimization may be terminated, and the new optimal lagrangian multiplier is equal to or less than zero, and the corresponding optimized first braking deceleration may be output.

Another case of this embodiment is that first, the first braking deceleration is input to a preset optimization model. Next, an optimal lagrangian multiplier is calculated based on the input first braking deceleration. If the optimal lagrangian multiplier is equal to or less than zero, the first braking deceleration may not be optimized.

It is understood that in the process of optimizing the first braking deceleration using the preset optimization model, the optimization may be terminated when the optimal lagrangian multiplier is equal to or less than zero. And the optimal Lagrangian multiplier is smaller than or equal to zero, and the corresponding optimized first braking deceleration is used as the output of the model.

In this way, the first braking deceleration can be optimized by using a preset optimization model, and the target braking deceleration pertinence and reliability are further improved by taking the optimized result as the target braking deceleration.

Alternatively, in one possible implementation of the present embodiment, before step 105, the driving data of the second vehicle may be specifically acquired; the second vehicle is a rear vehicle that travels behind the autonomous vehicle, and further, a second braking deceleration may be obtained based on travel data of the autonomous vehicle and travel data of the second vehicle, and the target braking deceleration may be obtained based on a preset condition, the first braking deceleration, and the second braking deceleration.

In this implementation manner, specifically, the second longitudinal distance between the autonomous vehicle and the second vehicle may be obtained based on the driving data of the autonomous vehicle and the driving data of the second vehicle, and then the second longitudinal distance, the driving data of the autonomous vehicle, and the driving data of the second vehicle may be calculated by using a preset distance algorithm, so as to obtain the second braking deceleration.

In this specific implementation, the second vehicle is a vehicle that travels behind the autonomous vehicle. The travel data of the second vehicle may include a speed of the second vehicle, position information of the second vehicle, a reaction time of the second vehicle, a maximum acceleration of the second vehicle, a minimum braking deceleration of the second vehicle, and a maximum braking deceleration of the second vehicle.

In this specific implementation, the preset distance algorithm may be a distance algorithm based on the RSS model that derives the maximum braking deceleration. Here, the derived maximum braking deceleration may be used as the second braking deceleration of the autonomous vehicle.

It will be appreciated that the preset distance algorithm and the preset safe distance algorithm in the foregoing implementation may be the same. The preset distance algorithm and the preset safe distance algorithm may be used to derive a maximum braking deceleration of a preceding vehicle of the two vehicles.

In the specific implementation process, the distance between the autonomous vehicle and the second vehicle can be obtained based on the position information of the autonomous vehicle and the position information of the second vehicle, and then the maximum braking deceleration of the autonomous vehicle can be derived by using a preset safe distance algorithm based on the distance between the autonomous vehicle and the second vehicle, the speed of the autonomous vehicle, the speed of the second vehicle, the reaction time of the second vehicle, the maximum acceleration of the second vehicle and the minimum braking deceleration of the second vehicle.

Specifically, the maximum braking deceleration of the automatically driven vehicle can be derived by using the following formula (7)Namely, the second braking deceleration:

wherein, the liquid crystal display device comprises a liquid crystal display device,for the distance between the autonomous vehicle and the second vehicle, i.e. the second longitudinal distance. Here, the autonomous vehicle is a front vehicle with respect to the second vehicle, which is a rear vehicle. />For the speed of an autonomous vehicle, i.e. the current speed of the preceding vehicle,/->For the speed of the second vehicle, i.e. the current speed of the rear vehicle,/>For the reaction time of the second vehicle, +.>For maximum acceleration of the second vehicle, +.>For maximum braking deceleration of the second vehicle, < > for >The maximum braking deceleration, i.e. the second braking deceleration, of the autonomous vehicle to be calculated for the derivation is also the maximum braking acceleration of the preceding vehicle.

In this way, the second longitudinal distance between the automatic driving vehicle and the second vehicle can be obtained based on the driving data of the automatic driving vehicle and the driving data of the second vehicle, and then the second longitudinal distance, the driving data of the automatic driving vehicle and the driving data of the second vehicle can be calculated by using a preset safe distance algorithm, so that the second braking deceleration can be obtained more accurately, the reliability of the second braking deceleration is improved, the rationality and the reliability of the target braking deceleration can be further improved, and the stability of the driving process of the automatic driving vehicle is further ensured.

Moreover, the target braking deceleration can be determined by combining the running data of the second vehicle, so that collision between the automatic driving vehicle and the cut-in vehicle can be avoided when the cut-in vehicle exists in front of the running, rear-end collision between the rear vehicle and the automatic driving vehicle is avoided, the stability of the running process of the automatic driving vehicle is further ensured, the safety and the stability of the running process of the rear vehicle of the automatic driving vehicle are further ensured, and the friendliness of the behavior strategy of the automatic driving vehicle to the running of the rear vehicle is further improved.

In a specific implementation of this implementation, the target braking deceleration may be obtained based on the first braking deceleration in response to the first braking deceleration and the second braking deceleration meeting a preset condition; or, in response to the first braking deceleration and the second braking deceleration not meeting a preset condition, performing optimization processing on the second braking deceleration to obtain the target braking deceleration.

In a specific implementation process of this implementation manner, the second braking deceleration may be optimized by using a preset optimization model, and the target braking deceleration may be obtained based on a result of the optimization.

In this implementation, the preset condition may include that the first braking deceleration is greater than zero, or that the first braking deceleration is less than zero, and that the first braking deceleration is greater than the second braking deceleration, i.e. that the absolute value of the first braking deceleration is less than the second braking deceleration.

One case of this particular implementation is that the target braking deceleration may be obtained based on the first braking deceleration in response to the first braking deceleration being greater than zero.

Another case of the present embodiment is that the target braking deceleration may be obtained based on the first braking deceleration in response to the first braking deceleration being smaller than zero and the first braking deceleration being larger than the second braking deceleration.

It is to be understood that, here, the first braking deceleration may be further optimized, and the optimized first braking deceleration may be used as the target braking deceleration, so that the braking process of the automatic driving vehicle may be smoother.

In still another case of the present embodiment, the second braking deceleration may be optimized to obtain the target braking deceleration in response to the first braking deceleration being smaller than the second braking deceleration being smaller than zero.

In this specific implementation process, in the process of performing optimization processing on the second braking deceleration to obtain the target braking deceleration, a preset optimization model may be specifically used to perform optimization processing on the second braking deceleration, so that the target braking deceleration may be obtained based on a result of the optimization processing.

It is to be understood that here, the first braking deceleration may be a braking deceleration of the autonomous vehicle with respect to the preceding vehicle, as well as an executable braking deceleration, i.e. a forward-running safety brake, determined by the autonomous vehicle on the basis of the preceding vehicle. The second braking deceleration may be a braking deceleration of the autonomous vehicle with respect to the rear vehicle, as well as a braking deceleration that the rear vehicle inferred the autonomous vehicle to perform, i.e., a backward safing brake. The fact that the first braking deceleration is smaller than the second braking deceleration is smaller than zero may be characterized in that the post-vehicle inferred braking deceleration is larger than the executable braking deceleration determined by the autonomous vehicle based on the preceding vehicle, i.e. the absolute value of the post-vehicle inferred braking deceleration is smaller than the absolute value of the executable braking deceleration determined by the autonomous vehicle based on the preceding vehicle, in which case there may be a problem with the autonomous vehicle following the rear vehicle if the second braking deceleration is not optimally processed.

In this specific implementation, the result of the optimization process may be taken as the target braking deceleration.

Here, the specific implementation manner of optimizing the second braking deceleration is the same as the specific implementation manner of optimizing the first braking deceleration, and the foregoing related specific description will not be repeated here.

In this way, the second braking deceleration can be optimized by using a preset optimization model, and the target braking deceleration is further improved in pertinence and reliability by taking the optimized result as the target braking deceleration.

Moreover, the target braking deceleration can be determined by combining the running data of the second vehicle, so that the stability of the running process of the automatic driving vehicle is ensured, and meanwhile, the safety and the stability of the running process of the rear vehicle of the automatic driving vehicle are also effectively ensured, thereby improving the friendliness of the behavior strategy of the automatic driving vehicle to the running of the rear vehicle.

It should be noted that, the various specific implementation procedures provided in the present implementation manner may be combined with each other to implement the step of obtaining the target braking deceleration in the control method of the autonomous vehicle according to the present embodiment. The detailed description may refer to the relevant content in the present implementation, and will not be repeated here.

Alternatively, in one possible implementation manner of the present embodiment, a pre-configured braking deceleration may be obtained specifically in response to the first longitudinal distance not reaching the preset pitch threshold, and the pre-configured braking deceleration may be taken as the target braking deceleration.

In the present implementation, the pre-configured braking deceleration may be a smaller braking deceleration, for example, the pre-configured braking deceleration may be-0.1 m/s ² 。

It will be appreciated that the first vehicle will typically not be braked immediately upon cutting into front of the autonomous vehicle, i.e. when the first longitudinal distance between the autonomous vehicle and the first vehicle does not reach the preset distance threshold. Therefore, when the first vehicle is just cut into front of the autonomous vehicle, the autonomous vehicle can be controlled to travel with a small braking deceleration with the pre-configured braking deceleration as the target braking deceleration of the autonomous vehicle, so that the autonomous vehicle takes a slow braking operation. Thus, the running stability of the automatic driving vehicle can be further ensured.

It will be appreciated that if the first vehicle were to slow down and brake immediately before cutting into the front of the autonomous vehicle, the first vehicle could be deemed to be an intentional off-vehicle, an abnormal driving behaviour, which may not be considered here.

It should be noted that, the specific implementation procedure provided in the present implementation manner may be combined with the various specific implementation procedures provided in the foregoing implementation manner to implement the control method of the autonomous vehicle of the present embodiment. The detailed description may refer to the relevant content in the foregoing implementation, and will not be repeated here.

Alternatively, in one possible implementation of the present embodiment, the driving data of the first vehicle may include longitudinal driving data, and the driving data of the autonomous vehicle may include longitudinal driving data. In step 101, the longitudinal travel data of the first vehicle may include a longitudinal speed of the first vehicle, a longitudinal maximum acceleration of the first vehicle, a longitudinal maximum/minimum brake deceleration of the first vehicle, longitudinal position information of the first vehicle, and the like.

The longitudinal travel data of the autonomous vehicle may include a longitudinal speed of the autonomous vehicle, a longitudinal maximum acceleration of the autonomous vehicle, a longitudinal maximum/minimum brake deceleration of the autonomous vehicle, longitudinal position information of the autonomous vehicle, and the like.

It is understood that the longitudinal maximum acceleration of the first vehicle, the longitudinal maximum/minimum brake deceleration of the first vehicle, and the longitudinal maximum acceleration of the automated guided vehicle, the longitudinal maximum/minimum brake deceleration of the automated guided vehicle may be preconfigured according to the type of vehicle and the running environment.

In a specific implementation of this implementation, the first longitudinal distance between the autonomous vehicle and the first vehicle, i.e. the first longitudinal distance in the current driving situation, is calculated based on the longitudinal position information of the first vehicle and the longitudinal position information of the autonomous vehicle.

In another specific implementation of this implementation, the first longitudinal distance between the autonomous vehicle and the first vehicle may also be measured based on a sensor onboard the autonomous vehicle.

In another specific implementation of this implementation, the longitudinal safety distance may be calculated based on longitudinal travel data of the first vehicle and longitudinal travel data of the autonomous vehicle using a preset longitudinal safety distance algorithm.

In the specific implementation process, the preset longitudinal safety distance algorithm may include a longitudinal safety distance algorithm for co-running based on an RSS model.

It will be appreciated that the first longitudinal distance and the longitudinal safety distance may be calculated by other existing methods, and are not particularly limited herein.

For a better understanding of the method according to the embodiment of the present application, the following describes the method according to the embodiment of the present application with reference to the accompanying drawings and specific application scenarios.

Fig. 2 is a flowchart of a control method of an automatic driving vehicle according to another embodiment of the present application, as shown in fig. 2.

Step 201, acquiring running data of an automatic driving vehicle, running data of a first vehicle and running data of a second vehicle.

In the present embodiment, the autonomous vehicle may also be referred to as a host vehicle, or a self-vehicle. The first vehicle may be a cut-in vehicle that may cut into the front of the vehicle, also referred to as a front vehicle or cut-in vehicle. The second vehicle is a vehicle that travels behind the autonomous vehicle, which may also be referred to as a rear vehicle.

In the present embodiment, in one control planning period, the running data of the current autonomous vehicle, the running data of the first vehicle, and the running data of the second vehicle may be acquired.

By way of example, one control programming cycle may be 0.1 seconds.

It will be appreciated that vehicle travel is controlled during an autonomous vehicle travel in accordance with successive control schedule periods.

Step 202, obtaining a first lateral distance and a lateral safety distance between the autonomous vehicle and the first vehicle based on the driving data of the first vehicle and the driving data of the autonomous vehicle.

Step 203, obtaining a first longitudinal distance and a longitudinal safety distance between the autonomous vehicle and the first vehicle based on the driving data of the first vehicle and the driving data of the autonomous vehicle.

Step 204, when the first lateral distance meets the lateral safety distance and the first longitudinal distance meets the longitudinal safety distance, the automatic driving vehicle continues to run based on the current running strategy.

Step 205, determining whether the first longitudinal distance reaches a preset distance threshold when the first lateral distance does not satisfy the lateral safety distance and the first longitudinal distance does not satisfy the longitudinal safety distance.

In this embodiment, the preset pitch threshold may be a distance between the first vehicle and the automatically driven vehicle corresponding to a preset maximum deceleration of the first vehicle. The preset maximum deceleration of the first vehicle may be an empirical value of the maximum deceleration set to cut into the vehicle in the case of cutting into the front of the autonomous vehicle.

For example, the preset maximum deceleration of the first vehicle may be-1 m/s ² The preset spacing threshold may be-1 m/s ² The distance between the corresponding first vehicle and the autonomous vehicle.

Step 206, obtaining the maximum braking deceleration of the first vehicle by using a preset safe distance algorithm based on the first longitudinal distance, the running data of the first vehicle and the running data of the automatic driving vehicle when the first longitudinal distance reaches the preset distance threshold.

Step 207, acquiring preset multi-stage braking deceleration; the multi-stage braking deceleration includes a plurality of braking decelerations ordered from small to large.

Step 208, when the maximum braking deceleration of the first vehicle is smaller than the minimum value of the multi-stage braking decelerations, the maximum braking deceleration of the first vehicle is adjusted to the minimum value.

Step 209, when the maximum braking deceleration of the first vehicle is between the minimum value and the maximum value of the multi-stage braking decelerations, acquiring a braking deceleration value adjacent to and smaller than the maximum braking deceleration of the first vehicle from the multi-stage braking decelerations, and adjusting the maximum braking deceleration of the first vehicle to the braking deceleration value.

In this embodiment, fig. 3 is a schematic diagram of a strategy for selecting a multi-level deceleration in a control method of an automatic driving vehicle according to another embodiment of the present application, as shown in fig. 3.

Specifically, the preset braking deceleration adjustment strategy may include adjusting the maximum braking deceleration of the first vehicle in accordance with a correlation between the preset multi-stage braking deceleration and the maximum braking deceleration of the first vehicle.

Specifically, the multi-stage braking deceleration includes a plurality of braking decelerations ordered from small to large. The plurality of braking decelerations may be selected from a predetermined deceleration range in accordance with a selection strategy of a multi-level deceleration. For example, the deceleration range may be selected from a predetermined deceleration range at predetermined intervals.

The predetermined interval may be, for example, 0.5 m/s ² The predetermined deceleration range may be [ -5m/s ² ，-1m/s ² ]Then the maximum braking deceleration of the first vehicle can be selected to be-1 m/s ² ，-1.5m/s ² ，……，-4.5 m/s ² ，-5m/s ² 。

Step 210, obtaining a first braking deceleration by using a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle.

Specifically, the first braking deceleration may be calculated using a preset braking algorithm by performing calculation processing based on the maximum braking deceleration of the first vehicle, the running data of the automatically driven vehicle, the running data of the first vehicle, and the longitudinal safety distance.

Here, as in the foregoing embodiment, the first braking deceleration may be calculated based on the maximum braking deceleration of the first vehicle, the running data of the autonomous vehicle, the running data of the first vehicle, and the longitudinal guard distance, using the foregoing equation (2).

It will be appreciated that the detailed description will be referred to the relevant content in the foregoing embodiments, and will not be repeated here.

Step 211, optimizing the first braking deceleration to obtain the optimized first braking deceleration.

Step 212, calculating a second longitudinal distance between the autonomous vehicle and the second vehicle based on the travel data of the autonomous vehicle and the travel data of the second vehicle.

Step 213, calculating a second braking deceleration by using a preset distance algorithm based on the second longitudinal distance, the driving data of the autonomous vehicle and the driving data of the second vehicle.

Here, as in the foregoing embodiment, the maximum braking deceleration of the autonomous vehicle, that is, the second braking deceleration, may be derived and calculated based on the second longitudinal distance, the running data of the autonomous vehicle, and the running data of the second vehicle, using the foregoing equation (7).

And 214, when the optimized first braking deceleration is smaller than the second braking deceleration and smaller than zero, performing optimization processing on the second braking deceleration by using a preset optimization model, and taking the result of the optimization processing as a target braking deceleration.

Step 215, taking the optimized first braking deceleration as the target braking deceleration when the optimized first braking deceleration is greater than zero or the first braking deceleration is less than zero and the optimized first braking deceleration is greater than the second braking deceleration.

Here, the detailed description of the optimization process of the second braking deceleration may be referred to the relevant content in the foregoing embodiment, and will not be repeated here.

And step 216, acquiring a pre-configured braking deceleration and taking the pre-configured braking deceleration as a target braking deceleration when the first longitudinal distance does not reach a preset interval threshold value.

In step 217, when the first lateral distance does not satisfy the lateral safety distance and the first longitudinal distance satisfies the longitudinal safety distance, a third braking deceleration is obtained based on the travel data of the autonomous vehicle, the travel data of the first vehicle, and the longitudinal safety distance, and a fourth braking deceleration is obtained based on the travel data of the autonomous vehicle and the travel data of the second vehicle.

And 218, when the third braking deceleration is smaller than the fourth braking deceleration and smaller than zero, performing optimization processing on the fourth braking deceleration by using a preset optimization model, so as to take the result of the optimization processing as the target braking deceleration.

Here, the detailed description of the optimization process for the fourth braking deceleration may be referred to the relevant content in the foregoing embodiment, and will not be repeated here.

Step 219, in the case where the third braking deceleration is greater than zero, or the third braking deceleration is less than zero, and the third braking deceleration is greater than the fourth braking deceleration, the third braking deceleration is taken as the target braking deceleration.

Step 220, controlling the running of the autonomous vehicle based on the target braking deceleration.

In the present embodiment, up to this point, the target braking deceleration may be regarded as the current braking deceleration of the autonomous vehicle. The autonomous vehicle may continue traveling based on the target braking deceleration.

In the present embodiment, fig. 4A is a schematic diagram of speed changes and braking deceleration changes of three types of vehicles based on the existing control method of the autonomous vehicle; fig. 4B is a schematic diagram of speed changes and braking deceleration changes of three types of vehicles in a control method of an autonomous vehicle according to an embodiment of the present application.

As shown in FIG. 4A, the horizontal axis may represent time (1 s, 2s, 3s,4 s), i.e., may be the control planning period for an autonomous vehicle, and the left-hand vertical axis may represent speed [ -10m/s,10 m/s)]The right vertical axis may represent braking deceleration [ -10m/s ² ,10m/s ² ]. Where curve 1 (blue dashed line) is the speed of the first vehicle, curve 2 (orange dashed line) is the speed of the autonomous vehicle, curve 3 (green dashed line) is the speed of the second vehicle, curve 4 (blue solid line) is the braking deceleration of the first vehicle, curve 5 (orange solid line) is the target braking deceleration of the autonomous vehicle, curve 6 (green solid line) is the braking deceleration of the second vehicle, and curve 7 (red solid line) is the backward braking deceleration of the autonomous vehicle, i.e. the second braking deceleration. In FIG. 4A, in The initial time period of the braking of the automatic driving vehicle is 0-1s, and the curve of the target braking deceleration of the automatic driving vehicle is below the curve of the backward braking deceleration of the automatic driving vehicle, so that the situation that the automatic driving vehicle is in rear-end collision is more likely to happen.

As shown in FIG. 4B, the horizontal axis may represent time (1 s, 2s, 3s,4 s), i.e., may be the control planning period for an autonomous vehicle, and the left-hand vertical axis may represent speed [ -10m/s,10 m/s)]The right vertical axis may represent braking deceleration [ -10m/s ² ,10m/s ² ]. Where curve 1 (blue dotted line) is the speed of the first vehicle, curve 2 (orange dotted line) is the speed of the autonomous vehicle, curve 3 (green dotted line) is the speed of the second vehicle, curve 4 (blue solid line) is the braking deceleration of the first vehicle, curve 5 (orange solid line) is the target braking deceleration of the autonomous vehicle, curve 6 (green solid line) is the braking deceleration of the second vehicle, and curve 7 (red solid line) is the backward braking deceleration of the autonomous vehicle, i.e. the second braking deceleration. In fig. 4B, the curve of the target braking deceleration of the autonomous vehicle is above the curve of the backward braking deceleration of the autonomous vehicle in the period of 0-1s at the start of executing the braking, and the gap between the target braking deceleration of the autonomous vehicle and the backward braking deceleration of the autonomous vehicle is more remarkable, so that the occurrence of rear-end collision of the rear-end vehicle can be effectively avoided when the autonomous vehicle brakes.

In combination with the comparison of FIGS. 4A and 4B, FIG. 4A shows a control method without a multi-step adjustment strategy, with an orange solid line having a very large braking deceleration, i.e., -5m/s, in the 0-1s period ² Is the upper hard limit for vehicle deceleration and fig. 4B illustrates a control method with a multi-level adjustment strategy that solves the problems associated with the method of fig. 4A.

Further, specifically, as shown in the respective curve states of fig. 4B, it can be seen that the speed of the autonomous vehicle and the speed variation of the second vehicle are smoother during the cut-in of the first vehicle, i.e., the running states of the autonomous vehicle and the second vehicle therebehind are relatively smooth. Moreover, the braking deceleration curve of the autonomous vehicle is relatively close to the desired braking deceleration curve of the autonomous vehicle. Therefore, by adopting the technical proposal in the embodiment, the braking deceleration of the automatic driving vehicle and the rear vehicle is controlled in a smaller and reasonable braking quantity through the multi-stage braking deceleration strategy, the safety and the stability of the running process of the automatic driving vehicle and the rear vehicle are effectively ensured, the friendliness of the behavior strategy of the automatic driving vehicle to the running of the rear vehicle is improved,

In addition, by adopting the technical scheme in the embodiment, the running scene that the front vehicle extremely cuts into the running direction of the self-vehicle can be better dealt with, and under the running scene, the safety and the stability of the running process of the self-driving vehicle and the rear vehicle can be effectively ensured.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

Fig. 5 is a block diagram showing a control apparatus for an autonomous vehicle according to an embodiment of the present application, as shown in fig. 5. The control apparatus 500 of the autonomous vehicle of the present embodiment may include an acquisition unit 501, an acquisition unit 502, an adjustment unit 503, a braking unit 504, and a control unit 505. Wherein, the acquiring unit 501 is configured to acquire running data of a first vehicle, running data of the autonomous vehicle, and a current first longitudinal distance between the first vehicle and the autonomous vehicle; the first vehicle is a front vehicle cut into a traveling direction of the autonomous vehicle; an obtaining unit 502, configured to obtain, in response to the first longitudinal distance reaching a preset distance threshold, a maximum braking deceleration of the first vehicle using a preset safe distance algorithm based on the first longitudinal distance, the driving data of the first vehicle, and the driving data of the autonomous vehicle; an adjusting unit 503, configured to adjust the maximum braking deceleration of the first vehicle by using a preset braking deceleration adjustment strategy; a braking unit 504 for obtaining a first braking deceleration by a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle; a control unit 505 for performing an optimization process on the first braking deceleration, determining a target braking deceleration, and controlling the travel of the autonomous vehicle based on the target braking deceleration.

It should be noted that, part or all of the control device for the autonomous vehicle according to the present embodiment may be an application located at the local terminal, or may be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) provided in the application located at the local terminal, or may be a processing engine located in a server on the network side, or may be a distributed system located on the network side, for example, a processing engine or a distributed system in an autopilot platform on the network side, which is not limited in this embodiment.

Optionally, in one possible implementation manner of this embodiment, the driving data of the first vehicle includes a speed of the first vehicle, the driving data of the autopilot vehicle includes a speed of the autopilot vehicle, and the obtaining unit 501 is specifically configured to derive the first longitudinal distance, the speed of the first vehicle, and the speed of the autopilot vehicle by using the preset safe distance algorithm; based on the result of the derivation process, a maximum braking deceleration of the first vehicle is obtained.

Alternatively, in one possible implementation manner of the present embodiment, the adjusting unit 503 may be specifically configured to obtain a preset multi-stage braking deceleration; the multi-stage braking deceleration comprises a plurality of braking decelerations which are ordered from small to large; and when the maximum braking deceleration of the first vehicle is smaller than the minimum value in the multi-stage braking deceleration, adjusting the maximum braking deceleration of the first vehicle to the minimum value.

Alternatively, in one possible implementation of the present embodiment, the adjusting unit 503 may be specifically configured to obtain, from the multi-stage braking decelerations, a braking deceleration value adjacent to and smaller than the maximum braking deceleration of the first vehicle when the maximum braking deceleration of the first vehicle is between the minimum and maximum values of the multi-stage braking decelerations; and adjusting the maximum braking deceleration of the first vehicle to the braking deceleration value.

Alternatively, in one possible implementation of the present embodiment, the braking unit 504 may be specifically configured to obtain the longitudinal safe distance between the first vehicle and the autonomous vehicle based on the running data of the first vehicle, the running data of the autonomous vehicle, and the adjusted maximum braking deceleration of the first vehicle; and obtaining a first braking deceleration by using a preset braking algorithm based on the running data of the first vehicle, the running data of the automatic driving vehicle and the longitudinal safety distance.

Alternatively, in one possible implementation of the present embodiment, the control unit 505 may be further configured to input the first braking deceleration into a preset optimization model; calculating an optimal Lagrangian multiplier based on the input first braking deceleration; when the optimal Lagrangian multiplier is less than or equal to zero, performing halving on the first braking deceleration; iteratively optimizing the first braking deceleration after the bipartite processing based on the optimal Lagrangian multiplier until a preset optimization termination condition is met, so as to obtain an optimization processing result; based on the result of the optimization process, a target braking deceleration is obtained.

Alternatively, in one possible implementation of the present embodiment, the control unit 505 may be further configured to acquire driving data of the second vehicle; the second vehicle is a rear vehicle that runs behind the autonomous vehicle; obtaining a second braking deceleration based on the travel data of the autonomous vehicle and the travel data of the second vehicle; the target braking deceleration is obtained based on a preset condition, the first braking deceleration, and the second braking deceleration.

Alternatively, in one possible implementation of the present embodiment, the control unit 505 may be further configured to obtain the target braking deceleration based on the first braking deceleration in response to the first braking deceleration and the second braking deceleration meeting a preset condition; or, in response to the first braking deceleration and the second braking deceleration not meeting a preset condition, performing optimization processing on the second braking deceleration to obtain the target braking deceleration.

Alternatively, in a possible implementation manner of the present embodiment, the control unit 505 may be further configured to perform an optimization process on the second braking deceleration using a preset optimization model; the target braking deceleration is obtained based on the result of the optimization process.

In this embodiment, the acquiring unit acquires, from the first acquiring unit, travel data of a first vehicle, travel data of the autonomous vehicle, and a current first longitudinal distance between the first vehicle and the autonomous vehicle; the first vehicle is a front vehicle cut into the running direction of the automatic driving vehicle, and further, the obtaining unit responds to the first longitudinal distance to reach a preset interval threshold value, based on the first longitudinal distance, the running data of the first vehicle and the running data of the automatic driving vehicle, the maximum braking deceleration of the first vehicle is obtained by utilizing a preset safe distance algorithm, the maximum braking deceleration of the first vehicle is regulated by utilizing a preset braking deceleration regulating strategy through the regulating unit, the maximum braking deceleration of the first vehicle is regulated by utilizing a preset braking deceleration regulating strategy, the first braking deceleration is obtained by utilizing a preset braking algorithm through the braking unit, the first braking deceleration can be optimally processed through the first braking deceleration, the target braking deceleration is determined, the running of the automatic driving vehicle is controlled based on the target braking deceleration, the maximum braking deceleration of the determined first vehicle is regulated according to the braking deceleration regulating strategy, the maximum braking deceleration of the first vehicle is more reasonable, the maximum braking deceleration of the first vehicle is regulated based on the first braking deceleration regulating strategy, the first vehicle is obtained, the first braking deceleration is more reasonable, the automatic driving vehicle can be controlled to be more reasonably driven in a collision condition with the running vehicle, and the running vehicle is more reasonable, and the situation of the automatic driving vehicle can be prevented from being driven, and the vehicle is more safe to be driven, and the running vehicle can be controlled.

In the technical scheme of the application, related personal information of the user, such as collection, storage, use, processing, transmission, provision, disclosure and other processes of images, attribute data and the like of the user, accords with the regulations of related laws and regulations and does not violate the popular regulations.

According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

According to an embodiment of the present application, further, there is also provided an autonomous vehicle including the provided electronic device, which may include an unmanned vehicle of the level L2 and above.

Fig. 6 shows a schematic block diagram of an example electronic device 600 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 606 is also connected to bus 604.

A number of components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 601 performs the respective methods and processes described above, for example, a control method of an autonomous vehicle. For example, in some embodiments, the method of controlling an autonomous vehicle may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the above-described control method of the autonomous vehicle may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the control method of the autonomous vehicle by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially, or in a different order, so long as the desired result of the technical solution of the present disclosure is achieved, and the present disclosure is not limited herein.

The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims

1. A control method of an autonomous vehicle, the method comprising:

2. The method of claim 1, wherein the travel data of the first vehicle comprises a speed of the first vehicle and the travel data of the autonomous vehicle comprises a speed of the autonomous vehicle, and wherein the obtaining the maximum braking deceleration of the first vehicle using a preset safe distance algorithm based on the first longitudinal distance, the travel data of the first vehicle, and the travel data of the autonomous vehicle comprises:

3. The method according to claim 1, wherein the adjusting the maximum braking deceleration of the first vehicle using a preset braking deceleration adjustment strategy includes:

4. A method according to claim 3, characterized in that the method further comprises:

5. The method according to claim 1, wherein the obtaining the first braking deceleration using a preset braking algorithm based on the adjusted maximum braking deceleration of the first vehicle includes:

6. The method of claim 1, wherein optimizing the first braking deceleration to determine a target braking deceleration comprises:

inputting the first braking deceleration into a preset optimization model;

7. The method according to claim 1, characterized in that the method further comprises:

8. The method of claim 7, wherein the obtaining the target braking deceleration based on the preset condition, the first braking deceleration, and the second braking deceleration comprises:

9. The method of claim 8, wherein the optimizing the second braking deceleration to obtain the target braking deceleration comprises:

Optimizing the second braking deceleration by using a preset optimizing model;

10. A control device for an autonomous vehicle, the device comprising:

11. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-9.

12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-9.

13. An autonomous vehicle comprising the electronic device of claim 11.