CN112373471B - Method, device, electronic equipment and readable medium for controlling vehicle running - Google Patents

Method, device, electronic equipment and readable medium for controlling vehicle running Download PDF

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CN112373471B
CN112373471B CN202110033180.3A CN202110033180A CN112373471B CN 112373471 B CN112373471 B CN 112373471B CN 202110033180 A CN202110033180 A CN 202110033180A CN 112373471 B CN112373471 B CN 112373471B
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CN112373471A (en
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王禅同
陈雨青
孙磊
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Heduo Technology Guangzhou Co ltd
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HoloMatic Technology Beijing Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/50—Barriers

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The embodiment of the disclosure discloses a method, a device, an electronic device and a readable medium for controlling vehicle running. One embodiment of the method comprises: acquiring obstacle vehicle information and target vehicle information of a target vehicle; generating a running task of the target vehicle according to the target vehicle information and the obstacle vehicle information; acquiring a sampling information set under the driving task; based on the sampled information set, a target vehicle travel track is generated. According to the implementation mode, data sampling is carried out according to different driving planning tasks, the calculated amount is reduced, the planning speed of the driving track is improved, and therefore the safety of the vehicle in the driving process is improved.

Description

Method, device, electronic equipment and readable medium for controlling vehicle running
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method, an apparatus, an electronic device, and a readable medium for controlling vehicle driving.
Background
The motion planning technology is an important technology in the field of automatic driving, and has the main function of planning a safe and controllable track with both comfort and time efficiency according to data provided by the sensing, positioning and decision-making modules and inputting the track to the control module. At present, when planning the motion of an automatic driving automobile, a planning method of an adaptive cruise or manual potential field method is often adopted to plan the track of a target vehicle so as to control the vehicle to run.
However, when the vehicle is controlled to run in the above manner, there are often technical problems as follows:
first, data such as time dimension, vehicle position, heading angle, speed, acceleration, jerk, track curvature, etc. need to be considered in the planning task. Thus, autodrive motion planning is a high-dimensional planning problem. However, due to the limitation of the computing power of the vehicle-mounted computing unit and the requirement of the automatic driving on the real-time performance, the direct solution of the high-dimensional planning problem is difficult to implement in engineering, and the accuracy of a planned route generated by the vehicle trajectory planning at a low latitude is low, so that the safety of the vehicle in the driving process is low.
Secondly, when the performance of the driving track is evaluated, the acceleration and the somatosensory stability are usually ensured through numerical calculation and amplitude limitation, and various performance indexes cannot be comprehensively considered, so that the planning result has deviation.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Some embodiments of the present disclosure propose a method, an apparatus, an electronic device, and a readable medium for controlling the travel of a vehicle to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a method for controlling travel of a vehicle, the method comprising: acquiring obstacle vehicle information and target vehicle information of a target vehicle; generating a driving task of the target vehicle according to the target vehicle information and the obstacle vehicle information; acquiring a sampling information set under the driving task; and generating a target vehicle running track based on the sampling information set.
In a second aspect, some embodiments of the present disclosure provide an apparatus for controlling travel of a vehicle, the apparatus comprising: a first acquisition unit configured to acquire obstacle vehicle information and target vehicle information of a target vehicle; a first generation unit configured to generate a travel task of the target vehicle based on the target vehicle information and the obstacle vehicle information; a second acquisition unit configured to acquire a sampling information set under the travel task; and a second generation unit configured to generate a target vehicle travel track based on the sampling information set.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: the target vehicle running track obtained by the method for controlling vehicle running of some embodiments of the present disclosure. The data are sampled according to different driving planning tasks, the calculated amount is reduced, and the planning speed of the driving track is improved, so that the safety of the vehicle in the driving process is improved. Specifically, the reason why the safety of the vehicle during running is low is that: data such as time dimension, vehicle position, heading angle, speed, acceleration, jerk, trajectory curvature, etc. need to be considered in the planning task. Thus, autodrive motion planning is a high-dimensional planning problem. However, due to the limitation of the computing power of the vehicle-mounted computing unit and the requirement of the automatic driving on the real-time performance, the direct solution of the high-dimensional planning problem is difficult to implement in engineering, and the accuracy of a planned route generated by the vehicle trajectory planning at a low latitude is low, so that the safety of the vehicle in the driving process is low. Based on this, the method for controlling vehicle travel of some embodiments of the present disclosure, first, obtains obstacle vehicle information and target vehicle information of a target vehicle. Therefore, data support is provided for the following running task of the detection target vehicle. Next, a travel task of the target vehicle is generated based on the target vehicle information and the obstacle vehicle information. And data are sampled according to different driving planning tasks, so that the calculated amount is reduced, and the planning speed of the driving track is improved. And thirdly, acquiring a sampling information set under the driving task. And providing data support for the subsequently generated target vehicle running track through the acquired sampling information set. That is, the sampling information set acquired according to different driving planning tasks can reduce the calculation amount of high latitude to generate the target vehicle driving track. And finally, generating a target vehicle running track based on the sampling information set. The data are sampled according to different driving planning tasks, so that the calculated amount is reduced, the planning speed of the driving track is improved, and the safety of the vehicle in the driving process is improved.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of a method for controlling vehicle travel according to some embodiments of the present disclosure;
FIG. 2 is a flow chart of some embodiments of a method for controlling vehicle travel according to the present disclosure;
FIG. 3 is a schematic block diagram of some embodiments of an apparatus for controlling travel of a vehicle according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of one application scenario of a method for controlling vehicle travel according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may acquire obstacle vehicle information 102 and target vehicle information 103 of a target vehicle; next, the computing device 101 may generate a travel task 104 of the target vehicle based on the target vehicle information 103 and the obstacle vehicle information 102; again, the computing device 101 may obtain the set of sampled information 105 under the travel task; finally, the computing device 101 may generate a target vehicle travel trajectory 106 based on the set of sampled information 105 described above. Alternatively, the computing device 101 may transmit the target vehicle travel track 106 to a control device of the target vehicle, so as to control the target vehicle to travel along the target vehicle travel track.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of a method for controlling vehicle travel according to the present disclosure is shown. The method for controlling the vehicle to run may include the steps of:
step 201, obtaining obstacle vehicle information and target vehicle information of a target vehicle.
In some embodiments, an execution subject (such as the computing device 101 shown in fig. 1) of the method for controlling vehicle travel may acquire the obstacle vehicle information and the target vehicle information of the target vehicle by wired connection or wireless connection. Wherein the obstacle vehicle information may include: and a time stamp corresponding to the obstacle vehicle position information and the obstacle vehicle position information. The target vehicle information of the target vehicle may include: and the target vehicle position information and the time stamp corresponding to the target vehicle position information. The obstacle vehicle position information and the target vehicle position information are both position information in a Frenet coordinate system. The obstacle vehicle position information may include: a longitudinal distance from a preset starting point and a lateral offset factor.
As an example, the obstacle vehicle position information included in the above-described obstacle vehicle information may be [ [1609320237], [100m, 1] ]. The target vehicle position information included in the above-mentioned target vehicle information may be [1609320237], [90m, 1] ].
And step 202, generating a running task of the target vehicle according to the target vehicle information and the obstacle vehicle information.
In some embodiments, the executing agent may be configured to respond to a determination that the time stamp corresponding to the obstacle vehicle information is the same as the time stamp corresponding to the target vehicle information. And determining distance information of the obstacle vehicle position information and the target vehicle position information. And determining a preset emergency braking task as the driving task in response to the fact that the distance information is smaller than a preset braking threshold value. Wherein, the preset braking threshold may be 30 m.
As an example, the obstacle vehicle position information included in the above-described obstacle vehicle information may be [100m, 1 ]. The target vehicle position information included in the above-mentioned target vehicle information may be [90m, 1 ]. As a result, the distance information may be 10 m. And is less than the preset brake threshold: 30 m. The preset emergency braking task is determined as the above-mentioned driving task.
In some optional implementations of some embodiments, the executing body generating the driving task of the target vehicle according to the target vehicle information and the obstacle vehicle information may include:
in a first step, in response to determining that the time stamp corresponding to the obstacle vehicle information matches the time stamp corresponding to the target vehicle information, a following distance is generated based on the target vehicle information and the obstacle vehicle information.
The matching means that the time stamp corresponding to the obstacle vehicle information is the same as the time stamp corresponding to the target vehicle information.
As an example, the obstacle vehicle position information included in the above-described obstacle vehicle information may be [ [1609320237], [100m, 1] ]. The target vehicle position information included in the above-mentioned target vehicle information may be [1609320237], [90m, 1] ]. The time stamp corresponding to the above-mentioned obstacle vehicle information may be [1609320237 ]. The timestamp corresponding to the target vehicle information may be [1609320237 ]. Accordingly, it is determined that the time stamp corresponding to the obstacle vehicle information matches the time stamp corresponding to the target vehicle information. Determining a following distance between the obstacle vehicle position information included in the obstacle vehicle information and the target vehicle position information included in the target vehicle information. The obstacle vehicle position information included in the above-mentioned obstacle vehicle information may be [100m, 1 ]. The target vehicle position information included in the above-mentioned target vehicle information may be [90m, 1 ]. It follows that the following distance may be 10 m.
And a second step of determining a preset cruise task as the travel task in response to determining that the following distance satisfies a cruise condition.
Wherein, the cruise condition may be: the following distance is greater than the cruise threshold. The cruise threshold may be 100 m.
And thirdly, in response to the fact that the following distance meets the following condition, determining a preset following task as the driving task.
Wherein, the following condition may be: the following distance is greater than a following threshold value and the following distance is less than or equal to a cruising threshold value.
As an example, the following threshold may be 60 m.
And fourthly, in response to the fact that the following distance meets the parking condition, determining a preset parking task as the driving task.
Wherein, the parking condition may be: the following distance is less than or equal to a following threshold value.
As an example, the following distance may be 10 m. And if the vehicle-following distance is less than the vehicle-following threshold value, determining the driving task as a parking task.
Step 203, acquiring a sampling information set under the driving task.
In some embodiments, the execution subject may obtain a sampling information set under the driving task in response to detecting the driving task. The driving task may be a cruising task, a car following task, or a parking task. And the sampling information in the sampling information set is the sampling information corresponding to the last timestamp.
In response to that the driving task is a preset cruise task, acquiring a sampling information set under the preset cruise task, wherein the sampling information in the sampling information set under the preset cruise task may include: a timestamp and a velocity value. Responding to the running task, and acquiring a sampling information set under the preset car following task, wherein the sampling information set under the preset car following task can comprise: a timestamp and location information. In response to that the driving task is a preset parking task, acquiring a sampling information set under the preset parking task, where sampling information in the sampling information set under the preset parking task may include: a timestamp and location information.
And step 204, generating a target vehicle running track based on the sampling information set.
In some embodiments, the execution subject may generate the target vehicle travel track based on the sampled information set. The sampling information in the sampling information set may include: relative distance information, relative velocity value, and type of target vehicle. And generating a target vehicle running track through a uniform motion model according to the sampling information set.
In some optional implementations of some embodiments, the executing body generating the target vehicle driving track based on the sampled information set may include:
firstly, determining a track information sequence to be detected of each piece of sampling information in the sampling information set to obtain a track information sequence set to be detected.
The track information to be detected in the track information sequence to be detected may include, but is not limited to, at least one of the following: a timestamp, a jerk value, a target vehicle velocity value, target vehicle position information, an actual following distance, a curvature, a relative velocity value, and a relative distance.
And secondly, determining the loss value of each track information sequence to be detected in the track information sequence set to be detected to obtain a loss value set.
The executing body may generate a loss value of each track information sequence to be detected in the track information sequence set to be detected by using the following formula:
Figure 534243DEST_PATH_IMAGE001
。
wherein,
Figure 859045DEST_PATH_IMAGE002
and representing the information of the track to be detected in the information sequence of the track to be detected.
Figure 697688DEST_PATH_IMAGE003
A time stamp is represented.
Figure 322704DEST_PATH_IMAGE004
Indicating the last timestamp.
Figure 87136DEST_PATH_IMAGE005
Indicating the last timestamp pair in the information sequence of the track to be detectedAnd the corresponding terminal performance loss value of the track information to be detected.
Figure 164813DEST_PATH_IMAGE006
And indicating the integral performance loss value corresponding to the track information to be detected corresponding to each timestamp in the track information sequence to be detected.
Figure 338305DEST_PATH_IMAGE007
The loss value is indicated.
Figure 817828DEST_PATH_IMAGE008
Representing a first loss value.
Figure 254626DEST_PATH_IMAGE009
Representing a second loss value.
Figure 819599DEST_PATH_IMAGE010
Representing a third loss value.
Figure 734466DEST_PATH_IMAGE011
Representing a fourth loss value.
Figure 334074DEST_PATH_IMAGE012
Representing a fifth loss value.
Figure 941773DEST_PATH_IMAGE013
Representing a sixth loss value.
Figure 226999DEST_PATH_IMAGE014
Representing a seventh loss value.
Figure 7873DEST_PATH_IMAGE015
Representing a first preset weight. The value range is [ 0. 100]。
Figure 461988DEST_PATH_IMAGE016
Representing a second preset weight. The value range is [ 0. 100]。
Figure 240588DEST_PATH_IMAGE017
Representing a third preset weight. The value range is [ 0. 100]。
Figure 514575DEST_PATH_IMAGE018
Representing a fourth preset weight. The value range is [ 0. 100]。
Figure 36823DEST_PATH_IMAGE019
Representing a fifth preset weight. The value range is [ 0. 100]。
Figure 345445DEST_PATH_IMAGE020
Representing a sixth preset weight. The value range is [ 0. 100]。
Figure 29367DEST_PATH_IMAGE021
Representing a seventh preset weight. The value range is [ 0. 1]。
Figure 56229DEST_PATH_IMAGE022
Representing the attenuation factor. The value range is [ 0. 100]。
Figure 913326DEST_PATH_IMAGE023
Representing a constant. The value was 2.72.
Figure 574989DEST_PATH_IMAGE024
And indicating the jerk value included in the track information to be detected.
Figure 492130DEST_PATH_IMAGE025
And indicating the target vehicle speed value included in the track information to be detected.
Figure 475129DEST_PATH_IMAGE026
Representing a preset desired speed value.
Figure 135918DEST_PATH_IMAGE027
Indicating integral performance loss corresponding to the difference value between the target vehicle speed value and the preset expected speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detectedLosing the value.
Figure 419132DEST_PATH_IMAGE028
And indicating a terminal performance loss value corresponding to the difference value between the target vehicle speed value included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected and the preset expected speed value.
Figure 710436DEST_PATH_IMAGE029
And indicating the position information of the target vehicle included in the track information to be detected.
Figure 446311DEST_PATH_IMAGE030
Indicating preset desired position information.
Figure 114052DEST_PATH_IMAGE031
And indicating a terminal performance loss value corresponding to the preset expected position information and target vehicle position information included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected.
Figure 251773DEST_PATH_IMAGE032
And indicating the relative velocity value included in the track information to be detected.
Figure 713978DEST_PATH_IMAGE033
And indicating the relative distance included by the track information to be detected.
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And the integral performance loss value corresponding to the ratio of the relative speed value to the relative distance included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected is represented.
Figure 641537DEST_PATH_IMAGE035
And representing the curvature included in the track information to be detected.
Figure 633764DEST_PATH_IMAGE036
To representAnd the integral performance loss value corresponding to a value obtained by multiplying the square and the curvature of the target vehicle speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected.
Figure 266871DEST_PATH_IMAGE037
Indicating a preset desired following distance.
Figure 774076DEST_PATH_IMAGE038
And indicating the actual following distance included by the track information to be detected.
The formula is used as an invention point of the embodiment of the disclosure, and solves the technical problems that in the prior art, the performance evaluation of the driving track is often performed, the stability of acceleration and body feeling is often ensured through numerical calculation and amplitude limitation, various performance indexes cannot be comprehensively considered, and the planning result has deviation. The factors that lead to deviations in the planning results are often as follows: when the performance of the driving track is evaluated, the stability of acceleration and body feeling is often guaranteed through numerical calculation and amplitude limitation, various performance indexes cannot be comprehensively considered, and the planning result has deviation. The accuracy of the generated planned trajectory can be improved if the above factors are solved. In order to achieve the effect, the method and the system divide different cost indexes based on different planning tasks. And corresponding to each specific performance index, a corresponding cost index is designed to evaluate the track performance. And obtaining the optimal running track by searching the global optimal solution. And the cost index is characterized by various loss values in the formula. The cost index of the motion planning can be divided into a terminal performance index and an integral performance index. The integral performance index is divided into a comfort cost index and a speed error cost index. The terminal performance indexes are divided into a terminal cost index, an occupation distance safety cost index, a collision time safety cost index, a lateral acceleration dynamics cost index and a collision detection safety cost index. By evaluating the comprehensive performance of the complete state information required by the automatic driving planning, the evaluation of the track is more comprehensive and accurate, and the safety of the vehicle in the driving process is improved.
And thirdly, selecting the loss value with the minimum loss value from the loss value set as a target loss value.
Wherein, selecting the loss value with the minimum loss value from the loss value set as the target loss value may include the following substeps:
the first substep is to determine the vertex sampling information set corresponding to the loss value set.
A second substep of determining the loss value satisfying the following condition set as a target loss value in response to determining that the loss value satisfying the following condition set exists in the loss value set.
Wherein, the condition group may be:
Figure 783620DEST_PATH_IMAGE039
。
wherein,
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and
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indicating a serial number.
Figure 366545DEST_PATH_IMAGE042
The number of vertex sample information in the vertex sample information set is indicated.
Figure 678315DEST_PATH_IMAGE043
Representing the bias coefficient. The value range may be [0, 1]]。
Figure 379555DEST_PATH_IMAGE044
Representing a preset initial loss value.
Figure 620044DEST_PATH_IMAGE045
The first one representing the above set of loss values
Figure 836261DEST_PATH_IMAGE041
And (4) loss value.
Figure 187608DEST_PATH_IMAGE046
Representing a preset coefficient. The value ranges from 0 to positive infinity.
Figure 743354DEST_PATH_IMAGE047
Representing the first of the vertex sample information sets
Figure 951482DEST_PATH_IMAGE040
Each vertex samples information.
Figure 592679DEST_PATH_IMAGE048
The first one representing the above set of loss values
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And (4) loss value.
Figure 220286DEST_PATH_IMAGE049
Representing the first of the vertex sample information sets
Figure 536998DEST_PATH_IMAGE041
Each vertex samples information.
Figure 727808DEST_PATH_IMAGE050
The vertex sample information in the vertex sample information set is represented.
Figure 653913DEST_PATH_IMAGE007
The loss values are shown above.
And fourthly, determining the track information sequence to be detected corresponding to the target loss value as the target track information sequence.
And fifthly, generating a target vehicle running track based on the target track information sequence.
Optionally, the target vehicle running track is sent to a control device of the target vehicle, so as to control the target vehicle to run along the target vehicle running track.
In some embodiments, the execution subject may send the target vehicle travel track to a control device of the target vehicle, where the control device of the target vehicle may be configured to control the target vehicle to travel along the target vehicle travel track.
The above embodiments of the present disclosure have the following advantages: first, obstacle vehicle information and target vehicle information of a target vehicle are acquired. Therefore, data support is provided for the following running task of the detection target vehicle. Next, a travel task of the target vehicle is generated based on the target vehicle information and the obstacle vehicle information. And data are sampled according to different driving planning tasks, so that the calculated amount is reduced, and the planning speed of the driving track is improved. And thirdly, acquiring a sampling information set under the driving task. The acquired sampling information set provides data support for the subsequent generation of the target vehicle running track. According to different driving planning tasks, the calculated amount of high latitude can be reduced by the acquired sampling information set. And finally, generating a target vehicle running track based on the sampling information set. And dividing different cost indexes based on different planning tasks. And corresponding to each specific performance index, a corresponding cost index is designed to evaluate the track performance. And obtaining the optimal running track by searching the global optimal solution. Therefore, the accuracy of path planning is improved, and the safety of the vehicle in the driving process is improved.
With further reference to fig. 3, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of an apparatus for controlling the travel of a vehicle, which correspond to those of the method embodiments illustrated in fig. 2, and which may be particularly applicable in various electronic devices.
As shown in fig. 3, an apparatus 300 for controlling the travel of a vehicle according to some embodiments includes: a first acquisition unit 301, a first generation unit 302, a second acquisition unit 303, and a second generation unit 304. Wherein the first acquisition unit 301 is configured to acquire obstacle vehicle information and target vehicle information of a target vehicle; a first generation unit 302 configured to generate a travel task of the target vehicle based on the target vehicle information and the obstacle vehicle information; a second acquiring unit 303 configured to acquire a sampling information set under the travel task; and a second generating unit 304 configured to generate a target vehicle running track based on the sampling information set.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to FIG. 4, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1) 400 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 404 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure 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 in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring obstacle vehicle information and target vehicle information of a target vehicle; generating a driving task of the target vehicle according to the target vehicle information and the obstacle vehicle information; acquiring a sampling information set under the driving task; and generating a target vehicle running track based on the sampling information set.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first acquisition unit, a first generation unit, a second acquisition unit, and a second generation unit. Where the names of these units do not constitute a limitation of the unit itself in some cases, for example, the first acquisition unit may also be described as a "unit that acquires obstacle vehicle information and target vehicle information of a target vehicle".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (7)

1. A method for controlling travel of a vehicle, comprising:
acquiring obstacle vehicle information and target vehicle information of a target vehicle;
generating a running task of the target vehicle according to the target vehicle information and the obstacle vehicle information;
acquiring a sampling information set under the driving task;
generating a target vehicle running track based on the sampling information set;
wherein the sampling information comprises at least one of: timestamp, velocity value and location information; and
generating a target vehicle driving track based on the sampling information set comprises:
determining a track information sequence to be detected of each piece of sampling information in the sampling information set to obtain a track information sequence set to be detected, wherein the track information to be detected in the track information sequence to be detected comprises at least one of the following items: a timestamp, a jerk value, a target vehicle velocity value, target vehicle position information, an actual following distance, a curvature, a relative velocity value, and a relative distance;
selecting the track information sequences to be detected which meet target conditions from the track information sequences to be detected as target track information sequences;
generating the target vehicle running track based on the target track information sequence;
wherein, the selecting the track information sequence to be detected which meets the target condition from the track information sequence set to be detected as the target track information sequence comprises:
determining a loss value of each track information sequence to be detected in the track information sequence set to be detected to obtain a loss value set;
selecting a loss value with the smallest loss value from the loss value set as a target loss value;
determining the track information sequence to be detected corresponding to the target loss value as the target track information sequence;
wherein, the determining the loss value of each track information sequence to be detected in the track information sequence set to be detected includes:
for each track information sequence to be detected in the track information sequence set to be detected, generating a loss value of the track information sequence to be detected by the following formula:
Figure 904439DEST_PATH_IMAGE001
，
wherein,
Figure 1708DEST_PATH_IMAGE002
representing the information of the track to be detected in the information sequence of the track to be detected,
Figure 568955DEST_PATH_IMAGE003
a time stamp is represented which is a time stamp,
Figure 336054DEST_PATH_IMAGE004
the last time stamp is indicated and is,
Figure 988752DEST_PATH_IMAGE005
representing a terminal performance loss value corresponding to the track information to be detected corresponding to the last timestamp in the track information sequence to be detected,
Figure 762149DEST_PATH_IMAGE006
indicating the integral performance loss value corresponding to the track information to be detected corresponding to each time stamp in the track information sequence to be detected,
Figure 183903DEST_PATH_IMAGE007
the value of the loss is represented by,
Figure 246537DEST_PATH_IMAGE008
which represents the value of the first loss to be,
Figure 261897DEST_PATH_IMAGE009
the value of the second loss is represented,
Figure 700969DEST_PATH_IMAGE010
a third value of the loss is represented,
Figure 118175DEST_PATH_IMAGE011
a fourth loss value is represented as a fourth loss value,
Figure 617289DEST_PATH_IMAGE012
a fifth loss value is represented as a function of,
Figure 979000DEST_PATH_IMAGE013
a sixth loss value is represented as a function of,
Figure 97129DEST_PATH_IMAGE014
a seventh loss value is represented by a value of,
Figure 493475DEST_PATH_IMAGE015
represents a first preset weight with a value range of [0, 100 ]]，
Figure 38857DEST_PATH_IMAGE016
Represents a second preset weight with a value range of [0, 100 ]]，
Figure 153444DEST_PATH_IMAGE017
Represents a third preset weight with a value range of 0, 100]，
Figure 75264DEST_PATH_IMAGE018
Represents a fourth preset weight with a value range of [0, 100 ]]，
Figure 467062DEST_PATH_IMAGE019
Represents a fifth preset weight with a value range of [0, 100 ]]，
Figure 307979DEST_PATH_IMAGE020
Represents a sixth preset weight with a value range of [0, 100 ]]，
Figure 50807DEST_PATH_IMAGE021
Represents a seventh preset weight, and the value range is [0, 1]]，
Figure 635372DEST_PATH_IMAGE022
Represents attenuation factor, and has a value range of 0, 100]，
Figure 896325DEST_PATH_IMAGE023
Represents a constant with a value of 2.72,
Figure 908143DEST_PATH_IMAGE024
indicating the jerk value included in the track information to be detected,
Figure 872688DEST_PATH_IMAGE025
indicating the target vehicle speed value included in the to-be-detected track information,
Figure 136311DEST_PATH_IMAGE026
a preset desired speed value is indicated and,
Figure 361756DEST_PATH_IMAGE027
indicating an integral performance loss value corresponding to the difference value between the target vehicle speed value and the preset expected speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 685421DEST_PATH_IMAGE028
a terminal performance loss value corresponding to the difference between the target vehicle speed value and the preset expected speed value included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected is represented,
Figure 261895DEST_PATH_IMAGE029
indicating the target vehicle position information included in the track information to be detected,
Figure 63629DEST_PATH_IMAGE030
indicating the preset desired position information that is to be set,
Figure 143581DEST_PATH_IMAGE031
indicating a terminal performance loss value corresponding to the preset expected position information and target vehicle position information included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected,
Figure 903726DEST_PATH_IMAGE032
indicating the relative velocity value included in the track information to be detected,
Figure 577284DEST_PATH_IMAGE033
to representThe information of the track to be detected comprises the relative distance,
Figure 307343DEST_PATH_IMAGE034
indicating an integral performance loss value corresponding to a ratio between a relative speed value and a relative distance included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 382746DEST_PATH_IMAGE035
represents the curvature included in the information of the track to be detected,
Figure 172848DEST_PATH_IMAGE036
the integral performance loss value corresponding to a value obtained by multiplying the square and the curvature of the target vehicle speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 596351DEST_PATH_IMAGE037
indicating a preset desired following distance,
Figure 5467DEST_PATH_IMAGE038
and representing the actual following distance included by the track information to be detected.
2. The method of claim 1, wherein the method further comprises:
and sending the target vehicle running track to a control device of the target vehicle so as to control the target vehicle to run along the target vehicle running track.
3. The method of claim 1, wherein the generating a driving mission of the target vehicle from the target vehicle information and the obstacle vehicle information comprises:
in response to determining that the timestamp corresponding to the obstacle vehicle information matches the timestamp corresponding to the target vehicle information, generating a following distance based on the target vehicle information and the obstacle vehicle information;
and generating a running task of the target vehicle based on the following distance.
4. The method of claim 3, wherein the generating a travel task for the target vehicle based on the following distance comprises:
determining a preset cruise task as the travel task in response to determining that the following distance satisfies a cruise condition, wherein the cruise condition includes: the following distance is greater than a cruise threshold;
in response to determining that the following distance satisfies a following condition, determining a preset following task as the driving task, wherein the following condition includes: the following distance is greater than a following threshold value and less than or equal to a cruising threshold value;
determining a preset parking task as the driving task in response to determining that the following distance satisfies a parking condition, wherein the parking condition includes: the following distance is smaller than or equal to a following threshold value.
5. An apparatus for controlling travel of a vehicle, comprising:
a first acquisition unit configured to acquire obstacle vehicle information and target vehicle information of a target vehicle;
a first generation unit configured to generate a travel task of the target vehicle based on the target vehicle information and the obstacle vehicle information;
a second acquisition unit configured to acquire a sampled information set under the travel task, wherein the sampled information includes at least one of: timestamp, velocity value and location information;
a second generation unit configured to generate a target vehicle travel track based on the sampling information set;
wherein the second generation unit is further configured to: determining a track information sequence to be detected of each piece of sampling information in the sampling information set to obtain a track information sequence set to be detected, wherein the track information to be detected in the track information sequence to be detected comprises at least one of the following items: a timestamp, a jerk value, a target vehicle velocity value, target vehicle position information, an actual following distance, a curvature, a relative velocity value, and a relative distance;
selecting the track information sequences to be detected which meet target conditions from the track information sequences to be detected as target track information sequences;
generating the target vehicle running track based on the target track information sequence;
wherein, the selecting the track information sequence to be detected which meets the target condition from the track information sequence set to be detected as the target track information sequence comprises:
determining a loss value of each track information sequence to be detected in the track information sequence set to be detected to obtain a loss value set;
selecting a loss value with the smallest loss value from the loss value set as a target loss value;
determining the track information sequence to be detected corresponding to the target loss value as the target track information sequence;
wherein, the determining the loss value of each track information sequence to be detected in the track information sequence set to be detected includes:
for each track information sequence to be detected in the track information sequence set to be detected, generating a loss value of the track information sequence to be detected by the following formula:
Figure DEST_PATH_IMAGE039
，
wherein,
Figure 466536DEST_PATH_IMAGE002
representing the information of the track to be detected in the information sequence of the track to be detected,
Figure 427538DEST_PATH_IMAGE003
a time stamp is represented which is a time stamp,
Figure 810109DEST_PATH_IMAGE004
the last time stamp is indicated and is,
Figure 881970DEST_PATH_IMAGE005
representing a terminal performance loss value corresponding to the track information to be detected corresponding to the last timestamp in the track information sequence to be detected,
Figure 666387DEST_PATH_IMAGE006
indicating the integral performance loss value corresponding to the track information to be detected corresponding to each time stamp in the track information sequence to be detected,
Figure 532712DEST_PATH_IMAGE007
the value of the loss is represented by,
Figure 527212DEST_PATH_IMAGE008
which represents the value of the first loss to be,
Figure 12552DEST_PATH_IMAGE009
the value of the second loss is represented,
Figure 41687DEST_PATH_IMAGE010
a third value of the loss is represented,
Figure 688701DEST_PATH_IMAGE011
a fourth loss value is represented as a fourth loss value,
Figure 436077DEST_PATH_IMAGE012
a fifth loss value is represented as a function of,
Figure 584161DEST_PATH_IMAGE013
a sixth loss value is represented as a function of,
Figure 77591DEST_PATH_IMAGE014
a seventh loss value is represented by a value of,
Figure 551297DEST_PATH_IMAGE015
represents a first preset weight with a value range of [0, 100 ]]，
Figure 398686DEST_PATH_IMAGE016
Represents a second preset weight with a value range of [0, 100 ]]，
Figure 84882DEST_PATH_IMAGE017
Represents a third preset weight with a value range of 0, 100]，
Figure 88611DEST_PATH_IMAGE018
Represents a fourth preset weight with a value range of [0, 100 ]]，
Figure 77426DEST_PATH_IMAGE019
Represents a fifth preset weight with a value range of [0, 100 ]]，
Figure 533815DEST_PATH_IMAGE020
Represents a sixth preset weight with a value range of [0, 100 ]]，
Figure 23703DEST_PATH_IMAGE021
Represents a seventh preset weight, and the value range is [0, 1]]，
Figure 757303DEST_PATH_IMAGE022
Represents attenuation factor, and has a value range of 0, 100]，
Figure 572813DEST_PATH_IMAGE023
Represents a constant with a value of 2.72,
Figure 391864DEST_PATH_IMAGE024
indicating the jerk value included in the track information to be detected,
Figure 419863DEST_PATH_IMAGE025
indicating the target vehicle speed value included in the to-be-detected track information,
Figure 867025DEST_PATH_IMAGE026
a preset desired speed value is indicated and,
Figure 728802DEST_PATH_IMAGE027
indicating an integral performance loss value corresponding to the difference value between the target vehicle speed value and the preset expected speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 894204DEST_PATH_IMAGE028
a terminal performance loss value corresponding to the difference between the target vehicle speed value and the preset expected speed value included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected is represented,
Figure 866839DEST_PATH_IMAGE029
indicating the target vehicle position information included in the track information to be detected,
Figure 434087DEST_PATH_IMAGE030
indicating the preset desired position information that is to be set,
Figure 325819DEST_PATH_IMAGE031
indicating a terminal performance loss value corresponding to the preset expected position information and target vehicle position information included in the track information to be detected corresponding to the last timestamp in the track information sequence to be detected,
Figure 119463DEST_PATH_IMAGE032
indicating the relative velocity value included in the track information to be detected,
Figure 754843DEST_PATH_IMAGE033
indicating the relative distance included by the track information to be detected,
Figure 51964DEST_PATH_IMAGE034
indicating an integral performance loss value corresponding to a ratio between a relative speed value and a relative distance included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 380177DEST_PATH_IMAGE035
represents the curvature included in the information of the track to be detected,
Figure 254592DEST_PATH_IMAGE036
the integral performance loss value corresponding to a value obtained by multiplying the square and the curvature of the target vehicle speed value included in the track information to be detected corresponding to each timestamp in the track information sequence to be detected,
Figure 831679DEST_PATH_IMAGE037
indicating a preset desired following distance,
Figure 373519DEST_PATH_IMAGE038
and representing the actual following distance included by the track information to be detected.
6. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
7. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-4.
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