CN115497317A - Target road section determination method, device, equipment, readable storage medium and product - Google Patents
Target road section determination method, device, equipment, readable storage medium and product Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
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Abstract
The disclosure provides a target road section determining method, a target road section determining device, a readable storage medium and a readable storage product, and relates to the field of artificial intelligence, in particular to the fields of automatic driving, intelligent transportation, vehicle and road cooperation and the like. The specific implementation scheme is as follows: acquiring a plurality of groups of target data of switching the driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the switched driving modes when the driving modes are switched; determining road sections matched with the target data according to the position information in the target data, and determining the driving mode switching times corresponding to the road sections; clustering each road section according to the switching times and preset clustering conditions to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times; and determining a target road section meeting preset test conditions in the road sections corresponding to the clusters. By determining the target road section, the automatic driving technology can be improved in a targeted manner based on the target road section.
Description
Technical Field
The present disclosure relates to the field of automated driving, intelligent transportation, and vehicle and road coordination in artificial intelligence, and in particular, to a method, an apparatus, a device, a readable storage medium, and a product for determining a target road segment.
Background
In the automatic driving process, the situation that the automatic driving mode is switched to the manual driving mode often occurs, and the situation represents that the automatic driving cannot cope with the current road condition and needs manual driving by a driver. If the number of times of switching automatic driving to manual driving is large, the automatic driving technology needs to be improved.
When the automatic driving technology is improved, intersections with large traffic flow are generally selected for road condition research, but when the road sections are adopted for optimizing the automatic driving technology, the optimization effect is often poor.
Disclosure of Invention
The present disclosure provides a target road segment determination method, apparatus, device, readable storage medium, and product for selecting a target road segment capable of improving an automatic driving optimization effect.
According to a first aspect of the present disclosure, there is provided a target road segment determining method, including:
acquiring a plurality of groups of target data of switching driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the switched driving modes when the driving modes are switched;
determining road sections matched with the target data according to the position information in the target data, and determining the driving mode switching times corresponding to the road sections;
clustering each road section according to the switching times and preset clustering conditions to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times;
and determining a target road section meeting preset test conditions in the road sections corresponding to the clusters.
According to a second aspect of the present disclosure, there is provided a target link determining apparatus including:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a plurality of groups of target data of which the driving modes are switched from automatic driving to manual driving, and the target data comprises position information of a target vehicle with the switched driving modes when the driving modes are switched;
the determining module is used for determining road sections matched with the target data according to the position information in the target data and determining the driving mode switching times corresponding to the road sections;
the clustering module is used for clustering the road sections according to the switching times and preset clustering conditions to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times;
and the processing module is used for determining a target road section meeting a preset test condition in the road sections corresponding to the clusters.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first aspect.
According to the technique of this disclosure, the current not good technical problem of autopilot technique promotion effect has been solved. By determining the target road section, the automatic driving technology can be improved in a targeted manner based on the target road section.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a diagram of a system architecture upon which the present disclosure is based;
fig. 2 is a schematic flowchart of a target link determining method according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a target link determining method according to yet another embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a target link determining method according to yet another embodiment of the present disclosure;
fig. 5 is a schematic flowchart of a target link determining method according to yet another embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a target map provided by an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a target link determining device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as 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 present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The present disclosure provides a target road segment determining method, device, apparatus, readable storage medium, and product, which are applied to automatic driving in artificial intelligence to determine a target road segment, thereby being capable of pertinently improving an automatic driving technology based on the target road segment.
It should be noted that the target road segment determining method, device, apparatus, readable storage medium and product provided by the present disclosure may be applied in any scenario of automatic driving technology optimization.
The existing automatic driving technology optimization method generally selects a road section with larger traffic flow to research the road condition, and then optimizes the automatic driving technology based on the research result. However, when the automated driving technique optimization is performed by the above method, the automated driving technique may be well performed on a link having a large traffic flow, and thus, it is not necessary to frequently switch the driving mode. Therefore, optimization of the automatic driving technology by using a road section with a large traffic flow is often poor in optimization effect.
In the process of solving the technical problem, the inventor finds out through research that in order to carry out optimization operation on the automatic driving technology in a targeted manner, a target road section with more times of switching from the automatic driving mode to the manual driving mode can be screened firstly. Because the number of times of switching the driving modes on the target road section is large, the representation of the automatic driving technology is poor in performance on the road section. Therefore, the target road section can be adopted to purposefully perform the lifting operation of the automatic driving technology.
Fig. 1 is a diagram of a system architecture on which the present disclosure is based, and as shown in fig. 1, the system architecture on which the present disclosure is based includes at least a target vehicle 11, a server 12. The server 12 is provided with a target road segment determining device, which may be written in C/C + +, java, shell, python, or other languages.
Based on the system architecture, the server 12 can obtain original data fed back by the target vehicle 11 in real time, determine a plurality of sets of target data of which the driving modes are switched from automatic driving to manual driving according to the original data, further determine a target road section meeting preset test conditions according to the target data, and perform automatic driving technology improvement operation based on the target road section.
Fig. 2 is a schematic flowchart of a target road segment determining method provided in the embodiment of the present disclosure, and as shown in fig. 2, the method includes:
The execution subject of the present embodiment is a target link determination device, which may be coupled in a server. The server can be in communication connection with a target vehicle with an automatic driving function, so that information interaction with the target vehicle can be carried out.
In the present embodiment, if the driving mode of the target vehicle driven on the link is frequently switched from the automated driving to the manual driving for each link, the automated driving technique used for characterizing the target vehicle may not perform well on the link. Therefore, the automatic driving technology can be purposefully lifted according to the road section.
Further, to enable determination of the target road segment, first a plurality of sets of target data may be determined. The target data may specifically be data of switching the driving mode from automatic driving to manual driving. The target data comprises position information of a target vehicle with a switched driving mode when the driving mode is switched. The location information may specifically be longitude and latitude where the target vehicle, whose driving mode is switched, is located when the driving mode is switched.
Step 202, determining road sections matched with the target data according to the position information in the target data, and determining the driving mode switching times corresponding to the road sections.
In the present embodiment, a road on a map may be divided into a plurality of links according to a preset division condition. For each target data, the road segment matched with the target data can be determined according to the position information of the switching operation in the target data and the position of each road segment.
After determining the links matching each target data, the number of target data corresponding to each link may be determined. And then the switching times of the driving modes in the road section can be determined according to the quantity of the target data corresponding to each road section.
It can be understood that, for any road segment, if the number of times of switching the driving modes in the road segment is small, the representation of the automatic driving technology in the road segment is good, and accordingly, the automatic driving technology cannot be purposefully improved by using the road segment. On the contrary, if the number of times of switching the driving modes in the road section is large, the thief characterizes that the automatic driving technology has a poor performance in the road section. At this time, the automatic driving technology can be effectively optimized by adopting the road section. And the performance of the subsequent automatic driving technology in the road section is improved.
And 203, clustering each road section according to the switching times and preset clustering conditions to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times.
In this embodiment, in order to facilitate the determination of the target link, a plurality of clusters may be provided in advance, wherein different clusters correspond to different switching times. For example, eight clusters of-1 to 6 may be set, where the switching times corresponding to each cluster may be set according to actual requirements, which is not limited by the present disclosure. 1 corresponding cluster can be outlier data that needs to be filtered out. For example, if there is only one or zero switching times in a certain road segment, the switching times may be divided into clusters corresponding to-1 as outlier data, and then filtered.
After the switching times corresponding to the road segments are respectively determined, clustering operation can be performed on the road segments according to the switching times and preset clustering conditions to obtain road segments corresponding to a plurality of clusters. For example, the clustering condition may be to divide the road segments into clusters with matching switching times according to the switching times of each road segment. For example, for each road segment, the road segments with at least two switching times may be clustered to obtain road segments corresponding to multiple clusters.
And 204, determining a target road section meeting preset test conditions in the road sections corresponding to the clusters.
In this embodiment, different test conditions may be set in advance according to actual needs, for example, the automatic driving technique may be improved by using a road segment whose switching frequency exceeds a preset frequency threshold, or the automatic driving technique may be improved by using a road segment whose switching frequency is within a preset frequency interval.
Therefore, after the clustering operation for each road segment is completed, a target road segment satisfying a preset test condition may be determined among road segments corresponding to a plurality of clusters.
Further, on the basis of any of the above embodiments, before the step 202, the method further includes:
aiming at each road in the map, dividing the road according to a preset length threshold value to obtain a plurality of road sections corresponding to the road.
In this embodiment, before determining the road segments matching the target data, each road may be divided to obtain a plurality of road segments.
Alternatively, different length thresholds may be set according to actual requirements. For example, the length threshold may be 500 meters. Alternatively, other lengths are possible, as the present disclosure is not limited in this regard.
For each road in the map, the road may be divided according to the length threshold to obtain a plurality of road segments corresponding to each road.
Further, on the basis of any of the above embodiments, after step 204, the method further includes:
and optimizing the automatic driving technology used by the target vehicle by adopting the target road section.
In this embodiment, after the target road segment is determined, in order to improve the performance of the automatic driving technology, the user experience is improved. The target road segment may be used to optimize the autonomous driving technique used by the target vehicle.
In the method for determining the target road section provided by this embodiment, the switching times of the driving mode switching in each road section are determined, clustering is further performed according to the switching times, and the target road section is determined according to the clustering result and the preset test condition. Therefore, the target road section with poor automatic driving technology performance can be effectively screened out, the automatic driving technology can be optimized according to the target road section in a targeted mode, and the accuracy and the efficiency of automatic driving technology optimization are improved.
Fig. 3 is a schematic flowchart of a target link determining method according to yet another embodiment of the present disclosure, and based on any one of the above embodiments, as shown in fig. 3, step 201 includes:
and 301, acquiring original data fed back by a plurality of target vehicles in real time.
And step 303, determining a plurality of groups of target data of switching the driving mode from automatic driving to manual driving according to the driving mode data.
In this embodiment, the target vehicle with the automatic driving function may feed back the raw data according to a preset frequency or a preset time during the driving process. The raw data may include driving data of the target vehicle during driving, including speed, acceleration, data collected by the sensor, and the like. Data relating to driving patterns is also included. Such as the driving mode used when reporting, the reporting time, the location information, etc.
Therefore, in order to facilitate the determination operation of the subsequent target link, the driving mode data may be filtered in the raw data according to a preset filtering condition, wherein the driving mode data includes the driving mode and the position information. The preset screening condition may include a data type, a data identifier, and the like that need to be currently acquired. And performing data screening operation in the original data according to the data type and the data identification.
And then, a plurality of groups of target data of which the driving modes are switched from automatic driving to manual driving can be determined according to the driving modes in the driving mode data.
Further, on the basis of any of the above embodiments, the driving pattern data further includes a vehicle identifier of the target vehicle and a data reporting time. Step 303 comprises:
and aiming at each vehicle identifier, acquiring a plurality of pieces of driving mode data to be processed corresponding to the vehicle identifier from the driving mode data.
And sequencing the plurality of pieces of driving mode data to be processed according to the sequence of the data reporting time from morning to evening to obtain the sequenced plurality of pieces of driving mode data to be processed.
And regarding each piece of sequenced driving mode data to be processed, taking the driving mode data to be processed and the driving mode data to be processed sequentially behind as a group of data sets.
And for each data group, if the switching of the driving modes corresponding to the two pieces of driving mode data to be processed in the data group is detected, determining the data group as the target data.
In this embodiment, the driving mode data further includes a vehicle identifier of the target vehicle and a data reporting time. After the driving pattern data is acquired, the driving pattern data may be filtered according to the dimensions of the target vehicle. Alternatively, for each vehicle identifier, a plurality of pieces of to-be-processed driving pattern data corresponding to the vehicle identifier may be acquired in the driving pattern data.
For a plurality of pieces of to-be-processed driving mode data corresponding to each vehicle identifier, the plurality of pieces of to-be-processed driving mode data can be sequenced according to the data reporting time in the to-be-processed driving mode data and the sequence from morning to evening, so that the sequenced plurality of pieces of to-be-processed driving mode data are obtained. The sorted pieces of driving pattern data to be processed may be as shown in table 1:
TABLE 1
Wherein 0 in the driving mode characterizes an automatic driving mode and 1 characterizes a manual driving mode.
And regarding each sorted to-be-processed driving mode data, taking the to-be-processed driving mode data and the to-be-processed driving mode data sequentially behind the to-be-processed driving mode data as a group of data groups. Wherein the data set may be as shown in table 2:
TABLE 2
And for each data group, if the switching of the driving modes corresponding to the two pieces of driving mode data to be processed in the data group is detected, determining the data group as target data.
For example, for the (0, 1) th data set in table 2, the driving mode in each driving mode data in the data set is 0, which indicates that the driving mode has not changed, and therefore, the data set may be discarded. For the (2, 3) th data group in table 2, the driving pattern of the first driving pattern data in the data group is 0, and the driving pattern of the second driving pattern data is 1. A change in the driving pattern occurs in the data set, and therefore, the data set can be determined as target data.
Further, on the basis of any of the above embodiments, the automatic driving mode corresponds to the first identifier, and the manual driving mode corresponds to the second identifier. Wherein, if it is detected that the driving modes corresponding to the two to-be-processed driving mode data in the data group are switched, determining the data group as the target data includes:
if it is detected that the driving mode data to be processed in the data group in advance includes a first identifier and the driving mode data to be processed in the data group in the end includes a second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are switched, and the data group is determined as the target data.
In this embodiment, in order to facilitate the determination of the target data with the changed driving mode, the automatic driving mode may be set to correspond to the first identifier, and the manual driving mode may be set to correspond to the second identifier.
And aiming at each data group, if it is detected that the driving mode data to be processed in the data group in advance comprises a first identifier and the driving mode data to be processed in the data group in the end comprises a second identifier, the switching of the driving modes of the two driving mode data corresponding to the data group is represented, and the automatic driving is switched to the manual driving. Therefore, the data group can be determined as the target data.
Further, on the basis of any of the above embodiments, the automatic driving mode corresponds to the first identifier, and the manual driving mode corresponds to the second identifier. The method further comprises the following steps:
if it is detected that the driving mode data to be processed in the data group firstly comprise the second identification and the driving mode data to be processed in the data group secondly comprise the first identification, determining that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and deleting the data group;
or if it is detected that the driving mode data to be processed in the data group both include the first identifier or the second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and the data group is deleted.
In this embodiment, in order to facilitate the determination of the target data with the changed driving mode, the automatic driving mode may be set to correspond to the first identifier, and the manual driving mode may be set to correspond to the second identifier.
For each data group, if it is detected that the driving mode data to be processed in the data group first includes the second identifier and the driving mode data to be processed in the data group later includes the first identifier, it is characterized that the driving mode data in the data group is switched from the manual mode to the automatic mode instead of being switched from the automatic mode to the manual mode.
Or, for each data group, if it is detected that the two pieces of driving pattern data to be processed in the data group both include the first identifier or the second identifier, it is characterized that the driving pattern in the data group is not cut, and the data group may be deleted.
According to the target road section determining method provided by the embodiment, after the original data is acquired, the driving mode data in the original data is acquired first, and the target data with the driving mode switching is further screened out from the driving mode data, so that the switching times of the driving mode switching in each road section can be accurately determined according to the target data. Providing a basis for the determination of subsequent target road segments.
Fig. 4 is a schematic flowchart of a target road segment determining method according to another embodiment of the present disclosure, where on the basis of any one of the foregoing embodiments, as shown in fig. 4, step 204 includes:
In this embodiment, in order to improve the efficiency of improving the automatic driving technology, a plurality of road segments with a large number of switching times may be used to perform the automatic driving technology optimization operation. Thus, the number of handovers corresponding to each cluster can be determined. And determining a plurality of road sections corresponding to the clusters with the switching times exceeding a preset time threshold value as target road sections.
In the target road section determining method provided by this embodiment, a plurality of road sections corresponding to a cluster whose switching times exceed a preset time threshold are determined as the target road section, so that the target road section with poor current automatic driving technology performance can be accurately determined. And then can carry out targeted lift operation to the automatic driving technique according to this target highway section.
Fig. 5 is a schematic flowchart of a target road segment determining method according to another embodiment of the present disclosure, and based on any one of the foregoing embodiments, as shown in fig. 5, after step 203, the method further includes:
and step 501, determining the position of each road section on the map according to the position information corresponding to the road section.
And 503, sending the target map to a terminal device for display.
In this embodiment, in order to enable the user to more intuitively view the behavior of the automatic driving technology on each road segment, different identifiers may be set for different clusters. The mark can be a mark with different shapes and different colors, or the road sections with different switching times are drawn into different colors, different gray scales and the like. Any identifier capable of distinguishing the road sections with different switching times may be used, and the disclosure is not limited thereto.
For each road segment, the position of the road segment on the map can be determined according to the position information corresponding to the road segment. And determining a cluster corresponding to the road section, and determining an identifier corresponding to the cluster. And drawing the road section by adopting the identifier to draw the position of the road section on the map to obtain the drawn target map.
Further, after the drawing operation of the map is completed and the target map is obtained, the target map may be transmitted to the terminal device. So that the user can view the target map on the terminal equipment.
Fig. 6 is a schematic diagram of a target map provided by the embodiment of the present disclosure, and as shown in fig. 6, identification indication information 62 corresponding to different clusters is arranged at an upper left corner of a target map 61, where the different clusters correspond to different grayscale colors. The road in the target map 61 is drawn according to the color of the cluster to which each road segment belongs. As shown in fig. 6, the dotted part in the map is the drawing color.
According to the target road section determining method provided by the embodiment, after clustering of each road section is completed, each road section is drawn on the map according to the corresponding identification of different clusters, so that a user can determine the road section with poor current automatic driving technology performance on the target map more intuitively, and the target road section is determined quickly.
Fig. 7 is a schematic structural diagram of a target road segment determining device according to an embodiment of the present disclosure, and as shown in fig. 7, the device includes: an acquisition module 71, a determination module 72, a clustering module 73, and a processing module 74. The acquiring module 71 is configured to acquire multiple sets of target data of switching the driving mode from automatic driving to manual driving, where the target data includes location information of a target vehicle where the driving mode is switched. And a determining module 72, configured to determine, according to the position information in each target data, a road segment matched with the target data, and determine the number of times of switching the driving mode corresponding to each road segment. And the clustering module 73 is configured to perform clustering operation on each road segment according to the switching times and preset clustering conditions to obtain road segments corresponding to multiple clusters, where different clusters correspond to different switching times. And a processing module 74, configured to determine a target road segment that meets a preset test condition among road segments corresponding to the plurality of clusters.
Further, on the basis of any one of the above embodiments, the apparatus further includes: the dividing module is used for dividing each road in the map according to a preset length threshold value to obtain a plurality of road sections corresponding to the road.
Further, on the basis of any one of the above embodiments, the apparatus further includes: and the optimization module is used for optimizing the automatic driving technology used by the target vehicle by adopting the target road section.
Further, on the basis of any one of the above embodiments, the obtaining module includes: and the original data acquisition unit is used for acquiring original data fed back by a plurality of target vehicles in real time. And the screening unit is used for screening the driving mode data in the original data according to a preset screening condition, wherein the driving mode data comprises a driving mode and position information. And the determining unit is used for determining a plurality of groups of target data of which the driving modes are switched from automatic driving to manual driving according to the driving mode data.
Further, on the basis of any of the above embodiments, the driving pattern data further includes a vehicle identifier of the target vehicle and a data reporting time. The determination unit includes: and the acquisition subunit is used for acquiring a plurality of pieces of to-be-processed driving mode data corresponding to the vehicle identifications in the driving mode data aiming at each vehicle identification. And the sequencing subunit is used for sequencing the plurality of pieces of driving mode data to be processed according to the sequence of the data reporting time from morning to evening to obtain a plurality of pieces of sequenced driving mode data to be processed. And the data processing subunit is used for regarding each piece of sequenced driving mode data to be processed, and regarding the driving mode data to be processed and the driving mode data to be processed sequentially behind the data to be processed as a group of data groups. And the determining subunit is used for determining the data group as the target data if the switching of the driving modes corresponding to the two pieces of driving mode data to be processed in the data group is detected for each data group.
Further, on the basis of any of the above embodiments, in the driving mode data, the automatic driving mode corresponds to a first flag, and the manual driving mode corresponds to a second flag. Wherein the determining subunit is to: if it is detected that the driving mode data to be processed in the data group in advance includes a first identifier and the driving mode data to be processed in the data group in the end includes a second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are switched, and the data group is determined as the target data.
Further, on the basis of any of the above embodiments, in the driving mode data, the automatic driving mode corresponds to a first flag, and the manual driving mode corresponds to a second flag. Wherein the apparatus further comprises: the deleting module is used for deleting the data group if the driving mode data to be processed in the data group before comprise the second identification and the driving mode data to be processed in the data group after comprise the first identification, and determining that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched;
or if it is detected that the driving mode data to be processed in the data group both include the first identifier or the second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and the data group is deleted.
Further, on the basis of any one of the above embodiments, wherein the processing module includes: and the switching frequency determining unit is used for determining the switching frequency corresponding to each cluster. And the target road section determining unit is used for determining a plurality of road sections corresponding to the clusters with the switching times exceeding a preset time threshold as the target road sections.
Further, on the basis of any one of the above embodiments, the apparatus further includes: and the position determining module is used for determining the position of each road section on the map according to the position information corresponding to the road section. And the drawing module is used for drawing the road section by adopting the position of the road section on the map, which is identified by the corresponding cluster, according to the cluster corresponding to the road section, so as to obtain a drawn target map. And the sending module is used for sending the target map to the terminal equipment for displaying.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
According to an embodiment of the present disclosure, the present disclosure also provides an electronic device including:
at least one processor. And
a memory communicatively coupled to the at least one processor. Wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of the embodiments described above.
According to an embodiment of the present disclosure, there is also provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of the above embodiments.
According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the apparatus 800 includes a computing unit 801 which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure 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/acts specified in the flowchart and/or block diagram to be performed. 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 this disclosure, 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. A 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 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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 can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (21)
1. A target road segment determination method, comprising:
acquiring a plurality of groups of target data of switching driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the switched driving modes when the driving modes are switched;
determining road sections matched with the target data according to the position information in the target data, and determining the driving mode switching times corresponding to the road sections;
clustering each road section according to the switching times and a preset clustering condition to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times;
and determining a target road section meeting preset test conditions in the road sections corresponding to the clusters.
2. The method of claim 1, wherein the obtaining a plurality of sets of target data for switching driving modes from automatic driving to manual driving comprises:
acquiring original data fed back by a plurality of target vehicles in real time;
screening driving mode data in the original data according to preset screening conditions, wherein the driving mode data comprise driving modes and position information;
and determining a plurality of groups of target data of switching the driving mode from automatic driving to manual driving according to the driving mode data.
3. The method of claim 2, wherein the driving pattern data further comprises a vehicle identification of a target vehicle and a data reporting time; the determining of the target data of switching a plurality of groups of driving modes from automatic driving to manual driving according to the driving mode data comprises the following steps:
aiming at each vehicle identification, acquiring a plurality of pieces of driving mode data to be processed corresponding to the vehicle identification from the driving mode data;
sequencing the plurality of pieces of driving mode data to be processed according to the sequence of the data reporting time from morning to evening to obtain a plurality of pieces of sequenced driving mode data to be processed;
regarding each sorted to-be-processed driving mode data, taking the to-be-processed driving mode data and the to-be-processed driving mode data sequentially behind the to-be-processed driving mode data as a group of data groups;
and for each data group, if the switching of the driving modes corresponding to the two pieces of driving mode data to be processed in the data group is detected, determining the data group as the target data.
4. The method of claim 3, wherein in the driving pattern data, the automatic driving pattern corresponds to a first indicator and the manual driving pattern corresponds to a second indicator;
wherein, if it is detected that the driving modes corresponding to the two to-be-processed driving mode data in the data group are switched, determining the data group as the target data includes:
if it is detected that the driving mode data to be processed in the data group in advance includes a first identifier and the driving mode data to be processed in the data group in the end includes a second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are switched, and the data group is determined as the target data.
5. The method of claim 3, wherein in the driving pattern data, the automatic driving pattern corresponds to a first indicator and the manual driving pattern corresponds to a second indicator;
the method further comprises the following steps:
if it is detected that the driving mode data to be processed in the data group firstly comprise the second identification and the driving mode data to be processed in the data group secondly comprise the first identification, determining that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and deleting the data group;
or if it is detected that the driving mode data to be processed in the data group both include the first identifier or the second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and the data group is deleted.
6. The method according to any one of claims 1 to 5, before determining the road segment matching the target data according to the position information in each target data, further comprising:
aiming at each road in the map, dividing the road according to a preset length threshold value to obtain a plurality of road sections corresponding to the road.
7. The method according to any one of claims 1-6, wherein the determining of the target road segments meeting the preset test condition in the road segments corresponding to the plurality of clusters comprises:
determining the corresponding switching times of each cluster;
and determining a plurality of road sections corresponding to the clusters with the switching times exceeding a preset time threshold value as the target road sections.
8. The method according to any one of claims 1 to 7, further comprising, after determining a target road segment satisfying a preset test condition among the road segments corresponding to the plurality of clusters:
and optimizing the automatic driving technology used by the target vehicle by adopting the target road section.
9. The method according to any one of claims 1 to 8, after the clustering operation is performed on each of the road segments according to the switching times and a preset clustering condition, and road segments corresponding to a plurality of clusters are obtained, the method further comprises:
for each road section, determining the position of the road section on a map according to the position information corresponding to the road section;
according to the clusters corresponding to the road sections, drawing the road sections by adopting the positions of the marks corresponding to the clusters on a map, and obtaining a drawn target map;
and sending the target map to terminal equipment for displaying.
10. A target link determination device comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a plurality of groups of target data of which the driving modes are switched from automatic driving to manual driving, and the target data comprises position information of a target vehicle with the switched driving modes when the driving modes are switched;
the determining module is used for determining road sections matched with the target data according to the position information in the target data and determining the driving mode switching times corresponding to the road sections;
the clustering module is used for clustering the road sections according to the switching times and preset clustering conditions to obtain road sections corresponding to a plurality of clusters, wherein different clusters correspond to different switching times;
and the processing module is used for determining a target road section meeting preset test conditions in the road sections corresponding to the clusters.
11. The apparatus of claim 10, wherein the means for obtaining comprises:
the original data acquisition unit is used for acquiring original data fed back by a plurality of target vehicles in real time;
the screening unit is used for screening driving mode data from the original data according to preset screening conditions, wherein the driving mode data comprise driving modes and position information;
and the determining unit is used for determining a plurality of groups of target data of switching the driving modes from automatic driving to manual driving according to the driving mode data.
12. The apparatus of claim 11, wherein the driving pattern data further comprises a vehicle identification of a target vehicle and a data reporting time; the determination unit includes:
the acquiring subunit is used for acquiring a plurality of pieces of to-be-processed driving mode data corresponding to the vehicle identifiers from the driving mode data aiming at each vehicle identifier;
the sequencing subunit is used for sequencing the plurality of pieces of driving mode data to be processed according to the sequence of the data reporting time from morning to evening to obtain a plurality of pieces of sequenced driving mode data to be processed;
the data processing subunit is used for regarding each piece of sequenced driving mode data to be processed, and taking the driving mode data to be processed and the driving mode data to be processed sequentially behind the data to be processed as a group of data groups;
and the determining subunit is used for determining the data group as the target data if the switching of the driving modes corresponding to the two pieces of driving mode data to be processed in the data group is detected for each data group.
13. The apparatus according to claim 12, in the driving mode data, the automatic driving mode corresponds to a first flag, and the manual driving mode corresponds to a second flag;
wherein the determining subunit is to:
if it is detected that the driving mode data to be processed in the data group in advance includes a first identifier and the driving mode data to be processed in the data group in the end includes a second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are switched, and the data group is determined as the target data.
14. The apparatus according to claim 12, in the driving mode data, the automatic driving mode corresponds to a first flag, and the manual driving mode corresponds to a second flag;
wherein the apparatus further comprises:
a deleting module, configured to delete a data group if it is detected that driving mode data to be processed before in the data group includes a second identifier and driving mode data to be processed after in the data group includes a first identifier, and if it is determined that driving modes corresponding to two driving mode data to be processed in the data group are not switched;
or if it is detected that the driving mode data to be processed in the data group both include the first identifier or the second identifier, it is determined that the driving modes corresponding to the two driving mode data to be processed in the data group are not switched, and the data group is deleted.
15. The apparatus of any of claims 10-14, further comprising:
the dividing module is used for dividing each road in the map according to a preset length threshold value to obtain a plurality of road sections corresponding to the road.
16. The apparatus of any one of claims 10-15, wherein the processing module comprises:
a switching frequency determining unit, configured to determine a switching frequency corresponding to each cluster;
and the target road section determining unit is used for determining a plurality of road sections corresponding to the clusters with the switching times exceeding a preset time threshold as the target road sections.
17. The apparatus according to any of claims 10-16, further comprising:
and the optimization module is used for optimizing the automatic driving technology used by the target vehicle by adopting the target road section.
18. The apparatus of any of claims 10-17, further comprising:
the position determining module is used for determining the position of each road section on a map according to the position information corresponding to the road section;
the drawing module is used for drawing the road section by adopting the position of the road section on a map by adopting the mark corresponding to the cluster according to the cluster corresponding to the road section to obtain a drawn target map;
and the sending module is used for sending the target map to terminal equipment for displaying.
19. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.
21. A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the method of any one of claims 1 to 9.
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