CN115497317B - Target road section determining method, device, equipment, readable storage medium and product - Google Patents
Target road section determining method, device, equipment, readable storage medium and product Download PDFInfo
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- 238000012163 sequencing technique Methods 0.000 claims description 6
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- 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
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- 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
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- 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
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/0969—Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
Abstract
The disclosure provides a target road section determining method, a device, equipment, a readable storage medium and a product, relates to the field of artificial intelligence, and particularly relates to the fields of automatic driving, intelligent transportation, vehicle-road coordination and the like. The specific implementation scheme is as follows: acquiring target data of switching multiple groups of driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the driving modes switched 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 operation is carried out on each road section according to the switching times and preset clustering conditions, and road sections corresponding to a plurality of clusters are obtained, wherein different clusters correspond to different switching times; and determining a target road section meeting a preset test condition from road sections corresponding to the clusters. By determining the target link, the autopilot technique can be specifically improved based on the target link.
Description
Technical Field
The disclosure relates to the fields of automatic driving, intelligent traffic, vehicle-road coordination and the like in artificial intelligence, in particular to a target road section determining method, a device, equipment, a readable storage medium and a product.
Background
In the automatic driving process, the automatic driving mode is often switched to the manual driving mode, and the situation indicates that the automatic driving cannot cope with the current road condition and the driver is required to manually drive. If the number of occurrences of automatic driving switching manual driving is large, the automatic driving technique needs to be improved.
When the automatic driving technology is improved, the road condition research is generally carried out at the intersection with a large traffic flow, but when the road section is adopted for optimizing the automatic driving technology, the optimizing effect is often poor.
Disclosure of Invention
The present disclosure provides a target segment determination method, apparatus, device, readable storage medium, and product for selecting a target segment capable of improving an autopilot optimization effect.
According to a first aspect of the present disclosure, there is provided a target segment determining method, including:
acquiring target data of switching multiple groups of driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the driving modes switched 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 operation is carried out on each road section according to the switching times and preset clustering conditions, and road sections corresponding to a plurality of clusters are obtained, wherein different clusters correspond to different switching times;
and determining a target road section meeting a preset test condition from road sections corresponding to the clusters.
According to a second aspect of the present disclosure, there is provided a target segment determining apparatus including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring target data of a plurality of groups of driving modes switched from automatic driving to manual driving, and the target data comprises position information of a target vehicle with the driving mode switched when the driving mode is 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 carrying out clustering operation on 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 the processing module is used for determining a target road section meeting a preset test condition from 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 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 storing 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 it can be read by at least one processor of an electronic device, the at least one processor executing the computer program causing the electronic device to perform the method of the first aspect.
The technology according to the disclosure solves the technical problem that the existing automatic driving technology is poor in lifting effect. By determining the target link, the autopilot technique can be specifically improved based on the target link.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for 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 flow chart of a target road segment determining method according to an embodiment of the disclosure;
fig. 3 is a flowchart of a target road segment determining method according to another embodiment of the disclosure;
fig. 4 is a flowchart of a target road segment determining method according to another embodiment of the disclosure;
fig. 5 is a flowchart of a target road segment determining method according to another embodiment of the 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 road section determining apparatus provided in an embodiment of the present disclosure;
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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 disclosure provides a target road section determining method, a device, equipment, a readable storage medium and a product, which are applied to automatic driving in artificial intelligence to determine a target road section, so that the automatic driving technology can be purposefully improved based on the target road section.
It should be noted that the method, the device, the equipment, the readable storage medium and the product for determining the target road section provided by the present disclosure may be applied to any scenario of optimizing an autopilot technology.
The existing automatic driving technology optimization method generally selects a road section with larger traffic flow to study road conditions, and further optimizes the automatic driving technology based on a study result. However, when the automatic driving technique is optimized by the above method, the automatic driving technique may perform well on the road section where the vehicle flow rate is large, and thus, frequent switching of the driving mode is not required. Therefore, the optimization of the automatic driving technology by adopting the road section with larger traffic flow often has poor optimization effect.
In solving the above-described technical problems, the inventors have found through studies that, in order to be able to perform an optimization operation of the automatic driving technique with pertinence, first, a target link having a large number of times of switching from the automatic driving mode to the manual driving mode may be screened. Since the number of driving mode switches on the target link is large, the characteristic autopilot technique does not perform well on the link. Thus, the lifting operation of the autopilot technique can be performed with pertinence using the target link.
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 and a server 12. The server 12 is provided with a target link determination device which can be written in a language such as C/c++, java, shell, or Python.
Based on the system architecture, the server 12 can acquire the original data fed back by the target vehicle 11 in real time, determine, according to the original data, target data for switching from automatic driving to manual driving in multiple groups of driving modes, and further determine, according to the target data, a target road section meeting a preset test condition, and perform automatic driving technology lifting operation based on the target road section.
Fig. 2 is a flow chart of a target road segment determining method according to an embodiment of the disclosure, as shown in fig. 2, the method includes:
step 201, obtaining target data of switching multiple groups of driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the switching driving modes when the driving modes are switched.
The execution subject of the present embodiment is a target link determination device, which may be coupled in a server. The server may be communicatively coupled to a target vehicle having an autopilot function to enable information interaction with the target vehicle.
In the present embodiment, if a driving mode of a target vehicle driving on a road section frequently appears to switch from automatic driving to manual driving for each road section, an automatic driving technique used to characterize the target vehicle does not perform well on the road section. Thus, the automatic driving technique can be purposefully operated in accordance with the road section.
Further, to enable determination of the target road segments, multiple sets of target data may first be determined. The target data may specifically be data that the driving mode is switched from automatic driving to manual driving. The target data comprises position information of a target vehicle with a driving mode switched when the driving mode is switched. The position information may specifically be the longitude and latitude of the target vehicle whose driving mode is switched when the driving mode is switched.
Step 202, determining a road section matched with the target data according to the position information in each target data, and determining the driving mode switching times corresponding to each road section.
In the present embodiment, the road on the map may be divided into a plurality of links according to a preset division condition. For each target data, the road section matched with the target data can be determined according to the position information of the switching operation in the target data and the positions of the road sections.
After determining the road segments matching each of the target data, the number of target data corresponding to each of the road segments may be determined. And the number of times of switching the driving mode in each road section can be determined according to the number of the target data corresponding to each road section.
It can be understood that, for any road segment, if the number of driving mode switching times in the road segment is small, the representation automatic driving technology performs well in the road segment, and accordingly, the automatic driving technology cannot be promoted in a targeted manner by using the road segment. Otherwise, if the number of switching times of the driving mode in the road section is more, the automatic driving technology is not well represented in the road section by the thief. In this case, the automatic driving technique can be effectively optimized by using the road section. The performance of the following automatic driving technology in the road section is improved.
And 203, 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.
In this embodiment, in order to facilitate determination of the target link, a plurality of clusters may be preset, where different clusters correspond to different switching times. For example, eight clusters from-1 to 6 may be set, where the number of times of switching corresponding to each cluster may be set according to actual requirements, which is not limited in this disclosure. The clusters corresponding to-1 may then be outliers that need to be filtered out. For example, if only one or zero times of switching is corresponding to a certain road section, the road section can be divided into clusters corresponding to-1 as outlier data, and then filtering is performed.
After the switching times corresponding to each road section are respectively determined, clustering operation can be carried out on each road section according to the switching times and preset clustering conditions, and road sections corresponding to a plurality of clusters are obtained. 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. Still for example, for each road segment, the road segments having at least two switching times may be clustered to obtain road segments corresponding to a plurality of clusters.
And 204, determining a target road section meeting a preset test condition from road sections corresponding to the clusters.
In this embodiment, different test conditions may be set in advance according to actual demands, for example, the automatic driving technique may be improved by using a road section whose switching number exceeds a preset number threshold, or the automatic driving technique may be improved by using a road section whose switching number is within a preset number 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 the road segments corresponding to the plurality of clusters.
Further, on the basis of any of the foregoing embodiments, before step 202, the method further includes:
and dividing each road in the map 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 to which the target data matches, each road may be first 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 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 links 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 section is determined, in order to be able to improve the performance of the autopilot technology, the user experience is improved. The target road segment may be employed to optimize the autopilot technology used by the target vehicle.
According to the target road section determining method, the switching times of the driving mode switching in each road section are determined, clustering is further carried out according to the switching times, and the target road section is determined according to the clustering result and preset test conditions. Therefore, the target road section with poor performance of the automatic driving technology can be effectively screened, and the optimization operation of the automatic driving technology can be carried out according to the target road section in a targeted manner, so that the accuracy and the efficiency of the optimization of the automatic driving technology are improved.
Fig. 3 is a flow chart of a target road segment determining method according to another embodiment of the disclosure, where, on the basis of any of the foregoing embodiments, as shown in fig. 3, step 201 includes:
Step 301, obtaining original data fed back by a plurality of target vehicles in real time.
Step 302, screening driving mode data in the original data according to preset screening conditions, wherein the driving mode data comprises driving modes and position information.
And 303, determining target data of switching the multiple groups of driving modes from automatic driving to manual driving according to the driving mode data.
In this embodiment, the original data may be fed back according to a preset frequency or a preset time during the driving of the target vehicle with the autopilot function. The original data can comprise driving data in the driving process of the target vehicle, including speed, acceleration, data acquired by a sensor and the like. Data relating to the driving mode is also included. Such as driving mode used in reporting, reporting time, location information, etc.
Therefore, in order to facilitate the determination operation of the subsequent target road section, the driving pattern data may be screened in the raw data according to a preset screening condition, wherein the driving pattern data includes the driving pattern and the location information. The preset screening conditions may include a data type, a data identifier, etc. that need to be acquired currently. And performing data screening operation in the original data according to the data type and the data identification.
Further, the target data for switching the plurality of sets of driving modes from automatic driving to manual driving can be determined from the driving modes in the driving mode data.
Further, on the basis of any one of the above embodiments, the driving mode data further includes a vehicle identifier of the target vehicle and a data reporting time. Step 303 comprises:
and for each vehicle identifier, acquiring a plurality of pieces of pending driving mode data 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 from the early to the late of the data reporting time to obtain the sequenced plurality of pieces of driving mode data to be processed.
And regarding each piece of ordered driving mode data to be processed, taking the driving mode data to be processed and the driving mode data to be processed which are sequentially behind as a group of data sets.
And for each data set, if the driving mode corresponding to the two pieces of driving mode data to be processed in the data set is detected to be switched, determining the data set as the target data.
In this embodiment, the driving pattern data further includes a vehicle identification of the target vehicle and a data reporting time. After the driving pattern data is acquired, the driving pattern data may be screened according to the dimension of the target vehicle. Alternatively, for each vehicle identifier, a plurality of pieces of pending driving pattern data corresponding to the vehicle identifier may be acquired in the driving pattern data.
And aiming at the plurality of pieces of to-be-processed driving mode data corresponding to each vehicle identifier, sorting the plurality of pieces of to-be-processed driving mode data according to the data reporting time in the to-be-processed driving mode data and the sequence from the morning to the evening to obtain the sorted plurality of pieces of to-be-processed driving mode data. The sorted pieces of driving mode data to be processed may be as shown in table 1:
TABLE 1
Wherein, 0 in the driving mode represents an automatic driving mode, and 1 represents a manual driving mode.
And regarding each piece of ordered driving mode data to be processed, taking the driving mode data to be processed and the driving mode data to be processed which are sequentially behind the driving mode data to be processed as a group of data sets. Wherein the data set may be as shown in table 2:
TABLE 2
And for each data set, if the driving mode corresponding to the two pieces of driving mode data to be processed in the data set is detected to be switched, determining the data set 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 is unchanged, and thus the data set may be discarded. For the (2, 3) data set in table 2, the driving pattern of the first driving pattern data in the data set is 0, and the driving pattern of the second driving pattern data is 1. A change in driving pattern occurs in the data set is characterized, and therefore, the data set can be determined as target data.
Further, on the basis of any one of the above embodiments, the automatic driving mode corresponds to a first identifier, and the manual driving mode corresponds to a second identifier. If it is detected that the driving modes corresponding to the two pieces of driving mode data to be processed in the data set are switched, determining the data set as the target data includes:
if the fact that the data of the previous to-be-processed driving mode in the data set comprises the first identifier and the data of the later to-be-processed driving mode in the data set comprises the second identifier is detected, the driving modes corresponding to the two to-be-processed driving mode data in the data set are determined to be switched, and the data set is determined to be the target data.
In this embodiment, in order to facilitate determination of target data in which the driving mode is changed, 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 set, if the data of the previous driving mode to be processed in the data set is detected to comprise a first identifier, and the data of the subsequent driving mode to be processed in the data set comprises a second identifier, the two driving mode data corresponding to the data set are characterized to be switched in driving mode, and automatic driving is switched to manual driving. Thus, the data group can be determined as target data.
Further, on the basis of any one of the above embodiments, the automatic driving mode corresponds to a first identifier, and the manual driving mode corresponds to a second identifier. The method further comprises the steps of:
if the fact that the data of the previous to-be-processed driving mode in the data set comprises the second identifier is detected, the fact that the data of the later to-be-processed driving mode in the data set comprises the first identifier is determined, and if the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, the data set is deleted;
or if the first identifier or the second identifier is detected to be included in the to-be-processed driving mode data in the data set, determining that the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, and deleting the data set.
In this embodiment, in order to facilitate determination of target data in which the driving mode is changed, 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 set, if the data of the previous driving mode to be processed in the data set is detected to comprise the second identifier, the data of the driving mode to be processed in the data set comprises the first identifier, the data of the driving mode in the data set is characterized to be switched from a manual mode to an automatic mode instead of being switched from the automatic mode to the manual mode, at the moment, it can be determined that the driving modes corresponding to the two data of the driving mode to be processed in the data set are not switched, and then the data set is deleted.
Or, for each data set, if it is detected that the two pieces of driving mode data to be processed in the data set include the first identifier or the second identifier, the data set may be deleted if it is indicated that no cut flower occurs in the driving mode in the data set.
According to the target road section determining method, after the original data are acquired, the driving mode data in the original data are acquired first, and the target data with the driving mode switching are 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 later according to the target data. Providing a basis for the determination of the subsequent target road segments.
Fig. 4 is a flow chart of a target road segment determining method according to another embodiment of the disclosure, where, based on any of the foregoing embodiments, as shown in fig. 4, step 204 includes:
step 401, determining the number of switching times corresponding to each cluster.
And step 402, determining a plurality of road sections corresponding to the clusters with the switching times exceeding a preset time threshold as the target road sections.
In this embodiment, in order to improve the efficiency of the automatic driving technology, a plurality of road sections 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 as target road sections.
According to the target road segment determining method, the plurality of road segments corresponding to the clusters with the switching times exceeding the preset times threshold are determined as the target road segments, so that the target road segments with poor performance of the current automatic driving technology can be accurately determined. And then the automatic driving technology can be lifted in a targeted manner according to the target road section.
Fig. 5 is a flow chart of a target road segment determining method according to another embodiment of the disclosure, where, on the basis of any of the foregoing embodiments, as shown in fig. 5, after step 203, the method further includes:
step 501, determining, for each road segment, the position of the road segment on the map according to the position information corresponding to the road segment.
And 502, drawing the road section by adopting a mark corresponding to the cluster on the map according to the cluster corresponding to the road section, and obtaining a drawn target map.
And step 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 performance of the autopilot technology on each road segment, different identifiers may be set for different clusters. The marks can be marks with different shapes, marks with different colors, or road sections with different switching times are drawn into different colors, different gray scales and the like. Any type of identification that can distinguish between road segments of different switching times may be employed, and this disclosure is not limited in this regard.
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 mark to the position of the road section on the map, thereby obtaining the drawn target map.
Further, after the drawing operation of the map is completed, the target map may be transmitted to the terminal device after the target map is obtained. So that the user can view the target map on the terminal device.
Fig. 6 is a schematic diagram of a target map provided in an embodiment of the present disclosure, as shown in fig. 6, in an upper left corner of a target map 61, identification indication information 62 corresponding to different clusters is provided, where different clusters correspond to different gray colors. The drawing operation is performed on the roads in the target map 61 according to the color of the cluster to which each road section belongs. As shown in fig. 6, the dot-like portion in the map is a drawn color.
According to the target road segment determining method, after the road segments are clustered, the road segments are drawn on the map according to the marks corresponding to the different clusters, so that a user can more intuitively determine the road segments with poor current automatic driving technology on the target map, and the determination of the target road segments is rapidly realized.
Fig. 7 is a schematic structural diagram of a target road segment determining device according to an embodiment of the present disclosure, 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 obtaining module 71 is configured to obtain target data for switching from automatic driving to manual driving in multiple sets of driving modes, where the target data includes position information of a target vehicle in which the driving mode is switched when the driving mode is switched. And a determining module 72, configured to determine a road segment matching the target data according to the position information in each target data, and determine the driving mode switching times corresponding to each road segment. The clustering module 73 is configured to perform a clustering operation on each of the road segments according to the switching times and a preset clustering condition, so as to obtain road segments corresponding to a plurality of clusters, where different clusters correspond to different switching times. And the processing module 74 is configured to determine a target road segment that meets a preset test condition from the road segments corresponding to the clusters.
Further, on the basis of any one of the foregoing 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 foregoing embodiments, the apparatus further includes: and the optimizing 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 foregoing embodiments, the acquiring module includes: the original data acquisition unit is used for acquiring original data fed back by the target vehicles in real time. And the screening unit is used for screening driving mode data in the original data according to preset screening conditions, wherein the driving mode data comprises driving modes and position information. And the determining unit is used for determining target data of switching the plurality of groups of driving modes from automatic driving to manual driving according to the driving mode data.
Further, on the basis of any one of the above embodiments, the driving mode 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 to-be-processed driving mode data according to the sequence from the early to the late of the data reporting time to obtain the sequenced plurality of pieces of to-be-processed driving mode data. And the data processing subunit is used for regarding each piece of ordered driving mode data to be processed, and taking the driving mode data to be processed and the driving mode data to be processed which are sequentially behind as a group of data groups. And the determining subunit is used for determining each data set as the target data if the driving mode corresponding to the two pieces of driving mode data to be processed in the data set is detected to be switched.
Further, on the basis of any one of the above embodiments, in the driving mode data, the automatic driving mode corresponds to a first identifier, and the manual driving mode corresponds to a second identifier. Wherein the determining subunit is configured to: if the fact that the data of the previous to-be-processed driving mode in the data set comprises the first identifier and the data of the later to-be-processed driving mode in the data set comprises the second identifier is detected, the driving modes corresponding to the two to-be-processed driving mode data in the data set are determined to be switched, and the data set is determined to be the target data.
Further, on the basis of any one of the above embodiments, in the driving mode data, the automatic driving mode corresponds to a first identifier, and the manual driving mode corresponds to a second identifier. Wherein the apparatus further comprises: the deleting module is used for deleting the data set if the fact that the driving mode data to be processed in the data set include the second identifier is detected, the driving mode data to be processed in the data set include the first identifier is determined to be not switched according to the driving modes corresponding to the two driving mode data to be processed in the data set;
Or if the first identifier or the second identifier is detected to be included in the to-be-processed driving mode data in the data set, determining that the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, and deleting the data set.
Further, on the basis of any one of the foregoing embodiments, 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 foregoing 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 mark corresponding to the cluster to the position of the road section on the map 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.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, including:
at least one processor. And
A memory communicatively coupled to the at least one processor. Wherein,
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 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 an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the 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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the apparatus 800 includes a computing unit 801 that 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 required for the operation of the device 800 can also be stored. The computing 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 the bus 804.
Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; 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, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. 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.
The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 801 performs the respective methods and processes described above, for example, a target section determination method. For example, in some embodiments, the target segment determination method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the target link determination method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the target road segment determination method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present 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/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of 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. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server 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 that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (19)
1. A target segment determination method, comprising:
acquiring target data of switching multiple groups of driving modes from automatic driving to manual driving, wherein the target data comprises position information of a target vehicle with the driving modes switched 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 operation is carried out on each road section according to the switching times and preset clustering conditions, and road sections corresponding to a plurality of clusters are obtained, wherein different clusters correspond to different switching times;
determining a target road section meeting a preset test condition in road sections corresponding to the clusters;
before determining the road section matched with the target data according to the position information in each target data, the method further comprises the following steps:
and dividing each road in the map according to a preset length threshold value to obtain a plurality of road sections corresponding to the road.
2. The method of claim 1, wherein the obtaining target data for switching from automatic driving to manual driving of multiple sets of driving modes 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 comprises driving modes and position information;
and determining target data of switching the multiple groups of driving modes from automatic driving to manual driving according to the driving mode data.
3. The method of claim 2, wherein the driving pattern data further includes a vehicle identification of a target vehicle and a data reporting time; the determining, according to the driving mode data, target data for switching from automatic driving to manual driving in multiple sets of driving modes includes:
For each vehicle identifier, acquiring a plurality of pieces of to-be-processed driving mode data corresponding to the vehicle identifier from the driving mode data;
sequencing the plurality of pieces of driving mode data to be processed according to the sequence from the early to the late of the data reporting time to obtain sequenced plurality of pieces of driving mode data to be processed;
regarding each piece of ordered driving mode data to be processed, taking the driving mode data to be processed and the driving mode data to be processed which are sequentially behind as a group of data sets;
and for each data set, if the driving mode corresponding to the two pieces of driving mode data to be processed in the data set is detected to be switched, determining the data set as the target data.
4. A method according to claim 3, wherein in the driving pattern data, the automatic driving pattern corresponds to a first identifier and the manual driving pattern corresponds to a second identifier;
if it is detected that the driving modes corresponding to the two pieces of driving mode data to be processed in the data set are switched, determining the data set as the target data includes:
if the fact that the data of the previous to-be-processed driving mode in the data set comprises the first identifier and the data of the later to-be-processed driving mode in the data set comprises the second identifier is detected, the driving modes corresponding to the two to-be-processed driving mode data in the data set are determined to be switched, and the data set is determined to be the target data.
5. A method according to claim 3, wherein in the driving pattern data, the automatic driving pattern corresponds to a first identifier and the manual driving pattern corresponds to a second identifier;
the method further comprises the steps of:
if the fact that the data of the previous to-be-processed driving mode in the data set comprises the second identifier is detected, the fact that the data of the later to-be-processed driving mode in the data set comprises the first identifier is determined, and if the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, the data set is deleted;
or if the first identifier or the second identifier is detected to be included in the to-be-processed driving mode data in the data set, determining that the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, and deleting the data set.
6. The method according to any one of claims 1-5, wherein the determining, among the segments corresponding to the plurality of clusters, a target segment that satisfies a preset test condition includes:
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 as the target road sections.
7. The method according to any one of claims 1-5, further comprising, after determining a target road segment that satisfies 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.
8. The method according to any one of claims 1-5, wherein the clustering operation is performed on each road segment according to the switching times and a preset clustering condition, and after obtaining road segments corresponding to a plurality of clusters, 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;
drawing the road section by adopting a mark corresponding to the cluster on the map according to the cluster corresponding to the road section, and obtaining a drawn target map;
and sending the target map to terminal equipment for display.
9. A target segment determining apparatus comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring target data of a plurality of groups of driving modes switched from automatic driving to manual driving, and the target data comprises position information of a target vehicle with the driving mode switched when the driving mode is 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 carrying out clustering operation on 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;
the processing module is used for determining a target road section meeting a preset test condition from road sections corresponding to the clusters;
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.
10. The apparatus of claim 9, wherein the acquisition module 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 in the original data according to preset screening conditions, wherein the driving mode data comprises driving modes and position information;
and the determining unit is used for determining target data of switching the plurality of groups of driving modes from automatic driving to manual driving according to the driving mode data.
11. The apparatus of claim 10, wherein the driving pattern data further comprises a vehicle identification of a target vehicle and a data reporting time; the determination unit includes:
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;
the sequencing subunit is used for sequencing the plurality of pieces of to-be-processed driving mode data according to the sequence from the early to the late of the data reporting time to obtain the sequenced plurality of pieces of to-be-processed driving mode data;
the data processing subunit is used for regarding each piece of ordered driving mode data to be processed, and taking the driving mode data to be processed and the driving mode data to be processed which are sequentially behind as a group of data groups;
and the determining subunit is used for determining each data set as the target data if the driving mode corresponding to the two pieces of driving mode data to be processed in the data set is detected to be switched.
12. The apparatus of claim 11, wherein in the driving pattern data, the automatic driving pattern corresponds to a first identifier and the manual driving pattern corresponds to a second identifier;
wherein the determining subunit is configured to:
if the fact that the data of the previous to-be-processed driving mode in the data set comprises the first identifier and the data of the later to-be-processed driving mode in the data set comprises the second identifier is detected, the driving modes corresponding to the two to-be-processed driving mode data in the data set are determined to be switched, and the data set is determined to be the target data.
13. The apparatus of claim 11, wherein in the driving pattern data, the automatic driving pattern corresponds to a first identifier and the manual driving pattern corresponds to a second identifier;
wherein the apparatus further comprises:
the deleting module is used for deleting the data set if the fact that the driving mode data to be processed in the data set include the second identifier is detected, the driving mode data to be processed in the data set include the first identifier is determined to be not switched according to the driving modes corresponding to the two driving mode data to be processed in the data set;
or if the first identifier or the second identifier is detected to be included in the to-be-processed driving mode data in the data set, determining that the driving modes corresponding to the two to-be-processed driving mode data in the data set are not switched, and deleting the data set.
14. The apparatus of any of claims 9-13, wherein the processing module comprises:
a switching frequency determining unit, configured to determine 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.
15. The apparatus of any of claims 9-13, further comprising:
and the optimizing module is used for optimizing the automatic driving technology used by the target vehicle by adopting the target road section.
16. The apparatus according to any one of claims 9-13, further comprising:
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;
the drawing module is used for drawing the road section by adopting the mark corresponding to the cluster on the map 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.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
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 1-8.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of claims 1-8.
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