CN115063974A - Road construction detection method, device, vehicle-mounted terminal, vehicle and medium - Google Patents

Road construction detection method, device, vehicle-mounted terminal, vehicle and medium Download PDF

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CN115063974A
CN115063974A CN202210645897.8A CN202210645897A CN115063974A CN 115063974 A CN115063974 A CN 115063974A CN 202210645897 A CN202210645897 A CN 202210645897A CN 115063974 A CN115063974 A CN 115063974A
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construction
road
avoidance
target data
road construction
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彭晓宇
崔茂源
孙连明
王超
刘泰言
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FAW Group Corp
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FAW Group Corp
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
    • E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
    • E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass

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  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Navigation (AREA)
  • Traffic Control Systems (AREA)

Abstract

The disclosure provides a road construction detection method, a road construction detection device, a vehicle-mounted terminal, a vehicle and a medium. The road construction detection method comprises the following steps: acquiring construction associated data of a road section to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; and determining the road construction condition of the road section to be detected according to the road construction events corresponding to different target data sources and the event confidence coefficients. By adopting the technical scheme, the construction condition of the road section to be detected is comprehensively judged, and the accuracy of the determination result of the road construction condition is improved.

Description

Road construction detection method, device, vehicle-mounted terminal, vehicle and medium
Technical Field
The embodiment of the disclosure relates to the technical field of intelligent traffic, in particular to a road construction detection method, a road construction detection device, a vehicle-mounted terminal, a vehicle and a medium.
Background
At present, with the accelerated development of road construction, the road construction sometimes happens, and the condition of traffic accidents caused by the road construction is also very common. Under the scene, in order to improve the safety of road driving, it is important to accurately detect the construction road section in advance.
Disclosure of Invention
The disclosure provides a road construction detection method, a road construction detection device, a vehicle-mounted terminal, a vehicle and a medium, so as to improve accuracy of a road construction condition determination result.
According to an aspect of the present disclosure, there is provided a road construction detection method, wherein the method includes:
acquiring construction associated data of a road section to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system;
respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources;
and determining the road construction condition of the road section to be detected according to the road construction event and the event confidence corresponding to different target data sources.
According to another aspect of the present disclosure, there is also provided a road construction detection device, wherein the device includes:
the construction associated data acquisition module is used for acquiring construction associated data of the road section to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system;
the construction event determining module is used for respectively determining road construction events and event confidence coefficients corresponding to the target data sources according to construction associated data of different target data sources;
and the road construction condition determining module is used for determining the road construction condition of the road section to be detected according to the road construction events corresponding to different target data sources and the event confidence coefficients.
According to another aspect of the present disclosure, there is also provided a vehicle-mounted terminal, including:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute any one of the road construction detection methods provided by the embodiments of the present disclosure.
According to another aspect of the present disclosure, a vehicle is also provided, wherein the vehicle is provided with a vehicle-mounted terminal capable of executing any one of the road construction detection methods provided by the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is also provided a computer-readable storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements any one of the road construction detection methods provided by the embodiments of the present disclosure.
According to the road construction detection scheme provided by the embodiment of the disclosure, construction associated data of a road section to be detected, which are provided by different target data sources, are obtained; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; and determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources. According to the scheme, the construction condition of the road section to be detected is comprehensively judged by using the construction associated data acquired by different target data sources, so that the condition that the accuracy of data provided by a single target data source is not high is avoided, and the accuracy of the determination result of the road construction condition is improved.
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
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a road construction detection method provided in an embodiment of the present disclosure;
fig. 2 is a flowchart of a road construction detection method provided in the second embodiment of the present disclosure;
fig. 3 is a flowchart of a road construction detection method provided in the third embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a road construction detection device provided in the fourth embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a vehicle-mounted terminal of a road construction detection method provided in the fifth embodiment of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not limiting of the disclosure. It should be further noted that, for the convenience of description, only some of the structures relevant to the present disclosure are shown in the drawings, not all of them.
Example one
Fig. 1 is a flowchart of a road construction detection method according to an embodiment of the present disclosure, where the present embodiment is applicable to detecting a condition of a construction road segment in a driving road, and the method may be executed by a road construction detection device, where the device may be implemented in a software and/or hardware manner, and may be integrated in an electronic device bearing a road construction detection function, such as a vehicle-mounted terminal or a smart phone. As shown in fig. 1, the method specifically includes:
and S110, acquiring construction associated data of the road section to be detected, which are provided by different target data sources.
The road section to be detected can be a road section on a driving route planned in advance for the vehicle to reach the destination, and can be a road section which the vehicle is going to enter.
The target data source may be a system used for providing road condition data of the road section to be detected. Optionally, the target data source may be at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system, a construction target detection system, and the like.
The construction related data may be data obtained by processing original data of the road section to be detected provided by the target data source, or may be original data of the road section to be detected directly provided by the target data source. Optionally, the construction related data may be presented in the form of at least one of an image, a text, a voice, and the like, which is not limited in this embodiment.
Specifically, at least one of the high-precision map positioning system, the navigation map positioning system, the avoidance behavior detection system, the construction target object detection system and the like can process the original data of the road section to be detected, which is detected by the high-precision map positioning system, the navigation map positioning system, the avoidance behavior detection system and the construction target object detection system, based on preset processing logic, so as to obtain construction associated data of the road section to be detected, so that the construction associated data can be used by the vehicle-mounted terminal. Processing logic may include, but is not limited to, data cleansing, compliance checking, and specification formats, among others.
And S120, respectively determining road construction events and event confidence degrees corresponding to the target data sources according to the construction associated data of different target data sources.
Among them, road construction is work performed to ensure the quality and safety of roads and road accessories. The road construction event is used for representing whether the road construction exists in the road section to be detected. The road construction event can be recorded as 'A', and when the road construction event exists in the road section to be detected, the road construction event can be recorded as '1'; when no road construction event exists in the road section to be detected, the road section to be detected can be marked as '0'.
The event confidence is used for representing the possibility of road construction on the road section to be detected. For example, the event confidence may be noted as "w". In this embodiment, the specific value of "w" is not limited at all, and it is only required to ensure that the value is not less than 0 and not more than 1.
Specifically, in this embodiment, the road construction events and the event confidence degrees corresponding to the construction related data of the road segment to be detected, which are provided by different target data sources, may be the same or different. For example, in the present embodiment, when the target data source is a high-precision map positioning system, the road construction event may be marked as "A 1 ", the event confidence may be noted as" w 1 "; when the target data source is a navigation map positioning system, the road construction event can be marked as' A 2 ", the event confidence may be noted as" w 2 "; when the target data source is an avoidance behavior detection system, the road construction event can be recorded as' A 3 ", the event confidence may be noted as" w 3 "; when the target data source is a construction target object detection system, the road construction event may be noted as "A 4 ", the event confidence may be noted as" w 4 ". Without loss of generality, w 1 ＜w 2 ＜w 3 ＜w 4 。
And S130, determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources.
The road construction condition can be that the road section to be detected has the road section under construction, or the road section to be detected has no road section under construction. Specifically, the determination of the road construction condition may be obtained by performing an operation on a road construction event and an event confidence corresponding to at least one target data source.
For example, the processing is performed according to the road construction event and the event confidence corresponding to different target data sources to determine the road construction condition of the road segment to be detected, which may be: and weighting and calculating the road construction event and the event confidence coefficient, and determining the road construction condition of the road section to be detected according to the calculation result.
For example, the processing is performed according to the road construction event and the event confidence corresponding to different target data sources to determine the road construction condition of the road segment to be detected, which may be: and inputting the road construction event and the event confidence coefficient into a pre-trained road construction prediction model, processing the road construction event and the event confidence coefficient by using the road construction prediction model, and determining the road construction condition of the road section to be detected. The road construction prediction model can be realized based on the existing machine learning model.
The construction associated data of the road section to be detected, which are provided by different target data sources, are obtained; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; and determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources. According to the scheme, the construction condition of the road section to be detected is comprehensively judged by using the construction associated data acquired by different target data sources, so that the condition that the accuracy of data provided by a single target data source is not high is avoided, and the accuracy of the determination result of the road construction condition is improved.
In the embodiments of the present disclosure, reference may be made to the description of the foregoing embodiments.
Example two
Fig. 2 is a flowchart of a road construction detection method provided in the second embodiment of the present disclosure, and in this embodiment, based on the foregoing embodiments, the operation of "obtaining construction related data of a road segment to be detected provided by different target data sources" is further refined into "selecting at least two target data sources from at least two candidate data sources according to a distance between a current position of a vehicle and the road segment to be detected; and acquiring the construction associated data of the road section to be detected, which is provided by each target data source, so as to perfect an acquisition mechanism of the construction associated data. As shown in fig. 2, the method includes:
s210, selecting at least two target data sources from the at least two candidate data sources according to the distance between the current position of the vehicle and the road section to be detected.
The current vehicle is any vehicle with a road section to be detected on a driving route.
The candidate data source can include at least two of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system, a construction target object detection system and the like.
The high-precision map positioning system can send out construction information in advance by a first preset distance (for example, 2 km-0.5 km); the navigation map positioning system can send out construction information in advance by a second preset distance (for example, 2 km-0.5 km); the avoidance behavior detection system can send out construction information in advance by a third preset distance (for example, 500 m-200 m); the construction target object detection system may send out construction information in advance of a fourth preset distance (e.g., 200m to 50 m).
Specifically, since the high-precision map positioning system and the navigation map positioning system usually remind a driver at a position far in front of the construction site, the driver can provide the vehicle with time for responding to the construction road in advance. To avoid that premature alerting causes a transition disturbance to the driving user, in an alternative embodiment the first and second preset distances are typically set to data in the order of kilometers, for example may each be set to 2 km. It should be noted that the first preset distance and the second preset distance may be the same or different in magnitude, and the disclosure does not limit this.
Specifically, since the detection object of the avoidance behavior is a vehicle ahead of the current vehicle, the third preset distance detected in advance by the avoidance behavior detection system may be set based on the detection range of the avoidance behavior detection system, and since the detection range is short, the third preset distance is usually set to data of hundred meters, for example, 500m to 200 m.
Specifically, since the detection of the construction target requires that the current vehicle is close to the construction area, a fourth preset distance detected in advance by the construction target detection system may be set based on the detection range of the construction target detection system, and since the detection range is short, the fourth preset distance is usually set to data of a hundred meters magnitude, for example, may be set to 200m to 50 m.
For example, the distance between the current position of the vehicle and the road section to be detected can be compared with a preset distance threshold; and selecting at least two target data sources from the at least two candidate data sources according to the comparison result. The preset distance threshold value can be set by a technician according to needs or empirical values, or determined by the perception distance of the candidate data source.
Optionally, a first preset distance threshold (e.g. 500m), a second preset distance threshold (e.g. 200m), and a third preset distance threshold (e.g. 50m) may be set according to the perceived distance of the candidate data source.
Since the different target data sources have different working distances of construction areas in front of the acquired data sources, the situation that construction exists in front of the acquired data sources according to the distance exists. In an optional embodiment, the distance selection interval of the target data source may be determined according to the first preset distance threshold, the second preset distance threshold, and the third preset distance threshold, and the corresponding target data source may be determined according to the distance selection interval.
In one embodiment, the boundary value of the first preset distance range may be determined according to a first preset distance threshold and a second preset distance threshold, and when the distance between the current position of the vehicle and the road segment to be detected belongs to the first preset distance range, the candidate data source (which may include, for example, a high-precision map positioning system and a navigation map positioning system) in the detection range of the first preset distance range is used as the target data source. For example, the first preset distance range may be (200m,500m ]. wherein the above description is only illustrative of the first preset distance range, it should not be understood as a specific limitation to the boundary values of the first preset distance range.
In one embodiment, the boundary value of the second preset distance range may be determined according to a second preset distance threshold and a third preset distance threshold, and when the distance between the current position of the vehicle and the road segment to be detected belongs to the second preset distance range, the candidate data source (which may include, for example, a high-precision map positioning system, a navigation map positioning system, and an avoidance behavior detection system) in the detection range of the second preset distance range is used as the target data source. For example, the second preset distance range may be (50m,200m ]. wherein the above description is only exemplary of the second preset distance range, it should not be understood as a specific limitation to the boundary values of the second preset distance range.
In a specific embodiment, a boundary value of a third preset distance range may be determined according to a third preset distance threshold, and when a distance between a current position of the vehicle and a road segment to be detected belongs to the third preset distance range, a candidate data source (which may include, for example, a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system, and a construction target object detection system) in the detection range of the third preset distance range is used as a target data source. For example, the third preset distance range may be (0,50 m), wherein the above-mentioned only exemplary illustration of the third preset distance range is not understood as a specific limitation to the boundary value of the third preset distance range, specifically, since the accuracy and reliability of the construction related data provided by different candidate data sources in different distance ranges are different, a target data source with relatively high accuracy and reliability may be selected from the candidate data sources in advance according to the distance between the current vehicle and the road segment to be detected, and the corresponding construction related data is provided, thereby laying a foundation for improving the accuracy and reliability of the subsequent road construction condition, and the data operation amount in the road construction detection process is reduced.
And S220, acquiring construction associated data of the road section to be detected, which is provided by each target data source.
The construction related data can be reference data capable of directly and/or indirectly reflecting road construction conditions, and the reference data comprises at least two of high-precision map reference data, navigation map reference data, avoidance reference data, construction target object reference data and the like.
The high-precision map reference data may be construction related data of a road segment to be detected provided by a high-precision map positioning system, for example, may be a regional high-precision map corresponding to a region around a navigation route, where the size of the region may be set or adjusted by a technician according to needs or experience values. In an alternative embodiment, since the high-precision map positioning system may send out the construction information in advance by a first preset distance (e.g., 2km to 0.5km), the area size may be set to the first preset distance.
The navigation map reference data may be construction related data of a road segment to be detected provided by a navigation map positioning system, for example, may be an area navigation map corresponding to an area around a navigation route, where the size of the area may be set or adjusted by a technician according to needs or experience values. In an alternative embodiment, since the navigation map positioning system may send out the construction information in advance of a second preset distance (e.g., 2km to 0.5km), the area size may be set to the second preset distance. It is worth noting that the high-precision map has a longer updating period and larger construction dynamic change relative to the navigation map, so that the construction associated data provided by the high-precision map positioning system is lower in comprehensiveness relative to the navigation map positioning system, and the navigation map is frequently updated, so that the accuracy of the provided construction associated data is higher relative to the high-precision map positioning system, and therefore, the construction associated data provided by two different map positioning systems can be complemented to provide rich and comprehensive construction associated data.
The avoidance reference data may be construction related data of the road section to be detected, which is provided by the avoidance behavior of the preceding vehicle detected by the avoidance behavior detection system, for example, avoidance behavior data generated by at least one preceding vehicle may be pointed to the front for construction to a certain extent, and is used for indirectly representing the construction condition of the road section to be detected in front. The number of the front vehicles is not limited in any way, and the front vehicles can be determined according to the detection capability of the avoidance behavior detection system.
The construction target object reference data may be reference data detected by the construction target object detection system for determining the presence or absence of the construction target object. For example, the construction target reference data may be a captured image from which subsequent road construction events are determined by the presence of the construction target in the image. The construction target object reference data is directly used for determining the construction target object, and the construction target object is a necessary reminding mark of a construction site, so that the construction target object reference data is used for directly representing the construction condition of the front road section to be detected. The construction target object may be an object for prompting pedestrians, motor vehicle drivers, non-motor vehicle drivers and other people passing through the construction road, and may include, but is not limited to, a cone, a road construction safety warning board and the like.
It should be noted that the corresponding detection ranges of the avoidance behavior detection system and the construction target object detection system are relatively close, so that relatively accurate avoidance reference data and construction target object reference data can be provided, and the accuracy of the road construction condition detection result can be improved.
Optionally, the avoidance behavior detection system may be a millimeter wave radar or a laser radar, and the hardware cost is low. Of course, the avoidance behavior detection system may also be other types of on-board sensors in order to improve the accuracy of the construction-related data.
Optionally, the construction target object detection system may be a vehicle-mounted camera or the like, and the hardware cost is low. To improve the accuracy of the construction-related data, the construction target detection system may also be other types of on-board sensors.
Specifically, the construction associated data of the road section to be detected provided by the target data source is obtained from the target data source with relatively high accuracy and reliability, and the obtained construction associated data can be at least two of high-precision map reference data, navigation map reference data, avoidance reference data, construction target object reference data and the like.
And S230, respectively determining road construction events and event confidence degrees corresponding to the target data sources according to the construction associated data of different target data sources.
In an alternative embodiment, the construction related data provided by the high-precision map positioning system can be a high-precision map, and whether a road construction event exists or not and the confidence level of the event are analyzed according to the data existing on the high-precision map.
In an optional embodiment, the construction related data provided by the navigation map positioning system may be a navigation map, and a construction location is marked on the navigation map, that is, whether a road construction event exists on the corresponding road segment to be detected, and the confidence level of the event.
In an optional embodiment, the construction related data corresponding to the avoidance behavior detection system may be detected avoidance behavior reference data, and it may be determined whether a road construction event exists and the magnitude of the event confidence according to the number of avoidance vehicles and/or avoidance degree data, so that the event confidence is more accurate. The avoidance degree data can be used for representing the emergency degree of avoiding the action of the vehicle.
Specifically, a first avoidance confidence coefficient can be determined according to the number of the avoided vehicles, and a second avoidance confidence coefficient can be determined according to the avoidance degree data. And determining an event confidence coefficient corresponding to the avoidance behavior detection system according to the first avoidance confidence coefficient and/or the second avoidance confidence coefficient.
The first avoidance confidence coefficient can be used for representing avoidance behaviors generated based on avoidance vehicles and presuming accuracy of front road construction. The first avoidance confidence coefficient is positively correlated with the quantity of the avoided vehicles, namely the quantity of the avoided vehicles is large, and the first avoidance confidence coefficient is high; the number of the avoided vehicles is small, and the first avoidance confidence coefficient is low.
The second avoidance confidence coefficient can be used for representing the avoidance degree based on the avoidance vehicle action and deducing the accuracy of the front road construction. The second avoidance confidence coefficient is positively correlated with the avoidance degree data of the avoided vehicle, namely the greater the avoidance degree of the avoided vehicle is, the higher the second avoidance confidence coefficient is; the smaller the avoidance degree of the avoided vehicle is, the lower the second avoidance confidence is.
The construction information of the road section to be detected is indirectly acquired through the avoidance behavior generated by the front vehicle at a short distance from the current vehicle, and the sensing range is short, so that the reliability is high, and the accuracy of the road construction condition is improved.
The avoidance degree data can include transverse avoidance degree data and/or longitudinal avoidance degree data, the transverse avoidance degree data can reflect the avoidance degree of the vehicle in the left-right direction in the avoidance degree data, and the longitudinal avoidance degree data can reflect the avoidance degree of the vehicle in the front-back direction in the avoidance degree data. Accordingly, a second avoidance confidence may be determined based on the lateral avoidance degree data and/or the longitudinal avoidance degree data.
Specifically, the lateral avoidance degree data may be used to represent the severity of the avoidance behavior of the vehicle in the left-right direction, for example, the lane change time of the vehicle may be long. The longitudinal avoidance degree data may be used to represent the severity data of the avoidance behavior of the vehicle in the front-rear direction, and may be, for example, the magnitude of the deceleration value of the vehicle.
In this embodiment, the second avoidance confidence is determined through the transverse avoidance degree data and/or the longitudinal avoidance degree data, so that the determination result of the second avoidance confidence is more accurate.
For example, the event confidence corresponding to the avoidance behavior detection system may be determined by the following method: if the number of the vehicles with the avoidance actions is recorded as an event a, the first avoidance confidence coefficient is w 3-1 (ii) a The lateral severity of the avoidance maneuver (which may be the time required to change lanes) is recorded as event b, and the lateral avoidance confidence level is recorded as w 3-2 The longitudinal severity (which may be the magnitude of the deceleration value) of the avoidance maneuver is recorded as event c, and the longitudinal avoidance confidence level is recorded as w 3-3 The second avoidance confidence includes a lateral avoidance confidence and a longitudinal avoidance confidence. Event confidence w corresponding to avoidance behavior detection system 3 ＝a×w 3-1 +b×w 3-2 +c×w 3-3 . The more the number of the vehicles with the avoidance actions, the greater the transverse intensity of the avoidance actions and/or the greater the longitudinal intensity of the avoidance actions, the higher the reliability of construction detection.
In an optional embodiment, the construction related data provided by the construction target detection system may be an original image, and the vehicle-mounted terminal may analyze the original image to identify whether a construction target, such as a cone or a barrel, exists in the image; the construction target object detection system can also directly determine whether the construction target object information exists in front according to the original image acquired by the construction target object detection system, so that the construction target object detection system can be used by the vehicle-mounted terminal. By the method, whether the road construction event exists in the road section to be detected or not and the corresponding event confidence coefficient are determined.
The event confidence coefficient can be data which comprehensively represents the confidence coefficient of the road construction event existing on the road section to be detected and is determined in real time when the high-precision map positioning system, the navigation map positioning system, the avoidance behavior detection system and the construction target object detection system determine whether the road construction event exists on the road section to be detected or not; but may be data set by a technician based on an empirical value. The specific determination mode of the event confidence coefficient is not limited, and only the numerical value of the event confidence coefficient is required to be ensured to be [0,1], wherein if the numerical value is larger, the higher the confidence coefficient is indicated; the smaller the value, the lower the reliability.
In a specific embodiment, when the target data source is a high-precision map positioning system and a navigation map positioning system, the sum of the event confidence coefficient determined by the high-precision map positioning system and the event confidence coefficient determined by the navigation map positioning system is 1, and the magnitude of the event confidence coefficient corresponding to each of the two systems is not limited; when the target data source is a high-precision map positioning system, a navigation map positioning system and an avoidance behavior detection system, the sum of the confidence degrees of the events respectively corresponding to the three systems is 1, and the confidence degrees of the events respectively corresponding to the three systems are not limited; when the target data source is a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system, the sum of the confidence degrees of the events corresponding to the four systems is 1, and the confidence degrees of the events corresponding to the four systems are not limited. Data determined in a particular manner may also be used, for example, the event confidence determined by the avoidance behavior detection system is determined by a weighted sum of a first avoidance confidence determined from the number of vehicles to be avoided and a second avoidance confidence determined from the avoidance degree data.
It should be noted that, when the sum of the confidence levels of the events corresponding to the target data sources is not 1, the activation function may be preset to perform activation processing on the confidence levels of the events of the target data sources, so as to update the confidence levels of the events of the target data sources, so that the sum of the updated confidence levels of the events of the target data sources is 1. The preset activation function may adopt at least one activation function in the prior art. Preferably, the preset activation function may be a softmax function.
Specifically, road construction events and event confidence degrees corresponding to different target data sources are determined according to different road construction events and event confidence degrees corresponding to the target data sources.
S240, determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources.
According to the embodiment of the disclosure, at least two target data sources are selected from at least two candidate data sources according to the distance between the current position of the vehicle and the road section to be detected; acquiring construction associated data of the road section to be detected, which is provided by each target data source; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; and determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources. According to the scheme, the construction associated data is obtained according to the distance difference between the current vehicle position and the road section to be detected, and when the candidate data source can provide relatively accurate construction associated data, the candidate data source is used as a target data source for obtaining the corresponding construction associated data; when the target data source cannot provide relatively accurate construction associated data, the candidate data source is forbidden to be used as the target data source, namely, the construction associated data is forbidden to be obtained from the corresponding candidate data source, so that the accuracy of the obtained construction associated data is improved, and meanwhile, the waste of data transmission bandwidth and calculation resources caused by the obtaining of the construction associated data with poor accuracy is avoided.
In the embodiments of the present disclosure, reference may be made to the description of the foregoing embodiments.
EXAMPLE III
Fig. 3 is a flowchart of a road construction detection method provided in the third embodiment of the present disclosure, and in this embodiment, based on the above embodiments, the operation of "determining the road construction condition of the road segment to be detected according to the road construction events and the event confidence levels corresponding to different target data sources" is further refined into "determining the road construction probability according to the road construction events and the event confidence levels corresponding to different target data sources; determining an intervention condition according to the distance between the current position of the vehicle and the road section to be detected; and (4) performing driving intervention on the current vehicle according to the road construction probability and the intervention condition so as to perfect the intervention mechanism when the construction condition exists in the road section to be detected. As shown in fig. 3, the method includes:
s310, selecting at least two target data sources from the at least two candidate data sources according to the distance between the current position of the vehicle and the road section to be detected.
And S320, acquiring construction associated data of the road section to be detected, which is provided by each target data source.
S330, respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources.
S340, determining road construction probability according to road construction events and event confidence degrees corresponding to different target data sources.
The road construction probability can be used for representing the possibility of road construction of the road section to be detected. The road construction probability may be determined by the road construction event and the event confidence. Optionally, weighted summation is performed through the road construction events and the confidence degrees of the corresponding events after the numerical values corresponding to different target data sources are quantized, so as to obtain the road construction probability. The larger the numerical value of the road construction probability is, the higher the possibility that the road construction section exists in the road section to be detected is; the smaller the numerical value of the road construction probability is, the smaller the possibility that the road construction section exists in the section to be detected is.
For example, when the target data source is a high-precision map positioning system and a navigation map positioning system, the road construction probability is determined according to the road construction event and the event confidence corresponding to different target data sources, and the determination may be: road construction probability Y ═ A 1 ×w 1 +A 2 ×w 2 . Wherein A is 1 Representing a corresponding road construction event when the target data source is a high-precision map positioning system; w is a 1 Representing the corresponding event confidence when the target data source is a high-precision map positioning system; a. the 2 Representing a corresponding road construction event when the target data source is a navigation map positioning system; w is a 2 And representing the corresponding event confidence when the target data source is a navigation map positioning system.
Illustratively, when the target data source is a high-precision map positioning system, a navigation map positioning system and an avoidance behavior detection system, the road construction probability is determined according to road construction events and event confidence degrees corresponding to different target data sources, and the method may further include: road construction probability Y ═ A 1 ×w 1 +A 2 ×w 2 +A 3 ×w 3 . Wherein A is 3 Representing a corresponding road construction event when the target data source is an avoidance behavior detection system; w is a 3 And representing the corresponding event confidence when the target data source is an avoidance behavior detection system.
Illustratively, when the target data source is a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system, and a construction target object detection system, the road construction probability is determined according to road construction events and event confidence degrees corresponding to different target data sources, and the method may further include: road construction probability Y ═ A 1 ×w 1 +A 2 ×w 2 +A 3 ×w 3 +A 4 ×w 4 . Wherein A is 4 Representing a corresponding road construction event when the target data source is a construction target object detection system; w is a 4 And representing the corresponding event confidence when the target data source is a construction target object detection system.
And S350, determining an intervention condition according to the distance between the current position of the vehicle and the road section to be detected.
Wherein the intervention condition may be used to characterize a minimum criterion to be met for intervention of the current vehicle. The intervention condition may be set according to a distance between the current vehicle and the road segment to be detected. Optionally, the intervention condition may be set by a technician according to experience, or may be set by a user according to his or her own habits. Alternatively, the number of intervention conditions may be at least one; alternatively, the kind of the intervention condition may be at least one.
Wherein the intervention condition may be generated based on the intervention threshold. The intervention threshold may be set or adjusted by a technician based on experience, or by a user based on his or her own habits.
Since the target data sources according to which the road construction probability is determined are different, the intervention threshold may be set according to the type and/or number of the target data sources according to. In view of the fact that the target data source is selected based on the distance between the position of the current vehicle and the road to be detected, the intervention threshold value of the intervention condition can be determined according to the distance between the current vehicle and the road section to be detected.
It can be understood that, because the distance between the current vehicle and the road section to be detected needs to be determined by combining the distance selection interval to which the distance belongs, the intervention threshold value in the intervention condition can be determined according to the distance belonging interval, so that the intervention can be performed in a differentiated manner under the condition that different distances between different current vehicles and the road section to be detected are different in the selection interval.
In a specific embodiment, if the distance between the current vehicle position and the road segment to be detected belongs to different preset distance ranges, the setting of the intervention threshold is different, and the corresponding intervention conditions generated based on the intervention threshold are also different. For example, a first intervention threshold may be set according to the first preset distance range, and a first intervention condition may be generated based on the first intervention threshold; a second intervention threshold value can be set according to the second preset distance range, and a second intervention condition is generated based on the second intervention threshold value; a third intervention threshold value may be set according to the aforementioned third preset distance range, and a third intervention condition may be generated based on the third intervention threshold value. The first intervention threshold, the second intervention threshold and the third intervention threshold may be used to characterize that an intervention condition may be generated based on the intervention thresholds when the distance between the current vehicle position and the road segment to be detected is within different preset distance ranges. In this embodiment, the sizes of the first intervention threshold, the second intervention threshold, and the third intervention threshold are not limited at all, and may be set or adjusted by a technician according to experience, or by a user according to their own habits. In particular, the first intervention threshold, the second intervention threshold and the third intervention threshold are of different magnitudes. The first intervention condition, the second intervention condition and the third intervention condition can be used for representing a minimum standard for intervention on the current vehicle when the distance between the current vehicle position and the road section to be detected is in different preset distance ranges. It is to be understood that the first intervention condition generated based on the first intervention threshold, the second intervention condition generated based on the second intervention threshold, and the third intervention condition generated based on the third intervention threshold are also different from each other.
In summary, different distance ranges correspond to different intervention conditions, i.e. the number of intervention conditions is at least one.
And S360, performing driving intervention on the current vehicle according to the road construction probability and the intervention condition.
Illustratively, if the road construction probability meets an intervention condition, driving intervention is carried out on the current vehicle; otherwise, the driving intervention of the current vehicle is prohibited.
Specifically, if the road construction probability is greater than an intervention threshold value in the intervention condition, driving intervention is performed on the current vehicle; otherwise, the driving intervention of the current vehicle is prohibited.
In an alternative embodiment, the intervention condition may comprise an on-air intervention condition.
The broadcasting intervention condition can be the minimum standard required to be reached for broadcasting reminding of the current vehicle. The broadcasting intervention condition can be set according to the type and/or the number of the target data sources, the distance between the current vehicle and the road section to be detected and the interval to which the distance between the current vehicle and the road section to be detected belongs. The setting of the broadcast intervention condition is not limited at all, and may be set by a technician according to experience or set by a user according to own habits.
Specifically, when the broadcasting intervention condition is met, broadcasting intervention is carried out on the current vehicle.
Specifically, the broadcasting intervention may include at least one of a method of reminding the driver of paying attention to the road ahead, a method of reminding the driver of holding a steering wheel, a method of prohibiting acceleration, and the like, which are capable of prompting. The broadcasting grade can be divided into primary reminding, secondary reminding and tertiary reminding. The first-level reminding can remind a driver of paying attention to a front road, the second-level reminding can remind the driver of paying attention to the front road and reminding the driver of holding a steering wheel, and the third-level reminding can remind the driver of paying attention to the front road and reminding the driver of holding the steering wheel and forbidding acceleration.
Optionally, when the current vehicle meets the broadcasting intervention condition, the road construction probability is compared with the intervention threshold value, and the broadcasting reminding level is determined.
Illustratively, performing on-air intervention on the current vehicle may include: if the distance between the current position of the vehicle and the road section to be detected is within the first preset distance range (such as (200m,500 m)), a first-level prompt is sent when the road construction probability is smaller than a first intervention threshold value, and a second-level prompt is sent when the road construction probability is larger than or equal to the first intervention threshold value.
Exemplarily, the performing of the broadcast intervention on the current vehicle may further include: if the distance between the current position of the vehicle and the road section to be detected is within the second preset distance range (such as (50m,200 m)), the second-level reminding is sent out when the road construction probability is smaller than the first intervention threshold value, and the third-level reminding is sent out when the road construction probability is larger than or equal to the first intervention threshold value and smaller than the second intervention threshold value.
Illustratively, performing on-air intervention on the current vehicle may further include: and if the distance between the current position of the vehicle and the road section to be detected is within the third preset distance range (such as (0,50 m)), sending out a secondary prompt when the road construction probability is smaller than the first intervention threshold, and sending out a tertiary prompt when the road construction probability is larger than or equal to the first intervention threshold and smaller than the second intervention threshold.
It should be noted that, in this embodiment, the number of times of performing the broadcast intervention reminding on the current vehicle is not limited at all, and may be set or adjusted by a technician according to experience, or set or adjusted by a user according to own habits.
In another alternative embodiment, the intervention condition may comprise a driving intervention condition.
The driving intervention condition may be a minimum standard required for driving intervention of the current vehicle. The driving intervention condition can be set according to the type and/or the number of the target data sources, the distance between the current vehicle and the road section to be detected, and the section to which the distance between the current vehicle and the road section to be detected belongs. The setting of the driving intervention condition in this embodiment is not limited at all, and may be set by a technician according to experience, or may be set by a user according to own habits.
Specifically, when the driving intervention condition is satisfied, the driving intervention is performed on the current vehicle.
The driving intervention may be at least one of comfort deceleration, lane change avoidance, emergency deceleration, and the like. The driving intervention level can be judged according to the surrounding environment of the current vehicle and/or the distance between the current vehicle and the construction scene and is divided into primary driving intervention and secondary driving intervention. The surrounding environment of the current vehicle may include, but is not limited to, a driving speed of the current vehicle, a distance between the current vehicle and a preceding vehicle and/or a following vehicle, a traffic flow on a lane to be avoided by lane changing, a vehicle speed, and the like. Specifically, the primary driving intervention may include comfortable deceleration or lane change avoidance, and the secondary driving intervention may include emergency deceleration or lane change avoidance.
The comfortable deceleration or lane change avoidance may be determined according to the current surrounding environment of the vehicle and/or the distance from the construction scene, and an appropriate deceleration or lane change acceleration is adopted, which is not limited in this embodiment. For example, the deceleration does not exceed 2m/s 2 Acceleration of lane changing is not more than 2m/s 2 。
The emergency deceleration or lane change avoidance may adopt an appropriate deceleration or lane change acceleration according to the current surrounding environment of the vehicle and/or the distance from the construction scene, which is not limited in this embodiment. For example, the deceleration exceeds 2m/s 2 Acceleration of lane change exceeding 2m/s 2 . It should be noted that the deceleration or lane change acceleration is assumed on the premise that the current vehicle is prevented from colliding with the surrounding vehicle and the current vehicle is not out of control.
Optionally, when the current vehicle meets the driving intervention condition, the road construction probability is compared with the intervention threshold value, and the driving intervention level is determined.
For example, the driving intervention on the current vehicle may include: and if the distance between the current vehicle position and the road section to be detected belongs to the second preset distance range (such as (50m,200 m)), performing primary driving intervention when the road construction probability is greater than or equal to a second intervention threshold value.
For example, the driving intervention on the current vehicle may further include: and if the distance between the current position of the vehicle and the road section to be detected is within the third preset distance range (such as (0,50 m)), performing primary driving intervention when the road construction probability is greater than or equal to the second intervention threshold and smaller than the third intervention threshold, and performing secondary driving intervention when the road construction probability is greater than or equal to the third intervention threshold.
In another alternative embodiment, the intervention condition may include a broadcast intervention condition and a driving intervention condition.
For example, intervening on the current vehicle may include: if the distance between the position of the current vehicle and the road section to be detected is within the second preset distance range (e.g., (50m,200 m)), when the road construction probability is greater than or equal to the first intervention threshold, intervening the current vehicle may include sending a third-level prompt and/or performing first-level driving intervening on the current vehicle.
For example, intervening on the current vehicle may further include: if the distance between the position of the current vehicle and the road section to be detected is within the third preset distance range (e.g., (50m,200 m)), when the road construction probability is greater than or equal to the first intervention threshold, intervening the current vehicle may include at least one of sending a third-level prompt, performing first-level driving intervening on the current vehicle, and performing second-level driving intervening on the current vehicle.
In the embodiment, through hierarchical intervention, the intervention information is more pertinent and better accepted by the user.
Specifically, when the intervention condition is met, the road construction probability is compared with an intervention threshold value, and the current vehicle is intervened.
According to the distance between the current position of the vehicle and the road section to be detected, at least two target data sources are selected from at least two candidate data sources; acquiring construction associated data of the road section to be detected, which is provided by each target data source; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; determining road construction probability according to road construction events and event confidence degrees corresponding to different target data sources; determining an intervention condition according to the distance between the current position of the vehicle and the road section to be detected; and driving intervention is carried out on the current vehicle according to the road construction probability and the intervention condition. According to the scheme, based on the identification of different target data sources for road construction, the probability of the front construction is comprehensively judged, the construction scene is responded by grading distance, the distance and the accuracy of the vehicle reacting to the construction scene are improved, and the probability of misoperation is reduced.
In the embodiments of the present disclosure, reference may be made to the description of the foregoing embodiments.
Example four
Fig. 4 is a schematic structural diagram of a road construction detection device provided in the fourth embodiment of the present disclosure. As shown in fig. 4, the road construction detecting device includes: a construction-related data acquisition module 410, a construction event determination module 420, and a road construction situation determination module 430.
The construction associated data acquisition module 410 is configured to acquire construction associated data of a road segment to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system;
the construction event determining module 420 is configured to determine, according to construction associated data of different target data sources, a road construction event and an event confidence corresponding to each target data source respectively;
the road construction condition determining module 430 is configured to determine the road construction condition of the road segment to be detected according to the road construction event and the event confidence corresponding to different target data sources.
The construction associated data of the road section to be detected, which are provided by different target data sources, are obtained; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system; respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources; and determining the road construction condition of the road section to be detected according to the road construction events and the event confidence degrees corresponding to different target data sources. According to the scheme, the construction condition of the road section to be detected is comprehensively judged by using the construction associated data acquired by different target data sources, so that the condition that the accuracy of data provided by a single target data source is not high is avoided, and the accuracy of the determination result of the road construction condition is improved.
In an optional embodiment, the construction-related data obtaining module 410 includes:
the target data source selection unit is used for selecting at least two target data sources from at least two candidate data sources according to the distance between the current position of the vehicle and the road section to be detected;
and the construction associated data unit is used for acquiring construction associated data of the road section to be detected, which is provided by each target data source.
In an optional embodiment, the construction association obtaining module 410 includes:
the avoidance reference data storage unit is used for storing the construction associated data if the target data source comprises the avoidance behavior detection system;
the construction event determining module 420 includes an event confidence determining unit, configured to correspondingly determine event confidence corresponding to each target data source according to construction associated data of different target data sources;
and the event confidence coefficient determining unit comprises an event confidence coefficient determining unit corresponding to the avoidance behavior detection system and is used for determining the event confidence coefficient corresponding to the avoidance behavior detection system according to the avoidance reference data.
In an alternative embodiment, the avoidance reference data includes data of a number of avoided vehicles and/or a degree of avoidance, and the event confidence determining unit includes:
the first avoidance confidence determining subunit is used for determining a first avoidance confidence according to the number of the avoided vehicles;
the second avoidance confidence determining subunit is used for determining a second avoidance confidence according to the avoidance degree data;
and the event confidence degree determining subunit is used for determining an event confidence degree corresponding to the avoidance behavior detection system according to the first avoidance confidence degree and/or the second avoidance confidence degree.
In an alternative embodiment, the avoidance degree data includes lateral avoidance degree data and/or longitudinal avoidance degree data, and the second avoidance confidence determining subunit includes:
and the second avoidance confidence determining slave unit is used for determining a second avoidance confidence according to the transverse avoidance degree data and/or the longitudinal avoidance degree data.
In an alternative embodiment, the road construction situation determination module 430 includes:
the road construction probability determining unit is used for determining the road construction probability according to road construction events and event confidence degrees corresponding to different target data sources;
the intervention condition determining unit is used for determining an intervention condition according to the distance between the current position of the vehicle and the road section to be detected;
and the driving intervention unit is used for driving intervention on the current vehicle according to the road construction probability and the intervention condition.
In an alternative embodiment, the intervention condition comprises an announcement intervention condition and a driving intervention condition, and the driving intervention unit comprises:
the broadcasting reminding unit is used for carrying out broadcasting reminding according to the broadcasting grade corresponding to the broadcasting intervention condition if the road construction probability meets the broadcasting intervention condition;
and the driving control unit is used for controlling the current vehicle to drive according to the driving grade corresponding to the driving intervention condition if the road construction probability meets the driving intervention condition.
The road construction detection device can execute the road construction detection method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of executing each road construction detection method.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related construction related data all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a vehicle-mounted terminal of a road construction detection method provided in the fifth embodiment of the present disclosure. In-vehicle terminal 50 is 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 in-vehicle terminal may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), 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. 5, the in-vehicle terminal 50 includes at least one processor 51, and a memory communicatively connected to the at least one processor 51, such as a Read Only Memory (ROM)52, a Random Access Memory (RAM)53, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 51 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM)52 or the computer program loaded from the storage unit 58 into the Random Access Memory (RAM) 53. In the RAM53, various programs and data necessary for the operation of the in-vehicle terminal 50 can also be stored. The processor 51, ROM52, and RAM53 are connected to each other by a bus 54. An input/output (I/O) interface 55 is also connected to bus 54.
Various components in the in-vehicle terminal 50 are connected to the I/O interface 55, including: an input unit 56 such as a keyboard, a mouse, or the like; an output unit 57 such as various types of displays, speakers, and the like; a storage unit 58 such as a magnetic disk, an optical disk, or the like; and a communication unit 59 such as a network card, modem, wireless communication transceiver, etc. The communication unit 59 allows the in-vehicle terminal 50 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The processor 51 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processors 51 include, but are not limited to, Central Processing Units (CPUs), Graphics Processing Units (GPUs), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, Digital Signal Processors (DSPs), and any suitable processors, controllers, microcontrollers, and the like. The processor 51 performs the various methods and processes described above, such as a road construction detection method.
In some embodiments, the road construction detection method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and/or installed on the in-vehicle terminal 50 via the ROM52 and/or the communication unit 59. When the computer program is loaded into RAM53 and executed by processor 51, one or more steps of the road construction detection method described above may be performed. Alternatively, in other embodiments, the processor 51 may be configured to perform the road construction detection 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 circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load 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. A computer program for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of this disclosure, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 may be implemented on a vehicle terminal 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 in-vehicle terminal. 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), blockchain networks, and the internet.
The computing 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 that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
In an alternative embodiment, the present disclosure also provides a vehicle that may be provided with an in-vehicle terminal as shown in fig. 5.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions of the present disclosure can be achieved.
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 in accordance with 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 (11)

1. A road construction detection method is characterized by comprising the following steps:
acquiring construction associated data of a road section to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system;
respectively determining road construction events and event confidence degrees corresponding to the target data sources according to construction associated data of different target data sources;
and determining the road construction condition of the road section to be detected according to the road construction events corresponding to different target data sources and the event confidence coefficients.
2. The method according to claim 1, wherein the obtaining of the construction related data of the road section to be detected, which is provided by different target data sources, comprises:
selecting at least two target data sources from at least two candidate data sources according to the distance between the current position of the vehicle and the road section to be detected;
and acquiring construction associated data of the road section to be detected, which is provided by each target data source.
3. The method of claim 2, wherein the construction-related data includes avoidance reference data if the target data source includes the avoidance behavior detection system;
correspondingly, respectively determining the event confidence corresponding to each target data source according to the construction associated data of different target data sources, including:
and determining an event confidence corresponding to the avoidance behavior detection system according to the avoidance reference data.
4. The method according to claim 3, wherein the avoidance reference data includes data on the number of vehicles to be avoided and/or the degree of avoidance;
correspondingly, the determining an event confidence corresponding to the avoidance behavior detection system according to the avoidance reference data includes:
determining a first avoidance confidence coefficient according to the number of the avoided vehicles;
determining a second avoidance confidence level according to the avoidance degree data;
and determining an event confidence coefficient corresponding to the avoidance behavior detection system according to the first avoidance confidence coefficient and/or the second avoidance confidence coefficient.
5. The method according to claim 4, wherein the avoidance degree data comprises lateral avoidance degree data and/or longitudinal avoidance degree data;
correspondingly, the determining a second avoidance confidence level according to the avoidance degree data includes:
and determining the second avoidance confidence level according to the transverse avoidance degree data and/or the longitudinal avoidance degree data.
6. The method according to any one of claims 2 to 5, wherein the determining the road construction condition of the road segment to be detected according to the road construction event and the event confidence corresponding to different target data sources comprises:
determining road construction probability according to the road construction events corresponding to different target data sources and the event confidence coefficients;
determining an intervention condition according to the distance between the position of the current vehicle and the road section to be detected;
and driving intervention is carried out on the current vehicle according to the road construction probability and the intervention condition.
7. The method of claim 6, wherein the intervention condition comprises a broadcast intervention condition and a driving intervention condition;
correspondingly, the driving intervention on the current vehicle according to the road construction probability and the intervention condition comprises the following steps:
if the road construction probability meets the broadcasting intervention condition, broadcasting reminding is carried out according to the broadcasting grade corresponding to the broadcasting intervention condition;
and if the road construction probability meets the driving intervention condition, driving control is carried out on the current vehicle according to the driving grade corresponding to the driving intervention condition.
8. The utility model provides a road construction detection device which characterized in that includes:
the construction associated data acquisition module is used for acquiring construction associated data of the road section to be detected, which are provided by different target data sources; the target data source comprises at least one of a high-precision map positioning system, a navigation map positioning system, an avoidance behavior detection system and a construction target object detection system;
the construction event determining module is used for respectively determining road construction events and event confidence coefficients corresponding to the target data sources according to construction associated data of different target data sources;
and the road construction condition determining module is used for determining the road construction condition of the road section to be detected according to the road construction events corresponding to different target data sources and the event confidence coefficients.
9. A vehicle-mounted terminal characterized by comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of road construction detection as claimed in any one of claims 1-7.
10. A vehicle characterized in that the vehicle is provided with the in-vehicle terminal of claim 9.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a road construction detection method according to any one of claims 1 to 7.
CN202210645897.8A 2022-06-08 2022-06-08 Road construction detection method, device, vehicle-mounted terminal, vehicle and medium Pending CN115063974A (en)

Priority Applications (1)

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CN110888358A (en) * 2019-11-25 2020-03-17 中国十九冶集团有限公司 Road construction detection system and method
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