CN115240450A - Intelligent traffic data acquisition equipment and method - Google Patents
Intelligent traffic data acquisition equipment and method Download PDFInfo
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- CN115240450A CN115240450A CN202210828425.6A CN202210828425A CN115240450A CN 115240450 A CN115240450 A CN 115240450A CN 202210828425 A CN202210828425 A CN 202210828425A CN 115240450 A CN115240450 A CN 115240450A
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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
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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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
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
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- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
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Abstract
The embodiment of the invention discloses intelligent traffic data acquisition equipment and a method, wherein the intelligent traffic data acquisition equipment comprises the following steps: unmanned aerial vehicle surveys module and vehicle; the unmanned aerial vehicle detection module is connected with the vehicle and used for receiving a control command sent by the vehicle and executing detection of an area corresponding to the control command; the vehicle is used for determining the current driving scene of the vehicle; determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; based on the detection result, a driving strategy is determined, wherein the driving strategy can be used for commanding the vehicle to perform intelligent driving. Therefore, the reference of driving behaviors can be realized more accurately, and the safety and the reliability of unmanned driving are facilitated.
Description
Technical Field
The invention relates to the technical field of vehicle intelligent control, in particular to intelligent traffic data acquisition equipment and method.
Background
With the rapid development of intelligent transportation technology, advanced information technology, data communication technology, sensor technology, electronic control technology, computer technology and the like are effectively and comprehensively applied to the whole transportation management system, so that a comprehensive transportation and management system which can play a role in a large range and all directions, and is real-time, accurate and efficient can be established. However, only depending on the existing monitoring deployment and electronic control technology, there is a possibility that the driving control cannot be accurately performed when there is an error in partial monitoring or data, so that the safety and reliability of driving cannot be guaranteed. Therefore, how to realize more accurate driving control and ensure the driving safety and reliability becomes a technical problem which needs to be solved urgently.
Disclosure of Invention
In order to solve the existing technical problems, the embodiment of the invention provides intelligent traffic data acquisition equipment and method.
In order to achieve the above purpose, the technical solution of the embodiment of the present invention is realized as follows:
the embodiment of the invention provides intelligent traffic data acquisition equipment, which comprises: unmanned aerial vehicle surveys module and vehicle;
the unmanned aerial vehicle detection module is connected with the vehicle and used for receiving a control command sent by the vehicle and executing detection of an area corresponding to the control command;
the vehicle is used for determining the current driving scene of the vehicle; determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; based on the detection result, a driving strategy is determined, wherein the driving strategy can be used for commanding the vehicle to perform intelligent driving.
Preferably, the vehicle is further adapted to at least one of:
according to the driving scene, if the driving scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the congestion time is greater than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset dispensing condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
Preferably, the vehicle is further configured to:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has terrain abnormality.
Preferably, the vehicle is further adapted to at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time length threshold and/or the preset interval time length according to the weather information;
and determining the landform information, and determining the time threshold and/or the preset interval time according to the landform information.
Preferably, the vehicle is further configured to:
and determining traffic light information of the current running road section, and determining the time length threshold value according to the traffic light information.
The embodiment of the invention also provides an intelligent traffic data acquisition method, which is applied to a vehicle, and the method comprises the following steps:
determining a current driving scene of the vehicle;
determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene;
if the preset conditions are met, sending a control instruction to an unmanned aerial vehicle detection module connected with the vehicle, wherein the control instruction is used for controlling the unmanned aerial vehicle detection module to detect the corresponding area;
and receiving a detection result sent by the unmanned aerial vehicle detection module, and determining a driving strategy, wherein the driving strategy can be used for commanding the vehicle to intelligently drive.
Preferably, the determining whether the preset condition for calling the unmanned aerial vehicle detection module to detect is met according to the driving scene includes at least one of the following:
according to the driving scene, if the driving scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined to be met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle to detect is determined not to be met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
Preferably, the method further comprises at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time threshold and/or the preset interval time according to the weather information;
and determining the landform information, and determining the time threshold and/or the preset interval time according to the landform information.
Preferably, the method further comprises:
and determining traffic light information of the current running road section, and determining the time threshold according to the traffic light information.
An embodiment of the present invention further provides a vehicle, including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is configured to implement the intelligent traffic data collection method applied to a vehicle as described above when the computer program is run by the processor.
An embodiment of the present invention further provides a computer storage medium, including: when the executable program is executed by the processor, the intelligent traffic data acquisition method applied to the vehicle is realized.
The intelligent traffic data acquisition equipment provided by the embodiment comprises an unmanned aerial vehicle detection module and a vehicle, wherein the unmanned aerial vehicle detection module is connected with the vehicle and used for receiving a control command sent by the vehicle and executing detection of a region corresponding to the control command; the vehicle is used for determining the current driving scene of the vehicle; determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene; if the preset conditions are met, sending a control instruction, and receiving a detection result detected by the unmanned aerial vehicle detection module; based on the detection results, a driving strategy is determined, wherein the driving strategy can be used for commanding the vehicle to carry out intelligent driving. Based on this, with the assistance of the unmanned aerial vehicle detection module, the embodiment of the invention can inform the vehicle of the detection result detected by the unmanned aerial vehicle detection module under the condition that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, so that the vehicle can determine how to command the vehicle to intelligently run according to the detection result. Therefore, the vehicle can be controlled more accurately based on the detection result of the unmanned aerial vehicle detection module, so that the driving control is more accurately realized, and the driving safety and reliability are guaranteed.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent traffic data acquisition device according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a smart traffic data collection method according to an embodiment of the present invention;
fig. 3 is a functional block diagram of an intelligent traffic data collecting device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a vehicle according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The technical solution of the present invention is further described in detail below with reference to the drawings and the specific embodiments of the present invention.
Fig. 1 is a view illustrating an intelligent traffic data collecting device according to an embodiment of the present invention, as shown in fig. 1, the intelligent traffic data collecting device 1 includes: an unmanned aerial vehicle detection module 10 and a vehicle 11;
the unmanned aerial vehicle detection module 10 is connected with the vehicle 11, and is used for receiving a control instruction sent by the vehicle 11 and executing detection of an area corresponding to the control instruction;
the vehicle 11 is used for determining the current driving scene of the vehicle; determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; based on the detection result, a driving strategy is determined, wherein the driving strategy can be used for commanding the vehicle to perform intelligent driving.
The unmanned aerial vehicle detection module 10 can be mounted on a vehicle 11; or may be unmanned aerial vehicle supply stations installed along the road. It should be noted that the drone supply station is used to store the drone detection module 10 that can be used for sharing, wherein the usage method of using the drone detection module 10 that can be used for sharing is similar to the usage method of the existing shared automobile or shared bicycle. What need supplement is that the unmanned aerial vehicle feed station can be every preset distance on the traffic route and deploy one, so, make things convenient for vehicle 11 to call the unmanned aerial vehicle of the nearest unmanned aerial vehicle feed station in position with the present position to survey module 10 according to the present position.
Here, the drone detection module 10, which may be a drone device, can be used to take a photograph of a specific area in the air.
The vehicle here may be an automobile or an electric vehicle or the like that travels on a public road. A vehicle is here understood to be a vehicle with a processing module, which can be used to process some computer program.
The driving scene includes, but is not limited to, driving road condition information, driving surrounding environment information, and the like. The road condition information here may be, for example, a congestion condition of a road, a topographic condition of a road, or the like; the traveling environment information may be, for example, the wet condition of the road. So, through the scene of traveling, confirm whether satisfy the unmanned aerial vehicle and survey the preset condition that the module surveyed to when satisfying preset condition, carry out the detection of unmanned aerial vehicle detection module, and assist to carry out intelligent driving based on the probe result, can ensure intelligent driving's security and reliability.
In some scenarios, if the vehicle is congested on a road, the vehicle cannot determine whether the front is congested due to traffic lights and the like or due to traffic accidents occurring in the front because the vehicle is long. If the congestion cause is known in advance, the vehicle can not get out of the way to change a new driving route as the vehicle approaches the congested area, and thus, the current situation of traffic jam can be caused. However, in the prior art, only the current situation of traffic congestion is presented to a navigation system, and the reason of the congestion is not notified, even if the current situation is notified, broadcasting may be performed through a radio station, and then if the radio station cannot timely grasp the true reason of the congestion, or if people do not listen to the radio station, especially for some unmanned vehicles, the reason of the congestion cannot be grasped in real time, so that the adjustment of the driving strategy cannot be made in time.
For another example, in some scenarios, especially for unmanned vehicles, such as vehicles that assist driving with infrared detection, the specific type of obstacle ahead cannot be known, which may result in some unnecessary route modifications for unmanned driving, for example, if the obstacle ahead is just a soft obstacle, the unmanned vehicle may normally pass through, but if the obstacle height may exceed the passing height of the unmanned vehicle in a natural state, if the obstacle ahead is not clearly identified, the vehicle may turn around or change the route. And through the detection of unmanned aerial vehicle detection module, after discerning the image information who surveys via neural network recognition model, can more accurately obtain the specific type of place ahead barrier to can reduce unnecessary route conversion.
For another example, in some scenes, in a route selected based on the existing big data statistics, if a deep pit or the like occurs on a road, an unnecessary traffic accident can be caused if a traffic big data system cannot be reported in time.
Based on this, in this embodiment, the vehicle may determine a current driving scene of the vehicle; determining whether preset conditions for calling the unmanned aerial vehicle detection module to detect are met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; determining a driving strategy based on the detection result, wherein the driving strategy can be used for commanding the vehicle to carry out intelligent driving; therefore, the unmanned aerial vehicle detection module can be utilized to detect the region which is required to be detected, for example, the congestion region is obtained, so that the reason of congestion is accurately known, the vehicle can be further assisted to carry out more accurate intelligent control, more accurate driving control is realized, and the safety and the reliability of the vehicle, especially the unmanned vehicle are guaranteed.
In some embodiments, the vehicle is further configured to at least one of:
according to the driving scene, if the driving scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined to be met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined not to be met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
Here, the time length threshold may be a time length defined in a specific situation, for example, a time length for changing the normal traffic light into the light; or the waiting time indicated by the link history traveling data. Always, if the congestion time exceeds the waiting time, the fact that the congestion in front is abnormal is indicated, and based on the condition, the unmanned aerial vehicle detection module is called, so that the reason of the congestion can be timely distinguished, and the driving strategy is timely adjusted.
The preset interval period may be set in advance according to different road conditions or driving requirements of the vehicle. It can be understood that the shorter the preset interval, the more accurate the obtained road information is, but the greater the resource consumption is. Therefore, a predetermined time interval is set for balancing the precise control and the resource consumption, for example, a half hour is detected.
Here, if the topography is unusual, also can regard as the condition of transferring unmanned aerial vehicle to survey the module to help the vehicle to accomplish intelligent driving better.
Therefore, through different calls the unmanned aerial vehicle detection module is set for the preset conditions for detection, the unmanned aerial vehicle detection module can be called only when the conditions of the same type or several types of conditions are met, and resource consumption can be saved.
Further, in some embodiments, the vehicle may be further configured to call the preset condition for the unmanned aerial vehicle detection module to perform detection in a comprehensive consideration manner in combination with the traffic condition information fed back by the traffic big data system.
Here, the traffic condition information fed back by the traffic big data system may be understood as traffic condition information of the existing big data statistics.
Therefore, the problem that the prediction is inaccurate due to the fact that the existing traffic big data system executes unmanned driving or intelligent driving can be solved, the traffic tool is helped to realize more accurate driving control, and safety and reliability of intelligent driving are guaranteed.
Certainly, in order to obtain the length of time threshold value more accurately, reduce the unnecessary unmanned aerial vehicle and survey the call of module, reduce the waste of resource. In some embodiments, the vehicle is further configured to at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time length threshold and/or the preset interval time length according to the weather information;
and determining the landform information, and determining the time length threshold and/or the preset interval time length according to the landform information.
Here, the historical travel data may be historical travel data of each vehicle of the current travel section, such as a speed of travel, a direction of travel, and the like. The current traveling data may be current traveling data of the vehicle, such as current traveling data, traveling direction, and the like. Therefore, according to the analysis of the data, whether the terrain abnormality exists in the current driving road section can be determined, and the unmanned aerial vehicle detection module can be called in the whole course to assist in driving detection and driving early warning for some abnormal terrains, so that the intelligent driving safety on the abnormal terrains is ensured.
Here, the weather information may be precipitation information in a current preset time period, or the like. It can be understood that, in the case of heavy rainfall, the driving speed of the vehicle may be slower than that in clear weather, and therefore, the duration threshold value may also need to be adjusted accordingly, for example, the duration of waiting for the traffic light may also be increased, so that in this embodiment, the duration threshold value may be increased to reduce unnecessary detection. Of course, if under the not good condition of weather, the possibility that causes the traffic accident also can increase, consequently, under this kind of condition, can be long through reducing the preset interval, through improving the detection frequency who calls unmanned aerial vehicle detection module promptly and reduce the emergence of traffic accident.
Here, the topographic information may be, for example, a flat land or a mountain land. It can be understood that, because landforms such as mountain region are complicated, the probability that traffic accident appears also has many improvements, consequently, can set for the different predetermined condition of calling unmanned aerial vehicle detection module through different landform information to can be favorable to the security to the intelligent driving of complicated landform information.
In some embodiments, the vehicle is further configured to:
and determining traffic light information of the current running road section, and determining the time length threshold value according to the traffic light information.
It can be understood that, if the number of the traffic lights on the current driving road section is larger, the light changing speed of the traffic lights is slower, the possibility of causing traffic jam is higher, and the time length of the traffic jam is longer. Therefore, through the traffic light information of confirming the current highway section of traveling, confirm the condition of the time of blocking up that is used for judging whether to call unmanned aerial vehicle detection module, confirm time threshold value promptly, can call unmanned aerial vehicle detection module more accurately, guarantee intelligent driving's security and reliability.
Based on this, in the above-mentioned embodiment, through the preset condition that dynamic adjustment called unmanned aerial vehicle and surveyed the module, can deal with various traffic situation to the security that resource consumption and guarantee intelligent driving have been balanced better.
It should be added that the above embodiments can be implemented independently or in any combination, and are not limited herein.
Referring to fig. 2, fig. 3 is a schematic flow chart of a smart traffic data collection method according to an embodiment of the present invention, as shown in fig. 2, the method applied to a vehicle includes:
step 201: determining a current driving scene of the vehicle;
step 202: determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene;
step 203: if the preset conditions are met, sending a control instruction to an unmanned aerial vehicle detection module connected with the vehicle, wherein the control instruction is used for controlling the unmanned aerial vehicle detection module to detect the corresponding area;
step 204: and receiving a detection result sent by the unmanned aerial vehicle detection module, and determining a driving strategy, wherein the driving strategy can be used for commanding the vehicle to intelligently drive.
In the embodiment, the vehicle determines the current driving scene of the vehicle; determining whether preset conditions for calling the unmanned aerial vehicle detection module to detect are met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; determining a driving strategy based on the detection result, wherein the driving strategy can be used for commanding the vehicle to carry out intelligent driving; therefore, the unmanned aerial vehicle detection module can be utilized to detect the region which is required to be detected, for example, the congestion region is obtained, so that the reason of congestion is accurately known, the vehicle can be further assisted to carry out more accurate intelligent control, more accurate driving control is realized, and the safety and the reliability of the vehicle, especially the unmanned vehicle are guaranteed.
In other embodiments, the determining, according to the driving scenario, whether a preset condition for invoking the drone detecting module to detect is met includes at least one of:
according to the running scene, if the running scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the running scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
In other embodiments, the method further comprises at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time threshold and/or the preset interval time according to the weather information;
and determining the landform information, and determining the time length threshold and/or the preset interval time length according to the landform information.
In other embodiments, the method further comprises:
and determining traffic light information of the current running road section, and determining the time length threshold value according to the traffic light information.
Here, it should be noted that: the description of the intelligent traffic data acquisition method item is similar to that of the intelligent traffic data acquisition equipment item, and the description of the beneficial effects of the intelligent traffic data acquisition equipment item is not repeated. For the technical details not disclosed in the embodiment of the intelligent traffic data collecting method of the present invention, please refer to the description of the embodiment of the intelligent traffic data collecting apparatus of the present invention.
Fig. 3 is a functional block diagram of an intelligent traffic data collecting apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus includes:
a first determining module 31, configured to determine a current driving scenario of the vehicle;
a second determining module 32, configured to determine whether a preset condition for invoking the unmanned aerial vehicle detection module to perform detection is met according to the driving scene;
the sending module 33 is configured to send a control instruction to the unmanned detection module connected to the vehicle if the preset condition is met, where the control instruction is used to control the unmanned detection module to perform detection on a corresponding area;
and the receiving module 34 is configured to receive the detection result sent by the unmanned detection module, and determine a driving strategy, where the driving strategy can be used to instruct the vehicle to perform intelligent driving.
In some embodiments, the second determining module 32 is further configured to at least one of:
according to the driving scene, if the driving scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined to be met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined not to be met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
In some embodiments, the apparatus further comprises at least one of:
the third determination module is used for determining historical driving data of a current driving road section and current driving data of the current driving road section and determining whether the current driving road section has terrain abnormality;
the fourth determining module is used for determining weather information and determining the time threshold and/or the preset interval time according to the weather information;
and the fifth determining module is used for determining the landform information and determining the time threshold and/or the preset interval time according to the landform information.
In some embodiments, the apparatus further comprises:
and the sixth determining module is used for determining traffic light information of the current running road section and determining the time length threshold value according to the traffic light information.
Here, it should be noted that: the description of the intelligent traffic data acquisition device item is similar to that of the intelligent traffic data acquisition method item applied to the vehicle, and the description of the beneficial effects of the method is not repeated.
As shown in fig. 4, embodiments of the present invention also provide a vehicle comprising a memory 42, a processor 41, and computer instructions stored on the memory 42 and executable on the processor 41; the processor 71, when executing the instructions, implements the steps of the intelligent traffic data collection method applied to the vehicle.
In some embodiments, memory 42 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), enhanced Synchronous SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 42 of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
And processor 41 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 41. The Processor 41 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 42, and the processor 41 reads the information in the memory 42 and performs the steps of the above method in combination with the hardware thereof.
In some embodiments, the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Yet another embodiment of the present invention provides a computer storage medium, which stores an executable program, and when the executable program is executed by the processor 41, the steps of the intelligent traffic data collection method as described in fig. 2 can be implemented.
In some embodiments, the computer storage medium may include: a U-disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: the technical schemes described in the embodiments of the present invention can be combined arbitrarily without conflict.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An intelligent traffic data collection device, the device comprising: unmanned aerial vehicle surveys module and vehicle;
the unmanned aerial vehicle detection module is connected with the vehicle and used for receiving a control instruction sent by the vehicle and executing detection of an area corresponding to the control instruction;
the vehicle is used for determining the current driving scene of the vehicle; determining whether preset conditions for calling the unmanned aerial vehicle detection module to detect are met or not according to the driving scene; if the preset condition is met, sending a control instruction; receiving a detection result detected by the unmanned aerial vehicle detection module based on the control instruction; based on the detection result, a driving strategy is determined, wherein the driving strategy can be used for commanding the vehicle to perform intelligent driving.
2. The apparatus of claim 1, wherein the vehicle is further configured to at least one of:
according to the running scene, if the running scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the running scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined to be met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is determined not to be met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
3. The apparatus of claim 2, wherein the vehicle is further configured to at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time threshold and/or the preset interval time according to the weather information;
and determining the landform information, and determining the time length threshold and/or the preset interval time length according to the landform information.
4. The apparatus of claim 2 or 3, wherein the vehicle is further configured to:
and determining traffic light information of the current running road section, and determining the time length threshold value according to the traffic light information.
5. An intelligent traffic data acquisition method applied to a vehicle, the method comprising:
determining a current driving scene of the vehicle;
determining whether a preset condition for calling the unmanned aerial vehicle detection module to detect is met or not according to the driving scene;
if the preset conditions are met, sending a control instruction to an unmanned aerial vehicle detection module connected with the vehicle, wherein the control instruction is used for controlling the unmanned aerial vehicle detection module to detect the corresponding area;
and receiving a detection result sent by the unmanned aerial vehicle detection module, and determining a driving strategy, wherein the driving strategy can be used for commanding the vehicle to intelligently drive.
6. The method according to claim 5, wherein the determining whether the preset condition for invoking the UAV detection module for detection is met according to the driving scenario comprises at least one of:
according to the running scene, if the running scene indicates that the congestion time is greater than a time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the running scene indicates that the congestion time is less than or equal to the time threshold, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the terrain is abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the terrain is not abnormal, the preset condition for calling the unmanned aerial vehicle detection module to detect is not met;
according to the driving scene, if the driving scene indicates that the detection time interval is greater than or equal to the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is met, or if the driving scene indicates that the detection time interval is less than the preset interval duration, determining that the preset condition for calling the unmanned aerial vehicle detection module to detect is not met.
7. The method of claim 6, further comprising at least one of:
determining historical driving data of a current driving road section and current driving data of the current driving road section, and determining whether the current driving road section has abnormal terrain;
determining weather information, and determining the time threshold and/or the preset interval time according to the weather information;
and determining the landform information, and determining the time threshold and/or the preset interval time according to the landform information.
8. The apparatus of claim 6 or 7, wherein the method further comprises:
and determining traffic light information of the current running road section, and determining the time length threshold value according to the traffic light information.
9. A vehicle, comprising: a processor and a memory for storing a computer program operable on the processor, wherein the processor is configured to implement the intelligent traffic data collection method of any one of claims 5 to 8 when the computer program is run.
10. A computer storage medium, comprising: the executable program, when executed by a processor, implements the intelligent traffic data collection method according to any one of claims 5 to 8.
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Application publication date: 20221025 |