CN114413924A - Vehicle danger avoiding method and system based on Internet of vehicles and readable storage medium - Google Patents
Vehicle danger avoiding method and system based on Internet of vehicles and readable storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent vehicle networks, in particular to a vehicle risk avoiding method and system based on the Internet of vehicles and a readable storage medium. The method comprises the following steps: acquiring disaster source information in a target area; calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area; when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, and acquiring position information of the corresponding danger-avoiding place according to the type of the disaster; and acquiring vehicle position information of each networked vehicle in the target area, and calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, so that the corresponding networked vehicle can complete danger avoiding based on the corresponding danger avoiding planning information. The invention also discloses a vehicle risk avoiding system and a readable storage medium. The vehicle risk avoiding method can realize regional multi-vehicle risk avoiding and can be applied to prediction of various different disasters.
Description
Technical Field
The invention relates to the technical field of intelligent vehicle networks, in particular to a vehicle risk avoiding method and system based on the Internet of vehicles and a readable storage medium.
Background
The disasters are various and serious in harm, especially the disasters such as fire disasters, hurricanes, typhoons, floods (waterlogging disasters), tsunamis and debris flows (geological disasters), seriously threaten the life and property safety of people and restrict the sustainable development of social economy. Further, as the popularity of automobiles increases, the loss of damage to automobiles in the event of a disaster becomes greater.
Aiming at the problem of how to avoid danger of vehicles when disasters happen, Chinese patent with publication number CN110415544B discloses a disaster weather early warning method and an automobile AR-HUD system, wherein the method comprises the following steps: acquiring a video image in front of an automobile; processing the video image, and extracting the image weather characteristics of the processed video image; inputting the image weather characteristics into a weather type prediction model, and identifying the weather type of the current environment of the automobile, wherein the weather type prediction model is obtained based on deep belief network training; and when the weather type is a disaster weather type, outputting early warning information through the automobile AR-HUD system.
The disaster weather early warning method in the existing scheme is also a vehicle danger avoiding method, and the disaster weather and the type thereof are predicted based on the video image obtained by the vehicle in front, and the disaster weather and the type information thereof are fed back to the vehicle, so that the vehicle can be assisted to complete danger avoiding when a disaster occurs. However, the existing vehicle risk avoiding method only aims at a single vehicle, and cannot assist a plurality of vehicles to complete risk avoidance at the same time, namely the vehicle risk avoiding comprehensiveness is poor. Meanwhile, the conventional vehicle risk avoiding method only aims at the prediction of disaster weather, but cannot be applied to the prediction of various disasters such as fire, debris flow, typhoon, flood and the like, so that the universality of vehicle risk avoiding is poor. Therefore, how to design a method capable of improving the safety and universality of vehicle danger avoidance is an urgent technical problem to be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: how to design a vehicle risk avoiding method based on the Internet of vehicles so as to realize regional multi-vehicle risk avoiding and be applied to the prediction of various different disasters, thereby improving the comprehensiveness and universality of vehicle risk avoiding.
In order to solve the technical problems, the invention adopts the following technical scheme:
a vehicle danger avoiding method based on the Internet of vehicles comprises the following steps:
s1: acquiring disaster source information in a target area;
s2: calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area;
s3: when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, and acquiring position information of the corresponding danger-avoiding place according to the type of the disaster;
s4: and acquiring vehicle position information of each networked vehicle in the target area, and calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, so that the corresponding networked vehicle can complete danger avoiding based on the corresponding danger avoiding planning information.
Preferably, the vehicle risk avoiding method based on the internet of vehicles further comprises the following steps:
s5: after occurrence of the predicted disaster, the predicted cancellation probability corresponding to the disaster is calculated based on the disaster source information in the target area, and when the predicted cancellation probability corresponding to the disaster reaches a set disaster ending threshold, the disaster cancellation is predicted.
Preferably, the vehicle risk avoiding method based on the internet of vehicles further comprises the following steps:
s6: and obtaining the current risk avoiding position information of each networked vehicle in the target area, and calculating a return planning path based on the current vehicle risk avoiding position information of the corresponding networked vehicle and the vehicle position information before risk avoiding, so that the corresponding networked vehicle can drive to the vehicle position before risk avoiding based on the corresponding return planning path.
Preferably, in step S1, the disaster source information includes, but is not limited to, video image information, smoke sensing information, water level sensing information, wind speed sensing information, temperature sensor information, and humidity sensing information.
Preferably, in step S1, the disaster source information further includes, but is not limited to, disaster warning information issued by a meteorology office, a geological office, and a fire department.
Preferably, in step S4, a vehicle start-stop state of the corresponding networked vehicle is further acquired.
Preferably, in step S4, when the vehicle start-stop state of the corresponding networked vehicle is the start state, a risk avoidance planning path is calculated and generated as risk avoidance planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoidance location, so as to guide the driver of the corresponding networked vehicle to drive to the corresponding risk avoidance location.
Preferably, in step S4, when the vehicle start-stop state of the corresponding networked vehicle is a stopped state, remote start information for starting the corresponding networked vehicle is generated, and then an automatic driving scheme is calculated and generated as risk avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoiding place, so that the corresponding networked vehicle can complete self-start based on the remote start information and automatically drive to the corresponding risk avoiding place based on the automatic driving scheme.
The invention also discloses a vehicle danger avoiding system based on the Internet of vehicles, and the vehicle danger avoiding method based on the invention is implemented and specifically comprises the following steps:
the information acquisition unit is used for acquiring disaster source information in a target area;
the server is used for calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area; when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, acquiring position information of the corresponding danger avoiding place according to the type of the disaster, simultaneously acquiring vehicle position information of each networked vehicle in a target area, calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, and sending the danger avoiding planning information to the corresponding networked vehicle;
and the networked vehicle is communicated with the server and is used for completing risk avoidance based on the received risk avoidance planning information.
The invention also discloses a readable storage medium, wherein a computer management program is stored on the readable storage medium, and when the computer management program is executed by a processor, the steps of the vehicle risk avoiding method based on the Internet of vehicles are realized.
Compared with the prior art, the vehicle danger avoiding method has the following beneficial effects:
according to the method and the device, the prediction occurrence probability of the disaster of the corresponding type is calculated through the disaster source information in the target area, and the position of the corresponding danger avoiding place is obtained based on the type of the disaster, so that the method and the device can be applied to prediction of various different disasters, and the danger avoiding can be completed by obtaining the corresponding danger avoiding place based on the type of the disaster, and therefore the universality of vehicle danger avoiding can be improved. Meanwhile, the risk avoiding planning information is generated through calculation of the vehicle position information of each networked vehicle and the position information of the risk avoiding place, so that all networked vehicles in the target area can complete risk avoidance based on the corresponding risk avoiding planning information, namely regional multi-vehicle risk avoidance can be realized, and the comprehensiveness of vehicle risk avoidance can be improved.
Furthermore, the method and the device can calculate the predicted disaster relief probability based on the disaster source information in the target area after the disaster occurs, and judge whether the disaster is relieved, so that each networked vehicle in the target area can know whether the disaster is relieved, and the vehicle risk avoiding effect can be improved. Finally, the return planning path is calculated based on the current vehicle danger avoiding position information of the corresponding networked vehicle and the vehicle position information before danger avoiding, so that each networked vehicle can drive to the vehicle position before danger avoiding based on the corresponding return planning path, namely, each networked vehicle can return to the original position for continuous driving through the optimal route when the disaster is relieved, and the vehicle can be better assisted to drive.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a logic block diagram of a vehicle danger avoiding method based on the Internet of vehicles.
Detailed Description
The following is further detailed by the specific embodiments:
the first embodiment is as follows:
the embodiment discloses a vehicle danger avoiding method based on the Internet of vehicles.
As shown in fig. 1, the vehicle danger avoiding method based on the internet of vehicles includes the following steps:
s1: acquiring disaster source information in a target area;
s2: calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area;
s3: when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, and acquiring position information of the corresponding danger-avoiding place according to the type of the disaster; and setting the disaster early warning threshold value according to corresponding national standards or related regulations.
S4: and acquiring vehicle position information of each networked vehicle in the target area, and calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, so that the corresponding networked vehicle can complete danger avoiding based on the corresponding danger avoiding planning information.
S5: after occurrence of the predicted disaster, the predicted cancellation probability corresponding to the disaster is calculated based on the disaster source information in the target area, and when the predicted cancellation probability corresponding to the disaster reaches a set disaster ending threshold, the disaster cancellation is predicted. The disaster ending threshold is set according to the corresponding national standard or related regulations.
S6: and obtaining the current risk avoiding position information of each networked vehicle in the target area, and calculating a return planning path based on the current vehicle risk avoiding position information of the corresponding networked vehicle and the vehicle position information before risk avoiding, so that the corresponding networked vehicle can drive to the vehicle position before risk avoiding based on the corresponding return planning path.
It should be noted that the management method of the shared unmanned sweeper disclosed by the invention can generate corresponding software codes or software services in a program programming mode, and further can be run and implemented on a server and a computer.
According to the method and the device, the prediction occurrence probability of the disaster of the corresponding type is calculated through the disaster source information in the target area, and the position of the corresponding danger avoiding place is obtained based on the type of the disaster, so that the method and the device can be applied to prediction of various different disasters, and the danger avoiding can be completed by obtaining the corresponding danger avoiding place based on the type of the disaster, and therefore the universality of vehicle danger avoiding can be improved. Meanwhile, the risk avoiding planning information is generated through calculation of the vehicle position information of each networked vehicle and the position information of the risk avoiding place, so that all networked vehicles in the target area can complete risk avoidance based on the corresponding risk avoiding planning information, namely regional multi-vehicle risk avoidance can be realized, and the comprehensiveness of vehicle risk avoidance can be improved.
Furthermore, the method and the device can calculate the predicted disaster relief probability based on the disaster source information in the target area after the disaster occurs, and judge whether the disaster is relieved, so that each networked vehicle in the target area can know whether the disaster is relieved, and the vehicle risk avoiding effect can be improved. Meanwhile, the return planning path is calculated based on the current vehicle danger avoiding position information of the corresponding networked vehicle and the vehicle position information before danger avoiding, so that each networked vehicle can drive to the vehicle position before danger avoiding based on the corresponding return planning path, namely, can return to the original position for continuous driving through the optimal route when the disaster is relieved, and can better assist the vehicle in driving.
In the specific implementation process, the disaster source information includes, but is not limited to, video image information, smoke sensing information, water level sensing information, wind speed sensing information, temperature sensor information, and humidity sensing information. Disaster source information is collected through various sensors, such as a visual sensor (camera), a smoke sensor, a water level sensor, a wind speed sensor, a temperature sensor, a humidity sensor and the like, and because the geographic environments of target areas are different, the types and the number of the sensors can be differentiated.
Disaster source information also includes, but is not limited to, disaster warning information issued by meteorology, geological, and fire departments.
According to the disaster source information acquisition method, the disaster source information is acquired through two information acquisition channels, so that the calculation accuracy of the disaster prediction occurrence probability is improved; and when a disaster happens, if the information of one information acquisition channel fails, the information of the other information acquisition channel can still play a role, so that disaster prediction is better assisted to be completed.
In particular embodiments, the types of disaster prediction include, but are not limited to, fires, hurricanes, typhoons, floods (flooding), tsunamis, and debris flows (geological disasters).
In this embodiment, a disaster prediction model of a corresponding type is established based on a neural network model and factors such as historical disasters and experiences, then the disaster prediction model is trained, the trained model is input as disaster source information, and output as a predicted occurrence probability or a predicted cancellation probability of the corresponding disaster type (in this embodiment, the predicted cancellation probability may be equal to 1-predicted occurrence probability). The establishment and training of the disaster prediction model can refer to the existing means, and are not described herein again.
For example: a disaster prediction model for flood (flood) can refer to a flood disaster prediction and early warning method based on QR-BC-ELM disclosed in a patent with the publication number of CN 112016839A; as a disaster prediction model for a debris flow (geological disaster), reference is made to "a method and a system for predicting a geological disaster based on a neural network" disclosed in patent publication No. CN 113919587A.
In the specific implementation process, the risk avoiding field needs to be preset in the target area according to the disaster prediction type, for example: the danger avoiding place 1: the ground is suitable for disasters: fire, hurricane typhoon, etc.; and (3) a danger avoiding place 2: in higher altitudes, avoid disasters: flood or tsunami, debris flow, etc. In the embodiment, only when the corresponding disaster occurs, the position information of the corresponding danger avoiding place is acquired.
In the specific implementation process, the vehicle starting and stopping states of the corresponding networked vehicles are also acquired. The risk avoiding planning method and the risk avoiding planning system can generate risk avoiding planning information correspondingly based on the starting and stopping states of the vehicle so as to better assist the vehicle to complete risk avoiding.
Specifically, when the vehicle start-stop state of the corresponding networked vehicle is the starting state, a risk avoiding planning path is calculated and generated as risk avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoiding place, so as to guide a driver of the corresponding networked vehicle to drive to the corresponding risk avoiding place.
According to the invention, the risk avoiding planning path is generated by calculating the vehicle position information of the networked vehicles and the position information of the corresponding risk avoiding places as risk avoiding planning information to guide the drivers of the networked vehicles to drive to the corresponding risk avoiding places, so that the networked vehicles can complete risk avoiding through the optimal route when a disaster occurs, and the risk avoiding effect of the vehicles can be ensured.
Specifically, when the vehicle start-stop state of the corresponding networked vehicle is a parking state, remote start information for starting the corresponding networked vehicle is generated, and then an automatic driving scheme is calculated and generated as danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, so that the corresponding networked vehicle can finish self-starting based on the remote start information and automatically drive to the corresponding danger avoiding place based on the automatic driving scheme. The networked vehicle which completes the automatic risk avoidance in the embodiment needs to have an automatic driving function.
According to the invention, the remote starting information for starting the networked vehicles is generated, and the automatic driving scheme is calculated and generated based on the vehicle position information of each networked vehicle and the position information of the corresponding danger avoiding place, so that the networked vehicles can be automatically started, and automatically drive to the corresponding danger avoiding place based on the automatic driving scheme, namely, the self-starting and the automatic driving of the vehicles can be realized, the danger avoiding of unmanned vehicles is completed, and the comprehensiveness of the vehicle danger avoiding can be further improved.
Example two:
the embodiment of the invention discloses a vehicle danger avoiding system based on Internet of vehicles.
The vehicle danger avoiding system based on the internet of vehicles is implemented based on the vehicle danger avoiding method in the first embodiment, and specifically comprises the following steps:
the information acquisition unit is used for acquiring disaster source information in a target area;
the server is used for calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area; when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, acquiring position information of the corresponding danger avoiding place according to the type of the disaster, simultaneously acquiring vehicle position information of each networked vehicle in a target area, calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, and sending the danger avoiding planning information to the corresponding networked vehicle;
and the networked vehicle is communicated with the server and is used for completing risk avoidance based on the received risk avoidance planning information.
In the specific implementation process, the vehicle danger avoiding systems corresponding to the target areas can share disaster source information, position information of danger avoiding places and danger avoiding planning information in real time, and the danger avoiding places are planned in a coordinated manner, so that faster and more accurate disaster prediction can be realized, the utilization rate of the danger avoiding places is improved, and property loss caused by disasters is further reduced.
According to the method and the device, the prediction occurrence probability of the disaster of the corresponding type is calculated through the disaster source information in the target area, and the position of the corresponding danger avoiding place is obtained based on the type of the disaster, so that the method and the device can be applied to prediction of various different disasters, and the danger avoiding can be completed by obtaining the corresponding danger avoiding place based on the type of the disaster, and therefore the universality of vehicle danger avoiding can be improved. Meanwhile, the risk avoiding planning information is generated through calculation of the vehicle position information of each networked vehicle and the position information of the risk avoiding place, so that all networked vehicles in the target area can complete risk avoidance based on the corresponding risk avoiding planning information, namely regional multi-vehicle risk avoidance can be realized, and the comprehensiveness of vehicle risk avoidance can be improved.
In a specific implementation process, the server can further execute the following steps:
after occurrence of the predicted disaster, the predicted cancellation probability corresponding to the disaster is calculated based on the disaster source information in the target area, and when the predicted cancellation probability corresponding to the disaster reaches a set disaster ending threshold, the disaster cancellation is predicted. The disaster ending threshold is set according to the corresponding national standard or related regulations.
And obtaining the current risk avoiding position information of each networked vehicle in the target area, and calculating a return planning path based on the current vehicle risk avoiding position information of the corresponding networked vehicle and the vehicle position information before risk avoiding, so that the corresponding networked vehicle can drive to the vehicle position before risk avoiding based on the corresponding return planning path.
According to the disaster recovery method and the disaster recovery device, after a disaster occurs, the predicted disaster recovery probability can be calculated based on the disaster source information in the target area, and whether the disaster is recovered or not is judged, so that each networked vehicle in the target area can know whether the disaster is recovered or not, and the vehicle risk recovery effect can be improved. Meanwhile, the return planning path is calculated based on the current vehicle danger avoiding position information of the corresponding networked vehicle and the vehicle position information before danger avoiding, so that each networked vehicle can drive to the vehicle position before danger avoiding based on the corresponding return planning path, namely, can return to the original position for continuous driving through the optimal route when the disaster is relieved, and can better assist the vehicle in driving.
In a specific implementation process, when the vehicle start-stop state of the networked vehicle is a starting state, the server calculates and generates a risk avoiding planning path as risk avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoiding place;
the networked vehicle can receive the risk avoidance planning path and generate corresponding map navigation display information to guide a driver to drive to a corresponding risk avoidance place.
According to the invention, the risk avoiding planning path is generated by calculating the vehicle position information of the networked vehicles and the position information of the corresponding risk avoiding places as risk avoiding planning information to guide the drivers of the networked vehicles to drive to the corresponding risk avoiding places, so that the networked vehicles can complete risk avoiding through the optimal route when a disaster occurs, and the risk avoiding effect of the vehicles can be ensured.
In a specific implementation process, when the vehicle starting and stopping state of the networked vehicle is a parking state, the server generates remote starting information for starting the corresponding networked vehicle, and then calculates and generates an automatic driving scheme as risk avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoiding place;
the networked vehicle can receive and complete self-starting based on the remote starting information, and simultaneously automatically drives to a corresponding danger avoiding place based on an automatic driving scheme.
According to the invention, the remote starting information for starting the networked vehicles is generated, and the automatic driving scheme is calculated and generated based on the vehicle position information of each networked vehicle and the position information of the corresponding danger avoiding place, so that the networked vehicles can be automatically started, and automatically drive to the corresponding danger avoiding place based on the automatic driving scheme, namely, the self-starting and the automatic driving of the vehicles can be realized, the danger avoiding of unmanned vehicles is completed, and the comprehensiveness of the vehicle danger avoiding can be further improved.
Example three:
disclosed in the present embodiment is a readable storage medium.
A readable storage medium having stored thereon a computer management like program which, when executed by a processor, implements the steps of the internet of vehicles based vehicle risk avoidance method of the present invention. The readable storage medium can be a device with readable storage function such as a U disk or a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (10)
1. A vehicle danger avoiding method based on the Internet of vehicles is characterized by comprising the following steps:
s1: acquiring disaster source information in a target area;
s2: calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area;
s3: when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, predicting the occurrence of the disaster, and acquiring position information of the corresponding danger-avoiding place according to the type of the disaster;
s4: and acquiring vehicle position information of each networked vehicle in the target area, and calculating and generating danger avoiding planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding danger avoiding place, so that the corresponding networked vehicle can complete danger avoiding based on the corresponding danger avoiding planning information.
2. The vehicle networking-based vehicle risk avoiding method of claim 1, further comprising:
s5: after occurrence of the predicted disaster, the predicted cancellation probability corresponding to the disaster is calculated based on the disaster source information in the target area, and when the predicted cancellation probability corresponding to the disaster reaches a set disaster ending threshold, the disaster cancellation is predicted.
3. The vehicle networking-based vehicle risk avoiding method of claim 2, further comprising:
s6: and obtaining the current risk avoiding position information of each networked vehicle in the target area, and calculating a return planning path based on the current vehicle risk avoiding position information of the corresponding networked vehicle and the vehicle position information before risk avoiding, so that the corresponding networked vehicle can drive to the vehicle position before risk avoiding based on the corresponding return planning path.
4. The vehicle networking-based vehicle risk avoiding method according to claim 1, wherein: in step S1, the disaster source information includes, but is not limited to, video image information, smoke sensing information, water level sensing information, wind speed sensing information, temperature sensor information, and humidity sensing information.
5. The vehicle networking-based vehicle risk avoiding method according to claim 4, wherein: in step S1, the disaster source information further includes, but is not limited to, disaster warning information issued by the meteorology, geological, and fire departments.
6. The vehicle networking-based vehicle risk avoiding method according to claim 1, wherein: in step S4, a vehicle start-stop state of the corresponding networked vehicle is also obtained.
7. The vehicle networking-based vehicle risk avoiding method according to claim 6, wherein: in step S4, when the vehicle start-stop state of the corresponding networked vehicle is the start state, a risk avoidance planning path is calculated and generated as risk avoidance planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoidance location, so as to guide the driver of the corresponding networked vehicle to travel to the corresponding risk avoidance location.
8. The vehicle networking-based vehicle risk avoiding method according to claim 6, wherein: in step S4, when the vehicle start-stop state of the corresponding networked vehicle is the parking state, remote start information for starting the corresponding networked vehicle is generated, and then an automatic driving scheme is calculated and generated as risk avoidance planning information based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoidance location, so that the corresponding networked vehicle can complete self-start based on the remote start information and automatically drive to the corresponding risk avoidance location based on the automatic driving scheme.
9. The vehicle danger avoiding system based on the internet of vehicles is characterized by being implemented based on the vehicle danger avoiding method based on the internet of vehicles in claim 1, and specifically comprising the following steps:
the information acquisition unit is used for acquiring disaster source information in a target area;
the server is used for calculating the predicted occurrence probability of the disaster of the corresponding type based on the disaster source information in the target area; when the predicted occurrence probability of the corresponding disaster reaches a set disaster early warning threshold value, the occurrence of the disaster is predicted, and the position information of the corresponding danger-avoiding place is obtained according to the type of the disaster; meanwhile, vehicle position information of each networked vehicle in the target area is obtained, risk avoiding planning information is calculated and generated based on the vehicle position information of the corresponding networked vehicle and the position information of the corresponding risk avoiding place, and the risk avoiding planning information is sent to the corresponding networked vehicle;
and the networked vehicle is communicated with the server and is used for completing risk avoidance based on the received risk avoidance planning information.
10. A readable storage medium, characterized in that a computer management like program is stored thereon, which when executed by a processor implements the steps of the vehicle networking based vehicle risk avoiding method according to any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115359680A (en) * | 2022-08-18 | 2022-11-18 | 中国第一汽车股份有限公司 | Parking place recommendation method and system in severe weather, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011169856A (en) * | 2010-02-22 | 2011-09-01 | Toyota Motor Corp | Route creation device |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN109935053A (en) * | 2019-03-26 | 2019-06-25 | 招商局重庆交通科研设计院有限公司 | Sudden landslide early warning system |
| JP2019132789A (en) * | 2018-02-02 | 2019-08-08 | アイシン・エィ・ダブリュ株式会社 | Information display system and information display program |
| CN111619582A (en) * | 2019-02-27 | 2020-09-04 | 丰田自动车株式会社 | Processing device, processing method, and processing program |
| JP2020161087A (en) * | 2019-03-28 | 2020-10-01 | 株式会社デンソーテン | Vehicle control device and vehicle control method |
| CN113335289A (en) * | 2021-05-06 | 2021-09-03 | 湖北亿咖通科技有限公司 | Vehicle disaster avoidance method and device and computer storage medium |
-
2022
- 2022-01-27 CN CN202210099463.2A patent/CN114413924A/en not_active Withdrawn
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011169856A (en) * | 2010-02-22 | 2011-09-01 | Toyota Motor Corp | Route creation device |
| JP2019132789A (en) * | 2018-02-02 | 2019-08-08 | アイシン・エィ・ダブリュ株式会社 | Information display system and information display program |
| CN111619582A (en) * | 2019-02-27 | 2020-09-04 | 丰田自动车株式会社 | Processing device, processing method, and processing program |
| CN109935053A (en) * | 2019-03-26 | 2019-06-25 | 招商局重庆交通科研设计院有限公司 | Sudden landslide early warning system |
| JP2020161087A (en) * | 2019-03-28 | 2020-10-01 | 株式会社デンソーテン | Vehicle control device and vehicle control method |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN113335289A (en) * | 2021-05-06 | 2021-09-03 | 湖北亿咖通科技有限公司 | Vehicle disaster avoidance method and device and computer storage medium |
Cited By (1)
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| CN115359680A (en) * | 2022-08-18 | 2022-11-18 | 中国第一汽车股份有限公司 | Parking place recommendation method and system in severe weather, electronic equipment and storage medium |
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