CN114758526A - Obstacle avoidance method and device based on Internet of vehicles big data and storage medium - Google Patents
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Abstract
The patent refers to the field of 'transmission of digital information'. Acquiring driving data of a driving vehicle on a target road section; analyzing the driving data to obtain the driving state of the vehicle, and judging whether the driving state of the vehicle is abnormal or not and whether the abnormality is caused by avoiding or touching an obstacle or not; and if the abnormal running state of the vehicle is caused by avoiding or touching an obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle. The scheme can find obstacles in time and early warn relevant vehicles as early as possible based on the big data of the Internet of vehicles.
Description
Technical Field
The embodiment of the application relates to the field of hybrid electric vehicles, in particular to an obstacle avoidance method and device based on internet of vehicles big data and a storage medium.
Background
In the daily driving process, a driver always encounters various obstacles on the road, sometimes does not find timely that the obstacle is avoided, damages are caused to a vehicle, and even safety problems are caused, and sometimes dangerous actions are made for avoiding the obstacle, so that driving safety is influenced.
Because most vehicles are not provided with a radar and a camera, a driver can only recognize obstacles by naked eyes when driving, and sometimes cannot find the obstacles in time. Even if the radar and the camera are carried, the detection range is limited due to the detection capability of the radar and the camera, and the driver cannot be reminded in time.
Disclosure of Invention
The embodiment of the application provides an obstacle avoidance method based on internet of vehicles big data, which is applied to a cloud server and comprises the following steps:
acquiring driving data of a driving vehicle on a target road section;
analyzing the driving data to obtain the driving state of the vehicle, and judging whether the driving state of the vehicle is abnormal or not and whether the abnormality is caused by avoiding or touching an obstacle or not;
and if the abnormal running state of the vehicle is caused by avoiding or touching an obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle.
The embodiment of the application provides an keep away barrier device based on car networking big data includes: the obstacle avoidance method based on the Internet of vehicles comprises a memory and a processor, wherein the memory stores a computer program, and the computer program realizes the steps of the obstacle avoidance method based on the Internet of vehicles big data when being executed by the processor.
The embodiment of the application provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the obstacle avoidance method based on the internet of vehicles big data.
According to the obstacle avoidance method based on the internet of vehicles big data, the cloud server obtains driving data of a driving vehicle on a target road section, analyzes the driving data to obtain the driving state of the vehicle, and judges whether the driving state of the vehicle is abnormal or not and whether the abnormality is caused by avoiding or touching an obstacle or not; and if the abnormal running state of the vehicle is caused by avoiding or touching an obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle. The obstacle avoidance method based on the big data of the internet of vehicles can share obstacle information to users in the road section based on the data sharing function of the internet of vehicles, and compared with the method that the users can only warn when arriving in a detection range in single-vehicle detection, the obstacle avoidance method provided by the embodiment can greatly advance early warning time, so that the drivers can have more sufficient time to deal with the front obstacle problem.
Other aspects will be apparent upon reading and understanding the attached figures and detailed description.
Drawings
The drawings are used for providing an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples, do not limit the disclosure.
Fig. 1 is a flowchart of an obstacle avoidance method based on internet of vehicles big data according to an embodiment of the present application;
fig. 2 is a schematic diagram of an obstacle avoidance device based on internet of vehicles big data according to an embodiment of the application.
Detailed Description
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed herein may also be combined with any conventional features or elements to form a unique inventive aspect as defined by the appended claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the appended claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Accordingly, the particular order of the steps set forth in the specification should not be construed as limitations on the claims appended hereto. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
The car networking technology realizes the network connection between cars, people, roads and service platforms by means of a new generation of information communication technology, improves the overall intelligent driving level of the cars, provides safe, comfortable, intelligent and efficient driving feeling and traffic service for users, improves the traffic operation efficiency and improves the intelligent level of social traffic service. The embodiment of the application applies the internet of vehicles technology to the obstacle warning of the road, judges whether the obstacle exists or not based on the vehicle running data, and warns the driver of the obstacle, so that the driver can have sufficient time to avoid the obstacle.
As shown in fig. 1, an embodiment of the present application provides an obstacle avoidance method based on internet of vehicles big data, which is applied to a cloud server, and includes:
step S10, acquiring the driving data of the driving vehicle on the target road section;
step S20, analyzing the driving data to obtain the driving state of the vehicle, and judging whether the driving state of the vehicle is abnormal and whether the abnormality is caused by avoiding or touching an obstacle;
and step S30, if the abnormal running state of the vehicle is caused by avoiding or touching the obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle.
In the obstacle avoidance method based on the internet of vehicles big data provided by the embodiment, the cloud server acquires the driving data of the driving vehicle on the target road section, analyzes the driving data to obtain the driving state of the vehicle, and judges whether the driving state of the vehicle is abnormal or not and whether the abnormality is caused by avoiding or touching an obstacle or not; and if the abnormal running state of the vehicle is caused by avoiding or touching an obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle. The obstacle avoidance method based on the big data of the internet of vehicles can share obstacle information to users in the road section based on the data sharing function of the internet of vehicles, and compared with the method that the users can only warn when arriving in a detection range in single-vehicle detection, the obstacle avoidance method provided by the embodiment can greatly advance early warning time, so that the drivers can have more sufficient time to deal with the front obstacle problem.
In some exemplary embodiments, the driving data of the vehicle includes at least one of the following data: vehicle speed, wheel speed, three-axis acceleration, and steering angle velocity.
In some exemplary embodiments, the determining whether the running state of the vehicle is abnormal and whether the abnormality is caused by avoiding or touching an obstacle includes:
determining that a driving state of a target vehicle is abnormal and the abnormality is caused by touching an obstacle if a plurality of sets of driving data of the target vehicle satisfy a first condition;
wherein the first condition comprises: it is detected that the fluctuation width of the vertical acceleration of the target vehicle is larger than a first threshold value and the variance of the wheel rotation speed is larger than a second threshold value.
In some exemplary embodiments, the determining whether the running state of the vehicle is abnormal and whether the abnormality is caused by avoiding or touching an obstacle includes:
if multiple groups of running data of a target vehicle meet the following second condition, judging that the running state of the target vehicle is abnormal avoidance;
when the running state of the target vehicle is avoidance abnormality, acquiring position information and running data of a vehicle in front of the target vehicle, and if there is no collision risk between the target vehicle and the vehicle in front, determining that the avoidance abnormality is caused by an avoidance obstacle;
wherein the second condition comprises: the vehicle speed variation width of the target vehicle is larger than the third threshold value, and the accompanying steering angular velocity is larger than the fourth threshold value and the horizontal acceleration is larger than the fifth threshold value when the vehicle speed variation width is larger than the third threshold value.
In some exemplary embodiments, the method further comprises:
acquiring a GNSS (Global Navigation Satellite System) positioning signal and map data of the vehicle if the abnormal driving state of the vehicle is caused by avoiding or touching an obstacle;
and marking the position of the abnormal running state of the vehicle on a map.
In some exemplary embodiments, the method further comprises:
if the target road section has obstacle marks of a plurality of vehicles on the map, acquiring the distribution situation of the obstacle marks, and determining the type of the obstacle according to the distribution situation of the obstacle marks: if a plurality of obstacle markers are distributed in a certain area of the target road section in a concentrated mode, judging that the obstacles are fixed obstacles; and if the plurality of obstacle marks are scattered at a plurality of positions in the target road section, judging that the obstacle is a moving obstacle.
In some exemplary embodiments, fixed obstacles such as potholes and objects that are not easily moved (large rocks, malfunctioning vehicles, etc.) after being subjected to external forces (impacts, high winds, etc.). Moving obstacles are objects (rollers, boxes, etc.) that move easily when subjected to external forces (impacts, strong winds, etc.).
In some exemplary embodiments, the method further comprises:
and after the type and the distribution position of the obstacles of the target road section are determined, transmitting the obstacle information to vehicles and traffic management departments in the Internet of vehicles. After obtaining the obstacle information of the target road section, the vehicles in the internet of vehicles can prepare to avoid the obstacle in advance.
In some exemplary embodiments, the method further comprises:
monitoring the vehicle driving state of a fixed obstacle area of a target road section: determining that the fixed obstacle has been cleared if a plurality of vehicles are monitored to pass through the fixed obstacle area and travel data does not satisfy the first condition.
In some exemplary embodiments, the method further comprises:
monitoring the vehicle driving state of the moving obstacle area of the target road section: determining that the moving obstacle has been cleared if a plurality of vehicles are monitored to pass through the moving obstacle area and travel data does not satisfy the second condition.
In some exemplary embodiments, the method further comprises:
and when the fixed obstacles or the moving obstacles on the target road section are cleared, clearing the marks of the obstacles on the map.
As shown in fig. 2, an embodiment of the present disclosure provides an obstacle avoidance device based on internet of vehicles big data, including: the obstacle avoidance method based on the Internet of vehicles comprises a memory and a processor, wherein the memory stores a computer program, and the computer program realizes the steps of the obstacle avoidance method based on the Internet of vehicles big data when being executed by the processor.
The embodiment of the disclosure provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the obstacle avoidance method based on the internet of vehicles big data are realized.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Claims (10)
1. An obstacle avoidance method based on Internet of vehicles big data is applied to a cloud server and comprises the following steps:
acquiring driving data of a driving vehicle on a target road section;
analyzing the running data to obtain the running state of the vehicle, and judging whether the running state of the vehicle is abnormal and whether the abnormality is caused by avoiding or touching an obstacle;
and if the abnormal running state of the vehicle is caused by avoiding or touching an obstacle, sending an alarm prompt to the running vehicle within the influence range of the obstacle.
2. The method of claim 1, wherein:
the determining whether the running state of the vehicle is abnormal and whether the abnormality is caused by evasion or touching of an obstacle includes:
if a plurality of sets of running data of a target vehicle meet the following first conditions, judging that the running state of the target vehicle is abnormal and the abnormality is caused by touching an obstacle;
wherein the first condition comprises: it is detected that the fluctuation width of the vertical acceleration of the target vehicle is larger than a first threshold value and the variance of the wheel rotation speed is larger than a second threshold value.
3. The method of claim 1, wherein:
the determining whether the running state of the vehicle is abnormal and whether the abnormality is caused by evasion or touching of an obstacle includes:
if multiple groups of running data of a target vehicle meet the following second condition, judging that the running state of the target vehicle is abnormal avoidance;
when the running state of the target vehicle is avoidance abnormality, acquiring position information and running data of a vehicle in front of the target vehicle, and if there is no collision risk between the target vehicle and the vehicle in front, determining that the avoidance abnormality is caused by an avoidance obstacle;
wherein the second condition comprises: the vehicle speed variation width of the target vehicle is larger than the third threshold, and the accompanying steering angular velocity is larger than the fourth threshold and the horizontal acceleration is larger than the fifth threshold when the vehicle speed variation width is larger than the third threshold.
4. The method of claim 1, wherein:
the method further comprises the following steps:
if the abnormal driving state of the vehicle is caused by avoiding or touching an obstacle, acquiring a Global Navigation Satellite System (GNSS) positioning signal and map data of the vehicle;
and marking the position of the abnormal running state of the vehicle on a map.
5. The method of claim 4, wherein:
the method further comprises the following steps:
if the target road section has obstacle marks of a plurality of vehicles on the map, acquiring the distribution situation of the obstacle marks, and determining the type of the obstacle according to the distribution situation of the obstacle marks: if a plurality of obstacle markers are distributed in a certain area of the target road section in a concentrated mode, judging that the obstacles are fixed obstacles; and if the plurality of obstacle marks are scattered at a plurality of positions in the target road section, judging that the obstacle is a moving obstacle.
6. The method of claim 5, wherein:
the method further comprises the following steps:
and after the type and the distribution position of the obstacles of the target road section are determined, transmitting the obstacle information to vehicles and/or traffic management departments in the Internet of vehicles.
7. The method of claim 2, wherein:
the method further comprises the following steps:
monitoring the vehicle driving state of a fixed obstacle area of a target road section: determining that the fixed obstacle has been cleared if a plurality of vehicles are monitored to pass through the fixed obstacle area and travel data does not satisfy the first condition.
8. The method of claim 3, wherein:
monitoring the vehicle driving state of the moving obstacle area of the target road section: determining that the moving obstacle has been cleared if a plurality of vehicles are monitored to pass through the moving obstacle area and travel data does not satisfy the second condition.
9. The utility model provides an keep away barrier device based on car networking big data, includes: a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor realizes the steps of the obstacle avoidance method based on the internet of vehicles big data of any one of the above claims 1 to 8.
10. A computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the obstacle avoidance method based on internet of vehicles big data of any one of the above claims 1 to 8 are implemented.
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