CN111444468A - Method, equipment and storage medium for determining driving risk - Google Patents
Method, equipment and storage medium for determining driving risk Download PDFInfo
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Abstract
The embodiment of the application discloses a method, equipment and a storage medium for determining driving risks, which are applied to an internet of vehicles, not only are the driving risk influence of first road condition information on a first target vehicle considered, but also the driving risk influence of multiple driving risks of N second target vehicles on the first target vehicle is considered, so that the determined driving risk condition of the first target vehicle is more practical. The method comprises the following steps: the method comprises the steps that a first target vehicle obtains first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer; determining a second driving risk state vector based on the first road condition information; and determining a third driving risk state vector based on each first driving risk state vector and each second driving risk state vector, wherein the third driving risk state vector is used for indicating the driving risk condition suffered by the first target vehicle.
Description
Technical Field
The embodiment of the application relates to the field of Internet of vehicles, in particular to a method, equipment and a storage medium for determining driving risks.
Background
The risk refers to the possibility of a certain loss occurring under a certain condition and period, and the driving risk in the internet of vehicles refers to the risk suffered by the current vehicle during driving.
At present, the conventional determination mode of the driving risk of the internet of vehicles depends on a sensor on the vehicle to acquire road condition information or acquire the road condition information from a cloud server, and then the vehicle calculates the collision risk between the vehicle and other vehicles according to the road condition information. However, the current method is only to consider the driving risks to be caused by the collision between vehicles, neglect other risk factors between different vehicles, and thus cause the driving risks finally determined by the vehicles not to be in accordance with the actual situation.
Disclosure of Invention
The embodiment of the application provides a method, equipment and a storage medium for determining driving risks, which are applied to an internet of vehicles, not only are the driving risk influence of first road condition information on a first target vehicle considered, but also the driving risk influence of multiple driving risks of N second target vehicles on the first target vehicle considered, so that the determined driving risk condition of the first target vehicle is more practical.
In view of this, the embodiments of the present application provide the following solutions:
in a first aspect, an embodiment of the present application provides a method for determining driving risk, where the method may include:
a first target vehicle acquires first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, wherein each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
the first target vehicle determining a second driving risk state vector based on the first road condition information;
the first target vehicle determines a third driving risk state vector based on each of the first and second driving risk state vectors, the third driving risk state vector being indicative of a driving risk condition experienced by the first target vehicle.
In a second aspect, embodiments of the present application provide a first target vehicle, which may include:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
the first determining unit is used for determining a second driving risk state vector according to the first road condition information;
a second determination unit, configured to determine a third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector, where the third driving risk state vector is used to indicate a driving risk situation suffered by the first target vehicle.
Optionally, with reference to the second aspect above, in a first possible implementation manner, the first target vehicle further includes:
a first sending unit, configured to send, to a server, a first vehicle identifier corresponding to each of the second target vehicles and a second vehicle identifier corresponding to the first target vehicle before determining a third driving risk state vector based on each of the first driving risk state vectors and the second driving risk state vector, where each of the first vehicle identifiers is used to instruct the server to determine a first risk transfer matrix corresponding to each of the second target vehicles, and the second vehicle identifier is used to instruct the server to determine a second risk transfer matrix corresponding to the first road condition information;
the acquiring unit is configured to receive each of the first risk transfer matrix and the second risk transfer matrix sent by the server;
correspondingly, the second determining unit includes:
a processing module, configured to obtain each first driving risk condition based on each first risk transfer matrix and each first driving risk state vector, and obtain a second driving risk condition based on the second risk transfer matrix and the second driving risk state vector;
a first determining module configured to determine a third driving risk state vector according to each of the first driving risk condition and the second driving risk condition.
Optionally, with reference to the second aspect and the first possible implementation manner of the second aspect, in a second possible implementation manner, the first road condition information includes risk states and the number of risk states, and the first determining unit includes:
a second determination module to determine a second driving risk state vector according to the risk states and the number of risk states.
Optionally, with reference to the second aspect and the first to second possible implementation manners of the first aspect of the second aspect, in a third possible implementation manner, the first target vehicle further includes:
a second sending unit, configured to send a risk request to each second target vehicle before acquiring first driving risk state vectors corresponding to N second target vehicles, respectively, so that each second target vehicle determines a corresponding first driving risk state vector based on the risk request;
correspondingly, the obtaining unit includes:
and the acquisition module is used for receiving the corresponding first driving risk state vector sent by each second target vehicle.
Optionally, with reference to the second aspect and the first to second possible implementation manners of the second aspect, in a fourth possible implementation manner, the second determining unit is further configured to determine N +1 risk states in a third driving risk state vector after determining the third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector;
determining a driving risk value based on the N +1 risk states and preset risk weights corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
Optionally, with reference to the third possible implementation manner of the second aspect, in a fifth possible implementation manner, the second determining unit is further configured to determine N +1 risk states in a third driving risk state vector after determining the third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector;
determining a driving risk value based on the N +1 risk states and preset risk weights corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
In a third aspect, an embodiment of the present application provides a computer device, including:
the method comprises the following steps: an input/output (I/O) interface, a processor and a memory,
the memory stores program instructions;
the processor is adapted to execute program instructions stored in the memory for implementing the method according to any one of the possible implementations of the first aspect as described above.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for performing the method according to any one of the possible implementation manners of the first aspect and the first aspect.
A fifth aspect of embodiments of the present application provides a computer program product comprising instructions which, when run on a computer or processor, cause the computer or processor to perform the method of any of the above aspects.
According to the technical scheme, the embodiment of the application has the following advantages:
in the embodiment of the application, the first driving risk state vectors corresponding to the N second target vehicles are obtained, and the second driving risk state vectors are determined based on the obtained first road condition information, so that the third driving risk state vector corresponding to the first target vehicle is determined based on each first driving risk state vector and each second driving risk state vector, and the driving risk condition of the first target vehicle is reflected by the third driving risk state vector. In the embodiment, not only the driving risk influence of the road condition information on the first target vehicle is considered, but also the driving risk influence of the multiple driving risks of the N second target vehicles on the first target vehicle is considered, so that the determined driving risk condition of the first target vehicle is more practical, and the driving risk warning experience can be provided for the driver in real time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application.
FIG. 1 is a schematic diagram of an architecture of a processing system in an embodiment of the present application;
fig. 2 is a schematic view of an application scenario provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of an embodiment of a method for determining driving risk provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of another embodiment of a method for determining driving risk provided by an embodiment of the present application;
FIG. 5 is a schematic representation of the transmission of driving risks provided in embodiments of the present application;
FIG. 6 is a schematic diagram of another embodiment of a method for determining driving risk provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of an embodiment of a first target vehicle provided by an embodiment of the present application;
fig. 8 is a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method, equipment and a storage medium for determining driving risks, which are applied to an internet of vehicles, not only are the driving risk influence of first road condition information on a first target vehicle considered, but also the driving risk influence of multiple driving risks of N second target vehicles on the first target vehicle considered, so that the determined driving risk condition of the first target vehicle is more practical.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the research and progress of artificial intelligence technology, the artificial intelligence technology is developed and applied in a plurality of fields, such as common smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned driving, automatic driving, unmanned aerial vehicles, robots, smart medical care, smart customer service, and the like.
The automatic driving technology generally comprises technologies such as high-precision maps, environment perception, behavior decision, path planning, motion control and the like, and the self-determined driving technology has wide application prospects. Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Computer Vision technology (CV) Computer Vision is a science for researching how to make a machine "see", and further refers to that a camera and a Computer are used to replace human eyes to perform machine Vision such as identification, tracking and measurement on a target, and further image processing is performed, so that the Computer processing becomes an image more suitable for human eyes to observe or transmitted to an instrument to detect. As a scientific discipline, computer vision research-related theories and techniques attempt to build artificial intelligence systems that can capture information from images or multidimensional data. Computer vision technologies generally include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content/behavior recognition, three-dimensional object reconstruction, 3D technologies, virtual reality, augmented reality, synchronous positioning, map construction, and other technologies, and also include common biometric technologies such as face recognition and fingerprint recognition.
At present, in the process of determining the driving risk of a vehicle, a conventional technical scheme is that after a sensor on the vehicle acquires road condition information or acquires the road condition information from a cloud server, the vehicle calculates the collision risk between the vehicle and other vehicles according to the road condition information. However, the current method is only to consider the driving risks to be caused by the collision between vehicles, neglect other risk factors between different vehicles, and thus cause the driving risks finally determined by the vehicles not to be in accordance with the actual situation.
In order to solve the above problem, in the embodiment of the present application, a method for determining driving risk is provided, and the method is applied to the processing system shown in fig. 1 and the application scenario described in fig. 2. Please refer to fig. 1, which is a schematic diagram of an architecture of a processing system according to an embodiment of the present application. As shown in fig. 1, the architecture diagram includes a first target vehicle, a server, and N second target vehicles, where N is an integer greater than 0, and the N second target vehicles are all transmission predecessors of the first target vehicle on the same transmission behavior. The first target vehicle determines second driving risk state vectors after acquiring the first road condition information, and acquires corresponding first risk state vectors from each second target vehicle, so that the first target vehicle can determine a third driving risk state vector based on each driving risk state vector and the first driving risk state vector corresponding to the first road condition information where the first target vehicle is located, namely the third driving risk state vector represents the risk of the first road condition information where the first target vehicle is located and other risks including collision risks caused by each transmission front vehicle to the first target vehicle. It should be understood that the above-mentioned server may be a cloud computing server, etc., and is not limited in this embodiment.
In addition, please refer to fig. 2, which is a schematic view of an application scenario provided in the embodiment of the present application. As can be seen from fig. 2, the application scenario includes a vehicle 1, a vehicle 2, and a vehicle 3, and the information flow is transferred from the vehicle 1 to the vehicle 2, and from the vehicle 2 to the vehicle 3. Assuming that the first target vehicle is the vehicle 2 in the figure, the vehicle before transmission corresponding to the vehicle 2 is the vehicle 1, that is, the second target vehicle is the vehicle 1; if the first target vehicle is the vehicle 3 in the figure, the vehicles before transmission corresponding to the vehicle 3 are the vehicle 1 and the vehicle 2, that is, the second target vehicles are the vehicle 1 and the vehicle 2, respectively.
It should be noted that the total number of vehicles depicted in fig. 2 may be other than 3, such as: 8, 9, etc., and the total number of vehicles is not limited in this embodiment, and the aforementioned 3 vehicles are only an exemplary illustration; in addition, the first target vehicle is not limited to the vehicle 2 or the vehicle 3, and the first target vehicle is represented as the vehicle 2 or the vehicle 3 only by a schematic illustration; the aforementioned second target vehicle is not limited to the vehicle 1, or the vehicle 1 and the vehicle 2, and the aforementioned representation of the transfer vehicle as the vehicle 1, or the vehicle 1 and the vehicle 2 is only an illustrative description, and is specifically determined according to the first target vehicle. Secondly, the information flow direction is not limited to the vehicle 1- > the vehicle 2- > the vehicle 3- >, and in practical applications, the information flow direction may also include the vehicle 1- > the vehicle 3- > the vehicle 2, and the like, and the foregoing vehicle 1- > the vehicle 2- > the vehicle 3 is only an illustrative description, and is not limited in the embodiment of the present application.
In addition, each second target vehicle is defined as a transfer preceding vehicle of the first target vehicle in such a manner that, in one continuous transfer behavior, if the driving risk of the second target vehicle can affect the driving behavior of the first target vehicle and the driving risk of the first target vehicle does not affect the driving behavior of the second target vehicle, it can be defined that the second target vehicle is a transfer preceding vehicle of the first target vehicle and the first target vehicle is an immediately following vehicle of the second target vehicle.
The method for determining driving risk in this embodiment may be applied to the system architecture shown in fig. 1, and may also be applied to other system architectures, which are not limited herein.
For convenience of understanding, a method for determining driving risk is provided in an embodiment of the present application, and please refer to fig. 3, which is a schematic diagram illustrating an embodiment of the method for determining driving risk provided in the embodiment of the present application.
As shown in fig. 3, a method for determining a driving risk provided in an embodiment of the present application may include:
301. the method comprises the steps that a first target vehicle obtains first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, wherein each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer.
In this embodiment, the first target vehicle may acquire, through its own sensor, the environmental information where the first target vehicle is located, that is, the first road condition information; or the first road condition information can be acquired from the cloud server. Specifically, the first road condition information may include, but is not limited to, weather information, road grade information, road viscosity information, visibility information, or traffic sign information, and the like, which is not limited in this embodiment of the present application.
In addition, since the N second target vehicles acquire the respective driving risk states and the driving risk state numbers, so that each second target vehicle determines the respective corresponding first driving risk state vector based on the respective corresponding driving risk states and driving risk state numbers, the first driving risk state vector corresponding to each second target vehicle can be respectively denoted as x _1 ═ (x _1, 1}, x _1, 2}, x _1}, x _2 ═ 2,1}, x _2, 2}, x _ N ═ x _ N,1}, x _ N,2}, x _ N }. In this way, the first target vehicle can acquire the risk influence of the corresponding second target vehicle on the first target vehicle from each first driving risk state vector only by acquiring each first driving risk state vector from each second target vehicle.
It should be noted that, since the N second target vehicles are all transmission front vehicles of the first target vehicle, in the embodiment of the present application, the N second target vehicles may be sequentially labeled by using 1, 2.
For example, the vehicle 3 in fig. 2 is taken as a first target vehicle, and the corresponding second target vehicles are the vehicle 1 and the vehicle 2. If the number of driving risk states acquired by the vehicle 1 is m _1, the driving risk states corresponding to the m _1 driving risk states are respectively: "rollover", "rear-end collision", "tire burst", and the like, at this time, the vehicle 1 determines a first driving risk state vector corresponding to the vehicle 1 based on the m _1 driving risk states, and the vector is denoted as x _1 ═ x \ {1,1}, x _ {1,2}, and x _ {1, m _1}, where x _ {1,1} represents an influence of a first type of driving risk state in the m _1 driving risk states on the vehicle 1, such as a risk influence of rollover on the vehicle 1; x _1, 2, and x _1 are respectively expressed as a second type of driving risk state of the m _1 driving risk states, and the influence of the m _1 type of driving risk state on the vehicle 1 will not be described in detail in this embodiment of the present application.
If the number of driving risk states acquired by the vehicle 2 is m _2, the driving risk states corresponding to the m _2 driving risk states are respectively: "race overtaking", "rear-end collision", "tire burst", "overspeed", and so on, in which the vehicle 2 determines a first driving risk state vector corresponding to the vehicle 2 based on the m _2 driving risk states, and is denoted as x _2 ═ x _ {2,1}, x _ {2,2},. once.,. x _ {2, m _2}, where x _ {2,2} represents an influence of a second type of driving risk state in the m _2 driving risk states on the vehicle 2, such as a risk influence of rear-end collision on the vehicle 2; x _2, and x _2, m _2 are respectively expressed as a second type of driving risk state of the m _2 driving risk states, and the influence of the m _2 type of driving risk state on the vehicle 2 will not be described in detail in this embodiment of the present application.
Optionally, in other embodiments, the first driving risk state vectors corresponding to the N second target vehicles respectively obtained for the first target vehicle may be obtained by:
the first target vehicle sends a risk request to each second target vehicle, so that each second target vehicle determines a corresponding first driving risk state vector based on the risk request;
the first target vehicle receives the corresponding first driving risk state vector sent by each second target vehicle.
That is to say, the first target vehicle sends the risk request to the N second target vehicles, so that each second target vehicle can determine the first driving risk state vector corresponding to the second target vehicle by analyzing the risk request when receiving the risk request. Each second target vehicle may not only calculate the first driving risk state vector before receiving the risk request, but also determine the first driving risk state vector by obtaining the driving risk state and the driving risk state number corresponding to each second target vehicle after receiving the risk request. Therefore, after each second target vehicle determines the corresponding first driving risk state vector, the first driving risk state vector can be fed back to the first target vehicle.
302. The first target vehicle determines a second driving risk state vector based on the first road condition information.
In an embodiment, after the first target vehicle acquires the first road condition information, the second driving risk state vector may be determined based on the first road condition information, so that the risk influence of the environmental factor of the first target vehicle on the first target vehicle may be reflected from the second driving risk state vector.
In other embodiments, since the first target vehicle acquires the first road condition information, the first road condition information includes the risk states and the number of risk states, that is, which risk states in the current environment may have risk influence on the first target vehicle. Thus, the first target vehicle is determining a second driving risk state vector based on the first road condition information, which may be determined by the risk states and the number of risk states.
For example, assuming that the number of risk states acquired by the first target vehicle is m _0, which is "rainy", "mud on road", "low visibility", etc., respectively, the second driving risk state vector determined by the first target vehicle may be denoted as x _0 ═ x _ {0,1}, x _ {0,2},. x _, x _ {0, m _0}, where x _ {0,1} may be indicated in the first road condition information, and a first risk state of the m _0 risk states indicates a risk influence on the first target vehicle, such as a risk influence on the first target vehicle from "rainy" and x _0, 2}, x _ {0, m _1} indicates a second risk state of the m _0 risk states, respectively, and the m _0 risk state indicates a risk influence on the first target vehicle, details will not be described in the embodiments of the present application.
303. The first target vehicle determines a third driving risk state vector based on each of the first and second driving risk state vectors, the third driving risk state vector being indicative of a driving risk situation experienced by the first target vehicle.
In an embodiment, after obtaining each first driving risk state vector and each second driving risk state vector, the first target vehicle may determine a third driving risk state vector based on each first driving risk state vector and each second driving risk state vector, that is, the first driving risk state vector corresponding to each second target vehicle and the second driving risk state vector in the environment where the first target vehicle is located are integrated to obtain N risk influences, which are generated by the vehicle and road condition information of the second target vehicle, on the first target vehicle.
Specifically, in other embodiments, the foregoing step 303 in fig. 3 may also be understood with reference to fig. 4, where fig. 4 is a schematic view of another embodiment of the method for determining driving risk provided by the embodiment of the present application. As shown in fig. 4, another method for determining driving risk provided by the embodiment of the present application may include:
s401, before a first target vehicle determines a third driving risk state vector based on each first driving risk state vector and each second driving risk state vector, the first target vehicle sends a first vehicle identifier corresponding to each second target vehicle and a second vehicle identifier corresponding to the first target vehicle to a server, each first vehicle identifier is used for instructing the server to determine a first risk transfer matrix corresponding to each second target vehicle, and the second vehicle identifier is used for instructing the server to determine a second risk transfer matrix corresponding to the first road condition information.
S402, the first target vehicle receives each first risk transfer matrix and each second risk transfer matrix sent by the server.
S403, the first target vehicle obtains each first driving risk condition based on each first risk transfer matrix and each first driving risk state vector, and obtains a second driving risk condition based on the second risk transfer matrix and the second driving risk state vector.
S404, the first target vehicle determines a third driving risk state vector based on each of the first driving risk condition and the second driving risk condition.
That is, it is understood that, because the risk transfer matrix may be a constant, or may change with time, and the risk transfer matrix corresponding to each second target vehicle and the risk transfer matrix corresponding to the first road condition information are preset in the server such as the cloud, the first target vehicle may send the first vehicle identifier corresponding to each second target vehicle and the second vehicle identifier of the first target vehicle to the server such as the cloud, so that the server such as the cloud determines the corresponding risk transfer matrix from the database based on each first vehicle identifier and each second vehicle identifier.
In this way, the first target vehicle can obtain, from a server such as a cloud, a risk transfer matrix from each second target vehicle to the first target vehicle, that is, the first risk transfer matrices corresponding to the N second target vehicles are respectively denoted as a _ { N +1,1}, a _ { N +1,2},. a _ { N +1, N }. In addition, the first target vehicle needs to acquire a risk transfer matrix, namely a second risk transfer matrix, which is recorded as a _ { n +1,0} and is used for transmitting the first road condition information to the first target vehicle from a server such as a cloud. For example, in the application scenario described in the foregoing fig. 2, assuming that the vehicle 3 is a first target vehicle, and the vehicles 1 and 2 are transmission predecessors of the vehicle 3, that is, the vehicles 1 and 2 are second target vehicles, then the first risk transmission matrix corresponding to the vehicle 1 is a _ { n +1,1}, and then the first risk transmission matrix corresponding to the vehicle 2 is a _ { n +1,2}, and so on, which will not be specifically described in the embodiment of the present application.
After the first target vehicle acquires each first risk transfer matrix and the first driving risk state vector, a risk transmission model from the driving risk of the second target vehicle to the first target vehicle may be established according to each first risk transfer matrix and the corresponding first driving risk state vector, that is, the first driving risk condition, which is denoted as a _ { n +1,1 }. x _1+ a _ { n +1, 2}. x _2+.. + a _ { n +1, n }. x _ n, where a _ { n +1,1 }. x _1 may represent the influence of the driving risk of the vehicle 1 on the driving risk of the vehicle n +1, and a _ { n +1, 2}. x _2,. a _, n }. x _ n may refer to a _ { n +1,1}, and will not be described herein.
After the first target vehicle acquires the second risk transfer matrix and the second driving risk state vector, a risk transmission model for transmitting the driving risk of the first road condition information to the first target vehicle can be established according to the second risk transfer matrix and the corresponding second driving risk state vector, that is, the second driving risk condition is marked as a _ { n +1,0 }. x _ 0.
Then the first target vehicle can process the first driving risk condition and the second driving risk condition to obtain a third driving risk state vector which is recorded as
The third driving risk state vector can effectively simulate the influence process of the driving risks of the N second target vehicles and the first road condition information on the driving risk of the first target vehicle. The driving risk transfer diagram provided in the embodiment of the present application can be understood with reference to fig. 5 in particular. As can be seen from fig. 5, the third driving risk state vector x _ { n +1} of the vehicle n +1 can be determined according to the second driving risk state vector x _0 and the second risk transfer matrix a _ { n +1,0} corresponding to the first road condition information, and the first driving risk state vectors x _1, x _2,. once, x _ n and the first risk transfer matrix a _ { n +1,1}, a _ { n +1,2},. once, a _ { n +1, n } corresponding to the vehicles 1 to n, respectively.
In addition, it should be noted that, when each of the first risk transfer matrix and the second risk transfer matrix is a constant, each of the first risk transfer matrix and the second risk transfer matrix may be stored in the first target vehicle in advance, in addition to being acquired from a server such as a cloud.
For further explanation of the method for determining driving risk provided in the embodiments of the present application, please refer to fig. 6, which is a schematic diagram of another embodiment of the method for determining driving risk provided in the embodiments of the present application.
As shown in fig. 6, a method for determining a driving risk provided in an embodiment of the present application may include:
601. the method comprises the steps that a first target vehicle obtains first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, wherein each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer.
602. The first target vehicle determines a second driving risk state vector based on the first road condition information.
603. The first target vehicle determines a third driving risk state vector based on each of the first and second driving risk state vectors, the third driving risk state vector being indicative of a driving risk situation experienced by the first target vehicle.
In this embodiment, the steps 601-603 can be understood by referring to the steps 301-303 described in fig. 3, which will not be described herein in detail.
604. The first target vehicle determining N +1 risk states in the third driving risk state vector;
605. and the first target vehicle determines a driving risk value based on the N +1 risk state information and a preset risk weight corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
That is, since each element in the third driving risk state vector can reflect the influence degree of the corresponding risk state on the driving risk of the first target vehicle, the first target vehicle may determine N +1 risk states in the third driving risk state vector, that is, x _ { N +1,1}, x _ { N +1,2}, x _ { N +1, m _ { N +1}, where the first target vehicle may obtain the risk state values corresponding to the N +1 risk states from a server such as a cloud, so that all the risk state values may be determined according to a weighted summation algorithm, and the driving risk degree of the first target vehicle may be specified by the driving risk values.
For example, assume that the third driving risk state vector includes 4 types of risk states, respectively: rollover, rear-end collision, tire burst and race racing, if the corresponding risk state values are 0.5, 0.3, 0.2, 0.4, and the preset risk values are 0.1, 0.2, 0.3 and 0.4, respectively, then the driving risk value is 0.1 x { n +1,1} +0.2 x { n +1,2} +0.3 x { n +1,3} +0.4 x { n +1,4} - [ 0.1 x 0.5+0.2 0.3+0.3 0.2+0.4 ═ 0.33. It should be understood that the aforementioned risk state values are 0.5, 0.3, 0.2, and 0.4, while the preset risk values of 0.1, 0.2, 0.3, and 0.4 are merely illustrative, and other parameters may be used in practical applications, and are not limited herein.
In the embodiment of the application, the first driving risk state vectors corresponding to the N second target vehicles are obtained, and the second driving risk state vectors are determined based on the obtained first road condition information, so that the third driving risk state vector corresponding to the first target vehicle is determined based on each first driving risk state vector and each second driving risk state vector, and the driving risk condition of the first target vehicle is reflected by the third driving risk state vector. In the embodiment, not only the driving risk influence of the road condition information on the first target vehicle is considered, but also the driving risk influence of the multiple driving risks of the N second target vehicles on the first target vehicle is considered, so that the determined driving risk condition of the first target vehicle is more practical, and the driving risk warning experience can be provided for the driver in real time.
The scheme provided by the embodiment of the application is mainly introduced from the perspective of a method. It is to be understood that the hardware structure and/or software modules for performing the respective functions are included to realize the above functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, functional modules of the apparatus may be divided according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
Referring to fig. 7, in the following detailed description of the first target vehicle 70 in the embodiment of the present application, fig. 7 is a schematic diagram of an embodiment of the first target vehicle 70 provided in the embodiment of the present application, where the first target vehicle 70 may include:
an obtaining unit 701, configured to obtain first road condition information and first driving risk state vectors corresponding to N second target vehicles, where each second target vehicle is a transmission leading vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
a first determining unit 702, configured to determine a second driving risk state vector according to the first road condition information;
a second determining unit 703, configured to determine a third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector, where the third driving risk state vector is used to indicate a driving risk situation suffered by the first target vehicle.
Optionally, on the basis of the embodiment corresponding to fig. 7, in another embodiment of the first target vehicle 70 provided in the embodiment of the present application, the first target vehicle 70 further includes:
a first sending unit, configured to send, to a server, a first vehicle identifier corresponding to each of the second target vehicles and a second vehicle identifier corresponding to the first target vehicle before determining a third driving risk state vector based on each of the first driving risk state vectors and the second driving risk state vector, where each of the first vehicle identifiers is used to instruct the server to determine a first risk transfer matrix corresponding to each of the second target vehicles, and the second vehicle identifier is used to instruct the server to determine a second risk transfer matrix corresponding to the first road condition information;
the obtaining unit 701 is configured to receive each first risk transfer matrix and each second risk transfer matrix sent by the server;
correspondingly, the second determining unit 703 includes:
a processing module, configured to obtain each first driving risk condition based on each first risk transfer matrix and each first driving risk state vector, and obtain a second driving risk condition based on the second risk transfer matrix and the second driving risk state vector;
a first determining module configured to determine a third driving risk state vector according to each of the first driving risk condition and the second driving risk condition.
Optionally, on the basis of the above-mentioned fig. 7 and the optional embodiment corresponding to fig. 7, in another embodiment of the first target vehicle 70 provided in this embodiment of the application, the first road condition information includes risk states and the number of the risk states, and the first determining unit 702 includes:
a second determination module to determine a second driving risk state vector according to the risk states and the number of risk states.
Optionally, on the basis of the foregoing fig. 7 and the optional embodiment of fig. 7, in another embodiment of the first target vehicle 70 provided in the embodiment of the present application, the first target vehicle 70 further includes:
a second sending unit, configured to send a risk request to each second target vehicle before acquiring first driving risk state vectors corresponding to N second target vehicles, respectively, so that each second target vehicle determines a corresponding first driving risk state vector based on the risk request;
correspondingly, the obtaining unit 701 includes:
and the acquisition module is used for receiving the corresponding first driving risk state vector sent by each second target vehicle.
Optionally, on the basis of the optional embodiments in fig. 7 and fig. 7, in another embodiment of the first target vehicle 70 provided in this embodiment of the present application, the second determining unit 703 is further configured to determine N +1 risk states in a third driving risk state vector after the first target vehicle determines the third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector;
determining a driving risk value based on the N +1 risk states and preset risk weights corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
The first target vehicle 70 in the embodiment of the present application is described above from the perspective of a modular functional entity, and the computer device in the embodiment of the present application is described below from the perspective of hardware processing. Fig. 8 is a schematic structural diagram of a computer device provided in an embodiment of the present application, which may include the first target vehicle 70 described above, and the like, and may generate a relatively large difference due to different configurations or performances, and which may include at least one processor 801, a communication line 807, a memory 803, and at least one communication interface 804.
The processor 801 may be a general-purpose Central Processing Unit (CPU), a microprocessor, an application-specific integrated circuit (server IC), or one or more ICs for controlling the execution of programs in accordance with the present invention.
The communication link 807 may include a path that conveys information between the aforementioned components.
The communication interface 804 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as ethernet, Radio Access Network (RAN), wireless local area networks (W L AN), etc.
The memory 803 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, which may be separate and coupled to the processor via a communication line 807. The memory may also be integral to the processor.
The memory 803 is used for storing computer-executable instructions for executing the present invention, and is controlled by the processor 801. The processor 801 is configured to execute computer-executable instructions stored in the memory 803 to implement the method for determining driving risk provided by the above-described embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
In particular implementations, the computer device may include multiple processors, such as processor 801, processor 802 in fig. 8, for example, as an embodiment. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
In particular implementations, the computer device may also include an output device 805 and an input device 806, as one embodiment. The output device 805 is in communication with the processor 801 and may display information in a variety of ways. The input device 806 is in communication with the processor 801 and may receive user input in a variety of ways. For example, the input device 806 may be a mouse, a touch screen device, or a sensing device, among others.
The computer apparatus described above may be a general-purpose device or a special-purpose device. In particular implementations, the computer device may be a desktop, laptop, nas server, wireless end device, embedded device, or a device with a similar structure as in fig. 8. The embodiment of the application does not limit the type of the computer equipment.
In the embodiment of the present application, the processor 801 included in the computer device further has the following functions:
acquiring first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, wherein each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
determining a second driving risk state vector based on the first road condition information;
determining a third driving risk state vector based on each of the first and second driving risk state vectors, the third driving risk state vector being indicative of a driving risk situation experienced by the first target vehicle.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the unit is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method of determining driving risk, comprising:
a first target vehicle acquires first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, wherein each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
the first target vehicle determining a second driving risk state vector based on the first road condition information;
the first target vehicle determines a third driving risk state vector based on each of the first and second driving risk state vectors, the third driving risk state vector being indicative of a driving risk condition experienced by the first target vehicle.
2. The method of claim 1, wherein prior to the first target vehicle determining a third driving risk state vector based on each of the first driving risk state vector, the second driving risk state vector, the method further comprises:
the first target vehicle sends a first vehicle identifier corresponding to each second target vehicle and a second vehicle identifier corresponding to the first target vehicle to a server, wherein each first vehicle identifier is used for instructing the server to determine a first risk transfer matrix corresponding to each second target vehicle, and the second vehicle identifier is used for instructing the server to determine a second risk transfer matrix corresponding to the first road condition information;
the first target vehicle receives each first risk transfer matrix and the second risk transfer matrix sent by the server;
correspondingly, the first target vehicle determining a third driving risk state vector based on each of the first driving risk state vector, the second driving risk state vector, comprising:
the first target vehicle obtains each first driving risk condition based on each first risk transfer matrix and each first driving risk state vector, and obtains a second driving risk condition based on the second risk transfer matrix and the second driving risk state vector;
the first target vehicle determines a third driving risk state vector based on each of the first and second driving risk conditions.
3. The method of claim 1 or 2, wherein the first road condition information includes a risk state and a number of the risk states, the first target vehicle determining a second driving risk state vector based on the first road condition information, comprising:
the first target vehicle determines a second driving risk state vector based on the risk states and the number of risk states.
4. The method according to any one of claims 1-3, further comprising, before the first target vehicle obtains the first driving risk state vectors corresponding to the respective N second target vehicles:
the first target vehicle sends a risk request to each second target vehicle, so that each second target vehicle determines a corresponding first driving risk state vector based on the risk request;
correspondingly, the first target vehicle obtains first driving risk state vectors corresponding to the N second target vehicles respectively, and the method includes:
the first target vehicle receives the corresponding first driving risk state vector sent by each second target vehicle.
5. The method of any of claims 1-3, wherein after the first target vehicle determines a third driving risk state vector based on each of the first driving risk state vector, the second driving risk state vector, the method further comprises:
the first target vehicle determining N +1 risk states in the third driving risk state vector;
and the first target vehicle determines a driving risk value based on the N +1 risk states and preset risk weights corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
6. The method of claim 4, wherein after the first target vehicle determines a third driving risk state vector based on each of the first driving risk state vector, the second driving risk state vector, the method further comprises:
the first target vehicle determining N +1 risk states in the third driving risk state vector;
and the first target vehicle determines a driving risk value based on the N +1 risk states and preset risk weights corresponding to the N +1 risk states, wherein the driving risk value is used for indicating the driving risk degree of the first target vehicle.
7. A first target vehicle, characterized by comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring first road condition information and first driving risk state vectors corresponding to N second target vehicles respectively, each second target vehicle is a transmission front vehicle of the first target vehicle in the same transmission behavior, N is greater than 0, and N is an integer;
the first determining unit is used for determining a second driving risk state vector according to the first road condition information;
a second determination unit, configured to determine a third driving risk state vector based on each of the first driving risk state vector and the second driving risk state vector, where the third driving risk state vector is used to indicate a driving risk situation suffered by the first target vehicle.
8. The first target vehicle of claim 7, further comprising:
a first sending unit, configured to send, to a server, a first vehicle identifier corresponding to each of the second target vehicles and a second vehicle identifier corresponding to the first target vehicle before determining a third driving risk state vector based on each of the first driving risk state vectors and the second driving risk state vector, where each of the first vehicle identifiers is used to instruct the server to determine a first risk transfer matrix corresponding to each of the second target vehicles, and the second vehicle identifier is used to instruct the server to determine a second risk transfer matrix corresponding to the first road condition information;
the acquiring unit is configured to receive each of the first risk transfer matrix and the second risk transfer matrix sent by the server;
correspondingly, the second determining unit includes:
a processing module, configured to obtain each first driving risk condition based on each first risk transfer matrix and each first driving risk state vector, and obtain a second driving risk condition based on the second risk transfer matrix and the second driving risk state vector;
a first determining module configured to determine a third driving risk state vector according to each of the first driving risk condition and the second driving risk condition.
9. A computer device, characterized in that the computer device comprises: an input/output (I/O) interface, a processor and a memory,
the memory has stored therein program instructions;
the processor is configured to execute program instructions stored in the memory to perform the method of any of claims 1-6.
10. A computer-readable storage medium comprising instructions that, when executed on a computer device, cause the computer device to perform the method of one of claims 1-6.
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