CN115691124A - Risk assessment method and device for urban road driving data - Google Patents
Risk assessment method and device for urban road driving data Download PDFInfo
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- CN115691124A CN115691124A CN202211273032.XA CN202211273032A CN115691124A CN 115691124 A CN115691124 A CN 115691124A CN 202211273032 A CN202211273032 A CN 202211273032A CN 115691124 A CN115691124 A CN 115691124A
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Abstract
The invention discloses a method and a device for evaluating dangers of urban road driving data, wherein the method comprises the following steps: s1, acquiring road driving safety parameters; s2, acquiring vehicle operation data; s3, encrypting and uploading the vehicle running data to a block chain network; s4, carrying out danger assessment on the vehicle running data in a game mode, and updating the state of the road running safety parameters; s5, obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter; and S6, mapping the vehicle behavior from a behavior database according to the risk value. By adopting the technical scheme of the invention, the danger evaluation is carried out on the states of the participatory vehicle nodes in different urban road environments, and the vehicle safety behavior operation is returned according to the evaluation result, so that the safety of the vehicle road data is ensured.
Description
Technical Field
The invention belongs to the technical field of risk assessment, and relates to a method and a device for assessing risks of urban road driving data.
Background
At present, data transmission and storage are carried out by establishing a centralized cloud database, an enterprise can directly supervise data transmitted by users and vehicles, the vehicles send requests to a server, and a system platform calls the cloud database to respond and return the data. The data request mode with the enterprise server as the center has risks, the server is down due to the fact that the instantaneous request exceeds the server load in unit time, the network cannot respond, the problem that the operation cost is too high due to the fact that the load level of the server is improved is solved. An attacker aiming at the car networking service can also modify the data resulting in a hazard.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a device for evaluating dangers of urban road driving data, wherein the method and the device are used for evaluating dangers of nodes of participating vehicles in different urban road environments and returning the dangers to vehicle safety behavior operation according to evaluation results to ensure the safety of the vehicle road data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a danger assessment method for urban road driving data comprises the following steps:
s1, acquiring road driving safety parameters;
s2, acquiring vehicle operation data;
s3, encrypting and uploading the vehicle running data to a block chain network;
s4, performing danger assessment on the vehicle running data in a game mode, and updating the state of the road running safety parameters;
s5, obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter;
and S6, mapping the vehicle behavior from a behavior database according to the risk value.
Preferably, the road running safety parameters are determined by vehicle self-inspection state, road position coordinates and road environment condition data in a relatively static state.
Preferably, the vehicle operation data includes: the speed of the vehicle at that time, the angular velocity at the next moment, and the surrounding environment road visual data shot by the radar and the vehicle-mounted camera.
Preferably, the step S4 is specifically: and dynamically adjusting and early warning state values in the interaction process of different vehicle nodes through a game according to the vehicle operation data, and grading scenes through early warning results to realize danger assessment.
The invention also provides a danger assessment device for urban road driving data, which comprises:
the first acquisition module is used for acquiring road driving safety parameters;
the second acquisition module is used for acquiring vehicle operation data;
the encryption module is used for encrypting and uploading the vehicle operation data to a block chain network;
the danger evaluation module is used for carrying out danger evaluation on the vehicle running data in a game mode and updating the state of the road running safety parameters;
the processing module is used for obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter;
and the mapping module is used for mapping the vehicle behavior from the behavior database according to the risk value.
Preferably, the road driving safety parameter is determined by a vehicle self-checking state, a road position coordinate and road environment condition data in a relatively static state.
Preferably, the vehicle operation data includes: the speed of the vehicle at that time, the angular velocity at the next moment, and the peripheral environment road vision data shot by the radar and the vehicle-mounted camera.
Preferably, the danger assessment module dynamically adjusts and pre-warns state values in the interaction process of different vehicle nodes through a game according to the vehicle running data, and grades scenes through pre-waring results to achieve danger assessment.
According to the technical scheme, the data security in network transmission is ensured by using a block chain technology, meanwhile, the danger evaluation is carried out on the states of the participating vehicle nodes in different urban road environments in a game theory mode, and the vehicle safety behavior operation is returned according to the evaluation result, so that the vehicle road data security is ensured.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a risk assessment method for urban road driving data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Example 1:
as shown in fig. 1, an embodiment of the present application provides a method for estimating a risk of urban road driving data, including the following steps:
s1, acquiring road driving safety parameters;
s2, acquiring vehicle operation data;
s3, encrypting and uploading the vehicle operation data to a block chain network;
s4, carrying out danger assessment on the vehicle running data in a game mode, and updating the state of the road running safety parameters;
s5, obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter;
and S6, mapping the vehicle behavior from a behavior database according to the risk value.
Embodiments of the invention utilize universe
Building a system model by theory, and realizing danger assessment facing urban road driving data by using a block chain technology; the system model is defined by using a universe set theory as follows:
S＝(A,B,F(λ,R 2 ,t),J,D)
the universe set theory comprises a quintuple module, wherein A is model input end data comprising sensor data of a vehicle and peripheral environment node data acquired by the vehicle in a form process; b, a data mapping mode is used for cleaning and screening after data input is unified, useless data are removed and data structure matching is carried out, vehicle data need to be encrypted firstly in the process, and after block chains are transmitted to different nodes, the receiving nodes decrypt to obtain data information, so that the accuracy of data transmission is ensured; f, a model data transformation process, wherein the optimal behavior operation in the current state is obtained through data interaction among different vehicle nodes, and different nodes are enabled to compete for high voting rate by utilizing a game theory mode so as to ensure the safety of an evaluation result; d, converting model data into mapping of a corresponding table, and giving a safety behavior operation at the moment after the vehicle finishes the evaluation of the environment state; j is the error value of the model game result, and the error value is also used as the mapping key weight to participate in the next game cycle.
As an implementation manner of the embodiment of the present invention, step S1 may specifically obtain the road driving safety parameter by the following manner: initializing vehicles and joining the vehicles into an Internet of vehicles network, wherein the safe transmission of data among the Internet of vehicles is ensured by a bottom layer blockchain network; the vehicle as a newly added node participates in the interaction with the surrounding road units, and the road driving safety parameter lambda of the current state is obtained from the Internet of vehicles platform, and the safety parameter is comprehensively calculated by the vehicle self-checking state, the road position coordinate and the road environment condition data in the relative static state.
As an implementation manner of the embodiment of the present invention, in step S2, the vehicle acquires vehicle operation data including a speed of the vehicle at that time, an angular velocity at the next time, and peripheral environment road vision data captured by the radar and the vehicle-mounted camera according to a built-in sensor.
As an implementation manner of the embodiment of the present invention, in step S3, the asymmetric encryption algorithm is used to encrypt and decrypt the vehicle operation data, the data plaintext a is encrypted by using the public key (n, e), n and e are public keys, the encrypted ciphertext is Y, and the encryption formula is: a. The e B (modn); decrypting the ciphertext Y by using a private key (n, d), wherein n and d are the private keys, the decrypted plaintext is A, and the decryption formula is B d ＝A(modn)。
As an implementation manner of the embodiment of the present invention, step S4 is: and dynamically adjusting and early warning state values in the interaction process of different vehicle nodes through a game according to the vehicle operation data, grading the scene through early warning results, and feeding back to vehicle safety behavior operation according to different early warning results. The method specifically comprises the following steps:
s41, defining participatory variables before the game, wherein the vehicle needs to send a transaction in the interactive process, and the base station closest to the transaction is used as the SN in the game 1 The remaining nodes of the forwarding are SN 2 . Assuming that the argument p represents the capability of forwarding the transaction, the more nodes included in the network range, the more voting weights, and the stronger the capability of forwarding the transaction. Let D the number of nodes participating in the network for a certain period of time. Let VW: voting rightRatio, transaction forwarding effort and voting weight set on the network by consensus verification. By setting a node consensus algorithm, each node tries to increase the voting rate of the node;
S42、SN 1 the maximum value for processing the transaction is determined, i.e. SN 1 The capacity cost of the transaction forwarding is fixed, the transaction to be forwarded is a demand function at the moment, the node transmission is distributed point-to-point transmission, and the VW is data feedback for successful verification of the forwarded transaction;
s43, because of SN 1 First of all, determine p 1 ，SN 2 Knowing SN 1 P of (a) 1 In the determination of p 1 While, SN 1 The SN must be considered 2 How to react, SN 1 Determined p 1 It is necessary to constrain the SN to follow the reaction function of all the received transactions at that time 1 The determined node demand number is a VW maximization value which is constrained by a reaction function following SN;
s44, setting the current time demand function as:
D＝D(p 1 +p 2 )a-b(p 1 +p 2 )
wherein p is 1 And p 2 Are respectively SN 1 And SN 2 The ability to forward transactions; a. b is a fixed constant of a linear function; assuming that the initial voting weights of the two nodes are the same, both are C = C 0 p，c 0 As constants, the values of the constants differ among different networks. First consider at a given SN 1 Under D, SN 2 Seeking an optimal solution p that maximizes own VW 2 Namely: maxp 2 [a-b(p 1 +p 2 )]-cp 2 P of the optimal solution 2 Is about p 1 Function p of 2 ＝g(p 1 )；
S45, knowing SN 2 After reaction for any given yield, SN 1 The optimal yield model is: maxp 1 [a-b(p 1 +p 2 )]-cp 1 ，s.t.p 2 ＝g(p 1 )；
Therefore, the following optimization model needs to be solved: maxp 2 [a-b(p 1 +p 2 )]-cp 2 To obtain p 2 ＝g(p 1 )；
S46, solving the following optimization model: maxp 1 [a-b(p 1 +p 2 )]-cp 1 ，s.t.p 2 ＝g(p 1 )；
S47, obtaining p 1 Substituting for p 2 ＝g(p 1 ) To obtain p 2 To obtain (p) at two-node equalization 1 ,p 2 ) Circularly executing secondary game for all nodes in the network;
s48, obtaining the balance parameter coefficients of different node vehicles at the unit running time through the game, updating the state of the road running safety parameter lambda in the step S1,
λ new ＝f(λ 0 ,p,t)；
wherein λ is new The update state of the safety parameter, f represents the course of the change of the vehicle parameter at time t, lambda 0 The state is the vehicle safety state at the last moment, p is the model state of different vehicle game equilibrium states, for example, the input state of the SN1 node at the moment is p 1 Input state p of SN2 node at this time 2 And t is the time interval from the last time to the receipt of new vehicle data.
As an implementation manner of the embodiment of the present invention, step S5 specifically includes:
s51, after obtaining a new safety state of the vehicle through a game theory, carrying out error analysis on environmental data, and carrying out comparison calculation according to data of an evaluation result at the previous moment of the data and feedback received real data to obtain an error parameter R 2 The following formula:
wherein, y i Is environmental data, R, acquired by the vehicle in the sensing process 2 The error degree of the result obtained by the vehicle game evaluation at the moment compared with the real environment data is shown,
representing the mean value of environmental data acquired during the vehicle operation over a time interval t, f i Indicating the resulting reference data predicted at the current state, SS res Is a predicted data and average value
Error of (S), SS tot Representing true data and mean values
The error of (2).
S52, calculating the risk value of the road data evaluation by the vehicle through the following formula:
risk＝F(λ,R 2 ,t)
where risk represents the risk value for the state at that time, the risk value being calculated from the common weight.
As an implementation manner of the embodiment of the present invention, in step S6, as shown in table 1, when the vehicle is just started, the value of the road driving safety parameter λ accessing to the network starts to change, the speed is low when the vehicle is accelerated and started, the vehicle interacts with the surrounding environment to be in a safe state, and the risk value is to be in a state of 0 to 0.2; and when the acceleration speed of the vehicle is stable, the synchronous interaction of the surrounding vehicles and the environmental information is started, and the game of the vehicle and the surrounding units requires win-win state values of both parties. When the risk value obtained by the vehicle game result is in a state of 0.2-0.4, the vehicle can safely run through the game result; when the risk value obtained by the vehicle game result is in a state of 0.4-1, the state of the vehicle is considered to be in a dangerous condition, and safety operations such as acceleration, deceleration, braking and the like are carried out to ensure the running safety of the vehicle.
TABLE 1
| risk value | Status of state | Acceleration of a vehicle | Behavior |
| 0-0.2 | Start state | a＝+s/v | Acceleration |
| 0.2-0.4 | Normal state | a＝0 | Holding |
| 0.4-0.6 | Wave dynamics | a＝±s/v | Acceleration/deceleration |
| 0.6-0.8 | State of risk | a＝-s/v | Speed reduction |
| 0.8-1 | Dangerous state | a＝-max | Brake |
The method aims at the problems in the data information security of the Internet of vehicles: the dangerous road conditions and the leakage in the data transmission process of the vehicles are participated in the Internet of vehicles. The vehicles can meet complex and changeable scenes in the process of traveling on urban roads, safety decisions of the vehicles according to different driving environments are important, and the fact that instructions are quickly and accurately obtained from the Internet of vehicles cloud server is also important. The block chain technology can ensure that data are not tampered in the network transmission process, road side units in the internet of vehicles can also provide quick service response for vehicles, and intelligent contracts can evaluate data of participated vehicle nodes to ensure data transmission safety. The embodiment of the invention firstly ensures the data security in network transmission by using a block chain technology, simultaneously carries out danger evaluation on the states of the participating vehicle nodes in different urban road environments in a game theory mode, and returns the evaluation results to vehicle safety behavior operation to ensure the vehicle road data security. At present, data safety interaction between vehicle networking is carried out by using a block chain technology, but scenes under dangerous conditions are not analyzed, and life and property safety of personnel can be endangered due to complex road conditions in the running process of vehicles on actual urban roads. Aiming at the problems, the embodiment of the invention carries out evaluation and early warning on the data state of the vehicle when the vehicle runs in the urban road, thereby ensuring the running safety of the vehicle.
The embodiment of the invention has the following technical effects:
1. according to the embodiment of the invention, based on the block chain technology, the urban road environment data acquired in the vehicle driving process is subjected to danger assessment, whether the vehicle is in a dangerous environment is subjected to state calculation, and the vehicle maps safety behaviors according to the state result, so that the driving safety of the vehicle is ensured.
2. The embodiment of the invention replaces the traditional danger assessment mode with a game theory mode, utilizes the interactive game of the vehicle and the surrounding environment state, and judges whether the critical value is in the risk interval by means of the real-time game result and the state change time, thereby achieving the purpose of data risk assessment.
Example 2:
the invention also provides a danger assessment device for urban road driving data, which comprises:
the first acquisition module is used for acquiring road driving safety parameters;
the second acquisition module is used for acquiring vehicle operation data;
the encryption module is used for encrypting and uploading the vehicle operation data to a block chain network;
the danger evaluation module is used for carrying out danger evaluation on the vehicle running data in a game mode and updating the state of the road running safety parameters;
the processing module is used for obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter;
and the mapping module is used for mapping the vehicle behavior from the behavior database according to the risk value.
As an implementation manner of the embodiment of the present invention, the road driving safety parameter is determined by a vehicle self-inspection state, a road position coordinate, and road environment condition data in a relatively static state.
As an implementation of the embodiment of the present invention, the vehicle operation data includes: the speed of the vehicle at that time, the angular velocity at the next moment, and the peripheral environment road vision data shot by the radar and the vehicle-mounted camera.
As an implementation manner of the embodiment of the invention, the risk assessment module dynamically adjusts and pre-warns state values in the interaction process of different vehicle nodes through a game according to the vehicle operation data, and grades a scene through a pre-warning result to achieve risk assessment.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (8)
1. A danger assessment method for urban road driving data is characterized by comprising the following steps:
s1, acquiring road driving safety parameters;
s2, acquiring vehicle operation data;
s3, encrypting and uploading the vehicle running data to a block chain network;
s4, carrying out danger assessment on the vehicle running data in a game mode, and updating the state of the road running safety parameters;
s5, obtaining a risk value of road data evaluation according to the updated state of the road driving safety parameter;
and S6, mapping the vehicle behavior from a behavior database according to the risk value.
2. The city road driving data-oriented risk assessment method according to claim 1, wherein the road driving safety parameter is determined by vehicle self-inspection state, road position coordinates, and road environment condition data in a relatively static state.
3. The city road driving data-oriented risk assessment method according to claim 2, wherein the vehicle operation data comprises: the speed of the vehicle at that time, the angular velocity at the next moment, and the peripheral environment road vision data shot by the radar and the vehicle-mounted camera.
4. The city road driving data-oriented risk assessment method according to claim 3, wherein the step S4 specifically comprises: and dynamically adjusting and early warning state values in the interaction process of different vehicle nodes through a game according to the vehicle operation data, and grading scenes through early warning results to realize danger assessment.
5. A danger assessment device for urban road driving data, characterized by comprising:
the first acquisition module is used for acquiring road driving safety parameters;
the second acquisition module is used for acquiring vehicle operation data;
the encryption module is used for encrypting and uploading the vehicle operation data to a block chain network;
the danger evaluation module is used for carrying out danger evaluation on the vehicle running data in a game mode and updating the state of the road running safety parameters;
the processing module is used for obtaining a risk value of road data evaluation according to the state of the updated road driving safety parameter;
and the mapping module is used for mapping the vehicle behavior from the behavior database according to the risk value.
6. The urban road driving data-oriented risk assessment device according to claim 5, wherein the road driving safety parameters are determined by vehicle self-inspection state, road position coordinates, and road environment condition data in a relatively static state.
7. The city road driving data oriented risk assessment device according to claim 6, wherein said vehicle operation data comprises: the speed of the vehicle at that time, the angular velocity at the next moment, and the surrounding environment road visual data shot by the radar and the vehicle-mounted camera.
8. The urban road driving data-oriented danger assessment device according to claim 7, wherein the danger assessment module dynamically adjusts and pre-warns state values in different vehicle node interaction processes through gaming according to the vehicle operation data, and grades scenes through pre-waring results to achieve danger assessment.
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