CN111710179B - Dynamic silence monitoring mixed area method based on traffic light state - Google Patents
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Abstract
The invention discloses a dynamic silence monitoring mixed area method based on traffic light states. At present, a plurality of academic strategies propose a method for using a pseudonym to protect the track privacy of a vehicle in the driving process, but the anonymous high efficiency, the driving safety, the realization convenience and the resource utilization cannot be considered simultaneously in the proposed method. The method comprises three parts: the method comprises the steps of a silent mixing area model based on traffic light state change, prediction based on traffic flow change selection tuning and automatic adjustment of dynamic silent length based on the two models. The dynamic silence monitoring mixed area method based on the traffic light state provided by the invention can simply and efficiently ensure privacy and driving safety during silence in an urban environment, and obviously improve the performance and resource utilization of the whole internet of vehicles safety application.
Description
Technical Field
The invention belongs to the field of vehicle pseudonym replacement in a vehicle network, and relates to a novel dynamic hybrid area design method allowing pseudonym replacement.
Background
In recent years, vehicle ad hoc networks (VANET) provide great help for the intelligent development of the internet of vehicles in the form of a dynamic and efficient topology structure, and each vehicle is used as a communication node to construct a network to allow the vehicles to communicate with all surrounding environments (V2X), and the aim is to provide a centerless dynamic ad hoc real-time topology network during road driving so as to mainly realize some safety applications such as cooperative driving, accident early warning and non-safety applications such as infotainment and the like. The safety application technology improves the running safety and traffic efficiency of the vehicle by providing continuous dynamic communication between the vehicle and the surrounding environment, the vehicle continuously broadcasts the current state information of the vehicle at the frequency of 1-10Hz in the running process, the current state information is also called as a beacon and comprises a vehicle identifier, a position, a speed, a direction, a system state and the like, the vehicle is helped to know the surrounding environment in advance, the vehicles run in a cooperation mode, the driver is reminded to obtain the buffer time for the upcoming accident, and the running safety of the road is greatly improved.
At the same time, however, due to the nature of its wireless broadcast, an eavesdropper can easily gather this security information with a very low cost receiver and deduce therefrom the detailed movement pattern of the vehicle. An attacker can gain access to when the driver goes out, goes home, goes to a hospital, works where, etc., which can seriously violate the privacy and even the security of the life and property of the driver. Therefore, while providing road security using VANET technology to reduce traffic accidents, it is important to protect the location privacy and trajectory privacy of the vehicle.
Many kana change schemes have been proposed in current research, and these schemes implement common change of the kana of multiple vehicles mainly by finding suitable change time or defining reasonable change place, which is generally divided into a strategy based on mixed region and a strategy based on mixed context; the method is characterized in that a mixed region-based strategy, such as setting of crossroads, parking lots, gas stations and other traffic-intensive regions, executes pseudonymous name change, exchange or encryption, is widely used in urban road environments due to convenience in implementation and deployment, but flexibility of places and driving safety are common defects of the strategy; the strategy based on the mixed context, such as real-time cooperative change or pseudonymization exchange with neighbors according to threshold values such as surrounding traffic density, vehicle speed and tracking probability in the vehicle driving process, has the characteristic of dynamic flexibility and is widely promoted in the road environment such as an expressway lacking infrastructure, but the important defects of the strategy are that the anonymity capacity is insufficient due to no interruption of space-time information and the energy consumption of vehicle resources is increased due to no infrastructure.
Disclosure of Invention
The invention aims to solve the problem that the traditional kana changing method cannot give consideration to anonymity, high efficiency, driving safety, realization convenience and resource utilization, and provides a dynamic silence monitoring mixed area (TLAS) method based on a traffic light state for urban environment.
On the basis of a traditional mixed area mechanism, the establishment and monitoring of the mixed area are designed through the state transformation of the traffic lights, so that a dynamic driving area suitable for silence is created for the vehicle, a new pseudonym is replaced when the vehicle is driven out of the area, and the privacy safety of the vehicle can be efficiently protected. Compared with the traditional mixed region, the method can realize a safer and more efficient pseudonymous name changing strategy; the dynamic silence length is realized through predictive algorithm modeling, the flexibility can be realized, and a scheme for minimizing resource consumption can be selected within the allowable range of errors and time consumption.
The method comprises the following steps:
step 1, monitoring the self state by using a silence monitoring mixed area model based on the traffic light state, and setting the shortest silence length L for the corresponding laneSMmin. Status of red or yellow when the initialization state isstartWhen red yellow, L is established backwards according to the zebra line positionSM＝LSMminA rectangular mixing zone of length. Status when the initialization Status is greenstartAnd (4) directly jumping to the step 2, wherein the traffic light contains an infrastructure RSU.
Step 2, when the state of the traffic light is changed into green StatustWhen green, the mixing-region silencing function I is turned offSMWhen the traffic light continuously monitors and collects the number of vehicles newly added in the area every second to record Data [ t [ < 0 >][vehicles]。
Step 3, when the state of the traffic light changes to yellow StatustWhen yellow, newly added vehicle information Data per second when green is collected [ t [ [ T ]][vehicles]Lane number information Lane, traffic light state time information TstatusAnd passed into the predictive model.
And 4, the prediction model can realize the selection and the optimization based on different scenes. By collecting various types of information, under the condition that the lane number and the green light time are suitable for the proportion, the model is used for selecting a minimum resource consumption prediction scheme within the tolerance of errors and the tolerance of the running time to predict the newly added vehicle information PreData [ t ] [ vehicles ] per second at the red light of the next stage.
Step 6, when the yellow light is finished, the traffic light broadcasts { silent zone starting identification ISMSilence area position coordinate POS ═ 1SMCurrent length of silence area LSMAnd establishing a silent zone with a new optimal length, keeping silent when the vehicle enters, replacing the pseudonym when the vehicle leaves, and simultaneously restarting the broadcast of the beacon.
And 7, periodically circulating the steps 2 to 6 until the task is finished.
The invention has the beneficial effects that: the invention can stably realize the performance of the intersection signal lamp simply by being deployed on the infrastructure of the intersection signal lamp in urban environment, and the establishment of the position of the mixed area not only ensures the driving safety of the vehicle in a busy area at the center of the intersection, but also can hide the lane changing intention before the vehicle turns, so that the change of the pseudonymous name is more confusable; the length dynamism of the mixing area realized by the prediction model can obviously realize the high efficiency of resource utilization while flexibly adjusting the length of the mixing area according to the traffic flow.
Drawings
Fig. 1 is a diagram of a dynamic silence monitoring mixed-zone (TLAS) pseudonymization policy model based on traffic light status.
Figure 2 is a diagram of the system architecture of the TLAS pseudonym system including a consideration of adversary modes.
Fig. 3 is a TLAS pseudonym scheme design flow diagram.
Detailed Description
The invention is further explained below with reference to the drawings, and the specific implementation steps are as follows.
Step 1, using the traffic light (containing infrastructure RSU) of the method, using a novel silence monitoring mixed area model based on the traffic light state to monitor the self state, and setting the shortest silence length L for the corresponding laneSMmin. Status of red or yellow when the initialization state isstartWhen red yellow, L is established backwards according to the zebra line positionSM＝LSMminA rectangular mixing zone of length. Status when the initialization Status is greenstartGo directly to step 2.
The invention provides a silence monitoring mixed area model based on traffic light states. Aiming at complex road conditions and miscellaneous traffic in cities, the traditional mixing area method is simply arranged at a crossroad with heavy traffic, and the problems of traffic safety in the middle of the crossroad and flexibility of size and position definition of the mixing area are not considered. The model selectively constructs a rectangular mixed area located a distance behind a zebra crossing based on traffic light state change, continuously monitors the beacon condition sent by traffic flow when the traffic light passes through a green light, and executes the construction of a silent mixed area as required when the traffic light state is a red light at the next stage by executing a prediction model.
Referring to fig. 1, fig. 1 is a diagram illustrating a pseudonymous policy model of a dynamic silence monitoring hybrid (TLAS) based on traffic light status according to the present invention. Wherein S1, S2, S3, S4 are set to the shortest silence length LsMminS5 is a dynamic wireless silent mixed zone that changes according to the traffic flow changes predicted from the respective monitoring information. Information is then broadcast by the traffic light infrastructure { silence area initiation identity ISMSilence area position coordinates POSSMCurrent length of silence area LSMThe establishment of a silent mixing zone.
Step 2, when the state of the traffic light is changed into green StatustWhen becoming greenTurning off the mixing zone muting function ISMWhen the traffic light continuously monitors and collects the number of vehicles newly added in the area every second to record Data [ t [ < 0 >][vehicles]。
Step 3, when the state of the traffic light is changed into yellow StatustWhen yellow, newly added vehicle information Data per second when green is collected [ t [ [ T ]][vehicles]Lane number information Lane, traffic light state time information TstatusAnd passed into the predictive model.
And 4, the prediction model can realize the selection and the optimization based on different scenes. By collecting various types of information, under the condition that the lane number and the green light time are suitable for the proportion, the model is used for selecting a minimum resource consumption prediction scheme within the tolerance of errors and the tolerance of the running time to predict the newly added vehicle information PreData [ t ] [ vehicles ] per second at the red light of the next stage. The prediction model is specifically introduced as follows:
and 4-1, realizing a prediction scheme.
The important strategy for realizing the dynamism of the invention is a prediction algorithm, which predicts the number of vehicles which are newly increased per second in the next time period of red light by obtaining the number of vehicles which are newly increased per second in the silent mixing area when the green light is continuously collected, and the sum of the number of vehicles which can stop at the red light is the total number of vehicles which can stop at the red light so as to adjust the length of the mixing area. On the basis, in order to adapt to real traffic scenes with variable traffic light duration and different traffic flows, the invention provides a prediction model based on different scene selection and optimization.
The data used for monitoring is the number of vehicles newly added in per second TLAS, and belongs to simple time series prediction, but the data quantity has the characteristics of small quantity, unstable fluctuation and weak trend. Aiming at such data characteristics, the invention can be realized by using four prediction algorithms, namely a simple averaging algorithm, an Auto-arima algorithm, a Prophet algorithm and an Lstm algorithm.
And 4-2, establishing an objective function.
Two important factors should be considered in the model objective function in the present invention: CPU energy consumption and memory occupation of the algorithm. The objective function thus minimizes resource consumption according to equation (1).
Min Q＝γ1*αCPU(p(xt,xt+1,…,xt+n))+γ2*βMemory(p(xt,xt+1,…,xt+n)) (1)
Wherein x represents a new vehicle number sequence in TLAS at the green light every second, and p (x) is a prediction result sequence under a prediction scheme. Alpha is alphaCPU(p(xt,xt+1,…,xt+n) And beta) andMemory(p(xt,xt+1,…,xt+n) Respectively represent the CPU resource situation and the memory situation consumed by the prediction scheme, and are given different weights, which are respectively marked as gamma1、γ2。
Step 4-3: setting a constraint condition: and (4) time limitation.
The invention requires collecting traffic flow conditions when vehicles enter a TLAS silent zone at a green light to predict the traffic flow conditions at a red light, so that the execution time of a prediction algorithm is required to be ensured in a period of a yellow light, as shown in the following formula (2).
t(p(xt,xt+1,…,xt+n))≤tyellow (2)
Wherein, t (p (x)t,xt+1,…,xt+n) Represents the execution time of the prediction scheme, tyellowIndicating the duration of the yellow light in units of s.
Step 4-4: setting a constraint condition: and (4) limiting the prediction error.
In the scheme, the smaller the prediction error is, the smaller the fluctuation range of the dynamic silent zone is, the smaller the influence on the traceability is, and the more stable the performance is. The number of vehicles with prediction errors is limited by the multiples of the number of the lanes as shown in the following formula (3), and the smaller the error coefficient is set, the higher the prediction accuracy requirement is.
acc(p(xt,xt+1,…,xt+n))≤nlane*w (3)
Wherein, acc (p (x)t,xt+1,…,xt+n) N) represents the prediction error case of the prediction schemelaneW represents the number of lanes in front of the intersection of the traffic light, and w represents an error coefficient.
And 4-5: and establishing a prediction model.
The optimal prediction model is established by comparing the development performance of 4 different prediction algorithm optimization schemes under different scenes. The method can meet the objective function to the maximum extent in the range allowed by setting constraint conditions such as prediction time limit and prediction accuracy limit, and selects the prediction algorithm with minimized CPU energy consumption and memory occupation.
step 6, when the yellow light is finished, the traffic light broadcasts { silent zone starting identification ISMSilence area position coordinate POS ═ 1SMCurrent length of silence area LSMAnd establishing a silent zone with a new optimal length, keeping silent when the vehicle enters, replacing the pseudonym when the vehicle leaves, and simultaneously restarting the broadcast of the beacon.
And 7, periodically circulating the steps 2 to 6 until the task is finished. Referring to fig. 2, in order to show the implementation manner of the entire TLAS pseudonym and the cyclic relationship of each module, the present invention considers the architecture diagram of the entire system, and the system includes (i) a management layer (central authority TA), (ii) an implementation layer (vehicle CAR and roadside unit or traffic light RSU), and (iii) a possible attack manner of the adversary layer (each detector). The invention divides the specific module contact flow modes into four types: pseudonym assignment flow, pseudonym execution flow, malicious event monitoring flow, and illegal trace flow. The module relation showing the periodic loop steps is a pseudonym execution stream, which is implemented as follows:
please refer to fig. 2, the pseudonym execution flow part of the implementation layer. In the driving process of vehicles in the internet of vehicles, current state information of the vehicles { vehicle identifiers (pseudonyms), positions, speeds, directions, system states and the like } (2-1, broadcasting beacons) is broadcasted through 1-10Hz frequency, so that coordinated driving among the vehicles is achieved to guarantee driving safety. Then RSU acquires the broadcast information through the signal transceiver and transmits the broadcast information to the information processing module, and the beacon is continuously collected through the green light to acquire the newly added vehicle information Data [ t ] per second][vehicles]Lane number information Lane and time information T of each state of traffic lightstatusAnd (2-2) information processing, transmitting various types of information to a prediction algorithm selection module to predict traffic flow of red light time in the next stage (2-3) in yellow light, and calculating the optimal silence length L of TLAS (traffic flow prediction) according to data returned by a prediction algorithm modelSM(2-4, calculating position), and setting the length L when the lamp is redSMSilence identification ISMAnd a silence start position POSSMBroadcast (2-5, broadcast location). When the vehicle receives the silence identifier and the length position of the silence area, the vehicle is transmitted to a pseudonymous name strategy selection module to select a pseudonymous name strategy (2-6, selection strategy) to be used, and when the vehicle leaves the silence area, a new pseudonymous name is obtained from a pseudonymous name storage unit to be replaced (2-7, pseudonymous name replacement), and the vehicle continues to drive to the next intersection. And the next intersection traffic light RSU is deployed to the mixing area according to the state again, and circulation is performed.
The above is the preferred implementation process of the present invention, and all the changes made according to the present invention technique, which produce the functional effects that do not exceed the scope of the present invention technical solution, belong to the protection scope of the present invention.
Claims (5)
1. A dynamic silence monitoring mixing zone method based on traffic light states is characterized in that: the method comprises the following steps:
step 1. Using traffic light status basedMonitoring the self state by the silence monitoring mixed area model, and setting the shortest silence length L for the corresponding laneSMmin；
Status of red or yellow when the initialization state isstartWhen red yellow, L is established backwards according to the zebra line positionSM＝LSMminA rectangular mixing zone of length;
status when the initialization Status is greenstartDirectly jumping to the step 2;
the traffic light comprises an infrastructure RSU, and the infrastructure RSU broadcasts information { silent zone starting identifier ISMSilence area position coordinates POSSMCurrent length of silence area LSMThe establishment of a silent mixed zone model is carried out;
step 2, when the state of the traffic light is changed into green StatustWhen green, the mixing-region silencing function I is turned offSMWhen the traffic light continuously monitors and collects the number of vehicles newly added in the area every second to record Data [ t [ < 0 >][vehicles]；
Step 3, when the state of the traffic light is changed into yellow StatustWhen yellow, newly added vehicle information Data per second when green is collected [ t [ [ T ]][vehicles]Lane number information Lane, traffic light state time information TstatusAnd transmitting the prediction algorithm;
step 4, the prediction algorithm is used for realizing selection and tuning based on different scenes; by collecting various types of information, under the condition that the ratio of the number of lanes to the duration of green light is suitable, a prediction algorithm is utilized to select a prediction scheme of minimized resource consumption within the tolerance of errors and the tolerance of running duration to predict newly added vehicle information PreData [ t ] [ vehicles ] per second at the time of red light of the next stage;
step 5, adding vehicle information PreData [ t ]][vehicles]Calculating the total number of vehicles staying in each lane at the red light stageredThen, the optimal silent length L which can just accommodate all vehicles is calculated by the vehicle numberSM；
Step 6, when the yellow light is finished, the infrastructure RSU broadcasts information { silent zone starting identifier ISMSilence area position coordinate POS ═ 1SMCurrent length of silence area LSMEstablishing a silent area with a new optimal length to keep the vehicle silent when entering, and replacing the pseudonym and simultaneously restarting the broadcast of the beacon when the vehicle leaves;
and 7, periodically circulating the steps 2 to 6 until the task is finished.
2. The dynamic silence monitoring mixing zone method based on traffic light status according to claim 1, characterized in that: the objective function in the prediction algorithm relates to CPU energy consumption and memory occupation, and the objective function is to minimize resource consumption.
3. The dynamic silence monitoring mixing zone method based on traffic light status according to claim 2, characterized in that: the execution time of the prediction algorithm is guaranteed to be within the time period of the yellow light.
4. The dynamic silence monitoring mixing zone method based on traffic light status according to claim 2, characterized in that: the prediction algorithm limits the number of vehicles with prediction error by a multiple of the number of lanes.
5. A dynamic silence monitoring mixing zone method based on traffic light status according to any of claims 2 to 4, characterized in that: the prediction algorithm adopts a simple average algorithm, an Auto-arima algorithm, a Prophet algorithm or an Lstm algorithm.
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