Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding

SHANGGUAN Wei; ZHA Yuan-yuan; FU Yao; ZHENG Si-fa; CHAI Lin-guo

doi:10.19818/j.cnki.1671-1637.2022.04.027

Volume 22 Issue 4

Aug. 2022

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Article Contents

Abstract

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0. 引言

1. Design of Trusted Interaction Architecture for Vehicle Road Collaboration

2. Information trustworthy interaction based on understanding of transportation business characteristics

3. 仿真验证与结果分析

4. conclusion

References

Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(4): 348-360

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SHANGGUAN Wei, ZHA Yuan-yuan, FU Yao, ZHENG Si-fa, CHAI Lin-guo. Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 348-360. doi: 10.19818/j.cnki.1671-1637.2022.04.027

Citation:

SHANGGUAN Wei, ZHA Yuan-yuan, FU Yao, ZHENG Si-fa, CHAI Lin-guo. Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 348-360. doi: 10.19818/j.cnki.1671-1637.2022.04.027

Citation:

SHANGGUAN Wei, ZHA Yuan-yuan, FU Yao, ZHENG Si-fa, CHAI Lin-guo. Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 348-360. doi: 10.19818/j.cnki.1671-1637.2022.04.027

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Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding

doi: 10.19818/j.cnki.1671-1637.2022.04.027

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3.
China Mobile Research Institute, Beijing 100053, China
4.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

Science and Technology Research and Development Program of China Railway N2021G045

More Information

Author Bio:
SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn
Received Date: 2021-12-12
Available Online: 2022-10-08
Publish Date: 2022-08-25

Abstract

Abstract

For the credible information interaction in vehicle-infrastructure cooperative environments, the processes of vehicle-vehicle and vehicle-infrastructure cooperative information interaction and the interaction requirements of different modes were analyzed, and a vehicle-infrastructure cooperative credible interaction framework was designed. A model of vehicle behavior state deduction and one of path perturbation factor quantification were constructed. A credibility calculation method for the vehicle object and level evaluation rules were designed. The credible authentication of vehicle object behavior was thereby achieved. A quantification model for the message urgency was built by understanding the effective traffic business characteristics. The low-resolution filtering strategy was used to preliminarily filter the message, and the message content was deeply understood on the basis of the support vector machine (SVM), thereby obtaining a multi-resolution interactive content cognition method. The Veins with OMNeT++ and SUMO simulators was used to build a simulation test environment. Simulation tests were carried out in open roads and intersection scenarios with different penetration rates of connected and automated vehicles (CAVs). The proposed vehicle-infrastructure cooperative credible interaction method was tested and verified. Research results show that the credibility identification for the vehicle-infrastructure cooperative information interaction can be effectively improved by understanding the traffic business characteristics. The average cognitive accuracy for the beacon location message achieved by the proposed method is 90.91%. It is 8.68% higher than that of the credible interaction method based on the timeliness detection. In the credible interaction verification experiment on the safety efficiency message, as the proportion of malicious vehicles increases, the traditional vehicle-infrastructure cooperative credible interaction method based on the voting mechanism is gradually held invalid. In contrast, an average accuracy of 94.96% is achieved by the proposed method under the condition that the single authentication delay is less than 13 ms. It is 3.05% higher than that of the traditional method based on the back propagation (BP) neural network. Moreover, a higher accuracy rate and a lower false negative rate of the credible interaction detection results can be obtained with a higher CAV penetration rate. Therefore, the needs of vehicle-infrastructure cooperative credible interaction can be met by the proposed method.

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0. 引言

In summary, this article combines the practical needs of typical vehicle to vehicle and vehicle to road collaborative information exchange, considers both traffic participants and interactive information content, and evaluates the credibility of vehicle entities based on vehicle behavior state deduction models and path disturbance factor quantification models; A quantitative model for message urgency and a multi-resolution cognitive model were constructed based on transportation business feature extraction to determine the credibility of interactive content; A trusted interaction method for vehicle road collaboration based on understanding of transportation business characteristics has been proposed, which solves the problems of cumbersome subject trust authentication and insufficient understanding of interaction content in the trusted interaction process of vehicle road collaboration systems, and improves the accuracy of information interaction trust authentication in complex transportation environments.

1. Design of Trusted Interaction Architecture for Vehicle Road Collaboration

In actual urban transportation environments, vehicles have certain perception blind spots due to obstacles such as buildings and trees. With the assistance of vehicle road coordination systems, vehicles can increase their comprehensive perception area through information exchange. Based on C-V2X communication technology, the perception information of roadside units and vehicle mounted units is fully exchanged and shared^[31]The information interaction of collaborative perception includes two types: vehicle road collaborative interaction and vehicle vehicle collaborative interaction.

1.1 车路协同式交互

Figure 1. Vehicle to infrastructure and vehicle to vehicle information interaction process

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1.2 Collaborative interaction between vehicles

Vehicles can detect other traffic participants around them through self perception devices such as cameras and radars, such asFigure 1As shown. The vehicle sends the processed information to other vehicles with communication devices around it through OBU. The receiving vehicle can formulate reasonable driving strategies based on the fused perception information, ensuring driving safety and improving traffic efficiency.

Figure 2. Framework of vehicle-infrastructure cooperative credible interaction

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Step 3： Extraction of effective business features. When receiving information from other vehicles or roadside interactions, effectively extract and deeply understand traffic business features based on trusted interaction requirements.

Step 4： Multi resolution content recognition. Based on the characteristics of transportation business, low resolution filtering and high-resolution cognition are applied to interactive content. If multi-resolution content cognition detection is used, it is judged as trustworthy and valid messages. Otherwise, it is judged as untrustworthy and invalid messages.

Step 5： Determine whether the message sender has a continuous interaction request. If the request is to continue interaction, proceed to Step 2. Otherwise, update the trust level library and end the current trusted interaction process.

2. Information trustworthy interaction based on understanding of transportation business characteristics

Firstly, the trustworthy authentication of the subject is achieved from two aspects: deducing the behavior state of the subject and quantifying the path disturbance factor; Secondly, a traffic business feature extraction and deep understanding method is proposed for the trustworthy interaction requirements in the vehicle road collaborative environment, and the multi-resolution content cognition of interactive information is achieved by integrating traffic business feature understanding; Finally, a vehicle road collaborative trusted interaction method is formed based on subject trusted authentication and centered on interactive content cognition.

2.1 Subject Trusted Authentication Based on Behavior State Assessment

Table 1. Mapping relationship between vehicle type and initial credibility

初始可信等级	车辆类型	初始信任程度	λ
P1	交管车、消防车、警车、救护车	可信	1.0
P2	公交车、出租车、网约车等	比较可信	0.8
P3	城管、水电城市公共设施维修车辆	基本可信	0.6
P4	私人车、单位车等普通车辆	不太可信	0.4
P5	故障、攻击或没进行年检的车等	不可信	0.2

| Show Table

DownLoad: CSV

$\Delta t=\left|t-t_{\mathrm{e}}\right|$

(1)

$\left\{ {\begin{array}{*{20}{l}} {\mathit{\Phi }\left( {{v_n}} \right) > 0}\\ {\frac{{{\rm{d}}\mathit{\Phi }\left( {{v_n}} \right)}}{{{\rm{d}}\Delta t}} < 0}\\ {\frac{{{\rm{d}}{\mathit{\Phi }^2}\left( {{v_n}} \right)}}{{{{\rm{d}}^2}\Delta t}} > 0} \end{array}} \right.$

(2)

Combining equation (2), toωConstruct vehicles for weighting coefficientsv_nThe behavior state trust function

$\mathit{\Phi }\left( {{v_n}} \right) = ƛ \left[ {\hbar \left( {{v_n}} \right)} \right]{{\rm{e}}^{ - \omega \left| {t - {t_{\rm{e}}}} \right|}}$

(3)

$G_j=\left\{c_k, e_{j k}, w_{j k}\right\}$

(4)

In the formula:c_kgroundc_jAdjacent RSU nodes that can be reached by departure;e_jkandw_jkThey are the corresponding road sections and road section weights.

In addition, the credibility calculation of the vehicle subject will be dynamically affected by the historical path selection behavior. Therefore, a path disturbance factor is introduced on the basis of the credibility of the behavior stateλ_rWhen the coupling degree between vehicle path selection and actual traffic status and vehicle destination path is high, its credibility is high, and the path disturbance factor is set to 1.0; On the contrary, path selection is not the main factor in evaluating the credibility of behavioral states, and the path perturbation factor is set to 0.5. The set of highly correlated path nodes consists of the optimal neighboring RSU nodes and the suboptimal neighboring RSU nodes. In order to fully consider the factors of urban road network topology and real-time traffic situation, the optimal neighboring RSU nodes are selected based on the shortest distance between nodes in a static environment, and the Dijkstra algorithm based on the shortest path is used for solving; The optimal selection of the next best neighboring RSU nodes based on the road traffic capacity between nodes in a dynamic environment, combined with real-time traffic road conditions, quantifies the travel time cost as road traffic impedance, and solves it using the node deletion method based on the Bureau of Public Road (BPR) function in the United States. The set of highly correlated path nodes for the jth RSU nodeN_jCan be expressed as

$N_j=\left\{c_a, c_b\right\} \quad j=1, 2, \cdots, q$

(5)

In summary, the path disturbance factor can be obtained as

$\lambda_{\mathrm{r}}= \begin{cases}1.0 & c_j \in N_{j-1} \\ 0.5 & c_j \notin N_{j-1}\end{cases}$

(6)

Vehicle subject behaviorV_BThe description of the vehicle includes its state (mainly referring to speed and position) and path selection. The RSU records the state information of the vehicle's main body during operation and forms a formal sequence in chronological order. The behavior sequence of the vehicle's main body is called

$V_{\mathrm{B}}=\left\{S_{\mathrm{R}, 1}, S_{\mathrm{R}, 2}, \cdots, S_{\mathrm{R}, q}\right\}$

(7)

In the formula:S_{R, j}For vehicles passing throughjPerformance of vehicle behavior status recorded by RSU.

The current RSU can achieve sequence extraction by interacting with the previous RSU passed by the vehicle. To avoid excessive communication resources occupied by interactions between RSUs in large-scale road networks, the number of hops for forwarding sequence information is set to one hop. Based on the analysis of vehicle subject behavior in combination with the requirements of trusted interactive subject authentication, a function is constructed from two aspects: real-time status and path selectionf₁(·) Quantify the credibility of the calculation subjectκ(0≤κ≤ 1) for

$\kappa=f_1\left(z_i, N_{j-1}, S_{R, j-1}, S_{\mathrm{R}, j}\right)=\lambda\left(z_i\right) \lambda_{\mathrm{r}} \mathrm{e}^{-\omega\left|t-t_{\mathrm{e}}\right|}$

(8)

2.2 Multi resolution content cognition integrating transportation business characteristics

According to the analysis of interaction requirements, the interaction information message contains 7 main characteristics of the generator and transmitter, corresponding to sender category, information type, information sending time, sender status description, information event description, vehicle reputation value, and information propagation hop count. The effective characteristics of the message are related to the sender type (roadside, vehicle) and the type of message sent (Security Efficiency Message (SEM), Beacon Location Message (BLM)). Due to the determinacy of the RSU location, the content of its beacon message will not be discussed. This article studies the effective characteristics of three types of message messages.

2.2.1 Effective feature extraction of roadside SEM messages

$\boldsymbol{M}_1=\left(T_{\mathrm{R}}, P_{\mathrm{R}}, E_{\mathrm{R}}, H_{\mathrm{R}}\right)$

(9)

2.2.2 Effective feature extraction of vehicle SEM messages

$\boldsymbol{M}_2=\left(T_{\mathrm{v}}, P_{\mathrm{V}}, E_{\mathrm{V}}, H_{\mathrm{V}}\right)$

(10)

2.2.3 车辆BLM报文的有效特征提取

If the message message isT_VTime is determined by the geographical location of the corresponding vehicleP_VIf generated, the effective features of the message are usedM₃Representing, i.e

$\boldsymbol{M}_3=\left(T_{\mathrm{V}}, P_{\mathrm{V}}\right)$

(11)

$\boldsymbol{M}_4=\left(T_{\mathrm{v}}, P_{\mathrm{V}}, E_{\mathrm{V}}\right)$

(12)

Figure 3. Typical SEM interaction scenario

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Construct a distance calculation function based on the latitude and longitude information of vehicle A and event location O sent by SEMf₂(·), calculate the distance between two placesD_OAdo

$D_{O A}=f_2\left(P_{A, \mathrm{~L} o}, P_{A, \mathrm{La}}, P_{O, \mathrm{~L} o}, P_{O, \mathrm{La}}\right)$

(13)

In the formula:P_{A, Lo}andP_{A, La}For vehicles respectivelyALocation latitude and longitude information;P_{O, Lo}andP_{O, La}They are the locations where the events occurredOThe latitude and longitude information.

SEM receives vehiclesBaccording toOPoint distance can be measured through its own sensors V2I/V2N、 Calculate the accident location based on the BLM of nearby vehicles in the alarm areaONearby reference traffic flowγ_O.M₄fusionD_OAgiveγ_OMulti dimensional fusion feature vector obtained from featuresM_f

$\boldsymbol{M}_{\mathrm{f}}=\left(T_{\mathrm{V}}, D_{O A}, \gamma_O, E_{\mathrm{V}}\right)$

(14)

Figure 4. Low-resolution message filtering process

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Step 1： Sender type discrimination. If the sender is identified as RSU based on the valid characteristics of the message, the trust level is the highest, marked as trustworthy, and the screening is ended; On the contrary, execute Step 2.

Step 3： Space urgency test. The message receiving vehicle only needs to focus on events that are highly relevant to the vehicle and calculate the distance impact factorλ_DIfλ_D=0. If the message describes that the location of the event is far away from the vehicle or behind the vehicle, ignore this message and end the filtering process. Otherwise, proceed to Step 4.

$U(x)= \begin{cases}w_{\mathrm{C}} \lambda_{\mathrm{T}} \lambda_{\mathrm{D}} \lambda_{\mathrm{P}} \mathrm{e}^{-w_{\mathrm{T}}\left(t_{x, \mathrm{r}}-t_{x, \mathrm{~g}}\right)-w_{\mathrm{S}} H_{\mathrm{V}} \rho(M)} & M \in \Gamma_{\mathrm{S}} \\ w_{\mathrm{C}} \lambda_{\mathrm{T}} \lambda_{\mathrm{D}} \lambda_{\mathrm{P}} \mathrm{e}^{-w_{\mathrm{T}}\left(t_{x, \mathrm{r}}-t_{x, \mathrm{~g}}\right)-w_{\mathrm{B}} \cdot \frac{v_{\mathrm{r}}-v_{\mathrm{g}} \mid}{v_{\mathrm{r}}}} \rho(M) & M \in \Gamma_{\mathrm{B}}\end{cases}$

(15)

3. 仿真验证与结果分析

3.1 Construction of typical interaction scenarios for vehicle road collaboration

To build a typical interaction scenario in the vehicle road collaborative environment, Veins, which includes OMNeT++and SUMO simulation simulators, was selected to construct the simulation environment. OMNeT++is a C++simulation framework used to build a network simulator. SUMO simulates local roads in Houchang Village, Haidian District, Beijing. The OpenStreetMap (OSM) map with longitude range of 116.268 1 °~116.303 0 ° E and latitude range of 40.050 2 °~40.071 0 ° N is imported into SUMO to form a simulation mapFigure 5As shown. Select from location A (40.050 1 ° N, 116.278 6 ° E) to B ground (40.056 5 ° N, 116.371 (7 ° E) Vehicle simulation data for straight sections.

Figure 5. Simulation road network topology

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3.1.1 开放道路仿真场景

Set the overtaking scenario for vehicles on open roads as overtaking vehicle V1 overtaking vehicle V2. As vehicle V3 is driving in an adjacent lane, vehicle V1 refers to the speed and position information of surrounding vehicles for overtaking behavior. Construct a vehicle to vehicle interaction scenario based on bidirectional four lane overtaking behavior, with simulation parameter settings as followsTable 2As shown.

Table 2. Simulation parameters of open road

项目	数值
车辆运行车道数	4
车辆运行长度/m	300
车辆长度/m	4
行驶车辆数/veh	100
最大行驶速度/(km·h^-1)	80
最小行驶速度/(km·h^-1)	0
最大加速度/(m·s^-2)	2.5
最大减速度/(m·s^-2)	9
初始速度/(km·h^-1)	30

| Show Table

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3.1.2 Simulation scenario of signalized intersection

Table 3. Simulation parameters of signalized intersection

项目	数值
车辆运行车道数	4
车辆运行长度/m	600
车辆长度/m	4
行驶车辆数/veh	500
最大行驶速度/(km·h^-1)	50
最小行驶速度/(km·h^-1)	0
最大加速度/(m·s^-2)	2.5
最大减速度/(m·s^-2)	9
初始速度/(km·h^-1)	30

| Show Table

DownLoad: CSV

3.2 车辆主体可信认证方法验证

Figure 6. Driving times of route AB during unblocked period

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Figure 7. Deduction errors of route AB during unblocked period

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Figure 8. Driving times of route AB during peak period

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Figure 9. Deduction errors of route AB during peak period

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Table 4. Comparison of vehicle state deduction errors %

推演方法	平均绝对相对误差		最大绝对相对误差
推演方法	畅通时段	高峰时段	畅通时段	高峰时段
本文方法	1.85	5.21	5.87	11.25
多元非线性拟合	1.93	5.89	5.32	12.58

| Show Table

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3.3 Verification of Trusted Interaction Methods Integrating Business Features

To verify the applicability of the vehicle road collaborative trusted interaction method based on the premise of subject behavior authentication and the core understanding of transportation business characteristics, experiments were conducted in typical scenarios of vehicle road collaboration using a multi-resolution content cognition method, in addition to the subject trusted authentication method based on PLS behavior state deduction. In order to quantify the corresponding experimental results, according toTable 5Determine the corresponding evaluation indicators based on the confusion matrix of various message classification results, including:A₁The invalid message is correctly identified as an invalid message;A₂The invalid message was mistakenly identified as a valid message;A₃The valid message was mistakenly identified as an invalid message;A₄The valid message is correctly identified as a valid message.

Table 5. Confusion matrix of message classification results

实际类别	分类结果
实际类别	无效消息	有效消息
无效消息	A₁	A₂
有效消息	A₃	A₄

| Show Table

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Define the True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), and Accuracy Rate (AR) for detecting invalid messages as follows:

$Q_1 =\frac{A_1}{A_1+A_2}$

(16)

$Q_2 =\frac{A_4}{A_4+A_3}$

(17)

$Q_3 =\frac{A_3}{A_3+A_4}$

(18)

$Q_4 =\frac{A_2}{A_1+A_2}$

(19)

$Q_5 =\frac{A_1+A_4}{A_1+A_2+A_3+A_4}$

(20)

3.3.1 信标位置消息可信交互方法验证

Figure 10. TPRs and TNRs of BLM credible interaction method

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Figure 11. FNRs and FPRs of BLM credible interaction method

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Table 6. Heterogeneous traffic scenario of open road %

交通场景	CAV占比	AHDV占比	CHDV占比	占比总计
路段场景1	25.0	37.5	37.5	100.0
路段场景2	50.0	25.0	25.0	100.0
路段场景3	75.0	12.5	12.5	100.0
路段场景4	100.0	0.0	0.0	100.0

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Figure 12. Test results of BLM at different permeabilities of CAV

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3.3.2 安全效率消息可信交互方法验证

Figure 13. Average authentication delays of SEM credible interaction

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Figure 14. Verification results of SEM credible interaction method

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For mixed traffic environments, HDVs in signalized intersection scenes are classified into four categories (HDV1~HDV4) based on the differences in vehicle kinematic characteristics and driver driving habits. The Intelligent Diver Model (IDM) in classical vehicle motion control models is used as the core control model for environmental vehicles in the longitudinal dimension,Table 7Description of mixed traffic scenarios at signalized intersections.

Table 7. Heterogeneous traffic scenarios of signalized intersection %

交通场景	CAV占比	HDV1占比	HDV2占比	HDV3占比	HDV4占比	占比总计
交叉口场景1	25.00	18.75	18.75	18.75	18.75	100.00
交叉口场景2	50.00	12.50	12.50	12.50	12.50	100.00
交叉口场景3	75.00	6.25	6.25	6.25	6.25	100.00
交叉口场景4	100.00	0.00	0.00	0.00	0.00	100.00

| Show Table

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Figure 15. Test results of SEM at different permeabilities of CAV

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4. conclusion

(4) This article mainly studies the interaction scenarios of open roads and signalized intersections in urban transportation environments. Subsequent research can be extended to more comprehensive typical interaction scenarios such as urban expressways and ramps, further considering and analyzing the impact of abnormal positioning and transmission devices on trustworthy interactions.

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Vehicle-infrastructure cooperative credible interaction method based on traffic business characteristics understanding

doi: 10.19818/j.cnki.1671-1637.2022.04.027