2021

Paul (Young Joun) Ha, Purdue University
Sikai Chen (chen1670@purdue.edu), Purdue University
Jiqian Dong, Purdue University
Runjia Du, Purdue University
Yujie Li, Purdue University
Samuel Labi, Purdue University

Highway bottleneck refers to the localized constriction of traffic flow in a given corridor when the traffic demand exceeds its available capacity. Active Traffic Management strategies are often adopted in real-time to address such sudden flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts speeds in upstream traffic to mitigate traffic showckwaves downstream, can be applied. However, because SH depends on driver awareness and compliance, it may not always be effective in mitigating congestion. The use of multiagent reinforcement learning for collaborative learning, is a promising solution to this challenge. By incorporating this technique in the control algorithms of connected and autonomous vehicle (CAV), it may be possible to train the CAVs to make joint decisions that can mitigate highway bottleneck congestion without human driver compliance to altered speed limits.  In this regard, we present an RL-based multi-agent CAV control model to operate in mixed traffic (both CAVs and human-driven vehicles (HDVs)). The results suggest that even at CAV percent share of corridor traffic as low as 10%, CAVs can significantly mitigate bottlenecks in highway traffic. Another objective was to assess the efficacy of the RL-based controller vis-à-vis that of the rule-based controller. In addressing this objective, we duly recognize that one of the main challenges of RL-based CAV controllers is the variety and complexity of inputs that exist in the real world, such as the information provided to the CAV by other connected entities and sensed information. These translate as dynamic length inputs which are difficult to process and learn from. For this reason, we propose the use of Graphical Convolution Networks (GCN), a specific RL technique, to preserve information network topology and corresponding dynamic length inputs. We then use this, combined with Deep Deterministic Policy Gradient (DDPG), to carry out multi-agent training for congestion mitigation using the CAV controllers. From the results of the simulation experiments, we find that for purposes of bottleneck congestion mitigation, the RL-based controller has superior performance compared with the rule-based controller.

Zulqarnain Khattak (khattakzh@ornl.gov), Oak Ridge National Laboratory
Jackeline Rios-Torres, Oak Ridge National Laboratory
Michael Fontaine, Virginia Transportation Research Council
Asad J. Khattak, University of Tennessee

Advances in communications technology have enabled the development of connected and automated vehicle (CAV) applications including adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) that could perform driving tasks with little human feedback. However, there are some key concerns and knowledge gaps regarding the impact of communications delay on CAV platoon stability. This study contributes by analyzing the impact of communications delay and other confounding factors on CAV platoon stability using the concept of driving volatility. Real-world experimental data from a field test were used to examine the lead vehicle (LV) and following vehicles (FVs) behavior in a five-vehicle platoon with multiple scenarios by developing switching regime models. Results show that CACC reduces volatility in both LV and FVs compared to ACC system which was expected due to the vehicular communication and motion synchronization of CACC. However, communication delay is observed to increase the likelihood of volatility and contribute to instability of platoons. FVs have more volatile behavior as compared to LV since the instability is transmitted through the string of the platoon. Other confounding factors such as disengagement and yaw rate contribute to volatility of the platoon.   The likelihood of volatility in both LV and FVs decreases with increases in gap to the preceding vehicle. These results have practical implications for considering communications delays in CAV performance analysis and for designing robust CACC controls with minimum delays in the future.

Seunghan Ryu (sr5ae@virginia.edu), University of Virginia
B. Brian Park, University of Virginia

In this paper, Platoon-Based Intersection Control Strategy (PBICS) at two adjacent intersections for fully connected automated vehicles is designed and evaluated. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of Vehicle-to-Intersection (V2I) and Vehicle-to-Vehicle (V2V) communications. The idea of the study is implementing vehicle platoons (e.g., consecutive vehicles in a row) to the intersection control problem. The core of PBICS is to decide optimal platoon size (i.e., number of vehicles in the platoon); because the efficiency of intersection relies on vehicle headway and arrival distribution. While the benefits of CAV-based signal free intersection control algorithms outperform existing signalized intersection control, few research efforts included simple formation of platooning without considering coordination. This paper focuses on the effectiveness of vehicle platooning and intersection coordination. Especially at an intersection with major and minor roads, vehicle platooning would have more benefits on travel time reduction with forming more frequent and longer platoons on major roads. A comparison between coordinated control (i.e., solving two-intersection traffic in one optimization problem) and separated control (i.e., two separate optimizations for each intersection) was conducted to see the coordination impacts. Simulation based evaluation results indicated that intersection coordination and vehicle platooning significantly improved intersection performance in congested traffic.

Zheng Chen (zc4ac@virginia.edu), University of Virginia
Longqian Qi, Tongji University
Jintao Lai, Tongji University
B. Brian Park, University of Virginia
Jia Hu, Tongji University

One of promising applications of connected and automated vehicles is cooperative adaptive cruise control (CACC) that allows vehicles maintain short time headways to improve mobility. However, CACC may not be able to fully realize benefits of vehicular connectivity due to its limited usability in the mixed traffic including unconnected human driven vehicles. Quasi-CACC applications including CACC with unconnected vehicle (CACCu) and human-in-the-loop CACC (hCACC) have been newly proposed. These quasi-CACC applications aim at partially achieving the favorable properties of CACC when the equipped vehicle does not meet the operational requirements of CACC. In this study, the network-wide impacts of the quasi-CACC applications on mixed traffic were investigated. A VISSIM-based traffic simulation testbed was developed incorporating heterogeneous vehicle types (i.e., THV, CHV and CAV) and vehicle dynamics model (i.e., poor, median and good). A typical highway segment with on/off ramps was built as the study site, and the behaviors of CAVs and Connected Human-driven Vehicles (CHVs) are modeled considering the new driving modes of quasi-CACC. A case study with 30% CAVs, 40% CHVs, and 30% traditional vehicles in the traffic was conducted. It was found that CACC alone only increased 6.5% network capacity compared to 100% traditional vehicle traffic. By contrast, CACC together with quasi-CACC applications doubled the benefit of CACC alone, leading to 14% increase in network capacity. These results indicate the remarkable potential of quasi-CACC applications in improving mobility in mixed traffic environment.

Farzaneh Azadi (faa85@pitt.edu), University of Pittsburgh
Nassim Motamedidehkordi, Parsons
Nikola Mitrovic, Siemens Mobility
Aleksandar Stevanovic, University of Pittsburgh

We recently proposed a concept, called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing and controlling directionally unrestricted traffic flows in automated vehicle environment. In CADLARIC each vehicle must position itself in a proper lane before it reaches the downstream intersection; therefore, the resolutions of conflicts between vehicles are made on the entire road surface instead only at intersections. However, the CADLARIC is infrastructurally demanding, requiring 6 lanes per intersection approach, conditions not frequently observed in the field. This paper extends CADLARIC into a more robust Combined Flexible Lane Assignment and Reservation-based Intersection Control (CFLARIC), which is much more applicable for real-world road geometries. The CFLARIC offers a spectrum of lane assignment schemas in combination with the appropriate reservation-based intersection control where the reservation-able zones properly match the deployed schemas. Unlike CADLARIC, CFLARIC does not have any restrictions on required number of lanes and can be easily deployed in field-like environments.  We test three such distinctive CFLARIC strategies on a three-intersection corridor in West Valley City, Utah, to investigate the impact of flexible lane assignment on measures of traffic efficiency and safety. The proposed CFLARIC scenarios are evaluated through a comparison with Fixed time Control (FTC) and Fully Reservation Based Intersection Control (FRBIC), both with conventional lane assignments. The results illustrate that CFLARIC scenarios: (i) outperform FTC and FRBIC in terms of the efficiency (delay and number of stops), and (ii) improve overall safety (by reducing number of conflicting situations when compared to FRBIC).

Mohammed Hadi, Florida International University
Kamar Amine, Florida International University
Thodsapon Hunsanon, Florida International University
Mahmoud Arafat, Florida International University

The rapid development of connected vehicle (CV) and cooperative automated vehicle (CAV) technologies in recent years calls for the assessment of the impacts of these technologies on system performance. Microscopic simulation can play a major role in assessing these impacts, particularly during the early stages of the adoption of the technologies and associated applications. This study develops a method to evaluate the safety benefits of Red-Light Violation Warning (RLVW), a CV-based Vehicle-to-Infrastructure application at signalized intersections, utilizing simulation. The study results confirm that it is critical to calibrate the probability to stop on amber in the utilized simulation model to reflect real-world driver behaviors when assessing RLVW impacts. Without calibration, the model is not able to assess the benefits of RLVW in reducing RLR and right-angle conflicts. Based on a surrogate safety assessment, the calibrated simulation models result shows that the CV-based RLVW can enhance the safety at signalized intersections by approximately 50.7% at 100% utilization rate of the application, considering both rear-end and right-angle conflicts.

Fang Zhang, Southeast University
Jian Lu, Southeast University
Xiaojian Hu, Southeast University

In this paper, we examine the practicability and investigate the mechanism of deploying autonomous vehicle/credit charging (AVCC) links on the mixed flow network under tradable credit scheme (TCS). Assuming that route choice behaviors of human-driven vehicle (HDV) and connected autonomous vehicle (CAV) users are characterized by SUE and UE principles, respectively, we formulate the mixed traffic equilibrium under TCS as a variational inequality (VI) problem with exogenous CAV penetration rate. Based on the VI formulation, a bi-level programming model is proposed to simultaneously determine the optimal credit charge scheme as well as deployment of AVCC links. Then a genetic algorithm is applied to solve the bi-level model, within which a modified Lagrangian dual algorithm embedded with diagonalization method is used to solve the lower-level problem. Based on the results from an example network, it is found that strategic deployment of AVCC links further improve the network performance compared with the case where only TCS is implemented. We also find that considering the initial flow distribution without policy intervention, different strategies are employed to deploy AVCC links under different CAV penetration rate, which provides useful insight for the deployment of AVCC links in practice. Moreover, we investigate the credit trading in the market and find that CAV users are more likely to be the beneficiary in the proposed scheme.

Meiqi Liu, Technische Universiteit Delft
Meng Wang, Technische Universiteit Delft
Serge Hoogendoorn, Technische Universiteit Delft
Jing Zhao, University of Shanghai for Science and Technology

This paper proposes a two-layered integrated control approach for optimizing traffic signals and cooperative vehicle trajectories at urban intersections. The upper layer sends feasible signal plans to the lower layer, and the lower layer can thereby compute the best objective function value by optimizing the trajectories under each feasible signal plan. After enumerating all feasible signal plans, the optimal signal plan is determined in the upper layer by comparing the objective function values. In the lower layer, the accelerations of the overall platoons are optimized to maximize riding comfort and minimize average travel delay, while satisfying the physical motion constraints and safe driving requirements. The red phase is determined by a logic constraint, which restricts vehicles to stay behind the stop-line within the signal cycle if they cannot pass at the green phase tail. Typical platoon performance at intersections, such as splitting, merging, accelerations and decelerations can be included in the lower layer. The integrated control approach can work under adaptive signals, and is flexible in incorparating different traffic movements during multiple signal phases. Simulation is performed to verify the performance of the integrated control approach. Three scenarios are designed to demonstrate the advantages on throughput and fuel economy, compared with two baseline scenarios of trajectory optimization and signal optimization separately. Analysis of the scenarios reveals insights into the optimal patterns on signals and vehicle trajectories.

Xishun Liao (xliao016@ucr.edu), University of California, Riverside
Xuanpeng Zhao, University of California, Riverside
Guoyuan Wu, University of California, Riverside
Matthew Barth, University of California, Riverside
Ziran Wang, Toyota Motor North America
Kyungtae Han, Toyota Motor North America
Sergei Avedisov, Toyota Motor North America
Prashant Tiwari, Toyota Motor North America

Ramp merging has been considered as one of the major causes of traffic congestion and accidents because of its chaotic nature. With the development of connected and automated vehicle (CAV) technology, cooperative ramp merging becomes one of the popular solutions to this problem. In a mixed traffic situation, CAVs will not only interact with each other, but also handle complicated situations with human-driven vehicles involved. In this paper, a game theory-based ramp merging strategy is developed for the optimal merging coordination of CAVs in the mixed traffic, which determines dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe inter-vehicle distance among the involved vehicles and harmonizing the speed of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulations under different penetration rates and different congestion levels are carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a traffic simulator. This platform allows the human driver to participate in the simulation, and also equip CAVs with more realistic sensing systems. In the traffic flow level simulation test, Unity takes over the sensing and control of all CAVs in the simulation, while SUMO handles the behavior of all legacy vehicles. The results show that the average speed of traffic flow can be increased up to 110%, and the fuel consumption can be reduced up to 77%, respectively.       Keywords: Mixed traffic, connected and automated vehicles, ramp merging, game theory, Unity-SUMO integration

Denis Monyo, University of North Florida
Thobias Sando, University of North Florida
Mike Soloka, University of North Florida

Secondary crashes (SCs) on freeways are one of the major causes of safety and operational deterioration in freeways.  Although SCs are fewer in number than primary incidents, their spatial-temporal effects may be more severe than primary incidents. SCs can result in deterioration of traffic flow conditions on freeways and adjacent arterials in addition to injuries and fatalities. Due to the limited nature of secondary crash data for scenarios that may not yet exist, such as operations under the connected vehicles (CV) environment, surrogate measures provide means to examine expected safety characteristics of such deployments. This study evaluated a freeway model of a segment on Florida’s Turnpike system using VISSIM – a microscopic simulation software. Trajectory files generated in VISSIM were imported in the SSAM software for conflict analysis to analyze the potential benefits of CVs in mitigating SCs. The study identified that the location of the primary incident, whether on the inside or outside lane, influences the number of conflicts generated as SCs. A primary incident on the outside lane resulted in more conflicts than the one on the inside lane. This is due to additional conflicts of on-ramp and off-ramp traffic. In both cases, the results showed a potential reduction of SCs with traffic conflicts of up to 98% at high CV market penetration and low traffic volume. Notably, the statistical analysis showed that the average number of conflicts was siginificantly reduced, even at lower CV market penetration.

Weijie Yu, Southeast University
Xuedong Hua, Southeast University
Wei Wang, Southeast University

Efficient vehicular wireless communication is the key to improve traffic safety and traffic efficiency for connected and automated vehicles (CAVs). However, the wireless communication is vulnerable to equipment failures and transmission environment, causing concerns on traffic safety. So far, transient communication failures of single CAV or CAV fleet and their safety impact have been widely studied, but the persistent communication failures occurring in the fixed area are rarely discussed. To fill in this gap, this study aims to evaluate the safety impact of regional communication failures under pure CAV environment. CAVs’ driving behaviors are reasonably simulated by comprehensively considering the processes of vehicle following, lane change, collision warning and driver’s takeover. Then we study three typical communication failures including random error, random delay and cyclical missing based on two-lanes freeway with a specific disturbed area and analyze their safety impact by building multiple scenarios. Lastly, the sensitivity on traffic safety of model parameters are tested. The results indicate disturbed factors, disturbed severity and disturbed coverage of various communication failures have differential safety impact on CAVs. The findings of this study are potential to provide useful suggestions for defending regional communication failures and improving longitudinal safety of CAVs.

Mutasem Alzoubaidi (malzouba@uwyo.edu), University of Wyoming
Adli Al Balbissi, University of Jordan
Ahmed Farid, University of Wyoming
Ahmed Al-Mansour, University of Jordan

In this paper, an economic analysis was conducted to assess alternative solutions to traffic congestion. They involved the integration of adaptive traffic signal control (ATSC) with connected vehicles (ATSC-CV) and the application of various conventional solutions. Three highly congested signalized intersections along a major urban arterial in Amman, Jordan were selected for detailed investigation. The operational performance of each alternative scenario was analyzed using VISSIM microsimulation software. The studied conventional scenarios included signal timing optimization, signal actuation and the upgrading of existing intersections to interchanges. There were unconventional scenarios, one of which two intersections were converted to interchanges while the third was converted to a continuous green-T intersection (CGTI). Further unconventional scenarios involved the deployment of ATSC-CV based systems assuming varying travel time reductions without implementing any geometric design changes. The results of this study have shown that the unconventional solution of upgrading two of the intersections to interchanges and the third to a CGTI produced the least mean vehicular travel time along the studied corridor. Also, results of analysis of variance (ANOVA) and post-hoc tests revealed that one of the alternative solutions, in which ATSC-CV based systems were assumed to reduce travel time by 37%, had the best benefit-to-cost ratio among all other alternatives. It was also found that having ATSC-CV based systems, assumed to curtail travel time by as low as 12%, was as economical as converting signalized intersections to interchanges.

Dong Nian, University of Cincinnati
Qinhua Jiang, University of California, Los Angeles
Yi Guo, University of Cincinnati
Jiaqi Ma (majiaqimark@gmail.com), University of California, Los Angeles

The purpose of this study is to document a case that applies analysis, modeling, and simulation (AMS) to investigate the effectiveness of connected vehicle (CV) based on Weather Responsive Management Strategies (WRMS). The goal is to address safety concerns on a real-world freeway corridor, a segment of the I–80 Connected Vehicle Testbed in Wyoming, under different weather conditions. This study simulates, evaluates, and discusses three CV-based WRMS applications: Early Lane Change (ELC), Forward Collision Warning (FCW) and Variable Speed Limit (VSL), all enabled by CV Traveler Information Messagess (TIM). The study designs operational alternatives for WRMS using CV data and develops an AMS tool using a microscopic traffic simulator VISSIM to understand the effectiveness of three WRMS under different scenarios, including various CV market penetration rates (MPR), weather conditions, and WRMS algorithm settings. The simulation results of this study provide operational insights that State and local transportation agencies may use in future strategic planning and real-time operations for their CV-based WRMS programs.

Ahmed Abdo, University of California, Riverside
Guoyuan Wu, University of California, Riverside
Nael Abu-Ghazaleh, University of California, Riverside

Connected vehicles offer a range of sophisticated services that benefit the vehicle owners, manufacturers, transportation authorities, and other mobility service providers. These services are based on underlying communication framework that enables vehicles, roads and other infrastructure, and our smart phones to communicate and share vital transportation information through advanced wireless communication technologies. Furthermore, vehicles on the road will likely be using wireless technology, GPS and other sensors to attain 360-degree awareness of nearby vehicles. This equipment will continually transmit position, direction, and speed, as well as other information, to other surrounding vehicles. With this great opportunity, connected vehicles can be exposed to a range of security and privacy threats such as location tracking or remote hijacking. In this paper, we use blockchain which is a disruptive technology that has been used in many applications from cryptocurrencies to smart contracts, as a potential solution to these challenges. The blockchain technology has the potential to revolutionize connected vehicles . It is far more secure than other record keeping systems because each new message transaction is encrypted and linked to the previous transaction. blockchain is impossible to be altered once formed. This immutable nature makes it safe from falsified information and hacks. Therefore, we propose a blockchain-based scheme to protect the vehicular ecosystem and increase its security. We present also an algorithm that uses blockchain to maintain trusted communication between vehicles and test it in a ramp merging application. p { margin-bottom: 0.1in; line-height: 115% } p { margin-bottom: 0.1in; line-height: 115% }

Khaled Shaaban (kshaaban@uvu.edu), Utah Valley University
Mazen Amin, Qatar University
Mohammed Alsoub, Qatar University

The increasing need for improving mobility and road safety has led developing countries to make significant changes in their infrastructure, especially when it comes to the modernization of the transport infrastructure. The aim of this paper is to present the experience and challenges of the implementation of Intelligent transportation systems (ITS) in Qatar, a developing country. ITS has been developed in Qatar and currently in the implementation stage. A detailed review of existing and proposed ITS technologies is provided. Many challenges were identified in order to achieve a fully functional, practical, and integratable ITS network. Some of these challenges include coordination with different stakeholders, adopting with other countries' equipment, keeping up with the technology, integration with existing systems, and budget constraints. The paper provides lessons that can benefit other developing countries going through the same transition.

Shradha Toshniwal (ar.shradhat@nevada.unr.edu), University of Nevada, Reno
Hao Xu, University of Nevada, Reno

The objective of this paper is to study the performance of LiDAR (Light Detection and Ranging), a remote sensing technology and its reliability under different condition of sensor surface and generate a system, to alarm the engineers for the upkeep required. Even though being advanced, no current single sensor system guarantees a perfect accurateness, with LiDAR the uncertainty of the cloud point which can be from a varied reasons i.e. from sensor itself, different condition of sensor surface or environmental reasons. To test the competence of the sensor, multiple experiments with variety of conditions were conducted, with 360° rotating Lidar sensor with 16 laser beams. Experiment no.1, evaluated if the sensor surface condition, influence the position of the objects detected, by conducting a simulation in well-controlled laboratory , in the Advanced Research Facility Building at University of Nevada, Reno (UNR), thus, comparing the effect in normal environment with a simulated environment. Experiment 2. would be on one the busiest intersection of McCarran and Evans, which would further increase the detection, accuracy with the road having various other obstacles to justify the results. In general, the results showed degradation in sensor performance due to surface obstacles, with the introduction of noise, and some lost data. Therefore, future work in software module development would consider, the system performance which could be checked to detect the malfunction of the sensor even before the installation, caused due to the surface obstacle.

Jiheng Huang, University of Central Florida
Shaurya Agarwal (shaurya.agarwal@ucf.edu), University of Central Florida

Intelligent vehicles have the potential to unlock a plethora of road congestion management and safety applications. This paper presents a novel framework that utilizes a physics informed deep learning approach suitable for time-sensitive intelligent vehicle applications. The framework is capable of training with low amounts of data and is computationally efficient. To demonstrate the applicability, we chose the traffic state estimation and prediction problem. Two case studies are performed that use synthetic data and field (NGSIM) data to demonstrate the potential of the proposed framework. The case studies consider realistic application scenarios with many practical aspects such as coverage areas of roadside units (RSUs), data loss in communication, sensor faults or cyber-attacks, the penetration level of intelligent vehicles, and communication range of communication protocol such as DSRC. Results are encouraging and show that the physics informed deep learning neural network outperforms the one without knowledge of the system's underlying physical laws in terms of accuracy and computational efficiency.

Pengyuan Sun (pengyuas@uci.edu), University of California, Irvine
Dingtong Yang, University of California, Irvine
R. Jayakrishnan, University of California, Irvine

A moving bottleneck on a road impedes the upstream traffic flow, which causes traffic oscillations, reduces traffic efficiency, and leads to extra fuel consumption.  This paper proposes a Dynamic Real-Time Trajectory Smoothing Algorithm (DRTSA) with the objective of minimized speed oscillations of the upstream vehicles following a moving bottleneck based on Vehicle-to-Vehicle (V2V) communication. The upstream traffic consists of connected autonomous vehicles (CAV) and human driving vehicles (HV). The proposed algorithm is operated under a rolling horizon scheme. Within each horizon, the algorithm devises an optimal acceleration scheme by discretizing the impeded period into sub-intervals and approximating the acceleration rates for each sub-interval. The optimized acceleration scheme will be implemented with the original car-following model every time step for trajectory smoothing. A set of simulations are performed to compare vehicle trajectories with and without the DRTSA. In addition, sensitivity analyses are conducted to test the algorithm performance from three aspects, namely, speed oscillation reduction, environmental benefit, and traffic efficiency improvement. The simulation results demonstrate that, by applying the proposed DRTSA, the speed variance and fuel consumption are reduced by 63.92% and 50.06% respectively, and the average travel distance within the impeded period increases by up to 46.24 meters. The results indicate that the proposed trajectory smoothing algorithm could smooth vehicle speed oscillations and bring extra traffic efficiency and environmental benefits as well.

Xue Liu, Wuhan University of Technology
Tony Qiu, University of Alberta
Tangtao Yang, Wuhan University
Haiyang Chen, Wuhan University of Technology

With the rapid development of the Intelligent Transportation System (ITS) and Connected Vehicle (CV) technology, Vehicle-to-Infrastructure (V2I) communication technologies have provided new solutions to traditional traffic safety and efficiency issues. However, current intelligent CV often provide positioning services only through a single GPS, and the positioning accuracy offered by these modules is often insufficient to support the safety and reliability of security applications. Thinking about how to enhance the positioning accuracy of GPS in a CV environment without adding additional equipment and using only the information that can be accessed by existing CV devices. This paper proposed a RSU-assisted GPS-RSS (Received Signal Strength) cooperative positioning method for a CV environment. The rough position information of the GPS is combined with RSS ranging and Dead Reckoning (DR) to obtain preliminary position estimated coordinates of the connected vehicles. Bayesian filtering is performed on the preliminary position estimate to obtain a more accurate preliminary position estimate. The final position estimated coordinates obtained after the data fusion is combined with the MAP data sent by the RSU to match the lane where the vehicle is located. Simulation and field tests verify each other, and the results show that the lane positioning accuracy of GPS can be improved within the range of OBU (On Board Unit) distance from the RSU (Road Side Unit). Keywords: Intelligent Transportation System, Vehicle-to-Infrastructure, Connected Vehicle, Received Signal Strength, Cooperative Positioning