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1. Evaluating the driving behavior of autonomous vehicles and human driver vehicles in mixed driver environments at a signalized intersection

   https://doi.org/10.1117/12.2613057  

   Yang, Xinyi; Ahmad, Salman; Nelson, Martha; Rogers, Joshua; Ren, Yihao; Hung, Ying; Lu, Pan (April 2022, Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele; Su, Zhongqing; Glisic, Branko (Ed.)

   With the rapid development of smart cities, interest in vehicle automation continues growing. Autonomous vehicles are becoming more and more popular among people and are considered to be the future of ground transportation. Autonomous vehicles, either with adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), provide many possibilities for smart transportation in a smart city. However, traditional vehicles and autonomous vehicles will have to share the same road systems until autonomous vehicles fully penetrate the market over the next few decades, which leads to conflicts because of the inconsistency of human drivers. In this paper, the performance of autonomous vehicles with ACC/CACC and traditional vehicles in mixed driver environments, at a signalized intersection, were evaluated using the micro-simulator VISSIM. In the simulation, the vehicles controlled by the ACC/CACC and Wiedemann 99 (W99) model represent the behavior of autonomous vehicles and human driver vehicles, respectively. For these two different driver environments, four different transport modes were comprehensively investigated: full light duty cars, full trucks, full motorcycles, and mixed conditions. In addition, ten different seed numbers were applied to each model to avoid coincidence. To evaluate the driving behavior of the human drivers and autonomous vehicles, this paper will compare the total number of stops, average velocity, and vehicle delay of each model at the signalized traffic intersection based on a real road intersection in Minnesota. 

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2. A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)

   https://doi.org/10.3390/vehicles3030032  

   Zhang, Hui; Zhang, Rongqing; Chen, Chen; Duan, Dongliang; Cheng, Xiang; Yang, Liuqing (September 2021, Vehicles)

   null (Ed.)

   In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs. 

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3. Agent Based Resilient Transportation Infrastructure With Surrogate Adaptive Networks

   https://doi.org/10.1115/DETC2020-22568  

   Apostol, Andre A.; Turner, Cameron J. (August 2020, c. ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract Connected autonomous intelligent agents (AIA) can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or with other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making can benefit autonomous vehicle technology. This research examines vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication strategies that use gathered data to ensure these agents make appropriate decisions under operational circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection to ultimately find an traffic flow that will minimizes average vehicle transit time to improve intersection efficiency. By considering each light and vehicle as an agent and providing for communication between agents, additional decision-making data can be transmitted. Improving agent based I2I communication and decision making will provide performance benefits to traffic flow capacities. 

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4. Cooperative Platooning with Mixed Traffic on Urban Arterial Roads

   https://doi.org/10.1109/IV51971.2022.9827105  

   Mu, Zeyu; Chen, Zheng; Ryu, Seunghan; Avedisov, Sergei S.; Guo, Rui; Park, B. Brian (January 2022, 2022 IEEE Intelligent Vehicles Symposium (IV))

   In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to- everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands. 

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5. Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

   https://doi.org/10.1177/0361198121994580  

   Gutesa, Slobodan; Lee, Joyoung; Besenski, Dejan (July 2021, Transportation Research Record: Journal of the Transportation Research Board)

   Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively. 

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