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1. Characterizing Connected and Automated Vehicle Platooning Vulnerability under Periodic Perturbation

   https://doi.org/10.1109/ITSC48978.2021.9565052  

   Wang, Pengcheng ; Wu, Xinkai ; He, Xiaozheng ( September 2021 , 2021 IEEE International Intelligent Transportation Systems Conference (ITSC))

   The performance of connected and automated vehicle (CAV) platoons, aimed at improving traffic efficiency and safety, depends on vehicle dynamics and communication reliability. However, CAVs are vulnerable to perturbations in vehicular communication. Such endogenous vulnerability can induce oscillatory dynamics to CAVs, leading to the failure of platooning. Differing from previous work on CA V platoon stability, this research exploits CAV platooning vulnerability under periodic perturbation by formulating the oscillatory dynamics as vibrations in a mechanical system. Akin to other mechanical systems, a CAV platoon has its inherent oscillation frequency, exhibiting unique characteristics in a perturbed travel environment. To this end,more »this paper proposes an approach to characterize the CAV platooning vulnerability using the mechanical vibration theory. The employed theory reveals that CAV platooning vulnerability mainly associates with its resonance frequency, through which a small periodic perturbation can amplify the platoon oscillation. The analytical formulation and simulation results show that preventing periodic perturbations from a platoon's resonance frequency is crucial to enhance the CAV platooning reliability and suppress large amplitude oscillations, helping to secure the expected benefits of CAV platoons.« less
2. Fully Distributed Optimization-Based CAV Platooning Control Under Linear Vehicle Dynamics

   https://doi.org/10.1287/trsc.2021.1100  

   Shen, Jinglai ; Kammara, Eswar Kumar ; Du, Lili ( March 2022 , Transportation Science)

   This paper develops distributed optimization-based, platoon-centered connected and autonomous vehicle (CAV) car-following schemes, motivated by the recent interest in CAV platooning technologies. Various distributed optimization or control schemes have been developed for CAV platooning. However, most existing distributed schemes for platoon centered CAV control require either centralized data processing or centralized computation in at least one step of their schemes, referred to as partially distributed schemes. In this paper, we develop fully distributed optimization based, platoon centered CAV platooning control under the linear vehicle dynamics via the model predictive control approach with a general prediction horizon. These fully distributed schemesmore »do not require centralized data processing or centralized computation through the entire schemes. To develop these schemes, we propose a new formulation of an objective function and a decomposition method that decomposes a densely coupled central objective function into the sum of multiple locally coupled functions whose coupling satisfies the network topology constraint. We then exploit locally coupled optimization and operator splitting methods to develop fully distributed schemes. Control design and stability analysis is carried out to achieve desired traffic transient performance and asymptotic stability. Numerical tests demonstrate the effectiveness of the proposed fully distributed schemes and CAV platooning control.« less
3. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   Jiang, L ( June 2021 , Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Suchmore »an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using realworld vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.« less
4. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   https://doi.org/10.1109/MT-ITS49943.2021.9529289  

   Jiang, Liming ; Xie, Yuanchang ; Wen, Xiao ; Chen, Danjue ; Li, Tienan ; Evans, Nicholas G. ( January 2021 , 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Suchmore »an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.« less
5. Rhythmic Control of Automated Traffic—Part II: Grid Network Rhythm and Online Routing

   https://doi.org/10.1287/trsc.2021.1061  

   Lin, Xi ; Li, Meng ; Shen, Zuo-Jun Max ; Yin, Yafeng ; He, Fang ( September 2021 , Transportation Science)

   Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a networkmore »and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer.« less