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1. 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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2. Assessment of Cooperative Adaptive Cruise Control in Mixed Traffic on Arterial Roads

   https://doi.org/10.1109/ITSC48978.2021.9565099  

   Mu, Zeyu; Chen, Zheng; Sakr, Ahmed Hamdi; Ryu, Seunghan; Wang, Tianye; Avedisov, Sergei S.; Guo, Rui; Park, B. Brian (January 2021, 2021 IEEE Intelligent Transportation Systems Conference (ITSC))

   In this paper, we investigated the performance of cooperative adaptive cruise control (CACC) algorithms in mixed traffic environments featuring connected automated vehicles (CAVs) and unconnected vehicles. For CAVs, we tested the recently proposed linear feedback control approach (Linear- CACCu) and adaptive model predictive control approach (A- MPC-CACCu) which have been tailored to extend CACC to mixed traffic environments. In contrast to most literature where CACC design and evaluation are performed on freeways, we focused on urban arterial roads using the CACC Field Operation Test Dataset from the Netherlands. We compared the performances of Linear-CACCu and A-MPC-CACCu to regular adaptive cruise control (ACC), where automated vehicles do not rely on connectivity, as well as human drivers. Performance comparison was done in terms of ego vehicle’s spacing error, acceleration, and energy consumption which relate to safety, driving comfort, and energy efficiency, respectively. Simulation results showed that CACCu algorithms significantly outper- formed the ACC and human drivers in these metrics. Moreover, we found that the fluctuations of the lead vehicle’s behavior due to changes in traffic signal phase have a significant impact on which CACCu is optimal (i.e., A-MPC-CACCu or Linear- CACCu). Thus, the CACC mode could be switched based on the expectation of traffic signal phase changes to assure better performance. 

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3. Adaptive control for reliable cooperative intersection crossing of connected autonomous vehicles

   https://doi.org/10.1002/msd2.12050  

   Wei, Yu; He, Xiaozheng_Sean (September 2022, International Journal of Mechanical System Dynamics)

   Abstract Rapid advances in vehicle automation and communication technologies enable connected autonomous vehicles (CAVs) to cross intersections cooperatively, which could significantly improve traffic throughput and safety at intersections. Virtual platooning, designed upon car‐following behavior, is one of the promising control methods to promote cooperative intersection crossing of CAVs. Nevertheless, demand variation raises safety and stability concerns when CAVs adopt a virtual platooning control approach. Along this line, this study proposes an adaptive vehicle control method to facilitate the formation of a virtual platoon and the cooperative crossing of CAVs, factoring demand variations at an isolated intersection. This study derives the stability conditions of virtual CAV platoons depending on the time‐varying traffic demand. Based on the derived stability conditions, an optimization model is proposed to adaptively control CAVs dynamics by balancing approaching traffic mobility and safety to enhance the reliability of cooperative crossing at intersections. The simulation results show that, compared to the nonadaptive control, our proposed method can increase the intersection throughput by 18.2%. Also, time‐to‐collision results highlight the advantages of the proposed adaptive control in securing traffic safety. 

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4. Cooperative Car-Following and Merging: A Novel Merge Control Strategy Considering Cooperative Adaptive Cruise Control and Courtesy

   Jiang, L. (January 2021, Proceedings of the 100th Transportation Research Board Annual Meeting)

   null (Ed.)

   This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative Car-following and Merging (CCM) control strategy is proposed considering the coexistence of Automated Vehicles (AVs) and Human-4 Driven Vehicles (HDVs). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AVs behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. AVs in this study do not need to be fully automated and can be at Level-1 automation. CCM only requires automated longitudinal control such as Adaptive Cruise Control (ACC) and information sharing among vehicles, and ACC is already commercially available on many new vehicles. Also, it does not need 100% ACC penetration, presenting itself as a promising and practical solution for improving traffic operations in lane reduction transition areas such as highway work zones. 

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5. Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems

   Wang, S; Wu, S; Lin, C; Boddupalli, S; Lin, C; Shih, C; Ray, S. (January 2021, 4th IEEE International Conference on Intelligent Transportation Systems - ITSC2021)

   null (Ed.)

   Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder-based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches. 

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