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1. Online predictive connected nd automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

   https://doi.org/https://doi.org/10.1016/j.trb.2020.12.009  

   Zhao, S ; Zhang, K. ( March 2021 , Transportation research)

   null (Ed.)

   For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project. 

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2. Impact of Cooperative Adaptive Cruise Control on Traffic Stability

   https://doi.org/10.1177/03611981221094822  

   Hung, Yun-Chu ; Zhang, Kuilin ( June 2022 , Transportation Research Record: Journal of the Transportation Research Board)

   Cooperative adaptive cruise control (CACC) is one of the popular connected and automated vehicle (CAV) applications for cooperative driving automation with combined connectivity and automation technologies to improve string stability. This study aimed to derive the string stability conditions of a CACC controller and analyze the impacts of CACC on string stability for both a fleet of homogeneous CAVs and for heterogeneous traffic with human-driven vehicles (HDVs), connected vehicles (CVs) with connectivity technologies only, and autonomous vehicles (AVs) with automation technologies only. We mathematically analyzed the impact of CACC on string stability for both homogeneous and heterogeneous traffic flow. We adopted parameters from literature for HDVs, CVs, and AVs for the heterogeneous traffic case. We found there was a minimum constant time headway required for each parameter design to ensure stability in homogeneous CACC traffic. In addition, the constant time headway and the length of control time interval had positive correlation with stability, but the control parameter had a negative correlation with stability. The numerical analysis also showed that CACC vehicles could maintain string stability better than CVs and AVs under low HDV market penetration rates for the mixed traffic case. 

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3. Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag

   https://doi.org/10.1177/03611981221091762  

   Tan, Yingtong ; Zhang, Kuilin ( May 2022 , Transportation Research Record: Journal of the Transportation Research Board)

   Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics. 

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4. Distributed Model Predictive Control for Connected and Automated Vehicles in the Presence of Uncertainty

   https://doi.org/10.1115/1.4054696  

   HomChaudhuri, Baisravan ; Bhattacharyya, Viranjan ( January 2022 , Journal of Autonomous Vehicles and Systems)

   This article focuses on the development of distributed robust model predictive control (MPC) methods for multiple connected and automated vehicles (CAVs) to ensure their safe operation in the presence of uncertainty. The proposed layered control framework includes reference trajectory generation, distributionally robust obstacle occupancy set computation, distributed state constraint set evaluation, data-driven linear model representation, and robust tube-based MPC design. To enable distributed operation among the CAVs, we present a method, which exploits sampling-based reference trajectory generation and distributed constraint set evaluation methods, that decouples the coupled collision avoidance constraint among the CAVs. This is followed by data-driven linear model representation of the nonlinear system to evaluate the convex equivalent of the nonlinear control problem. Finally, to ensure safe operation in the presence of uncertainty, this article employs a robust tube-based MPC method. For a multiple CAV lane change problem, simulation results show the efficacy of the proposed controller in terms of computational efficiency and the ability to generate safe and smooth CAV trajectories in a distributed fashion.

    

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5. 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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