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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Cooperative Car-Following and Merging: A Novel Merge Control Strategy Considering Cooperative Adaptive Cruise Control and Courtesy
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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- Award ID(s):
- 1734521
- NSF-PAR ID:
- 10257297
- Date Published:
- Journal Name:
- Proceedings of the 100th Transportation Research Board Annual Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Given the aging infrastructure and the anticipated growing number of highway work zones in the U.S.A., it is important to investigate work zone merge control, which is critical for improving work zone safety and capacity. This paper proposes and evaluates a novel highway work zone merge control strategy based on cooperative driving behavior enabled by artificial intelligence. The proposed method assumes that all vehicles are fully automated, connected, and cooperative. It inserts two metering zones in the open lane to make space for merging vehicles in the closed lane. In addition, each vehicle in the closed lane learns how to adjust its longitudinal position optimally to find a safe gap in the open lane using an off-policy soft actor critic reinforcement learning (RL) algorithm, considering its surrounding traffic conditions. The learning results are captured in convolutional neural networks and used to control individual vehicles in the testing phase. By adding the metering zones and taking the locations, speeds, and accelerations of surrounding vehicles into account, cooperation among vehicles is implicitly considered. This RL-based model is trained and evaluated using a microscopic traffic simulator. The results show that this cooperative RL-based merge control significantly outperforms popular strategies such as late merge and early merge in terms of both mobility and safety measures. It also performs better than a strategy assuming all vehicles are equipped with cooperative adaptive cruise control.more » « less
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Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings.more » « less
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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.more » « less
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