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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. Such 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. 

In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), the Data-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design. 

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. 

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. 

ABSTRACT Human infection challenge studies involving the intentional infection of research participants with a disease-causing agent have recently been suggested as a means to speed up the search for a vaccine for the ongoing coronavirus disease 2019 (COVID-19) outbreak. Calls for challenge studies, however, rely on the expected social value of these studies. This value represents more than the simple possibility that a successful study will lead to the rapid development and dissemination of vaccines but also some expectation that this will actually occur. I show how this expectation may not be realistic in the current political moment and offer potential ways to make sure that any challenge trials that arise actually achieve their goals.