Driving automation systems offer both opportunities and challenges in addressing environmental issues. It is essential to clarify: (1) the environmental impacts of driving automation systems-equipped vehicles (referred to as automated vehicles, i.e., AVs in this review) and (2) the methods for leveraging AVs to improve the environment. To answer these questions, this study systematically reviews relevant literature from the past decade, focusing on the air environment and traffic operation. We also pay additional attention to the link between environmental impacts and improvement methods. The findings reveal that the environmental impact of AVs is two-sided, and it is primarily determined by connectivity, penetration rate, and traffic demand. When traffic demand is low, integrating AVs into traffic can lead to environmental improvements. However, under high traffic demand, the ability of AVs to improve the environment depends on their connectivity and the scale to form a multi-vehicle cooperation. Both micro-level traffic parameters and macro-level traffic scenarios affect the environmental benefits of AVs. While micro-parameters can be adjusted by altering the settings of AVs to realize positive environmental effects, macro-scenario conditions must be adapted by AVs through control and management methods to achieve ecological goals. Several representative methods from both vehicle control and traffic management perspectives are discussed. Generally, the existing control and management methods can be categorized into four phases: individual AV control, cooperative control of multiple AVs, facility-based management methods, and network-based ecological schemes. Finally, this study outlines five potential research directions for the sustainable development of AVs.

Autonomous intersection management (AIM) has been widely investigated over the past two decades as a solution for signal-free intersections in a fully connected and autonomous environment. However, existing AIM approaches lack consideration for crossing pedestrians, especially in a way that enables pedestrians to cross streets safely while optimizing upcoming vehicles at the intersection to maintain traffic continuity. To address this issue, this paper proposes an Automated Pedestrian Shuttle (APS) service system to transport pedestrians across the street. Using a coupled space–time network modeling method, an optimization model, which incorporates pedestrian ride strategies and aims to minimize the total crossing time, is first established for the APS operation scheme. A Shuttle-by-Shuttle-Planning (SBSP) algorithm is developed for model solving to determine the APS operation route and the departure time from each station along the route. Then, a timing schedule optimization model is proposed to adjust the departure time of APS at each station and the time of main-lane vehicles entering an intersection to avoid vehicle conflicts. To ensure that the main-lane vehicles can enter the intersection on time, a trajectory optimization model is proposed to provide a specific driving strategy for each main-lane vehicle. A rolling horizon strategy is adopted to bridge the different optimization time windows. Finally, the effectiveness of the proposed APS service system is demonstrated through numerical simulation experiments. The results show that (1) the APS service can effectively help pedestrians cross the street with minor delays to main-lane vehicles; (2) The dynamic-route scheme is adaptable to the needs of randomly arriving pedestrians due to its demand-responsive nature; (3) The service efficiency of the system is related to the number of APSs, and the more APSs, the higher the service efficiency under the premise of unsaturation; (4) The designed SBSP algorithm significantly reduces the model-solving time compared to the commercial solver, making the APS service system potentially applicable for field implementation.