Nowadays, Automated Vehicle (AV) technology is gaining attention as a candidate to improve the efficiency of Bus Rapid Transit (BRT) systems. However, there are still some challenges in AV technology including limited perception range and lack of cooperation capability in mixed traffic situations with drivers. The emerging Connected and Automated Vehicles (CAVs) and Cooperative Intelligent Transportation System (C-ITS) offer an unprecedented opportunity to solve such challenges. As a result, this study presents a framework for Connected and Automated BRT (CA-BRT), including a cloud-based architecture and a deep reinforcement learning system for Sectionalized Speed Guidance (SSG) system designed for CAVs. The proposed framework is field-tested in Sejong City in South Korea, where there are various road environments such as bus stops, overpasses, underground tunnels, intersections, and crosswalks. The driving performance of the proposed system is compared with different types of control scenarios, and the results from the field tests show that the proposed system improves the driving performance of the AVs in various aspects including driving safety, ride comfort, and energy efficiency with downstream information obtained from road infrastructures.

如今，自动驾驶汽车 (AV) 技术作为提高快速公交 (BRT) 系统效率的候选技术而受到关注。然而，自动驾驶技术仍然存在一些挑战，包括感知范围有限以及在混合交通情况下缺乏与驾驶员的合作能力。新兴的联网和自动驾驶汽车 (CAV) 和协同智能交通系统 (C-ITS) 为解决此类挑战提供了前所未有的机会。因此，本研究提出了一个连接和自动化 BRT (CA-BRT) 的框架，包括一个基于云的架构和一个深度强化学习系统，用于为 CAV 设计的分段速度引导 (SSG) 系统。拟议的框架在韩国世宗市进行了现场测试，那里有各种道路环境，如公交车站、立交桥、地下隧道、十字路口和人行横道。将所提出的系统的驾驶性能与不同类型的控制场景进行比较，现场测试的结果表明，所提出的系统在驾驶安全性、乘坐舒适性和能源效率等多个方面提高了自动驾驶汽车的驾驶性能。从道路基础设施获得的信息。