Risk‐based maximum speed advisory system for driving safety of connected and automated bus - Sehyun Tak; Sari Kim; Donghoun Lee (IET Intelligent Transport Systems)
Abstract Bus rapid transit (BRT) system is a cost‐effective way to provide public transportation service. However, it faces some challenges such as reduced labour productivity and increasing fuel costs. One solution is introducing automated vehicles (AV) to reduce operational expenses. However, there are still limitations on completely replacing human drivers even in limited operational design domains (ODD). Furthermore, AVs often suffer from poor driving stability in some roadways, such as abrupt changes in road geometry. To enhance the driving safety of AV‐based BRT services, this study develops a new connected and automated bus (CAB) system using a cloud‐based traffic management centre with cooperative intelligent transportation systems. The proposed system introduces risk‐based maximum speed advisory system (RMSAS), which controls the maximum advisory speed of CAB to reduce its driving risk. This research evaluates the performance of RMSAS by comparing it to other driving modes, such as human‐driven vehicles and conventional AVs, based on real‐world field operational tests. The result shows that the proposed system outperforms other driving modes in terms of driving risks, particularly in some road geometry‐related ODDs. Hence, this research concludes that the proposed system can be applied to the AV‐based BRT service for uprating its safety performance.