| Abstract: |
Although originally developed for vehicle-centric applications, connectivity technologies show promise for improving awareness of nearby road users and mitigating potential conflicts. Their scope now extends to vulnerable road users (VRUs), including bicyclists. These technologies involve various communication paradigms between vehicles, bicyclists, and infrastructure, increasingly designed to address safety challenges for both drivers and cyclists. However, full connectivity is unlikely in the near term, making it essential to study mixed connectivity environments, networks where some but not all users are connected. Such studies should examine how road users experience situations in different connectivity roles, both connected and unconnected, using objective and subjective performance measures.
Driving simulators, and more recently, bicycle simulators, have been extensively utilized to: 1. evaluate engineering interventions aimed at enhancing traffic performance, and 2. analyze road user behavior. Prior research in this domain has predominantly focused on simulations involving a single human agent, wherein interactions occur with virtual agents governed by the simulation platform. To complement the previous research, this study presents a novel approach where a unique simulator framework is used, in which two human agents, a driver and a bicyclist, interact within a simulation. Specifically, the study investigates potential conflicting events between a human-controlled driver and a human-controlled bicyclist under various levels of connectivity.
Moreover, while most prior driving and bicycle simulator studies have evaluated the safety benefits of connected technologies using hypothetical networks, this study develops a high-fidelity Digital Twin. To replicate an authentic multimodal traffic environment, the study uses a heavy multimodal Delaware Avenue, a two-lane one-way eastbound roadway, located in downtown Newark, DE, USA. A Digital Twin is powered by two synchronized co-simulation platforms: 1. CARLA (Car Learning to Act), an open-source simulator, is employed to provide an immersive and realistic experience for human participants, and 2. PTV Vissim 2025 manages the surrounding background traffic environment.
This research firstly addresses a fundamental question: can an experimental setup that integrates a high-fidelity Digital Twin with a dual human-in-the-loop simulator in Virtual Reality successfully reproduce severe conflicts between a bicyclist and a driver? The evaluation was conducted in two stages. First, the study assessed whether the simulated bicyclist–vehicle interactions met established surrogate safety thresholds for severe conflicts. Second, it examined whether participants perceived these events as conflicts through analysis of their self-reported feedback and physiological responses.
The subsequent stage of the analysis investigated bicyclist–vehicle conflicts within a mixed connectivity environment, focusing on how collision warning information influenced physiological stress responses, perception and anticipation abilities, and how connectivity affected users’ decision-making. |