The development of intelligent transportation has generated many ultra reliable low latency communication (URLLC) tasks, which require sufficient communication and computation resources for task offloading and processing. Although mobile edge computing (MEC) provides a promising solution, its efficiency is subject to the limited knowledge and analysis capability on the physical networks. Therefore, in this paper, we propose a digital twin (DT) empowered MEC framework to strengthen the MEC task offloading efficiency in cellular vehicle-to-everything (C-V2X) networks. Our proposed DT is constructed through a hybrid data-driven and model-driven approach to capture the realistic transportation network features. Then, DT leverages the metric of time to collision to predict vehicular safety levels and estimates the corresponding URLLC task requirements of future time slots. The prediction results are further utilized to make decisions on the URLLC resource reservation. Different from conventional studies, we consider the influence of DT's inaccurate predictions (i.e., the prediction with error) on the resource allocations. Specifically, the inaccurate DT prediction results are considered as uncertain constraints of the resource reservation problem. A robust parameter from the robust optimization is adopted to adjust the tradeoff between the problem uncertainty and solution optimality degree. Further, we leverage the optimized resource reservation results to construct the task offloading problem. The problem is decoupled into two sub-problems of channel resource allocation and computation resource allocation, respectively. And a two-stage matching algorithm is developed to solve each sub-problem based on the resource reservation constraints. Finally, realistic road information is mapped into DT for simulations. Simulation results validate the advantages of our proposed approach by comparing with existing schemes.

This study utilized Virtual Reality (VR) experiments to investigate pedestrian-autonomous vehicle interaction in shared spaces. In the VR experiment, pedestrians attempt to cross the road under different conditions, including the presence of another pedestrian, different external Human-Machin-Interfaces, AV driving styles, and road conditions. We employed an innovative VR setup that enabled two pedestrians to interact in real time with physical movements within an immersive VR environment. Overall, we found that the presence of multiple pedestrians significantly influenced pedestrian movement dynamics during road crossing. Additionally, the relative standing position had a significant impact on the distant pedestrians regarding time before crossing and vehicle-gazing behavior. While previous studies predominantly focused on pedestrian-AV interaction with a single pedestrian, this study takes an important step forward in terms of theory, methods, and relevance by considering interactions between multiple pedestrians and AVs. The findings establish a basis for further exploration of pedestrian-AV interaction in shared space.