The field of autonomous systems and mobility is rapidly evolving, with a focus on developing innovative solutions for real-world problems. Recent research has emphasized the importance of cooperative autonomous vehicle systems, with a focus on effective coordination between multiple agents to enhance traffic efficiency, fuel economy, and road safety. Scalable testbed platforms, such as ConvoyNext, are being developed to facilitate the real-world evaluation of cooperative driving behaviors. Additionally, researchers are exploring the application of machine learning techniques, such as Conformal Predictive Systems, to improve the accuracy of order fulfillment time forecasting and distributional forecasting. The development of datasets, such as CU-Multi, is also underway to support the evaluation of multi-robot data association methods. Furthermore, benchmarks like URB are being created to standardize the evaluation of collective routing strategies for connected autonomous vehicles. Noteworthy papers in this area include ConvoyNext, which demonstrates a comprehensive and open-access testbed for advancing research in cooperative autonomous vehicle systems, and Conformal Predictive Distributions for Order Fulfillment Time Forecasting, which introduces a novel framework for distributional forecasting of order fulfillment time.