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In the realm of connected autonomous vehicles, the integration of data from both onboard and edge sensors is vital for environmental perception and navigation. However, the fusion of this sensor data faces challenges due to timestamp disparities, particularly when edge devices are involved. The Robotic Operating System (ROS) addresses this with synchronization policies like Approximate and Exact Time, along with the newer Synchronizing the Earliest Arrival Messages (SEAM). Understanding SEAM’s performance in edge-assisted environments is crucial yet under-explored. This paper presents a comprehensive analysis of SEAM synchronization within ROS. Our study focuses on critical latency metrics for ROS message synchronization in edge-assisted autonomous driving. Specifically, we analyze two key latency metrics, the passing latency and reaction latency, which are needed to analyze the end-to-end delay and reaction time on the system level. We conduct experiments under different settings to evaluate the precision of our proposed latency upper bounds against the maximum experimental latency in simulation. 

Accurate road networks play a crucial role in modern mobile applications such as navigation and last-mile delivery. Most existing studies primarily focus on generating road networks in open areas like main roads and avenues, but little attention has been given to the generation of community road networks in closed areas such as residential areas, which becomes more and more significant due to the growing demand for door-to-door services such as food delivery. This lack of research is primarily attributed to challenges related to sensing data availability and quality. In this paper, we design a novel framework called SmallMap that leverages ubiquitous multi-modal sensing data from last-mile delivery to automatically generate community road networks with low costs. Our SmallMap consists of two key modules: (1) a Trajectory of Interest Detection module enhanced by exploiting multi-modal sensing data collected from the delivery process; and (2) a Dual Spatio-temporal Generative Adversarial Network module that incorporates Trajectory of Interest by unsupervised road network adaptation to generate road networks automatically. To evaluate the effectiveness of SmallMap, we utilize a two-month dataset from one of the largest logistics companies in China. The extensive evaluation results demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving a precision of 90.5%, a recall of 87.5%, and an F1-score of 88.9%, respectively. Moreover, we conduct three case studies in Beijing City for courier workload estimation, Estimated Time of Arrival (ETA) in last-mile delivery, and fine-grained order assignment.