Teleoperation provides a critical fallback for autonomous vehicles (AVs), enabling remote operators or automated systems to assume control when autonomous functionality is insufficient. However, network latency remains a key challenge that can degrade control accuracy and compromise safety. This study systematically evaluates the impact of network latency on teleoperated driving through system-level experiments using a physical robotic vehicle. Controlled delays ranging from 0 ms to 240 ms were introduced via a custom latency injection module, isolating latency effects from human factors. Path-following accuracy and speed regulation were assessed using a pure-pursuit controller and LiDAR-based localization. Results show that increasing latency leads to progressive degradation in both lateral and longitudinal control, with performance declining sharply beyond 160 ms. These findings provide quantitative benchmarks for latency tolerance in real-time teleoperation and offer practical guidance for designing resilient communication and control architectures for connected and autonomous vehicles.

We introduce a drone-centric workflow that complements fixed trail cameras to deliver objective, standardized, and scalable assessments of park and trail infrastructure. Traditional human audits, such as PARA and the Boston Block Walk, are labor intensive and subjective and lack the corridor scale perspective needed for comprehensive maintenance planning. Our system addresses this gap by using (i) a multirotor UAV equipped with synchronized thermal and RGB cameras to document surface conditions over vast areas and quantify defect dimensions, and (ii) low-cost fixed cameras to continuously monitor user counts, activity types, and intensity (MET) values. Drone imagery is calibrated via homography to convert pixel measurements to real-world units, and a thermal RGB data fusion pipeline detects cracks, moisture-softened patches, and other defects. A case study on a community tennis court demonstrated a mean absolute percentage error (MAPE) of 11.6% between drone-estimated and tape-measured crack lengths. Along Delaware's Jack A. Markell and James F. Hall trails, camera-based usage analytics revealed patterns consistent with intercept surveys: ≈ 20% of the users were cyclists and ≈ 98% engaged in recreational activities, while estimating speed-derived METs for walkers, runners, and cyclists. Compared to an 11-mile manual audit that required 26 raters, our approach reduced costs by approximately $4,000 and produced reusable digital evidence. We conclude that drones are the necessary backbone for corridor-scale, time-bounded condition assessments, while fixed cameras remain essential for long-term use monitoring; together, they enable auditable, repeatable, and cost-effective management of urban greenspace.

The exploration of software Over-the-Air (OTA) updates for automotive applications is currently very limited. Our work introduces an edge-assisted framework for automotive OTA updates that carefully accounts for various factors, including different software models in vehicles, communication distances, and cluster sizes. We present valuable insights using key evaluation metrics like update speed, data transmission efficiency, and success rate, accompanied by a thorough scalability analysis. Our research involves three distinct vehicle software models: ResNet-18 (46.8 MB), ResNet-50 (102.5 MB), and Faster R-CNN (175.2 MB). These models are used to evaluate update performance across eight distance categories ranging from 0 to 21 meters with a 3-meter interval. We also utilize diverse computing platforms to assess the success rate and conduct a comprehensive scalability analysis. This innovative approach significantly advances our understanding and practical implementation of OTA updates in the automotive field.