This paper introduces a novel deep learning framework that enhances vehicle re-identification (ReID) accuracy by integrating visual and temporal data. Vehicle ReID, which identifies target vehicles from large volumes of traffic data, is essential for continuous tracking in large-scale monitoring scenarios involving multiple Unmanned Aerial Vehicles (UAVs). UAV-based monitoring, while offering a comprehensive bird’s-eye view (BEV), faces key challenges: loss of uniquely identifiable features and reliance on visual data, which struggles with vehicles of similar appearance. To overcome these issues, our approach incorporates traffic-oriented features based on shockwave theory to model predictable vehicle travel times. Methods have been tested with data from one of the largest drone experiments with 10 drones monitoring 20 intersections for one week in the city of Songdo in Seoul Area. Experimental results demonstrate a 36.8\% improvement in ReID accuracy over traditional methods, highlighting the potential of UAV-based solutions for robust and scalable traffic monitoring.

The replication package containing data and code to reproduce this study' s results contains:
 • code generated or adapted via GPT-based prompt methods,
 • detailed evaluation scripts (Python) and analysis notebooks,
 • extensive persona prompts used in the guided LLM methodology,
 • supplementary hints and strategy documentation for persona modelling, and
 • an extracted subset of the dataset corresponding to the study' s tables and results.

Dataset: MiD2017 Tabellenband Deutschland

This article presents FollowGen, a conditional diffusion model for vehicle trajectory prediction in car-following scenarios. Unlike existing diffusion-based approaches that introduce conditions only during the denoising stage, FollowGen incorporates a scaled noise conditioning mechanism in the forward process to embed historical motion features, and employs a cross-attention transformer in the reverse process to explicitly model interactions between leading and following vehicles. Experiments demonstrate that FollowGen consistently outperforms state-of-the-art baselines, achieving higher accuracy and robustness in diverse car-following environments. 

To address the limited extensibility of standardized message format this study proposes a modular, edge-intelligent framework — The mobility Operating System (mOS) — integrated with a mixed-reality testbed for realistic validation of infrastructure-guided autonomous vehicle coordination. We analyzed to verify the feasibility of the C-V2X Framework for autonomous vehicle guidance in real-time at the physical testbed. The dataset was collected from the testbed experiment to analyze framework performance (speed profiles, post-encroachment time, and numerical error) and service performance (latency, jitter, and packet loss).