This paper investigates the role of Vehicle-to-Vehicle (V2V) communications in enhancing road safety, with a focus on the Emergency Electronic Brake Lights (EEBL) application. We analyze a scenario with an abruptly decelerating vehicle on a highway, and we derive the conditions for trailing vehicles to either prevent collisions or maintain a safe following distance. We simulate the scenario using ns-3, with a focus on Fifth-Generation (5G) New Radio (NR) to provide the V2V communication needed for the EEBL application. The findings indicate that the use of the EEBL application over NR V2V significantly enhances road safety by preventing forward collisions that would otherwise occur due to delayed human driver reactions.

With the growing significance of data analysis, several studies aim to provide precise answers to users' natural language questions from tables, a task referred to as tabular question answering (TQA). The state-of-the-art TQA approaches are limited to handling only single-table questions. However, real-world TQA problems are inherently complex and frequently involve multiple tables, which poses challenges in directly extending single-table TQA designs to handle multiple tables, primarily due to the limited extensibility of the majority of single-table TQA methods. This paper proposes AutoTQA, a novel Auto nomous T abular Q uestion A nswering framework that employs multi-agent large language models (LLMs) across multiple tables from various systems (e.g., TiDB, BigQuery). AutoTQA comprises five agents: the User , responsible for receiving the user's natural language inquiry; the Planner , tasked with creating an execution plan for the user's inquiry; the Engineer , responsible for executing the plan step-by-step; the Executor , provides various execution environments (e.g., text-to-SQL) to fulfill specific tasks assigned by the Engineer ; and the Critic , responsible for judging whether to complete the user's natural language inquiry and identifying gaps between the current results and initial tasks. To facilitate the interaction between different agents, we have also devised agent scheduling algorithms. Furthermore, we have developed LinguFlow, an open-source, low-code visual programming tool, to quickly build and debug LLM-based applications, and to accelerate the creation of various external tools and execution environments. We also implemented a series of data connectors, which allows AutoTQA to access various tables from multiple systems. Extensive experiments show that AutoTQA delivers outstanding performance on four representative datasets.

Proximity Services (ProSe) and Wi-Fi are two promising technologies that may provide support for Mission Critical Voice (MCV) applications in remote and rural areas by enabling Device-to-Device (D2D) communication. In this paper, several performance metrics of ProSe and Wi-Fi are evaluated and compared side-by-side under various configurations. The ns-3 simulation results show that ProSe outperforms Wi-Fi in terms of coverage range and access time with a medium traffic load, while Wi-Fi has a shorter access time under a light traffic load. In addition, with various user densities, ProSe offers better coverage range and access time a majority of the time. The evaluation in this paper provides insights to first responders on what to expect with either technology and how to improve the performance by adjusting different system parameters.

Public safety organizations around the world started migrating toward Long-Term Evolution (LTE) networks to support the increasing needs for video and data. To address the unique voice communication requirements of first responders, the 3rd Generation Partnership Project (3GPP) introduced new capabilities that aim at providing similar functionalities as the traditional Land Mobile Radio (LMR) systems, namely, Direct Mode communication and mission critical push-to-talk (MCPTT). Direct Mode communication, also called Proximity Services (ProSe), allows public safety users to communicate directly with each other regardless of the network status. MCPTT was the first mission critical service, and first application, standardized by 3GPP to provide both on- and off-network voice capability. Assessing the performance of those capabilities is critical to accelerate their deployment and adoption by first responders. In this study, we evaluate the performance of an off-network mode MCPTT device over ProSe by focusing on the access time, a measure of the delay incurred before a user can talk. We develop analytical models for various types of calls and verify the accuracy of the predicted access time using ns-3 simulations. We perform sensitivity analysis to show the validity of the models for various scenarios. Finally, we show how the models can be used to guide parameter configuration for both MCPTT and ProSe to optimize the performance.

A multiple target tracking method based on Gaussian mixture probability hypothesis density (GM-PHD) filter is given under the condition of asynchronous measurement in radar system, named Cross Sector GM-PHD. The Cross Sector GM-PHD process does not require all measured values measured at the same time, but also can deal the multiple targets tracking problem with changing number of targets. The results for engineering applications of the multiple target tracking based on Cross Sector GM-PHD filtering in radar system are introduced. Results showed that the Cross Sector GM-PHD filtering can effectively solve the problems of Track initiation, organizing batch, and so on, under the condition of low detection probability.