This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility (UAM) vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolutional Neural Networks (CNNs). Trojan attacks work by embedding covert triggers within a model’s training data. These triggers cause specific failures under certain conditions, while the model continues to perform normally in other situations.We assessed the vulnerability of Urban Autonomous Aerial Vehicles (UAAVs) using the DroNet framework. Our experiments showed a significant drop in accuracy, from 96.4% on clean data to 73.3% on data triggered by Trojan attacks. To conduct this study, we collected a custom dataset and trained models to simulate real-world conditions. We also developed an evaluation framework designed to identify Trojan-infected models. This work demonstrates the potential security risks posed by Trojan attacks and lays the groundwork for future research on enhancing the resilience of UAM systems.

In this paper, we propose a conflict-free multi-agent flight scheduling that ensures robust separation in constrained airspace for Urban Air Mobility (UAM) operations application. First, we introduce Pairwise Conflict Avoidance (PCA) based on delayed departures, leveraging kinematic principles to maintain safe distances. Next, we expand PCA to multi-agent scenarios, formulating an optimization approach that systematically determines departure times under increasing traffic densities. Performance metrics, such as average delay, assess the effectiveness of our solution. Through numerical simulations across diverse multi-agent environments and real-world UAM use cases, our method demonstrates a significant reduction in total delay while ensuring collision-free operations. This approach provides a scalable framework for emerging urban air mobility systems.

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold (ε) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. To assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.62±1%, 99.96±0.1%, 99.88±0.1%, and 98.38±0.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of AVs against GPS spoofing threats.

In traffic safety research, simulation tools are considered more straightforward and cost-effective than direct observations of real-world conditions, especially when dealing with scenarios that may not exist in reality. The tools include traffic micro-simulation tools (e.g., SUMO) and driver simulators developed in game engines (e.g., Unity). However, the tools also have limitations. For example, the equations used to simulate human behavior may not always reflect real-world behavior accurately, and driver simulators’ lack of realistic traffic systems affect the interaction between the simulator vehicle and other vehicles. Co-simulation allows two different simulation tools to exchange data to enhance the capabilities of each tool, but many traffic safety researchers currently spend significant amounts of time, effort, and budget working on their own version of a co-simulation tool to integrate, for example, a traffic micro-simulation tool such as SUMO with a driver simulator such as the Unity game engine. This situation takes time away from focusing on the goal of improving traffic safety. In this paper, we developed an open-source traffic co-simulation tool. Development involved three tasks: 1. integration of SUMO and Unity; 2. development of a 2D and 3D environment (a 3D road environment in Unity was generated from a 2D road environment in SUMO); and 3. development of a 3D model of a simulator vehicle and development of a VR-based driver simulator. We named our tool SUMO2Unity and believe that it can significantly help traffic safety researchers to conduct future research aimed at improving traffic safety.

In this paper the design and development of microstrip disk resonator for measuring the amount of Napropamide in water is presented. Different type of disk resonator coated with poly pyrrol donated by single-wall carbon nanotubes is simulated using CST software. From simulation results, the resonant frequency of the disk resonator is changed form 7.4 GHz to 6.8 GHz after applying napropamide to the top of resonator. This technology has the capability to construct network sensor systems to measure the amount of napropamide in shielded environment in future research.