Distracted driving is a major factor in road accidents and fatalities, particularly due to the widespread use of mobile phones. This study aims to detect visual driving distractions using AI models, focusing on feature extraction and machine learning techniques to create an accurate detection method. Twenty-six participants took part in a simulated driving experiment using Euro Truck Simulator to create realistic driving scenarios. Participants completed two distinct scenarios: one involving a standard GPS-guided route and another while reading from a handheld smartphone to simulate distraction. Data was collected from both the simulator and an eye-tracking device, capturing critical metrics such as vehicle speed, steering angle, pedal usage, gaze direction, and head movement. The collected data was preprocessed by removing irrelevant timestamps, scaling values, and synchronizing the timestamps to a frequency of 20 Hz to ensure consistency. From this processed data, key features such as mean, standard deviation, minimum, maximum, and median values were extracted at 1 Hz intervals to provide a robust statistical summary of each signal. Several machine learning models were applied, with the Gaussian Process Classifier demonstrating the best performance, achieving an accuracy of 0.976 and an AUC of 0.996. These results highlight the potential of AI-driven systems to significantly enhance driving safety by accurately identifying distracted driving behaviors, paving the way for real-time driver monitoring solutions.
The systems engineering and robotics communities evolve in parallel, although they share common concepts and related issues. This article proposes a state-of-the-art of publications tackling both Systems Engineering (SE) and Robotics, to identify added value of Systems Engineering for Robotics, and lessons learnt for Systems Engineering from robotics use cases. In the framework of the Systems Engineering community, this synthesis of works from academics, industrial and government agencies should ease the building of SE-based seamless integrated framework for architecture, design, verification and validation of heterogeneous swarms of collaborating unmanned ground vehicles and unmanned aerial vehicles.
The attempt to backdoor SSH via an infiltration operation in one of its dependencies, namely the XZ library, illustrated the importance of robust software supply chain security. One of the tools to achieve this objective is software bill of materials (SBOM). In this paper, we proposed enriching data from our internal SBOM tooling with maintenance-related metadata from GitHub and Node Package Manager (npm) repositories to get a better understanding of our supply chain, regarding its susceptibility to XZ-type infiltration. We defined several features that we believed contributed to this risk and calculated a risk metric to identify the most extreme cases to test the validity of our approach. In doing so, we did not identify any case of supply chain attacks but identified libraries that could be susceptible to it. In addition, during the data analysis we encountered several cases where developers were making seemingly irrational tradeoffs between productivity gains and technical debt, including cyber security risks.
Bangladesh, grappling with high population density and severe traffic congestion, has witnessed a surge in air travel demand due to inadequate transportation infrastructure. This trend has propelled the growth of the domestic aviation industry, with several private companies and a government-owned entity operating domestic flights. However, monopolistic tendencies and barriers to entry pose challenges for new market entrants. In response, this paper presents a comprehensive analysis of Bangladesh's domestic aviation sector, focusing on comprehensive analysis of growth opportunities. Over a four-month period, our study constructs a comprehensive dataset from diverse sources, including flight data, operational expenditures, passenger surveys, and interviews, to evaluate the scope and growth potential within the domestic aviation market. Leveraging machine learning algorithms and novel uncertainty quantification techniques like prediction intervals, we investigate the profitability of flights on different routes based on operational costs and ticket fares. Prediction intervals perform better than point predictions in this case, as they offer a range of likely outcomes, providing more valuable insights into potential profit margins, offering valuable guidance for new entrants seeking opportunities in the market. The dataset's trustworthiness is ensured through rigorous validation, with a trustworthiness score of 83.5 %. Additionally, the dataset is available upon request to facilitate further research and collaboration.
A network created with Low Earth Orbit (LEO) satellites has advantages, such as lower latencies and network equity to remote Internet regions. However, the inherent mobility of a LEO satellite network sets it apart from its terrestrial counterpart. Studying how LEO networks are structured and interconnected is essential for understanding the performance and efficiency of LEO network topologies. This paper explores the realm of LEO satellite networks created using Inter Satellite Links (ISL). We simulated three topologies: Sparse, +Grid, and Extended +Grid. We evaluated their performance on a major LEO satellite constellation and recorded key network characteristics. We assess the simplicity, hop count, latency, and stability (the count of total path changes over a 10-minute interval) of these connection strategies in simulated LEO networks. Our evaluations highlight that although the Sparse topology is simpler than the other two, it has a higher latency than the +Grid topology. Our results indicate that using an Extended +Grid topology over a +Grid topology can reduce hop count down to 50% in a LEO mega-constellation while using the same amount of ISL hardware. Although Sparse Topology shows similar hop count and stability results, its additional latency makes it less desirable to +Grid and Extended +Grid topologies. The trade-offs between these attributes and performance and reliability across diverse topologies in LEO satellite networks need further research.
