Intelligent Traffic Control with Smart Speed Bumps
Traffic congestion and safety continue to pose significant challenges in urban environments. In this paper, we introduce the Smart Speed Bump (SSBump), a novel traffic calming solution that leverages the Internet of Things (IoT) and innovative non-Newtonian fluid materials to enhance road safety, optimize emergency response times, and improve the overall driving experience. The SSBump uses IoT sensors to detect and communicate with emergency vehicles, reducing response times by temporarily deflating. These sensors also analyze traffic patterns and inform data-driven decisions. Additionally, the SSBump uses an Oobleck mixture that adapts its behavior based on the velocity of approaching vehicles, resulting in a safer and more comfortable experience for drivers. This study commences with an overview of the prevalent traffic congestion, followed by a discussion on various available options in this domain. Subsequently, the paper explores the advantages of smart speed bumps and their operational mechanisms. Finally, it presents a comprehensive analysis of the results, its challenges, and the prospects of the work. The findings of this research demonstrate the potential of the SSBump system to revolutionize traffic control, emergency response time, and the driving experience in smart cities, making it a game-changing innovation for advanced transportation systems.
Graph Neural Network based Log Anomaly Detection and Explanation
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. Most existing log anomaly detection methods take a log event count matrix or log event sequences as input, exploiting quantitative and/or sequential relationships between log events to detect anomalies. Unfortunately, only considering quantitative or sequential relationships may result in many false positives and/or false negatives. To alleviate this problem, we propose a graph-based method for unsupervised log anomaly detection, dubbed Logs2Graphs, which first converts event logs into attributed, directed, and weighted graphs, and then leverages graph neural networks to perform graph-level anomaly detection. Specifically, we introduce One-Class Digraph Inception Convolutional Networks, abbreviated as OCDiGCN, a novel graph neural network model for detecting graph-level anomalies in a collection of attributed, directed, and weighted graphs. By coupling the graph representation and anomaly detection steps, OCDiGCN can learn a representation that is especially suited for anomaly detection, resulting in a high detection accuracy. Importantly, for each identified anomaly, we additionally provide a small subset of nodes that play a crucial role in OCDiGCN's prediction as explanations, which can offer valuable cues for subsequent root cause diagnosis. Experiments on five benchmark datasets show that Logs2Graphs performs at least on par state-of-the-art log anomaly detection methods on simple datasets while largely outperforming state-of-the-art log anomaly detection methods on complicated datasets.
Effects of Explanation Specificity on Passengers in Autonomous Driving
The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving. We extended an existing data-driven tree-based explainer algorithm by adding a rule-based option for explanation generation. We generated auditory natural language explanations with different levels of specificity (abstract and specific) and tested these explanations in a within-subject user study (N=39) using an immersive physical driving simulation setup. Our results showed that both abstract and specific explanations had similar positive effects on passengers' perceived safety and the feeling of anxiety. However, the specific explanations influenced the desire of passengers to takeover driving control from the autonomous vehicle (AV), while the abstract explanations did not. We conclude that natural language auditory explanations are useful for passengers in autonomous driving, and their specificity levels could influence how much in-vehicle participants would wish to be in control of the driving activity.