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Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night. In this study, we present and empirically assess the benefits of high-density LiDAR in low-light and dark conditions—a persistent challenge in VRU detection when compared to traditional RGB traffic cameras. Robust detection and tracking algorithms were utilized for analyzing VRU-to-vehicle and vehicle-to-vehicle interactions using the LiDAR data. The analysis explores the effectiveness of two DA metrics based on the i.e. Post Encroachment Time (PET) and Minimum Distance Safety Envelope (MDSE) formulations in identifying potentially unsafe scenarios for VRUs at the Tempe intersection. The codebase for the data pipeline, along with the high-density LiDAR dataset, has been open-sourced with the goal of benefiting the AV research community in the development of new methods for ensuring safety at urban traffic intersections. 

Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles. These components, in conjunction with a set of defined metrics based on our prior work on Operational Safety Assessment (OSA) by the Institute of Automated Mobility (IAM), allow us to conduct a detailed analysis of driving safety. Our results reveal the distribution of these metrics under various real-world car-following scenarios and characterize the impact of different parameters and thresholds in the metrics. By enabling a data-driven approach to address driving safety in car-following scenarios, our work can empower traffic operators and policymakers to make informed decisions and contribute to a safer, more efficient future for road transportation systems. 

The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera. Bespoke localization and tracking algorithms are created for the LIDAR and cameras. In total, there are 26 different scenarios of the test vehicles navigating the testbed intersection; for this work, we are only considering car following scenarios. The LIDAR data collected from the infrastructure-based and onboard vehicle-based sensors system are used to perform object detection and multi-target tracking to estimate the velocity and position information of the test vehicles and use these values to compute OSA metrics. The comparison of the performance of the two systems involves the localization and tracking errors in calculating the position and the velocity of the subject vehicle, with the real-time differential GPS data serving as ground truth for velocity comparison and tracking results from the drone for OSA metrics comparison. 

Hands-on practice is a critical component of cybersecurity education. Most of the existing hands-on exercises or labs materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. With the advantages of big data and natural language processing (NLP) technologies, constructing a large knowledge graph and mining concepts from unstructured text becomes possible, which motivated us to construct a machine learning based lab exercising plan for cybersecurity education. In the research presented by this paper, we have constructed a knowledge graph in the cybersecurity domain using NLP technologies including machine learning based word embedding and hyperlink-based concept mining. We then utilized the knowledge graph during the regular learning process based on the following approaches: 1. We constructed a web-based front-end to visualize the knowledge graph, which allows students to browse and search cybersecurity-related concepts and the corresponding interdependence relations; 2. We created a personalized knowledge graph for each student based on their learning progress and status; 3.We built a personalized lab recommendation system by suggesting more relevant labs based on students’ past learning history to maximize their learning outcomes. To measure the effectiveness of the proposed solution, we have conducted a use case study and collected survey data from a graduate-level cybersecurity class. Our study shows that, by leveraging the knowledge graph for the cybersecurity area study, students tend to benefit more and show more interests in cybersecurity area. 

This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.