“…Finally, our ultimate goal would be to estimate how many expected buses we need during a similar pandemic. Our vision is that autonomous-driven bus systems will be a future goal for transit systems [25].…”
The communication revolution that happened in the last ten years has increased the use of technology in the transportation world. Intelligent Transportation Systems wish to predict how many buses are needed in a transit system. With the pandemic effect that the world has faced since early 2020, it is essential to study the impact of the pandemic on the transit system. This paper proposes the leverage of Internet of Things (IoT) devices to predict the number of bus ridership before and during the pandemic. We compare the collected data from Kobe city, Hyogo, Japan, with data gathered from a college city in Virginia, USA. Our goal is to show the effect of the pandemic on ridership through the year 2020 in two different countries. The ultimate goal is to help transit system managers predict how many buses are needed if another pandemic hits.
“…Finally, our ultimate goal would be to estimate how many expected buses we need during a similar pandemic. Our vision is that autonomous-driven bus systems will be a future goal for transit systems [25].…”
The communication revolution that happened in the last ten years has increased the use of technology in the transportation world. Intelligent Transportation Systems wish to predict how many buses are needed in a transit system. With the pandemic effect that the world has faced since early 2020, it is essential to study the impact of the pandemic on the transit system. This paper proposes the leverage of Internet of Things (IoT) devices to predict the number of bus ridership before and during the pandemic. We compare the collected data from Kobe city, Hyogo, Japan, with data gathered from a college city in Virginia, USA. Our goal is to show the effect of the pandemic on ridership through the year 2020 in two different countries. The ultimate goal is to help transit system managers predict how many buses are needed if another pandemic hits.
“…These electronic controls are governed by a custom Robot Operation System (ROS) program that utilizes as inputs a 3-D LiDAR for localization and obstacle detection, electronic sensors for steering column position, and user-selected destination from the GUI. For additional technical details on the project, please see El-Tawab, Sprague, and Mufti (2020); El-Tawab, Sprague, and Stewart, et al (2020). Figure 1.Autonomous vehicle testbed.…”
As automated and autonomous systems become more widely available, the ability to integrate them into environments seamlessly becomes more important. One cognitive construct that can predict the use, misuse, and disuse of automated and autonomous systems is trust that a user has in the system. The literature has explored not only the predictive nature of trust but also the ways in which it can be evaluated. As a result, various measures, such as physiological and behavioral measures, have been proposed as ways to evaluate trust in real-time. However, inherent differences in the measurement approaches (e.g., task dependencies and timescales) raise questions about whether the use of these approaches will converge upon each other. If they do, then the selection of any given proven approach to trust assessment may not matter. However, if they do not converge, it raises questions about the ability of these measures to assess trust equally and whether discrepancies are attributable to discriminant validity or other factors. The present study used various trust assessment techniques for passengers in a self-driving golf-cart. We find little to no convergence across measures, raising questions that need to be addressed in future research.
“…In the context of today's Internet of Things (IoT)-driven world, where autonomous agents are becoming increasingly integrated into various aspects of daily life, from self-driving cars [1] to robotic platforms for retirement communities [2], and collaborative drones in smart cities [3], evaluating the robustness of these autonomous robot systems is paramount. As such, precise and robust range measurements play a critical role in ensuring the correct operation of the majority of autonomous mobile robotic systems, serving as a fundamental element in many mapping and localization algorithms [4,5].…”
Range-measuring sensors play a critical role in autonomous driving systems. While Light Detection and Ranging (LiDAR) technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions -the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, Radio Detection and Ranging (RADAR), and Depth-Camera. Based on accurate experimental evaluation the paper's findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving.
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