5 accepted papers
Autonomous driving systems (ADSs) rely on massive amounts of sensed data to train their underlying deep neural networks (DNNs). Common sensor hardware migrations can render an existing DNN-dependent pipeline inadequate. This necessitates the development of bespoke transformations to adapt new sensor
Although Driving Automation Systems (DASs) are rapidly becoming more advanced and ubiquitous, they are still confined to specific Operational Design Domains (ODDs) over which the system must be trained and validated. Yet, each DAS has a bespoke and often informally defined ODD, which makes it intrac
Users are often the last barrier in the detection and correction of abnormal behavior in autonomous systems, so understanding what can be expected from users in various contexts is crucial to the performance of such systems. This letter presents the first study characterizing a user ability to timel
Multirotor unmanned aerial systems (UASs) are often used to transport a variety of payloads. However, the maximum time that the cargo can remain airborne is limited by the flight endurance of the UAS. In this letter, we present a novel approach for two multirotors to transfer a payload between them
This project seeks to generate small Unmanned Aerial System (sUAS) flight paths that are broadly understood by the general population and can communicate states about both the sUAS and its understanding of the world. Previous work in sUAS flight paths has sought to communicate intent, destination, o