19 citations · 51 across the 13 of their papers we have counts for
13 papers
Driving Accessibility: Shifting the Narrative & Design of Automated Vehicle Systems for Persons With Disabilities Through a Collaborative Scoring System
Savvy Barnes, Maricarmen Davis, Josh Siegel
Automated vehicles present unique opportunities and challenges, with progress and adoption limited, in part, by policy and regulatory barriers. Underrepresented groups, including i…
Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)
Tashfain Ahmed, Josh Siegel
This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on cons…
Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learning
Siddhartha Bhattacharya, Daniel Helo, Joshua Siegel
Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregat…
Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles
Mohammadali Saffary, Nishan Inampudi, Joshua E. Siegel
As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for th…
The AI Mechanic: Acoustic Vehicle Characterization Neural Networks
Adam M. Terwilliger, Joshua E. Siegel
In a world increasingly dependent on road-based transportation, it is essential to understand vehicles. We introduce the AI mechanic, an acoustic vehicle characterization deep lear…
Automatic Exam Evaluation based on Brain Computer Interface
Hameda F Balat, M A El-dosuky, El-Saeed Abd M El-Razek +1
Brain computer interface applications can be used to overcome learning problems, especially student anxiety, lack of focus, and lack of attention. This paper introduces a system ba…
Game and Simulation Design for Studying Pedestrian-Automated Vehicle Interactions
Georgios Pappas, Joshua E. Siegel, Jacob Rutkowski +1
The present cross-disciplinary research explores pedestrian-autonomous vehicle interactions in a safe, virtual environment. We first present contemporary tools in the field and the…
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics
Joshua E. Siegel, Umberto Coda
We survey the state-of-the-art in offboard diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic approaches. We identify promising app…
Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)
Piyush Gupta, Demetris Coleman, Joshua E. Siegel
Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-ca…
A Distributed `Black Box' Audit Trail Design Specification for Connected and Automated Vehicle Data and Software Assurance
Gregory Falco, Joshua E. Siegel
Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up-to-date to ensure long-term system performance. Upda…
A gamified simulator and physical platform for self-driving algorithm training and validation
Joshua E. Siegel, Georgios Pappas, Konstantinos Politopoulos +1
We identify the need for a gamified self-driving simulator where game mechanics encourage high-quality data capture, and design and apply such a simulator to collecting lane-follow…
Automated Non-Destructive Inspection of Fused Filament Fabrication Components Using Thermographic Signal Reconstruction
Joshua E. Siegel, Maria F. Beemer, Steven M. Shepard
Manufacturers struggle to produce low-cost, robust and complex components at manufacturing lot-size one. Additive processes like Fused Filament Fabrication (FFF) inexpensively prod…
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Yongbin Sun, Yue Wang, Ziwei Liu +2
Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud sampl…