activity
20182021
most citedDeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

83 citations · 117 across the 6 of their papers we have counts for

collaborators

9 papers

cs.SE2021

A Novel Spatial-Temporal Specification-Based Monitoring System for Smart Cities

Meiyi Ma, Ezio Bartocci, Eli Lifland +2

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against ci…

cs.LG202130 cited

Safe Multi-Agent Reinforcement Learning via Shielding

Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato +3

Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e.g., no unsafe states are ev…

cs.LG202183 cited

DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

Erfan Pakdamanian, Shili Sheng, Sonia Baee +3

Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may…

cs.RO20201 cited

Towards Personalized Explanation of Robot Path Planning via User Feedback

Kayla Boggess, Shenghui Chen, Lu Feng

Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaborati…

cs.RO2020

Towards Transparent Robotic Planning via Contrastive Explanations

Shenghui Chen, Kayla Boggess, Lu Feng

Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanati…

cs.LO2019

A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors

Josephine Lamp, Simone Silvetti, Marc Breton +2

Type I Diabetes (T1D) is a chronic disease in which the body's ability to synthesize insulin is destroyed. It can be difficult for patients to manage their T1D, as they must contro…