90 citations
- Honda Research Institute, USAUS9 papers
- Carnegie Mellon UniversityUS6 papers
- University of California, BerkeleyUS6 papers
- Boston UniversityUS4 papers
- Duke UniversityUS3 papers
- Honda (Germany)DE3 papers
- Honda (Japan)JP3 papers
- Brown UniversityUS2 papers
- Cornell UniversityUS2 papers
- Massachusetts Institute of TechnologyUS2 papers
- Purdue University West LafayetteUS2 papers
- University of DelawareUS2 papers
7 papers · 1 filter
Deep Tactile Experience: Estimating Tactile Sensor Output from Depth Sensor Data
Karankumar Patel, Soshi Iba, Nawid Jamali
Tactile sensing is inherently contact based. To use tactile data, robots need to make contact with the surface of an object. This is inefficient in applications where an agent need…
Clustering Human Trust Dynamics for Customized Real-time Prediction
Jundi Liu, Kumar Akash, Teruhisa Misu +1
Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively…
A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment
Meera Ratnagiri, Clare O'Dwyer, Logan E. Beaver +3
In this paper, we investigate the problem of a last-mile delivery service that selects up to available vehicles to deliver packages from a centralized depot to delivery…
A Digital Smart City for Emerging Mobility Systems
Raymond M. Zayas, Logan E. Beaver, Behdad Chalaki +2
The increasing demand for emerging mobility systems with connected and automated vehicles has imposed the necessity for quality testing environments to support their development. I…
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Jiachen Li, Fan Yang, Hengbo Ma +3
Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of his…
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
Sangjae Bae, David Isele, Kikuo Fujimura +1
This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic…