Search Results for author:
Found 8 papers, 3 papers with code
Cooperative Advisory Residual Policies for Congestion Mitigation
Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding
Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language.
Towards Co-operative Congestion Mitigation
The effects of traffic congestion are widespread and are an impedance to everyday life.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset
In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.
Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians.
