To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …

We present a new algorithm for predicting the near-term trajectories of road agents in dense
traffic videos. Our approach is designed for heterogeneous traffic, where the road agents …

We present a novel approach for traffic forecasting in urban traffic scenarios using a
combination of spectral graph analysis and deep learning. We predict both the low-level …

A major challenge to deploying robots widely is navigation in human-populated
environments, commonly referred to as social robot navigation. While the field of social …

The core value of simulation-based autonomy tests is to create densely extreme traffic
scenarios to test the performance and robustness of the algorithms and systems. Test …

Traditional video-based human activity recognition has experienced remarkable progress
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …

In order to make an accurate prediction of vehicle trajectory in a dynamic environment, a
Unidirectional and Bidirectional LSTM (UB‐LSTM) vehicle trajectory prediction model …

We present a new measure, CMetric, to classify driver behaviors using centrality functions.
Our formulation combines concepts from computational graph theory and social traffic …

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic
environment composed of human drivers and do not adapt to local conditions and socio …

Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …