Mobility trajectory data is of great significance for mobility pattern study, urban computing,
and city science. Self-driving, traffic prediction, environment estimation, and many other …

Predicting motions of surrounding vehicles is critically important to help autonomous driving
systems plan a safe path and avoid collisions. Although recent social pooling based LSTM …

In highly interactive driving scenarios, the actions of one agent greatly influence those of its
neighbors. Planning safe motions for autonomous vehicles (AVs) in such interactive …

Understanding social interactions between a vehicle and its surrounding agents enables
effective path prediction, which is critical for the motion planning and safe navigation of …

Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge
due to pedestrians' unpredictable movements and behavior. The potential for risky situations …

Road surface wear leads to the formation of cracks and holes known as potholes. Potholes
disrupt the smooth flow of traffic and can lead to accidents. The Intelligent Driver (ID) model …

Connected and automated vehicles (CAVs) provide enormous opportunities for improving
fuel economy, safety, and capacity of the transportation system. In this article, an eco-driving …

A new microscopic traffic model is proposed that characterizes driver response according to
reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a …

In this contribution we study the “Bando-follow the leader” car-following model, a second
order ordinary differential equation, for its well-posedness. Under suitable conditions, we …

Autonomous agents (robots) face tremendous challenges while interacting with
heterogeneous human agents in close proximity. One of these challenges is that the …