No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

Abstract We propose\textit {JFP}, a Joint Future Prediction model that can learn to generate
accurate and consistent multi-agent future trajectories. For this task, many different methods …

Mobile robots struggle to integrate seamlessly in crowded environments such as pedestrian
scenes, often disrupting human activity. One obstacle preventing their smooth integration is …

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 …

We present a new method for multi-agent planning involving human drivers and
autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging …

Radar is widely employed in many applications, especially in autonomous driving. At
present, radars are only designed as simple data collectors, and they are unable to meet …

We focus on robot navigation in crowded environments. To navigate safely and efficiently
within crowds, robots need models for crowd motion prediction. Building such models is …

Contingency planning, wherein an agent generates a set of possible plans conditioned on
the outcome of an uncertain event, is an increasingly popular way for robots to act under …

Many autonomous agents, such as intelligent vehicles, are inherently required to interact
with one another. Game theory provides a natural mathematical tool for robot motion …