Abstract We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE,
for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE …

We consider the problem of predicting the future trajectory of scene agents from egocentric
views obtained from a moving platform. This problem is important in a variety of domains …

In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …

Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous
driving. Most existing work focuses on predicting marginal trajectories for each agent …

Accurate long-term trajectory prediction in complex scenes, where multiple agents (eg,
pedestrians or vehicles) interact with each other and the environment while attempting to …

Temporal prediction is critical for making intelligent and robust decisions in complex
dynamic environments. Motion prediction needs to model the inherently uncertain future …

Developing safe human-robot interaction systems is a necessary step towards the
widespread integration of autonomous agents in society. A key component of such systems …

Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …

Reliable multi-agent trajectory prediction is crucial for the safe planning and control of
autonomous systems. Compared with single-agent cases, the major challenge in …