Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …

In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for
human trajectory prediction. Inspired by human navigation, we model the task of trajectory …

We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …

Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …

The historical trajectories previously passing through a location may help infer the future
trajectory of an agent currently at this location. Despite great improvements in trajectory …

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …

Predicting the future behavior of moving agents is essential for real world applications. It is
challenging as the intent of the agent and the corresponding behavior is unknown and …

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety
of autonomous driving. Recent motion prediction approaches attempt to achieve such …

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 …

Widely adopted motion forecasting datasets substitute the observed sensory inputs with
higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred …