Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …

The success of motion prediction for autonomous driving relies on integration of information
from the HD maps. As maps are naturally graph-structured, investigation on graph neural …

In this paper, we propose GOHOME, a method leveraging graph representations of the High
Definition Map and sparse projections to generate a heatmap output representing the future …

Prediction of human motions is key for safe navigation of autonomous robots among
humans. In cluttered environments, several motion hypotheses may exist for a pedestrian …

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 …

In this paper, we propose HOME, a framework tackling the motion forecasting problem with
an image output representing the probability distribution of the agent's future location. This …

Determining the motion behavior of inexhaustible categories of traffic participants is critical
for autonomous driving. In recent years, there has been a rising concern in performing class …

The ability to predict the future movements of other vehicles is a subconscious and effortless
skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for …

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 …

Variational autoencoder (VAE) has widely been utilized for modeling data distributions
because it is theoretically elegant, easy to train, and has nice manifold representations …