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 …

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 …

Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …

Motion forecasting is a key module in an autonomous driving system. Due to the
heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low …

Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …

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 …

We present a novel framework for motion forecasting with Dual Consistency Constraints and
Multi-Pseudo-Target supervision. The motion forecasting task predicts future trajectories of …

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 …

Due to the stochasticity of human behaviors, predicting the future trajectories of road agents
is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …

Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …