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 …

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 …

The task of motion forecasting is critical for self-driving vehicles (SDV s) to be able to plan a
safe maneuver. Towards this goal, modern approaches reason about the map, the agents' …

Predicting the future motion of road participants is crucial for autonomous driving but is
extremely challenging due to staggering motion uncertainty. Recently, most motion …

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 …

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 …

Automated vehicles need to not only perceive their environment, but also predict the
possible future behavior of all detected traffic participants in order to safely navigate in …

Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …

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 …