With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

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 …

Abstract Reasoning about human motion is an important prerequisite to safe and socially-
aware robotic navigation. As a result, multi-agent behavior prediction has become a core …

High-definition maps (HD maps) are a key component of most modern self-driving systems
due to their valuable semantic and geometric information. Unfortunately, building HD maps …

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 a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

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 …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …