End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …

In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …

Many studies in the field of robot navigation have focused on environment representation
and localization. The goal of map representation is to summarize spatial information in …

We present Dictionary Fields, a novel neural representation which decomposes a signal into
a product of factors, each represented by a classical or neural field representation, operating …

Planning an optimal route in a complex environment requires efficient reasoning about the
surrounding scene. While human drivers prioritize important objects and ignore details not …

We propose a novel knowledge distillation framework for effectively teaching a sensorimotor
student agent to drive from the supervision of a privileged teacher agent. Current distillation …

We propose XVO, a semi-supervised learning method for training generalized monocular
Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and …

Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on
the internet can advance generalized intelligent systems, ie, to robustly scale across …