We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …

Predicting how the world can evolve in the future is crucial for motion planning in
autonomous systems. Classical methods are limited because they rely on costly human …

The research community has increasing interest in autonomous driving research, despite
the resource intensity of obtaining representative real world data. Existing self-driving …

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future
behavior of other traffic participants. Existing works either perform object detection followed …

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 …

The ability to reliably perceive the environmental states, particularly the existence of objects
and their motion behavior, is crucial for autonomous driving. In this work, we propose an …

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating
through complex urban traffic scenes. Bird's-eye-view roadmap information provides …

We consider the problem of predicting the future trajectory of scene agents from egocentric
views obtained from a moving platform. This problem is important in a variety of domains …

Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …

Vision-centric autonomous driving has recently raised wide attention due to its lower cost.
Pre-training is essential for extracting a universal representation. However current vision …