  We present the results of our autonomous racing virtual challenge, based on
the newly-released Learn-to-Race (L2R) simulation framework, which seeks to
encourage interdisciplinary research in autonomous driving and to help advance
the state of the art on a realistic benchmark. Analogous to racing being used
to test cutting-edge vehicles, we envision autonomous racing to serve as a
particularly challenging proving ground for autonomous agents as: (i) they need
to make sub-second, safety-critical decisions in a complex, fast-changing
environment; and (ii) both perception and control must be robust to
distribution shifts, novel road features, and unseen obstacles. Thus, the main
goal of the challenge is to evaluate the joint safety, performance, and
generalisation capabilities of reinforcement learning agents on multi-modal
perception, through a two-stage process. In the first stage of the challenge,
we evaluate an autonomous agent's ability to drive as fast as possible, while
adhering to safety constraints. In the second stage, we additionally require
the agent to adapt to an unseen racetrack through safe exploration. In this
paper, we describe the new L2R Task 2.0 benchmark, with refined metrics and
baseline approaches. We also provide an overview of deployment, evaluation, and
rankings for the inaugural instance of the L2R Autonomous Racing Virtual
Challenge (supported by Carnegie Mellon University, Arrival Ltd., AICrowd,
Amazon Web Services, and Honda Research), which officially used the new L2R
Task 2.0 benchmark and received over 20,100 views, 437 active participants, 46
teams, and 733 model submissions -- from 88+ unique institutions, in 58+
different countries. Finally, we release leaderboard results from the challenge
and provide description of the two top-ranking approaches in cross-domain model
transfer, across multiple sensor configurations and simulated races.
