  The development of Autonomous Driving (AD) systems in simulated environments
like CARLA is crucial for advancing real-world automotive technologies. To
drive innovation, CARLA introduced Leaderboard 2.0, significantly more
challenging than its predecessor. However, current AD methods have struggled to
achieve satisfactory outcomes due to a lack of sufficient ground truth data.
Human driving logs provided by CARLA are insufficient, and previously
successful expert agents like Autopilot and Roach, used for collecting
datasets, have seen reduced effectiveness under these more demanding
conditions. To overcome these data limitations, we introduce PRIBOOT, an expert
agent that leverages limited human logs with privileged information. We have
developed a novel BEV representation specifically tailored to meet the demands
of this new benchmark and processed it as an RGB image to facilitate the
application of transfer learning techniques, instead of using a set of masks.
Additionally, we propose the Infraction Rate Score (IRS), a new evaluation
metric designed to provide a more balanced assessment of driving performance
over extended routes. PRIBOOT is the first model to achieve a Route Completion
(RC) of 75% in Leaderboard 2.0, along with a Driving Score (DS) and IRS of 20%
and 45%, respectively. With PRIBOOT, researchers can now generate extensive
datasets, potentially solving the data availability issues that have hindered
progress in this benchmark.
