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.

为了克服数据限制问题，我们引入了 PRIBOOT，一种利用有限人类日志和特权信息的专家代理。与之前的方法相比，PRIBOOT 是第一个在 Leaderboard 2.0 中实现了 75% 的路线完成率（RC），以及 20% 驾驶评分（DS）和 45% 的违规率评分（IRS）。有了 PRIBOOT，研究人员可以生成大量数据集，潜在地解决了在该基准中妨碍进展的数据可用性问题。