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.

翻译：在CARLA等模拟环境中开发自动驾驶系统对于推进现实世界汽车技术至关重要。为了推动创新，CARLA引入了比其前身更具挑战性的Leaderboard 2.0。然而，由于缺乏足够的真实数据，当前的自动驾驶方法难以取得令人满意的结果。CARLA提供的人类驾驶日志不足，而先前用于收集数据集的成功专家代理（如Autopilot和Roach）在这些更苛刻的条件下效果也大打折扣。为了克服这些数据限制，我们引入了PRIBOOT——一种利用有限人类日志与特权信息的专家代理。我们专门针对这一新基准的需求开发了一种新颖的鸟瞰图表示法，并将其处理为RGB图像以促进迁移学习技术的应用，而非使用一组掩码。此外，我们提出了违规率评分（IRS），这是一种新的评估指标，旨在对长距离路线上的驾驶性能提供更均衡的评估。PRIBOOT是首个在Leaderboard 2.0中实现75%路线完成率（RC）的模型，其驾驶评分（DS）和IRS分别为20%和45%。借助PRIBOOT，研究人员现在可以生成大规模数据集，有望解决一直阻碍该基准进展的数据可用性问题。