对比三大自动驾驶榜单，揭示运动规划现状与瓶颈。
Self-driving cars: Are we there yet?
- 以CARLA v2.0为统一平台，兼容性改造模型进行公平对比。
- 发现当前方法在复杂场景下规划鲁棒性不足，泛化能力待提升。
- 适合关注自动驾驶算法评估与未来研究方向的从业者。
自动驾驶仍是活跃的研究领域，旨在使车辆能够感知动态环境，预测交通参与者（如车辆、行人、骑行者）的未来轨迹，并规划安全高效的行驶路径。为推动该领域发展，多个竞赛平台和基准测试已建立，提供标准化数据集与评估协议。其中，CARLA组织、nuPlan以及Waymo Open Dataset的排行榜成为评估运动规划算法的主要基准，各自提供独特的数据集和涵盖广泛驾驶场景与条件的挑战性规划任务。本文对这三个排行榜中的运动规划方法进行了全面对比分析。为确保公平且统一的评估，我们采用CARLA排行榜v2.0作为共同评估平台，并对所选模型进行适配改造。通过揭示现有方法的优势与局限，我们识别出当前主流趋势、共性挑战，并提出推进运动规划研究的潜在方向。
原文摘要 · Abstract (English)
Autonomous driving remains a highly active research domain that seeks to enable vehicles to perceive dynamic environments, predict the future trajectories of traffic agents such as vehicles, pedestrians, and cyclists and plan safe and efficient future motions. To advance the field, several competitive platforms and benchmarks have been established to provide standardized datasets and evaluation protocols. Among these, leaderboards by the CARLA organization and nuPlan and the Waymo Open Dataset have become leading benchmarks for assessing motion planning algorithms. Each offers a unique dataset and challenging planning problems spanning a wide range of driving scenarios and conditions. In this study, we present a comprehensive comparative analysis of the motion planning methods featured on these three leaderboards. To ensure a fair and unified evaluation, we adopt CARLA leaderboard v2.0 as our common evaluation platform and modify the selected models for compatibility. By highlighting the strengths and weaknesses of current approaches, we identify prevailing trends, common challenges, and suggest potential directions for advancing motion planning research.
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