Core Insights - The article discusses the limitations of traditional modular approaches in autonomous driving and introduces the ResAD framework, which aims to improve efficiency and safety by using an end-to-end model that focuses on learning necessary adjustments from a baseline trajectory [2][50]. Group 1: Framework Overview - ResAD framework proposes a shift from directly predicting future trajectories to learning the necessary adjustments from a physical baseline trajectory, termed "inertial reference line" [2][50]. - The model focuses on understanding the reasons for trajectory adjustments, such as obstacles and traffic rules, rather than memorizing data correlations [50]. Group 2: Methodology - The ResAD framework incorporates a "normalized residual trajectory modeling" approach, which simplifies the learning problem by defining trajectory predictions as adjustments to a reference line [11][50]. - The framework employs a "point-wise residual normalization" technique to balance the optimization weights of near and far trajectory points, ensuring that critical adjustments are not overlooked [20][50]. Group 3: Testing and Results - Real-world testing demonstrated the effectiveness of the ResAD framework, showcasing its ability to handle complex driving scenarios and respond intelligently to dynamic obstacles [6]. - In benchmark evaluations, ResAD achieved state-of-the-art performance on NAVSIM v1 and v2, with a PDMS score of 88.6 and an EPDMS score of 85.5, indicating high safety and efficiency in route completion [38][39]. Group 4: Comparative Analysis - ResAD outperformed existing models like DiffusionDrive in various metrics, including lane adherence and route completion efficiency, highlighting its superior trajectory generation capabilities [41][39]. - The article emphasizes the importance of the unique trajectory modeling strategy in ResAD, which allows for the generation of contextually relevant and diverse trajectories without relying on a static trajectory library [10][41].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Zhiyu Zheng等 编辑 | 自动驾驶之心 想让车子自己开，传统方法得像搭积木：先"看"（感知），再"猜"（预测），最后"做决定"（规划）。这套流程环环相扣，一个环节出错，后面全跟着错， 既不高效，也不安全。 于是， 端到端自动驾驶 成了一条新路。它想让AI像老司机一样，直接把看到的（传感器数据）变成要走的路线（未来轨迹）。想法很美好，但现实很骨 感：现有的端到端模型，大多在死磕一个问题—— "未来的轨迹长啥样？" 为了解决这些问题，地平线、华科和武大的团队提出了 ResAD 框架。核心思想很简单： 不直接预测整条轨迹，而是先给一个"惯性参考线"——就是车子如 果不动方向盘会走的路线。然后，让模型只学习一个"调整量"（残差），即为了安全行驶，需要偏离这根参考线多少。 这样一来，学习目标就从 "轨迹是什么？" 变成了 "为什么要调整方向？" 。模型被迫去关注那些导致调整的真实原因，比如障碍物、交通规则等，而不是死 记硬背数据里的巧合。 我们 ...