End-to-end autonomous driving has rapidly progressed, enabling joint perception and planning in complex environments. In the planning stage, state-of-the-art (SOTA) end-to-end autonomous driving models decouple planning into parallel lateral and longitudinal predictions. While effective, this parallel design can lead to i) coordination failures between the planned path and speed, and ii) underutilization of the drive path as a prior for longitudinal planning, thus redundantly encoding static information. To address this, we propose a novel cascaded framework that explicitly conditions longitudinal planning on the drive path, enabling coordinated and collision-aware lateral and longitudinal planning. Specifically, we introduce a path-conditioned formulation that explicitly incorporates the drive path into longitudinal planning. Building on this, the model predicts longitudinal displacements along the drive path rather than full 2D trajectory waypoints. This design simplifies longitudinal reasoning and more tightly couples it with lateral planning. Additionally, we introduce a planning-oriented data augmentation strategy that simulates rare safety-critical events, such as vehicle cut-ins, by adding agents and relabeling longitudinal targets to avoid collision. Evaluated on the challenging Bench2Drive benchmark, our method sets a new SOTA, achieving a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety

翻译：端到端自动驾驶技术发展迅速，已能在复杂环境中实现感知与规划的联合处理。在规划阶段，当前最先进的端到端自动驾驶模型将规划解耦为并行的横向与纵向预测。尽管有效，这种并行设计可能导致：i) 规划路径与速度间的协调失效；ii) 未能充分利用行驶路径作为纵向规划的先验信息，从而冗余编码静态信息。为解决这些问题，我们提出一种新颖的级联框架，将纵向规划显式地以行驶路径为条件，实现协调且具备碰撞感知能力的横向与纵向规划。具体而言，我们引入一种路径条件化建模方法，将行驶路径显式整合到纵向规划中。在此基础上，模型沿行驶路径预测纵向位移而非完整的二维轨迹路径点。该设计简化了纵向推理过程，并使其与横向规划更紧密耦合。此外，我们提出一种面向规划的数据增强策略，通过添加交通参与者和重标定纵向避撞目标，模拟车辆切入等罕见的安全关键事件。在具有挑战性的Bench2Drive基准测试中，我们的方法创造了新的最先进水平，驾驶评分达到89.07，成功率达73.18%，显著提升了规划协调性与安全性。