Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods use parallel planning architectures, they fail to explicitly couple speed decisions with agent behavior along the driving path, leading to suboptimal coordination. To address this, we propose a cascaded framework that transforms longitudinal planning from an independent prediction task into a path-conditioned reasoning process. On the model side, we introduce an anchor-based regression design that conditions longitudinal prediction on the lateral drive path, and reformulate longitudinal planning as 1D displacement prediction along the path. This reduces geometric uncertainty and sharpens the model's focus on interaction-driven dynamics. On the data side, we introduce a planning-oriented data augmentation strategy that simulates rare safety-critical events by programmatically inserting agents and relabeling longitudinal targets to enforce collision avoidance. Evaluated on the challenging Bench2Drive benchmark, our method achieves SOTA performance with a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety. Further evaluation on Fail2Drive confirms strong generalization to rare edge cases where parallel formulations typically fail. Project page:https://yanhaowu.github.io/AlignDrive/.

翻译：实用化自动驾驶要求模型具备时空推理能力，通过排除不安全结果实现泛化。现有最先进（SOTA）方法采用并行规划架构，但未能将速度决策与沿行驶路径的智能体行为显式耦合，导致协同性欠佳。为解决该问题，我们提出级联框架，将纵向规划从独立预测任务转化为路径条件化推理过程。在模型层面，我们引入基于锚点的回归设计使纵向预测受限于横向行驶路径，并将纵向规划重构为沿路径的一维位移预测。此举降低了几何不确定性，强化了模型对交互驱动动力学的关注。在数据层面，我们提出规划导向的数据增强策略，通过程序化插入智能体并重新标注纵向目标来模拟罕见安全关键事件，强制实现碰撞规避。在具有挑战性的Bench2Drive基准测试中，本方法以89.07的驾驶得分和73.18%的成功率达SOTA性能，展现出显著提升的协同性与安全性。Fail2Drive上的进一步评估验证了对并行方案通常失效的罕见边缘案例的强泛化能力。项目主页见：https://yanhaowu.github.io/AlignDrive/。