End-to-end autonomous driving is increasingly adopting a multimodal planning paradigm that generates multiple trajectory candidates and selects the final plan, making candidate-set design critical. A fixed trajectory vocabulary provides stable coverage in routine driving but often misses optimal solutions in complex interactions, while scene-adaptive refinement can cause over-correction in simple scenarios by unnecessarily perturbing already strong vocabulary trajectories.We propose CdDrive, which preserves the original vocabulary candidates and augments them with scene-adaptive candidates generated by vocabulary-conditioned diffusion denoising. Both candidate types are jointly scored by a shared selection module, enabling reliable performance across routine and highly interactive scenarios. We further introduce HATNA (Horizon-Aware Trajectory Noise Adapter) to improve the smoothness and geometric continuity of diffusion candidates via temporal smoothing and horizon-aware noise modulation. Experiments on NAVSIM v1 and NAVSIM v2 demonstrate leading performance, and ablations verify the contribution of each component. Code: https://github.com/WWW-TJ/CdDrive.

翻译：端到端自动驾驶正日益采用多模态规划范式，该范式生成多个轨迹候选并选择最终规划，这使得候选集设计至关重要。固定的轨迹词汇表在常规驾驶中提供稳定的覆盖范围，但在复杂交互中常常遗漏最优解；而场景自适应优化在简单场景中可能通过对原本已很强的词汇表轨迹进行不必要的扰动而导致过度修正。我们提出CdDrive，该方法保留原始词汇表候选，并通过词汇表条件化的扩散去噪生成场景自适应候选进行增强。两种候选类型由一个共享的选择模块联合评分，从而在常规和高度交互场景中均实现可靠性能。我们进一步引入HATNA（视界感知轨迹噪声适配器），通过时间平滑和视界感知噪声调制来改善扩散候选的平滑度和几何连续性。在NAVSIM v1和NAVSIM v2上的实验展示了领先的性能，消融实验验证了各组件的贡献。代码：https://github.com/WWW-TJ/CdDrive。