用扩散模型动态优化轨迹候选集，兼顾常规与复杂场景表现
A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Driving
- 固定候选集+扩散生成的自适应候选联合优化
- 在NAVSIM v1/v2上实现领先性能，多场景稳定表现
- 适合追求端到端自动驾驶鲁棒性的研究者与工程师
端到端自动驾驶正采用多模态规划范式，生成多个轨迹候选并选择最优路径，候选集设计至关重要。固定轨迹词汇表在常规驾驶中覆盖稳定，但在复杂交互场景中常遗漏最优解；而场景自适应精修可能在简单场景中过度修正已优候选。我们提出CdDrive，保留原始词汇候选，并通过词汇条件扩散去噪生成场景自适应候选。两类候选由共享选择模块联合评分，确保在常规与高度交互场景中均表现可靠。进一步引入HATNA（时序感知轨迹噪声适配器），通过时间平滑和时程感知噪声调节提升扩散候选的平滑性与几何连续性。在NAVSIM v1和NAVSIM v2上的实验验证了领先性能，消融实验确认各组件贡献。代码开源：https://github.com/WWW-TJ/CdDrive。
原文摘要 · Abstract (English)
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.
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