Achieving safe and stylized trajectory planning in complex real-world scenarios remains a critical challenge for autonomous driving systems. This paper proposes the SDD Planner, a diffusion-based framework designed to effectively reconcile safety constraints with driving styles in real time. The framework integrates two core modules: a Multi-Source Style-Aware Encoder, which employs distance-sensitive attention to fuse dynamic agent data and environmental contexts for heterogeneous safety-style perception; and a Style-Guided Dynamic Trajectory Generator, which adaptively modulates priority weights within the diffusion denoising process to generate user-preferred yet safe trajectories. Extensive experiments demonstrate that SDD Planner achieves state-of-the-art performance. On the StyleDrive benchmark, it improves the SM-PDMS metric by 3.9% over WoTE, the strongest baseline. Furthermore, on the NuPlan Test14 and Test14-hard benchmarks, SDD Planner ranks first with overall scores of 91.76 and 80.32, respectively, outperforming leading methods such as PLUTO. Real-vehicle closed-loop tests further confirm that SDD Planner maintains high safety standards while aligning with preset driving styles, validating its practical applicability for real-world deployment.

翻译：在复杂的现实场景中实现安全且风格化的轨迹规划，仍然是自动驾驶系统面临的关键挑战。本文提出SDD规划器，一种基于扩散模型的框架，旨在实时有效地协调安全约束与驾驶风格。该框架集成了两个核心模块：多源风格感知编码器，其采用距离敏感注意力机制融合动态智能体数据与环境上下文，以实现异构的安全-风格感知；以及风格引导的动态轨迹生成器，其在扩散去噪过程中自适应地调节优先级权重，以生成用户偏好且安全的轨迹。大量实验表明，SDD规划器实现了最先进的性能。在StyleDrive基准测试中，其SM-PDMS指标相较于最强基线WoTE提升了3.9%。此外，在NuPlan Test14和Test14-hard基准测试中，SDD规划器分别以91.76和80.32的综合得分排名第一，超越了PLUTO等领先方法。实车闭环测试进一步证实，SDD规划器在保持高安全标准的同时，能够与预设驾驶风格保持一致，验证了其在现实世界部署中的实际适用性。