用扩散模型实现安全与驾驶风格的实时协调规划
Safe and Stylized Trajectory Planning for Autonomous Driving via Diffusion Model
- 基于扩散模型，融合动态车辆与环境信息进行风格化感知
- 在StyleDrive上比最强基线提升3.9%的风格匹配度，在NuPlan上分别达91.76和80.32分
- 支持真实车辆闭环测试，适合追求个性化驾驶风格的自动驾驶系统
在复杂现实场景中实现安全且具风格的轨迹规划仍是自动驾驶系统的核心挑战。本文提出SDD Planner，一种基于扩散模型的实时框架，有效协调安全约束与驾驶风格。该框架包含两个核心模块：多源风格感知编码器，通过距离敏感注意力融合动态交通参与者数据与环境上下文，实现异构的安全-风格感知；风格引导的动态轨迹生成器，自适应调节扩散去噪过程中的优先权重，生成符合用户偏好且安全的轨迹。大量实验表明，SDD Planner达到领先性能：在StyleDrive基准上，相比最强基线WoTE，SM-PDMS指标提升3.9%；在NuPlan Test14与Test14-hard基准上，总体得分分别为91.76和80.32，优于PLUTO等主流方法。真实车辆闭环测试进一步验证其在保持高安全性的同时，可精准对齐预设驾驶风格，具备实际部署潜力。
原文摘要 · Abstract (English)
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.
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