Safe and Stylized Trajectory Planning for Autonomous Driving via Diffusion Model

TL;DR

This paper introduces the SDD Planner, a diffusion-based framework for autonomous driving that ensures safety and style adherence in trajectory planning through multi-source perception and style-guided generation, achieving state-of-the-art results.

Contribution

The paper presents a novel diffusion model framework integrating style-aware perception and style-guided trajectory generation for safe, stylized autonomous driving.

Findings

Outperforms baselines on StyleDrive with 3.9% improvement in SM-PDMS

Ranks first on NuPlan Test14 and Test14-hard benchmarks

Maintains high safety standards in real-vehicle tests

Abstract

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.