NavDrive: Safety-Enhanced End-to-End Autonomous Driving With Navigation-Guided Diffusion Policy

Automated vehicles (AVs) are transforming urban transportation systems, as end-to-end autonomous driving models show great promise in enhancing traffic safety and operational efficiency. Despite these advances, their performance in highly interactive driving scenarios remains limited due to insufficient decision-making diversity and the absence of explicit safety guarantees. To address these challenges, we propose NavDrive, a safety-enhanced end-to-end autonomous driving framework that formulates planning as a multi-modal generative process.Specifically, NavDrive integrates navigation-based guidance into a diffusion policy. To focus on decision-critical information, a Decision-Aware Channel Fusion (DCF) module adaptively emphasizes regions involving key interactions between the ego vehicle and surrounding agents. Furthermore, a safety-aware generative planner refines trajectory samples toward feasible regions via the Target-Prior Diffusion Transformer (TDiT), which explicitly embeds physical constraints to ensure safe and human-aligned driving behaviors. Extensive experiments on the NAVSIM and nuScenes benchmarks demonstrate that NavDrive consistently outperforms existing baselines, delivering substantial gains in planning quality, safety, and robustness under complex and adverse conditions. The details will be available at https://github.com/zgchongbo/NavDrive