RealDrive: Retrieval-Augmented Driving with Diffusion Models

• URL: http://arxiv.org/abs/2505.24808v1
• Date: Fri, 30 May 2025 17:15:03 GMT
• Title: RealDrive: Retrieval-Augmented Driving with Diffusion Models
• Authors: Wenhao Ding, Sushant Veer, Yuxiao Chen, Yulong Cao, Chaowei Xiao, Marco Pavone,
• Abstract summary: Learning-based planners generate human-like driving behaviors by learning to reason about nuanced interactions from data.<n>Data-driven approaches often struggle with rare, safety-critical scenarios and offer limited controllability over the generated trajectories.<n>We propose RealDrive, a Retrieval-Augmented Generation framework that initializes a diffusion-based planning policy by retrieving the most relevant expert demonstrations from the training dataset.
• Score: 42.6467760755688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rule-based planners. Nonetheless, data-driven approaches often struggle with rare, safety-critical scenarios and offer limited controllability over the generated trajectories. To address these challenges, we propose RealDrive, a Retrieval-Augmented Generation (RAG) framework that initializes a diffusion-based planning policy by retrieving the most relevant expert demonstrations from the training dataset. By interpolating between current observations and retrieved examples through a denoising process, our approach enables fine-grained control and safe behavior across diverse scenarios, leveraging the strong prior provided by the retrieved scenario. Another key insight we produce is that a task-relevant retrieval model trained with planning-based objectives results in superior planning performance in our framework compared to a task-agnostic retriever. Experimental results demonstrate improved generalization to long-tail events and enhanced trajectory diversity compared to standard learning-based planners -- we observe a 40% reduction in collision rate on the Waymo Open Motion dataset with RAG.

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