# LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving

arXiv: https://arxiv.org/abs/2505.18198 · HTML: https://mlsanity.com/paper/2505.18198/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2025.

## Built on by (most cited first, 4 of 4)

- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems](https://mlsanity.com/paper/2512.24385/) (2025)
- [SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling](https://mlsanity.com/paper/2506.21041/) (2025)
- [VERIA: Verification-Centric Multimodal Instance Augmentation for Long-Tailed 3D Object Detection](https://mlsanity.com/paper/2603.24294/) (2026)

## Abstract (arXiv)

3D perception plays an essential role for improving the safety and performance of autonomous driving. Yet, existing models trained on real-world datasets, which naturally exhibit long-tail distributions, tend to underperform on rare and safety-critical, vulnerable classes, such as pedestrians and cyclists. Existing studies on reweighting and resampling techniques struggle with the scarcity and limited diversity within tail classes. To address these limitations, we introduce LTDA-Drive, a novel LLM-guided data augmentation framework designed to synthesize diverse, high-quality long-tail samples. LTDA-Drive replaces head-class objects in driving scenes with tail-class objects through a three-stage process: (1) text-guided diffusion models remove head-class objects, (2) generative models insert instances of the tail classes, and (3) an LLM agent filters out low-quality synthesized images. Experiments conducted on the KITTI dataset show that LTDA-Drive significantly improves tail-class detection, achieving 34.75% improvement for rare classes over counterpart methods. These results further highlight the effectiveness of LTDA-Drive in tackling long-tail challenges by generating high-quality and diverse data.