生成式 AI 用于自动驾驶：一篇综述
Generative AI for Autonomous Driving: A Review
一篇综述探讨了生成式 AI 如何推动自动驾驶发展，涵盖静态地图创建、动态场景生成、轨迹预测和车辆运动规划等任务。文章比较了 VAE、GAN、生成式 Transformer 和扩散模型等多种生成方法在自动驾驶应用中的能力与局限，并讨论了混合方法、相关数据集及五个核心挑战：安全性、可解释性、实时能力、训练与基准测试。
Authors:Katharina Winter, Abhishek Vivekanandan, Rupert Polley, Yinzhe Shen, Christian Schlauch, Mohamed-Khalil Bouzidi, Bojan Derajic, Natalie Grabowsky, Annajoyce Mariani, Dennis Rochau, Giovanni Lucente, Harsh Yadav, Firas Mualla, Adam Molin, Sebastian Bernhard, Christian Wirth, Ömer Şahin Taş, Nadja Klein, Fabian B. Flohr, Hanno Gottschalk
Abstract:Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how generative models can enhance automotive tasks, such as static map creation, dynamic scenario generation, trajectory forecasting, and vehicle motion planning. By examining multiple generative approaches ranging from Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to Generative Transformers (GTs) and Diffusion Models (DMs), we highlight and compare their capabilities and limitations for AD-specific applications. Additionally, we discuss hybrid methods integrating conventional techniques with generative approaches, and emphasize their improved adaptability and robustness. We also identify relevant datasets and outline open research questions to guide future developments in GenAI. Finally, we discuss five core challenges: safety, interpretability, realtime capabilities, training and benchmarking. We also present recommendations for image generation, dynamic scenario generation, and planning.
| Comments: | This work has been submitted to the IEEE for possible publication |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2505.15863 [cs.CV] |
| (or arXiv:2505.15863v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2505.15863 arXiv-issued DOI via DataCite |
Submission history
From: Katharina Winter [view email]
[v1]
Wed, 21 May 2025 07:59:18 UTC (8,932 KB)
[v2]
Tue, 13 Oct 2026 07:38:50 UTC (1,562 KB)
来源：arXiv：cs.AI · arxiv.org