生成式 AI 在自动驾驶中的应用综述
Generative AI for Autonomous Driving: A Review
一篇综述梳理了生成式 AI 在自动驾驶中的应用，涵盖静态地图创建、动态场景生成、轨迹预测与车辆运动规划等任务，并对比了 VAE、GAN、生成式 Transformer 与扩散模型的能力与局限。文章还讨论了传统方法与生成方法结合的混合方案、相关数据集，以及安全、可解释性、实时性、训练与基准测试五大核心挑战，并给出图像生成、动态场景生成与规划方面的建议。
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