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This arXiv survey maps how generative models (VAEs, GANs, generative transformers, diffusion models) are being applied to autonomous driving tasks such as map creation, scenario generation, trajectory forecasting, and motion planning, and compares their strengths and limits. For AI readers, the useful content is the comparison of approaches, the hybrid methods, and the open problems it flags: safety, interpretability, real-time performance, and benchmarking. As a review, it gives a structured overview of the field rather than new empirical results.
Generative AI for Autonomous Driving: A Review
arXiv:2505.15863v2 Announce Type: replace-cross 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.
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.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2505.15863 [cs.CV]
(or arXiv:2505.15863v2 [cs.CV] for this version)
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