Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908836473864192 |
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| author | Gao, Yuan Piccinini, Mattia Zhang, Yuchen Wang, Dingrui Moller, Korbinian Brusnicki, Roberto Zarrouki, Baha Gambi, Alessio Totz, Jan Frederik Storms, Kai Peters, Steven Stocco, Andrea Alrifaee, Bassam Pavone, Marco Betz, Johannes |
| author_facet | Gao, Yuan Piccinini, Mattia Zhang, Yuchen Wang, Dingrui Moller, Korbinian Brusnicki, Roberto Zarrouki, Baha Gambi, Alessio Totz, Jan Frederik Storms, Kai Peters, Steven Stocco, Andrea Alrifaee, Bassam Pavone, Marco Betz, Johannes |
| contents | For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, which represent a new generation of pre-trained, general-purpose AI models, developers can process heterogeneous inputs (e.g., natural language, sensor data, HD maps, and control actions), enabling the synthesis and interpretation of complex driving scenarios. In this paper, we conduct a survey about the application of foundation models for scenario generation and scenario analysis in autonomous driving (as of May 2025). Our survey presents a unified taxonomy that includes large language models, vision-language models, multimodal large language models, diffusion models, and world models for the generation and analysis of autonomous driving scenarios. In addition, we review the methodologies, open-source datasets, simulation platforms, and benchmark challenges, and we examine the evaluation metrics tailored explicitly to scenario generation and analysis. Finally, the survey concludes by highlighting the open challenges and research questions, and outlining promising future research directions. All reviewed papers are listed in a continuously maintained repository, which contains supplementary materials and is available at https://github.com/TUM-AVS/FM-for-Scenario-Generation-Analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis Gao, Yuan Piccinini, Mattia Zhang, Yuchen Wang, Dingrui Moller, Korbinian Brusnicki, Roberto Zarrouki, Baha Gambi, Alessio Totz, Jan Frederik Storms, Kai Peters, Steven Stocco, Andrea Alrifaee, Bassam Pavone, Marco Betz, Johannes Robotics Artificial Intelligence For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, which represent a new generation of pre-trained, general-purpose AI models, developers can process heterogeneous inputs (e.g., natural language, sensor data, HD maps, and control actions), enabling the synthesis and interpretation of complex driving scenarios. In this paper, we conduct a survey about the application of foundation models for scenario generation and scenario analysis in autonomous driving (as of May 2025). Our survey presents a unified taxonomy that includes large language models, vision-language models, multimodal large language models, diffusion models, and world models for the generation and analysis of autonomous driving scenarios. In addition, we review the methodologies, open-source datasets, simulation platforms, and benchmark challenges, and we examine the evaluation metrics tailored explicitly to scenario generation and analysis. Finally, the survey concludes by highlighting the open challenges and research questions, and outlining promising future research directions. All reviewed papers are listed in a continuously maintained repository, which contains supplementary materials and is available at https://github.com/TUM-AVS/FM-for-Scenario-Generation-Analysis. |
| title | Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.11526 |