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[2506.11526] Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Summary
This survey explores the role of foundation models in enhancing scenario generation and analysis for autonomous driving, addressing limitations of traditional methods.
Why It Matters
As autonomous vehicles increasingly rely on complex scenario testing for safe navigation, understanding how foundation models can improve scenario generation is crucial. This paper provides insights into methodologies, datasets, and future research directions, which are vital for advancing autonomous driving technology.
Key Takeaways
- Foundation models can synthesize diverse driving scenarios beyond traditional methods.
- The paper presents a unified taxonomy of models relevant to scenario generation.
- It reviews methodologies, datasets, and benchmarks for evaluating scenario generation.
- Open challenges and future research directions are identified to guide further exploration.
- A continuously maintained repository of reviewed papers and supplementary materials is available.
Computer Science > Robotics arXiv:2506.11526 (cs) [Submitted on 13 Jun 2025 (v1), last revised 16 Feb 2026 (this version, v4)] Title:Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis Authors:Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz View a PDF of the paper titled Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis, by Yuan Gao and 14 other authors View PDF Abstract: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 gener...