Application of foundation models for autonomous driving: a survey of data synthesis

Song Gao; Bolin Gao; Peng Wei; Jianpeng Guo; Meng Yuan; Cheng Han; Yueyun Xu

doi:10.1117/12.3054764

20 December 2024 Application of foundation models for autonomous driving: a survey of data synthesis

Song Gao, Bolin Gao, Peng Wei, Jianpeng Guo, Meng Yuan, Cheng Han, Yueyun Xu

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134213B (2024) https://doi.org/10.1117/12.3054764
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

With the evolution of data-driven autonomous driving technology, transferring driving responsibility from humans to machines is now feasible. Addressing the long-tail distribution problem in autonomous driving data is crucial for enhancing safety. Recent advances in foundational models, including large language models (LLMs) and generative AI models, have significantly improved data synthesis capabilities. This paper reviews the application of foundation models in three key areas: sensor data synthesis, traffic flow synthesis, and world models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Song Gao, Bolin Gao, Peng Wei, Jianpeng Guo, Meng Yuan, Cheng Han, and Yueyun Xu "Application of foundation models for autonomous driving: a survey of data synthesis", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134213B (20 December 2024); https://doi.org/10.1117/12.3054764

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