World Models for Autonomous Driving: An Initial Survey

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process. World models have emerged as a transformative approach, enabling auton...

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Bibliographic Details
Main Authors Guan, Yanchen, Liao, Haicheng, Li, Zhenning, Hu, Jia, Yuan, Runze, Li, Yunjian, Zhang, Guohui, Xu, Chengzhong
Format Journal Article
LanguageEnglish
Published 04.03.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Computer Science - Robotics
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Summary:In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process. World models have emerged as a transformative approach, enabling autonomous driving systems to synthesize and interpret vast amounts of sensor data, thereby predicting potential future scenarios and compensating for information gaps. This paper provides an initial review of the current state and prospective advancements of world models in autonomous driving, spanning their theoretical underpinnings, practical applications, and the ongoing research efforts aimed at overcoming existing limitations. Highlighting the significant role of world models in advancing autonomous driving technologies, this survey aspires to serve as a foundational reference for the research community, facilitating swift access to and comprehension of this burgeoning field, and inspiring continued innovation and exploration.
DOI:10.48550/arxiv.2403.02622