World Models for Autonomous Driving: An Initial Survey
Yanchen Guan Haicheng Liao Zhenning Li Guohui Zhang Chengzhong Xu
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.