World Models for Autonomous Driving: An Initial Survey
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916238047838208 |
|---|---|
| author | Guan, Yanchen Liao, Haicheng Li, Zhenning Hu, Jia Yuan, Runze Li, Yunjian Zhang, Guohui Xu, Chengzhong |
| author_facet | Guan, Yanchen Liao, Haicheng Li, Zhenning Hu, Jia Yuan, Runze Li, Yunjian Zhang, Guohui Xu, Chengzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02622 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | World Models for Autonomous Driving: An Initial Survey Guan, Yanchen Liao, Haicheng Li, Zhenning Hu, Jia Yuan, Runze Li, Yunjian Zhang, Guohui Xu, Chengzhong Machine Learning Artificial Intelligence Robotics 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. |
| title | World Models for Autonomous Driving: An Initial Survey |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2403.02622 |