Download pdf
World models for autonomous driving
- 1 Tongji University
* Author to whom correspondence should be addressed.
Abstract
Advancements in autonomous driving have been achieved due to the increasing use of artificial intelligence. The existing autonomous driving technology is insufficient to handle intricate traffic conditions. In autonomous driving, the world model is a crucial and innovative technology. Utilizing sensors and pre-existing knowledge, a world model can enhance the autonomous driving system's comprehension of the surroundings, offer essential data for future judgments, and enhance the system's resilience. The paper employs literature analysis and review methods to investigate the research and implementation of world models in autonomous driving, including environment perception and modeling, path planning, decision-making, and safety. This study examines the use of artificial intelligence technology in autonomous driving and analyzes the research and application of the world model in this field. It offers new insights for addressing challenging scenarios in autonomous driving and enhancing the safety of the system.
Keywords
autonomous driving, artificial intelligence, world model, security
[1]. Johan Engstrom, Shu-Yuan Liu, Azadeh Dinparastdjadid, Camelia Simoiu. Modeling road user response timing in naturalistic settings: a surprise-based framework. arXiv preprint, 2023.
[2]. Anna Dawid, Yann LeCun. Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. arXiv preprint, 2023.
[3]. Tesla Research Team. Foundation Models for Autonomy. Conference on Computer Vision and Pattern Recognition (CVPR 2023 Workshop).
[4]. Anthony Hu, Lloyd Russell; Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, Gianluca Corrado. GAIA-1: A Generative World Model for Autonomous Driving. arXiv preprint, 2023.
[5]. Wayve Research Team. Introducing GAIA-1: A Cutting-Edge Generative AI Model for Autonomy. https://wayve.ai/thinking/introducing-gaia1/, 2023.
[6]. Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep learning[J].Nature,2015,Vol.521(7553): 436-444.
[7]. He, Kaiming, Zhang Xiangyu, Ren Shaoqing, Sun, Jian. Deep Residual Learning for Image Recognition[J].2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR),2016,: 770-778.
[8]. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection[A].2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)[C],2016.
[9]. Ashish Vaswani, Noam Shazeer, Niki Parmar. Attention is all you need[A].NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems[C],2017.
[10]. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [A]. ICLR 2021.
[11]. Waymo Research Team. Progress towards Scalable Deployment of Autonomous Driving. Conference on Computer Vision and Pattern Recognition (CVPR 2023 Workshop).
[12]. Pei Sun; Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov. Scalability in Perception for Autonomous Driving: Waymo Open Dataset[A].2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)[C],2020.
[13]. Wayve Research Team. LINGO-1: Exploring Natural Language for Autonomous Driving. https://wayve.ai/thinking/lingo-natural-language-autonomous driving/, 2023.
Cite this article
Chen,Y. (2024). World models for autonomous driving. Applied and Computational Engineering,75,14-18.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).