Think2Drive: Efficient Reinforcement Learning by Thinking with Latent World Model for Autonomous Driving (in CARLA-V2)
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Qu, Y., Huang, Z., Sheng, Z., Chen, J., Leng, Y., Labi, S., & Chen, S. (2025) · arXiv · doi:10.48550/arxiv.2505.16377 · Cited by 1 · see citing works
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- Authors
- Li, Q., Jia, X., Wang, S., & Yan, J.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Yan, Junchi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024-11-24
- Pages
- 142-158
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4404654834
- Cited by
- 42
- References
- 29
- Citations by year
- 2026: 21 · 2025: 19 · 2024: 2
- FWCI
- 12.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- autonomous driving, reinforcement learning, latent world models, model-based reinforcement learning, corner cases, driving scenarios, training efficiency, benchmark
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