Driving Into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving
Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang
2024年份
104顶会引用
摘要
distinct driving maneuvers, and determines the optimal trajectory according to the image-based rewards. Evaluation on real-world driving datasets verifies that our method could generate high-quality, consistent, and controllable multiview videos, opening up possibilities for real-world simulations and safe planning.
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引用它的顶会 Paper104
- Vista: A Generalizable Driving World Model with High Fidelity and Versatile ControllabilityShenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta 等NeurIPS 2024 · 被引用 403 次
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- FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous DrivingShuang Zeng, Xinyuan Chang, Mengwei Xie, Xinran Liu 等NeurIPS 2025 · 被引用 228 次
- Unified Vision-Language-Action ModelYuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang 等ICLR 2026 · 被引用 144 次
- RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement LearningHao Gao, Shaoyu Chen, Bo Jiang, Bencheng Liao 等NeurIPS 2025 · 被引用 92 次
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