Cover Image

FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Hongbin, Yang, Yiming, Zhang, Yifan, Zheng, Chaoda, Feng, Jie, Wang, Sheng, Wang, Zhennan, Chen, Shijia, Wang, Boyang, Zhang, Yu, Liu, Xianming, Cui, Shuguang, Li, Zhen
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving
by: Zheng, Chaoda, et al.
Published: (2026)

Latent Chain-of-Thought World Modeling for End-to-End Driving
by: Tan, Shuhan, et al.
Published: (2025)

DriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving
by: Lin, Hongbin, et al.
Published: (2025)

Enhancing End-to-End Autonomous Driving with Latent World Model
by: Li, Yingyan, et al.
Published: (2024)

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
by: Wang, Linbo, et al.
Published: (2026)

DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving
by: Wang, Tianqi, et al.
Published: (2024)

Cognitive-Hierarchy Guided End-to-End Planning for Autonomous Driving
by: Wang, Zhennan, et al.
Published: (2025)

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
by: Zheng, Yupeng, et al.
Published: (2025)

ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving
by: Zhang, Jinqing, et al.
Published: (2026)

Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
by: Ge, Junhao, et al.
Published: (2025)

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
by: Zhang, Lingjun, et al.
Published: (2026)

FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
by: Zeng, Zhiyuan, et al.
Published: (2025)

DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
by: Liao, Bencheng, et al.
Published: (2024)

FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains
by: Liu, Jiashuo, et al.
Published: (2026)

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving
by: Guan, Yanchen, et al.
Published: (2025)

Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
by: Zhang, Bozhou, et al.
Published: (2025)

ComDrive: Comfort-Oriented End-to-End Autonomous Driving
by: Wang, Junming, et al.
Published: (2024)

Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
by: Hao, Ruiyang, et al.
Published: (2025)

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
by: Cui, Yixin, et al.
Published: (2025)

Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
by: Zhang, Jiaru, et al.
Published: (2026)

MAP: End-to-End Autonomous Driving with Map-Assisted Planning
by: Yin, Huilin, et al.
Published: (2025)

ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
by: Sheng, Zihao, et al.
Published: (2026)

FlowDrive: Energy Flow Field for End-to-End Autonomous Driving
by: Jiang, Hao, et al.
Published: (2025)

Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
by: Zheng, Chaoda, et al.
Published: (2024)

IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving
by: Zhang, Chenghao, et al.
Published: (2026)

Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving
by: Hua, Zhihua, et al.
Published: (2026)

Exploring the Causality of End-to-End Autonomous Driving
by: Li, Jiankun, et al.
Published: (2024)

Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
by: Geng, Jiaheng, et al.
Published: (2025)

UniSTPA: A Safety Analysis Framework for End-to-End Autonomous Driving
by: Kou, Hongrui, et al.
Published: (2025)

SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation
by: Wang, Ruoyu, et al.
Published: (2026)

DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving
by: Shang, Shuyao, et al.
Published: (2025)

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
by: Yasarla, Rajeev, et al.
Published: (2026)

GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
by: Liu, Shuai, et al.
Published: (2025)

Unified End-to-End V2X Cooperative Autonomous Driving
by: Li, Zhiwei, et al.
Published: (2024)

X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference
by: Zeng, Yixiao, et al.
Published: (2026)

Active Learning from Scene Embeddings for End-to-End Autonomous Driving
by: Jiang, Wenhao, et al.
Published: (2025)

Attack End-to-End Autonomous Driving through Module-Wise Noise
by: Wang, Lu, et al.
Published: (2024)

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
by: Zheng, Zhiyu, et al.
Published: (2025)

MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving
by: Duan, Yiqun, et al.
Published: (2024)

Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
by: Liu, Genjia, et al.
Published: (2024)