Image de couverture de livre

PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Gao, Yinfeng, Zhang, Qichao, Liu, Deqing, Xia, Zhongpu, Li, Guang, Ma, Kun, Chen, Guang, Ye, Hangjun, Chen, Long, Ding, Da-Wei, Zhao, Dongbin
Format:	Preprint
Publié:	2026
Sujets:	Robotics Computer Vision and Pattern Recognition
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
par: Liu, Deqing, et autres
Publié: (2025)

Learning from Mistakes: Post-Training for Driving VLA with Takeover Data
par: Gao, Yinfeng, et autres
Publié: (2026)

MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving
par: Wang, Junli, et autres
Publié: (2026)

Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving
par: Xing, Zebin, et autres
Publié: (2025)

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
par: Zheng, Yupeng, et autres
Publié: (2025)

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
par: Wang, Junli, et autres
Publié: (2026)

Dream to Drive with Predictive Individual World Model
par: Gao, Yinfeng, et autres
Publié: (2025)

UncAD: Towards Safe End-to-end Autonomous Driving via Online Map Uncertainty
par: Yang, Pengxuan, et autres
Publié: (2025)

TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning
par: Xing, Zebin, et autres
Publié: (2025)

DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
par: Yang, Pengxuan, et autres
Publié: (2026)

ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
par: Liu, Xueyi, et autres
Publié: (2025)

ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
par: Li, Yongkang, et autres
Publié: (2025)

WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving
par: Yang, Pengxuan, et autres
Publié: (2025)

Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
par: Zheng, Yupeng, et autres
Publié: (2024)

PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
par: Zheng, Yupeng, et autres
Publié: (2024)

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
par: Zheng, Yinan, et autres
Publié: (2026)

AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
par: Yan, Tianyi, et autres
Publié: (2025)

GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
par: Zhang, Yunpeng, et autres
Publié: (2024)

GenAD: Generative End-to-End Autonomous Driving
par: Zheng, Wenzhao, et autres
Publié: (2024)

TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving
par: Jiang, Xuefeng, et autres
Publié: (2025)

VGGDrive: Empowering Vision-Language Models with Cross-View Geometric Grounding for Autonomous Driving
par: Wang, Jie, et autres
Publié: (2026)

CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving
par: Ma, Enhui, et autres
Publié: (2025)

Closing the Navigation Compliance Gap in End-to-end Autonomous Driving
par: Wu, Hanfeng, et autres
Publié: (2025)

LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
par: Mei, Xiaodong, et autres
Publié: (2026)

Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
par: Ding, Kairui, et autres
Publié: (2024)

End-to-end Autonomous Driving: Challenges and Frontiers
par: Chen, Li, et autres
Publié: (2023)

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
par: Zhang, Yuhang, et autres
Publié: (2025)

WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model
par: Zhang, Songyan, et autres
Publié: (2024)

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
par: Wang, Linbo, et autres
Publié: (2026)

Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving
par: Lin, Haohong, et autres
Publié: (2025)

Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments
par: Noguchi, Chihiro, et autres
Publié: (2025)

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
par: Zheng, Zhiyu, et autres
Publié: (2025)

PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
par: Chen, Zhili, et autres
Publié: (2023)

GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
par: Zheng, Wenzhao, et autres
Publié: (2024)

VDRive: Leveraging Reinforced VLA and Diffusion Policy for End-to-end Autonomous Driving
par: Guo, Ziang, et autres
Publié: (2025)

UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
par: Li, Yongkang, et autres
Publié: (2026)

FocalAD: Local Motion Planning for End-to-End Autonomous Driving
par: Sun, Bin, et autres
Publié: (2025)

MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models
par: Chen, Zhongpu, et autres
Publié: (2025)

WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving
par: Zhu, Ziyue, et autres
Publié: (2025)

Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations
par: Tang, Zuojin, et autres
Publié: (2024)