Copertina

Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zheng, Yupeng, Yang, Pengxuan, Xia, Zhongpu, Zhang, Qichao, Zheng, Yuhang, Gu, Songen, Jin, Bu, Zhang, Teng, Lu, Ben, Han, Chao, Lang, Xianpeng, Zhao, Dongbin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Robotics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving
di: Xing, Zebin, et al.
Pubblicazione: (2025)

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

WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving
di: Yang, Pengxuan, et al.
Pubblicazione: (2025)

UncAD: Towards Safe End-to-end Autonomous Driving via Online Map Uncertainty
di: Yang, Pengxuan, et al.
Pubblicazione: (2025)

PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
di: Zheng, Yupeng, et al.
Pubblicazione: (2024)

TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
di: Liu, Deqing, et al.
Pubblicazione: (2025)

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

DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
di: Yang, Pengxuan, et al.
Pubblicazione: (2026)

GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
di: Zheng, Wenzhao, et al.
Pubblicazione: (2024)

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

Data Scaling Laws for End-to-End Autonomous Driving
di: Naumann, Alexander, et al.
Pubblicazione: (2025)

PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
di: Gao, Yinfeng, et al.
Pubblicazione: (2026)

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
di: Wang, Junli, et al.
Pubblicazione: (2026)

TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving
di: Jiang, Xuefeng, et al.
Pubblicazione: (2025)

MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving
di: Wang, Junli, et al.
Pubblicazione: (2026)

Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation
di: Ma, Enhui, et al.
Pubblicazione: (2024)

UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation
di: Jin, Bu, et al.
Pubblicazione: (2025)

GenAD: Generative End-to-End Autonomous Driving
di: Zheng, Wenzhao, et al.
Pubblicazione: (2024)

Embodied Cognition Augmented End2End Autonomous Driving
di: Niu, Ling, et al.
Pubblicazione: (2025)

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
di: Song, Ziying, et al.
Pubblicazione: (2025)

Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
di: Duan, Yiqun, et al.
Pubblicazione: (2024)

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

SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
di: Sun, Wenchao, et al.
Pubblicazione: (2024)

OccTENS: 3D Occupancy World Model via Temporal Next-Scale Prediction
di: Jin, Bu, et al.
Pubblicazione: (2025)

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

Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
di: Ding, Kairui, et al.
Pubblicazione: (2024)

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

Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving
di: Sun, Jiangxin, et al.
Pubblicazione: (2026)

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

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

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
di: Zheng, Yinan, et al.
Pubblicazione: (2026)

From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
di: Li, Yueyuan, et al.
Pubblicazione: (2024)

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

Beyond Imitation: Constraint-Aware Trajectory Generation with Flow Matching For End-to-End Autonomous Driving
di: Liu, Lin, et al.
Pubblicazione: (2025)

What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving
di: Zhou, Hongkuan, et al.
Pubblicazione: (2023)

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

TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning
di: Xing, Zebin, et al.
Pubblicazione: (2025)

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
di: Lu, Yuhang, et al.
Pubblicazione: (2025)

FocalAD: Local Motion Planning for End-to-End Autonomous Driving
di: Sun, Bin, et al.
Pubblicazione: (2025)

ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving
di: Cui, Can, et al.
Pubblicazione: (2025)