Cover Image

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xing, Shuo, Qian, Chengyuan, Wang, Yuping, Hua, Hongyuan, Tian, Kexin, Zhou, Yang, Tu, Zhengzhong
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning Robotics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EMMA: End-to-End Multimodal Model for Autonomous Driving
by: Hwang, Jyh-Jing, et al.
Published: (2024)

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
by: Xing, Shuo, et al.
Published: (2024)

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

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

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

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

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

LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving
by: Qiao, Zhijie, et al.
Published: (2025)

DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model
by: Yu, Rui, et al.
Published: (2025)

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

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

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
by: Jiang, Bo, et al.
Published: (2024)

DriveSafer: End-to-End Autonomous Driving with Safety Guidance
by: Sural, Shounak, et al.
Published: (2026)

GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving
by: Wan, Chi, et al.
Published: (2025)

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

Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM
by: Qin, Jack, et al.
Published: (2025)

UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
by: Wang, Yuping, et al.
Published: (2025)

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
by: Jiang, Bo, et al.
Published: (2024)

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving
by: Shao, Hao, et al.
Published: (2026)

HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
by: Cho, Hoonhee, et al.
Published: (2026)

ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
by: Li, Yongkang, et al.
Published: (2025)

AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
by: Wu, Yanhao, et al.
Published: (2026)

RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations
by: Xing, Ruidan, et al.
Published: (2025)

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

Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
by: Jia, Xiaosong, et al.
Published: (2024)

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

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

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

Using Ensemble Diffusion to Estimate Uncertainty for End-to-End Autonomous Driving
by: Wintel, Florian, et al.
Published: (2025)

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
by: Han, Jianhua, et al.
Published: (2025)

End-to-End Autonomous Driving through V2X Cooperation
by: Yu, Haibao, et al.
Published: (2024)

DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
by: Jia, Xiaosong, et al.
Published: (2025)

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

DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation
by: Yu, Haibao, et al.
Published: (2025)

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
by: Yang, Zhenjie, et al.
Published: (2025)

An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
by: Azad, Maryam Sadat Hosseini, et al.
Published: (2026)

Latent Chain-of-Thought World Modeling for End-to-End Driving
by: Tan, Shuhan, 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)

Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving
by: Xiong, Ziliang, et al.
Published: (2025)

ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration
by: Zhu, Minjie, et al.
Published: (2025)