Cover Image

FlowDrive: Energy Flow Field for End-to-End Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Hao, Zhang, Zhipeng, Gao, Yu, Sun, Zhigang, Wang, Yiru, Heng, Yuwen, Wang, Shuo, Chai, Jinhao, Chen, Zhuo, Zhao, Hao, Sun, Hao, Zhang, Xi, Jiang, Anqing, Hu, Chuan
Format:	Preprint
Published:	2025
Subjects:	Robotics Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving
by: Wang, Yiru, et al.
Published: (2026)

DiffVLA++: Bridging Cognitive Reasoning and End-to-End Driving through Metric-Guided Alignment
by: Gao, Yu, et al.
Published: (2025)

UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
by: Gao, Yu, et al.
Published: (2026)

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
by: Chai, Jinhao, et al.
Published: (2025)

DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion
by: Sun, Zhigang, et al.
Published: (2025)

Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive
by: Wang, Yiru, et al.
Published: (2026)

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
by: Jiang, Hao, et al.
Published: (2025)

DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
by: Jiang, Anqing, et al.
Published: (2025)

ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models
by: Wang, Yiru, et al.
Published: (2026)

IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
by: Jiang, Anqing, et al.
Published: (2025)

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

SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction
by: Jiang, Anqing, et al.
Published: (2025)

GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving
by: Liu, Lin, et al.
Published: (2025)

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

GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
by: Xing, Zebin, et al.
Published: (2025)

FlowDrive: moderated flow matching with data balancing for trajectory planning
by: Wang, Lingguang, et al.
Published: (2025)

StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving
by: Hao, Ruiyang, et al.
Published: (2025)

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

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving
by: Jiao, Siwen, et al.
Published: (2025)

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

End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023
by: Lan, Gongjin, et al.
Published: (2023)

WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model
by: Zhang, Songyan, et al.
Published: (2024)

TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
by: Wang, Jiawei, et al.
Published: (2025)

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

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

GMF-Drive: Gated Mamba Fusion with Spatial-Aware BEV Representation for End-to-End Autonomous Driving
by: Wang, Jian, et al.
Published: (2025)

Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
by: Zeng, Xinyu, et al.
Published: (2026)

End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation
by: Guo, Mingzhe, et al.
Published: (2024)

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

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

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

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

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

PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Driving
by: Yuan, Chengran, et al.
Published: (2025)

UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving
by: Lu, Hao, et al.
Published: (2025)

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

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

Unified Map Prior Encoder for Mapping and Planning
by: Zhang, Zongzheng, et al.
Published: (2026)

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

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