Cover Image

DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Wenru, Liu, Pei, Ma, Jun
Format:	Preprint
Published:	2025
Subjects:	Robotics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
by: Xin, Ren, et al.
Published: (2025)

Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
by: Liu, Wenru, et al.
Published: (2024)

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)

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

Utilizing Navigation Paths to Generate Target Points for Enhanced End-to-End Autonomous Driving Planning
by: Shen, Yuanhua, et al.
Published: (2024)

Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
by: Song, Ziying, et al.
Published: (2025)

Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
by: Zhou, Hang, et al.
Published: (2024)

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

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

ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving
by: Feng, Renju, et al.
Published: (2025)

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

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

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

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

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

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

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

FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving
by: Wang, Baoyun, et al.
Published: (2026)

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

CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems
by: Liu, Haichao, et al.
Published: (2025)

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
by: Wozniak, Maciej K., et al.
Published: (2025)

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
by: Liu, Haochen, et al.
Published: (2025)

Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles
by: Liu, Wenru, et al.
Published: (2024)

Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning
by: Zhang, Zhiwei, et al.
Published: (2025)

A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Driving
by: Wu, Zhengfei, et al.
Published: (2026)

LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving
by: Paul, Pranjal, et al.
Published: (2024)

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

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

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

AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving
by: Huang, Wenhui, et al.
Published: (2026)

Prioritizing Perception-Guided Self-Supervision: A New Paradigm for Causal Modeling in End-to-End Autonomous Driving
by: Huang, Yi, et al.
Published: (2025)

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

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

VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
by: Zhao, Rui, et al.
Published: (2026)

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

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

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

SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving
by: Ryu, Wonjeong, et al.
Published: (2025)

SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
by: You, Junwei, et al.
Published: (2025)