Cover Image

Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lian, Zhexi, Wang, Haoran, Yan, Xuerun, Lin, Weimeng, Zhang, Xianhong, Chen, Yongyu, Hu, Jia
Format:	Preprint
Published:	2026
Subjects:	Robotics Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
by: Hu, Jia, et al.
Published: (2025)

XBG: End-to-end Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration
by: Cardenas-Perez, Carlos, et al.
Published: (2024)

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

Human-Machine Shared Control Approach for the Takeover of CACC
by: Wang, Haoran, et al.
Published: (2024)

ZTRS: Zero-Imitation End-to-end Autonomous Driving with Trajectory Scoring
by: Li, Zhenxin, et al.
Published: (2025)

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

Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
by: Zheng, Yupeng, et al.
Published: (2024)

VDRive: Leveraging Reinforced VLA and Diffusion Policy for End-to-end Autonomous Driving
by: Guo, Ziang, et al.
Published: (2025)

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
by: Lei, Mingyue, et al.
Published: (2025)

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

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
by: Hu, Jia, et al.
Published: (2024)

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

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

A simulation platform calibration method for automated vehicle evaluation: accurate on both vehicle level and traffic flow level
by: Hu, Jia, et al.
Published: (2025)

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

Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
by: Yang, Zhenjie, et al.
Published: (2025)

DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
by: Yuan, Chengran, et al.
Published: (2024)

MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulation
by: He, Baotian, et al.
Published: (2024)

METDrive: Multi-modal End-to-end Autonomous Driving with Temporal Guidance
by: Guo, Ziang, et al.
Published: (2024)

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

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

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
by: Zheng, Xiaoji, et al.
Published: (2025)

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

End-to-end Autonomous Driving: Challenges and Frontiers
by: Chen, Li, et al.
Published: (2023)

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning
by: Huang, Zhiyu, et al.
Published: (2024)

Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving
by: Guo, Yipin, et al.
Published: (2024)

ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation
by: Yu, Zhengcheng, et al.
Published: (2026)

Pre-trained Transformer-Enabled Strategies with Human-Guided Fine-Tuning for End-to-end Navigation of Autonomous Vehicles
by: Hu, Dong, et al.
Published: (2024)

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

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

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)

End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning
by: Kishky, Mahmoud M., et al.
Published: (2024)

Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
by: Murdoch, Andrew, et al.
Published: (2023)

ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
by: Lu, Han, et al.
Published: (2024)

Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations
by: Tang, Zuojin, et al.
Published: (2024)

PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
by: Chen, Zhili, et al.
Published: (2023)

TrajDiff: End-to-end Autonomous Driving without Perception Annotation
by: Gui, Xingtai, et al.
Published: (2025)

FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction
by: Zhang, Yanghai, et al.
Published: (2025)

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