# Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving

arXiv: https://arxiv.org/abs/2606.08170 · HTML: https://mlsanity.com/paper/2606.08170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [LMDrive: Closed-Loop End-to-End Driving with Large Language Models](https://mlsanity.com/paper/2312.07488/) (2023)
- [A Recurrent Vision-and-Language BERT for Navigation](https://mlsanity.com/paper/2011.13922/) (2020)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning](https://mlsanity.com/paper/2310.10103/) (2023)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving](https://mlsanity.com/paper/2407.21293/) (2024)
- [ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Models](https://mlsanity.com/paper/2504.10757/) (2025)

## Abstract (arXiv)

With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still face severe challenges in complex interaction and long-tail scenarios due to distribution shift and causal confusion. These limitations often result in a lack of human-level decision-making flexibility and safety in extreme conditions. To overcome this limitation, this paper proposes a Human-in-the-Loop Online Behavior Cloning frame work (HiL-OBC) for autonomous driving, which aims to deeply integrate the cross-modal perceptual capabilities of LFMs with the high-level driving intelligence of human experts. Specifically, HiL-OBC deployment is executed through three critical phases: policy initialization with human intervention, latent behavioral modeling with Bayesian policy adaptation, and online deploy ment and updates. Furthermore, we design a Multi-modal Online Behavior Cloning (MOBC) model, which optimizes the base driving policy online through a lightweight network architecture, a takeover trigger mechanism, and a multi-variant loss function, thereby enhancing the system's decision-making robustness in complex environments. We evaluated the HiL-OBC on the LangAuto-Human CARLA benchmark. Experimental results demonstrate that the driving policies optimized via the human-in-the-loop mechanism achieve substantial performance gains: the DS of StructNav, LFG, and LMDrive increased by 47.25%, 31.59%, and 32.12%, respectively, with a simultaneous of various experimental settings and key components highlights the advantages of human-in-the-loop learning in improving decision-making robustness and overall driving performance.