Author name cluster

Dong Han

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

Enhui Ma
Lijun Zhou
Tao Tang
Jiahuan Zhang
Junpeng Jiang
Zhan Zhang
Dong Han
Kun Zhan

End-to-end planning methods are the de-facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long-tail problem (i.e., rare but safety-critical failure cases). In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with structured 3D layouts, can enable a fully automated pipeline to self-correct such failure cases. We first introduce an agent to simulate the role of product manager, dubbed PM-Agent, which formulates data requirements to collect data similar to the failure cases. Then, we use a generative model that can simulate both data collection and annotation. However, existing generative models struggle to generate high-fidelity data conditioned on 3D layouts. To address this, we propose DriveSora, which can generate spatiotemporally consistent videos aligned with the 3D annotations requested by PM-Agent. We integrate these components into our self-correcting agentic system, CorrectAD. Importantly, our pipeline is end-to-end model agnostic and can be applied to improve any end-to-end planner. Evaluated on both nuScenes and a more challenging in-house dataset across multiple end-to-end planners, CorrectAD corrects 62.5% and 49.8% of failure cases, reducing collision rates by 39% and 27%, respectively.

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AAAI Conference 2026 Conference Paper

Realistic Face Reconstruction from Facial Embeddings via Diffusion Models

Dong Han
Yong Li
Joachim Denzler

With the advancement of face recognition (FR) systems, privacy-preserving face recognition (PPFR) systems have gained popularity for their accurate recognition, enhanced facial privacy protection, and robustness to various attacks. However, there are limited studies to further verify privacy risks by reconstructing realistic high-resolution face images from embeddings of these systems, especially for PPFR. In this work, we propose the face embedding mapping (FEM), a general framework that explores Kolmogorov-Arnold Network (KAN) for conducting the embedding-to-face attack by leveraging pre-trained Identity-Preserving diffusion model against state-of-the-art (SOTA) FR and PPFR systems. Based on extensive experiments, we verify that reconstructed faces can be used for accessing other real-word FR systems. Besides, the proposed method shows the robustness in reconstructing faces from the partial and protected face embeddings. Moreover, FEM can be utilized as a tool for evaluating safety of FR and PPFR systems in terms of privacy leakage. All images used in this work are from public datasets.

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NeurIPS Conference 2024 Conference Paper

A Siamese Transformer with Hierarchical Refinement for Lane Detection

Zinan Lv
Dong Han
Wenzhe Wang
Danny Z. Chen

Lane detection is an important yet challenging task in autonomous driving systems. Existing lane detection methods mainly rely on finer-scale information to identify key points of lane lines. Since local information in realistic road environments is frequently obscured by other vehicles or affected by poor outdoor lighting conditions, these methods struggle with the regression of such key points. In this paper, we propose a novel Siamese Transformer with hierarchical refinement for lane detection to improve the detection accuracy in complex road environments. Specifically, we propose a high-to-low hierarchical refinement Transformer structure, called LAne TRansformer (LATR), to refine the key points of lane lines, which integrates global semantics information and finer-scale features. Moreover, exploiting the thin and long characteristics of lane lines, we propose a novel Curve-IoU loss to supervise the fit of lane lines. Extensive experiments on three benchmark datasets of lane detection demonstrate that our proposed new method achieves state-of-the-art results with high accuracy and efficiency. Specifically, our method achieves improved F1 scores on the OpenLane dataset, surpassing the current best-performing method by 5. 0 points.

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ECAI Conference 2024 Conference Paper

SinLane: Siamese Visual Transformer via Pyramid Feature Integration for Lane Detection

Zinan Lv
Dong Han
Wenzhe Wang
Danny Z. Chen

Lane detection is an important yet challenging task in autonomous driving systems. Based on the development of the Visual Transformer, early Transformer-based lane detection studies have achieved promising results in some scenarios. However, for complex road conditions such as uneven illumination intensity and heavy traffic, the performance of these methods remains limited and may even be worse than that of contemporaneous CNN-based methods. In this paper, we propose a novel Transformer-based end-to-end network, called SinLane, that attains the attention weights focusing on the sparse yet meaningful locations and improves the accuracy of lane detection in complex environments. SinLane is composed of a novel Siamese Visual Transformer structure and a novel Feature Pyramid Network (FPN) structure called Pyramid Feature Integration (PFI). We utilize the proposed PFI to better integrate global semantics and finer-scale features and to promote the optimization of the Transformer. Moreover, the designed Siamese Visual Transformer is combined with multiple levels of the PFI and is employed to refine the multi-scale lane line features output from the PFI. Extensive experiments on three benchmark datasets of lane detection demonstrate that our SinLane achieves state-of-the-art results with high accuracy and efficiency. Specifically, our SinLane improves the accuracy by over 3% compared to the current best-performing Transformer-based method for lane detection on CULane.

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ICRA Conference 2021 Conference Paper

Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems

Boyi Liu
Lujia Wang 0001
Xinquan Chen
Lexiong Huang
Dong Han
ChengZhong Xu 0001

A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics, which is inspired by the peer-assisted learning in cognitive psychology and pedagogy. PARL implements data collaboration with the framework of cloud robotic systems. Both data and models are shared by robots to the cloud after semantic computing and training locally. The cloud converges the data and performs augmentation, integration, and transferring. Finally, fine tune this larger shared dataset in the cloud to local robots. Furthermore, we propose the DAT Network (Data Augmentation and Transferring Network) to implement the data processing in PARL. DAT Network can realize the augmentation of data from multi-local robots. We conduct experiments on a simplified self-driving task for robots (cars). DAT Network has a significant improvement in the augmentation in self-driving scenarios. Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.

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IROS Conference 2015 Conference Paper

The calibration device and method of humanoid finger sensor based on multimodal perception

Meng Chen
Ping Tang
Dong Han

Depending on a humanoid finger sensor with multimodal perception capability, physical properties such as pressure, temperature, texture features, surface roughness, micro vibration, etc. , could be easily extracted when the sensor contacts with different objects. In this paper, an innovative calibration device is created, as well as the motor controllers, centre control system, hardware and software system. The Least Square method is proposed for data fitting and parameter predicting, which is used to implement precise calibration for pressure and its distribution. The Frequency Spectrum Analysis method is presented by using Fast Fourier Transform to determine different physical properties of objects under certain frequency range and energy distribution. With the analysis of calibration data and results, the contact force and temperature range are obtained in good linearity, meanwhile the method for how to distinguish objects with similar texture features is put forward by using the Frequency Spectrum Analysis. This study provides a set of systematic calibration methods for the humanoid robot fingers, which can be served as an efficient reference to perceive multimodal objects.

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