Pan He

AAAI Conference 2026 Conference Paper

MOBA: A Material-Oriented Backdoor Attack Against LiDAR-Based 3D Object Detection Systems

• Saket Sanjeev Chaturvedi
• Gaurav Bagwe
• Lan Emily Zhang
• Pan He
• Xiaoyong Yuan

LiDAR-based 3D object detection is widely used in safety-critical systems. However, these systems remain vulnerable to backdoor attacks that embed hidden malicious behaviors during training. A key limitation of existing backdoor attacks is their lack of physical realizability, primarily due to the digital-to-physical domain gap. Digital triggers often fail in real-world settings because they overlook material-dependent LiDAR reflection properties. On the other hand, physically constructed triggers are often unoptimized, leading to low effectiveness or easy detectability. This paper introduces Material-Oriented Backdoor Attack (MOBA), a novel framework that bridges the digital–physical gap by explicitly modeling the material properties of real-world triggers. MOBA tackles two key challenges in physical backdoor design: 1) robustness of the trigger material under diverse environmental conditions, 2) alignment between the physical trigger's behavior and its digital simulation. First, we propose a systematic approach to selecting robust trigger materials, identifying titanium dioxide (TiO₂) for its high diffuse reflectivity and environmental resilience. Second, to ensure the digital trigger accurately mimics the physical behavior of the material-based trigger, we develop a novel simulation pipeline that features: (1) an angle-independent approximation of the Oren–Nayar BRDF model to generate realistic LiDAR intensities, and (2) a distance-aware scaling mechanism to maintain spatial consistency across varying depths. We conduct extensive experiments on state-of-the-art LiDAR-based and Camera-LiDAR fusion models, showing that MOBA achieves a 93.50% attack success rate, outperforming prior methods by over 41%. Our work reveals a new class of physically realizable threats and underscores the urgent need for defenses that account for material-level properties in real-world environments.

TMLR Journal 2026 Journal Article

OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control

• Darryl C. Jacob
• Xinyu Liu
• Muchao Ye
• Xiaoyong Yuan
• Pan He

Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional reinforcement learning–based TSC methods function as black boxes, providing little to no insight into their decisions. Although large language models (LLMs) could provide the needed interpretability through natural language reasoning, they face challenges such as limited memory and difficulty in deriving optimal policies from sparse environmental feedback. Existing TSC methods that apply reinforcement fine-tuning to LLMs face notable training instability and deliver only limited improvements over pretrained models. We attribute this instability to the long-horizon nature of TSC: feedback is sparse and delayed, most control actions yield only marginal changes in congestion metrics, and the resulting weak reward signals interact poorly with policy-gradient optimization. We introduce OracleTSC, which addresses these issues through: (1) a reward hurdle mechanism that filters weak learning signals by subtracting a calibrated threshold from environmental feedback, and (2) preventing policy degeneracy by maximizing the probability of the chosen answer, which promotes consistent decision-making across multiple responses. Experiments on the standard LibSignal benchmark demonstrate that our approach enables a compact model (LLaMA3-8B) to achieve substantial improvements in traffic flow, with a $75%$ reduction in travel time and $67%$ decrease in queue lengths over the pretrained baseline while preserving interpretability through natural language explanations. Furthermore, the method exhibits strong cross-intersection generalization: a policy trained on one intersection transfers to a structurally distinct intersection with $17%$ lower travel time and $39%$ lower queue length, all without any additional finetuning for the target topology. These findings show that uncertainty-aware reward shaping could stabilize reinforcement fine-tuning and provide a new perspective for improving its effectiveness in TSC tasks.

EAAI Journal 2025 Journal Article

Prediction of high-performance concrete compressive strength using Decision Tree-Guided Artificial Neural Network Pretraining approach

• Yang Zhang
• Xinghai Yuan
• Xuanpeng Zhang
• Heng Wang
• Pan He
• Ling Luo
• Chuanyun Xu

Deep learning models exhibit substantial nonlinear learning capabilities, enabling them to predict the compressive strength of high-performance concrete based on its mix proportions and to discern the intricate nonlinear relationships between these proportions and compressive strength. However, the data-hungry nature of deep learning necessitates extensive training datasets on mix proportions, and generating such large-scale, high-quality concrete mix datasets incurs significant costs. To address this issue, this study proposes a Decision Tree-guided Artificial Neural Network Pretraining (TANNP) approach, aimed at training high-accuracy concrete compressive strength prediction models using small-scale datasets. This approach synergizes the advantages of decision trees in managing small datasets and the powerful learning capabilities of neural networks. The TANNP method was experimentally validated on a concrete compressive strength dataset. The results demonstrate that compared to directly employing neural networks, the TANNP approach significantly improves performance metrics: the coefficient of determination increased from 0. 91 to 0. 97, the root mean square error decreased from 5. 10 MPa to 3. 26 MPa (a reduction of approximately 36. 1%), the mean absolute error was reduced from 3. 00 MPa to 2. 10 MPa, the mean absolute percentage error dropped from 10. 72% to 7. 24%, and the A20-Index improved from 0. 88 to 0. 92, achieving state-of-the-art results on this dataset. These findings indicate that the TANNP approach effectively mitigates the data-hunger issue inherent in deep learning models and significantly enhances the accuracy of concrete compressive strength predictions.

IJCAI Conference 2024 Conference Paper

BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection

• Saket S. Chaturvedi
• Lan Zhang
• Wenbin Zhang
• Pan He
• Xiaoyong Yuan

3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting “triggers” to poison the training dataset, backdoor attacks manipulate the detector's prediction for inputs containing these triggers. Existing backdoor attacks against 3D object detection primarily poison 3D LiDAR signals, where large-sized 3D triggers are injected to ensure their visibility within the sparse 3D space, rendering them easy to detect and impractical in real-world scenarios. In this paper, we delve into the robustness of 3D object detection, exploring a new backdoor attack surface through 2D cameras. Given the prevalent adoption of camera and LiDAR signal fusion for high-fidelity 3D perception, we investigate the latent potential of camera signals to disrupt the process. Although the dense nature of camera signals enables the use of nearly imperceptible small-sized triggers to mislead 2D object detection, realizing 2D-oriented backdoor attacks against 3D object detection is non-trivial. The primary challenge emerges from the fusion process that transforms camera signals into a 3D space, compromising the association with the 2D trigger to the target output. To tackle this issue, we propose an innovative 2D-oriented backdoor attack against LiDAR-camera fusion methods for 3D object detection, named BadFusion, for preserving trigger effectiveness throughout the entire fusion process. The evaluation demonstrates the effectiveness of BadFusion, achieving a significantly higher attack success rate compared to existing 2D-oriented attacks.

AAAI Conference 2022 Conference Paper

Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions

• Pan He
• Patrick Emami
• Sanjay Ranka
• Anand Rangarajan

In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point clouds as continuous probability density functions using Gaussian mixture models. Scene flow estimation is therefore converted into the problem of recovering motion from the alignment of probability density functions, which we achieve using a closed-form expression of the classic Cauchy-Schwarz divergence. Unlike existing nearest-neighbor-based approaches that use hard pairwise correspondences, our proposed approach establishes soft and implicit point correspondences between point clouds and generates more robust and accurate scene flow in the presence of missing correspondences and outliers. Comprehensive experiments show that our method makes noticeable gains over the Chamfer Distance and the Earth Mover’s Distance in real-world environments and achieves state-of-the-art performance among selfsupervised learning methods on FlyingThings3D and KITTI, even outperforming some supervised methods with ground truth annotations.

ICML Conference 2021 Conference Paper

Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations

• Patrick Emami
• Pan He
• Sanjay Ranka
• Anand Rangarajan 0001

Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize. However, we observe that methods for learning these representations are either impractical due to long training times and large memory consumption or forego key inductive biases. In this work, we introduce EfficientMORL, an efficient framework for the unsupervised learning of object-centric representations. We show that optimization challenges caused by requiring both symmetry and disentanglement can in fact be addressed by high-cost iterative amortized inference by designing the framework to minimize its dependence on it. We take a two-stage approach to inference: first, a hierarchical variational autoencoder extracts symmetric and disentangled representations through bottom-up inference, and second, a lightweight network refines the representations with top-down feedback. The number of refinement steps taken during training is reduced following a curriculum, so that at test time with zero steps the model achieves 99. 1% of the refined decomposition performance. We demonstrate strong object decomposition and disentanglement on the standard multi-object benchmark while achieving nearly an order of magnitude faster training and test time inference over the previous state-of-the-art model.

AAAI Conference 2016 Conference Paper

Reading Scene Text in Deep Convolutional Sequences

• Pan He
• Weilin Huang
• Yu Qiao
• Chen Loy
• Xiaoou Tang