arXiv subjects

Zhuang Zhang

Publications and source records attributed to Zhuang Zhang.

11 recordsLinked to original sources

arXiv2026

Topology Enhanced MARL for Multi-Agent Cooperative Decision-Making of CAVs

Decentralized multi-agent cooperative decision-making in continuous environments is fundamentally bottlenecked by the curse of dimensionality, where undirected exploration typically converges to conservative local optima. We propose Topology-Enhanced Multi-Agent Reinforcement Learning (TPE-MARL) to reformulate multi-agent exploration as a structured topological traversal. We introduce the Game Topology Tensor, utilizing locality-sensitive hashing to project the continuous physical manifold into a discrete quotient space. This abstraction operates as an adversarial Information Bottleneck, decoupling strategic coordination intents from environmental noise. Within this space, a dual intrinsic reward mechanism drives exploration: a novelty reward maximizes the marginal entropy of visited topologies, while a collaboration reward, optimized via a variational Evidence Lower Bound (ELBO), minimizes conditional entropy to exploit cooperative joint configurations. Evaluations demonstrate that TPE-MARL achieves near-optimal decision distributions, closely approximating the theoretical bounds established by a Monte Carlo Tree Search (MCTS) oracle. Furthermore, physical testbed experiments validate the framework's zero-shot out-of-distribution (OOD) generalization. Supported by a spatial relaxation mechanism, the learned representations reliably execute dynamic negotiations, such as cooperative zipper-merging, exhibiting inherent robustness against real-world covariate shifts and actuation latencies.

cs.AI↗

arXiv2026

Interferometric molecular line observations toward the 21 μm protoplanetary nebula IRAS 06530-0213

The identification of the $21\,μ\mathrm{m}$ feature in some protoplanetary nebulae remains a longstanding puzzle, whose interpretation requires characterization of the molecular gas environments of associated sources. Here, we present high-resolution interferometric observations from the Northern Extended Millimeter Array toward the $21\,μ\mathrm{m}$ protoplanetary nebula IRAS 06530-0213. Multiple molecular transitions of $\mathrm{HC_3N}$, $\mathrm{C_4H}$, and $\mathrm{SiC_2}$ are detected and spatially resolved. We analyze these spectral lines alongside previously reported CO and $^{13}\mathrm{CO}$ emission toward this source. Differing from the extended barrel-like molecular structure traced by CO and $^{13}\mathrm{CO}$, $\mathrm{HC_3N}$ and $\mathrm{C_4H}$ are predominantly concentrated in the inner low-latitude equatorial zones. Reproducing the observed morphology requires $\mathrm{HC_3N}$ to possess a more inwardly concentrated abundance distribution compared with CO and $^{13}\mathrm{CO}$, implying that the inner equatorial regions provide favorable conditions for carbon-chain chemistry.

astro-ph.GA↗

arXiv2026

Appearance-Preserving Refinement of Generated 3D Assets for Monochromatic Fabrication

Recent advances in 3D mesh generation have enabled the creation of visually realistic assets. However, much of their visual fidelity is encoded in textures rather than geometry. When such assets are fabricated using monochromatic materials, texture information is largely lost, causing visually important details to disappear even when the original geometry is faithfully preserved. A key challenge is that the geometric perturbations required to recover texture-dependent appearance cues often introduce sharp local features and high-frequency surface structures, which may increase stress concentration and fabrication risk. In this paper, we present GenMF, an appearance-oriented geometry refinement framework for monochromatic fabrication. GenMF transforms texture-dependent visual cues into geometry-induced shading effects and formulates geometry refinement as a balance between appearance preservation and fabrication-oriented robustness. To discourage structurally and narrow the gap between simulation and physical manufacturing, we further introduce a differentiable stress-aware regularization based on a learned thermal-stress predictor. Experimental results demonstrate that GenMF significantly improves appearance preservation under monochromatic rendering while reducing stress concentration under a consistent thermo-mechanical simulation setting. Physical 3D printing examples further show that the refined geometries preserve more recognizable visual details while remaining suitable for fabrication. These results suggest that appearance-aware geometry refinement provides an effective bridge between generated 3D assets and fabrication-ready monochromatic objects.

cs.GR↗

arXiv2026

The Influence of Aliphatic Components on the Aromatic Emission Characteristics of Polycyclic Aromatic Hydrocarbons

Intensity ratios of aromatic emission features are widely used to diagnose the size and ionization state of polycyclic aromatic hydrocarbons (PAHs) in astronomical environments. However, PAHs are known to typically carry aliphatic side chains, a structural feature that may compromise the reliability of traditional diagnostic methods. This study systematically investigates the effects of aliphatic components on the aromatic emission properties of PAHs. Based on theoretical data from the NASA Ames PAH IR Spectroscopic Database, we compare the emission behavior of purely aromatic PAHs with those containing aliphatic substituents, revealing that aliphatic functionalization may modify the intensity ratio of the 11.2 $μ$m band relative to the 7.7 $μ$m and 3.3 $μ$m bands. This potentially leads to misidentification of their ionization state if molecular structural effects are neglected. Further analysis indicates that the impact of aliphatic components on diagnostic band ratios strongly depends on PAH size: small PAHs exhibit significant emission ratio shifts, deviating from traditional size/ionization trends, while larger PAHs are minimally affected. Despite these shifts, the classic $(I_{11.2/7.7})$ versus $(I_{11.2/3.3})$ diagnostic grid remains largely applicable to mixed aromatic-aliphatic PAHs, although some systematic calibration may be needed. Our findings emphasize the necessity for caution when interpreting PAH band ratios in aliphatic-rich environments, as variations in PAH molecular composition may distort inferences about physical conditions.

astro-ph.GA↗

arXiv2026

SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras

High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense arrays of tens or even hundreds of synchronized cameras. Dependence on such costly lab setups severely limits practical scalability. To this end, we propose a sparse-camera dynamic reconstruction framework that exploits abundant yet inconsistent generative observations. Our key innovation is the Spatio-Temporal Distortion Field, which provides a unified mechanism for modeling inconsistencies in generative observations across both spatial and temporal dimensions. Building on this, we develop a complete pipeline that enables 4D reconstruction from sparse and uncalibrated camera inputs. We evaluate our method on multi-camera dynamic scene benchmarks, achieving spatio-temporally consistent high-fidelity renderings and significantly outperforming existing approaches. Project page available at https://inspatio.github.io/sparse-cam4d/

cs.CV↗

arXiv2025

Hybrid Differential Reward: Combining Temporal Difference and Action Gradients for Efficient Multi-Agent Reinforcement Learning in Cooperative Driving

In multi-vehicle cooperative driving tasks involving high-frequency continuous control, traditional state-based reward functions suffer from the issue of vanishing reward differences. This phenomenon results in a low signal-to-noise ratio (SNR) for policy gradients, significantly hindering algorithm convergence and performance improvement. To address this challenge, this paper proposes a novel Hybrid Differential Reward (HDR) mechanism. We first theoretically elucidate how the temporal quasi-steady nature of traffic states and the physical proximity of actions lead to the failure of traditional reward signals. Building on this analysis, the HDR framework innovatively integrates two complementary components: (1) a Temporal Difference Reward (TRD) based on a global potential function, which utilizes the evolutionary trend of potential energy to ensure optimal policy invariance and consistency with long-term objectives; and (2) an Action Gradient Reward (ARG), which directly measures the marginal utility of actions to provide a local guidance signal with a high SNR. Furthermore, we formulate the cooperative driving problem as a Multi-Agent Partially Observable Markov Game (POMDPG) with a time-varying agent set and provide a complete instantiation scheme for HDR within this framework. Extensive experiments conducted using both online planning (MCTS) and Multi-Agent Reinforcement Learning (QMIX, MAPPO, MADDPG) algorithms demonstrate that the HDR mechanism significantly improves convergence speed and policy stability. The results confirm that HDR guides agents to learn high-quality cooperative policies that effectively balance traffic efficiency and safety.

cs.AI↗

arXiv2025

A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms

Reinforcement learning (RL) shows great potential for optimizing multi-vehicle cooperative driving strategies through the state-action-reward feedback loop, but it still faces challenges such as low sample efficiency. This paper proposes a differentiated reward method based on steady-state transition systems, which incorporates state transition gradient information into the reward design by analyzing traffic flow characteristics, aiming to optimize action selection and policy learning in multi-vehicle cooperative decision-making. The performance of the proposed method is validated in RL algorithms such as MAPPO, MADQN, and QMIX under varying autonomous vehicle penetration. The results show that the differentiated reward method significantly accelerates training convergence and outperforms centering reward and others in terms of traffic efficiency, safety, and action rationality. Additionally, the method demonstrates strong scalability and environmental adaptability, providing a novel approach for multi-agent cooperative decision-making in complex traffic scenarios.

cs.AI↗

arXiv2025

A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles

To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyzing the parallel actions in the multi-vehicle joint action space in the partial-steady-state traffic flow, the parallel update method can quickly exclude potential dangerous actions, thereby increasing the search depth without sacrificing the search breadth. The proposed method is tested in a large number of randomly generated traffic flow. The experiment results show that the algorithm has good robustness and better performance than the SOTA reinforcement learning algorithms and heuristic methods. The vehicle driving strategy using the proposed algorithm shows rationality beyond human drivers, and has advantages in traffic efficiency and safety in the coordinating zone.

cs.MA↗

arXiv2024

LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues

This paper presents an accurate and robust Structure-from-Motion (SfM) pipeline named LiVisSfM, which is an SfM-based reconstruction system that fully combines LiDAR and visual cues. Unlike most existing LiDAR-inertial odometry (LIO) and LiDAR-inertial-visual odometry (LIVO) methods relying heavily on LiDAR registration coupled with Inertial Measurement Unit (IMU), we propose a LiDAR-visual SfM method which innovatively carries out LiDAR frame registration to LiDAR voxel map in a Point-to-Gaussian residual metrics, combined with a LiDAR-visual BA and explicit loop closure in a bundle optimization way to achieve accurate and robust LiDAR pose estimation without dependence on IMU incorporation. Besides, we propose an incremental voxel updating strategy for efficient voxel map updating during the process of LiDAR frame registration and LiDAR-visual BA optimization. Experiments demonstrate the superior effectiveness of our LiVisSfM framework over state-of-the-art LIO and LIVO works on more accurate and robust LiDAR pose recovery and dense point cloud reconstruction of both public KITTI benchmark and a variety of self-captured dataset.

cs.CV↗

arXiv2023

The beaming effect for Fermi-LAT-detected FR-I radio galaxies

Our knowledge of Giga-electron volt (GeV) radio galaxies has been revolutionized by the Fermi-LAT Telescope, which provides an excellent opportunity to study the physical properties of GeV radio galaxies. According to the radio power and morphology, radio galaxies can be separated into Fanaroff-Riley Type I radio galaxies (FR-Is) and Type II radio galaxies (FR-IIs). In this paper, we consider the unification of FR-Is and BL Lacertae objects (BL Lacs), and assume FR-Is to be a standard candle to discuss the beaming effect for Fermi-LAT-detected FR-Is. Our main conclusions are as follows: (1) The estimated Doppler factors for 30 Fermi-LAT-detected FR-Is are in a range of $δ_{\rm{I}}=0.88-7.49$. The average Doppler factor ($<δ_{\rm{I}}>=2.56\pm0.30$) of the 30 FR-Is is smaller than that ($<δ_{\rm{BL}}>=10.28\pm2.03$) of the 126 Fermi-LAT-detected BL Lacs, supporting the unification model that FR-Is are regarded as the misaligned BL Lacs with smaller Doppler factors; (2) We propose that different regions of FR-Is in the plot of the $γ$-ray luminosity against the photon spectral index $(\log L_γ-α_{\rm{ph}})$ may indicate the different beaming effects; (3) The average Doppler factor of the 6 TeV FR-Is is similar to that of the 24 non-TeV FR-Is, which implies that the difference between the TeV and GeV emissions is not driven by the beaming effect in the Fermi-LAT-detected FR-I samples.

astro-ph.HE↗

arXiv2023

Vehicle Trajectory Prediction based Predictive Collision Risk Assessment for Autonomous Driving in Highway Scenarios

For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction of OVs is proposed in this paper. Firstly, the vehicle trajectory prediction is formulated as a sequence generation task with long short-term memory (LSTM) encoder-decoder framework. Convolutional social pooling (CSP) and graph attention network (GAN) are adopted for extracting local spatial vehicle interactions and distant spatial vehicle interactions, respectively. Then, two basic risk metrics, time-to-collision (TTC) and minimal distance margin (MDM), are calculated between the predicted trajectory of OV and the candidate trajectory of AV. Consequently, a time-continuous risk function is constructed with temporal and spatial risk metrics. Finally, the vehicle trajectory prediction model CSP-GAN-LSTM is evaluated on two public highway datasets. The quantitative results indicate that the proposed CSP-GAN-LSTM model outperforms the existing state-of-the-art (SOTA) methods in terms of position prediction accuracy. Besides, simulation results in typical highway scenarios further validate the feasibility and effectiveness of the proposed predictive collision risk assessment method.

cs.RO↗