arXiv subjects

Xiyang Wang

Publications and source records attributed to Xiyang Wang.

At least 19 recordsLinked to original sources

arXiv2026

BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation

Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back to refine the action model. However, the bottleneck of this loop lies in the simulators' inability to generate behaviorally plausible responses by surrounding agents, making generated data both unrealistic in interaction and imbalanced in distribution. We introduce BehaviorWorldGen, a framework that closes the loop between action models and world simulators through controllable behavior-aware structured world generation. Its core component is BehaviorFlow, a meta-action-conditioned traffic-flow model that injects interpretable behavior controls and jointly generates multi-agent rollouts. BehaviorFlow realizes the specified agent behaviors while allowing surrounding vehicles to respond to the ego and to one another. The resulting rollouts are rendered by a world simulator into realistic multi-view observations, which are paired with corrected interaction-aware trajectories for action-model refinement. Since BehaviorWorldGen uses structured trajectories as the interface between its modules, it is compatible with diverse action models and world simulators. Experiments on world generation, scene extrapolation, and policy refinement demonstrate consistent improvements, with the largest benefits concentrated on difficult interactive scenarios.

cs.RO↗

arXiv2026

WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving

Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent feature prediction. However, V-JEPA is built around random-mask completion and deterministic regression, making it fundamentally ill-suited for autonomous driving planning that demands future-directed prediction tightly coupled with action. To address this, we rethink the V-JEPA paradigm and present WA-JEPA, a V-JEPA-native world-action model designed for autonomous driving planning. Instead of random spatiotemporal masking, WA-JEPA employs hybrid future-masked pre-training, where the model infers future latents from observed context. Departing from deterministic regression, we recast future prediction as conditional flow matching over latent futures, which substantially improves the model's ability to generate plausible future latents for downstream planning. Finally, a joint future-action predictor is proposed to denoise future scene tokens and ego trajectories together in a unified spatiotemporal latent space, allowing action supervision to directly shape planning-relevant world representations. Pre-trained on nuPlan videos and fine-tuned on NAVSIM, WA-JEPA reaches 91.7 EPDMS on NAVSIM-v2, surpassing the strongest end-to-end and world-action baselines by 1.6 and 1.3 EPDMS, and, without HUGSIM-specific fine-tuning, attains the best HD-Score of 0.4462 on the closed-loop HUGSIM benchmark under the same evaluation protocol. These results validate V-JEPA-native world-action modeling as a powerful and scalable paradigm for autonomous driving planning. Code is available at https://github.com/AFARI-Research/WA-JEPA.

cs.CV↗

arXiv2026

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models

Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) models capture interaction-aware temporal dependencies via causal factorization, but their step-wise decoding leads to error accumulation and suboptimal global structure. In contrast, diffusion models optimize trajectories globally but lack explicit causal constraints, making them unreliable in interactive and safety-critical scenarios. This dichotomy reveals a deeper issue: existing methods treat causal modeling and global optimization as separate paradigms, without a principled way to unify them within a single trajectory distribution. To address this, we propose ChainFlow-VLA, which unifies causal generation and global refinement within a unified probabilistic framework. We formulate planning as a mixture over AR-induced modes and learn Vision-Language Model (VLM)-conditioned residual distributions over these modes. An autoregressive generator (Chain) produces a discrete set of causal trajectory modes, followed by a diffusion-based refiner (Flow) that leverages VLM hidden states as semantic priors to perform mode-conditioned correction in residual space while preserving causal structure. This straightforward conditioning seamlessly injects high-level scene understanding into fine-grained trajectory adjustments. Experiments demonstrate that ChainFlow-VLA achieves robust planning in ambiguous and long-tail scenarios, achieving a state-of-the-art score of 94.85 on the NAVSIM v1 leaderboard, matching human-level performance (94.8). Code will be available at https://github.com/AFARI-Research/ChainFlow-VLA.

cs.CV↗

arXiv2026

Development of a sub-100 ps Time-of-Flight detector with SiPM-readout scintillator for measurement of cosmic muon velocity

Accurate Time-of-Flight (TOF) measurement with sub-100 picosecond resolution is a critical requirement for particle identification in future high-energy physics experiments, such as the Belle II $K_{L}$ and Muon (KLM) detector upgrade. Achieving this precision with large-area Silicon Photomultipliers (SiPMs) is challenging due to the inherent junction capacitance, which degrades signal rise time. In this work, we developed and evaluated a high-time-resolution cosmic ray detector based on plastic scintillators and customized SiPM arrays. To optimize the readout for block-shaped scintillators, we systematically compared different sensor topologies. We demonstrate that a multi-face readout topology, utilizing low-capacitance 4-series (4S) SiPM modules coupled to four faces of the scintillator, achieves an excellent coincidence time resolution of approximately 68 ps, outperforming the $\sim$100 ps resolution of the concentrated 4-series 3-parallel (4S3P) hybrid topology. Furthermore, to validate the system's practical performance, we successfully measured well-known cosmic ray observables, specifically the relativistic muon velocity via TOF reconstruction. These results highlight the potential of the multi-face 4S configuration as a high-precision solution for future TOF detector upgrades.

physics.ins-det↗

arXiv2026

ERNIE 5.0 Technical Report

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.

cs.CL↗

arXiv2025

Development of a time calibration system for the KLM upgrade in the Belle II experiment

To meet the stringent time calibration requirements for the Belle II experiment upgrade, particularly for its large-size KL and Muon Detector comprising tens of thousands of scintillator channels with time resolutions better than 100 ps, we developed a compact and high-speed time calibration system. The system utilizes a laser diode as its light source, integrated with a fast pulse laser drive circuit that employs high-speed switching GaN FETs and gate drivers. A prototype was constructed and rigorously evaluated using scintillators, achieving timing resolutions of about 13 ps for a single calibration channel. Furthermore, internal deviations among calibration channels were analyzed, with most measurements remaining within 250 ps. These results highlight the system's precision, scalability, and suitability for large-scale particle physics experiments.

physics.ins-det↗

arXiv2025

Multipath Component-Enhanced Signal Processing for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has gained traction in academia and industry. Recently, multipath components (MPCs), as a type of spatial resource, have the potential to improve the sensing performance in ISAC systems, especially in richly scattering environments. In this paper, we propose to leverage MPC and Khatri-Rao space-time (KRST) code within a single ISAC system to realize high-accuracy sensing for multiple dynamic targets and multi-user communication. Specifically, we propose a novel MPC-enhanced sensing processing scheme with symbol-level fusion, referred to as the "SL-MPS" scheme, to achieve high-accuracy localization of multiple dynamic targets and empower the single ISAC system with a new capability of absolute velocity estimation for multiple targets with a single sensing attempt. Furthermore, the KRST code is applied to flexibly balance communication and sensing performance in richly scattering environments. To evaluate the contribution of MPCs, the closed-form Cram\'er-Rao lower bounds (CRLBs) of location and absolute velocity estimation are derived. Simulation results illustrate that the proposed SL-MPS scheme is more robust and accurate in localization and absolute velocity estimation compared with the existing state-of-the-art schemes.

eess.SP↗

arXiv2025

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation

Controllable trajectory generation guided by high-level semantic decisions, termed meta-actions, is crucial for autonomous driving systems. A significant limitation of existing frameworks is their reliance on invariant meta-actions assigned over fixed future time intervals, causing temporal misalignment with the actual behavior trajectories. This misalignment leads to irrelevant associations between the prescribed meta-actions and the resulting trajectories, disrupting task coherence and limiting model performance. To address this challenge, we introduce Autoregressive Meta-Actions, an approach integrated into autoregressive trajectory generation frameworks that provides a unified and precise definition for meta-action-conditioned trajectory prediction. Specifically, We decompose traditional long-interval meta-actions into frame-level meta-actions, enabling a sequential interplay between autoregressive meta-action prediction and meta-action-conditioned trajectory generation. This decomposition ensures strict alignment between each trajectory segment and its corresponding meta-action, achieving a consistent and unified task formulation across the entire trajectory span and significantly reducing complexity. Moreover, we propose a staged pre-training process to decouple the learning of basic motion dynamics from the integration of high-level decision control, which offers flexibility, stability, and modularity. Experimental results validate our framework's effectiveness, demonstrating improved trajectory adaptivity and responsiveness to dynamic decision-making scenarios. We provide the video document and dataset, which are available at https://arma-traj.github.io/.

cs.RO↗

arXiv2025

DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling

Accurate and efficient modeling of agent interactions is essential for trajectory generation, the core of autonomous driving systems. Existing methods, scene-centric, agent-centric, and query-centric frameworks, each present distinct advantages and drawbacks, creating an impossible triangle among accuracy, computational time, and memory efficiency. To break this limitation, we propose Directional Rotary Position Embedding (DRoPE), a novel adaptation of Rotary Position Embedding (RoPE), originally developed in natural language processing. Unlike traditional relative position embedding (RPE), which introduces significant space complexity, RoPE efficiently encodes relative positions without explicitly increasing complexity but faces inherent limitations in handling angular information due to periodicity. DRoPE overcomes this limitation by introducing a uniform identity scalar into RoPE's 2D rotary transformation, aligning rotation angles with realistic agent headings to naturally encode relative angular information. We theoretically analyze DRoPE's correctness and efficiency, demonstrating its capability to simultaneously optimize trajectory generation accuracy, time complexity, and space complexity. Empirical evaluations compared with various state-of-the-art trajectory generation models, confirm DRoPE's good performance and significantly reduced space complexity, indicating both theoretical soundness and practical effectiveness. The video documentation is available at https://drope-traj.github.io/.

cs.RO↗

arXiv2025

R&D of KLM Upgrade for Direct Measurement of Neutral Hadron Momentum via Time-of-Flight in Belle II

Accurate momentum determination of a neutral hadron, such as a KL meson or a neutron, remains a significant challenge in particle physics and nuclear physics experiments. The Belle II experiment presents an opportunity to address this challenge through an upgrade incorporating Time-of-Flight (TOF) capability for its large KL and Muon Detector (KLM). We investigate the feasibility of momentum determination via TOF measurement. To achieve high time resolution for the KLM upgrade, we conduct research and development of cost-effective plastic scintillators in collaboration with GaoNengKeDi Company, and technology utilizing silicon photomultipliers(SiPMs) arrays. A bulk attenuation length of 120 \pm 7 cm has been achieved with a 135 cm-long sample, along with a time resolution of 70 \pm 7 ps at its midpoint. A 50 cm-long scintillator demonstrates an exceptional time resolution of 47 \pm 2 ps. These results highlight the potential of the proposed technology for improving neutral hadron momentum measurements in an upgraded Belle II KLM detector.

physics.ins-det↗

arXiv2025

Scintillation and Timing Performance of a 3at% Yttrium-Doped Barium Fluoride Crystal

We report the scintillation and timing performance of a new developed 200 * 20 mm * 20 mm large size barium fluoride crystal doped with 3at% yttrium (BaF2:Y) to enhance the application for high time resolution. This doping effectively suppresses the slow scintillation component while maintaining most of the fast component, as confirmed by X-ray excited luminescence measurements. The BaF2:Y crystal demonstrated a transmittance of near 90% in the visible spectrum and a light response uniformity parameter of delta = (-2.74 +- 1.15)% when coupled with the tail end. The actual yttrium content varied from 2.1at% near the seed end to 3.7at% at the tail end. The assembled large BaF2:Y detector with silicon photomultipliers exhibited a time resolution of (82.2 +- 2.6) ps using constant fraction discrimination method in a cosmic ray test and (140.1 +- 3.8) ps using a low fixed threshold method in a beam test at Shanghai Synchrotron Radiation Facility with an 1.35 GeV electron beam. These results indicate the significant potential of BaF2:Y crystal for various applications, such as detectors for particle physics and nuclear physics.

physics.ins-det↗

arXiv2024

Multipath Component-Aided Signal Processing for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.

eess.SP↗

arXiv2024

MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving

This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack

cs.CV↗

arXiv2024

StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction

3D multi-object tracking and trajectory prediction are two crucial modules in autonomous driving systems. Generally, the two tasks are handled separately in traditional paradigms and a few methods have started to explore modeling these two tasks in a joint manner recently. However, these approaches suffer from the limitations of single-frame training and inconsistent coordinate representations between tracking and prediction tasks. In this paper, we propose a streaming and unified framework for joint 3D Multi-Object Tracking and trajectory Prediction (StreamMOTP) to address the above challenges. Firstly, we construct the model in a streaming manner and exploit a memory bank to preserve and leverage the long-term latent features for tracked objects more effectively. Secondly, a relative spatio-temporal positional encoding strategy is introduced to bridge the gap of coordinate representations between the two tasks and maintain the pose-invariance for trajectory prediction. Thirdly, we further improve the quality and consistency of predicted trajectories with a dual-stream predictor. We conduct extensive experiments on popular nuSences dataset and the experimental results demonstrate the effectiveness and superiority of StreamMOTP, which outperforms previous methods significantly on both tasks. Furthermore, we also prove that the proposed framework has great potential and advantages in actual applications of autonomous driving.

cs.CV↗

arXiv2023

Design and test for the CEPC muon subdetector based on extruded scintillator and SiPM

A combination of scintillator, wavelength shifting (WLS) fiber, and silicon photomultiplier (SiPM) shows an excellent performance in the `$K_{L}$ and $\mu$ detector (KLM)' of the Belle II experiment. In this study, we present the R&D efforts for a similar detection technology utilizing a new scintillator and SiPM. This technology can be applied to a muon detector for the proposed CEPC experiment. The R&D encompasses the investigation of the performance of a new 150 cm-long scintillator, the NDL SiPM with a sensitive surface of $\times$ 3 mm, or the Hamamatsu MPPC with a sensitive surface of 1.3 mm $\times$ 1.3 mm. Additionally, it includes the construction of a detector strip and the methods employed to achieve excellent light collection. Cosmic ray tests reveal efficient photon collections by NDL SiPM or MPPC, with efficiencies well above 90% using a threshold of 8 p.e.. The time resolutions for hits at the far end of a scintillator strip are better than 1.7 ns. The observed performance lays the foundation for advancing R&D including prototype modules aiming for reference Technical Design Report of CEPC detector recently.

physics.ins-det↗

arXiv2023

Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

In currently available literature, no tracking-by-detection (TBD) paradigm-based tracking method has considered the localization confidence of detection boxes. In most TBD-based methods, it is considered that objects of low detection confidence are highly occluded and thus it is a normal practice to directly disregard such objects or to reduce their priority in matching. In addition, appearance similarity is not a factor to consider for matching these objects. However, in terms of the detection confidence fusing classification and localization, objects of low detection confidence may have inaccurate localization but clear appearance; similarly, objects of high detection confidence may have inaccurate localization or unclear appearance; yet these objects are not further classified. In view of these issues, we propose Localization-Guided Track (LG-Track). Firstly, localization confidence is applied in MOT for the first time, with appearance clarity and localization accuracy of detection boxes taken into account, and an effective deep association mechanism is designed; secondly, based on the classification confidence and localization confidence, a more appropriate cost matrix can be selected and used; finally, extensive experiments have been conducted on MOT17 and MOT20 datasets. The results show that our proposed method outperforms the compared state-of-art tracking methods. For the benefit of the community, our code has been made publicly at https://github.com/mengting2023/LG-Track.

cs.CV↗

arXiv2023

You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking

In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance. In this paper, a new end-to-end multi-object tracking framework is proposed, which integrates object detection and multi-object tracking into a single model. The proposed tracking framework eliminates the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, the regression confidence of historical trajectories is investigated, and the possible states of a trajectory (weak object or strong object) in the current frame are predicted. Then, a confidence fusion module is designed to guide non-maximum suppression for trajectories and detections to achieve ordered and robust tracking. Thirdly, by integrating historical trajectory features, the regression performance of the detector is enhanced, which better reflects the occlusion and disappearance patterns of objects in real world. Lastly, extensive experiments are conducted on the commonly used KITTI and Waymo datasets. The results show that the proposed framework can achieve robust tracking by using only a 2D detector and a 3D detector, and it is proven more accurate than many of the state-of-the-art TBD-based multi-modal tracking methods. The source codes of the proposed method are available at https://github.com/wangxiyang2022/YONTD-MOT.

cs.CV↗

arXiv2023

3D Multi-Object Tracking Based on Uncertainty-Guided Data Association

In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Namely, the inherent uncertainties existing in tracks and detections are overlooked. In this work, we discard the commonly used deterministic tracks and deterministic detections for data association, instead, we propose to model tracks and detections as random vectors in which uncertainties are taken into account. Then, based on the Jensen-Shannon divergence, the similarity between two multidimensional distributions, i.e. track and detection, is evaluated for data association purposes. Lastly, the level of track uncertainty is incorporated in our cost function design to guide the data association process. Comparative experiments have been conducted on two typical datasets, KITTI and nuScenes, and the results indicated that our proposed method outperformed the compared state-of-the-art 3D tracking algorithms. For the benefit of the community, our code has been made available at https://github.com/hejiawei2023/UG3DMOT.

cs.CV↗