arXiv subjects

Jiaming Zhong

Publications and source records attributed to Jiaming Zhong.

6 recordsLinked to original sources

arXiv2026

Learning Agent-based Model Predictive Control for Holistic Vehicle Performance

Agent-based model predictive control (AMPC) has recently been proposed as a distributed scheme that collaborates with all agents to achieve optimal holistic performance. However, its optimality highly depends on the prediction accuracy that requires all agents or their contributions to be known, which is too idealistic for actual implementation. This research proposes a novel practical hybrid control scheme - learning agent-based MPC (LAMPC), combining the model-based AMPC approach and data-based learning methods to improve the holistic vehicle performance for multi-agent systems. The Gaussian process regression (GPR) enhanced by an online data management strategy serves as the learning core to predict unknown contributions. A novel multi-step prediction mechanism leverages the GPR learning potential along the horizon. The predicted mean, representing the learned unknown contributions, completes the system model in the MPC for more accurate control. Meanwhile, a stochastic framework is formulated to guarantee control safety and feasibility using soft chance constraints based on the prediction variance. Both simulations and experiments show that, with the learning capability, LAMPC outperforms the traditional AMPC. LAMPC can achieve higher tracking performance in well-learned scenarios and always guarantee constraint satisfaction even in less-learned scenarios. Moreover, the proposed hybrid control scheme is efficient for real-time implementation and is flexible to any control agent topology.

cs.RO↗

arXiv2026

MPF-Net: Exposing High-Fidelity AI-Generated Video Forgeries via Hierarchical Manifold Deviation and Micro-Temporal Fluctuations

With the rapid advancement of video generation models such as Veo and Wan, the visual quality of synthetic content has reached a level where macro-level semantic errors and temporal inconsistencies are no longer prominent. However, this does not imply that the distinction between real and cutting-edge high-fidelity fake is untraceable. We argue that AI-generated videos are essentially products of a manifold-fitting process rather than a physical recording. Consequently, the pixel composition logic of consecutive adjacent frames residual in AI videos exhibits a structured and homogenous characteristic. We term this phenomenon `Manifold Projection Fluctuations' (MPF). Driven by this insight, we propose a hierarchical dual-path framework that operates as a sequential filtering process. The first, the Static Manifold Deviation Branch, leverages the refined perceptual boundaries of Large-Scale Vision Foundation Models (VFMs) to capture residual spatial anomalies or physical violations that deviate from the natural real-world manifold (off-manifold). For the remaining high-fidelity videos that successfully reside on-manifold and evade spatial detection, we introduce the Micro-Temporal Fluctuation Branch as a secondary, fine-grained filter. By analyzing the structured MPF that persists even in visually perfect sequences, our framework ensures that forgeries are exposed regardless of whether they manifest as global real-world manifold deviations or subtle computational fingerprints.

cs.CV↗

arXiv2025

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset for Vehicle-Infrastructure Cooperation in Adverse Weather

Vehicle-infrastructure (V2I) cooperative perception can substantially extend the range, coverage, and robustness of autonomous driving systems beyond the limits of onboard-only sensing, particularly in occluded and adverse-weather environments. However, its practical value is still difficult to quantify because existing benchmarks do not adequately capture large-scale multi-node deployments, realistic communication conditions, and adverse-weather operation. This paper presents CoInfra, a deployable cooperative infrastructure perception platform comprising 14 roadside sensor nodes connected through a commercial 5G network, together with a large-scale dataset and an open-source system stack for V2I cooperation research. The system supports synchronized multi-node sensing and delay-aware fusion under real 5G communication constraints. The released dataset covers an eight-node urban roundabout under four weather conditions (sunny, rainy, heavy snow, and freezing rain) and contains 294k LiDAR frames, 589k camera images, and 332k globally consistent 3D bounding boxes. It also includes a synchronized V2I subset collected with an autonomous vehicle. Beyond standard perception benchmarks, we further evaluate whether infrastructure sensing improves awareness of safety-critical traffic participants during roundabout interactions. In structured conflict scenarios, V2I cooperation increases critical-frame completeness from 33%-46% with vehicle-only sensing to 86%-100%. These results show that multi-node infrastructure perception can significantly improve situational awareness in conflict-rich traffic scenarios where vehicle-only sensing is most limited.

cs.RO↗

arXiv2025

DriveSOTIF: Advancing Perception SOTIF Through Multimodal Large Language Models

Human drivers possess spatial and causal intelligence, enabling them to perceive driving scenarios, anticipate hazards, and react to dynamic environments. In contrast, autonomous vehicles lack these abilities, making it challenging to manage perception-related Safety of the Intended Functionality (SOTIF) risks, especially under complex or unpredictable driving conditions. To address this gap, we propose fine-tuning multimodal large language models (MLLMs) on a customized dataset specifically designed to capture perception-related SOTIF scenarios. Benchmarking results show that fine-tuned MLLMs achieve an 11.8\% improvement in close-ended VQA accuracy and a 12.0\% increase in open-ended VQA scores compared to baseline models, while maintaining real-time performance with a 0.59-second average inference time per image. We validate our approach through real-world case studies in Canada and China, where fine-tuned models correctly identify safety risks that challenge even experienced human drivers. This work represents the first application of domain-specific MLLM fine-tuning for SOTIF domain in autonomous driving. The dataset and related resources are available at github.com/s95huang/DriveSOTIF.git

cs.RO↗

arXiv2025

SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes

Autonomous driving systems must operate smoothly in human-populated indoor environments, where challenges arise including limited perception and occlusions when relying only on onboard sensors, as well as the need for socially compliant motion planning that accounts for human psychological comfort zones. These factors complicate accurate recognition of human intentions and the generation of comfortable, socially aware trajectories. To address these challenges, we propose SAP-CoPE, an indoor navigation system that integrates cooperative infrastructure with a novel 3D human pose estimation method and a socially-aware model predictive control (MPC)-based motion planner. In the perception module, an optimization problem is formulated to account for uncertainty propagation in the camera projection matrix while enforcing human joint coherence. The proposed method is adaptable to both single- and multi-camera configurations and can incorporate sparse LiDAR point-cloud data. For motion planning, we integrate a psychology inspired personal-space field using the information from estimated human poses into an MPC framework to enhance socially comfort in human-populated environments. Extensive real-world evaluations demonstrate the effectiveness of the proposed approach in generating socially aware trajectories for autonomous systems.

cs.RO↗

arXiv2023

Augmented Kinesthetic Teaching: Enhancing Task Execution Efficiency through Intuitive Human Instructions

In this paper, we present a complete and efficient implementation of a knowledge-sharing augmented kinesthetic teaching approach for efficient task execution in robotics. Our augmented kinesthetic teaching method integrates intuitive human feedback, including verbal, gesture, gaze, and physical guidance, to facilitate the extraction of multiple layers of task information including control type, attention direction, input and output type, action state change trigger, etc., enhancing the adaptability and autonomy of robots during task execution. We propose an efficient Programming by Demonstration (PbD) framework for users with limited technical experience to teach the robot in an intuitive manner. The proposed framework provides an interface for such users to teach customized tasks using high-level commands, with the goal of achieving a smoother teaching experience and task execution. This is demonstrated with the sample task of pouring water.

cs.RO↗