The rapid development of IoT technology has promoted the integration and networked fusion of massive heterogeneous sensors. However, traditional Gaussian-Markov frameworks struggle to characterize nonlinear long-term correlated errors within systems and network cooperative effects, limiting inter-node interoperability. To address this, we propose a generalized nonlinear stochastic process (GNSP) theoretical framework, constructing nonlinear operators with long-term memory characteristics, and employing fractional calculus tools to achieve unified analysis of multiple error types within and across nodes. Based on this, we derive distributed error propagation laws on Lie group spaces, establish boundary criteria based on Wasserstein metrics, and improve nonlinear filters to design a dual-layer fusion architecture combining single-node hybrid adaptive filtering and inter-node collaborative optimization. Using vehicle networking GNSS/INS systems as an example, experimental results show that the proposed single-node strategy improves position, velocity, and attitude accuracy by an average of 11.71%, 9.82%, and 12.83% respectively in complex environments such as urban canyons and mountainous forest areas. Meanwhile, through inter-node parameter sharing and cooperative strategies of federated filtering, further V2V collaboration improves performance by 8.5%-12.7%, maintaining average improvements of 16.85%, 18.37%, and 21.14% even under limited communication conditions. This effectively addresses the challenges of distributed sensing errors in IoT environments.

The pantograph-catenary system (PACs) is the only channel for high-speed trains to obtain electric energy, and its state is crucial to the safety of train operation. There are two key indicators to measure the PACs state: the tension state of the catenary dropper and the zig-zag value of the PACs contact point. With the development of deep learning, on-board vision inspection equipment has gradually become an effective means of PACs state inspection. However, since more and more deep learning black boxes are used in the railway industry, the reliability estimation of their decisions has become an unavoidable challenge. As for PACs state detection, there are two other challenges: the lack of defective dropper sample and the difficulty in distinguishing between true and false contact points. To overcome the challenge of defective sample scarcity, this paper proposes to detect dropper defect by the semantic features extracted by a multi-task pantograph-catenary system state detection network (MPCN), which is trained using only normal droppers. Aiming at the contact point tracking, a multiple hypothesis tracking tree (MHTT) algorithm is proposed to track the contact points using the change rule of the contact point sequence. Meanwhile, in the semantic space, domain knowledge is combined with Bayesian Hypothesis testing to monitor the runtime reliability of the MPCN. In this way, the PACs state detection and the MPCN reliability monitoring can be implemented in a unified framework. The effectiveness of our method has been verified in various scenarios of various railway lines.

The concept of shared control has garnered significant attention within the realm of human-machine hybrid intelligence research. This study introduces a novel approach, specifically a dynamic control authority allocation method, for implementing shared control in autonomous vehicles. Unlike conventional mixed-initiative control techniques that blend human and vehicle inputs with weights determined by predefined index, the proposed method utilizes optimization-based techniques to obtain an optimal dynamic allocation for human and vehicle inputs that satisfies safety constraints. Specifically, a convex quadratic programm (QP) is constructed incorporating control barrier functions (CBF) for safety and control Lyapunov functions (CLF) for satisfying automated control objectives. The cost function of the QP is designed such that human weight increases with the magnitude of human input. A smooth control authority transition is obtained by optimizing over the change rate of the weight instead of the weight itself. The proposed method is verified in lane-changing scenarios with human-in-the-loop (HmIL) and hardware-in-the-loop (HdIL) experiments. Results show that the proposed method outperforms index-based control authority allocation method in terms of agility, safety and comfort.