This article studies the formation tracking problem of a team autonomous underwater vehicles (AUVs) with ocean current disturbances. A distributed Lyapunov-based model predictive controller (DLMPC) is designed such that AUVs can keep desired while reference trajectory, despite presence external The DLMPC inherits stability and robustness extended state observer (ESO)-based auxiliary control law invokes online optimization to improve performance multi-AUV system. closed-loop system guaranteed...

Autonomous marine vehicles (AMVs) have received considerable attention in the past few decades, mainly because they play essential roles broad applications such as environmental monitoring and resource exploration. Recent advances field of communication technologies, perception capability, computational power advanced optimization algorithms stimulated new interest development AMVs. In order to deploy constrained AMVs complex dynamic maritime environment, it is crucial enhance guidance...

Driver drowsiness detection is of great significance in improving driving safety and has been widely studied recent years. However, some existing methods have not fully utilized the drowsiness-related information, are susceptible to interference from redundant information input data. To address these issues, a video-based driver method according key facial features including landmarks local areas (VBFLLFA) proposed this paper. In order utilize related exclude head movement obtained through...

<div>The vehicle dynamic state is essential for stability control and decision-making of intelligent vehicles. However, these states cannot usually be measured directly need to obtained indirectly using additional estimation algorithms. Unfortunately, most the existing methods ignore effect data loss on accuracy. Furthermore, high-order filters have been proven that can significantly improve performance. Therefore, a second-order fault-tolerant extended Kalman filter (SOFTEKF) designed...

This study investigates resilient platoon control for constrained intelligent and connected vehicles (ICVs) against F-local Byzantine attacks. We introduce a distributed model-predictive platooning framework such ICVs. seamlessly integrates the predesigned optimal with model predictive (DMPC) optimization introduces unique attack detector to ensure reliability of transmitted information among vehicles. Notably, our strategy uses previously broadcasted specialized convex set, termed...

This survey paper explores the emergent domain of electric vertical takeoff and landing vehicles (eVTOLs), emphasizing critical role autonomous navigation capabilities essential for their effective integration operation in complex urban environments. Pioneering this review is introduction a novel six-level autonomy concept eVTOLs, categorizing them based on degree intelligence. The offers comprehensive state-of-the-art developments that together fortify functionality with special focus...

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions practical contexts remains a challenge. Departing from conventional PEMS that rely on historical or static traffic data, we introduce real-time traffic-aware for improved performance. To better understand interplay between host and its surrounding traffic, use Transformer network as...

In this paper, an asynchronous stochastic self-triggered distributed MPC (DMPC) control scheme is proposed for vehicular platoon systems under coupled state constraints and additive disturbance. considered systems, each vehicle broadcasts its predicted as beacon information to neighbouring vehicles through the ad-hoc network (VANET). To reduce communication burden in VANET, proactively determines next sampling time instant by solving DMPC problem at instant. The formulated utilizing local...

This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed address this problem. At triggering instants, each agent solves a local optimization based on system states predicted neighbors, determines its next instant, broadcasts state trajectory...

This paper addresses the consensus problem of linear discrete-time Multi-Agent Systems (MASs) under conditions input constraints and bounded time-varying communication delays. We propose a novel framework for such constrained MASs that incorporates an offline optimal design unconstrained systems to achieve convergence, along with online robust Distributed Model Predictive Control (DMPC) accommodate constraints. Our accomplishes near-optimal performance by minimizing divergence between DMPC...

Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme proposed estimate TRFC in the case of missing measurements. First, unscented Kalman filter (FTUKF) developed for estimating longitudinal and lateral forces condition sensor signal loss. Then, TRFCs are estimated separately with FTUKF based information. Next, an event-driven multi-model fusion method degree data loss designed perform...

This study addresses the challenges and solutions for achieving flexible resilient platooning in Intelligent Connected Vehicles (ICVs) under diverse constraints. We focus on enabling vehicles to freely join or leave platoon maintaining resilience against adversarial cyberattacks within network. propose a hierarchical distributed coordination framework that combines high-level event-driven cluster with lower-level decoupled longitudinal lateral control designs. Each normal vehicle updates its...

Precise path tracking and agilely avoiding obstacles are essential for the stability safety of autonomous driving. In this paper, we introduce a uniform safe control strategy that combines obstacle avoidance with via barrier function. Unlike conventional hierarchical collision methods, our approach employs an integral heuristic function addresses planning reference trajectory problems simultaneously. Via this, complex following problem is simplified into tractable yaw angle problem. We then...

Making predictions of future frames is a critical challenge in autonomous driving research. Most the existing methods for video prediction attempt to generate simple and fixed scenes. In this paper, we propose novel effective optical flow conditioned method task with an application complex urban contrast previous work, model only requires sequences training testing. Our uses rich spatial-temporal features sequences. The takes advantage motion information extracting from maps between neighbor...

The integrated motion control and intelligent energy management problems of hybrid electric vehicles (HEVs) can be divided into the sub-problems fuel consumption minimization, battery electrical thermal management, trajectory tracking. HEVs generally pose different requirements on underlying objectives concerning working scenarios (e.g., acceleration, cruise, brake cycles). In this paper, two priority-driven multi-objective model predictive (MoMPC) approaches are developed, which facilitate...

This article explores the challenge of achieving scalable and constrained consensus in general linear multiagent systems (MASs), where agents can occasionally join leave network. Two distributed model predictive control (DMPC)-based methods are developed to tackle scalability, performance, constraint challenges. The first approach uses an innovative online DMPC optimization that integrates with a predesigned protocol, ensuring satisfaction while consensus. second method leverages tracking...

Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most previous works focus on generating novel views certain objects with a fixed background. However, for some applications, such as virtual reality or robotic manipulations, large changes in background may occur due the egomotion camera. Generated large-scale environment be distorted if structure is not considered. In this work, we propose fully convolutional network, that can take advantage...