Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous development. This paper proposes deep reinforcement learning (DRL)-based strategy an emergency situation. Three influencing factors, including efficiency, accuracy passengers' comfort, are fully considered satisfied by proposed strategy. First, lane-changing process analyzed detail, which include critical factors design Second, we...

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging cooperation have improved varying degrees, but they are still full challenges in practical Especially for cooperative positioning, addition performance methods, cooperators may provide false data due attacks or selfishness, which can seriously affect accuracy. By fully exploiting...

The accuracy of the ML model is essential for further development AI-enabled CAVs. With increasing complexity on-board sensor systems, large amount raw data available learning can however cause big communication burdens and security issues. To alleviate cost yet improve machine with preserved privacy an important issue to address in In this article, we survey existing literature toward efficient secured a dynamic wireless environment. particular, BCL framework CAVs presented. enables...

Edge caching is being explored as a promising technology to alleviate the network burden of cellular networks by separating computing functionalities away from base stations. However, service capability existing scheme limited fixed edge infrastructure when facing uncertainties users requests and locations. The vehicular caching, which uses moving vehicles cache carriers, regarded an efficient method solve above problem. This paper studies effectiveness in content centric developing...

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of Collision Avoidance (CA) system used solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, can make CA more accurate effective, thereby improving driving safety. In addition, assistance Artificial Intelligence (AI) technology adapt environment facilitate fast decisions. Considering current lack a...

Currently, the complex traffic environment challenges fast and accurate response of a connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to collaborate share knowledge. To remedy that, this paper proposes selective federated reinforcement learning (SFRL) strategy achieve online knowledge aggregation improve accuracy environmental adaptability driving model. First, we propose framework that allows participants use other make corresponding actions, thereby...

A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing data between DT and user (VUE). In this paper, we consider heterogeneous networks (HetVNets) in which a VUE can connect to network through different networks. The HetVNets improve efficiency communication by providing seamless connections. However, uneven distribution VUEs dynamics make environment more complex. Therefore, propose selection algorithm for synchronization DTs HetVNets, where behaviour...

Inappropriate lane following and changing behaviors of connected autonomous vehicles (CAVs) can result in accidents, such as rear-end collision side collision. To remedy that, the use deep reinforcement learning (DRL) for driving decisions is currently a widely used promising solution. In this case, accuracy effectiveness machine (ML) model quite essential artificial intelligence (AI)-enabled CAVs. This article proposes blockchain-based collective (BCL) framework lane-changing systems. Four...

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, adaptive ability of connected autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration integrates learning capabilities machine interpretation expert systems (ESs) a unified architecture form hybrid guidance system, which not only solves "bottleneck" knowledge acquisition during construction but also "black box" phenomenon decision-making process. First, an...

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces system reliability problem while decentralized one cannot satisfy management flexibility. In addition, when privacy preservation scheme that prevents information leakage encounters user selection desires detailed participants, how balance this...

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as typical architecture IoT, can efficiently facilitate various digital intelligent IoT applications. In the DSNs, due to asymmetry in coverage focus diversity detection angle directional sensors, how enhance performance with limited sensors becomes new challenge. To this end, we develop novel redeployment scheme based on minimum...

While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches system design. This led the emergence of novel human–machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power create collaborative framework that meets demands large-scale, complex tasks enables symbiosis. Human...

LLM-based autonomous agents often fail to execute complex web tasks that require dynamic interaction, largely due the inherent uncertainty and complexity of these environments. Existing typically rely on rigid, expert-designed policies specific certain states actions, lacking flexibility generalizability needed adapt unseen tasks. In contrast, humans excel by exploring unknowns, continuously adapting strategies based new observations, resolving ambiguities through exploration. To emulate...

Magnesium phosphate cement (MPC) continues to gain attention in the field of biomedicine. However, its suboptimal mechanical strength and weak biological activity hinder wider clinical application. Given excellent characteristics bioglass fiber (BGF), In this study, magnesium bone (BMPC) containing MPC BGF with different concentrations (0%, 10%, 20%) are fabricated. Called (MPC, 10BMPC, 20BMPC) respectively. BGF-induced strengthening is verified through physical chemical performance tests....

The integration of mathematical methods with artificial intelligence (AI) and mobile edge computing (MEC) has emerged as a promising research direction to address the growing complexity intelligent distributed systems. To chart landscape this interdisciplinary field, we first examine recent surveys that primarily focus on architectural designs, learning paradigms, system-level deployments in AI. However, these studies largely overlook theoretical foundations essential for ensuring...

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm avoidance. Three key issues are addressed by applying neural network approach, including noises positioning, inaccurate risk assessment, enhanced comfort level of passengers. First, to filter wireless vehicular communications leveraged; accurate relative...

This article explores the optimal offloading strategy in Internet of Vehicles (IoVs), which is challenged by three issues. First, resources edge servers are shared multiple vehicles, leading to random changes over time. Second, as a vehicle would drive across consecutive servers, needs consider overall along trip. Third, at each vehicle, computing tasks arrive continuously when driving. dictates not only current status but also futuristic tasks. To tackle these issues, we propose digital...

Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload tasks. However, due the high mobility of vehicles, dynamics wireless conditions, uncertainty arrival tasks, it is difficult a single vehicle determine optimal offloading strategy. In this paper, we propose Digital Twin (DT) empowered task framework Internet Vehicles. As software agent residing in cloud, DT can obtain both global network information by using communications...