The increasing demand for road transportation is placing significant pressure on infrastructures, leading to congestions and, in some cases, a higher risk of road accidents. As a result, the introduction of Autonomous Vehicles (AVs) has become a central topic in transportation research. One of the foremost challenges associated with AV management is the control of intersections and roundabouts, where high levels of uncertainty and the need to adapt to rapidly evolving traffic conditions contribute to increased management complexity. This paper presents a systematic literature review aimed at investigating studies that address AVs at intersections, both signalized and unsignalized, as well as roundabouts. It examines control methods, driving strategies, and communication technologies applied in these scenarios, highlighting similarities and differences across various intersection types. Before a fully autonomous environment is achieved, hybrid traffic conditions, where AVs share road infrastructure with human-driven vehicles, will occur. Interestingly, only a small portion of the studies address such conditions. Nevertheless, managing AVs at intersections in hybrid traffic presents two additional challenges. First, human drivers' behavior is difficult to model and predict. Second, older human-driven vehicles are likely to lack communication capabilities. To address these challenges, specially designed control methodologies and driving strategies must be developed.
The advancement of intelligent vehicles represents a pivotal step in enhancing road safety, operational efficiency, and seamless communication within transport systems. However, the development of intelligent vehicle solutions poses significant challenges, requiring interdisciplinary collaboration across automotive engineering, telecommunications, road safety, and cybersecurity. Cooperative Intelligent Transport Systems (C-ITS) including intelligent vehicles must be conceptualized as complex Systems of Systems (SoS), which, while offering transformative potential, also introduce novel safety risks and failure modes. This survey article investigates the impact of dynamically changing vehicle parameters on the operational safety of intelligent vehicles and examines the dual role of wireless communication technologies in both enhancing and potentially compromising system safety.We highlight the critical vulnerabilities inherent in wireless communication, underscoring the need for robust cybersecurity measures to be integrated into the design and development of intelligent vehicle systems. Our survey provides a comprehensive analysis of the interconnected domains that form the foundation of intelligent vehicle ecosystems, emphasizing the intricate interplay between cyber and physical components. We stress the importance of aligning communication protocols, cybersecurity frameworks, and vehicle safety mechanisms to ensure reliable and resilient system performance. Furthermore, we identify and explore safety risks arising from both intentional and unintentional disruptions, offering valuable insights into existing research gaps and opportunities for future innovation.The objective of this article is to assist researchers and practitioners in navigating the multifaceted challenges associated with developing safe and secure intelligent vehicle systems.
This paper presents an optimization-driven framework for bidirectional charging and computation resource allocation in electric vehicle (EV) networks. The proposed framework leverages multi-tier time-of-use (TOU) pricing to dynamically regulate energy and computation interactions based on real-time demand and system conditions. Both moving and parked EVs act as energy and computation prosumers, enabling grid-to-vehicle (G2V) and vehicle-to-grid (V2G) energy exchange while simultaneously utilizing or contributing computational resources during both mobility and parking periods. In low-demand intervals, EVs perform cost-efficient charging and computation offloading to edge servers (ESs), whereas in high-demand intervals, they support the system by participating in V2G energy trading and contributing computational resources. This bidirectional interaction enhances resource utilization and improves overall economic efficiency for EV owners. To achieve system-wide cost minimization and revenue maximization, the framework incorporates a mobility-aware route selection mechanism, where EVs select optimal routes and resource interaction strategies based on real-time pricing and resource availability. Simulation results demonstrate that the proposed framework reduces total system cost by up to 27% and increases net profit by 25% on average compared to existing approaches. These results highlight the scalability and economic effectiveness of the proposed framework for future smart grid and intelligent transportation systems (ITS).
Effective power flow management in Electric Vehicles (EVs) that utilize multiple energy sources is essential for improving performance and reducing costs. A Hybrid Energy Storage System (HESS) that combines batteries and fuel cells leverages the benefits of both technologies to maximize efficiency and extend the vehicle's range. A Hybrid Storage Participation Index (HSPI) based power management strategy is introduced to facilitate effective power management operations. The HSPI provides an advanced power management approach that ensures optimal power distribution between the battery and the fuel cell. This strategy minimizes energy wastage, extends the vehicle's range, reduces wear and tear, and enhances overall performance, making hybrid systems more efficient and sustainable. The results show that the proposed approach reduces weight by decreasing the battery capacity from 40.5 kWh to 28 kWh without compromising functionality. The proposed strategy optimizes using HESS to ensure that each component operates within its ideal range, enhancing overall efficiency and performance. The major concerns of current EVs, such as limited range and long charging times, are also addressed. Fuel Cell Battery Hybrid Electric Vehicle (FCBHEV) technology offers a noticeable increase in range, providing a driving distance of 280 km, which enhances its utility value and justifies its price. As hydrogen is anticipated to play a significant role in future energy systems, FCBHEVs present a high-performing and cost-effective alternative to purely battery electric vehicles, serving as a viable option for environmentally friendly mobility.
This paper presents a novel prescribed-time path-following control framework for underactuated surface vehicles (USVs) operating in cluttered environments with limited sensing and communication resources. Unlike existing asymptotic or finite-time methods requiring velocity measurements, the proposed approach guarantees prescribed-time convergence in a velocity-free manner. A smooth finite-range potential function is integrated with a flexible prescribed-performance mechanism to ensure safe obstacle avoidance without violating tracking constraints. A nonlinear prescribed-time extended state observer (NPTESO) is developed to estimate unmeasured velocities and disturbances with convergence time independent of initial conditions. Furthermore, hybrid input quantization effects are explicitly addressed via adaptive compensation, ensuring robustness under limited velocity sensing and quantized actuation. Numerical simulations demonstrate fast convergence, improved tracking accuracy, and reduced control effort in obstacle-populated environments.
