Jams and phase transitions in heterogeneous lattice model integrating the continuous delayed feedback control to boycott cyber-attacks under connected autonomous and human driven vehicles environment

Chaos Solitons & Fractals, Год журнала: 2024, Номер 189, С. 115606 - 115606

Опубликована: Окт. 8, 2024

Язык: Английский

---

A Review on Security Challenges in V2X Communications Technology for VANETs

IEEE Access, Год журнала: 2025, Номер 13, С. 31069 - 31094

Опубликована: Янв. 1, 2025

Язык: Английский

---

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

Sustainability, Год журнала: 2024, Номер 16(19), С. 8336 - 8336

Опубликована: Сен. 25, 2024

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, radar that have been made available on CAVs are able to detect anomalies roads, including potholes, surface cracks, or roughness. also describes advanced data processing techniques of detected with sensors, machine learning algorithms, fusion, edge computing, which enhance accuracy reliability in assessment. Together, these support instant safety long-term maintenance cost reduction proactive strategies. Finally, this article provides a comprehensive review the state-of-the-art future directions monitoring systems traditional smart roads.

Язык: Английский

---

Strategic Participation of Electric Vehicles in Vehicle-to-Grid within a Microgrid System: A Decentralized Optimization Approach

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103144 - 103144

Опубликована: Окт. 1, 2024

Язык: Английский

---

Survey of Autonomous Vehicles’ Collision Avoidance Algorithms

Sensors, Год журнала: 2025, Номер 25(2), С. 395 - 395

Опубликована: Янв. 10, 2025

Since the field of autonomous vehicles is developing quickly, it becoming increasingly crucial for them to safely and effectively navigate their surroundings avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods precise obstacle identification, sophisticated path-planning that guarantee follow dependable safe paths, decision-making systems allow adaptable reactions a range driving situations. survey also emphasizes how Machine Learning can improve efficacy avoidance. Combined, these techniques necessary enhancing dependability safety systems, ultimately increasing public confidence game-changing technology.

Язык: Английский

---

Traffic and Vehicle Management in Roundabouts Through Systems Based on Dedicated Short-Range Communications and Visible Light Communications

Electronics, Год журнала: 2025, Номер 14(2), С. 317 - 317

Опубликована: Янв. 15, 2025

This study introduces an innovative system integrating visible light communication (VLC) and Dedicated Short-Range Communication (DSRC) technologies to enhance traffic flow safety in urban roundabouts. The approach optimizes vehicle coordination minimizes collision risks through advanced management algorithms infrastructure-to-vehicle communication. Simulations conducted on nine roundabouts Suceava municipality demonstrate that the hybrid VLC–DSRC improves efficiency by over 80% significantly reduces delays congestion. Results emphasize potential of this integrated solution modernizing systems while ensuring a high level road safety.

Язык: Английский

---

Adoption and Implementation Trends of Vehicle-to-Everything (V2X) Technologies: A Comprehensive Bibliometric Analysis

Studies in computational intelligence, Год журнала: 2025, Номер unknown, С. 329 - 344

Опубликована: Янв. 1, 2025

Язык: Английский

---

Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN

Sensors, Год журнала: 2025, Номер 25(3), С. 696 - 696

Опубликована: Янв. 24, 2025

Establishing a safe and stable routing path for source–destination pair is necessary regardless of the weather conditions. The reason this that vehicles can improve safety on road by exchanging messages updating each other current conditions both roads vehicles. This paper intends to solve problem when foggy make it difficult drivers travel, especially people encounter emergency situations have no option but drive in weather. Although literature offers few solutions problem, one has considered integrating software-defined networking into vehicular networks create an optimal path. Moreover, significance mention adverse travel following maintaining constant distance, which leads formation platoon. Considering this, we propose heterogeneous communication protocol network establish using platoons highways. Different cases were tested show how behave high connectivity sparsity, achieving maximum delivery ratio 99%, delay 2 ms, overhead 55%, acceptable number hops compared reference schemes.

Язык: Английский

---

Vspp: Verifiable, shareable, and privacy-preserving access control scheme for IoV

Pervasive and Mobile Computing, Год журнала: 2025, Номер unknown, С. 102014 - 102014

Опубликована: Янв. 1, 2025

Язык: Английский

---

VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous Vehicles

World Electric Vehicle Journal, Год журнала: 2025, Номер 16(2), С. 82 - 82

Опубликована: Фев. 6, 2025

Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, insufficient coverage critical edge cases multi-vehicle interactions. This paper introduces VRDeepSafety, a novel scalable VR simulation platform that overcomes these limitations integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, signal prioritization, enhance accident prediction mitigation. VRDeepSafety generates comprehensive featuring interactions, coordinated sensor visual, LiDAR, radar, streams. Evaluated with our deep-learning model, VRFormer, which uniquely fuses data using probabilistic Bayesian inference, as well hierarchical Kalman particle filter structure, achieved an 85% accuracy (APA) at 2 s horizon, 17% increase 3D object detection precision (mAP), 0.3 reduction response time, outperforming single-vehicle baseline. Furthermore, integration increased APA 15%. Extending the horizon 3–4 reduced 70%, highlighting trade-off between time accuracy. The high-fidelity integrated provide valuable rigorous tool developing safer more responsive AVs.

Язык: Английский

---