Spoofer Detection Framework for V2X Systems via Tensor-Based DoA Estimation and YOLO-Based Object Detection

5G is the new wireless technology. In vehicles, it is used to support the network associates for sensor on the provide and roads V2X service to drivers and pedestrians. The 5G V2X (vehicle to everything) communication is a benefit to us, for low latency, high reliability and large communication coverage. In this novel, we survey how the 5G technology is used in autonomous vehicles. Mainly we review the architecture of 5G, V2X communication and its uses. And then we discuss mobile network evolution. we also provide the advantages of 5G technology in autonomous vehicles and 5G design principles. This paper also outlines how 5G technology is used in Artificial Intelligence (AI). Finally, for a future generation, there is predictable to pay more attentions and effort into developing 5G V2X services.

Road safety applications envisaged for vehicular ad hoc networks (VANETs) depend largely on the exchange of messages to deliver information to concerned vehicles. Safety applications as well as inherent VANET characteristics make data dissemination an essential service and a challenging task. We are developing a decentralized efficient solution for broadcast data dissemination through two game-theoretical mechanisms. Besides, VANETs can also include autonomous vehicles (AVs). AVs might represent a revolutionary new paradigm that can be a reality in our cities in the next few years. AVs do not need a driver to work; instead, they should copy a proper human behavior to adapt the driving according to the current circumstances, such as speed limit, pedestrian crossing street or wheather conditions. We will develop an AV software module including artificial intelligence (AI) techniques so that AVs can interact with the dynamic scenario throughout time. Finally, we also will include electrical vehicles (EV) in the VANET, so that special services such as finding and reserving an EV charging station place will be welcome. In addition, we are developing a multimetric geographic routing protocol for VANETs to transmit H.265 video (traffic accident, traffic state, commercial….) over VANETs.

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management.

The implementation of Artificial Intelligence (AI) into transportation systems signifies a fundamental change that holds the potential to improve overall sustainability, safety, and efficiency. However, this transformation is not without its challenges, particularly concerning human factors and ergonomics. Firstly, the ergonomic integration of AI systems in transportation requires careful consideration of human-AI interaction. Unlike traditional systems, AI-driven technologies often involve complex algorithms that are not inherently intuitive to human operators. This complexity can lead to a disconnect between how the system operates and how users understand it. For instance, in autonomous vehicles, drivers may be required to take control in certain scenarios. The ergonomic challenge here lies in ensuring that the transition between AI and human control is seamless and that the user is adequately informed and prepared to take over. Another significant challenge is addressing the variability in human behavior and expectations. AI systems, designed based on standard models of behavior, may not adequately account for the wide range of human responses and interactions. This gap can lead to scenarios where AI systems behave in unexpected or counterintuitive ways to human users, potentially causing confusion and reducing the overall efficiency and safety of the transportation system. CAE Advanced Air Mobility (AAM) research case study focuses on the implementation and certification of AAM in the FAA/EASA environment.The reliability and trustworthiness of AI systems also pose a major ergonomic challenge. To fully integrate AI into transportation, users must trust these systems. This trust depends on the transparency and explainability of AI decision-making processes. Nevertheless, numerous AI algorithms, especially those rooted in deep learning, are frequently regarded as 'black boxes' owing to their intricacy and absence of interpretability. Developing AI systems that are both advanced and transparent is a significant hurdle that needs to be addressed.Data privacy and security are also paramount concerns. AI systems in transportation rely heavily on data, including personal and sensitive information. Ensuring the privacy and security of this data while utilizing it for AI processes is a complex challenge, requiring robust encryption methods and strict data handling policies. Moreover, the ergonomic aspect involves designing systems that not only protect data but also respect user privacy preferences and norms.The implementation of AI in transportation is a multifaceted challenge, requiring a holistic approach that considers human factors and ergonomics. As we move towards more AI-integrated transportation systems, it is essential to address these challenges through interdisciplinary research, collaboration, and a user-centered design approach.

The Internet of Things (IoT) has major implications in the transportation industry. Autonomous Vehicles (AVs) aim at improving day-to-day activities such as delivering packages, improving traffic, and the transportations of goods. AVs are not limited to ground vehicles but also include aerial and sea vehicles with a wide range of applications. The IoT systems consisting of a collection of AVs have come to be known as the Internet of Transportation systems. While such IoT systems manage large quantities of sensor data, much of the data is also sent to a cloud for offline analysis. While there is great potential in AVs and the improvements it can make to the transportation industry, security and privacy concerns pose new challenges that need to be addressed as we move forward. In addition, Artificial Intelligence techniques are also becoming crucial for such IoT systems to be able to intelligently manage the AVs. This paper discusses AI and security for cloud-based Internet of Transportation Systems.

The artificial intelligence (AI) is creating the great impacts and revolution in the technological world, education world, industry world, and business world. The concepts in artificial intelligence are improving themselves day by day, and it is becoming a part of human’s everyday activities. It plays a major role in the human life, and the situation becomes the people cannot live without this AI. In this book chapter, the categorization of artificial intelligence based on the autonomous vehicles and its other applications is to be discussed in brief. The artificial intelligence technique is based on the artificial neural network (ANN), machine learning (ML), deep neural network (DNN), recurrent neural network (RNN), and convolution neural network (CNN). To implement these types of neural network, the system should use the programming languages like MATLAB programming, Python programming, R programming, etc.

In recent years, object detection has become a crucial component in various applications such as surveillance, autonomous vehicles, medical imaging, and smart cities. This project focuses on object detection using state-of-the-art algorithms YOLOv8 and YOLOv9. Leveraging the power of deep learning, YOLO (You Only Look Once) models are renowned for their speed and accuracy in identifying multiple objects within images and video streams. The proposed system allows users to upload images or videos and utilizes the webcam for live object detection. The frontend is built using Streamlit, ensuring an intuitive and interactive user interface, while the backend is implemented in Python and executed on Google Colab to leverage GPU acceleration. The system processes the input through pre-trained YOLO models, identifies objects, and displays them with bounding boxes and confidence scores. This project demonstrates how advanced object detection algorithms can be integrated into user-friendly applications, promoting accessibility and real-world deployment. Additionally, it serves as a foundation for further enhancements such as object tracking, anomaly detection, and integration with IoT systems for smarter decision-making in dynamic environments.

<p><em>This article examines the challenges of autonomous motor vehicles for Queensland road and criminal laws</em>. <em>Autonomous vehicles refer to motor vehicles where driver decision making has been augmented or replaced by intelligent systems. Proponents of autonomous vehicles argue that they will virtually eliminate road accidents, boost productivity and provide significant environmental benefits. The key issue is that autonomous vehicles challenge the notion of human responsibility which lies at the core of Queensland’s road and criminal laws. The road rules are predicated on a driver in control of the vehicle, the intoxication regime is concerned with the person in charge of the vehicle and the dangerous driving offences require a person who operates a vehicle. Notwithstanding this challenge, it can be seen that much of Queensland’s law is adaptable to autonomous vehicles. However, there are some identifiable anomalies that require reform.</em></p>

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

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

This paper examines the application of artificial intelligence (AI) in desalination. The study explored AI techniques, including machine learning, neural networks, and genetic algorithms, to enhance system efficiency and reduce energy costs. Case studies assessed the impact of AI on desalination systems, including those powered by renewable energy sources. Key findings revealed that AI-driven systems improved water quality, reduced energy consumption by up to 50%, and enabled predictive maintenance, minimizing downtime. Challenges in integrating AI with renewable energy-powered water treatment and desalination systems were addressed by analyzing hybrid setups combining solar, wind, and battery storage with reverse osmosis (RO) and multi-stage flash (MSF) technologies. These systems demonstrated critical improvements in energy efficiency, making desalination more viable for arid and remote areas. Fault detection algorithms and predictive maintenance emerged as pivotal AI applications, significantly reducing maintenance costs and enhancing reliability. This study was concluded by identifying challenges such as the intermittent nature of renewable energy and the complexities of designing scalable hybrid AI systems. Future research should further refine AI techniques, advancing sustainable and energy-efficient water treatment solutions.

Due to the increasing rate of population in the world, the demand for food has also risen a lot day by day. There are several solutions to deal with this issue, and one of them is that maximizing the yield for agricultural products. It can be achieved by designing such robots that can analyze the field for yield estimation. While there are a lot of robotics solutions that leverage the camera or light detection and ranging (LiDAR) sensor data, almost none of them compared these two methods in the scope of agricultural robotics. In this study, we have simulated different kinds of object detection methods and we try to find which one is more suitable for specific tasks such as color detection and size distinction in the agrarian industry. The main contribution is that the advantages of two sensors are utilized, with the goal of agricultural robotics. Results show that while camera-based object detection is better for color-oriented operations, LiDAR-based object detection is preferable for the operations where size distinction is important. We can achieve over 95% accuracy rate on average for both object detection methods. KeywordsRoboticsVisionLiDARObject detectionAgricultureYield estimation

Object detection is a central theme for many Artificial Intelligence (AI) applications such as autonomous vehicles, surveillance etc. The algorithms providing this capability rely on training data being available in corresponding sensor mode. Having co-located data from multiple sensor modes enhances the detection confidence, but the availability of training data in desired sensor mode is not always readily available, which slows down progress. In this paper, we investigate the ability to translate images from one sensor mode to another, on a single fixed camera dataset, using conditional Generative Adversarial Network (cGAN). Specifically, images are transferred from Electro-Optical (EO) to Infra-Red (IR) images and vice-versa using cGAN models, which are generative models that learn the data distribution in a minimax game setting. To investigate the usability of such transferred images, we apply object detection algorithm on ground truth and transferred images and compare their performance. The results indicate that transferred images match closely to real images and object detection has good performance on transferred images, especially when the object size is large.

A multistage space---time equalizer (STE) is proposed to blindly separate signals received by an antenna array from different sources simultaneously. Neither the direction of arrival (DOA) nor a training sequence is assumed to be available at the receiver. The only assumption is that the transmitted signals satisfy the constant modulus property, which is valid for many modulation schemes, and can be exploited by the multi-modulus algorithm. Each stage consists of an adaptive beamformer, a DOA estimator and an equalizer. Its function is to jointly combat multi-user interference and the effect of fading channels between sources and the antenna. An adaptive version of the basic structure of generalized sidelobe canceller (GSC), called adaptive GSC, is presented which can track a user and strongly attenuate other users with different DOAs. The possibly time-varying DOA for each user is estimated using the phase shift between the outputs of two subarray beamformers at each stage. The estimated DOAs are used to improve multi-user interference rejection and to compute the input to the next stage. In order to significantly alleviate inter-stage error propagation and provide a fast convergence, a mean-square-error sorting algorithm is proposed which assigns detected sources to different stages according to the reconstruction error. Further, to speed up the convergence, a simple, yet efficient, DOA estimation algorithm is proposed which can provide good initial DOAs for the multistage STE. Simulation results illustrate the performance of the proposed STE and show that it can deal effectively with changing DOAs and time-varying channels.