Intelligent Vehicular Systems Research Articles

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Video Blockchain: A Decentralized Approach for Secure and Sustainable Networks with Distributed Video Footage from Vehicle-Mounted Cameras in Smart Cities

In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance data for observation, thereby enhancing overall situational awareness. We utilize the decentralized nature of blockchain to implement a vehicle-based surveillance system across a smart city. To ensure reliability, the integration of two cryptographic functions, hashing and signing, with the blockchain is employed. This integration ensures secure and tamper-proof solutions for the existing intelligent surveillance system. In this paper, our primary focus is on combining blockchain technology to achieve sustainable and robust smart solutions for intelligent vehicular distributed video networks while eliminating the need for third-party intermediaries. Through extensive experiments and analysis, we demonstrate the effectiveness and feasibility of our proposed video blockchain approach. The results indicate that this innovative framework provides enhanced security, privacy, and scalability for intelligent vehicular distributed networks in smart cities, paving the way for a connected and efficient urban environment.

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems.

A Parallel Intelligence-Driven Resource Scheduling Scheme for Digital Twins-Based Intelligent Vehicular Systems

Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

Intelligent vehicular systems and smart city applications are the fastest growing Internet-of-Things (IoT) implementations at a compound annual growth rate of 30%. In view of the recent advances in IoT devices and the emerging new breed of IoT applications driven by artificial intelligence (AI), the fog radio access network (F-RAN) has been recently introduced for the fifth-generation (5G) wireless communications to overcome the latency limitations of cloud-RAN (C-RAN). We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments. We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) to efficiently utilize the limited resources at the network edge. For each service request in a cluster, the EC decides which FN to execute the task, i.e., locally serve the request at the edge, or to reject the task and refer it to the cloud. We formulate the problem as infinite-horizon Markov decision process (MDP) and propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy. The performance of the proposed DRL-based slicing method is evaluated by comparing it with other slicing approaches in dynamic environments and for different scenarios of design objectives. Comprehensive simulation results corroborate that the proposed DRL-based EC quickly learns the optimal policy through interaction with the environment, which enables adaptive and automated network slicing for efficient resource allocation in dynamic vehicular and smart city environments.

A Drone-Aided Blockchain-Based Smart Vehicular Network

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.

Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving

Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.

IEEE Access Special Section Editorial: Communication, Control, and Computation Issues in Heterogeneous Vehicular Networks

The automotive industry has recently shifted from developing advanced vehicles to concentrating on safety and comfort, which stimulates the development of new intelligent vehicles with advanced driver assistance systems and autonomous driving [item 1) in the Appendix]. However, the current technology is still far from meeting the practical requirements of autonomous driving. The existing control approaches are mainly based on expensive sensors and cameras equipped in vehicles. Thanks to the rapid development of advanced communication and computation techniques, vehicles easily communicate and cooperate to each other [item 2) in the Appendix]. Meanwhile, the control of vehicles become more reliable and cost-efficient with the communication between vehicles [item 3) in the Appendix]. Therefore, communication, computing and control (3C) will play a critical role in the development of next generation intelligent vehicular systems [item 4) in the Appendix].

Ingenious vehicular system to evade collisions

Driver’s response is indispensable in the aversion of collisions and in stringent circumstances. WiB-IVS (Wireless Broadcast Intelligent Vehicular System) is a proposed design for Intelligent Vehicular Systems enabling Vehicle to Infrastructure (V2I) communication for effectuating an interconnected network of vehicles for easier and faster information sharing in order to prevent collisions. The system centralizes on driver’s response and thus aims to bring extrinsic information to the driver, stimulating situational awareness. An indication system fulfils this purpose and helps the driver respond to the scenario ahead. Information transmission is facilitated by communication between vehicle and broadcasting station based on User Datagram Protocol (UDP). The design approach caters to the problems of the collision at sharp or blind turns, offsite intersections and those occurring due to low visibility in extreme weather conditions such as fog. A pilot model design to test for the effectiveness of the proposed system architecture is also put forth in this paper. The pilot test results confirm the system’s rapid response and its key potential for the future.

A Low-cost, Installable Intelligent Helper Module for Automobiles

Ground vehicles are being intelligent day by day. But in today's market the price of an autonomous, which is still in its experimental phase, or a semi-autonomous intelligent vehicular system is too high. Apart from the usage of expensive sensors, previously proposed vehicular networking systems for intelligent vehicles need network provider and communication towers like cellular communication networks which is both time consuming and costly to implement. Moreover, the mechatronics part, which controls the vehicle is very much different from a traditional intra-vehicular mechanism making it very difficult to convert a regular vehicle, e.g. a car, into an intelligent one. Due to these facts, these overpriced systems are not suitable for the underdeveloped countries where, these are somewhat more needed. In this paper, I have developed a very cheap intelligent system for providing guidance to the driver while driving. This module will not be connected to the hardware directly, which made it an easy-to-install ―helper module‖ for any kind of ground vehicles. However, this module will reduce the accident rate by collecting and analyzing surrounding data, communicating with nearby vehicles (peer to peer) while overtaking and providing continuous guidelines for safe driving. Unlike other systems this could easily be deployed in the underdeveloped countries because of its ultra-low cost.

Modeling the Lane Contention Algorithm for Control in Intelligent Vehicular Systems

Delegating the responsibility of safe driving on an autonomous vehicle needs the autonomous system to be extremely robust. In an attempt to design a robust guidance system for the autonomous vehicle we decided to go refer the most complex system designed which is “humans”. In humans we peculiarly observe that there exists an array of senses we possess that provide the required robustness and efficiency in chores that we perform. It clearly displays that in human beings the various senses we possess precisely complement each other to support a process. On this basis we propose an introduction to optimization of lane detection and tracking. Also the scalability of computation for smart vehicles such as auto park assist, cruise mode or auto lane keep assist continue to grow for driverless manoeuvre thus removing the possibility of manual or judgmental error in driving. We model a ground vehicle in a turning manoeuvre, such as a step lane-change where modeling process follows that of a vehicle undergoing translational as well as rotational motion. We shall be using this to design the control algorithm for executing a lane change maneuver under a safety set of rules using Dynamic Matrix Control which is a subset of the Model Predictive Control. This requires that we transform the vehicle lateral variable into the fixed coordinates. The application depends on recursively obtained sensor readings in a feedback loop mode for processing and deploying a corrective action tot exploits non-linear functions of the state and finds control inputs such as state of system, position, acceleration, peer movement to recursively estimate and improve the quality of resulting estimation for collision avoidance and target localization.

An Agentmining Framework for Intelligent Vehicular System

Abstract During the last decades, the evolution of wireless technologies has allowed researches to design communication systems where vehicles participate in the communication networks. Vehicular Ad-Hoc Network is an important component of Intelligent Transportation System, which has a future potential in terms of a rich set of applications that it can provide to its commuters. Various approaches has been proposed in recent years for the design of intelligent VANET but most of the proposed works are limited to provide a complete road information to vehicles. For this reason, to develop a sophisticated framework which should disseminate up-to-the minute information about existing or impending traffic-related events has gained recent attention. The proposed framework exploit concepts data mining, machine learning and agent technology to model intelligent vehicular system contributes safer and more efficient roads by providing timely information and decision making capability to vehicle driver. Our work shows the techniques and methods resulting from the field of agent mining applied to many aspects including intelligent traffic control, dynamic routing, congestion management, decision support, modeling and simulation. The current research related to design a shell to control and monitor vehicles by sending intelligent traffic report and warning messages. Simulation results shows communication among agents in a collaborative environment.