Inter-vehicular Communication Research Articles

Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

Improved S-AF and S-DF Relaying Schemes Using Machine Learning Based Power Allocation Over Cascaded Rayleigh Fading Channels

We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n, for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.

Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control

The design of cooperative adaptive cruise control is critical in mixed traffic flow, where connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist. Compared with pure CAVs, the major challenge is how to handle the prediction uncertainty of HDVs, which can cause significant state deviation of CAVs from planned trajectories. In most existing studies, model predictive control (MPC) is utilized to replan CAVs' trajectories to mitigate the deviation at each time step. However, as the replan process is usually conducted by solving an optimization problem with information through inter-vehicular communication, MPC methods suffer from heavy computational and communicational burdens. To address this limitation, a robust platoon control framework is proposed based on tube MPC in this paper. The prediction uncertainty is dynamically mitigated by the feedback control and restricted inside a set with a high probability. When the uncertainty exceeds the set or additional external disturbance emerges, the feedforward control is triggered to plan a ``tube'' (a sequence of the set), which can bound CAVs' actual trajectories. As the replan process is usually not required, the proposed method is much more efficient regarding computation and communication, compared with the MPC method. Comprehensive simulations are provided to validate the effectiveness of the proposed framework.

Bluetooth 5 performance analysis for inter-vehicular communications

Previous work has demonstrated the feasibility of Bluetooth Low Energy (BLE) as an alternative technology for data transfers in inter-vehicular communication (IVC) scenarios. Bluetooth 5.x core specifications enhance the trade-off between energy requirements, communication range and flexibility. In this paper, we aim to analyse the potential of Bluetooth 5 features for VANET applications, proposing a connectionless communication system. By means of field experiments, we evaluate long range and 2 × speed features, defining a set of communication scenarios. This allows us to test both Bluetooth 5.x range and application throughput. The evaluation includes experiments of V2I communications carried out under real highway traffic conditions. The experiments conducted demonstrate that communication ranges up to 300 m may be achieved depending on the communications scenario. The results also show how throughput degrades as the distance between devices increases. The results obtained are used to discuss future work, aimed at deeper analysing Bluetooth 5 features for VANET applications, completing the development of our prototype and evaluating VANET connectionless communications with the features included in the latest Bluetooth 5.2 specification.

Provable Secure Identity-Based Anonymous and Privacy-Preserving Inter-Vehicular Authentication Protocol for VANETS Using PUF

The concept of the smart city and the extensive use of vehicles are evolving progressively. This evolution makes vehicular ad-hoc network (VANET) a widely used network for inter-vehicular communication to get information about speed, traffic congestion, road condition, and vehicle location. This information is obtained from a public network, which is prone to various attacks. Generally speaking, data security in VANET is one of the most crucial tasks. We found many authentication protocols for VANETs; however, most of these protocols are insecure due to impersonation, replay, denial of service attacks, etc. Moreover, the existing protocols utilized heavy computation and communication costs, making them incompatible for resource-constrained VANET applications. This article presents a lightweight privacy-preserving authentication protocol for VANETs to offer a secure and efficient data transmission mechanism over a public channel. We analyze that our protocol is lightweight, safe, and scalable compared to related protocols. The communication and computation cost of our protocol is less concerning the state-of-the-art protocols. Hence, it is overt from the observed facts that our protocol is more reliable, convenient, and practical for secure data transmission in VANETs.

Multihop V2U Path Availability Analysis in UAV-Assisted Vehicular Networks

The work presented in this article aims at improving the ground vehicle connectivity in the context of an intermittent vehicle-to-UAV (V2U) communication scenario where vehicles opportunistically establish time-limited connectivity with passing by unmanned aerial vehicles (UAVs) serving as flying base stations responsible for routing incoming vehicle data over backbone networks and/or the Internet. As opposed to existing work in the literature where vehicles are only allowed to establish direct connectivity with in-range UAVs, this work aims at also exploiting the possible formation of vehicular clusters and, hence, the feasibility of intervehicular communications to establish multihop paths connecting source vehicles to destination UAVs. A mathematical model is presented for the purpose of capturing the nodal (i.e., vehicles and UAVs) mobility dynamics and derive an expression for the overall V2U connectivity probability as well as the overall average vehicle connection time. Extensive simulations are conducted in order to adduce the validity and accuracy of the proposed model and provide further insights into the connectivity sensibility to fundamental system parameters.

Methods of Formation Control for a Group of Mobile Robots (a Review)

The multi-robot formation control is an essential issue in robotics. This review focuses on important lines of research on current contr ol is s ues and s tr ateg ies on a g r oup of unmanned autonomous vehicles/r obots for mation. In this paper, we pr ovide a br ief description of each method characterizing its key benefits and drawbacks. A multilayered classification of both centralized and decentralized formation control methods is proposed. We consider the classification of robot communication topologies in terms of centralized control. Seminal works dedicated to the practical application of centralized approach are briefly discussed. The majority of centralized methodsare represented by a " leader-follower" approach, taking into account the robot’s dynamics models. Furthermore, the most common models of vehicle dynamics are mentioned. In the framework of decentralized approach, behaviour-based algorithms, as well as swarm algorithms, are discussed. Then, we present an outlook of both centralized and decentralized virtual structure methods used in robot formation control. The described modifications of these methods allow tracing the evolution of the virtual structure approach to hybrid algorithms used for cooperative movement of a group of robots. This paper deals with formation control approach considering communication delays and low carrying capacity in an inter-vehicular communication network as very few works discussed this issue despite its relevance. We pointed out the main development trends of formation control approaches. The most effective approach is the integration of various methods of the formation control so that their disadvantages are nullified. As the same time, the most common disadvantage of discussed formation control methods is their weak conceptual framework in terms of kinematic and dynamic constraints of robots.

MADRaS : Multi Agent Driving Simulator

Autonomous driving has emerged as one of the most active areas of research as it has the promise of making transportation safer and more efficient than ever before. Most real-world autonomous driving pipelines perform perception, motion planning and action in a loop. In this work we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. Given a start and a goal state, the task of motion planning is to solve for a sequence of position, orientation and speed values in order to navigate between the states while adhering to safety constraints. These constraints often involve the behaviors of other agents in the environment. MADRaS provides a platform for constructing a wide variety of highway and track driving scenarios where multiple driving agents can be trained for motion planning tasks using reinforcement learning and other machine learning algorithms. MADRaS is built on TORCS, an open-source car-racing simulator. TORCS offers a variety of cars with different dynamic properties and driving tracks with different geometries and surface. MADRaS inherits these functionalities from TORCS and introduces support for multi-agent training, inter-vehicular communication, noisy observations, stochastic actions, and custom traffic cars whose behaviors can be programmed to simulate challenging traffic conditions encountered in the real world. MADRaS can be used to create driving tasks whose complexities can be tuned along eight axes in well-defined steps. This makes it particularly suited for curriculum and continual learning. MADRaS is lightweight and it provides a convenient OpenAI Gym interface for independent control of each car. Apart from the primitive steering-acceleration-brake control mode of TORCS, MADRaS offers a hierarchical track-position – speed control mode that can potentially be used to achieve better generalization. MADRaS uses a UDP based client server model where the simulation engine is the server and each client is a driving agent. MADRaS uses multiprocessing to run each agent as a parallel process for efficiency and integrates well with popular reinforcement learning libraries like RLLib. We show experiments on single and multi-agent reinforcement learning with and without curriculum

A Selective reliable communication to reduce broadcasting for cluster based VANET

Vehicular Ad hoc Network (VANET) is one of the subset of Mobile Ad hoc Network (MANET) and it is a self-organised system with a group of vehicles, which are capable of short-range communication using On Board Unit (OBU). This unit is comprised with the vehicles that are possible to communicate with the nearby vehicles. VANETs rely on heavy broadcast transmission due to sharing data (messages) between the nearby vehicles about the traffic, collision and so on. This redundant information spoils the nature of VANET that affects the inter-vehicular communication, rebroadcasting and information on collision. This message transmission increases largely as the number of vehicles increases. This problem is typically named as broadcast storm and it is relatively reduced through the proposed Selective Reliable Communication (SRC) Protocol. Through a reliable communication, packets are retransmitted to reduce a number of transmission in the network within the acceptable level of QoS. The proposed SRC protocol automatically detect the vehicle clusters as “Zone of Interest”. Generally, the proposed protocol forwards the packets to the cluster-heads and the cluster-head forwards the packets to the cluster-members. The proposed protocol outperforms than the existing protocols in terms of Throughput, Packet Delivery Ratio (PDR) and Average delay.

Simulation-Based Evaluation of Cooperative Maneuver Coordination and Its Impact on Traffic Quality

<div class="section abstract"><div class="htmlview paragraph">Cooperative Maneuver Coordination (CMC) is one of the cornerstone technologies on the way to automated and connected driving of the future. The goal of this technology is to increase traffic safety and efficiency, by solving potential conflict situations on the road, based on cooperative maneuver planning, negotiation and decision-making, with the aid of inter-vehicular communication. This innovative topic represents a wide area for research projects, e.g., such as German funded project IMAGinE. A variety of different approaches for CMC has already been implemented and evaluated in the related work of the recent past. However, due to the high system complexity in general and vast testing effort in particular of these solutions, their actual impact on the traffic quality has not yet been extensively addressed, and therefore must be further investigated. In order to fulfill this task, one needs a methodology with appropriate evaluation metrics alongside with a suitable testing environment, in order to obtain comprehensive results. The scientific contribution of the work at hand involves a simulation-based evaluation methodology for CMC. For this, we will propose a novel CMC algorithm, which is built upon a direct trajectory exchange via inter-vehicular communication and a decentralized decision-making process, suitable for both manually operated as well as autonomous vehicles. In order to examine this algorithm, we will introduce a co-simulation environment with automatic scenario generation and multi-instances capability, which consists of a coupled simulation of traffic flow and vehicle dynamics. Eventually, we will present a set of metrics, which we determined in order to evaluate effectiveness and efficiency of our CMC algorithm, considering its impact on various aspects of the traffic quality.</div></div>

UFMC Waveform and Multiple-Access Techniques for 5G RadCom

In recent years, multiple functions traditionally realized by hardware components have been replaced by digital-signal processing, making radar and wireless communication technologies more similar. A joint radar and communication system, referred to as a RadCom system, was proposed to overcome the drawbacks of the conventional existent radar techniques while using the same system for intervehicular communication. Consequently, this system enhances used spectral resources. Conventional orthogonal frequency division multiplexing (OFDM) was proposed as a RadCom waveform. However, due to OFDM’s multiple shortcomings, we propose universal filtered multicarrier (UFMC), a new 5G waveform candidate, as a RadCom waveform that offers a good trade-off between performance and complexity. In addition to that, we propose multicarrier code division multiple access (MC-CDMA) as a multiple-access (MA) technique that can offer great performance in terms of multiuser detection and power efficiency. Moreover, we study how UFMC filter length and MC-CDMA spreading sequences can impact overall performance on both radar and communication separately under a multipath channel. Analysis of the bit error rate (BER) of the UFMC waveform was performed in order to confirm the experiment results.

Link Setup Time Reduction by FILS on IEEE 802.11-Based Inter-Vehicular Communications

This paper reports the performance of link setup time reduction outlined by IEEE 802.11ai, which is also known as Fast Initial Link Setup (FILS), in real intermittent inter-vehicular communications. Fast link establishment is a significant concern in communications between mobile devices with high mobility, such as passing vehicles on roads, because short link setup times enable vehicles to transfer larger amounts of application data. However, secure links are also important because they prevent both unauthorized access by unauthenticated users and misinformation circulation by malicious persons. Conventional security protocols such as IEEE 1609.2 and IEEE 802.11i/IEEE 802.1X, which is also known as theWi-Fi Protected Access 2 Extensible Authentication Protocol (WPA2-EAP), archive user authentication in vehicular networks but often take several seconds to establish a secure link due to numerous frame exchanges. In contrast, FILS is designed to establish a secure WPA2-EAP link in 100 ms using cached authentication information. However, since the effectiveness of FILS in real vehicular networks has not yet been reported, this paper describes experiments that clarify its setup time reduction abilities in 2.4 GHz IEEE 802.11n-based inter-vehicular communications by measuring the initial link setup times between two passing vehicles in a real environment. The results show that FILS significantly reduces the initial link setup times between the passing vehicles to around 150 ms and increases the size of application data transferred between vehicles. Additionally, it is demonstrated that FILS always establishes a secure link, while Protected EAP (PEAP) sometimes fails. Finally, in communications between vehicles passing each other at the relative speed of 80 km/h, we confirm that the FILS link setup time reduction effectively increases transferrable application data sizes by 10MB compared with WPA2-PEAP.

Monte Carlo Tree Search and Cognitive Hierarchy Theory for Interactive-Behavior Prediction in Fast Trajectory Planning and Automated Lane Change

AbstractPredicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communications and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. The main goal of this paper is to develop a fast algorithm that predicts the future states of the neighboring vehicles. The proposed workflow employs Monte Carlo Tree Search (MCTS) along with an on-policy learning technique for fast trajectory planning in multi-lane highway traffic scenarios. Also, for the inclusion of behavioral aspects, cognitive hierarchy and level-K game theories are utilized to predict the reaction and decision of the surrounding drivers. Simulation case studies demonstrate that our proposed approach is real-time implementable and can often avoid collision in difficult simulated confrontations.

A Survey on Junction Selection based Routing Protocols for VANETs

Objectives: To compare significant position-based routing protocols based on their underlying techniques such as junction selection mechanisms that provide vehicle-to-vehicle communications in city scenarios. Background: Vehicular Adhoc Network is the most significant offshoot of Mobile Adhoc Networks which is capable of organizing itself in an infrastructure-less environment. The network builds smart transportation which facilitates deriving in-terms of traffic safety by exchanging timely information in a proficient manner. Findings: The main features of vehicular adhoc networks pertaining to the city environment like high mobility, network segmentation, sporadic interconnections, and impediments are the key challenges for the development of an effective routing protocol. These features of the urban environment have a great impact on the performance of a routing protocol. This study presents a brief survey on the most substantial position-based routing schemes premeditated for urban inter-vehicular communication scenarios. These protocols are provided with their operational techniques for exchanging messages between vehicles. A comparative analysis is also provided, which is based on various important factors such as the mechanisms of intersection selection, forwarding strategies, vehicular traffic density, local maximum conquering methods, mobility of vehicular nodes, and secure message exchange. Application/Improvements: the outcomes observed from this paper motivate us to improve routing protocol in terms of security, accuracy, and reliability in vehicular adhoc networks. Furthermore, it can be employed as a foundation of references in determining literature that are worth mentioning to the routing in vehicular communications.

LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move.

The industrial development of the last few decades has prompted an increase in the number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. The reliability and effectiveness of V2X communication greatly depends on communication architecture and the associated wireless technology. Addressing this challenge, a device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture is proposed with LoRa wireless technology. The proposed system takes a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the proposed architecture offers modularity and compact design, making it ideal for legacy systems without requiring any additional hardware. Testing and analysis suggest the proposed system can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from 15–50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload. At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data packet every 53.34 m with reliable transitions.

Integrating Intervehicular Communications, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss

Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enables vehicle following with desired inter-vehicular distances. Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizes additional inter-vehicular wireless communication to share vehicle states such as acceleration to enable shorter gap following. Both ACC and CACC rely on range sensors such as radar to obtain the actual inter-vehicular distance for gap-keeping control. The range sensor may lose detection of the target, the preceding vehicle, on curvy roads or steep hills due to limited angle of view. Unfavourable weather conditions, target selection failure, or hardware issue may also result in target detection loss. During target detection loss, the vehicle following system usually falls back to Cruise Control (CC) wherein the follower vehicle maintains a constant speed. In this work, we propose an alternative way to obtain the inter-vehicular distance during target detection loss to continue vehicle following. The proposed algorithm integrates inter-vehicular communication, accurate vehicle localization, and a digital map with lane center information to approximate the inter-vehicular distance. In-lab robot following experiments demonstrated that the proposed algorithm provided desirable inter-vehicular distance approximation. Although the algorithm is intended for vehicle following application, it can also be used for other scenarios that demand vehicles' relative distance approximation. The work also showcases our in-lab development effort of robotic emulation of traffic for connected and automated vehicles.

Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics

This study addresses the platoon formation control problem of multiple off-axle hitching tractor–trailers with limited communication ranges, under model uncertainties and external disturbances, without any collision and without velocity and acceleration measurements for the first time. Towards this end, a new second-order Euler–Lagrange formulation of tractor–trailers is introduced under the prescribed performance design procedure that preserves all structural properties of the tractor–trailer dynamics. Then, a prescribed performance non-linear transformation, a saturated filtered tracking error, radial basis function neural networks, an adaptive robust controller, and a high-gain observer are creatively employed to design a novel platoon output-feedback controller, which forces the vehicles to construct a desired convoy while guaranteeing the robust performance against unmodelled dynamics and external forces and ensuring inter-vehicular communication maintenance, collision avoidance between each successive pair in the convoy of vehicles, and some preassigned desired response specifications of platoon formation errors including overshoot/undershoot, convergence speed, and ultimate tracking accuracy. By utilising a Lyapunov-based stability analysis, a semi-global uniform ultimate boundedness of formation errors is ensured with prescribed performance. Finally, simulation results illustrate the efficacy of the proposed control system.

Development of a Mobile Inter-Vehicular Communication System Based on Gossip Algorithm

<h1>Mobile Inter-vehicular communication network is an ad hoc network that allows peer vehicles to share and receive data. The existing inter-vehicular communication approach uses data from a limited number of data sources located in distant areas. This leads to high latency caused by the short-range antennas used to connect the peers. Inter-vehicular networks are dynamic and self-organized networks that do not use any external infrastructure to send and receive data. Currently, inter-vehicular networks only exist as part of a hybrid system and hence there is a need for a faster inter-vehicular network which can function independently. In this study, we developed a model which uses the gossip algorithm to send and receive data. The results show that over time, in a peer-to-peer network, the distance to be covered and the delay time are reduced. In the current models, however, the delay and distance covered remain constant. The results show that a peer-to-peer model is faster.</h1><p> </p>

Managing the Movement of Virtual Vehicles in Cloud Environment Using Reinforcement Learning Algorithm and Fuzzy Logic

Traffic monitoring methods have remarkably high costs. Hence, using intelligent vehicles can reduce the costs related to infrastructures such as equipment application costs, system operation and its maintenance. Inter-vehicular communication provides the opportunity in the cloud environment for enhancing communication area. In this paper, distribution of a large volume of information by roadside units to the vehicular network is inspected based on effective vehicle-to-vehicle communication in cloud environment. Given the nature of vehicular networks, the topology of these networks changes rapidly. Therefore, a distributed pattern with simple protocols is taken into consideration, rather than focusing on centralized structures. By applying no-rate coding in roadside units and the utilization of vehicles as information carriers, an effective method is presented for distributing information to all nodes (even to nodes belonging to separate clusters) within the network. Furthermore, fuzzy logic, as a precise system for imprecise and approximate conditions in vehicular networks, and reinforcement learning, as an appropriate algorithm for dynamic environments, for managing the movement of vehicles are used. As a result, network efficiency is enhanced and data transmission delay in the network is reduced.

An Improved Clustering Method Vanet Routing and Communication

The rising popularity of network based technologies has witnessed growing interests in research of inter-vehicular communications. In this scenario, Vehicular Ad-hoc Network (VANET) has evolved as the largely admired network traffic routing and control system. The main idea of this work is to enhance quality of clustering approach implemented in VANET. To achieve this, authors proposed the addition of two new elements in the existing clustering architecture, namely, the number of cluster heads required in a specific simulation area and the selection of paramount candidate to be used as cluster head. Supervised learning technique is implemented in the employed methodology. The proposed architecture is evaluated in terms of Jitter and Packet Delivery Ratio (PDR). The simulation results demonstrated that the node variation with PDR shows relatively higher average PDR for polynomial kernel as compared to the average PDR for linear kernel.