V2I Communication Research Articles

Use of Emerging Internet of Things Technologies for Developing Infrastructure-to-Vehicle Communication Systems

Infrastructure-to-vehicle (I2V) communication is an essential part of an intelligent transportation system. Bluetooth low energy (BLE) is one of the emerging Internet of Things (IoT) technologies that can enable the exchange of data at low-energy consumption. This study evaluated the application of IoT technologies, in particular BLE beacons—I2V communication devices to support autonomous vehicle (AV) operation in a real-world setting. An AV development platform built on a Lexus RX450h car was used to perform the evaluation. The testing program included two controlled-environment experiments and two real-world validation experiments. The controlled-environment experiments were used to select the BLE beacon settings to be considered. The validation experiments were performed in two different settings: a city street and an interstate highway in the City of Cincinnati in Ohio. To this end, beacons were attached to 10 different traffic signs in a city street as well as to a side barrier of an interstate highway. The ability of the AV to detect the traffic signs and the beacons attached to the side barrier was evaluated at different speeds. The study revealed promising results and indicated that the BLE beacon technology could potentially be a reliable solution to develop I2V communication systems that can be easily deployed to modify existing transportation infrastructure.

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning.

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle's distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

At present, there is still a lack of relevant theoretical guidance on the deployment of roadside RSU on expressways. In the face of the coexistence of V2V and V2I communication in the future, the deployment adjustment after the penetration of intelligent vehicles is not considered. Therefore, this paper proposes a roadside RSU deployment income model in consideration of the influence of V2V and V2I communication. Based on the optimal income of roadside RSU nodes, it achieves the optimization of the RSU deployment range and determines the optimal deployment spacing through the forwarding and relaying role of V2V communication so as to achieve cost savings. In terms of RSU coverage of positive income, it considers the impact of intelligent vehicles and reconstructs the traditional information flow–traffic flow coupling theory to innovatively realize the modeling of income within the information life cycle. In terms of the information transmission deficit, the WSN node energy loss model is reconstructed with permeability. Also, in terms of the construction and maintenance costs, the cost models are constructed for different cluster lengths. In order to provide a basis for expressway sensor network deployment, MATLAB software (version R2016B) is used to analyze the three-dimensional relationship between expressway traffic density, intelligent vehicle permeability, and roadside RSU deployment spacing as well as to determine the optimal roadside RSU deployment spacing with the income model. Finally, the model reliability is validated by the Warshell algorithm and the Kmeans clustering algorithm.

Automotive Radar Optimization Design in a Spectrally Crowded V2I Communication Environment

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Routing Protocols for Vehicle-to-Vehicle (V2V) Networks

VANET, short for Vehicle Ad-hoc Network, is an emerging technology with unique characteristics that differentiate it from previous ad-hoc networks. Designing an effective routing protocol for V2V (vehicle-to-vehicle) communication and V2I (vehicle-to-roadside infrastructure) communication is particularly challenging due to the dynamic nature of topology and frequent disconnections. VANET plays a crucial role in the development of Intelligent Transportation Systems (ITS) aimed at enhancing traffic flow and safety, primarily due to the high occurrence of traffic accidents. However, existing VANET routing protocols face limitations in effectively handling diverse traffic scenarios. To ensure future communication between vehicles for road safety, it is essential to develop appropriate routing protocols. This paper focuses on analyzing the advantages and disadvantages of routing protocols that can contribute to the development of new or improved routing protocols in the near future.

VANETs Security Using Cryptography

Abstract: The usage of Vehicular Ad-hoc Networks, often known as VANET, is becoming more common as the frequency of accidents in the modern world continues to rise. In situations like these, VANET makes accessible a broad variety of protective applications that may shield people from injury while they are operating motor vehicles. These applications can be used to prevent accidents. Additionally, it lessens the amount of collisions that take place, which in turn lessens the amount of damage that is sustained by cars, drivers, and passengers. By giving information to drivers about the flow of traffic along congested roads, VANETs make it possible for drivers to make better use of the time they have available. On the other hand, two security issues that are tied to the safety of drivers are the protection of an individual's right to privacy about their location and the maintenance of identifying information inside the network. Both of these issues are connected to the network. Many protocols need an infrastructure that enables the efficient distribution of keys, the revocation of keys, and the protection of sensitive communications by utilising a number of different cryptographic techniques in order to defend themselves against a wide variety of attacks and to guarantee that driver privacy is maintained. This is necessary in order to ensure that driver privacy is maintained. It is necessary to do extra work in the fields of computers and communication in order to implement a number of the security methods that are used for encrypting and authenticating V2V and V2I communications. These methods may be found in a variety of places. Because of this, it is essential to carry out research into a broad range of different cryptographybased approaches in order to boost the viability of various cryptography-based protocols and to improve the overall performance of these protocols. Because of the high mobility of nodes in the network, a suitable cryptographic technique is necessary. This method must be able to create short keys and short messages, take a little amount of processing time, and have a sufficient degree of security throughout the length of a key.

Towards a novel magnetic asphalt mixture containing ceramic ferrites for intelligently encoding road traffic sign information

With the development of smart road transport, the use of magnetic coding units (MCU) to store and transmit road traffic sign information is a novel technical solution that emerged in recent years for advancing the V2I communication. In this work, MCU was prepared by partly replacing the aggregates and mineral powder in the AC-13 asphalt mixture (AM) with ceramic ferrites (CF). It was found that the MCU was able to produce a stable and detectable magnetic field after magnetization, while its spatial distribution pattern is programmable. In order to provide technical guidance for MCU design, environmental impacts including erosion and thermal cycling on the magnetic performance of MCU were explored over in further. The following conclusions were obtained: (1) The experimental measurements show that CF has a remanence of more than 20 emu/g and a coercivity of more than 1 k Oe, and as a result, the addition of CF effectively improves the magnetic flux density and detectable range of MCU. (2) Satisfactory corrosion resistance of MUC is observed, which is mainly attributed to the stable hexagonal magnetoplumbite-type crystal structure and large binding energy of Fe3+ within the mixture. (3) Little irreversible flux density loss of the MCU is observed in a large number of thermal cycles in a range of 20–60 °C, which is attributed to the high curie temperature of the CF. Increasing the CF doping can enhance the Fe-O exchange interaction, thus reducing the reduction ratio of the MCU magnetic field at 60 ℃. (4) MCU material has typical viscoelastic response characteristics under mechanical loading, while its dynamic modulus is close to that of ordinary AC-13 AM. (5) The economic cost of using MCU to encode traffic sign information is merely 20.25% of that of conventional traffic signs, which supports further its application potential.

A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management

City zones have become increasingly overcrowded due to the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a significant issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication protocol for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed protocol support minimizes the end-to-end (E2E) delay and route request and reduces link failure. Our protocol’s leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The protocols are centered on short-range peer-to-peer (P2P) wireless communication in a 400 m radius cluster. They include innovative P2P wireless communications on VANET using minimized resources. The proposed protocols deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

Proof of Travel for Trust-Based Data Validation in V2I Communication

Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication security is effective in identifying falsified and malicious V2X data. Each vehicle in a given region can be a witness to report on the misbehavior of other nearby vehicles, which will then be added to a "blacklist." However, there may not exist enough witness vehicles that are willing to opt-in in the early stage of connected-vehicle deployment. In this paper, we propose a "whitelisting" approach to V2X security, titled Proof-of-Travel (POT), which leverages the support of roadside infrastructure. Our goal is to transform the power of cryptography techniques embedded within Vehicle-to-Infrastructure (V2I) protocols into game-theoretic mechanisms to incentivize connected-vehicle data sharing and validate data trustworthiness simultaneously. The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra costs for normal vehicles, which naturally want to move from the origin to the destination. The POT protocol is used to enhance the security of previous voting-based data validation algorithms for V2I crowdsensing applications. For the POT-enhanced voting, we prove that all vehicles choosing to cheat are not a pure Nash equilibrium using game-theoretic analysis. Simulation results suggest that the POT-enhanced voting is more robust to malicious data.

Outage Performance of RIS-Assisted V2I Communications With Inter-Cell Interference

With the ability of enhancing desired signals, Reconfigurable Intelligent Surface (RIS) is expected to be applied to vehicular networks which require ultra-reliable communications. In this paper we propose a promising RIS-assisted vehicle-to-infrastructure (V2I) network model in highway environment. Then under Rayleigh fading channels, our goal is to derive the downlink outage probability (OP) of the proposed V2I system with considering the inter-cell interference, i.e., interference from neighbor road side units (RSUs) and RISs. To achieve this intractable derivation, the probability density functions (PDFs) of desired signal power and interference-plus noise power are calculated by leveraging Gauss-Chebyshev quadrature and moment matching. Numerical results verify the accuracy of our derivation and it is shown that RISs can enhance the desired signal with causing little interference to neighbor cells, so that the OP of V2I communications can be greatly reduced.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

Assessing Connected Vehicle’s Response to Green Light Optimal Speed Advisory From Field Operational Test and Scaling Up

What are the main factors related to road or service configuration influencing the response behaviour of connected vehicles? How does it evolve with respect to the Market Penetration Rate (MPR) of connected vehicles? Here are some questions raised by this paper with a focus made on Green Light Optimal Speed Advisory (GLOSA) strategy. Such a system, based on V2I communication, aims at providing speed advice/recommendations when approaching an intersection to adjust speed and enhance fuel consumption. The message is displayed on the Human Machine Interface (HMI) of the connected vehicles and a response is expected from the driver. This paper derives its interest in the response behaviour of the driver to HMI. It develops a two-stage methodology based on (i) Field Operational Test to collect realistic inputs (e.g., response rate, delay, deceleration profile, etc.) and (ii) a simulated environment used for extending the findings to non-observed cases (e.g. higher MPR). Besides, the methodology that is well-fitted for generic evaluation and comparison of pilots sites&#x2019; conclusions, one further contribution lies in the process to select the explaining factors. Factors are targeted among features of (i) the road configuration (e.g. number of lanes), (ii) the service configuration (e.g. activation distance), or (iii) the individual route choice and traffic conditions. Among others, it is highlighted that the activation distance plays a significant role in the response behaviour and, depending on the cycle duration, a short activation distance might be completely inefficient, while a true environmental impact requires high MPR.

Optimization of Speed and Network Deployment for Reliable V2I Communication in the Presence of Handoffs and Interference

Vehicle-to-infrastructure (V2I) communication is becoming indispensable for successful roll-out of connected and autonomous vehicles (CAVs). While increasing the CAVs’ speed improves the average CAV traffic flow, it increases communication handoffs (HOs) thus reducing wireless data rates. Furthermore, unplanned density of active base-stations (BSs) may result in severe interference which negatively impacts CAV data rate. In this letter, we first characterize macroscopic traffic flow by considering log-normal distribution of the spacing between CAVs. We then derive novel closed-form expressions for the exact HO-aware rate outage probability and ergodic capacity in a large-scale network with interference. Then, we formulate a traffic flow maximization problem to optimize the speed of CAVs and deployment density of BSs with HO-aware rate constraints and collision avoidance constraints. Our numerical results validate the closed-form analytical expressions, extract useful insights about the optimal speed and BS density, and highlight the key trade-offs between the HO-aware data rates and CAV traffic flow.

Multiple re-configurable intelligent surfaces based physical layer eavesdropper detection for V2I communications

Inspired by the promising potential of re-configurable intelligent surface (RIS)-aided transmission in achieving the vision of 6th Generation (6G) network, we analyze the security model for a vehicular-to-infrastructure (V2I) network by considering multiple RISs (M-RIS) on buildings to act as passive relays at fixed distances from a source. In addition, multiple eavesdroppers are presented in the vicinity of the intended destination. Our aim is to enhance the secrecy capacity (SC) and to minimize secrecy outage probability (SOP) in presence of multiple eavesdroppers with the help of M-RIS in V2I communications. We propose a key-less physical layer security using beam-forming by exploiting M-RIS. The proposed approach assumes the concept of detecting eavesdroppers before the information can be transmitted via beam-forming by utilizing M-RIS. The results reveal that with consideration of M-RIS and beam-forming, the achievable SC and SOP performance is significantly improved while imposing minimum power consumption and fewer RIS reflectors.

Stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic

This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. Stochastic model predictive control (SMPC) strategy is employed at the high-level, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position-dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features; 2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.36 \,\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.26 \,\%$</tex-math></inline-formula> , respectively.

Smart optimization in 802.11p media access control protocol for vehicular ad hoc network

&lt;span lang="EN-US"&gt;The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.&lt;/span&gt;

A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

An agent-based platform to evaluate V2X routing road traffic scenarios

Traffic congestion is a recurrent problem in both urban and non-urban road networks. It impacts several key issues such as higher overall gas consumption, vehicle emissions, and noise. Traffic information available on time to vehicles is a key element for helping to reduce these issues. Nowadays, modern vehicles produce information via its own sensors that can be communicated to other vehicles on the road. For example, average speed or fuel consumption or if it was applied the ABS due to slipping in the previous segment road. This information could be exchanged between vehicles on road to help them in their driving decisions. This is known as V2V communication capability. When this information can be exchanged also with Infrastructure elements of the road, it is known as V2X communication. Current approaches for simulators in the context of V2X communication are devoted to the design and evaluation of the wireless technologies suitable for the efficient exchange of information, they are not designed to deal with the usage that drivers can do with the traffic information exchanged. In this paper, an agent based model is presented to test the importance of traffic information exchange for reducing traffic congestion. In this model, vehicles can use no communication, vehicles to vehicle (V2V) or vehicle to infrastructure (V2I) communications. The functional architecture of the proposed model is structured in several layers including the road network knowledge model, the road traffic model and the interaction protocols. The model allows the creation of different traffic scenarios, based on traffic flow, type of vehicles, etc. Results drawn from the executions of diverse traffic scenarios show the relevance that information exchange (via V2I and V2V communications) has to calculate efficient itineraries in real-time, and thus, reducing the overall consequences of traffic congestion.

A Safe and Robust Autonomous Intersection Management System Using a Hierarchical Control Strategy and V2I Communication

Connected autonomous vehicles can significantly improve the safety and mobility of urban transportation systems. However, these systems are vulnerable to model uncertainties, wireless communication impairments, and external disturbances. In this article, we propose a new autonomous intersection management (AIM) system, called hierarchical model predictive control (HMPC). In HMPC, the intersection coordination unit (ICU) in a global centralized layer is responsible for assigning a safe speed to each vehicle while minimizing the system's cost. In the Local decentralized layer, each vehicle is responsible for tracking the reference speed assigned by the ICU, while avoiding collisions. In our method, each vehicle can use its own sensors to monitor its close surroundings, and take its own decisions on its movements, independent on the control decisions sent from the ICU. We investigate the safety, scalability and robustness of HMPC compared with two well-known AIM methods based on centralized and decentralized control strategies. For the evaluation, we use simulation of urban mobility (SUMO). Further, we study the scalability and performance of the algorithms in the presence of communication impairments associated with wireless channels. Our simulation results show that HMPC can safely handle high traffic flow rates. Also, HMPC is robust to uncertainties caused by the wireless communication.

Decentralized Resource Allocation-Based Multiagent Deep Learning in Vehicular Network

Resource allocation (RA) has a significant impact on vehicular network performance. With high mobility, RA is more challenging, as the number of vehicles in close proximity changes dynamically in the nonstationary environment. In this article, we propose a multiagent double deep Q-networks scheme to stabilize the system and maximize the sum-capacity of the vehicle-to-infrastructure (V2I) links, while satisfying the reliability and delay constraints for vehicle-to-vehicle (V2V) links. To avoid interference caused by unstable V2V links, a transmission mode selection is considered in the scheme design. In addition, we introduce a binarized weight algorithm to accelerate the deep neural network learning process and, therefore, improve the computational complexity of our scheme. Through extensive simulations and complexity analysis, we demonstrate that the proposed scheme yields excellent performance in terms of the sum-rate and probability rate of V2I and V2V communication modes. We also compare the proposed scheme with binarized weights with other algorithms in terms of accuracy evaluation.