V2V Communication Research Articles

A Car-Following Model for Connected and Automated Vehicles With Heterogeneous Time Delays Under Fixed and Switching Communication Topologies

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow.

Novel Optical Scattering-Based V2V Communications With Experimental Analysis

Taking advantage of the proliferation of optical wireless communications, this paper proposes a new paradigm of optical scattering (or ultraviolet) vehicle-to-vehicle (V2V) communication with experimental analysis based on extensive channel measurements at a wavelength of 266 nm. This new optical scattering-based V2V technology can ease a fundamental requirement of pointing, acquisition, and tracking (PAT) commonly found in conventional optical V2V systems and can also provide an additional robust capability of efficient non-line-of-sight (NLOS) V2V transmission. Based on the experiments, it is demonstrated that in a mobile V2V channel where the dynamics are changing rapidly, the scattering of the UV photons in the upper atmosphere produces some unique characteristics, resulting in greater randomness and severe fading. From the experimental results, we characterize the channel by a non-stationary time-frequency-selective fading channel with a statistical description of the time-frequency-varying power profile. An examination of the measured data reveals that the distribution of the fading statistics follows the Rayleigh distribution but a departure from the Rayleigh distribution is also observed. In addition, to facilitate the real-time capture of the dynamic behavior of the UV-based V2V channel, a Kalman filter-based algorithm is presented to estimate the time-varying optical scattering channel parameters. Finally, some open issues and challenges in the optical scattering-based V2V technology are presented.

V2X Communication for Message Transmission and Warning Detection

Currently, the way that smart cars operate is that they have sensors all around the vehicle to identify and learn about adjacent objects. However, the precision of these sensors frequently has a relatively short range. Reliable long-distance data collection is becoming increasingly important as autonomous driving systems gain in popularity. The suggested remedy for this problem is a gadget that would be mounted on every moving vehicle. This gadget would transmit data about the car (size, speed, acceleration, position, heading, etc.) and receive data about nearby cars with comparable equipment. This eliminates the need for intelligent vehicles to guess what other vehicles are doing around them. Vehicles can instead get this information directly from other vehicles on the road and spend more resources monitoring/tracking non-vehicular objects and feed their information directly to an intelligent system for further complex decision making. This report mainly consists of collecting data for M2M communication and analyzing data for V2V based protocols; establishing simple V2V communication with software tool and sample data; code for V2V communication using CCA with the help of AoDV; design and simulation of hardware architecture in its application. The simulation is performed on an open-source software which is created on its latest release. The test cases run is in a simulated environment of nodal communication representing clusters of vehicles on the radar interacting with each other. These nodes have reactive protocols such as Co- operative Collision Avoidance (CCA) and Ad-Hoc On Demand Distance Vectoring (AoDV). These aim at providing early warning and message routing using unicast and multicast. The results presented in this report show that the through-put, network-life, energy consumed and distance between the nodes are crucial factors that help route message passing between nodes in a given Wireless Sensor Network (WSN). The results obtained from the project discuss WSN communication using AoDV and CCA, along with this the hardware application is built on the idea to transfer vehicle information such as acceleration and displacement using the ESP-32 control module to establish communication that validates the message transmission between these modules in offline mode.

Scheduling and Power Control for Connectivity Enhancement in Multi-Hop I2V/V2V Networks

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs’ coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

Performance Analysis Using Full Duplex Discovery Mechanism in 5G-V2X Communication Networks

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

Smart Card-Based Identity Management Protocols for V2V and V2I Communications in CCAM: A Systematic Literature Review

Besides developing new Cooperative, Connected and Automated Mobility (CCAM) services for the improvement of road safety and travel experience, researchers are considering protection mechanisms to ensure the security of these services and the safety of involved users (drivers but also, e.g., cyclists and pedestrians). In particular, several Identity Management (IDM) protocols have been designed as the first line of defence against external attackers. Among these protocols, a promising trend in research consists in the use of a Smart Card (SC) as a technical enabler for strong authentication. Indeed, many SC-based IDM protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications in real-time CCAM services have been proposed in the literature which present interesting features and promising usability experimental results. However, this research line is far from being exhausted, especially considering the recent spread of SC technologies and use cases. For this reason, in this paper we propose a systematic literature review on SC-based IDM protocols for real-time CCAM services. In particular, we identify characterising assumptions of CCAM scenarios and extrapolate a unified high-level view of the steps composing a SC-based IDM protocol. Then, we present a detailed survey of several SC-based IDM protocols. Finally, we identify trends in research and formulate guidelines and useful recommendations to provide a solid base which researchers can use as a starting point for the design of new and improved SC-based IDM protocols for CCAM scenarios.

Service-Oriented Real-Time Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles

This paper presents a real-time vehicle speed planning system called the real-time energy-optimal deceleration planning system (RT-EDPS). Connectivity and autonomous driving technologies that provide map and navigation data, traffic light information, and front detection sensor data are exploited to perceive and forecast upcoming deceleration events. A parameterized polynomial-based deceleration model is employed as the deceleration strategy. Real driving test data that characterize the physical limits of regenerative braking are used to model the objective function and constraints of the parameterized deceleration commands. The proposed RT-EDPS involves two speed-planning strategies with different scales of the planning horizon. Within a deceleration event horizon ascertained by the signal phase and timing data of traffic signals and 3D geographic map data, a dynamic programming-based energy-efficient deceleration strategy is used to generate long-sighted speed profiles. While the scheduled vehicle speed is being tracked, if a preceding vehicle is detected within a predefined threshold range, then a model predictive control-based energy-efficient deceleration strategy that incorporates the traffic status ahead of the ego vehicle and exploits V2V communication with the preceding vehicles is activated to re-plan the speed trajectory of the vehicle to guarantee a collision-free distance for the preceding vehicle. In virtual driving experiments, we record energy-recuperation efficiency gains of over 40% as compared to human drivers.

A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

Safety risk focused analysis of V2V communication especially considering cyberattack sensitive network performance and vehicle dynamics factors

Future transportation is expected to be connected, cooperative, and highly automated. Classic automated vehicular functions are developed based on safety principles primarily, but the reliability and cybersecurity of wireless communication processes pose new challenges for the automotive industry for connected vehicles, representing Connected, Cooperative and Automated Mobility (CCAM) systems as cyber-physical systems. Since CCAM system safety is significantly affected by network performance metrics, we need to consider communication factors such as end-to-end latency and packet delivery ratio during the safety evaluation of highly automated vehicle functions. This study presents a methodological framework which links the safety of the CCAM systems with the cyber security sensitive network performance metrics, as well as the vehicle dynamics factors in order to evaluate the effect of intentional or unintentional communication failures. Accordingly, we introduced a new approach to characterize the safety risk of specific Vehicle-to-Vehicle (V2V) applications. Beyond this, severity of collision and message reception probability functions were introduced to further investigate the safety of the analyzed application. Finally, the values of the safety risk index for different warning intervals are summed, which suggest that the safety effect of specific function-related parameters could be and shall be represented during the function development process.

An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

Anonymous authentication is a critical step in safeguarding vehicle privacy and security in VANETs. VANETs connected with blockchain are gaining popularity as a means to increase the effectiveness of anonymous authentication across many security domains. However, present blockchain-assisted authentication systems cannot successfully achieve anonymity since colluding RSUs or vehicles may acquire linkability via the same retrieved record, hence destroying anonymity. To solve the problem, the proposed work offers an unlinkable anonymous signature-based authentication for VANET to ensure collusion resistance. To provide V2R unlinkability, a trusted authority issues anonymous parameters that conceal the vehicle's identification from RSUs and other vehicles in the VANET system. The vehicle user produces anonymous signatures, and RSUs validate them during anonymous authentication. Moreover, the proposed authentication methods are based on an anonymous certificateless signature (ACS) approach that is computationally more efficient and provably safe against eternal forgery in the random oracle model. Additionally, the proposed work guarantees that neither an RSU nor a vehicle has the authority to divulge users’ true identities. Hence, the proposed system has stringent unlinkability and better anonymity, and it enhances the efficiency of V2R and V2V communications considerably according to security analysis and performance assessment.

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Vehicle-to-Vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. The 3rd Generation Partnership Project (3GPP) is designed to support V2V communication without any infrastructure using sensing-based Semi-Persistent Scheduling (SPS) in order to avoid resource collisions for Cooperative Awareness Messages (CAMs) transmission. However, the legacy system suffers from significant collisions in a traffic congestion environment and resource waste in light traffic conditions. To address these problems, in this paper we propose a novel dynamic resource scheduling algorithm for real-time group broadcasting, inspired by the Lyapunov optimization framework, where the optimization objective is to minimize time-average failure probability subject to queue stability. Through extensive simulations, we show that the proposed scheduling algorithm outperforms the standard in both traffic jams and leisurely road environments.

Collision Avoidance Predictive Motion Planning Based on Integrated Perception and V2V Communication

Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, existing research studies have only considered the applications of either of these approaches, which neglect their necessary interactions in real-world AV motion planning systems. To address this problem, we design an AV motion planning strategy based on motion prediction and V2V communication. Specifically, we propose the perception system and V2V communication module to provide real-time traffic and vehicular information to the participated AVs. Then, we formulate the AV lane-change motion planning problem through the scope of model predictive control based problem, as well as proposing the method on learning optimal motion planning by means of a novel deep learning technique. We conduct extensive case studies to evaluate the performance of the proposed system model. Our experimental results demonstrate the effectiveness of the proposed system model under various traffic conditions. In addition, the robustness of the perception system is guaranteed by utilizing the Car Learning to Act (CARLA) system with available V2V communication.

Secure V2V and V2I Communication in Intelligent Transportation Using Cloudlets

Intelligent Transportation System (ITS) is a vision which offers safe, secure and smart travel experience to drivers. This futuristic plan aims to enable vehicles, roadside transportation infrastructures, pedestrian smart-phones and other devices to communicate with one another to provide safety and convenience services. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in ITS offers ability to exchange speed, heading angle, position and other environment related conditions amongst vehicles and with surrounding smart infrastructures. In this intelligent setup, vehicles and users communicate and exchange data with random untrusted entities (like vehicles, smart traffic lights or pedestrians) whom they don't know or have met before. The concerns of location privacy and secure communication further deter the adoption of this smarter and safe transportation. In this paper, we present a secure and trusted V2V and V2I communication approach using edge infrastructures where instead of direct peer to peer communication, we introduce trusted cloudlets to authorize, check and verify the authenticity, integrity and ensure anonymity of messages exchanged in the system. Moving vehicles or road side infrastructure are dynamically connected to nearby cloudlets, where security policies can be implemented to sanitize or stop fake messages and prevent rogue vehicles to exchange messages with other vehicles. We also present a formal attribute-based model for V2V and V2I communication, called AB-ITS, along with proof of concept implementation of the proposed solution in AWS IoT platform. This cloudlet supported architecture complements direct V2V or V2I communication, and serves important use cases such as accident or ice-threat warning and other safety applications. Performance metrics of our proposed architecture are also discussed and compared with existing ITS technologies.

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them. Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.

String Stability of Connected Vehicle Platoons Under Lossy V2V Communication

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.

Use of Traffic Signal Lights in Vehicle– to– Vehicle Communication on Underground Mine Ramps

To minimise collisions and road accidents, road safety systems require Vehicle– to– Vehicle (V2V) and Vehicle– to– Road (V2R) communication infrastructure. Two-way traffic flow dynamics in single-lane underground mine haulage ramps do affect productivity of ramp in times of ore transportation to the surface for processing. Optical wireless communication offers no health hazard, low power consumption, non-licensed channels and high bandwidth. In this research, V2V communication on an underground mine ramp has been actualised making use of traffic signal lights, Radio Frequency (RF) Transmitters (Tx) and Receivers (Rx), and a traffic signal lights module. Simulation outcomes confirm orderly movement of two heavy vehicles on the haulage ramp. This development stands to minimise the incidents occurrence rate in mine ramp haulage systems.

Development of secure Internet of Vehicle Things (IoVT) for smart transportation system

Intelligent Transportation Systems (ITS) have attracted significant attention for their potential to improve transportation in urban and highway contexts as automotive infrastructures and automobiles have progressed. The characteristics of heterogeneity, constrained resources, scaling requirements, an uncontrolled environment, and so on make security and privacy concerns far more difficult to tackle than they are today. The high availability required by IoT networks may risk the integrity and security of data carried over the network. Because the majority of security threats occur during data routing activities, developing a safe routing protocol for IoT networks has emerged as the most important research subject. The efficient procedures required to allow secure data transfer for autos enabled by IoT technology are detailed here. Using a novel cryptography-based routing mechanism, this study demonstrates how to protect V2V and V2I communications in the context of the VANET against a wide range of cyber threats. It is a data encryption and decryption technology based on attributes and identity that has been designed to have the lowest computational overhead while retaining the best level of security. To provide efficient message authentication and integrity while ensuring vehicle privacy and safeguarding vehicular communications, a hybrid cryptography system integrating ABS and IBS, named AIBS (Attribute-Identity Based Signature), was created. The recommended cryptography-based solution is tested in the presence of prospective attackers on the network while using the NS2 tool. The simulation results have been measured in terms of average throughput, Packet Delivery Ratio (PDR), communication delay, and communication overhead.

VABLOCK: A blockchain-based secure communication in V2V network using icn network support technology

Vehicular Ad hoc Network (VANET) provides efficient communication among vehicles (V2V). The communication among all the vehicles complies with the on-demand, which contains a secure and trustable mechanism to ensure trustable communication. The modification in the communication information may result in falsified information. Secure data is very important in V2V communication to save the lives of pedestrians and drives by delivering secure and accurate information. To address the problem and achieve secure communication in V2V, we proposed a new blockchain-based message dissemination technique to secure V2V communication. With the passive need for adaptable and sufficient content delivery, information-centric networking (ICN) is adopted to enhance trustworthiness communication in VANET. We used Cluster-based secure communication through ICN-based VANETs. As VANET is open, ICN provides direct content requests and responses without location dependency. ICN-based VANET enhances caching abilities. The blockchain-based security protocol is implemented to secure efficient communication without altering the messages. The protocol achieves privacy, security, and trust for detecting malicious nodes in VANET. Additionally, the Clustering technique is applied to adopt in-range communication. Proposed-Caching framework enhances security and provides on-demand data to vehicles. NS-2 simulator is used to simulate the Proposed VABLOCK approach. Experimental results are performed and compared with relevant techniques, which show enhanced results based on cache hit ratio, one hop count, malicious node detection, and delivery ratio. We demonstrate that proposed caching improves results on selected parameters based on results.

Optimized modulation order for V2V communication over index-modulated radar signals

Embedding information messages in radar signals through index modulation is a promising approach for dual function radar and communication in automotive systems. In this paper, we optimize the index modulation order to maximize the communication performance without compromising the radar operation. We derive novel expressions for the number of successfully transmitted bits per symbol and the target detection probability as functions of the road layout, traffic conditions and index modulation order. Results show that in light traffic conditions, index modulation can achieve up to 12 bits/symbol when line of sight is present between the transmit and receive vehicles with up to 140% improvement compared to the case with absent line of sight. Finally, we show that the received radar signal can be used, at the receive vehicle, to estimate the location of the transmitting vehicle via a maximum-likelihood estimator.

VB-Kalman Based Localization for Connected Vehicles With Delayed and Lost Measurements: Theory and Experiments

Traditionally, connected vehicles (CVs) share their own sensor data that relies on the satellite with their surrounding vehicles by vehicle-to-vehicle (V2V) communication. However, the satellite-based signal sometimes may be lost due to environmental factors. Time-delays and packet dropouts may occur randomly by V2V communication. To ensure the reliability and accuracy of localization for CVs, a novel variational Bayesian (VB)-Kalman method is developed for unknown and time varying probabilities of delayed and lost measurements. In this VB-Kalman localization method, two random variables are introduced to indicate whether a measurement is delayed and available, respectively. A hierarchical model is then formulated and its parameters and state are simultaneously estimated by the VB technique. Experimental results validate the proposed method for the localization of CVs in practice.