V2V Links Research Articles

Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users

PurposeSince the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).Design/methodology/approachAn optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.FindingsThe performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.Originality/valueThere is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

AoI-Driven Power Allocation and Batch Sampling Control for V2V Status Update Communications

This paper focuses on power allocation and sampling rate control in a spectrum-sharing vehicle-to-vehicle (V2V) status update network, where data packets are sampled in batches at the transmitter of the V2V links. In particular, we aim to minimize the overall power consumption while satisfying the age of information (AoI) requirement of all links. Firstly, we analyze the average AoI of the resulting queueing system with periodical packet batch arrivals and geometrically distributed packet service time. Then, the primal problem is decomposed into two subproblems, i.e., sampling rate optimization and transmit power optimization. Finally, a computationally efficient algorithm with polynomial-time complexity is developed that optimally solves the two subproblems and thus solves the original problem with global optimality. Simulation results validate our average AoI analysis and the proposed algorithm.

Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.

Coalitional Game Based Resource Allocation in D2D-Enabled V2V Communication

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

A V2I and V2V Collaboration Framework to Support Emergency Communications in ABS-Aided Internet of Vehicles

Providing reliable and flexible emergency communications for the Internet of Vehicles (IoV) is challenging in post-disaster scenarios, especially when communications infrastructures are unavailable or seriously damaged. Under such circumstances, it is highly demanded to deploy the air base station (ABS) to restore connectivity in disaster areas. Although the ABS can offer improved performance, the massive access of communications devices and the rising demand for many emergency-related services will bring new challenges to the ABS-aided IoV. In this paper, we propose a vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) collaboration framework to support emergency communications. Specifically, we incorporate non-orthogonal multiple access (NOMA) into the ABS-aided IoV. By using the successive interference cancellation (SIC) technique, V2I and V2V links can operate in four spectrum reuse modes, which are the extension of the conventional underlaying reuse mode. To fully exploit the advantages of the NOMA enhanced collaboration framework, we formulate a total power consumption minimization problem by jointly considering the mode selection, power control, and the channel state information (CSI) latency. The formulated problem is constrained by the reliability and rate requirements, and the decoding threshold of the SIC technique. Due to the NP-hard characteristics of the formulated problem, we decouple it first, and then adopt the graphical method to derive the analytical solution of optimal power control policies. Afterwards, we can get every feasible spectrum reuse mode. Among the feasible modes, we select the mode with minimum power consumption as the optimal mode. Finally, simulation results demonstrate that our proposed algorithm outperforms the current works in term of the total power consumption, e.g., by adopting the ABS and the NOMA technology, the total power consumption can be reduced by 22.3% and 40.9%, respectively.

Cooperative vehicular networks over Nakagami-[formula omitted] fading: Joint power control and spectrum scheduling

Facilitated by the rapid development of wireless communication technologies and the broad deployment of cellular networks, vehicular communications have evolved towards ultra-reliable low-latency communications (URLLC). In this paper, we consider a device-to-device (D2D)-enabled vehicular networks, where the uplink from roadside units (RSUs) to the base station (BS) shares spectrum with D2D-enabled vehicle-to-vehicle (V2V) links. The sum capacity of RSUs is maximized by jointly optimizing the transmit power and orthogonal spectrum bands scheduling, with the minimum data rate of RSUs and the reliability of V2V links guaranteed. Specifically, under Nakagami fading channels, the statistical channel state information (CSI) of vehicular communication links is utilized instead of the instantaneous CSI to adapt to the extra overheads resulting from frequent channel estimation. Moreover, user fairness is also considered, through maximizing the minimum ergodic capacity achieved by RSUs. To solve the formulated non-convex problem, we decomposed it into a power allocation subproblem that is analytically solved, and a spectrum bands scheduling subproblem that is tackled by resorting to the Hungarian method. The validity of the proposed methods is confirmed by numerical results.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Power Control and Fuzzy Pairing in V2X Communications

In this article, a joint power control and pairing-based resource allocation problem in the vehicle-to-everything (V2X) network are studied. This V2X network consists of a vehicle-to-infrastructure (V2I) link and a vehicle-to-vehicle (V2V) link. Therefore, the resource allocation problem has been formulated with the objective function of maximizing the V2I link's rate with the constraints of quality of service. Due to its NP hard nature, the original resource allocation problem is divided into two subproblems of power control and pairing. The power control subproblem is transformed into a convex problem using an exponential variable change. A fuzzy decision-making method is exploited to solve the pairing subproblem to have a highly reliable and stable V2V link. Then, the proposed joint power control and pairing-based resource allocation technique is evaluated with a simulated highway and compared with similar pairing algorithms. These performance comparisons reveal that the stability of the proposed resource allocation technique outperforms the other related methods.

Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network: A Multi-Agent Deep Reinforcement Learning Approach

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

Connectivity Analysis with Co-Channel Interference for Urban Vehicular Ad Hoc Networks

In urban vehicular ad hoc networks (VANETs), the complex channel environment and co-channel interference resulted in the uncertain delay of inter-vehicle packet transmission, which causes serious delay jitter. Connectivity is proposed as a key metric to describe this uncertainty. However, existing works lack a discussion of inter-vehicle connectivity in urban VANETs, particularly with regards to the process of transmitting packets between vehicles. In this paper, we analyze the connectivity probability of urban VANETs under co-channel interference with both complete and incomplete information. When the information is complete, we model the time-varying nature of co-channel interference and channel fading as delay jitter and analyze inter-vehicle connectivity in a time-varying environment. Then, when complete information is unavailable, we estimate the probability distribution of co-channel interference by combining the distribution of multiple parameters with the free space propagation model (Friss) model and Nakagami-m fading model. The expression for the connectivity performance of vehicle-to-vehicle (V2V) links is derived from the signal interference plus noise ratio (SINR) of the destination V2V link. Finally, we analyze the implications of various factors on connectivity, such as the transmit power of the signal, the arrival rate of packets, the number of channels and vehicles, and the distance between the transmitting vehicle and the receiving vehicle. The numerical analysis shows that co-channel interference and signal fading significantly affect inter-vehicle connectivity.

Learning Based Multi-Channel Access in Vehicular Networks With Illegal Operators and Users

This paper investigates the multi-channel access (MCA) problem for vehicular networks in presence of illegal operators and users. Due to the limited spectrum resource, vehicle-to-vehicle (V2V) links need to reuse the frequency spectrum allocated for vehicle-to-infrastructure (V2I) links in networks. For high-mobility environments of vehicular networks, the fast-varying channel conditions will inevitably lead to the significant uncertainty of the acquired channel state information (CSI). Hence, the traditional centralized MCA methods, which rely on the CSI, could not be performed in a timely manner. We formulate the MCA of the vehicle users as a distributed optimization problem, by maximizing the sum capacity of V2I links while guaranteeing the reliability of V2V links as well as reducing the cost due to frequency hopping. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework is introduced to tackle this problem, where each vehicle user, connecting with either a V2I or V2V link, acts as a learning agent to make spectrum access decisions rapidly and locally to meet the latency requirement. In order to adapt to the dynamic environment, we further propose a delayed interaction aided MADDPG algorithm, which enables online training for the distributed MCA problem. Moreover, to guarantee the proposed algorithm to converge stably, we optimize the selecting weights of experience traces and propose a Batch Prioritized Experience Replay (BPER) strategy to ensure that the high priority traces can be learned timely. Compared with baselines, simulation results showcase remarkable performance gain of our proposed algorithm.

Cellular V2X Communications in the Presence of Big Vehicle Shadowing: Performance Analysis and Mitigation

In Intelligent Transportation Systems (ITS), vehicular networks are enabling technologies for onboard data services such as traffic safety, user infotainment, etc. Vehicular networks face many challenges when it comes to providing satisfactory quality of service, mainly because of issues that arise from unreliable communication in unfavorable propagation conditions. One prime example is Vehicle-to-Vehicle (V2V) communication in the presence of big vehicles that present obstacles in the communication path of smaller vehicles, where the signal strength decays drastically due to the big vehicle shadowing. As a result, the communication range is shortened, and the safety message dissemination capability is reduced. In this paper, we analyze the impact of big vehicle shadowing on V2V communications, taking into account Cellular Vehicle-to-Everything (C-V2X) networks. A geometric as well as a stochastic approach is employed to analyze the length of shadow regions for conventional cars and big vehicles on the road in different scenarios. This paper analyses the effect of large vehicles shadowing on a V2V communication link as a function of the shadow region length. A beamforming-based signal reception technique is proposed in order to mitigate packet collisions caused by hidden nodes. Considering relaying operation by big vehicles, three relaying schemes are proposed to improve the V2V message dissemination performance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed schemes.

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.

A Comprehensive Investigation on Multi-User Interference Effects in Vehicular Visible Light Communications.

Vehicular visible light communications (VLC) are considered a suitable technology for vehicular platooning applications. Nevertheless, this domain imposes strict performance requirements. Although numerous works have shown that VLC technology is compatible with platooning applications, existing studies are mainly focused on the physical layer performances, mostly ignoring the disruptive effects generated by neighboring vehicular VLC links. Nevertheless, the 5.9 GHz Dedicated Short Range Communications (DSRC) experience has shown that mutual interference can significantly affect the packed delivery ratio, pointing out that these effects should be analyzed for vehicular VLC networks as well. In this context, this article provides a comprehensive investigation focused on the effects of mutual interference generated by neighboring vehicle-to-vehicle (V2V) VLC links. Therefore, this work provides an intensive analytical investigation based on simulation and also on experimental results that demonstrate that although ignored, the influence of mutual interference is highly disruptive in vehicular VLC applications. Hence, it has been shown that without preventive measures, the Packet Delivery Ratio (PDR) can decrease below the imposed 90% limit for almost the entire service area. The results have also shown that although less aggressive, multi-user interference affects V2V links even in short-distance conditions. Therefore, this article has the merit of emphasizing a new challenge for vehicular VLC links and points out the importance of multiple-access techniques integration.

Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles

In this paper, we investigate the resource block (RB) allocation problem in cellular vehicle-to-everything (C-V2X) networks mode 3, where the cellular networks schedule the RBs for direct vehicular communications. First, we establish the communication model and introduce the effective capacity and queuing theory to describe the reliability of vehicle-to-vehicle (V2V) links. Then, we introduce the α-fair function and formulate the joint power control and RB allocation problem considering the allocation fairness and the different quality-of-service (QoS) requirements for vehicle-to-infrastructure (V2I) and V2V links. Our objective is to maximize the sum capacity of all V2I links with the α-fair function while guaranteeing the allocation fairness among V2I links and the transmission reliability for each V2V pair. To achieve this objective, we propose a novel matching game theory algorithm based on the exchanged preference profiles between the two participant sets, i.e., V2V and V2I links. Simulation results show that our proposed algorithm is adaptive to the dynamic vehicular network and achieves better efficiency and fairness trade-offs, outperforming the classic allocation method.

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Cooperative driving of heterogeneous uncertain nonlinear connected and autonomous vehicles via distributed switching robust PID-like control

In this work, the leading-tracking control problem for heterogeneous and uncertain nonlinear autonomous vehicles is addressed. These latter communicate their status information over a wireless communication network and engage in cooperative movements in extra-urban traffic environment. The nonlinear vehicle dynamics are affected by time-varying parameter uncertainties, and the air-drag reduction caused by platooning is explicitly considered. The variation of V2V links as a result of cooperative manoeuvres is modelled by incorporating switching into the communication network topology. To this aim, we develop a novel robust distributed Proportional-Integral-Derivative (PID)-like control protocol which ensures that all vehicles within the platoon robustly track the behaviour of the leader, despite the presence of both unknown parameters uncertainties and network switching. The analytical stability of the proposed control strategy is demonstrated using Lyapunov theory. Sufficient stability conditions are expressed as a set of feasible Linear Matrix Inequalities (LMIs) whose solution allows the robust control gains to be properly tuned. The effectiveness of the approach is evaluated via the Mixed Traffic Simulator (MiTraS) co-simulation platform, which combines MATLAB/Simulink with SUMO microscopic traffic simulator. The exhaustive simulation analysis, involving the Monte Carlo method, is carried out considering different cooperative platooning manoeuvres, and confirms the theoretical derivation.