Inter-vehicular Communication Research Articles

Distributed vehicular platoon control considering communication delays and packet dropouts

The influence of both inter-vehicular communication delays and packet dropouts on platoon control performance is studied. First, the conditions for achieving platoon mean square stability (MSS) are derived for three cases: undirected information flow (UIF) topology with time delays and identical packet losses, general information flow topology (IFT) with time delays and identical packet losses, and general IFT with time delays and non-identical packet losses. These conditions explicitly reveal how packet dropout rates, time delays, and IFT jointly affect vehicular stability. Second, in order to mitigate the impact of time delays and packet dropouts on platoon MSS, some feasible controllers are proposed for each one of the three cases by solving the corresponding modified Riccati inequalities (MRIs). In comparison with existing results, the provided conditions are easily determinable, and the offered controllers are simple to implement, with low computational complexity. Furthermore, they expand upon existing research findings. Finally, simulations are conducted to compare different time delays and packet loss rates, which validate the theoretical discoveries, also demonstrate the robustness and feasibility of the proposed methods using a platoon of passenger cars and realistic vehicle dynamics.

Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles

In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies.

Mobility in pedestrian communication simulations: Impact of microscopic models and solutions for integration

Modeling Pedestrian-to-X (P2X) communication plays an important role for use-cases such as crowd networking, wireless contact tracing or the evaluation of Intelligent Transportation Systems (ITS) for the protection of Vulnerable Road Users (VRUs). Since in general the mobile communication depends on the position of the pedestrians, their mobility needs to be modeled. Often simplified mobility models such as the random-waypoint or cellular automata based models are used. However, for ad hoc networks and Inter-Vehicular Communication (IVC), it is well-known that a detailed model for the microscopic mobility has a strong influence — which is why state-of-the-art simulation frameworks for IVC often combine vehicular mobility and network simulators. Therefore, this paper investigates to what extent a detailed modeling of the pedestrian mobility on an operational level influences the results of P2X communication and its applications.We model P2X scenarios within the open-source coupled simulation environment CrowNet. It enables us to simulate the identical P2X scenario while varying the pedestrian mobility simulator as well as the used model. In a first step, two communication scenarios (pedestrian to server via 5G New Radio, pedestrian to pedestrian via PC5 Sidelink) are investigated in different mobility scenarios. Initial results demonstrate that time- and location-dependent factors represented by detailed microscopic mobility models can have a significant influence on the results of wireless communication simulations, indicating a need for detailed pedestrian mobility models in particular for scenarios with pedestrian crowds.While for scenarios with a focus on pedestrian communications this can be achieved by applying locomotion models available in pedestrian dynamics simulators, modeling vehicle-to-pedestrian communication is challenging: Commonly used vehicular mobility simulators apply very simplified pedestrian mobility models; pedestrian dynamics simulators often have no or very limited support for modeling vehicular traffic. Therefore, in a second step, we present a simple approach to simultaneously couple two microscopic mobility simulators (one for modeling pedestrian and one for modeling vehicular mobility) with a pedestrian communication simulation. We implement the concept and apply it in an example scenario modeling vehicle-to-pedestrian communication in a part of the city center of Munich, where we evaluate the influence on application metrics such as the accuracy of the position information received for a pedestrian.

A Novel Joint Channel Estimation and Symbol Detection Receiver for Orthogonal Time Frequency Space in Vehicular Networks.

A vehicular network embodies a specialized variant of wireless network systems, characterized by its capability to facilitate inter-vehicular communication and connectivity with the encompassing infrastructure. With the rapid development of wireless communication technology, high-speed and reliable communication has become increasingly important in vehicular networks. It has been demonstrated that orthogonal time frequency space (OTFS) modulation proves effective in addressing the challenges posed by high-mobility environments, as it transforms the time-varying channels into the delay-Doppler domain. Motivated by this, in this paper, we focus on the theme of integrated sensing and communication (ISAC)-assisted OTFS receiver design, which aims to perform sensing channel estimation and communication symbol detection. Specifically, the estimation of the sensing channel is accomplished through the utilization of a deep residual denoising network (DRDN), while the communication symbol detection is performed by orthogonal approximate message passing (OAMP) processing. The numerical results demonstrate that the proposed ISAC system exhibits superior performance and robustness compared to traditional methods, with a lower complexity as well. The proposed system has great potential for future applications in wireless communication systems, especially in challenging scenarios with high mobility and interference.

An Efficient and Physically Secure Privacy-Preserving Key-Agreement Protocol for Vehicular Ad-Hoc Network

The popularity of vehicles promotes the evolution of smart cities. This development makes vehicular ad-hoc network (VANET) a widely used inter-vehicular communication to obtain information about road conditions, speed, vehicle location and traffic congestion. Such a public network is vulnerable to different security threats. Overall, the security of private data in VANET is a critical task. It has been observed that various authentication protocols have been devised for VANETs. However, most of the proposed protocols are not secure and reliable because of different security threats, including denial of service, replay, forgery and impersonation attacks, etc. Furthermore, the existing protocols used extra communication overhead and computational cost, so they become infeasible for resource-constrained environments. In this article, we design a lightweight and secure privacy-preserving key agreement protocol for VANETs using the hashing technique, which provides an efficient and secure data transmission mechanism over a public communication channel. Detailed security analysis shows that the proposed protocol is secure against various attacks. To evaluate the performance of the protocol, we simulate key cryptographic operations of vehicles, a roadside unit, and a trusted party agent on Arduino, low-end and high-end devices, respectively. The experimental results show that compared with the other related protocols, the computational cost and communication overhead of our protocol are reduced on average by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.797\%$</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">$22.06 \%$</tex-math> </inline-formula> , respectively. Additionally, simulation results on NS3 show that our protocol consistently achieves a packet delivery ratio higher than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99.91 \%$</tex-math> </inline-formula> . Therefore, our protocol is secure and reliable for VANET environment.

Deep Reinforcement Learning Approach for V2X Managed Intersections of Connected Vehicles

Intersections are major bottlenecks for road traffic, as well as the origin of many accidents. Efficient management of traffic at intersections is required to ensure both safety and efficiency. Yet, the traditional solutions (static signs, traffic lights) are limited in their efficiency as they consider the flow of vehicles and not the vehicles at the microscopic level. By using inter-vehicular communication of connected vehicles, recent works have shown the possibility to have a great increase in the number of evacuated vehicles thanks to the possibility to give an individual right-of-way directly to each vehicle. In this context of intersections of cooperative vehicles, the scheduling of this right-of-way in order to maximize the throughput of the intersection is still a challenging task, with regard to the hybrid and dynamic aspects of the problem. In this paper, we propose an approach based on Deep Reinforcement Learning (DRL) to efficiently distribute the right-of-way to each vehicle. A Markov Decision Process model of intersections of cooperative vehicles, enabling the application of DRL, is proposed. The performance of the DRL-based scheduling is then compared with classic traffic lights, and with two state-of-the-art cooperative scheduling policies, showing the benefits of the approach (increase of the flow, reduction of CO2 emissions).

ROS-Based Collaborative Driving Framework in Autonomous Vehicular Networks

This paper investigates the optimal data transmission for collaborative driving in autonomous vehicular networks (AVNs). We consider that vehicles communicate with each other based on the definition of Robot Operating System (ROS), which is a pervasively adopted middleware operating system for autonomous vehicles such as Baidu Apollo. ROS defines a publish/subscribe scheme for inter-vehicular communications, in which vehicles subscribe to “topics” published by adjacent vehicles; a “topic” is related to the real-time sensing/computing data that a vehicle intends to share with nearby neighbors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , radar or camera images captured. By sharing various sensing data using different topics at the real-time, vehicles in the same area therefore can collaboratively drive with cooperative perception and computing. However, multiple data flows for different topics would coexist along overlapping transmission paths during this process. As a result, a fundamental issue raised is how to schedule the contending data flows in the mobile and bandwidth-constrained vehicular networks. In this paper, we model the ROS-based publish/subscribe scheme as an optimization problem which jointly considers the power allocation and conflict avoidance of the communication process in AVNs. By applying the Lyapunov Optimization, we decompose the model to calculate the power and sub-carrier proportion on each node while avoiding link conflicts, and finally obtain the optimal resource allocation strategy. By combining the corresponding link conflict constraints, we are able to encapsulate the optimization model in a stable set to efficiently avoid link conflicts, and thereby reduce the resource waste caused by the link interference and data flow conflicts. Using simulations, we show that our method has good advantages in resource optimization.

Spectral Efficient Modem Design With OTFS Modulation for Vehicular-IoT System

A 5G network’s use-case in the Internet of Things (IoT) is a breakthrough, offering networks the ability to handle billions of connected devices with the proper blend of speed, latency, and cost. The IoT networks implemented in high-speed scenarios like intra and intervehicular communications in autonomous driving vehicles, high-speed vehicles, and trains will experience a Doppler effect. Orthogonal frequency-division multiplexing, the popular transmission technology for existing new-radio IoT (NR-IoT), is limited in providing reliable connections in high-speed vehicular scenarios. The performance of such system degrades with higher-order antenna configuration due to the lack of channel state information in highly mobile environments. The recently proposed orthogonal time-frequency space (OTFS) modulation is a strong contender that can handle high mobility but requires efficient transceiver design to be deployed in vehicular NR-IoT (V-IoT) systems. To conserve the resources and minimize the air time of the devices, we have proposed an embedded pilot design in the Delay–Doppler domain for the V-IoT systems. In the designed frame structure, the pilot’s position is optimized as per the vehicle speed to maximize the spectral efficiency of the system. The increase in spectral efficiency is at the cost of interference in the channel search region of the received Delay–Doppler domain OTFS signal. So a new joint estimator and the low-complex detector are proposed to handle the interference. The proposed efficient transceiver design with the spectral efficient pilot patterns allows us to conserve resources and remove complex encoder–decoders like the low-density parity check in NR-IoT.

Lightweight and Secure Multi-Factor Authentication Scheme in VANETs

The idea of a smart city and the utilization of vehicles are emerging rapidly. For inter-vehicular communication, this progression builds Vehicular Ad-Hoc Network (VANET) an extensively used network. The VANET is used to obtain information regarding the road's condition, location of vehicles, speed, and traffic congestion description. The exchange of information in VANET is public which leads to severe security threats. Generally, one of the most crucial tasks in VANET is the security of data. So it becomes one of the urgent tasks of researchers. We have reviewed several authentication schemes for VANETs. Still, most schemes are not secure due to several attacks such as replay, denial of services, impersonation attacks, etc. Furthermore, the existing schemes used higher communication and computational costs, and they are also incompatible with VANET applications that are resource-constrained. This article presents the lightweight and secure multi-factor authenticated key agreement scheme for VANETs to ensure efficient and secure transmission of data via an open channel. Our presented scheme is secure, scalable, and lightweight compared to existing schemes due to its low computation and communication cost. The formal and informal security analysis proved that our scheme is more reliable, practical, and secure against several unknown adversarial attacks in VANETs.

Online Learning for Network Resource Allocation

Motivation. Connectivity and ubiquity of computing devices enabled a wide spectrum of network applications such as content delivery, interpersonal communication, and intervehicular communication. New use cases (e.g., autonomous driving, augmented reality, and tactile internet) require satisfying user-generated and machine-generated demand with stringent low-latency and high-bandwidth guarantees.

Performance of Non-Orthogonal Multiple Access (NOMA) Systems Over $N$-Nakagami-m Multipath Fading Channels for 5G and Beyond

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user.

Distributed Adaptive Consensus Protocol for Connected Vehicle Platoon With Heterogeneous Time-Varying Delays and Switching Topologies

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective.

A novel adaptive fuzzy reinforcement learning controller for a platoon of off-axle hitching tractor-trailers with a prescribed performance and path curvature compensation

This paper addresses an artificial intelligence-based platoon control (AIPC) problem of autonomous off-axle hitching tractor-trailer wheeled mobile robots (TTWMRs) in a planar motion. Towards this end, a novel saturated nonlinear interval type-2 neuro-fuzzy reinforcement learning controller is proposed to construct a convoy-like motion of TTWMRs. To overcome the cutting-corner phenomenon in the platoon motion, the curvature of the reference trajectory is estimated and the cutting-corner behaviour is prevented. In order to deal with the situation where the velocity and acceleration signals are not measurable or corrupted by the measurement noise, a high-gain observer (HGO) is employed. The prescribed performance control (PPC) is also engaged in the control design procedure to guarantee inter-vehicular communication preservation, collision prevention between each consecutive pair in the platoon, singularity cancellation, and some prespecified desired characteristics of the convoy formation tracking errors such as maximum undershoot/overshoot, convergence rate, and final tracking precision. The Lyapunov-based stability analysis reveals semi-global uniform ultimate boundedness (SGUUB) convergence of tracking errors for the entire platoon system. With the suggested control framework, simulations and comparisons are conducted in which the control performance is considerably improved compared with the existing literature.

Blockchain-Integrated Multiagent Deep Reinforcement Learning for Securing Cooperative Adaptive Cruise Control

Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.

Resisting bad mouth attack in vehicular platoon using node-centric weight-based trust management algorithm (NC-WTM)

Individuals use Vehicular Ad Hoc Networks (VANETs) to secure, reliable communication networks as a viable route of transmission. Platooning uses Inter-Vehicular Communication to supervise a group of vehicles (IVC). The information communicated, especially acceleration, location, and speed, can then be used to respond by longitudinal control legislation. Platoons require help between those vehicles to improve fuel efficiency, security, and various crises associated with driver errors. Despite these advances, the communication network must govern vehicles unprotected from adequate threat vectors, which malicious attacks might exploit. Considering that connected cars between trusts could increase security, whereas all active automobiles can develop and spread legitimate, accurate, and recognized information within the network. So, considering badmouth, this work proposes a unique node centric weight-based trust management algorithm (NC-WTM). By quickly recognising and deleting fraudulent vehicles and their generated messages, the NC-WTM (node-centric weighted trust management) technique improves aggregate platoon security. The NC-WTM beats the robust and privacy-preserving reputation management scheme (RPRep) and the attack resistant trust management scheme (ART) trust designs regarding accuracy, recall, and F-score. Metrics, such as precision results of 78%, recall of 69.3%, F-score of 60.4%, and accuracy of 89%, are achieved and optimised in this trust model. Abbreviations: VANETs: vehicular ad hoc networks; IVC: inter-vehicular communication; NC-WTM: node-centric weight-based trust management algorithm; WTM: weight-based trust management algorithm; RPRep: robust and privacy-preserving reputation management scheme; ART: attack-resistant trust management scheme; MANET: mobile ad hoc network; DSRC: dedicated short-range communication; WAVE: wireless access in vehicular environment; IVC: inter-vehicular communication; I2V: infrastructure-to-vehicle; V2I: vehicle-to-infrastructure; V2V: vehicle-to-vehicle; TA: trust authority; RSU: road side unit; OBU: on-board unit; GPS: global positioning system; WSN: wireless sensor network; VASNETs: vehicular sensor networks; CCW: cooperative collision warning; BMA: bad mouth attack; TDMA: time division multiple access; GDVAN: greedy detection for VANETs; SMTS: spider monkey time synchronization; SVM: support vector machine; DST: Dempster-Shafer theory of evidence; TA: trust authority; PCA: puzzle-based co-authentication; VLC: visible light communication; NE: Nash equilibrium; RTB: request-to-broadcast; CTB: clear-to-broadcast; RREQ: route request message; RREP: route reply; DDR: data disseminate ratio; Dir: direct trust; IDir: indirect trust; TCE: trust computation error; PDR: packet delivery ratio

Experimentation of a road hazard anticipation system based on vehicle cooperation

This paper presents a complete system for anticipating road hazard based on inter-vehicular communication. It relies on efficient cooperative strategies between vehicles, namely distributed data fusion for hazard detection and conditional transmissions for alert propagation. It is built upon cheap embedded hardware and specific embedded software dedicated to dynamic vehicular networks. We describe the system, its architecture and the cooperative strategies. Road experiments involving 10 vehicles are reported. Real-life in-lab experiments complete the study with performance evaluation. These experiments emphasize the complementarity of both cooperative strategies. They show the practical interest of the whole system as well as its truthfulness and robustness even against poor networking conditions. They prove its efficiency to provide a significant anticipation period of road hazards.

A Provably Secure Identity Based Authenticated Key Agreement Protocol with Multiple PKG Compatibility for Inter-Vehicular Ad hoc Networks

Vehicular Ad hoc Networks (VANETs) facilitate the timely dissemination of road traffic information, including early warnings related to road conditions, weather updates, traffic congestion and collisions. The information exchanged in VANETs is crucial for maintaining uninterrupted traffic flow, minimizing road mishaps, expediting emergency services and analyzing potential detours in case of congestion. In order to ensure the legitimacy of the interchanged messages and to preserve confidentiality, it is essential to establish secure authenticated channels among the communicating automotive nodes. Considering the highly dynamic nature of VANETs and the diversity of the participating vehicular nodes, Identity based Authenticated Key Agreement (ID-AKA) protocol in the multiple Private Key Generator (PKG) setting, is an ideal cryptographic technique for securing inter-vehicular communications. We cryptanalyze the existing pairing-free multiple PKG compatible ID-AKA (mPKG-ID-AKA) protocol designed for securing VANETs and prove that the protocol lacks weak Perfect Forward Secrecy (wPFS). Our investigations also reveal that previous attempts to model mPKG-ID-AKA protocols are computation-intensive and provide weaker security guarantees. To this end, we propose an efficient eCK secure mPKG-ID-AKA protocol based on the gap Diffie-Hellman assumption. Experimental analysis suggests that the proposed protocol attains the highest computation efficiency, when compared to the existing mPKG-ID-AKA schemes and is therefore highly suitable for securing inter-vehicular communications.

Design of Microstrip Tchebyscheff Phased Array Antennas for Inter-Vehicular Communication

To enhance the safety and comfort of a driver, the wireless technology is used for inter-vehicular communication (IVC). During the lane change in highways, the driver of a high speed vehicle can exploit the antenna technology using a microcontroller. Here, a 4-element Tchebyscheff phased array of microstrip antennas for IVC application is designed at millimeter wave frequency band of 60GHz. The details of this array design with feed network, its design layout and fabricated prototype are presented. Another 4-element microstrip Tchebyscheff phased array for 6GHz IVC application is designed and the measured result is compared with the simulated result.

A Study of Error Reduction in Optical Triangulation for Short-Range Intervehicle Positioning

The long-existing problem of high positioning inaccuracy by triangulation in the far region in short-range vehicle-to-vehicle (V2V) communication environments is investigated in this study. The triangulation is achieved by sensing angle of arrival with two optical signal-direction sensors developed in our previous works. The measured signal-direction error can be categorized into two different kinds, namely systematic error and random error. The former is mainly incurred by imperfect calibration of hardware and misalignment of signal-direction sensors as well as multipath scattering of the signal. The latter is caused by random noises. Both of them lead to positioning error. By utilizing the specific characteristics pertaining to the triangulation measurements, it is possible to extract the target location and increase the accuracy with an optimization algorithm. With the aid of a general-purpose optimization subroutine together with two proper objective functions developed in this study, this work shows the viability of this idea. With the technique proposed in this paper, precise hardware calibration and careful alignment of signal-direction sensors are unnecessary. The problem of multipath scattering can also be mitigated. This work indicates a possible direction to treat the problem of high inaccuracy in noisy and ill-geometric environments in triangulation measurement. The abovementioned signal-direction sensors are currently constructed for infrared light. By changing the signal sources and detectors, they can also be fabricated for visible light. Such an alteration of transmission medium is beneficial for the currently emerging short-range visible-light intervehicular communications.

A QoE-Driven Pricing Scheme for Inter-Vehicular Communications With Four-Stage Stackelberg Game

A QoE-Driven Pricing Scheme for Inter-Vehicular Communications With Four-Stage Stackelberg Game