Autonomous Vehicular Networks Research Articles

A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles

The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to engage in leisure activities such as watching VR videos and playing games while travelling. Before driverless cars can become a “third living space”, open challenges confront the provision of immersive content (e.g., VR videos and gaming) with a high level of Quality of Experience (QoE). Current autonomous vehicular network technologies focus on navigation and safety and rely on ubiquitous high-speed 5G/6G network connectivity to provide a seamless streaming experience in AVs. The compounded risks of cyber- and motion-sickness are identified as barriers to the enjoyment of in-car VR entertainment technologies. The influence of AV car networks in different traffic conditions is identified as a key factor to minimize a person’s risk of sickness and increase overall QoE. However, assessing the QoE of immersive media consumption in AV to its full potential is a difficult task that requires proper standard guidelines and directions. Existing surveys focus only on a specific metaverse or transportation system. These surveys lack a comprehensive view of the entire immersive media consumption process, QoE factors and aspects, and QoE standards for assessing immersive content in vehicular networks. To address these issues, we present an overview of the research landscape in the context of QoE and network standards that highlights the need for experimental studies to inform and consolidate the methodologies and recommendations for standardization of immersive content QoE assessment in AVs. Research and user studies have not yet yielded standards or recommendations describing assessment methodologies to evaluate immersive systems, such as exist for traditional video and images. A framework is outlined to provide a comprehensive understanding of the QoE influencing factors and aspects that will assist in adapting, augmenting, and producing standards for immersive entertainment in autonomous vehicles.

Evaluating the impact of microservice-centric computations in internet of vehicles

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in .NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems.

Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

Secure Federated Learning in Quantum Autonomous Vehicular Networks

With rapid developments of artificial intelligence (AI) and wireless communication technologies, autonomous vehicular networks (AVNs) have emerged to provide convenient, comfortable, and safe driving services. Especially, due to the provable and unconditional security, quantum communication technology is increasingly being applied to promote the security of data delivery in AVNs, which forms the promising quantum AVNs (QAVNs). Federated learning (FL) is essential in QAVNs to implement intelligent and autonomous driving, where extensive distributed data is utilized to cooperatively train AI models, preserve user privacy, and reduce resource consumption. However, the FL unavoidably faces several possible privacy and security problems as a result of the intricate interconnections between several autonomous vehicles or infrastructures. In this article, we investigate the security and privacy of FL in QAVNs. Specifically, we first establish the system architecture, including quantum key distribution protocol, space-air-ground integrated network (SAGIN)-enabled QAVNs model, and FL framework in QAVNs. Furthermore, we describe the privacy and security threats to FL in QAVNs, i.e., inference attack, eavesdropping attack, and sybil attack. Following that, we give a case study for designing a secure FL scheme using the local differential privacy mechanism and the homomorphic encryption mechanism. Finally, we suggest several potential future research directions in QAVNs.

Latency-Energy Tradeoff in Connected Autonomous Vehicles: A Deep Reinforcement Learning Scheme

Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing closer to user equipment (UEs) at the edge of the access network by delivering various services to the UEs with limited processing power and battery. However, in fifth-generation and beyond 5G (B5G) networks, where UEs’ service requests and locations change dynamically, the deployment of static edge server deployments may lead to an increase in latency and total energy consumption. This paper presents a latency-energy-aware, efficient task offloading scheme for connected autonomous vehicular networks. Firstly, vehicles are assembled into clusters, in which vehicle can transmit tasks to the other vehicle, while on the other hand, the VEC server is used for processing the data. We developed a joint resource allocation and offloading decision optimization problem to minimize network latency and total energy usage. Due to the non-convex character of the optimization issue, we employed the Markov decision process (MDP) to convert it to a reinforcement learning (RL) problem. Then, we used a soft-actor critic-based scheme to achieve the optimal policy for resource allocation and task offloading to reduce the total latency and energy consumption for connected autonomous vehicles. Simulation analysis reveals that the proposed scheme attains 46.6% and 17.2% lesser delay, and 28.8% and 20.0% consumes less energy than the Hybrid DRL with Genetic Algorithm (HDRL-GA) and DRL based collaborative Data Scheduling (DRL-CDSS) state-of-art schemes.

Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks

Vehicular ad hoc networks consist of self-organizing nodes using multi-hop wireless links for communication without any infrastructure support. Traditionally, ad hoc routing protocols use the minimum hop count for their routing metric since a smaller number of transmissions is typically equivalent to a higher throughput, lower delay, and minimal power consumption. However, with the muti-rate capability of emerging radio interfaces, e.g., 802.11ax/be standards, the min-hop metric no longer results in high throughput. For instance, if the higher data rate links are selected for the route, it could result in a higher throughput even if the route takes more hop counts. In this paper, we propose a high throughput routing scheme, called MARV, which makes two key contributions. MARV searches for high throughput paths using an on-demand route searching algorithm so that the routing overhead is smaller compared to other multi-rate-aware routing schemes. MARV also searches for multiple paths to maintain both min-hop and high-throughput paths to select the adequate path depending on the data packet size. We conduct simulations to demonstrate that MARV outperforms not only min-hop path metrics but also previously proposed high-throughput metrics.

Agile gravitational search algorithm for cyber-physical path-loss modelling in 5G connected autonomous vehicular network

Based on the characteristics of the 5 G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5 G (or the so-called 6 G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5 G and Beyond 5 G (or 6 G) will operate over millimetre-wave (mmWave) bands. These network standards require suitable path loss (PL) models to guarantee effective communication over the network standards of CAV. The existing PL models suffer heavy signal losses and interferences at mmWave bands and may not be suitable for cyber-physical (CP) signal propagation. This paper develops an Agile Gravitational Search Algorithm (AGSA) that mitigates the PL and signal interference problems in the 5G–NR network for CAV. On top of that, a modified Okumura-Hata model (OHM) suitable for deployment in CP terrestrial mobile networks is derived for the CAV-CPS application. These models are tested on the real-world 5 G infrastructure. Results from the simulated models are compared with measured data for the modified, enhanced model and four other existing models. The comparative evaluation shows that the modified OHM and AGSA performed better than existing OHM, COST, and ECC-33 models by 90%. Also, the modified OHM demonstrated reduced signal interference compared to the existing models. In terms of optimisation validation, the AGSA scheme outperforms the Genetic algorithm, Particle Swarm Optimisation, and OHM models by at least 57.43%. On top of that, the enhanced AGSA outperformed existing PL (i.e., Okumura, Egli, Ericson 999, and ECC-33 models) by at least 67%, thus presenting the potential for efficient service provisioning in 5G-NR driverless car applications.

Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles.

It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for streaming applications of autonomous vehicle passengers. It leverages cutting-edge 6G wireless technology to create a network with high-speed transmission and broad coverage, ensuring smooth streaming application performance. The key features of bandwidth allocation for diverse streaming applications in this work include bandwidth relay and pre-loading of video clips assisted by vehicle-to-vehicle communication. Through simulations, this research effectively showcases the algorithm's ability to fulfill the bandwidth needs of diverse streaming applications for autonomous vehicle passengers. Specifically, during periods of peak user bandwidth demand, it notably increases the bandwidth accessible for streaming applications. On average, users experience a substantial 55% improvement in the bandwidth they can access. This validation affirms the viability and promise of the proposed approach in efficiently managing the intricate complexities of bandwidth allocation issues for streaming services within the connected autonomous vehicular networks.

Intelligent data offloading learning model for autonomous vehicular networks

AbstractWhen individuals are accustomed to receiving information in automobiles, mobile data offloading is becoming more common. However, the effects of movement of the vehicle in correspondence to relative speed and direction between vehicles, have a significant impact on mobile data offloading. An innovative deep learning algorithm namely, intelligent deep neural network (IDNN) is proposed for vehicle data offloading and an optimal algorithm namely, quasi opposition based C‐hen swarm optimization (QOCSO) is proposed for efficient vehicle resource allocation. Initially, the vehicles in the vehicular networks are clustered with the help of the cosine similarity‐based K‐means algorithm for transmitting the data in an energy‐aware manner. Then cluster heads (CHs) are optimally selected for the generated clusters using Boltzmann selection probability‐based earth worm algorithm. The selected CHs are responsible for collecting the data from the cluster members and that is forwarded to the roadside unit. Then the suitable mobile edge servers are selected according to the IDNN algorithm that offloads the data from the CHs to the appropriate server. These received tasks of the vehicles could be stored as a blockchain for providing security to the vehicular network and finally, the resource allocation of the incoming tasks to the vehicles is performed using the QOCSO algorithm. Experiment findings reveal that both offloading and resource scheduling techniques outperform existing state‐of‐the‐art vehicular network algorithms.

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.

Performance of Cooperative Detection in Joint Communication-Sensing Vehicular Network: A Data Analytic and Stochastic Geometry Approach

The increasing complexity of urban environments introduces additional uncertainty to the deployment of the autonomous vehicular network. A novel road infrastructure cooperative detection model using Joint Communication and Sensing (JCS) technology is proposed in this article to simultaneously achieve high-efficient communication and obstacle detection for urban autonomous vehicles. To suppress the performance fluctuation caused by shadowing and obstruction to the JCS signals, we first derive the statistic of road obstacles from the Geographic Information System (GIS). Then, the analysis of JCS channel characteristics and shadowing factors are presented using Line-of-Sight and Non-Line-of-Sight (LoS and NLoS) channel models under the complex urban scenario. A stochastic geometry approach is applied to analyze the interference factors and the probability distribution of successful JCS detection and communication. Simulations have been made to verify the cooperative detection model by probability analysis based on LoS and NLoS channels, and the numerical results demonstrate several different optimization methods for the deployment of JCS road infrastructures. Finally, we simulated and analyzed a deployment optimization method for JCS road infrastructures that complied with the standard of urban traffic-spot structure placement.

Vehicular Networks Dynamic Grouping and Re-Orchestration Scenarios

The topological structure in vehicular communication networks presents challenges for sustaining network connectivity on the road. Highway dynamics, for example, encourage the need for an adaptive and flexible structure to handle the rapid events of vehicles joining and leaving the road. Such demand aligns with the advancement made in software-defined networks and related dynamic network re-orchestration. This paper discusses the development of a virtual model that represents the operation of an autonomous vehicular network. It also investigates the ability to re-orchestrate the topology through software definition while running the various operational phases. Network self-formation, network expansion, retraction via vehicular members joining and leaving, and network self-healing when a topological rupture occurs as a result of a key member leaving the network are the key grouping phases. The communication approach is analyzed based on the status of network members and their ability to assume the various network roles. The concept is tested using both a Contiki–Cooja network simulator and a MATLAB analytical modeling tool to reflect the operation and performance of the grouping approach under various road scenarios. The outcome of the analysis reflects the ability of the group to be formulated within a measured latency considering the various network parameters such as communication message rate. The approach offers tools for managing the dynamic connectivity of vehicular groups and may also be extended to assume the function of an on-road network digital twin during the lifetime of a given group.

Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication.

A MEC architecture for a better quality of service in an Autonomous Vehicular Network

Designing 5G networks and related applications such as the Internet of Things (IoT), Cellular and autonomous vehicular networks (AVNET) is a challenge. Indeed, these networks are subjected to a number of constraints that play a crucial role in the network’s quality of service (QoS). Among these constraints, we have the management of computing, storage, bandwidth resources and low latency requirements. If in the past cloud computing has been used to avoid networks being affected by some of the previous constraints, that technology negatively affects the low latency requirements required by AVNETs for example. Multi-access Edge Computing (MEC) has recently emerged as a palliative solution to cloud computing. MEC aims to bring computing and storage resources from Cloud Data Center to Edge Data Center nearer to the User Equipment (UEs) in order to reduce the UEs’ requests latency for an improvement of the network’s QoS. Many MEC architectures have been proposed for AVNET. These solutions make use of Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC) or Network Slicing (NS) technologies. Some of these techniques combine partially these technologies while others do not. But to the best of our knowledge, none of them combines all these technologies to obtain a better QoS. In this paper, we combine the SDN, NFV, SFC and NS technologies to efficiently manage the MEC server resources for guaranteeing the QoS requirements in AVNETs. The QoS parameters we consider are latency, computing, storage and bandwidth resources. In that way, we first present a MEC server mathematical resource management model, then, we propose a new MEC architecture adapted to AVNETs that uses the aforementioned technologies also as the mathematical model we first present to manage the bandwidth, computing, and storage resources. The simulation results show that these resources are well managed, resulting to a low end to end latency for autonomous vehicles (AVs) requests executing on edge/cloud servers when the direct communication of AVs with cloud server takes long end to end delays, ensuring a better QoS for the AVNET.

Secure Content Delivery for Connected and Autonomous Trucks: A Coalition Formation Game Approach

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme.

Scalable Collaborative Intrusion Detection in Autonomous Vehicular Networks: A hierarchical framework based on game theory

Autonomous Vehicles (AVs) networks are vulnerable to security attacks targeting data integrity and service availability. To successfully operate in emerging automated highway systems (AHS), securing communications among the AVs and with the transportation infrastructure is paramount. An Intrusion Detection System (IDS) is the foremost defense artifact against malicious AV activities. However, individual IDS on board of the AV may not be sufficient enough against diverse and increasingly emerging new threats. In addition, not all security vendors synchronize their update of new IDS security features. Therefore, the collaboration among individual IDSs of AVs enables the vehicles to benefit from the collective knowledge and information of their neighbors and achieve more accurate intrusion detection. This paper presents a hierarchical framework for a Collaborative Intrusion Detection Network (CIDN) that allows the AV platoons to share detection knowledge based on trust and increase the accuracy of their individual IDS. The proposed CIDN involves intra-platoon and inter-platoon collaborations to improve scalability. The experimental results show that collaborative IDSs yield higher accuracy than individual IDSs in detecting misbehaviors in AV networks while achieving scalability.

Eco-Friendly Disaster Evacuation Framework for 6G Connected and Autonomous Vehicular Networks

Crowd and evacuation management have been active areas of research and study over recent years. Connected Autonomous Vehicles (CAVs) could provide safe and vital life-saving reinforcement when evacuating populations from a large-scale disaster site. However, navigation in disaster-affected environments is vulnerable to the presence of unpredictable moving obstacles. Existing solutions perform optimal route determination while considering the presence of a single rescue vehicle. To optimize path planning and enhance energy efficiency in the presence of a fleet of rescue vehicles and Unmanned Aerial Vehicles (UAVs), multiple sources and target destinations, we propose a Multi-Agent Deep Reinforcement Dijkstra (MADRD) algorithm in a sixth generation (6G) assisted environment to achieve cooperative navigation in the Route Planning Framework. The MADRD algorithm allows vehicles to learn from the experiences and behavior of other CAVs to obtain an optimal path from the source to the affected target site. Route selection and throttle parameters are optimized to achieve efficient fuel consumption, reduced travel time and lower traffic congestion. We simulate the Route Planning Framework on a large-scale real-world road network. The experimental results indicate that MADRD achieves 40% less fuel consumption, 45% higher road throughput and 34% less evacuation time than its state-of-the-art counterparts.

Synchronizing tasks for distributed learning in connected and autonomous vehicles

Autonomous driving relies greatly on deep learning to comprehend the surroundings and activities of the road systems. The learning models are traditionally trained off-line and used during driving. However, recent research on federated learning has enabled distributed deep learning for model adaptation with new data inputs from end users. Similarly, the recent research on continual learning has enabled the upgrading of the learned model with newer rounds of training, without losing previously acquired knowledge. For autonomous and/or connected vehicles, these mean it is possible to take new, maybe real time, data inputs from various sensors from multiple vehicles in vicinity, to train and update the preloaded models. The updated models can improve the safety and reduce human involvements when driving through unfamiliar situations. This paper tackles one important issue, namely, the model data dissemination for enabling such a distributed learning. The model data dissemination consists of steps of soliciting workers, transmitting model, and collecting the updates. In a mixed autonomous vehicles and connected vehicles network scenario, communication overhead and network dynamics are the major challenges. We present analyses on critical latency issues pertained to the various aspects of the model data dissemination to gain insights on the feasibility of federated learning for such a scenario. Also, we introduce a communication architecture and a publish-subscribe system for the model data dissemination. Our system is built with an information-centric networking paradigm via a tiered edge network architecture. Such a system organizes the steps of the model data dissemination clearly and manages the dynamics of the participating vehicles easily. The results show that the presented system reduces overall communication overhead and delay. It also provides high resiliency to packet losses in the mixed wireless connected and autonomous vehicular network.

NESEPRIN: A new scheme for energy-efficient permutation routing in IoT networks

Internet of Things (IoT) consists of a variety of heterogeneous interconnected devices called objects or things. These objects are generally equipped with sensing, processing and wireless communication capabilities. Unfortunately, these capabilities are not enough compared to those of devices in traditional networks. For example, objects have low battery power, limited memory storage, and less processing power. There are some cases where objects have to communicate with each other. In an Autonomous Vehicular Network for example, when a vehicle needs to change its direction, it has to alert other vehicles in the same network. This is known as the permutation routing problem. More precisely, the permutation routing problem in IoT occurs when some things of the network possess items that belong to others. The goal is to send items to their respective owners. A number of solutions to the problem have been proposed in literature which focus mainly on Wireless Sensor Networks (where the memory size of objects are the same). In this paper, we propose an efficient permutation routing scheme for a single-hop IoT network (the memory size differs from one object to another). The proposed NESEPRIN protocol consists of two phases: In the first phase, we solve the permutation routing problem in a single-hop environment with a single channel, and secondly we generalize the previous solution to a network with multiple channels. Our solution makes use of the wake and sleep technique to improve the energy conservation of objects. The simulation results show that our protocol outperforms the existing protocols designed to solve the permutation routing problem in terms of energy saving when the volume of data to route is large. NESEPRIN is the better candidate to solve the permutation routing problem in a single-hop multi-channels IoT environment where the volume of data to route is huge.

BCC: Blockchain-Based Collaborative Crowdsensing in Autonomous Vehicular Networks

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.