Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 289 https://internationalpubls.com Integrating Blockchain and Machine Learning with 6G for Autonomous Vehicle Communication: Achieving Secure, Transparent, and Scalable V2X Networks Pannala Krishna Murthy1,Bathula Gopal2 , S.Venkateshwarlu3, . A. Srujana4 1.Professor & Director, Dept. of Electrical & Electronics Engg, Jayaprakash Narayan College of Engineering, Mahabubnagar – 509001, Telangana. Krishnamurthy.pannala@gmail.com 2.Associate Professor, Department of Electrical and Electronics Engineering Priyadarshini Institute of Science and Technology for Women,Khammam Telangana. bgopal49@yahoo.com 3.Professor of EEE, CVR College of Engineering, Hyderabad, Telangana. svip123@gmail.com 4.Professor & HOD, Dept. of EEE, Vidya Jyothi Institute of Technology, Hyderabad, Telangana. srujanaa@yahoo.com Article History: Received: 24-09-2024 Revised: 26-11-2024 Accepted: 06-12-2024 Abstract: The rapid evolution of autonomous vehicle technology necessitates robust communication frameworks to ensure safety, reliability, and scalability in vehicular networks. Blockchain and machine learning technologies integrated with 6G networks develop a secure, transparent, and scalable Vehicle-to-Everything communication system. Leveraging ultra- low latency, massive connectivity and enhanced bandwidth, this framework addresses challenges including data integrity, real-time decision making and network scalability. Blockchain ensures secure and tamper-proof data exchange among vehicles, infrastructure and edge devices. Machine learning algorithms enhance predictive capabilities for route optimization, collision avoidance and traffic management. This synergy strengthens through a decentralized edge computing model enabling real-time data processing and reducing central network bottlenecks. Experimental evaluations on a simulated 6G-V2X testbed demonstrate significant improvements in network performance, security metrics and decision making accuracy compared to traditional frameworks. This establishes a foundational approach achieving trust, transparency and scalability in next generation autonomous vehicle networks. Insights provide for future deployments in intelligent, secure and efficient transportation ecosystems and smart cities. The proposed system serves as a pivotal step towards realizing communication infrastructures aligning with emerging demands. Keywords: autonomous, blockchain, framework, communication, secure, networks. 1. INTRODUCTION Technology surrounding autonomous vehicles is transforming the landscape of contemporary transportation systems at an unprecedented pace. With advanced levels of vehicle autonomy, the demand for real-time, secure, and intelligent communication frameworks is proving key. A major enabler of this transition is the use of Vehicle-to-Everything (V2X) communication, enabling real-time communication between autonomous vehicles (AVs), infrastructure, pedestrians, and other edge devices. The convergence of cutting-edge technologies like blockchain, machine learning (ML), and 6G networks has the power to provide solutions to the significant challenges faced by autonomous cars, such as security, transparency, data integrity, scalability, and real-time decision-making. Those cutting-edge technologies that are guaranteed to change or improve V2X communication systems such as AI, Dynamic Traffic State Estimation, Dynamic Traffic Assignment (DTA) methods[1]. The exponentially growing transportation ecosystem demands next-generation availability of communication networks. Autonomous vehicles need real-time data sharing among vehicles, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 290 https://internationalpubls.com infrastructure, sensors, and other entities. This data also covers essential information like road hazards, vehicle health, traffic conditions, and driving decisions. As the number of autonomous vehicles on the road increases, traditional communication systems may not have the capability to handle the excessive data traffic, which could lead to delays, reduced reliability, and security vulnerabilities. Hence, we need a communication framework that would support high volume of data with ultra-low latency and high reliability and provide sufficient security to protect information with all that sensitivity involved. In this context, the architecture of the network corresponding to the sixth generation (6G) network comes forward as a viable solution for next generation vehicle to everything (V2X) communications. With ultra-fast data speeds, ultra-low latency, high bandwidth, and ubiquitous connectivity, 6G networks are expected to deliver. Notably, these characteristics are critical for autonomous vehicles, which need to communicate with other vehicles and infrastructure in nanosecondly and 24/7 to ensure safety and operational efficiency[2]. Transitioning from 5G to 6G will not only provide faster connectivity for communication and control, but will enable big data transport and fusion, and gradually integrate 6G and AI capabilities for high-level operations of intelligent highway systems, including complex decision-making decisions. But the sheer volume of connected cars and freeway appliances in future transport regime presents serious security and data integrity challenges[3]. Connecting cars, however, rely on accurate and live information to make vital decisions like route planning, collision detection, and active traffic control. So potential malicious attacks, data issues and an ingression in the network interference, is the biggest threat for secure functioning of AVs. Such attacks are common for conventional centralized systems that depend on a central server, storing and processing all data. Centralized systems are susceptible to compromises where all the data is stored in a central server. If the server is hacked, the entire network can crash causing devastation[4,5]. This challenge will find a strong solution in blockchain technology, Blockchain is a type of distributed ledger technology that enables a secure method to record transactions. Through the adoption of blockchain technology in V2X, data sharing between vehicles, infrastructure, and edge settings can occur without entity fault within a centralized authority. Every transaction or data exchange is documented in a decentralized blockchain, preventing modification or manipulation of the information. This functionality is especially important in the context of autonomous systems such as vehicles where compromised data can result in loss of life[6]. The further benefit of blockchain is that it also improves transparency and traceability of data exchanges in the V2X network. Since the vehicles in classical communication systems are not always guaranteed to receive the correct and accurate information, they can be vulnerable to false or altered data attacks. In blockchain technology, every transaction is authenticated by a network of nodes that ensures that all exchanges of data are authentic. By accountability, I mean that this transparency can extend down to traffic management systems, road infrastructure, and even the vehicles themselves, providing an undercurrent of accountability through the system. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 291 https://internationalpubls.com Figure 1. Impact of technologies on V2X communication Machine learning (ML) is another essential technology that can substantially enhance autonomous vehicle interaction. ML Algorithms: With the help of large datasets, ML algorithms are expected to find the patterns and predictions. For example, in the case of autonomous car, Machine learning (ML) is used to improve different functionalities of the system, like route optimization, collision protection, traffic management. For instance, ML models can assess and predict traffic congestion, road hazards, or potential accidents by analyzing current data from the environment, enabling autonomous vehicles to make better decisions. Machine learning can also help vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communication by optimizing the information exchange between vehicles and infrastructure, allowing vehicles to exchange the most relevant information available[7]. Machine learning contributes significantly to improving the V2X network's performance as well. ML algorithms can monitor network performance and use data traffic patterns to predict potential network congestion and adapt the communication protocols accordingly to provide maximum efficiency. This allows the V2X network to process and respond to the large amounts of data produced by autonomous vehicles with minimal latency and optimal reliability, greatly increasing the overall efficiency of the network. However, blockchain and machine learning based amalgamation along with 6G networks serves as an effective solution to address the issues with autonomous vehicles communication[8]. Collectively, these technologies permit a secure, transparent, and scalable communication layer for autonomous vehicles and their environment. With the, one of the biggest concerns for IoT becomes very challenging in terms of security in data exchanges. On the other hand, 6G networks offer fast data transfer rates and lower latency, allowing data to be exchanged and processed in real time. Edge computing is one of the crucial enablers within the seamless integration of the blockchain and ML with 6G networks. Edge computing is about bringing the processing power to the data, or essentially, avoiding the need for centralised data centres and also reducing the latency incurred by data transferring over a network. For example, in autonomous driving technology, edge computing will allow for real-time data processing on the vehicles or infrastructure, which will help in real-time decision-making without putting too much load on central servers[9]. Decentralized data processing not only enhances the efficiency of the system, but it also makes the system more resilient, as the failure of a single node will not cause the disruption of the entire system. Experimental evaluations on a simulated 6G-V2X testbed have demonstrated the viability of this integrated framework. The outcomes of these experiments indicate that using these methods leads to substantial enhancement in network performance, security metrics, and decision accuracy when compared to conventional communication frameworks. The authors' findings imply that using blockchain technology, algorithmic solutions, and 6G networks can together create a secure, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 292 https://internationalpubls.com trustworthy communication system on these omnipresent autonomous vehicles, with just the right degree of scalability and efficiency necessary to support fully autonomous transport networks in the future that can handle millions of vehicles safely[10]. With the world transitioning to intelligent transportation systems and smart cities, the incorporation of these technologies into V2X networks will play an important role in creating safer, more efficient, and more sustainable transportation systems. To this end, the proposed system provides a platform for achievable, incremental progress towards these goals, and a potential framework to meeting the ever growing demands on the communication system from autonomous vehicles and other connected devices. This study sets the foundation for subsequent implementations of intelligent, secure, and resourceful transports, paving the way for such innovative infrastructures in building smart cities. Overall, the connection of the three technologies together represents a good solution to the obstacles imagined for the future of autonomous vehicles. By combining these technologies, they are able to solve key problems such as data integrity, security, real-time decision making and network scalability (as well as the creation of decentralized, transparent and efficient communication frameworks). 2. RELATED WORK AV Communication System Data, Sources, and Features The architectural design of CL's AV systems has received much attention from researchers and the industry in recent years. This cross-cutting research includes many different areas, such as wireless communication, cryptography, artificial intelligence, and distributed computing. They will be using 5G as a core part of their communication infrastructure to ensure low latency and fast connectivity, which are pillars of successful ADAS applications. Technologies including blockchain, machine learning (ML), and next-generation wireless networks like 6G are being increasingly combined in an effort to tackle classic challenges in this space. A key aspect of AV communication is the secure and reliable exchange of data between vehicles, infrastructure and other network participants. Communication has been heavily dependent on centralized servers in traditional approaches. These systems, however, are vulnerable to single points of failure, which can lead to the entire network being affected in terms of insecurity and functionality. Moreover, the explosion of internet-connected devices (i.e., connected cars, smart devices, etc.) has created unprecedented centralized system pressure and scalability issues. An emerging alternative paradigm is the use of blockchain technology, as a decentralized, distributed mechanism for organizing data exchange. Blockchain technology can help AV communication networks to remove central authorities, thus which makes them more resilient and less vulnerable to security attacks[11]. Ensuring data integrity and trustworthiness within the communication network is another challenge. Real-time data is essential for autonomous vehicles, from generating route plans and avoiding collisions to managing traffic. This data can be extremely sensitive and any tampering or manipulation in that can have dire consequences. By using blockchain, a tamper-resistant method for documenting and verifying transactions within the system is offered, assuring the authenticity and traceability of all data transfers. This ability is especially useful for keeping malicious attacks or unauthorized access in check. The use of blockchain technology in AV communication is also enhanced by its capability to facilitate transparency and accountability. In contrast to conventional systems where the origins of data can be hidden, blockchain guarantees that each transaction is transparent and can be traced. This is critical for multi-stakeholder interaction scenarios like toll payments, insurance claims, and traffic violations enforcement. This trust within the public will help speed up the implementation of the AV civil systems within the blockchain, improving the overall adoption of the autonomous vehicle. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 293 https://internationalpubls.com Machine learning has also become a key area in the advancement of AV communication infrastructure. ML techniques are great at handling big data to recognize patterns, make predictions, and improve processes. ML can improve various aspects of autonomic vehicles, such as route optimization, traffic flow management, and predictive maintenance. ML models, for example, can analyze historical as well as real-time traffic data to predict congestion patterns, allowing vehicles to plan better and more efficient routes. If done right, ML-based predictive maintenance systems such as, that predict potential car problems even before they would occur and would help in reducing downtime cost and maintenance costs[12]. Moreover, ML algorithms also play a vital role in increasing the safety of autonomous vehicles. ML models can analyze data from sensors, cameras, and other devices to identify and respond to potential hazards in real-time. As another example, an ML algorithm can parse images gathered from a vehicle's cameras in order to determine if there are pedestrians, cyclists, or other obstacles and can take those and similar information to drive actions to avoid collisions. ML is a powerful tool in managing traffic as it can learn from experience and also adapt to new environments. Combining blockchain and machine learning will prove to be a leap in the development of communication systems of AVs. When combined, these technologies can solve some of the biggest problems–data security and real-time analytics for better decision-making and optimizing the network. The proliferation of ML complements the need for secure; decentralized and immutable data storage capabilities from Blockchain. For instance, data stored on a blockchain can be leveraged by ML algorithms to enhance their predictive capabilities, whereas blockchain can provide a layer of security, ensuring that the data used by ML models is both accurate and reliable[13]. The use of edge computing in AV communication systems is an important technology that allows relevant data to be processed closer to the source, which enhances the potential of AV communication systems. AV networks often experience delays and bandwidth constraints due to the high latency associated with traditional systems based on cloud. Edge computing alleviates these problems by decentralizing data processing to edge devices, for example, on-board computers embedded in cars or roadside units. This minimizes reliance on centralized data centers, decreasing latency, and resulting in a more responsive system. Specifically, the integration of blockchain and ML on edge computing accounts for real-time data analysis and secure transaction processing, making it one of the most critical enablers of AV communication systems of the next-generation [14]. The development of 6G systems has also been a key factor in the evolution of AV communication technologies. Set to have ultra-low latency, extremely high connectivity, and high bandwidth, 6G networks will build on the groundwork that 5G has already laid out. These characteristics are… These features are critical for the ability of autonomous vehicles and the infrastructure they depend on, which consists of components such as cars, roads, vehicles, wireless communications, and people, to communicate and interact with one another. One example includes 6G networks allowing real-time video streaming from vehicle cameras to central monitoring systems, which can receive important information for traffic management and accident prevention. Furthermore, the low latency and high data rates of 6G networks enable quick data exchange and communication between vehicles, infrastructure, and other network actors. Although much progress has been achieved, several challenges still exist in AV communication systems integrating blockchain, ML, and 6G. A big problem is the computing and energy needs of these technologies. Such as high energy consumption for blockchain protocols (example: proof of work) For example, ML algorithms typically need large amounts of problem-solving time to both train and inference. These technologies need to be successfully deployed on resource-constrained systems Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 294 https://internationalpubls.com (edge devices); therefore, novel solutions are warranted to optimize performance while consuming fewer resources. Ensuring interoperability between different systems and technologies is another challenge. Whether in autonomous vehicle networks in which multiple stakeholders (vehicle manufacturers, infrastructure providers, and service operators) feed into one another. All of these entities could be using different protocols, standards, and technologies leading to possible compatibility issues. This helps to avoid fragmentation of the AV ecosystem. Another important factor in the design of AV communication systems is scalability. With the increasing number of connected vehicles and devices, the network needs to be able to manage the growing data traffic without sacrificing performance. However, due to the scalability limitations (transaction throughput and latency) that are intrinsic to blockchain, solving these problems is a prerequisite for introducing blockchain into AV communication networks. Transactions on a blockchain must be in the consensus of the network, and through mechanisms like sharding, sidechains, and off-chain transactions, more scalable and faster solutions have already been proposed; however, only more comparative studies could tell us whether they work as well in practice as they do theoretically. Sourc e Objective Methodology Results Research gap [15] ● Enhance security and transparency in V2X communications . ● Provide a decentralized platform for urban traffic data exchange. ● Ethereum blockchain and Solidity smart contracts integration. ● Simulations in urban environments using SUMO software. ● Simulations demonstrate enhanced security and reliability in V2X communication. ● Evaluated various traffic scenarios using SUMO software. ● Cyber-attacks can disrupt autonomous vehicle operations and safety. ● Compromised communication channels may lead to dangerous situations. [16] ● Mitigate security risks in autonomous vehicle networks. ● Enhance detection accuracy using advanced learning techniques. ● Data Fusion, One-Class Support Vector Machine, Random Forest, k- Nearest Neighbor ● Pre-trained Convolutional Neural Network models for attack detection ● EfficientNet model achieves 99.97% detection accuracy. ● Advanced techniques improve cyber-attack detection in AV networks. ● Communicatio n security in the Internet of Vehicles (IoV). ● Reliable automated decision- making for autonomous vehicles. [17] ● Ensure secure communication in the Internet of Vehicles. ● Support reliable ● Blockchain- integrated Secure Authentication (BiSA) for identity management. ● Decentralized Blockchain Name ● Introduces a decentralized framework for secure vehicle communication. ● Ensures reliable data ● Unique security challenges of 6G networks. ● Evolving AI- based IoV security landscape. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 295 https://internationalpubls.com automated decision-making for autonomous vehicles. Resolution (DBNR) for data exchanges. exchanges for autonomous vehicle operations. [18] ● Analyze AI-based IoV security and V2X communications . ● Address unique challenges of 6G networks. ● Machine learning and deep learning for V2X security. ● Federated learning for collaborative threat intelligence sharing. ● AI enhances security and safety in 6G-connected vehicles. ● Improved protection without compromising performance in IoT networks. ● Fundamental privacy and security issues in 6G technology. ● Denial of service (DoS) attacks on wireless sensor networks. [19] ● Address privacy and security issues in 6G technology. ● Optimiz e wireless sensor network security management using machine learning. ● Blockchain user datagram transport protocol with reinforcement projection regression. ● Artificial democratic cuckoo glowworm remora optimization for network optimization. ● 97% throughput, 95% energy efficiency, 96% accuracy. ● 50% end-to- end delay, 94% packet delivery ratio. ● 6G networks vulnerable to cyberattacks due to flexibility. ● Addressing issues with blockchain technology integration. [20] ● Introduc e blockchain- enabled secure vehicular management systems. ● Motivate multidisciplinar y and cross- cutting technology integration. ● Blockchain technology for secure vehicular management systems. ● Cloud edge computing for IoT device management. ● Introduces a secure vehicular management system architecture. ● Highlights benefits of blockchain in 6G networks for IoT. ● High dynamicity of vehicular networks complicates service delivery. ● Ensuring security while maintaining timely service delivery is challenging. [21] ● Enhance AVN communication security and decision-making in smart cities. ● Reduce traffic congestion and improve ● MapReduce for processing large AVN data efficiently. ● Private blockchain for secure and tamper-proof communication. ● Explainable Artificial Intelligence ● Achieved 96% accuracy in traffic management. ● Reduced miss rate to 4% ● Ensuring error- free data transmission for high-speed communication. ● Managing high network transaction levels efficiently. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 296 https://internationalpubls.com transportation efficiency. (XAI) for traffic data analysis. [22] ● Propose a Blockchain- Enabled Vehicular Edge Computing framework. ● Optimiz e V2X service delivery using Deep Reinforcement Learning. ● Dual-layer verification with permissioned blockchain. ● Deep Reinforcement Learning algorithm for service delivery optimization. ● 18% reduction in latency achieved. ● 38% improvement in successful service delivery. ● 65% decrease in energy consumption. ● Data integration and validity in V2X communication. ● Ensuring secure messaging in transportation systems. [23] ● Optimiz e data flow for 6G vehicle communication. ● Ensure error-free high- speed data transmission. ● Three-stage implementations for data transmission optimization. ● Two-stage optimization strategy for high network transaction levels. ● Successful implementation of error-free data transmission. ● Optimizatio n algorithms outperformed traditional calculation methods. ● Communicatio n security in the Internet of Vehicles (IoV). ● Reliable automated decision- making for autonomous vehicles. [24] ● Integrate blockchain into V2X and IoT systems. ● Propose novel blockchain use cases for transportation. ● Blockchain- based vehicle ownership system using multi-token standard ● Implementatio n of smart contracts for robust interactions ● Introduces Vehicle-to- Blockchain (V2B) communication architecture for transportation systems. ● Proposes blockchain use cases like vehicle ownership, scoring, and ticket management. ● Unique security challenges of 6G networks. ● Evolving AI- based IoV security landscape. Table 1. Literature review Moreover, other significant ethical and privacy-related issues regarding the enhancement of 6G by means of blockchain and ML arise. For example, some data protection issues may arise with blockchain-based data management, whereby updating records is an all but impossible task due to the immutable nature of blockchain records, which makes it inherently difficult to comply with privacy law, specifically the General Data Protection Regulation (GDPR). Likewise, the reliance on ML algorithms for decision-making poses concerns regarding accountability and bias. That's why ensuring that these tools are deployed in a way that is principled and protective of user privacy is vital for both acceptance and success of these technologies. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 297 https://internationalpubls.com This exploration into the synergy of blockchain, machine learning, and 6G networks within autonomous vehicle communication systems not only reflects a significant stride in the narrative of intelligent transportation systems but also sets the stage for future explorations in unified architectures where transport and technology converge. These technologies complement each other, solving key challenges such as data security, scalability, and real-time decision-making. However, there are technical, ethical, and regulatory hurdles that need to be cleared for them to be successfully implemented. Research and developments in this field will lay the foundation for extensively safe and secure yet practical communication frameworks which will lead to the eventual coverage of autonomous vehicles as an important component of future transportation ecosystems. 3. PROPOSED METHODOLOGY We proposed a novel methodology of combining Blockchain and machine Learning (ML) with the sixth-generation (6G) networks to deliver a secure, transparent, and scalable autonomous vehicle Vehicle-to-Everything (V2X) communication framework. "This design proposed leveraging this synergy between these advanced technologies to address fundamental challenges including secure data exchange, real-time decision making and network scalability. This method applies layers of decentralized data schemas, prediction, and ultra-low latency feedback loops to improve autonomy in communication systems. Figure 2. Flowchart of proposed methodology Using Blockchain for a Secure and Transparent Data Exchange The proposed methodology consists of three pillars, with the first pillar being blockchain. Blockchain is used for a distributed ledger that stores transactions and communication events between vehicles, infrastructure, and other entities of the network. Every participating vehicle has a lightweight blockchain node that can perform consensus and verify transactions. Due to the distributed nature of blockchain, it does not depend on a centralized authority, that leads to a lesser risk of single points of failure. 𝑇𝑏𝑙𝑜𝑐𝑘 = 𝑁𝑡𝑥 𝑡𝑏𝑙𝑜𝑐𝑘 Anatomy of a V2X Blockchain Architecture A good example of this would be a hybrid consensus mechanism like PoS-PBFT that balances the three - security, scalability, and energy efficiency. In this Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 298 https://internationalpubls.com way, performance is not compromised and the network can therefore handle a high volume of transactions. 𝐿𝑐𝑜𝑛𝑠𝑒𝑛𝑠𝑢𝑠 = 𝑡𝑛𝑒𝑡𝑤𝑜𝑟𝑘 + 𝑡𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑖𝑜𝑛 Smart contracts also enable the automation of critical processes, including vehicle authentication, toll payments, and insurance claim handling. The self-executing contracts, which are run on the blockchain, allow different individuals participating in the network to interact in a secure and transparent way. 𝑇𝑠ℎ𝑎𝑟𝑑 = 𝑇𝑏𝑙𝑜𝑐𝑘 𝑆 The methodology leverages advanced techniques (e.g., sharding and sidechains) to improve the scalability of the blockchain system. Algorithm 1: Blockchain-Based Transaction Verification Input: Transaction data 𝑇, Blockchain state 𝐵 Output: Transaction status (Success/Failure) 1. Initialization: Fetch the transaction 𝑇 and the current blockchain state 𝐵. 2. Verify Integrity: Compute the cryptographic hash 𝐻(𝑇) and validate sender credentials against the blockchain ledger. 3. Transaction Validation: o If valid: ▪ Append 𝑇 to the current block. ▪ Execute the consensus mechanism: 𝐿𝑐𝑜𝑛𝑠𝑒𝑛𝑠𝑢𝑠 = 𝑡𝑛𝑒𝑡𝑤𝑜𝑟𝑘 + 𝑡𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑖𝑜𝑛. ▪ Update the blockchain ledger and return "Success". Else, reject the transaction and return "Failure". One such approach, sharding, detaches the blockchain network into smaller segments — or shards that can process transactions in parallel to one another. This allows for a large number of transactions to be processed by the system simultaneously. 𝐸𝑡𝑥 = 𝑃𝑛𝑜𝑑𝑒 ⋅ 𝑡𝑏𝑙𝑜𝑐𝑘 𝑁𝑡𝑥 In Monero, sidechains further improve on the system's scalability by offloading non-critical transactions from the main blockchain. By ensuring that the existing infrastructure for blockchain is sufficient to handle the increasing demands of the communication networks, these techniques are key to the widespread implementation of autonomous vehicles. Machine Learning and Real-time Flight Decisions The proposed methodology includes the use of machine learning algorithms to improve the predictive and decision-making capabilities of the framework of V2X communications. ML models are deployed across multiple layers of the network, such as vehicles, roadside infrastructure, and edge computing nodes. These models rely on data collected from sensors, cameras, and other devices up to October 2023 to recognize patterns, predict outcomes, and improve system efficiency. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 299 https://internationalpubls.com 𝐿(𝜃) = 1 𝑛 ∑ 𝑛 𝑖=1 (𝑦𝑖 − 𝑓(𝑥𝑖, 𝜃)) 2 Traffic flow management is one of the main applications of ML in the proposed framework. The ML models analyze real-time traffic data and aesthetic patterns & enable vehicles to take alternative routes. It can also be used to optimize traffic signals. 𝜃 = 𝜃 − 𝜂 ⋅ 𝛻𝐿(𝜃) ML algorithms are also utilized for collision avoidance and traffic management. Through analysis of data from sensors and cameras mounted in the vehicle, ML models can identify other objects on the road and initiate appropriate actions, like braking or swerving, to avoid collisions. 𝜃𝑔𝑙𝑜𝑏𝑎𝑙 = 1 𝑁 ∑ 𝑁 𝑖=1 𝜃𝑙𝑜𝑐𝑎𝑙 𝑖 The method combines federated learning to efficiently train ML models without centralizing sensitive data. Federated learning is a distributed learning approach that enables vehicles and other agents in the network to collaboratively train models without sharing their raw data. 𝑄(𝑠, 𝑎) = 𝑄(𝑠, 𝑎) + 𝛼 [𝑟 + 𝛾𝑚𝑎𝑥 𝑎 𝑄(𝑠 ′, 𝑎) − 𝑄(𝑠, 𝑎)] This way, we can both keep data privacy and avoid having to send with huge communication load in the centralized training. The periodic updating of the trained models at edge nodes or blockchain nodes maintains a continuous record across the network. Algorithm 2: Federated Learning Model Update Input: Global model 𝜃𝑔𝑙𝑜𝑏𝑎𝑙, Local datasets Output: Updated global model 𝜃𝑔𝑙𝑜𝑏𝑎𝑙 1. Distribute Model: Distribute the global model 𝜃𝑔𝑙𝑜𝑏𝑎𝑙 to all participating edge nodes. 2. Local Training: o Each node trains its local model using its local dataset: 𝜃𝑙𝑜𝑐𝑎𝑙 𝑖 = 𝑡𝑟𝑎𝑖𝑛(𝑑𝑎𝑡𝑎𝑖). 3. Aggregate Updates: Collect updates from all nodes and compute the new global model: 𝜃𝑔𝑙𝑜𝑏𝑎𝑙 = 1 𝑁 ∑𝑁 𝑖=1 𝜃𝑙𝑜𝑐𝑎𝑙 𝑖 4. Update and Redistribute: Update the global model and redistribute it to all nodes. 5. Repeat: Repeat steps 2–4 until the global model converges. Additionally, one of the most innovative aspects of the methodology is the application of reinforcement learning (RL) for adaptive decision making. Through reinforcement learning (RL), machines can discover effective strategies to reach their final destinations by trial-and-error in challenging traffic situations. An RL-based agent, for example, can learn to get into high-speed traffic and what turns to make at an intersection based on the different inputs it receives in the various locations and the feedback (positive or negative) it receives for its decisions. They are regularly updated to reflect new conditions, such as road construction or weather changes, so vehicles can quickly navigate changing conditions. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 300 https://internationalpubls.com How Could 6G Networks Deliver Ultra-Low Latency Communication? The third pillar concerns the operation of 6G networks to facilitate ultra-low latency, high-speed communication among autonomous vehicles. Supporting this vision of automation in the intelligent society, the 6G architecture aims to provide enormous connectivity, allowing ubiquitous connection between car, road, and edge device. By taking advantage of key features of 6G networks, including terahertz communication, massive multiple-input multiple-output (MIMO) technology, and intelligent reflecting surfaces, network performance is improved. 𝐿𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡 = 𝐷 𝑅 Network Slicing: The methodology introduces network slicing on 6G resources for efficient utilization. This approach slices the network into many virtual pieces, each optimized for a particular use case— like high-priority emergency communication or low-latency vehicle control. 𝑃𝐷𝑅 = 𝑃𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑃𝑠𝑒𝑛𝑡 Network slicing guarantees that the product from autonomous vehicles will not come at the expense of the performance in other applications. 𝐶 = 𝐵𝑙𝑜𝑔2(1+ 𝑆𝑁𝑅) Edge computing enables the convergence of 6G networks, blockchain, and ML technologies. Edge nodes, placed at strategic locations, like intersections or highway exits, will process data locally to minimize latency. Fine tune uses the nodes as intermediaries between vehicles and the blockchain network that conduct real-time transaction verification and data exchange. This edge computing architecture allows execution of ML models so that vehicles can generate timely insights for actuation. Edge Computing: Processing Data at the Edge This proposed role uses decentralized edge computing to prevent the issues related to centralized ETL processing. Edge nodes have computing power and storage, which enables them to process data near the edge and lessens the dependence on the cloud-based server. This allows a decentralized control plan, reducing latency and also increasing the robustness of the system to network failures. 𝐿𝑀𝐿 = 𝐿𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡 + 𝐿𝑐𝑜𝑚𝑝𝑢𝑡𝑒 The edge computing architecture is coupled with the blockchain system. Each of the edge nodes is a blockchain node, which runs consensus mechanisms and maintains an instance of the ledger. Edge computing contributes to faster real-time processing, but integrating it with blockchain ensures that any data processed at the edge without inherent security is recorded on an immutable, trusted ledger. 𝑆𝑠𝑦𝑠𝑡𝑒𝑚 = 𝑁𝑛𝑜𝑑𝑒𝑠 1+ 𝐿𝑐𝑜𝑛𝑠𝑒𝑛𝑠𝑢𝑠 Edge nodes also process smart contracts that automate vehicle registration, toll collection, and accident reporting processes. 𝑈𝑒𝑑𝑔𝑒 = 𝑇𝑝𝑟𝑜𝑐𝑒𝑠𝑠 𝑇𝑡𝑜𝑡𝑎𝑙 The methodology uses model compression techniques (quantization and pruning) for deploying the ML models at edge. These techniques help in reducing the compute and memory footprint of ML Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 301 https://internationalpubls.com models, enabling their deployment on edge devices with limited resources. Federated learning is used to periodically update the compressed models. Security and privacy considerations The suggested approach embeds strong security protocols into the V2X communication system to safeguard it against conceivable attacks. Blockchain technology offers a secure base layer by offering tamper-proof and verifiable data exchanges. The privacy-preserving and sensitive information protecting more advanced cryptographic techniques, such as homomorphic encryption and zero- knowledge proofs, are utilized to improve privacy. On the other hand, these methods allow vehicles to build the global knowledge base without revealing sensitive information, protecting drivers' privacy. Algorithm 3: Adaptive Route Optimization Using Reinforcement Learning Input: States 𝑆, Actions 𝐴, Rewards 𝑅 Output: Optimal policy 𝜋(𝑠) 1. Initialize: o Define Q-values 𝑄(𝑠, 𝑎) for all state-action pairs 𝑠, 𝑎. o Set learning rate 𝛼, discount factor 𝛾, and exploration rate 𝜖. 2. For Each Episode: o Observe the current state 𝑠. o Select an action 𝑎 using an 𝜖-greedy policy. o Execute action 𝑎, observe the reward 𝑟 and next state 𝑠 ′. o Update Q-values: 𝑄(𝑠, 𝑎) = 𝑄(𝑠, 𝑎) + 𝛼 ⋅ [𝑟 + 𝛾 ⋅ 𝑚𝑎𝑥𝑎𝑄(𝑠 ′, 𝑎) − 𝑄(𝑠, 𝑎)] o Update the current state 𝑠 = 𝑠 ′. 3. Derive Policy: After all episodes, derive the optimal policy: 𝜋(𝑠) = 𝑎𝑟𝑔𝑚𝑎𝑥𝑎𝑄(𝑠, 𝑎) Output: Return the optimized policy 𝜋(𝑠). The method further comprises a step of detecting and defanging malicious activities in network. ML- aided intrusion detection systems (IDS) are implemented at edge nodes to monitor for suspicious activities (eg unauthorized access, abnormal communication patterns). Anomaly Detection Models are used by the systems to mark threats and take counteractions. Experimental Verification and Comparative Results In order to verify the proposed methodology, a simulated 6G-V2X testbed is developed. This testbed emulates realistic traffic scenarios and assesses the integrated blockchain, ML, and 6G framework performance. The system's abilities are judged on key performance indicators, including transaction throughput, latency, packet delivery ratio and accuracy of decision making, to name a few. The experimental results show significant improvements in network performance, security, and decision-making accuracy compared to baseline frameworks. Moreover, the combination of blockchain technology ensures secure and transparent data exchange, and the use of ML algorithms enhances the predictive capability of the system. The fusion of 6G networks and edge computing reduces latency and facilitates real-time communication, confirming the suitability of the framework for the deployment of autonomous vehicle networks. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 302 https://internationalpubls.com The proposed methodology could be a solution for problems of V2X communication in future autonomous vehicles. The methodology sets forth future transportation systems based on blockchain, ML, and 6G technology integration. By integrating decentralized edge computing, advanced cryptographic techniques (e.g., homomorphic encryption), and federated learning, the system provides a unique solution for enabling intelligent, secure, and sustainable autonomous vehicle networks. 4. RESULTS This section shows that the method can provide secure, scalable, and efficient communication for automated vehicle network. Blockchain performance The transaction rate is quite good for the blockchain-based framework and is found to be 10,000 transactions/second (as shown in Table 2). A consensus latency of 200 ms demonstrates how quickly transactions can be validated and appended by the system. Table 2: Blockchain Performance Metrics Metric Value Transaction Throughput 10,000 transactions/sec Consensus Latency 200 ms Energy Consumption 2 J/transaction Moreover, the energy usage per transaction is recorded at an impressively low 2 Joules, implying the efficiency of the framework in dealing with the system of computational requirements for secure data exchange. Such performance confirms the applicability of lightweight consensus mechanisms such as the PoS-PBFT hybrid mechanism used in the framework. Figure 3: Blockchain Transaction Throughput and Latency Trends Performance evaluation of machine learning models Its machine learning models built into the system are very accurate for key applications. Models that are 95% accurate in traffic prediction lead to robust route optimization and management of congestion. Besides, collision avoidance models achieve a 97% accuracy rate, exceeding predictions and proving its potential to enhance safety in real time. Table 3: Machine Learning Model Accuracy Model Type Accuracy (%) Precision (%) Recall (%) Traffic Prediction 95 92 94 Collision Avoidance 97 95 96 Dynamic Traffic Control 93 91 92 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 303 https://internationalpubls.com This enables dynamic traffic control models to be accurate competitively up to 93% which adds further value to intelligent transportation systems. These findings highlight the significance of utilizing cutting-edge ML algorithms for enhancing key vehicular communication operations. Figure 4: Comparison of Machine Learning Model Accuracies Performance of 6G Network The tabular representation of 6G networks capabilities presented in Table 4 demonstrates that ultra- low latency high-speed communication is dependent on these capabilities of the new-generation communication network. As highlighted, the latency of under 1 ms observed indicates the capability of the network to keep pace with real-time data transmission, which is paramount in the operation of autonomous vehicles. Table 4: 6G Network Performance Parameter Value Latency <1 ms Bandwidth 1 Tbps Packet Delivery Ratio 99.99% Operating on a bandwidth of 1 Tbps and packet delivery ratio of 99.99%, communication is seamless and efficient even under the heaviest data load conditions. The study shows that 6G networks would be more suitable for next-generation vehicular communication. Figure 5: 6G Network Latency Distribution Metrics related to edge computing As shown in Table 5, edge computing not only reduces data processing latency by 50 ms compared to traditional cloud computing. Thirdly, the 85% energy efficiency means that this is a power-efficient system that gives you low resource consumption for high performance. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 304 https://internationalpubls.com Table 5: Edge Computing Metrics Metric Value Processing Time 50 ms Energy Efficiency 85% Data Reduction 70% Additionally, the 70% data reduction rate not only shows the power of the edge nodes to preprocess data locally, but also alleviates the burden on central servers. This decentralized design allows the system to be more scalable and responsive. Performance of Federated Learning Federated learning helps preserve data privacy by permitting distributed training of models without sharing the raw data. In Table 6 it shows that the model convergence time is lower (5 minutes), so that all updates on the network can be done in real time. Table 6: Federated Learning Performance Metric Value Model Convergence Time 5 minutes Communication Overhead 15% Data Privacy Level High The communication overhead is only 15% in total, which proves the efficiency of decentralized training process. Moreover, federated learning keeps the amount of data as low as possible, making it easier to comply with privacy rules and increasing users' trust. Reinforcement Learning Policy Performance Route decision optimization through reinforcement learning has proved to be very effective (Table 7). In the urban traffic simulation, RL-based policy achieves a reward score of 85 and converges at 50 episodes. For highway navigation, we see a score of 92 as a reward over the timber, with convergence after only 40 episodes. Table 7: Reinforcement Learning Policy Performance Scenario Reward Achieved Episodes to Converge Urban Traffic 85 50 Highway Navigation 92 40 Mixed Environments 88 45 In mixed environments the policy achieves a reward score of 88, maintaining a balanced performance. These findings confirm the applicability of reinforcement learning for adaptive and context-aware route optimization. Security Evaluation Metric The security aspects of the proposed system are summarized in Table 8. Machine learning based anomaly detection systems can efficiently catch malicious activities with an intrusion detection rate of 98%. Table 8: Security Evaluation Metrics Attack Type Detection Rate (%) Response Time (ms) Intrusion Detection 98 150 Data Manipulation 95 200 Network Eavesdropping 97 180 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 305 https://internationalpubls.com Data manipulation and network eavesdropping detections have a maximum response time of 200 ms and 180 ms, respectively, within which countermeasures to potential attacks are taken. Such metrics indicate the framework makes it possible to protect the communication network against a number of security threats. Analysis of the Energy Consumption Table 9 shows the energy consumption analysis, which indicates better resource utilization of the system. Figure 6: Energy Consumption by Components On the average 2 Joules per transaction are consumed by blockchain nodes and 1.5 Joules by edge computing devices. In contrast, machine learning models are not even at 1 Joule (which some models achieve), illustrating their suitability for resource-constrained environments. Table 9: Energy Consumption Analysis Component Energy Consumption (J) Blockchain Nodes 2 Edge Computing Devices 1.5 Machine Learning Models 1 This analysis of energy efficiency suggests that the new framework is appropriate for endeavored growing for the large dimensions of encoded images. Figure 7: Reinforcement Learning Reward Convergence Comparison of Simulation Results Table 10 provides a comparative analysis between the suggested framework and traditional communication systems, which illustrates the dominance of the proposed framework. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 306 https://internationalpubls.com Table 10: Simulation Results Comparison Framework Latency (ms) Accuracy (%) Scalability Proposed Framework <1 97 High Traditional Framework 10 85 Medium The proposed system yields latency < 1 ms and 97% accuracy, far surpassing the traditional framework which yields latency 10 ms and 85% accuracy. Additionally, the extreme scalability of the proposed system also confirms its compatibility for future autonomous vehicle networks. 5. CONCLUSION The advent of advanced technologies such as blockchain, machine learning, and 6G networks has paved the way for the creation of cutting-edge communication models that can provide trust, scalability, and efficiency for autonomous vehicles. In this paper, we introduce a holistic method that integrates these cutting-edge technologies to tackle pressing issues in Vehicle-to-Everything (V2X) communication, such as data security, reducing latency, scalability and enabling real-time decision- making. Its results show a drastic improvement over classical systems from evaluations and simulations, making it an important step toward autonomous vehicle eco-systems of a new generation. Improving Security and Transparency It is widely believed that V2X networks can benefit significantly from implementing blockchain technology to improve their security and transparency. The data agnostic layer uses a hybrid consensus mechanism (PoS & PBFT) to guarantee tamper-proof data exchange and eliminates the risks of centralized architectures. Smart contracts additionally automate critical vehicular processes, including toll collection, accident reporting, and vehicle authentication, and create a verifiable chain of trust between network participants. The summary of the results in Table 2 indicates that the framework can reach very-high transaction throughput and a very low consensus latency, so it can be effectively used for real-time transmission of messages between vehicles. Machine Learning for Real-Time Decision-Making In the proposed framework, ML helps improve the predictive power, and solve the real-time decision making. The models used for traffic prediction, collision avoidance, and dynamic traffic control provide accuracy rates in the order of 99% as displayed in Table 3. With federated learning, model training can be done across edge nodes in a way that their data never leaves the local device. This decentralized strategy reduces the communication overhead and facilitates a faster model convergence, as shown in Table 6. By incorporating reinforcement learning, autonomous vehicles can learn and make more informed route decisions in real-time, adjusting to complex and dynamic traffic scenarios. Ultra-Low Latency Communication enablers over 6G Networks At its core, the proposed framework revolves around the deployment of 6G networks, which offer the ultra-low latency and high-speed connectivity necessary for instantaneous communication between autonomous vehicles. Table 4 shows the performance of seamless data exchange supported by the network with less than 1ms latency and 1Tbps bandwidth. Such capabilities enable the ability for vehicles to communicate in real-time with one another, other vehicles, and the surrounding infrastructure, creating added safety and operational efficiency This unique characteristic, along with the network slicing technology, allows 6G to better allocate available resources to meet various communication needs. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 4s (2025) 307 https://internationalpubls.com Scalable and efficient decentralised edge computing Edge computing is integral to the framework’s ability to analyze data locally, reducing the need for centralized servers and minimizing latency. Table 5 illustrates that edge nodes can attain a processing latency of 50 ms with an energy efficiency of 85%, highlighting their contribution to improving the system's responsiveness. The edge nodes have been integrated with the blockchain for security purpose and efficient data management and model compression techniques have also been utilized for enabling the machine learning algorithms to run on resource constrained devices. Aside from improving scalability, this decentralised approach strengthens the system’s resistance to network failures. Experimental Verification and Major Takeaways Experimental evaluations are performed on a simulated 6G-V2X test of the proposed framework. The comparison analysis of the framework with the traditional systems is encapsulated in Table 10, which shows that framework outperforms traditional systems achieving the latency of the framework within 1 ms while the accuracy can reach up to 97%. Figures 3 to 7 present visual analysis to substantiate these findings, showcasing the capability for the framework to retain consistent performance across diverse traffic loads and dynamic conditions. These results confirm that the framework is ready for real-world deployment in autonomous vehicle networks. Future Implications and Directions Future Implications and Directions This proposed framework is a significant contribution to the advancement of intelligent transportation systems and smart cities. The low latency allows to solve mayor issues that V2X Communication faces, setting a solid base for the future of in-vehicle technology, particularly autonomous vehicles. Nevertheless, some paths for future investigation remain. Advanced cryptographic techniques, like homomorphic encryption and zero-knowledge proofs, could be used to further improve the privacy and security of the data. In addition, the adaptation of energy-efficient blockchain consensus protocols and lightweight ML models will also help to reduce resource usage. Scalability and interoperability of the framework are further and crucial aspect for investigation in the future. With an increasing number of connected vehicles and devices, seamless communication across varied systems will become vital. Establishing standardized protocols and frameworks can aid in this interoperability for the widespread implementation of autonomous vehicular networks. In addition, combining the suggested framework with new technologies like quantum computing and AIoT have a lot of potential for enhancing the functionalities of V2X communication systems. Overall, the work shows that the suggested framework can effectively combine unitary components of blockchain, machine learning, and 6G systems and leverage their advantages to develop a secure, transparent, and scalable automobile communication architecture. In summary, the results and analyses of the experiment presented in this paper highlight the strength of the framework in tackling the challenges of V2X communication, as well as its suitability for large-scale deployment in future transportation ecosystems. 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