# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 2 Related Research  
Blockchain technology has been extensively explored as a means of ensuring data integrity and security across various domains, including autonomous vehicle systems [12]. Traditional blockchain-based platforms such as Ethereum and Hyperledger employ cryptographic hashing and distributed consensus mechanisms to prevent unauthorized data modifications. However, these solutions face inherent scalability limitations, particularly in real-time environments that demand low-latency data validation [13].  
To improve blockchain performance in asynchronous networks, several Byzantine Fault-Tolerant (BFT) consensus algorithms have been proposed. PBFT (Practical Byzantine Fault Tolerance) [14] and Tendermint [15], for instance, adopt a partially synchronous network model to realize consensus under normal operating conditions. Nevertheless, these approaches often encounter network bottlenecks and computational overhead due to their reliance on sequential transaction processing. To address these inefficiencies, asynchronous BFT (ABFT) mechanisms have been introduced [16]. Hashgraph utilizes a gossip-based protocol to achieve ABFT, enabling parallel transaction processing while ensuring data integrity and immutability.  
Recent studies have underscored the advantages of hashgraph in AI-driven applications. [PERSON] et al. [17] investigated its integration into distributed AI models, demonstrating its effectiveness in managing high-throughput data transactions with minimal latency. Alternative consensus protocols such as HoneyBadgerBFT [18] and BEAT [19] have introduced probabilistic mechanisms aimed at enhancing communication efficiency in adversarial network environments. These techniques have been successfully applied to decentralized AI training frameworks, improving both data availability and security while offering viable alternatives to conventional blockchain-based data management approaches.  
However, the application of hashgraph in autonomous driving systems remains largely underexplored. Existing implementations have primarily focused on financial services and general AI data management. Recent studies have emphasized the potential of hashgraph-based key management frameworks for secure vehicle-to-vehicle communication, employing logical key hierarchies and batch rekeying to minimize transaction latency and adapt effectively to dynamic traffic conditions [20]. Concurrently, directed acyclic graph (DAG)-based blockchain architectures have emerged as scalable alternatives to conventional blockchains. These systems enable high-throughput, low-latency processing, rendering them particularly well-suited for real-time autonomous vehicle coordination and microtransaction scenarios, such as electric vehicle (EV) charging [21]. Moreover, the integration of blockchain with artificial intelligence (AI) and fog computing has been proposed to enhance the reliability of AI-driven decision-making in autonomous systems by ensuring secure and tamper-resistant data processing [22]. Further research is warranted to evaluate its feasibility in safety-critical environments where real-time data processing and decision-making are essential.  
As summarized in Table 1, previous studies have addressed only individual aspects, such as hash storage or real-time verification, without presenting an integrated framework. In contrast, our approach combines smart contracts, real-time validation, and cross-platform benchmarking between Hedera and Ethereum, addressing both theoretical limitations and deployment challenges in blockchain-based AI data verification.