# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 1 Introduction  
The global adoption of autonomous vehicles is rapidly transforming modern transportation systems [1]. Companies such as Tesla and Waymo are at the forefront of developing self-driving technologies, while cities such as San Francisco are actively deploying driverless taxi services [2]. At present, China is making substantial progress in autonomous vehicle research through large-scale, government-backed initiatives, underscoring its ambition to become a global leader in this field. These efforts are closely tied to advancements in deep learning and artificial intelligence (AI). Despite significant progress, ensuring the reliability and safety of autonomous vehicles remains a critical challenge. These systems must function effectively under unpredictable conditions, such as adverse weather, unexpected road obstacles, and deviations from predefined routes. Their performance heavily depends on the quality of training data; poor or biased data can result in critical decision-making errors [3]. For instance, if an AI system misinterprets a partially obscured or degraded traffic sign, this may result in incorrect speed adjustments or hazardous driving behavior, posing serious safety risks. While various factors influence the reliability of AI models, this study focuses on ensuring the integrity and accuracy of training data. To address this issue, blockchain technology has attracted considerable attention from researchers and developers. Leveraging robust cryptographic mechanisms--such as hash functions and digital signatures--blockchain helps to prevent data tampering [4]. Moreover,distributed ledger technology (DLT) enhances system resilience by eliminating single points of failure, thereby improving overall data reliability. These properties render blockchain a promising tool for safeguarding training datasets against unauthorized modifications. In the context of autonomous driving AI, blockchain has been explored as a means of securing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, ensuring the integrity of the data exchanged among autonomous vehicles and mitigating risks such as data manipulation and identity forgery. Recent studies have proposed blockchain-based frameworks to protect self-driving vehicles from cyberattacks [5]. These frameworks ensure the verifiability and authenticity of shared sensor data, which are crucial for AI-driven decision-making in real-time traffic environments [6]. Beyond securing communication channels, blockchain technologies have also been employed to manage training and sensor datasets in AI-driven environments. This is particularly important in scenarios where data integrity, traceability, and verifiability are essential for building secure and trustworthy AI models. Several studies have proposed blockchain-based frameworks that enable the secure storage, sharing, and validation of AI-related data, especially in domains such as autonomous driving and industrial IoT [7; 8]. By leveraging blockchain's decentralized and tamper-resistant characteristics, these approaches enhance the transparency and reliability of end-to-end AI pipelines. However, conventional blockchain architectures face inherent limitations that restrict their applicability to real-time AI applications [9]. High computational overhead, slow transaction speeds, and limited scalability remain significant challenges. Many blockchain networks rely on computationally intensive consensus algorithms, resulting in excessive energy consumption and high operational costs. Furthermore, blockchain transactions are typically processed sequentially, causing network congestion and increased latency. These limitations pose critical barriers for autonomous driving systems, where real-time data verification and low-latency decision-making are essential for ensuring safety and performance. Overcoming these barriers requires the exploration of alternative distributed ledger technologies that offer greater efficiency and scalability.