# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 6 Conclusions and Future Research  
Our experimental findings validated Hedera Hashgraph as a more efficient and scalable alternative to Ethereum for managing secure datasets in AI training workflows. Its low latency and high throughput render it particularly suitable for real-time applications, especially in the domain of autonomous vehicle systems. Ensuring the integrity of training data is essential for building reliable AI models, particularly in safety-critical contexts such as autonomous driving. The proposed framework overcomes the limitations of conventional blockchain-based methods by enabling faster verification, minimizing computational overhead, and enhancing scalability through the use of an asynchronous Byzantine Fault-Tolerant (aBFT) consensus algorithm. Despite the promising results, this study has several limitations. First, the evaluation was conducted in a simulated environment using CARLA and has not yet been validated under real-world deployment conditions. Second, the  
Figure 4: Uploading time comparison.  
Figure 5: Checking time comparison.  
current system primarily focuses on time and cost efficiency without assessing other dimensions such as energy consumption or resilience against potential security threats. Third, the dataset used was limited in diversity, consisting of a fixed set of images that may not fully capture the variability encountered in actual driving scenarios. Addressing these limitations will be a key focus of future research.  
Future studies will focus on integrating deep learning models that leverage the verified datasets for improved AI inference and decision-making accuracy. Additionally, we plan to further optimize smart contract execution on the Hedera platform by investigating lightweight transaction encoding and parallelized logic structures. Field-scale evaluations will be conducted in real-world autonomous driving scenarios to assess the robustness of the system under continuous high-frequency data ingestion. Beyond autonomous vehicles, the proposed framework can be extended to other data-intensive fields--such as medical diagnostics, industrial automation, and smart city infrastructure--where tamper-evident and trustworthy data validation is vital for system performance and safety.  
Conceptualization, Y.S.; Methodology, Y.S.; Software, Y.S. and Y.C.; Investigation, Y.S.; Writing--original draft preparation, Y.S.; Supervision, Y.P.; Project administration, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00207391: Development of Hashgraph-based Blockchain Enhancement Scheme and Implementation of Testbed for Autonomous Driving). The APC was funded by the same agency.  
Not applicable.  
The simulation data generated using the CARLA autonomous driving simulator are available upon reasonable request.  
The authors thank the Institute of Information and Communications Technology Planning and Evaluation (IITP) for their support.  
The authors declare no conflict of interest.