This research investigates the challenges faced by traditional encryption methods in practical communication scenarios, particularly regarding resource limitations. Furthermore, it examines the incorporation of Deep Reinforcement Learning (DRL) in autonomous vehicles to manage their trajectories within the context of Connected and Autonomous Vehicles (CAVs).
The research methodology includes applying Deep Reinforcement Learning (DRL) techniques to manage the trajectories of autonomous cars in the realm of connected and autonomous vehicles (CAVs). Additionally, it involves a thorough investigation into the deployment of Blockchain technology, consensus procedures, and decentralized data storage mechanisms.
The results demonstrate that traditional encryption is not feasible for real-world communications with limited resources. Furthermore, integrating DRL and Blockchain technology proves to be effective in improving the performance of autonomous car systems, lowering training expenses, and creating secure, globally accessible government-managed transportation systems that enhance data integrity and accessibility.
In summary, the study emphasizes that traditional encryption methods are not suitable for practical communication situations and highlights the notable progress enabled by DRL in managing autonomous vehicle trajectories. Additionally, the incorporation of Blockchain technology not only guarantees secure data transfer but also lays the groundwork for a transportation blockchain accessible worldwide, transforming the future of the industry.
Read the research here; https://bit.ly/3K2cOm4
The Open Transportation Journal
10.2174/0126671212291400240315084722
Navigating the Future of Secure and Efficient Intelligent Transportation Systems using AI and Blockchain
28-Mar-2024