Advancing Autonomous Driving Through Federated Learning

Abstract

Autonomous driving is a widely studied field in both academic and industrial areas. Current solutions primarily concentrate on enhancing accuracy through centralized training using vast amounts of data. Continuous improvement of autonomous driving is essential to ensure safety, efficiency, and adaptability. This chapter will give a sight of intelligent, autonomous, and multi-agents in collaboration with AI. Furthermore, it will include some existing examples of autonomous driving cars. In conjunction with the concerns, the chapter will include a better understanding of federated learning and how we can practically implement it. The authors will delve into the principles, benefits, and challenges associated with federated learning in the context of autonomous driving. Also, the chapter will dive deeply into the challenges faced using centralized learning in self-driving cars and will explain the techniques and solutions to overcome the same problems and techniques for performance evaluation and optimization, along with case studies and real-world implementations. The final result of this chapter is the solution proposed regarding a safer approach for operation of autonomous vehicles, which is Federated Learning; a decentralized approach to machine learning that leverages the power of distributed computing and collaboration to enable efficient and privacy-preserving machine learning on de-centralized datasets. To make significant advances in autonomous vehicle technology, while protecting the privacy of individuals, this enables a pooling of intelligence among various entities like vehicles. The chapter highlights the collaborative approach that drives its success, exploring the concept of advancing autonomous driving through Federated Learning.