Key points are not available for this paper at this time.
The advancement of autonomous vehicles (AVs) heavily relies on their ability to process high volumes of sensor data and make real-time decisions. This paper explores how the integration of data engineering, machine learning (ML), artificial intelligence (AI), and a cohesive hardware-software approach can further enhance the performance and safety of AVs. We propose a comprehensive framework that leverages advanced data engineering techniques for efficient data management, employs state-of-the-art ML models for accurate perception and prediction, and utilizes AI- driven strategies for decision-making and control. The proposed solutions are designed to be effective in areas with limited internet connectivity and can operate on low- powered hardware, even with outdated software.
Brahma Reddy Katam (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: