A review of common practices and challenges in autonomous driving

Abstract

Autonomous driving technologies have garnered significant attention in recent years, promising transformative impacts on transportation systems. The landscape of transportation is undergoing a profound transformation with a focus on achieving autonomy in vehicles, ranging from advanced driver assistance systems (ADAS) to the ambitious goal of fully autonomous vehicles. This article delves into the complexity of autonomous driving, exploring both the advancements driving this paradigm shift and the intricate challenges impeding its seamless integration into daily life. The journey toward autonomy involves breakthroughs in sensor technology, artificial intelligence, and connectivity, with a crucial emphasis on sensor fusion for precise navigation. The review highlights key advancements in machine learning, computer vision, and sensor technologies that underpin autonomous driving systems, offering insights into their current capabilities and limitations. The synthesis of this review aims to provide a holistic understanding of the current state of autonomous driving, facilitating informed discussions among researchers, practitioners, and the broader public. By shedding light on both achievements and challenges, this paper contributes to the ongoing discourse on the future of autonomous driving and informs the development of strategies to address the complexities inherent in achieving widespread adoption of this transformative technology.