<![CDATA[Autonomous vehicles (AVs) have the potential to transform the transportation industry by improving road safety, reducing traffic congestion, and enhancing fuel efficiency. Significant progress has been made in autonomous vehicle (AV) technologies, especially in sensor systems, machine learning, and artificial intelligence. These advancements enable vehicles to navigate complex environments and make real-time decisions. Despite these advancements, numerous challenges remain in ensuring the safety, reliability, and acceptance of AVs. Key issues include sensor fusion, the ability to handle unpredictable scenarios, the development of universally accepted regulatory frameworks, and public trust in autonomous systems. Furthermore, ethical dilemmas, such as decision-making in unavoidable accident situations, present additional concerns. The deployment of AVs also raises questions about the impact on employment in driving-dependent industries and the infrastructure needed to support these technologies. This paper reviews the current state of AV development, examining the progress made in simulation-based testing, sensor technology, and decision-making algorithms. Additionally, it discusses the challenges that still need to be addressed, including safety concerns, regulatory barriers, and societal implications. The paper concludes by outlining potential areas for future research, such as improving sensor reliability, enhancing machine learning algorithms, integrates an analysis of simulation-based testing, decision-making algorithms, and sensor technologies with a forward-looking discussion on legal frameworks, public trust, and employment impacts, offering a holistic perspective on the path toward AV integration.]]>
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