Cruising into the Future: Navigating the Challenges and Advancements in Autonomous Vehicle Technology

Abstract

Autonomous vehicles have numerous challenges facing today's driving in urban areas. The installation of artificial intelligence in autonomous cars has become capable of communicating with road users and determining the user's intentions. With the help of AI, computer vision, sensors, machine learning, deep learning, and other fields of study, they analyze the environment for autonomous cars. The behavior of users aids in the understanding and analysis of various factors such as the environment, traffic lights, and demographics. In this paper, we determine these factors involve autonomous vehicle surveying to assess the behavior of pedestrian drivers. Autonomous vehicles use various techniques and classifiers to analyze pedestrian behavior further approaches. We sincerely need help to review interaction challenges and independent vehicle design approaches. Understanding is complex reasoning to communicate visual perception. The main aim of this survey is to discuss the various sensors and their roles in autonomous vehicles. Communication technologies facilitate transmission in three main categories short-range, medium-range, and long-range. In addition, the automotive industry is developing and manufacturing to connect autonomous vehicles to communication safety and promote transportation. The survey also focuses on protecting and implementing various attacks that threaten the security of the CAVs. Hence, AV's environment of CAVs highlights the future and current challenges to identify the security issues-based communication networks and cyber-security risks. The cyber-attacks have many types, but we will focus on the primary attacks or damage to the data.