Rademics Research Institute

Abstract

The convergence of artificial intelligence (AI), autonomous vehicles (AVs), and electric mobility is poised to redefine modern transportation systems, driving advancements in sustainability, energy management, and smart infrastructure. This chapter explores the integration of AI-powered technologies in autonomous electric vehicles (AEVs) and examines the pivotal role they play in optimizing vehicle performance, enhancing energy efficiency, and creating connected ecosystems. Key developments in machine learning, reinforcement learning, and smart charging infrastructure are discussed in the context of improving vehicle autonomy and enabling seamless communication between vehicles and urban infrastructure. Additionally, the chapter addresses the significant challenges to public trust and acceptance of AEVs, emphasizing the importance of transparent safety measures, regulatory frameworks, and ethical considerations in building consumer confidence. By highlighting the intersection of AI, electric vehicle energy management, and smart cities, this chapter provides a comprehensive outlook on the future of intelligent mobility, underscoring the transformative potential of AEVs in fostering sustainable, efficient, and safe transportation networks.

Introduction

The automotive industry is undergoing a monumental transformation driven by the convergence of artificial intelligence (AI) and electric vehicle (EV) technologies [1]. AI-powered autonomous electric vehicles (AEVs) represent the pinnacle of this evolution, offering promising solutions to address many of the environmental, economic, and safety challenges associated with traditional internal combustion engine vehicles [2]. With advancements in machine learning, deep learning, and reinforcement learning, AEVs are poised to revolutionize how transportation systems operate [3]. These vehicles are designed to operate without direct human intervention, relying on AI algorithms to perceive their environment, make real-time decisions, and optimize driving performance [4]. As the demand for sustainable transportation grows, AEVs, with their combination of autonomous driving capabilities and zero-emission electric powertrains, provide a compelling alternative to conventional vehicles, offering the potential to reduce carbon emissions, improve road safety, and alleviate urban congestion [5].

AI plays a central role in enhancing the functionality and performance of autonomous electric vehicles [6]. One of the key challenges in autonomous driving is the ability to navigate complex and dynamic environments [7]. Autonomous systems must process massive amounts of data from sensors such as LiDAR, radar, and cameras to build an accurate model of the vehicle's surroundings [8]. AI algorithms, particularly deep learning and reinforcement learning techniques, enable AEVs to learn from this data, continuously improving their ability to navigate safely and efficiently [9]. By leveraging vast amounts of real-time data, AI systems can make rapid decisions, such as adjusting speed, changing lanes, or avoiding obstacles, ensuring the vehicle operates with a level of precision and safety that surpasses human drivers. As these AI systems evolve, they are expected to handle increasingly complex scenarios, such as navigating through crowded urban streets or dealing with unpredictable weather conditions, making autonomous driving a reality for everyday transportation [10].

The integration of electric vehicles with autonomous driving capabilities presents an additional set of challenges and opportunities [11]. While electric mobility significantly reduces carbon emissions and reliance on fossil fuels, the deployment of electric vehicles on a large scale requires a robust and efficient energy infrastructure [12]. This challenge is compounded when considering the energy demands of autonomous vehicles, which require extensive computing power for real-time decision-making [13]. To address this, AI-powered systems are being integrated into EV energy management to optimize energy consumption and enhance vehicle efficiency [14]. AI can intelligently manage charging schedules, optimize energy usage during driving, and enable vehicle-to-grid (V2G) interactions, where EVs can supply stored energy back to the grid during peak demand. These innovations not only enhance the sustainability of electric vehicles but also support the broader goal of creating a smart, interconnected energy ecosystem that facilitates the transition to renewable energy sources [15].