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@article{187983,
        author = {M.Dileep Kumar and J.Sravan kumar and S.Rajendra Prasad and R.S.R. Krishnam Naidu},
        title = {Comprehensive Review of Autonomous Electric Vehicle Technology},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {802-808},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187983},
        abstract = {The convergence of autonomous driving and electric vehicle (EV) technologies is catalyzing a paradigm shift in transportation, offering transformative gains in safety, sustainability, and system efficiency. Autonomous Electric Vehicles (AEVs) synergistically integrate battery-electric propulsion with advanced automation (SAE Levels 1–5), powered by heterogeneous sensor suites—cameras, LiDAR, radar, ultrasonic systems, and inertial measurement units—and orchestrated through high-performance computing and AI-driven decision-making architectures. This review presents a systematic analysis of the AEV landscape, with emphasis on sensor fusion strategies, vehicle platforms, and control-system integration. We contextualize the historical and societal drivers—ranging from climate imperatives to traffic safety crises—that have accelerated AEV development. Synthesizing two decades of research, we highlight pivotal advances in perception, localization, path planning, and co-optimized energy management. A detailed comparative assessment of leading commercial and prototype AEVs—including Tesla’s Model series, Waymo’s Jaguar I-PACE and Zeekr RT, GM Cruise Origin, Mercedes-Benz EQS with DRIVE PILOT, and BYD Seal—examines their sensor configurations, compute hardware, battery technologies, and autonomy stacks. Despite rapid progress, critical challenges persist: sensor reliability in adverse weather, ethical AI frameworks, cybersecurity vulnerabilities, battery–autonomy co-design, V2X infrastructure dependency, and the absence of scalable validation protocols. While Level 2+/3 AEVs are increasingly deployed in controlled environments, the realization of safe, equitable, and scalable Level 4/5 autonomy demands interdisciplinary collaboration across robotics, materials science, regulatory policy, and human factors engineering. This review serves as a foundational resource for researchers, industry stakeholders, and policymakers navigating the multidimensional trajectory toward zero-emission, fully autonomous mobility ecosystems.},
        keywords = {Autonomous Electric Vehicles (AEVs), Sensor Fusion, LiDAR, Computer Vision, SAE Autonomy Levels, Battery Management Systems (BMS), Deep Learning for Perception, Vehicle-to-Everything (V2X), Functional Safety (ISO 26262), Ethical AI},
        month = {December},
        }