Enhancing Safety and Efficiency in Autonomous Vehicles through Integrated AI Technologies


  • Clement Varaprasad Karu Faculty of Engineering, Sohar University, PO Box. 44|PC311, Suhar, Sultanate of Oman
  • Asadi Srinivasulu Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (crcCARE), Global Centre for Environmental Remediation/College of Engineering, Science & Environment, ATC Building | The University of Newcastle | Callaghan NSW 2308 | Australia


Autonomous vehicles, Artificial intelligence, Transportation technology, Implementation challenges, Policy analysis, Real-world validation, Deep learning algorithms, Safety and efficiency


Autonomous vehicles (AVs) stand as a groundbreaking innovation set to transform global transportation systems. Outfitted with sensors, artificial intelligence (AI), and control systems, AVs hold the promise of revolutionizing transportation by ensuring safer and more efficient travel, effectively eliminating human error and enabling seamless navigation. Nonetheless, current research on AVs reveals significant shortcomings across various domains, impeding the realization of their full potential. A notable deficiency identified in the literature is the scant attention given to specific implementations. While numerous studies provide comprehensive overviews or assessments of AV technology, they frequently lack in-depth discussions on practical applications or validation in real-world scenarios, resulting in a disconnect between theory and practice. Furthermore, papers concentrating on policy analysis or offering guidance for policymakers often lack the necessary technical depth, neglecting critical technical aspects and viable solutions. Additionally, many papers fail to sufficiently address associated challenges or constraints, offering superficial discussions on progress and opportunities without thoroughly exploring potential obstacles. This lack of comprehensive solutions exacerbates the issue, leaving significant challenges unaddressed. To address these limitations, a proposed approach could harness advanced AI methodologies, such as deep learning algorithms trained on extensive real-world driving data. Through the integration of these technologies, researchers can develop robust and verified solutions to tackle specific challenges within AV technology, ultimately enhancing safety and efficiency. Comparative evaluations between existing and proposed technologies, incorporating metrics like accuracy, precision, loss, iterations, epochs, and time complexity, will further clarify the effectiveness of integrated AI technologies in advancing AV systems.