AI-BASED MANAGEMENT OF AUTONOMOUS TRANSPORT ROBOTS A COMPREHENSIVE SCIENTIFIC REVIEW
Keywords:
Autonomous Vehicles, Artificial Intelligence, Deep Learning, Sensor Fusion, V2X Communication, LiDAR, Self-Driving Systems, Transport Robotics, Safety AI, Smart MobilityAbstract
The rapid advancement of artificial intelligence (AI) has catalyzed a transformational shift in transportation systems worldwide. This paper investigates the application of AI-driven management systems for autonomous transport robots (ATRs), encompassing self-driving vehicles, delivery drones, and logistics robots. We examine the core technological pillars — machine learning, computer vision, sensor fusion, and vehicle-to-everything (V2X) communication — and evaluate their integration within real-time decision-making frameworks. Statistical data, market projections, and comparative analyses are presented alongside architectural diagrams and performance benchmarks. The paper further discusses safety protocols, ethical challenges, regulatory landscapes, and future trajectories through 2030. Findings indicate that AI-managed autonomous transport systems can reduce traffic accidents by up to 74% and improve logistics efficiency by 35–60%, presenting compelling arguments for accelerated adoption supported by appropriate policy frameworks.
References
[1] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
[2] Nilsson, N. J. (1984). Shakey the Robot. SRI Technical Note 323. SRI International, Menlo Park, CA.
[3] McKinsey & Company. (2023). Autonomous Driving’s Future: Convenient and Connected. McKinsey Center for Future Mobility.
[4] Grand View Research. (2024). Autonomous Vehicle Market Size, Share & Trends Analysis Report, 2024–2030. GVR-4-68040-013-7.
[5] National Highway Traffic Safety Administration (NHTSA). (2022). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. DOT HS 812 506.
[6] KPMG International. (2024). Autonomous Vehicles Readiness Index. KPMG International Cooperative.
[7] Waymo LLC. (2024). Waymo Safety Report: On the Road to Fully Self-Driving. Waymo Annual Safety White Paper.
[8] International Telecommunication Union (ITU). (2024). IMT-2020 Standards for Intelligent Transportation and V2X. ITU-R M.2410.
[9] Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767.
[10] Alahi, A., et al. (2016). Social Force: Human Trajectory Prediction in Crowded Spaces. CVPR 2016, IEEE Conference on Computer Vision.
[11] Tesla, Inc. (2023). Tesla Vehicle Safety Report Q4 2023. NHTSA Voluntary Safety Self-Assessment.
[12] Hecht, J. (2018). Lidar for Self-Driving Cars. Optics and Photonics News, 29(1), 26–33.
[13] Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2018). On a Formal Model of Safe and Scalable Self-Driving Cars. arXiv:1708.06374.
[14] Singapore Smart Nation Initiative. (2023). Annual Report on Intelligent Transport Systems Deployment. Smart Nation Digital Government Office.
[15] European Commission. (2023). C-Roads Platform: C-ITS Services and Deployment Report. Connecting Europe Facility.
[16] Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The Social Dilemma of Autonomous Vehicles. Science, 352(6293), 1573–1576.
[17] Awad, E., et al. (2018). The Moral Machine Experiment. Nature, 563(7729), 59–64.
[18] Miller, C., & Valasek, C. (2015). Remote Exploitation of an Unaltered Passenger Vehicle. DEF CON 23 Proceedings.
[19] Sha, H., et al. (2023). LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving. arXiv:2310.03026.
[20] Davies, M. (2021). Taking Neuromorphic Computing to the Next Level with Loihi 2. Intel Labs White Paper.
[21] NVIDIA Corporation. (2024). DRIVE Sim: High-Fidelity Autonomous Vehicle Simulation Platform. NVIDIA Developer Documentation.
[22] FAA. (2024). Urban Air Mobility (UAM) Concept of Operations, Version 2.0. Federal Aviation Administration.
[23] Taddeo, M., & Floridi, L. (2018). How AI Can Be a Force for Good. Science, 361(6404), 751–752.
