Journal of Advancements in Robotics Review Article

An Insight Review of Autonomous Vehicle Architecture, Sensors, and Challenges

  1. Qutaiba I. Ali Department Computer Engineering, College of Engineering, University of Mosul
  2. Zeina Ali M. Department Computer and Information Engineering, College of Electronics Engineering, Ninevah University

Abstract

Autonomous vehicles (AVs) are revolutionizing transportation by integrating advanced sensors, artificial intelligence, and communication networks to enhance safety and efficiency. This review explores the architecture of AVs, focusing on perception, localization, path planning, and control. A detailed analysis of AV sensors, including LiDAR (light detection and ranging), radar, cameras, and inertial navigation systems, highlights their roles, advantages, and limitations. Additionally, the paper examines in-vehicle and inter-vehicle communication networks, such as CAN (controller area network), LIN (local interconnect network), FlexRay, and Ethernet, which facilitate real-time data exchange. The study also addresses the key challenges AVs face, including cybersecurity threats, data processing, legal policies, and ethical concerns. By synthesizing recent advancements and ongoing challenges, this paper provides a comprehensive understanding of the state of AV technologies and their future prospects.

Keywords

References (37)

  1. Van Brummelen J, O’Brien M, Gruyer D, Najjaran H. Autonomous vehicle perception: the technology of today and tomorrow. Transport Res Part C: Emerg Technol. 2018; 89: 384–406.
  2. SAE International. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. [Online]. SAE International. 4970, no. 724. 2018. pp. 1–5. Available at https://www.sae.org/standards/content/j3016_202104/
  3. Tesla. AI & Robotics [Online]. Tesla. 2025. Available at https://www.tesla.com/AI.
  4. Fayyad J, Jaradat MA, Gruyer D, Najjaran H. Deep learning sensor fusion for autonomous vehicle perception and localization: a review. Sensors. 2020; 20 (15): 4220.
  5. BMW Group. Intel and Mobileye Team Up to Bring Fully Autonomous Driving to Streets by 2021. [Online]. BMW Group Press Club. 2021. Available at https://www.press.bmwgroup.com/deutschland/article/detail/T0261586EN/bmw-group-intel-and-mobileye-team-up-to-bring-fully-autonomous-driving-to-streets-by-2021
  6. Yeong DJ, Velasco-Hernandez G, Barry J, Walsh J. Sensor and sensor fusion technology in autonomous vehicles: a review. Sensors. 2021; 21 (6): 2140.
  7. Katrakazas C, Quddus M, Chen WH, Deka L. Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transport Res Part C Emerg Technol. 2015; 60: 416–442.
  8. Pendleton SD, Andersen H, Du X, Shen X, Meghjani M, Eng YH, Rus D, Ang MH. Perception, planning, control, and coordination for autonomous vehicles. Machines. 2017; 5 (1): 6.
  9. Kaviani S, O'Brien M, Van Brummelen J, Najjaran H, Michelson D. INS/GPS localization for reliable cooperative driving. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, British Columbia, Canada, May 15–18, 2016. pp. 1–4.
  10. Kato S, Tsugawa S, Tokuda K, Matsui T, Fujii H. Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications. IEEE Trans Intell Transport Syst. 2002; 3 (3): 155–161.
  11. Campbell S, O'Mahony N, Krpalcova L, Riordan D, Walsh J, Murphy A, Ryan C. Sensor technology in autonomous vehicles: a review. In: 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK, June 21–22, 2018. pp. 1–4.
  12. Petit F. The Beginnings of LiDAR—A Time Travel Back in History. [Online]. Blickfeld.com. April 23, 2020. Available at https://www.blickfeld.com/blog/the-beginnings-of-lidar/
  13. Rahimi A, He Y. A review of essential technologies for autonomous and semi-autonomous articulated heavy vehicles. In: Proceedings of the Canadian Society for Mechanical Engineering International Congress, Charlottetown, Prince Edward Island, Canada, June 21–24, 2020. pp. 1–8.
  14. Jahromi BS. Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles. PhD Dissertation. Chicago, IL, USA: University of Illinois at Chicago; 2019.
  15. Sume A, Gustafsson M, Herberthson M, Janis A, Nilsson S, Rahm J, Orbom A. Radar detection of moving targets behind corners. IEEE Trans Geosci Remote Sensing. 2011; 49 (6): 2259–2267.
  16. Ziebinski A, Cupek R, Erdogan H, Waechter S. A survey of ADAS technologies for the future perspective of sensor fusion. In: Nguyen N, Iliadis L, Manolopoulos Y, Trawiński B, editors. Computational Collective Intelligence: 8th International Conference, ICCCI 2016 Cham, Switzerland: Springer International Publishing; 2016. pp. 135–146.
  17. Ignatious HA, Khan M. An overview of sensors in autonomous vehicles. Procedia Computer Sci. 2022; 198: 736–741.
  18. Choi E, Song H, Kang S, Choi JW. High-speed, low-latency in-vehicle network based on the bus topology for autonomous vehicles: automotive networking and applications. IEEE Vehic Technol Mag. 2021; 17 (1): 74–84.
  19. Ayala R, Mohd TK. Sensors in Autonomous Vehicles: A Survey. Journal of Autonomous Vehicles and Systems. 2021;1(3). doi:10.1115/1.4052991
  20. Muoio D. Tesla's Autopilot System Is Partially to Blame for a Fatal Crash, Federal Investigators Say. [Online]. Business Insider. September 12, 2017. Available at https://www.businessinsider.com/tesla-autopilot-fatal-crash-ntsb-2017-9
  21. Efrati A. Uber Finds Deadly Accident Likely Caused by Software Set to Ignore Objects on Road. [Online]. The Information 5, No. 7, 2018. Available at https://www.theinformation.com/articles/ uber-finds-deadly-accident-likely-caused-by-software-set-to-ignore-objects-on-road
  22. Wang J, Liu J, Kato N. Networking and Communications in Autonomous Driving: A Survey. IEEE Communications Surveys & Tutorials. 2019;21(2):1243-1274. doi:10.1109/comst.2018.2888904
  23. Fagnant DJ, Kockelman K. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice. 2015;77:167-181. doi:10.1016/j.tra.2015.04.003
  24. Navet N, Simonot-Lion F. In-vehicle communication networks – a historical perspective and review. In: Zurawski R, editor. Industrial Communication Technology Handbook. 2nd edition. New York, NY, USA: Taylor & Francis; 2013. pp. 1204–1223.
  25. Tuohy S, Glavin M, Hughes C, Jones E, Trivedi M, Kilmartin L. Intra-Vehicle Networks: A Review. IEEE Transactions on Intelligent Transportation Systems. 2015;16(2):534-545. doi:10.1109/tits.2014.2320605
  26. Lo Bello L, Patti G, Leonardi L. A Perspective on Ethernet in Automotive Communications—Current Status and Future Trends. Applied Sciences. 2023;13(3):1278. doi:10.3390/app13031278
  27. Cataldo C. Ethernet Network in the Automotive Field: Standards, Possible Approaches to Protocol Validation and Simulations. Master’s Thesis. Turin, Italy: Politecnico di Torino; 2021.
  28. Anderson JM, Nidhi K, Stanley KD, Sorensen P, Samaras C, Oluwatola OA. Autonomous Vehicle Technology: A Guide for Policymakers. Santa Monica, CA, USA: RAND Corporation; 2014. Available at https://www.rand.org/pubs/research_reports/RR443-2.html
  29. Bagloee SA, Tavana M, Asadi M, Oliver T. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation. 2016;24(4):284-303. doi:10.1007/s40534-016-0117-3
  30. Sivaraman S. Learning, Modeling, and Understanding Vehicle Surround Using Multi-Modal Sensing. PhD Dissertation. San Diego, CA, USA: University of California, San Diego; 2013. Available at https://escholarship.org/uc/item/05h602hb
  31. Chowdhury M, Dey K. Intelligent Transportation Systems-A Frontier for Breaking Boundaries of Traditional Academic Engineering Disciplines [Education]. IEEE Intelligent Transportation Systems Magazine. 2016;8(1):4-8. doi:10.1109/mits.2015.2503199
  32. Sharma R. Big Data for Autonomous Vehicles. Studies in Computational Intelligence. 2021:21-47. doi:10.1007/978-3-030-65661-4_2
  33. Ali QI. Securing solar energy‐harvesting road‐side unit using an embedded cooperative‐hybrid intrusion detection system. IET Inform Security. 2016; 10 (6): 386–402.
  34. Ibrahim Q. Design & implementation of high-speed network devices using SRL16 reconfigurable content addressable memory (RCAM). Int Arab J e Technol. 2011; 2 (2): 72–81.
  35. Alhabib MH, Ali QI. Internet of autonomous vehicles communication infrastructure: a short review. Diagnostyka. 2023; 24 (3): 2023302.
  36. Ali QI. Realization of a robust fog-based green VANET infrastructure. IEEE Syst J. 2022; 17 (2): 2465–2476.
  37. Ali QI, Jalal JK. Practical design of solar-powered IEEE 802.11 backhaul wireless repeater. In: 2014 6th International Conference on Multimedia, Computer Graphics and Broadcasting, Hainan, China, December 20–23, 2014. pp. 9–12.
Support