Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

AI-Based Techniques for Autonomous Vehicle Collision Detection and Mitigation

PDF
  • Dr. Ricardo Garzón
Dr. Ricardo Garzón
Professor of Industrial Engineering, Universidad de los Andes (Venezuela)
Cover

Published 30-12-2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Ricardo Garzón, “AI-Based Techniques for Autonomous Vehicle Collision Detection and Mitigation”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 199–219, Dec. 2023, Accessed: Sep. 16, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/97

Abstract

The possibility of using advanced infrastructures, for monitoring, protecting, and providing autonomous vehicles with useful warnings, also in the presence of potentially harmful events on a single carriageway road, has recently led to a massive increase of interest in these technologies. The advanced solutions for steering actuator management and intelligent driver assistance, together with the extreme potential of communication systems (especially V2I, but also V2V), guarantee safe obstacle avoidance when the onboard devices are no longer sufficient to decide on the correct action [1].

One of the most crucial functionalities of autonomous vehicle control systems is their ability to avoid collisions with other vehicles and pedestrians [2]. In general, collision avoidance systems have a wide range of solutions to control the vehicle in critical situations, such as the warning of onboard systems, physical intervention on the brake or on the steering wheel, as well as complete management of the vehicle during autonomous driving. The most advanced systems can manage these solutions by identifying the best one for the specific situation, also involving additional data coming from the external world on the basis of calculations performed on received information. The perception of the environment is performed by using radar, cameras, lidar, and, when available, Taillight-based speed assist and control (TSAC) cameras. The latter technology enables vehicle-to-vehicle (V2V) communications, as well as vehicle-to-infrastructure (V2I) communications.

PDF

Downloads

Download data is not yet available.

References

  1. [1] V. Savic, E. M. Schiller, and M. Papatriantafilou, "Distributed Algorithm for Collision Avoidance at Road Intersections in the Presence of Communication Failures," 2017. [PDF]
  2. [2] F. Jiménez, J. Eugenio Naranjo, and Óscar Gómez, "Autonomous Manoeuvring Systems for Collision Avoidance on Single Carriageway Roads," 2012. ncbi.nlm.nih.gov
  3. Tatineni, Sumanth. "Cloud-Based Business Continuity and Disaster Recovery Strategies." International Research Journal of Modernization in Engineering, Technology, and Science5.11 (2023): 1389-1397.
  4. Vemori, Vamsi. "From Tactile Buttons to Digital Orchestration: A Paradigm Shift in Vehicle Control with Smartphone Integration and Smart UI–Unveiling Cybersecurity Vulnerabilities and Fortifying Autonomous Vehicles with Adaptive Learning Intrusion Detection Systems." African Journal of Artificial Intelligence and Sustainable Development3.1 (2023): 54-91.
  5. Shaik, Mahammad, Leeladhar Gudala, and Ashok Kumar Reddy Sadhu. "Leveraging Artificial Intelligence for Enhanced Identity and Access Management within Zero Trust Security Architectures: A Focus on User Behavior Analytics and Adaptive Authentication." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 1-31.
  6. Tatineni, Sumanth. "Security and Compliance in Parallel Computing Cloud Services." International Journal of Science and Research (IJSR) 12.10 (2023): 972-1977.
  7. [7] A. Jafar Md Muzahid, S. Fauzi Kamarulzaman, M. Arafatur Rahman, S. Akbar Murad et al., "Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework," 2023. ncbi.nlm.nih.gov
  8. [8] D. Iberraken and L. Adouane, "Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches," 2023. [PDF]
  9. [9] D. Fernández Llorca, R. Hamon, H. Junklewitz, K. Grosse et al., "Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness," 2024. [PDF]
  10. [10] H. Abdi Khojasteh, A. Abbas Alipour, E. Ansari, and P. Razzaghi, "An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles," 2018. [PDF]
  11. [11] M. Tropea, F. De Rango, N. Nevigato, L. Bitonti et al., "SCARE: A Novel Switching and Collision Avoidance pRocEss for Connected Vehicles Using Virtualization and Edge Computing Paradigm," 2021. ncbi.nlm.nih.gov
  12. [12] A. Biswas and H. C. Wang, "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain," 2023. ncbi.nlm.nih.gov
  13. [13] Z. Wei, F. Zhang, S. Chang, Y. Liu et al., "MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review," 2021. [PDF]
  14. [14] A. Al Mamun, P. Ping Em, M. Jakir Hossen, B. Jahan et al., "A deep learning approach for lane marking detection applying encode-decode instant segmentation network," 2023. ncbi.nlm.nih.gov
  15. [15] D. Katare, D. Perino, J. Nurmi, M. Warnier et al., "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services," 2023. [PDF]
  16. [16] J. J. Lin, J. I. Guo, V. Malligere Shivanna, and S. Y. Chang, "Deep Learning Derived Object Detection and Tracking Technology Based on Sensor Fusion of Millimeter-Wave Radar/Video and Its Application on Embedded Systems," 2023. ncbi.nlm.nih.gov
  17. [17] M. Abdou and H. Ahmed Kamal, "SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System," 2022. ncbi.nlm.nih.gov
  18. [18] V. Adewopo, N. Elsayed, Z. Elsayed, M. Ozer et al., "AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities," 2023. [PDF]
  19. [19] S. Chen, B. Liu, C. Feng, C. Vallespi-Gonzalez et al., "3D Point Cloud Processing and Learning for Autonomous Driving," 2020. [PDF]
  20. [20] F. García, F. Jiménez, J. Javier Anaya, J. María Armingol et al., "Distributed Pedestrian Detection Alerts Based on Data Fusion with Accurate Localization," 2013. ncbi.nlm.nih.gov
  21. [21] Z. Liu, G. Wen, W. Liu, T. X. Tan et al., "Research on automatic emergency steering collision avoidance and stability control of intelligent driving vehicle," 2023. ncbi.nlm.nih.gov
  22. [22] H. Li, L. Zhou, and A. Knoll, "BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving," 2023. [PDF]
  23. [23] A. Biglari and W. Tang, "A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme," 2023. ncbi.nlm.nih.gov
  24. [24] R. Sun, Q. Cheng, D. Xue, G. Wang et al., "GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application," 2017. ncbi.nlm.nih.gov
  25. [25] M. Bujňák, R. Pirník, K. Rástočný, A. Janota et al., "Spherical Robots for Special Purposes: A Review on Current Possibilities," 2022. ncbi.nlm.nih.gov
  26. [26] S. Grigorescu, T. Cocias, B. Trasnea, A. Margheri et al., "Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles," 2020. [PDF]
  27. [27] D. Garikapati and S. Sudhir Shetiya, "Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms," 2024. [PDF]
  28. [28] H. Thierschmann, H. Cetinay, M. Finkel, A. J. Katan et al., "Local electrodynamics of a disordered conductor model system measured with a microwave impedance microscope," 2019. [PDF]
  29. [29] S. Atakishiyev, M. Salameh, H. Yao, and R. Goebel, "Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions," 2021. [PDF]