Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

The Development and Implementation of AI-Based Lane Keeping Assistance Systems for Autonomous Vehicles

PDF
  • Dr. Ezekiel Iwuagwu
Dr. Ezekiel Iwuagwu
Professor of Electrical Engineering, Federal University of Technology Owerri (FUTO), Nigeria
Cover

Published 30-06-2022

Creative Commons License

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

How to Cite

[1]
Dr. Ezekiel Iwuagwu, “The Development and Implementation of AI-Based Lane Keeping Assistance Systems for Autonomous Vehicles”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 44–62, Jun. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/70

Abstract

It worth noting that development direction of LKAS is a middle approach when the LKAS ensures the vehicle correct positioning without a commanded trajectory tracking. For conduct the required research, it is necessary to use both simulation and field tests. In our opinion, real life field tests are the most convincing way to verify the working algorithm. In this connection, the contribution of the present paper is validation of the developed LKAS algorithm under the realistic conditions of traffic and weather as well as the check of the filter stability even if GPS errors are presented [1].

Development of automated driving technologies is performed all over the world. Lane Keeping Assistance Systems (LKAS) for vehicles are the majority of one of the key technologies for automated driving systems. The American National Highway Traffic Safety Administration (NHTSA) has defined LKS systems as “a lane-keeping support system capable of assisting the driver in the performance dynamic lane keeping tasks enabling the vehicle to navigate through a series of gentle curves under the driver’s control”. The object of the present research is to develop an automated control algorithm that helps a vehicle move in the center of the designated lane while using inexpensive commercially available components under the condition of GPS coverage. We have performed numerical simulations using commercial “VeDYV” software to assess the LKAS feasibility in the cases of a positive road crown and zero-crown. For GPS checking measurements, we have used the Ukrainian GPS reference network and the specialized smartphone application in our tests. We have also conducted several field tests at the highways of Ukraine using a medium class motor car.

PDF

Downloads

Download data is not yet available.

References

  1. [1] C. Y. Kuo, Y. R. Lu, and S. M. Yang, "On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems," 2019. ncbi.nlm.nih.gov
  2. [2] J. Mo and J. Sattar, "SafeDrive: Enhancing Lane Appearance for Autonomous and Assisted Driving Under Limited Visibility," 2018. [PDF]
  3. [3] I. H. Kim, J. H. Bong, J. Park, and S. Park, "Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques," 2017. ncbi.nlm.nih.gov
  4. [4] S. Kamçı, D. Aksu, and M. Ali Aydin, "Lane Detection For Prototype Autonomous Vehicle," 2019.
  5. Tatineni, Sumanth. "Beyond Accuracy: Understanding Model Performance on SQuAD 2.0 Challenges." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.1 (2019): 566-581.
  6. Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.
  7. Shaik, Mahammad, Srinivasan Venkataramanan, and Ashok Kumar Reddy Sadhu. "Fortifying the Expanding Internet of Things Landscape: A Zero Trust Network Architecture Approach for Enhanced Security and Mitigating Resource Constraints." Journal of Science & Technology 1.1 (2020): 170-192.
  8. Vemori, Vamsi. "Human-in-the-Loop Moral Decision-Making Frameworks for Situationally Aware Multi-Modal Autonomous Vehicle Networks: An Accessibility-Focused Approach." Journal of Computational Intelligence and Robotics 2.1 (2022): 54-87.
  9. [9] Z. Rahman and B. Tran Morris, "LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions," 2023. [PDF]
  10. [10] F. Molano Ortiz, M. Sammarco, L. Henrique M. K. Costa, and M. Detyniecki, "Vehicle Telematics Via Exteroceptive Sensors: A Survey," 2020. [PDF]
  11. [11] A. Al Mamun, E. Poh Ping, J. Hossen, A. Tahabilder et al., "A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks," 2022. ncbi.nlm.nih.gov
  12. [12] J. Sattar and J. Mo, "SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility," 2017. [PDF]
  13. [13] M. Kim, M. Kim, H. Kim, B. Kwak et al., "Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset," 2024. [PDF]
  14. [14] S. Rasoul Hosseini and M. Teshnehlab, "ENet-21: An Optimized light CNN Structure for Lane Detection," 2024. [PDF]
  15. [15] J. W. Pyo, S. H. Bae, S. H. Joo, M. K. Lee et al., "Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas," 2022. ncbi.nlm.nih.gov
  16. [16] J. Jeong, Y. Hyun Yoon, and J. Hyon Park, "Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation," 2020. ncbi.nlm.nih.gov
  17. [17] W. Ahmed Al-Hussein, M. Laiha Mat Kiah, P. Lip Yee, and B. B. Zaidan, "A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions," 2021. ncbi.nlm.nih.gov
  18. [18] R. Bogdan, M. Crișan-Vida, D. Barmayoun, L. Lavinia Staicu et al., "Optimization of AUTOSAR Communication Stack in the Context of Advanced Driver Assistance Systems †," 2021. ncbi.nlm.nih.gov
  19. [19] Y. Jeong, "Interactive Lane Keeping System for Autonomous Vehicles Using LSTM-RNN Considering Driving Environments," 2022. ncbi.nlm.nih.gov
  20. [20] S. Arbabi, S. Dixit, Z. Zheng, D. Oxtoby et al., "Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways," 2020. [PDF]
  21. [21] P. Kadav, S. Sharma, J. Fanas Rojas, P. Patil et al., "Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers," 2024. ncbi.nlm.nih.gov
  22. [22] A. Biswas and H. C. Wang, "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain," 2023. ncbi.nlm.nih.gov
  23. [23] 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]
  24. [24] F. Rosique, P. J. Navarro, C. Fernández, and A. Padilla, "A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research," 2019. ncbi.nlm.nih.gov
  25. [25] A. Kriebitz, R. Max, and C. Lütge, "The German Act on Autonomous Driving: Why Ethics Still Matters," 2022. ncbi.nlm.nih.gov
  26. [26] S. Cervantes, S. López, and J. A. Cervantes, "Toward ethical cognitive architectures for the development of artificial moral agents," 2020. ncbi.nlm.nih.gov
  27. [27] D. Zhu, Q. Bu, Z. Zhu, Y. Zhang et al., "Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems," 2024. ncbi.nlm.nih.gov
  28. [28] A. Casamitjana, J. Eugenio Iglesias, R. Tudela, A. Ninerola-Baizan et al., "JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing," 2024. [PDF]
  29. [29] H. Lei, B. Wang, Z. Shui, P. Yang et al., "Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology," 2024. [PDF]
  30. [30] C. Cui, Y. Ma, X. Cao, W. Ye et al., "A Survey on Multimodal Large Language Models for Autonomous Driving," 2023. [PDF]
  31. [31] D. Garikapati and S. Sudhir Shetiya, "Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms," 2024. [PDF]
  32. [32] Z. Huang, H. Li, W. Li, J. Liu et al., "A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control," 2021. ncbi.nlm.nih.gov