AI-Powered Predictive Control in Autonomous Vehicles

Authors

  • Prasad Rayi, Rajasree Yandra, Rama Subbanna, Sakthivel S.

Keywords:

AI-Enabled Autonomous Driving, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science,Vehicle, Vehicle Reliability

Abstract

Autonomous driving (AD) is an innovative technology poised to transform the future of transportation. In addition to providing a chance for enhanced road safety by minimizing human mistakes, the implementation of autonomous driving will increase traffic efficiency by facilitating superior driving and stability in traffic flow, as powerful predictive analytics algorithms may be built. This research emphasizes that the dynamics of an autonomous vehicle (AV) interacting with human drivers is a weakly collective, open-system complex that is essentially temporal and representation-hierarchical. To address the problem of achieving AI-enabled autonomous driving, we created predictive planning that incorporates perceptual and learning modules for task-relevant scene comprehension in operational and tactical planning. The discussion regarding AI-enabled transportation effectively distinguishes between functional and realization levels while integrating them within system engineering. The dynamics visualization framework for AI-enabled AD systems may be easily adapted to other analogous systems and processes inside vast complex systems.

Downloads

Download data is not yet available.

References

Adile Akpunar, and Serdar Iplikci, “Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors,” Energies, vol. 13, no. 5, pp. 1-17, 2020.

Pedro Bautista-Camino et al., “Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion,” Energies, vol. 15, no. 5, pp. 1-23, 2022.

Boliang Yi et al., “Real-Time Integrated Vehicle Dynamics Control and Trajectory Planning with MPC for Critical Maneuvers,” IEEE Intelligent Vehicles Symposium (IV), Sweden, pp. 584-589, 2016.

Anne Collin et al., “Autonomous Driving Systems Hardware and Software Architecture Exploration: Optimizing Latency and Cost Under Safety Constraints,” Systems Engineering, vol. 23, no. 3, pp. 327-337, 2020.

Yong Chen et al., “Accurate and Efficient Approximation of Clothoids Using Bezier Curves for Path Planning,” IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1242-1247, 2017.

H. Bassi, and Y.A. Mobarak, “State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach,” Technology & Applied Science Research, vol. 7, no. 2, pp. 1436-1443, 2017.

Do Trong Tu, “Enhancing Road Holding and Vehicle Comfort for an Active Suspension System Utilizing Model Predictive Control and Deep Learning,” Technology & Applied Science Research, vol. 14, no. 1, pp. 12931-12936, 2024.

Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles,” vol. 352, no. 6293, pp. 1573-1576, 2016.

Matthew Brown et al., “Safe Driving Envelopes for Path Tracking in Autonomous Vehicles,” Control Engineering Practice, vol. 61, pp. 307-316, 2017.

Ahmed AbdElmoniem et al., “A Path Tracking Algorithm Using Predictive Stanley Lateral Controller,” International Journal of Advanced Robotic Systems, vol. 17, no. 6, 2020.

Akinobu Goto, Takashi Fukushige, and Takeshi Kimura, “Real-Time Trajectory Planning for Autonomous Driving in Urban Area Based on Dynamic Programming,” Transactions of Society of Automotive Engineers of Japan, vol. 52, no. 3, pp. 639-644, 2021.

Noor Hafizah Amer et al., “Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges,” Journal of Intelligent & Robotic Systems, vol. 86, pp. 225-254, 2016.

Brian Paden et al., “A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 33-55, 2016.

Patrick Weyers, Alexander Barth, and Anton Kummert, “Driver State Monitoring with Hierarchical Classification,” 21st International Conference on Intelligent Transportation Systems (ITSC), USA, pp. 3239-3244, 2018.

Andrea Minari, Aurelio Piazzi, and Alessandro Costalunga, “Polynomial Interpolation for Inversion-Based Control,” European Journal of Control, vol. 56, pp. 62-72, 2020.

Abdeslam Boularias et al., “Learning Qualitative Spatial Relations for Robotic Navigation,” Proceedings of 25th International Joint Conference on Artificial Intelligence, pp. 4130 - 4134, 2016.

Anouer Bennajeh et al., “Bilevel Decision-Making Modeling for an Autonomous Driver Agent: Application in the Car-Following Driving Behavior,” Applied Artificial Intelligence, vol. 33, no. 13, pp. 1157-1178, 2019.

Ezzatollah Asgharizadeh, Mohammadreza Taghizadeh Yazdi, and Abdolkarim Mohammadi Balani, “An Output-Oriented Classification of Multiple Attribute Decision-Making Techniques Based on Fuzzy C-Means Clustering Method,” International Transactions in Operational Research, vol. 26, no. 6, pp. 2476-2493, 2017.

Bandana Barman, Ria Kanjilal, and Anirban Mukhopadhyay, “Neuro-Fuzzy Controller Design to Navigate Unmanned Vehicle with the Construction of Traffic Rules to Avoid Obstacles,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 24, no. 3, pp. 433-449, 2016.

Downloads

Published

21.09.2023

How to Cite

Prasad Rayi. (2023). AI-Powered Predictive Control in Autonomous Vehicles. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 1041–1047. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7489

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.