Exploring the Potential of Q-Learning Offers a Promising Pathway towards Achieving Artificially Intelligent Driving Capabilities
Abstract
This research focuses on Artificially Intelligent driving techniques that are being used to train several machinelearning models to achieve complete human-like driving skills. Artificially Intelligent driving consists of training a machine to drive on a provided path to any vehicle (a car in this case) while simultaneously following all the traffic routes, providing passenger comfort and vehicle and passenger safety. In this research, since most of the available artificially intelligent driving models are set to work upon a predetermined path provided by the user and can only follow that path, I intend to further this model by providing a completely random path to the model and then evaluate its efficiency, the resources it requires to complete its whole path to training the model so that it can adapt to the randomly provided path with much faster speed and more accuracy as compared to traditional Artificially Intelligent vehicle driving models.
References
[2] H. Pradana, M.-S. Dao, and K. Zettsu, “Augmenting ego-vehicle for traffic near-miss and accident classification dataset using manipulating conditional style translation,” in 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2022, pp. 1–8.
[3] Z. Li, K. W. E. Cheng, and K. K. W. Chan, “An overview of factors influencing the mass adoption of self-driving vehicles,” in 2022 IEEE 9th International Conference on Power Electronics
Systems and Applications (PESA), 2022, pp. 1–5.
[4] C.-C. Chen, Y.-H. Guan, N. R. Novianda, C.-C. Teng, and M.-H. Yen, “Real-time traffic sign detection for self-driving and energy-saving driving based on yolov4 neural network,” in 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2022, pp. 208– 211.
[5] M. A. Pandya, P. Siddalingaswamy, and S. Singh, “Explainability of image classifiers for targeted adversarial attack,” in 2022 IEEE 19th India Council International Conference (INDICON), 2022, pp. 1–6.
[6] L. Jia, H. Zhong, X. Wang, L. Huang, and Z. Li, “How do injected bugs affect deep learning?” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022, pp. 793–804.
[7] A. Rangesh, B. Zhang, and M. M. Trivedi, “Gaze preserving cyclegans for eyeglass removal and persistent gaze estimation,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 377 386, 2022.
[8] A. Rangesh, N. Deo, R. Greer, P. Gunaratne, and M. M. Trivedi, “Autonomous vehicles that alert humans to take-over controls: Modeling with real-world data,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 231–236.
[9] S. Q. Zulqarnain and S. Lee, “An efficient driver selection algorithm for controlling multiple vehicles in remote driving,” in 2021 International Conference on Information Networking (ICOIN), 2021, pp. 20–23.
[10] S. Khanal, K. Thar, and E.-N. Huh, “Route-based proactive content caching using self-attention in hierarchical federated learning,” IEEE Access, vol. 10, pp. 29 514–29 527, 2022.
[11] S. Khanal, K. Thar, M. D. Hossain, and E.-N. Huh, “Proactive content caching at self-driving car using federated learning with edge cloud,” in 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), 2021, pp. 129–134.
[12] L. Kumar, D. Choudhury, A. R. Paduri, S. Kumar, D. Sahoo, J. Murthy, and N. Darapaneni, “Electric vehicle (ev) preventive diagnostic system: Solution for thermal management of battery packs using aiot,” in 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), 2023, pp. 0041–0046.
[13] A. Ndikumana, N. H. Tran, D. H. Kim, K. T. Kim, and C. S. Hong, “Deep learning based caching for self-driving cars in multi-access edge computing,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2862–2877, 2021.
[14] H. Abualsaud, S. Liu, D. B. Lu, K. Situ, A. Rangesh, and M. M. Trivedi, “Laneaf: Robust multi-lane detection with affinity fields,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7477–7484, 2021.
[15] B. Jang, M. Kim, G. Harerimana, and J. Kim, “Q-learning algorithms: A comprehensive classification and applications,” IEEE Access, vol. PP, pp. 1–1, 09 2019.
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AUTHOR RESPONSIBILITIES
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
Author/Employer Rights
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
Reprint/Republication Policy
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
GENERAL TERMS
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :
Creative Commons Attribution 4.0 International License.
