AI-Based Approaches for Autonomous Vehicle Safety and Risk Management
Abstract
From a positive perspective, when the functions and enabling capabilities of the AVs progress fast, the contributory percentage of traditional functional components to the AV will drop rapidly, which will simplify the formulation, allowing more psychological effort and energy to integrate human-aware, human-like traits/capabilities/concepts for the AVs with the aim to improve the societal perception, safety trustworthiness and ethical issues of the new type of partners sharing the transportation systems. Hence, the significant tasks and technical features that the AI-based approaches will embrace are potential safety enhancements, potential safety security risks, future societal acceptance towards AI-based AV safety and risk management.
Downloads
References
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788.
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices." Asian Journal of Research in Computer Science 16.4 (2023): 239-256.
Shahane, Vishal. "Investigating the Efficacy of Machine Learning Models for Automated Failure Detection and Root Cause Analysis in Cloud Service Infrastructure." African Journal of Artificial Intelligence and Sustainable Development2.2 (2022): 26-51.
Muthusubramanian, Muthukrishnan, and Jawaharbabu Jeyaraman. "Data Engineering Innovations: Exploring the Intersection with Cloud Computing, Machine Learning, and AI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 76-84.
Tillu, Ravish, Bhargav Kumar Konidena, and Vathsala Periyasamy. "Navigating Regulatory Complexity: Leveraging AI/ML for Accurate Reporting." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 149-166.
Sharma, Kapil Kumar, Manish Tomar, and Anish Tadimarri. "AI-driven marketing: Transforming sales processes for success in the digital age." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 250-260.
Abouelyazid, Mahmoud. "Natural Language Processing for Automated Customer Support in E-Commerce: Advanced Techniques for Intent Recognition and Response Generation." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 195-232.
Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, "CARLA: An Open Urban Driving Simulator," in Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 2017, pp. 1-16.
M. Bojarski et al., "End to End Learning for Self-Driving Cars," in arXiv preprint arXiv:1604.07316, 2016.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, "DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2722-2730.
S. Thrun et al., "Stanley: The Robot that Won the DARPA Grand Challenge," in Journal of Field Robotics, vol. 23, no. 9, pp. 661-692, 2006.
M. Buehler, K. Iagnemma, and S. Singh, The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, Springer, 2009.
A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3354-3361.
J. K. Tsotsos et al., "Modeling Driver Behavior for Safe Autonomous Vehicle Operation," in IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 4, pp. 570-579, 2008.
M. Bahram, C. Hubmann, A. Lawitzky, M. Aeberhard, and D. Wollherr, "A Combined Model for Interaction Between Autonomous Vehicles and Vulnerable Road Users," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 5, pp. 1538-1550, 2016.
J. A. Bagnell et al., "An Integrated System for Autonomous Off-Road Driving," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2007, pp. 1251-1256.
J. P. V. D. Hoek, J. E. Naranjo, C. González, and T. de Pedro, "Automated Driving: The Role of Human Factors," in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 637-647, 2013.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, "A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles," in IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 33-55, 2016.
S. Behere and M. Törngren, "A Functional Architecture for Autonomous Driving," in Proceedings of the First International Workshop on Automotive Software Architecture (WASA), 2015, pp. 3-10.
Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," in Nature, vol. 521, no. 7553, pp. 436-444, 2015.
Y. Bengio, "Learning Deep Architectures for AI," in Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009.
R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," in Journal of Basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.
J. Ziegler et al., "Making Bertha Drive—An Autonomous Journey on a Historic Route," in IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 2, pp. 8-20, 2014.
J. K. Tsotsos, R. Fan, S. Mitra, and D. Doupe, "Improving Road Safety for Autonomous Vehicles Through Deep Reinforcement Learning," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1-12, 2020.
D. Silver et al., "Mastering the Game of Go with Deep Neural Networks and Tree Search," in Nature, vol. 529, pp. 484-489, 2016.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
