AI-Driven Approaches for Autonomous Vehicle Fleet Coordination and Routing
Keywords:
AV oscillation infrastructure, behavioural interactionsAbstract
The paper is mostly concerned with first understanding the large-scale population behavioural interactions in a system of car-shares of many different types—not necessarily from the same companies or with the same sizes or capabilities—all driven in concert with some other vehicles of various types and any amount of private cars. Only partly in competition with car-shares, we have a second fundamental source of artificial intelligence/autonomy-centric coordination: autonomous logistics. At the same time as car-shares and partners, autonomous logistics (e.g. droneHaulage—long distance and small parceled; droidDeliver small distance and localized) co-use shared AVs—either that are always AVs or that become AVs just for the last portion of each optimal whole journey—across scales from ways to streets to buildings. The principal uses of autonomous logistics vehicles require coordination too, although with quite different routing priorities, cost functions and penalty functions. Nonetheless, these are sufficiently similar in dynamic terms to consider simultaneously in one study different types of vehicles for goods carrying, and an additional type for worthy attention today with high technological readiness levels, for passengers, with no cargo but a bit of luggage maybe. They can share the schedule-able adaptive mobility and AV oscillation infrastructure and thus could be directly coordinated to nearly no extra work, although with necessary systematic complementarity and safety through all stages.
Downloads
References
A. Arizala, A. Zubizarreta, and J. Pérez, "A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach," 2022. ncbi.nlm.nih.gov
G. Wu, K. Zhao, J. Cheng, and M. Ma, "A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search," 2022. ncbi.nlm.nih.gov
M. Lujak, S. Giordani, A. Omicini, and S. Ossowski, "Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation," 2024. [PDF]
Tatineni, Sumanth. "Federated Learning for Privacy-Preserving Data Analysis: Applications and Challenges." International Journal of Computer Engineering and Technology 9.6 (2018).
Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.
Mahammad Shaik, et al. “Envisioning Secure and Scalable Network Access Control: A Framework for Mitigating Device Heterogeneity and Network Complexity in Large-Scale Internet-of-Things (IoT) Deployments”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 1-24, https://dlabi.org/index.php/journal/article/view/1.
H. Billhardt, A. Fernández, M. Lujak, and S. Ossowski, "Agreement Technologies for Coordination in Smart Cities," 2024. [PDF]
D. Garikapati and S. Sudhir Shetiya, "Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms," 2024. [PDF]
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
S. Mariani, G. Cabri, and F. Zambonelli, "Coordination of Autonomous Vehicles: Taxonomy and Survey," 2020. [PDF]
S. Malik, M. Ahmed Khan, and H. El-Sayed, "Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges," 2021. ncbi.nlm.nih.gov
Y. Shi, Y. Liu, Y. Qi, and Q. Han, "A Control Method with Reinforcement Learning for Urban Un-Signalized Intersection in Hybrid Traffic Environment," 2022. ncbi.nlm.nih.gov
Z. Li, S. Yuan, X. Yin, X. Li et al., "Research into Autonomous Vehicles Following and Obstacle Avoidance Based on Deep Reinforcement Learning Method under Map Constraints," 2023. ncbi.nlm.nih.gov
M. Faris, M. Zanon, and P. Falcone, "Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections," 2024. [PDF]
Z. Qin, H. Zhu, and J. Ye, "Reinforcement Learning for Ridesharing: An Extended Survey," 2021. [PDF]
F. Paparella, K. Chauhan, T. Hofman, and M. Salazar, "Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting," 2022. [PDF]
undefined Zulkarnain and T. Dwi Putri, "Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach," 2021. ncbi.nlm.nih.gov
S. Cui, Q. Sun, and Q. Zhang, "A Time-Dependent Vehicle Routing Problem for Instant Delivery Based on Memetic Algorithm," 2022. ncbi.nlm.nih.gov
R. Santin, L. Assis, A. Vivas, and L. C. A. Pimenta, "Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage," 2021. ncbi.nlm.nih.gov
A. Abouelrous, L. Bliek, and Y. Zhang, "Digital Twin Applications in Urban Logistics: An Overview," 2023. [PDF]
D. Pandya and G. Kumar, "Applying Industry 4.0 technologies for the sustainability of small service enterprises," 2022. ncbi.nlm.nih.gov
A. Abbas-Turki, Y. Mualla, N. Gaud, D. Calvaresi et al., "Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling," 2023. ncbi.nlm.nih.gov
S. Paiva, M. Abdul Ahad, G. Tripathi, N. Feroz et al., "Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges," 2021. ncbi.nlm.nih.gov
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]
W. Wei and J. Wang, "Ethical Decision-making for Autonomous Driving based on LSTM Trajectory Prediction Network," 2023. [PDF]
S. Kumar Reddy Manne, S. Zhu, S. Ostadabbas, and M. Wan, "Automatic Infant Respiration Estimation from Video: A Deep Flow-based Algorithm and a Novel Public Benchmark," 2023. [PDF]
A. Acharya, "Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective," 2014. [PDF]
A. Taik, Z. Mlika, and S. Cherkaoui, "Clustered Vehicular Federated Learning: Process and Optimization," 2022. [PDF]
D. Hettiachchi, K. Ji, J. Kennedy, A. McCosker et al., "Designing and Evaluating Presentation Strategies for Fact-Checked Content," 2023. [PDF]
L. Luxmi Dhirani, N. Mukhtiar, B. Shankar Chowdhry, and T. Newe, "Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review," 2023. ncbi.nlm.nih.gov
O. Rinchi, A. Alsharoa, I. Shatnawi, and A. Arora, "The Role of Intelligent Transportation Systems and Artificial Intelligence in Energy Efficiency and Emission Reduction," 2024. [PDF]
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]
D. Katare, D. Perino, J. Nurmi, M. Warnier et al., "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services," 2023. [PDF]
A. Biswas and H. C. Wang, "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain," 2023. ncbi.nlm.nih.gov
J. Wang, X. Guo, and X. Yang, "Efficient and Safe Strategies for Intersection Management: A Review," 2021. ncbi.nlm.nih.gov
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
