Abstract

Automated "vehicles (AVs) have the potential to positively affect transportation and smart cities in many ways. Vehicle platooning, in which one automobile follows closely after another, might benefit from AV technology, which could reduce the amount of space between vehicles. However, well-made AVs have the potential to have a far larger impact [27]. Currently designed roads and highways will need to be modified when autonomous vehicles (AVs) gain popularity [14]. We need to start planning now to make the most of AVs' potential in smart transportation networks. Researching and capitalising on the distinctive features of AVs holds great promise for advancing technology and creating AV systems with various additional advantages. This is because there are "the following three major categories of research topics: Traffic data management including autonomous vehicles and road infrastructure [15].

Vehicle-to-grid "use cases for autonomous vehicles (V2G). A battery is a typical source of energy for AVs. When there is a mismatch between supply and demand in a smart grid, electricity production prices might go up [16]. One solution is to leverage the enormous battery capacity of AVs to maintain and balance the power grid. If the amount of energy produced exceeds the amount of energy needed, we may utilise the surplus to charge the AVs. Similarly, we may discharge the AVs to deliver extra power to the grid if demand exceeds supply [26]. Parking garages equipped for vehicle-to-grid services were made available to AVs through a centralised scheduling system. To solve the ILP version of the coordinated parking problem [17], we shall employ a decentralised approach. However, V2G services are limited in their adaptability since AVs are confined to a single parking location. Only by counting automobiles can V2G services be recognised, but this issue also has to account for the power exchanged and voltage effect caused by these vehicles. Therefore, the actual power flow of AVs must be considered while scheduling charging periods. As a result, figuring out parking spots for autonomous vehicles is crucial for both vehicle-to-vehicle and vehicle-to-pedestrian interactions "rebalancing, and it's an important field to research [18].

hematics education "cities

Keywords

• Automated Vehicles,
• Smart Transportation Network

References

• . K.-F. CHU, E. R. Magsino, I. W.-H. Ho, and C.-K. Chau, “Index coding of point cloud-based road map data for autonomous driving,” in Proceedings of the 2017 IEEE 85th Vehicular Technology Conference, Jun. 2017.
• . D. Bevly, X. Cao, M. Gordon, G. Ozbilgin, D. Kari, B. Nelson, J. Woodruff,
• . M. Barth, C. Murray, A. Kurt, et al., “Lane change and merge maneuvers for connected and automated vehicles: A survey,” IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 105–120, Mar. 2016.
• . Tesla. (2020). Model S, [Online]. Available: https://www.tesla.com/ models.
• . DARPA. (2007). Urban challenge, [Online]. Available: http://archive. darpa.mil/grandchallenge/.
• . Waymo. (2020). Journey, [Online]. Available: http : / / waymo . com / journey/.
• . Tesla. (2020). Autopilot, [Online]. Available: https://www.tesla.com/ Autopilot/.
• . M. Pavone, “Autonomous mobility-on-demand systems for future urban mobility,” in AutonomesFahren, Springer, 2015, pp. 399–416.
• . [10] Waymo LLC. (2020). Waymo one, [Online]. Available: https://waymo. com/waymo-one/.
• . A. Y. S. Lam, Y. W. Leung, and X. Chu, “Autonomous-vehicle public transportation system: Scheduling and admission control,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 5, pp. 1210–1226, May 2016.
• . S. Sukkarieh, E. M. Nebot, and H. F. Durrant-Whyte, “A high integrity IMU/GPS navigation loop for autonomous land vehicle applications,” IEEE Transactions on Robotics and Automation, vol. 15, no. 3, pp. 572–578, Jun. 1999.
• . S. S. M. Ali, B. George, L. Vanajakshi, and J. Venkatraman, “A multiple inductive loop vehicle detection system for heterogeneous and lane-less traffic,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 5, pp. 1353–1360, May 2012.
• . J. Zhang, F. Y. Wang, K. Wang, W. H. Lin, X. Xu, and C. Chen, “Data- driven intelligent transportation systems: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624–1639, Dec. 2011.
• . H. Hartenstein and L. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE Communications magazine, vol. 46, no. 6, pp. 164–171, Jun. 2008.
• . M. Pavone, S. L. Smith, E. Frazzoli, and D. Rus, “Robotic load balancing for mobility-on-demand systems,” The International Journal of Robotics Research, vol. 31, no. 7, pp. 839–854, 2012.
• . S. Michel and B. Chidlovskii, “Stochastic optimization of public transport schedules to reduce transfer waiting times,” in Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2), IEEE, 2016.
• . A. Y. S. Lam, J. J. Q. Yu, Y. Hou, and V. O. K. Li, “Coordinated autonomous vehicle parking for vehicle-to-grid services: Formulation and distributed algorithm,” IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 4356–4366, Sep. 2018.
• . W. Lee, S. Tseng, J. Shieh, and H. Chen, “Discovering traffic bottlenecks in an urban network by spatiotemporal data mining on location-based services,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1047–1056, Dec. 2011, Issn: 1524-9050.
• . B. Wolshon and L. Lambert, “Reversible lane systems: Synthesis of practice,”Journal of Transportation Engineering, vol. 132, no. 12, pp. 933–944, 2006.
• . M. Hausknecht, T. C. Au, P. Stone, D. Fajardo, and T. Waller, “Dynamic lane reversal in traffic management,” in Proceedings of the IEEE 14th International Conference on Intelligent Transportation Systems, Oct. 2011, pp. 1929–1934.
• . G. E. P. Box and D. A. Pierce, “Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,” Journal of the American Statistical Association, vol. 65, no. 332, pp. 1509–1526, 1970.
• . X. Li, G. Pan, Z. Wu, G. Qi, S. Li, D. Zhang, W. Zhang, and Z. Wang, “Prediction of urban human mobility using large-scale taxi traces and its applications,” Frontiers in Computer Science, vol. 6, no. 1, pp. 111–121, Feb. 2012.
• . L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas, “Predicting taxi-passenger demand using streaming data,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1393–1402, Sep. 2013.
• . N. Davis, G. Raina, and K. Jagannathan, “A multi-level clustering approach for forecasting taxi travel demand,” in Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, Nov. 2016, pp. 223–228.
• . K. Zhao, D. Khryashchev, J. Freire, C. Silva, and H. Vo, “Predicting taxi demand at high spatial resolution: Approaching the limit of predictability,” in Proceedings of the IEEE International Conference on Big Data, Dec. 2016, pp. 833–842.
• . K. Zhang, Z. Feng, S. Chen, K. Huang, and G. Wang, “A framework for passengers demand prediction and recommendation,” in Proceedings of the IEEE International Conference on Services Computing, Jun. 2016, pp. 340– 347.
• . A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc., 2012, pp. 1097–1105.

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