Advancing urban planning and autonomous vehicles integration through scaled models
DOI:
https://doi.org/10.5902/2179460X86771Keywords:
Autonomous vehicles, Urban planning, Scaled modelsAbstract
In the evolving landscape of urban planning and transportation, the integration of autonomous vehicles (AVs) into the urban environment presents a transformative opportunity. This paper explores the potential of scaled models in advancing urban planning and AV integration, highlighting the intricate interdependence of transportation systems, urban planning, and socio-economic factors. The emergence of AVs promises unparalleled efficiency, safety, and environmental sustainability in urban mobility. However, their successful integration necessitates meticulous planning and a comprehensive understanding of the urban landscape. Scaled models offer a dynamic platform for urban planners and policymakers to simulate, assess, and strategize the incorporation of AVs into cities, enabling the visualization of potential changes and the formulation of sustainable and equitable development strategies. Despite the promising prospects of scaled models, challenges such as scaling accuracy and the simplification of complex urban dynamics persist. Addressing these challenges is crucial for bridging the gap between model experiments and real-world urban complexities. By harnessing the power of scaled models, this paper aims to deepen our understanding of the interaction between AVs and urban environments and to strategize their integration, marking a significant step towards smarter, safer, and more sustainable cities.
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Ahn, H., Rizzi, A., Colombo, A., & Del Vecchio, D. (2015). Experimental testing of a semi-autonomous multi-vehicle collision avoidance algorithm at an intersection testbed. IEEE International Conference on Intelligent Robots and Systems, 2015-December, 4834–4839. https://doi.org/10.1109/IROS.2015.7354056
Andert, E., Khayatian, M., & Shrivastava, A. (2017). Crossroads: Time-Sensitive Autonomous Intersection Management Technique. Proceedings - Design Automation Conference, Part 128280. https://doi.org/10.1145/3061639.3062221
Anindyaguna, K., Basjaruddin, N. C., & Saefudin, D. (2016). Overtaking assistant system (OAS) with fuzzy logic method using camera sensor. 2016 2nd International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering, ICIMECE 2016, 89–94. https://doi.org/10.1109/ICIMECE.2016.7910420
Bae, I., Moon, J., Park, H., Kim, J. H., & Kim, S. (2013). Path generation and tracking based on a Bézier curve for a steering rate controller of autonomous vehicles. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 436–441. https://doi.org/10.1109/ITSC.2013.6728270
Banister, D. (2008). The sustainable mobility paradigm. Transport Policy, 15(2), 73–80. https://doi.org/10.1016/j.tranpol.2007.10.005
Bellomo, N., Piccoli, B., & Tosin, A. (2012). Modeling crowd dynamics from a complex system viewpoint. Mathematical Models and Methods in Applied Sciences, 22(supp02), 1230004. https://doi.org/10.1142/S0218202512300049
Bettencourt, L. M. A. (2020). Urban growth and the emergent statistics of cities. Science Advances, 6(34). https://doi.org/10.1126/sciadv.aat8812
Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022). Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing. IEEE Open Journal of Intelligent Transportation Systems, 3, 458–488. https://doi.org/10.1109/ojits.2022.3181510
Caleffi, F., Rodrigues, L. da S., Stamboroski, J. da S., Rorig, B. V., Santos, M. M. C. dos, Zuchetto, V., & Raguzzoni, Í. B. (2023). A systematic review of hardware technologies for small-scale self-driving cars. Ciência e Natura, 45(esp. 1), 84071. https://doi.org/10.5902/2179460X84071
Eckelman, M. J. (2013). Life cycle assessment in support of sustainable transportation. Environmental Research Letters, 8(2), 021004. https://doi.org/10.1088/1748-9326/8/2/021004
Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/J.TRA.2015.04.003
Filho, E. V., Guedes, N., Vieira, B., Mestre, M., Severino, R., Gonçalves, B., Koubaa, A., & Tovar, E. (2020). Towards a Cooperative Robotic Platooning Testbed. 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 332–337. https://doi.org/10.1109/ICARSC49921.2020.9096132
Gao, C., Li, Z., Zhao, Y., Zhu, Z., & Jia, Z. (2022). Design of a Scaled-car Platform for Extreme Driving Conditions. 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), 743–747. https://doi.org/10.1109/ICARM54641.2022.9959231
Garcia‐Dorado, I., G. Aliaga, D., & V. Ukkusuri, S. (2014). Designing large‐scale interactive traffic animations for urban modeling. Computer Graphics Forum, 33(2), 411–420. https://doi.org/10.1111/cgf.12329
Hamzah, M. S., Fadillah, N., Maulana, D. W., Joni, I. M., Panatarani, C., & Faizal, F. (2022). Development of Single-board Computer-based Self-Driving Car Model using CNN-Controlled RC Car. Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, 1805–1812. https://doi.org/10.1109/ICEARS53579.2022.9751873
Hossain, S., Doukhi, O., Jo, Y., & Lee, D. J. (2020). Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment. International Conference on Control, Automation and Systems, 2020-October, 1231–1236. https://doi.org/10.23919/ICCAS50221.2020.9268370
Hussain, R., & Zeadally, S. (2019). Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys and Tutorials, 21(2), 1275–1313. https://doi.org/10.1109/COMST.2018.2869360
Hyldmar, N., He, Y., & Prorok, A. (2019). A Fleet of Miniature Cars for Experiments in Cooperative Driving. 2019 International Conference on Robotics and Automation (ICRA), 3238–3244. https://doi.org/10.1109/ICRA.2019.8794445
Jahoda, P., Cech, J., & Matas, J. (2020). Autonomous Car Chasing. 16th European Conference on Computer Vision, ECCV 2020, 337–352. https://doi.org/10.1007/978-3-030-66823-5_20
Kannapiran, S., & Berman, S. (2020). Go-CHART: A miniature remotely accessible self-driving car robot. IEEE International Conference on Intelligent Robots and Systems, 2265–2272. https://doi.org/10.1109/IROS45743.2020.9341770
Kee, C. Y., Chua, C., Zubair, M., & Ang, L. K. (2022). Fractional modeling of urban growth with memory effects. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(8). https://doi.org/10.1063/5.0085933
Kim, J., Schwartz, M., & Zarzycki, A. (2016). The Wave of Autonomous Mobility:Architecture Facilitating Indoor Autonomous Navigation. Proceedings of the 34th ECAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 53–58. https://doi.org/10.52842/conf.ecaade.2016.1.053
Koopman, P., & Wagner, M. (2017). Autonomous Vehicle Safety: An Interdisciplinary Challenge. IEEE Intelligent Transportation Systems Magazine, 9(1), 90–96. https://doi.org/10.1109/MITS.2016.2583491
Li, N., Oyler, D. W., Zhang, M., Yildiz, Y., Kolmanovsky, I., & Girard, A. R. (2018). Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems. IEEE Transactions on Control Systems Technology, 26(5), 1782–1797. https://doi.org/10.1109/TCST.2017.2723574
Litman, T. (2023). Autonomous Vehicle Implementation Predictions: Implications for Transport Planning. Victoria Transport Policy Institute. Retrieved from Https://Www.Vtpi.Org/Avip.Pdf.
Mozaffari, S., Al-Jarrah, O. Y., Dianati, M., Jennings, P., & Mouzakitis, A. (2022). Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review. IEEE Transactions on Intelligent Transportation Systems, 23(1), 33–47. https://doi.org/10.1109/TITS.2020.3012034
Muraleedharan, A., Okuda, H., & Suzuki, T. (2022). Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving. IEEE Transactions on Intelligent Vehicles, 7(1), 11–20. https://doi.org/10.1109/TIV.2021.3062730
Narazani, M., Eghtebas, C., Jenney, S. L., & Mühlhaus, M. (2019). Tangible urban models: two-way interaction through 3D printed conductive tangibles and AR for urban planning. Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 320–323. https://doi.org/10.1145/3341162.3343810
O’Kelly, M., Sukhil, V., Abbas, H., Harkins, J., Kao, C., Pant, Y. V., Mangharam, R., Agarwal, D., Behl, M., Burgio, P., & Bertogna, M. (2019). F1/10: An Open-Source Autonomous Cyber-Physical Platform. http://arxiv.org/abs/1901.08567
O’Kelly, M., Zheng, H., Karthik, D., & ... (2020). F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning. Machine Learning Research, 123, 77–89. http://proceedings.mlr.press/v123/o-kelly20a.html
Raza, A., Zhong, M., & Safdar, M. (2022). Evaluating Locational Preference of Urban Activities with the Time-Dependent Accessibility Using Integrated Spatial Economic Models. International Journal of Environmental Research and Public Health, 19(14), 8317. https://doi.org/10.3390/ijerph19148317
Sweeting, W. J., & Winfield, P. H. (2012). Future transportation: Lifetime considerations and framework for sustainability assessment. Energy Policy, 51, 927–938. https://doi.org/10.1016/j.enpol.2012.09.055
Verma, A., Bagkar, S., Allam, N. V. S., & Raman, A. (2021). Implementation and Validation of Behavior Cloning using Scaled Vehicles. SAE Technical Papers 2021. https://doi.org/10.4271/2021-01-0248
Williams, G., Goldfain, B., Drews, P., Rehg, J. M., & Theodorou, E. A. (2018). Best Response Model Predictive Control for Agile Interactions between Autonomous Ground Vehicles. Proceedings - IEEE International Conference on Robotics and Automation, 2403–2410. https://doi.org/10.1109/ICRA.2018.8462831
Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., & Liu, P. (2018). What drives people to accept automated vehicles? Findings from a field experiment. Transportation Research Part C: Emerging Technologies, 95, 320–334. https://doi.org/10.1016/J.TRC.2018.07.024
Zhang, T., Tao, D., Qu, X., Zhang, X., Lin, R., & Zhang, W. (2019). The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, 98, 207–220. https://doi.org/10.1016/J.TRC.2018.11.018
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