{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:08:35Z","timestamp":1769904515553,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T00:00:00Z","timestamp":1582848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["GRK 2159"],"award-info":[{"award-number":["GRK 2159"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a B\u00e9zier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also B\u00e9zier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.<\/jats:p>","DOI":"10.3390\/a13030052","type":"journal-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T09:30:36Z","timestamp":1582882236000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6872-5150","authenticated-orcid":false,"given":"Mohamed","family":"Abdelaal","sequence":"first","affiliation":[{"name":"The Institute of Cartography and Geoinformatics, University of Hannover, D-30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5042-6742","authenticated-orcid":false,"given":"Steffen","family":"Sch\u00f6n","sequence":"additional","affiliation":[{"name":"Institute of Geodesy, University of Hannover, D-30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s00607-011-0171-y","article-title":"Sensitivity-based feedforward and feedback control for uncertain systems","volume":"94","author":"Rauh","year":"2012","journal-title":"Computing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Beyersdorfer, S., and Wagner, S. (2013, January 6\u20139). Novel model based path planning for multi-axle steered heavy load vehicles. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728268"},{"key":"ref_3","unstructured":"Wang, J., Steiber, J., Surampudi, B., and Member, S. (2008, January 11\u201313). Autonomous Ground Vehicle Control System for High-Speed and Safe Operation. Proceedings of the American Control Conference, Seattle, WA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Quirynen, R., Berntorp, K., and Cairano, S.D. (2018, January 27\u201329). Embedded Optimization Algorithms for Steering in Autonomous Vehicles based on Nonlinear Model Predictive Control. 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Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s19194289","article-title":"Advanced estimation techniques for vehicle system dynamic state: A survey","volume":"19","author":"Jin","year":"2019","journal-title":"Sensors"},{"key":"ref_28","unstructured":"Kong, J., Pfeiffer, M., Schildbach, G., and Borrelli, F. (July, January 28). Kinematic and Dynamic Vehicle Models for Autonomous Driving Control Design. 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