{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:59:07Z","timestamp":1770271147359,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two components: sensor analytics and path finder. The sensor analytics component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.<\/jats:p>","DOI":"10.3390\/s20216103","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"6103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Global Path Planner for Safe Navigation of Autonomous Vehicles in Uncertain Environments"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9293-4905","authenticated-orcid":false,"given":"Mohammed","family":"Alharbi","sequence":"first","affiliation":[{"name":"Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, USA"},{"name":"College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia"}]},{"given":"Hassan A.","family":"Karimi","sequence":"additional","affiliation":[{"name":"Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/JRA.1985.1087002","article-title":"Navigation for an Intelligent Mobile Robot","volume":"1","author":"Crowley","year":"1985","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1007\/978-3-030-32520-6_67","article-title":"PROBE: Preparing for Roads in Advance of Barriers and Errors","volume":"Volume 1069","author":"Arai","year":"2020","journal-title":"Proceedings of the Future Technologies Conference (FTC) 2019"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF01386390","article-title":"A note on two problems in connexion with graphs","volume":"1","author":"Dijkstra","year":"1959","journal-title":"Numer. Math."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"A Formal Basis for the Heuristic Determination of Minimum Cost Paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/70.508439","article-title":"Probabilistic roadmaps for path planning in high-dimensional configuration spaces","volume":"12","author":"Kavraki","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1177\/02783640122067453","article-title":"Randomized kinodynamic planning","volume":"20","author":"LaValle","year":"2001","journal-title":"Int. J. Robot. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","article-title":"A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles","volume":"1","author":"Paden","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s40595-014-0035-4","article-title":"Path planning for autonomous vehicle based on heuristic searching using online images","volume":"2","author":"Hoang","year":"2015","journal-title":"Vietnam. J. Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Goodrich, M.T., and Pszona, P. (2014, January 4\u20137). Two-phase Bicriterion Search for Finding Fast and Efficient Electric Vehicle Routes. Proceedings of the 22Nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA.","DOI":"10.1145\/2666310.2666382"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Baum, M., Dibbelt, J., Pajor, T., and Wagner, D. (2013, January 5\u20138). Energy-optimal routes for electric vehicles. Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Orlando, FL, USA.","DOI":"10.1145\/2525314.2525361"},{"key":"ref_11","unstructured":"Dillmann, R., Beyerer, J., Hanebeck, U.D., and Schultz, T. (2010). The shortest path problem revisited: Optimal routing for electric vehicles. KI 2010: Advances in Artificial Intelligence, Springer. Chapter 2."},{"key":"ref_12","unstructured":"Eisner, J., Funke, S., and Storandt, S. (2011, January 7\u201311). Optimal route planning for electric vehicles in large networks. Proceedings of the National Conference on Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sachenbacher, M., Leucker, M., Artmeier, A., and Haselmayr, J. (2011, January 7\u201311). Efficient energy-optimal routing for electric vehicles. Proceedings of the National Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v25i1.7803"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1002\/net.20033","article-title":"An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems","volume":"44","author":"Feillet","year":"2004","journal-title":"Networks"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ejor.2012.11.040","article-title":"New exact method for large asymmetric distance-constrained vehicle routing problem","volume":"226","author":"Almoustafa","year":"2013","journal-title":"Eur. J. Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cie.2014.02.013","article-title":"Multiple-criterion shortest path algorithms for global path planning of unmanned combat vehicles","volume":"71","author":"Han","year":"2014","journal-title":"Comput. Ind. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0921-8890(93)90005-W","article-title":"The application of neural networks to optimal robot trajectory planning","volume":"11","author":"Simon","year":"1993","journal-title":"Robot. Auton. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/41.824136","article-title":"Global minimum-jerk trajectory planning of robot manipulators","volume":"47","author":"Piazzi","year":"2000","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0004-3702(98)00023-X","article-title":"Planning and acting in partially observable stochastic domains","volume":"101","author":"Kaelbling","year":"1998","journal-title":"Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1287\/opre.21.5.1071","article-title":"The optimal control of partially observable Markov processes over a finite horizon","volume":"21","author":"Smallwood","year":"1973","journal-title":"Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1287\/opre.26.2.282","article-title":"The optimal control of partially observable Markov processes over the infinite horizon: Discounted costs","volume":"26","author":"Sondik","year":"1978","journal-title":"Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1177\/0278364909341659","article-title":"The belief roadmap: Efficient planning in belief space by factoring the covariance","volume":"28","author":"Prentice","year":"2009","journal-title":"Int. J. Robot. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Platt, R., Tedrake, R., Kaelbling, L., and Lozano-P\u00e9rez, T. (2010, January 27\u201330). Belief space planning assuming maximum likelihood observations. Proceedings of the Robotics: Science and Systems VI, Zaragoza, Spain.","DOI":"10.15607\/RSS.2010.VI.037"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1177\/0278364911406562","article-title":"LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information","volume":"30","author":"Abbeel","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bry, A., and Roy, N. (2011, January 9\u201313). Rapidly-exploring random belief trees for motion planning under uncertainty. Proceedings of the 2011 IEEE international conference on robotics and automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980508"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1177\/0278364912456319","article-title":"Motion planning under uncertainty using iterative local optimization in belief space","volume":"31","author":"Patil","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1109\/TASE.2016.2517124","article-title":"Stochastic extended LQR for optimization-based motion planning under uncertainty","volume":"13","author":"Sun","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_28","unstructured":"Kaplan, E., and Hegarty, C.J. (2017). Understanding GPS\/GNSS: Principles and Applications, Artech House Inc. [3rd ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"139","DOI":"10.14358\/PERS.78.2.139","article-title":"Integrated global navigation satellite system (iGNSS) QoS prediction","volume":"78","author":"Roongpiboonsopit","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/JSEN.2017.2654359","article-title":"Multiple faulty GNSS measurement exclusion based on consistency check in urban canyons","volume":"17","author":"Hsu","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hasirlioglu, S., Doric, I., Lauerer, C., and Brandmeier, T. (2016, January 19\u201322). Modeling and simulation of rain for the test of automotive sensor systems. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535399"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hasirlioglu, S., Doric, I., Kamann, A., and Riener, A. (2017, January 4\u20137). Reproducible Fog Simulation for Testing Automotive Surround Sensors. Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia.","DOI":"10.1109\/VTCSpring.2017.8108566"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., and Stork, W. (2019, January 9\u201312). Weather Influence and Classification with Automotive Lidar Sensors. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814205"},{"key":"ref_34","unstructured":"Sasiadek, J.Z., and Wang, Q. (1999, January 10\u201315). Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle. Proceedings of the 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), Detroit, MI, USA."},{"key":"ref_35","unstructured":"Bento, L.C., Nunes, U., Moita, F., and Surrecio, A. (2005, January 16). Sensor fusion for precise autonomous vehicle navigation in outdoor semi-structured environments. Proceedings of the 2005 IEEE Intelligent Transportation Systems, Vienna, Austria."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. Control"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Driankov, D., Hellendoorn, H., and Reinfrank, M. (1993). An Introduction to Fuzzy Control, Springer Science & Business Media.","DOI":"10.1007\/978-3-662-11131-4"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6103\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:29:03Z","timestamp":1760178543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,27]]},"references-count":37,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216103"],"URL":"https:\/\/doi.org\/10.3390\/s20216103","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,27]]}}}