{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:12:37Z","timestamp":1777389157637,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,15]],"date-time":"2016-01-15T00:00:00Z","timestamp":1452816000000},"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>This paper describes a real-time motion planner based on the drivers\u2019 visual behavior-guided rapidly exploring random tree (RRT) approach, which is applicable to on-road driving of autonomous vehicles. The primary novelty is in the use of the guidance of drivers\u2019 visual search behavior in the framework of RRT motion planner. RRT is an incremental sampling-based method that is widely used to solve the robotic motion planning problems. However, RRT is often unreliable in a number of practical applications such as autonomous vehicles used for on-road driving because of the unnatural trajectory, useless sampling, and slow exploration. To address these problems, we present an interesting RRT algorithm that introduces an effective guided sampling strategy based on the drivers\u2019 visual search behavior on road and a continuous-curvature smooth method based on B-spline. The proposed algorithm is implemented on a real autonomous vehicle and verified against several different traffic scenarios. A large number of the experimental results demonstrate that our algorithm is feasible and efficient for on-road autonomous driving. Furthermore, the comparative test and statistical analyses illustrate that its excellent performance is superior to other previous algorithms.<\/jats:p>","DOI":"10.3390\/s16010102","type":"journal-article","created":{"date-parts":[[2016,1,16]],"date-time":"2016-01-16T03:30:25Z","timestamp":1452915025000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Drivers\u2019 Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving"],"prefix":"10.3390","volume":"16","author":[{"given":"Mingbo","family":"Du","sequence":"first","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"},{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Mei","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huawei","family":"Liang","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rulin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"},{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1002\/rob.20265","article-title":"Motion planning in urban environments","volume":"25","author":"Ferguson","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1109\/TCST.2008.2012116","article-title":"Real-time motion planning with applications to autonomous urban driving","volume":"17","author":"Kuwata","year":"2009","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., and How, J. (2008, January 18\u201321). Motion planning in complex environments usingclosed-loop prediction. Proceedings of the AIAA Guidance, Navigation Control Conference, Honolulu, HI, USA.","DOI":"10.2514\/6.2008-7166"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TITS.2012.2198214","article-title":"Local path planning for off-road autonomous driving with avoidance of static obstacles","volume":"13","author":"Keonyup","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2010). The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, Springer-Verlag.","DOI":"10.1007\/978-3-642-03991-1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/JPROC.2006.888394","article-title":"Systems for safety and autonomous behavior in cars: The DARPA grand challenge experience","volume":"95","author":"Stiller","year":"2007","journal-title":"IEEE Proc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17548","DOI":"10.3390\/s140917548","article-title":"Motion Planning for Autonomous vehicle Based on Radial Basis Function Neural in Unstructured Environment","volume":"14","author":"Chen","year":"2014","journal-title":"Sensors"},{"key":"ref_8","unstructured":"Du, M., Chen, J., Zhao, P., Liang, H., Xin, Y., and Mei, T. (June, January 31). An Improved RRT-based Motion Planner for Autonomous Vehicle in Cluttered Environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"LaValle, S.M. (2006). Planning Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9780511546877"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF01386390","article-title":"A note on two problems in connection with graphs","volume":"1","author":"Dijkstra","year":"1959","journal-title":"Numer. Math."},{"key":"ref_11","unstructured":"Koren, Y., and Borenstein, J. (1991, January 9\u201311). Potential field methods and their inherent limitations for mobile robot navigation. Proceedings of the IEEE Conference on Robotics and Automation, Sacramento, CA, USA."},{"key":"ref_12","unstructured":"Likhachev, M., Ferguson, D., Gordon, G., Thrun, S., and Stenz, A. (2005, January 5\u201310). Anytime dynamic A*: An anytime, Replanning algorithm. Proceedings of the International Conference on Automated Planning and Scheduling, Monterey, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.trc.2015.09.011","article-title":"Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions","volume":"60","author":"Katrakazas","year":"2015","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_14","unstructured":"Gu, T.Y., and Dolan, J.M. (2012). Intelligent Robotics and Applications, Springer."},{"key":"ref_15","unstructured":"Liang, M., Yang, J., and Zhang, M. (2012, January 6\u20137). A two-level path planning method for on-road autonomous driving. Proceedings of the IEEE 2012 2nd International Conference on Intelligent System Design and Engineering Application (ISDEA), Sanya, China."},{"key":"ref_16","unstructured":"Pivtoraiko, M., and Kelly, A. (2009, January 12\u201317). Fast and feasible deliberative motion planner for dynamic environments. Proceedings of the International Conference on Robotics and Automation (ICRA), Kobe, Japan."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, C., Dom\u00ednguez, R., Fern\u00e1ndez-Llorca, D., Alonso, J., and Sotelo, M. (2013). Autonomous Navigation and Obstacle Avoidance of a Micro-bus. Int. J. Adv. Robot. Syst.","DOI":"10.5772\/56125"},{"key":"ref_18","unstructured":"Ferguson, D., and Stentz, A. (2005). The Field D*Algorithm for Improved Pathplanning and Replanning in Uniform and Nonuniform Cost Environments, Robotics Institute, Carnegie Mellon University. Technical Report."},{"key":"ref_19","unstructured":"Kuffner, J., and LaValle, S. (2000, January 24\u201328). RRT-connect: An efficient approach to single-query path planning. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jaillet, L., Hoffman, J., van den Berg, J., Abbeel, P., Porta, J., and Goldberg, K. (2011, January 25\u201330). EG-RRT: Environment-guided random trees for kinodynamicmotion planning with uncertainty and obstacles. Proceedings of the IROS, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048409"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Melchior, N.A., and Simmons, R. (2007, January 10\u201314). Particle RRT for path planning withuncertainty. Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy.","DOI":"10.1109\/ROBOT.2007.363555"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1177\/0278364911406761","article-title":"Sampling-based algorithms for optimalmotion planning","volume":"30","author":"Karaman","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jeon, J.H., Karaman, S., and Frazzoli, E. (2011, January 12\u201315). Anytime computation of time-optimal off-road vehicle maneuvers using the RRT*. Proceedings of the IEEE 50th Conference on Decision and Control and European Control, Orlando, FL, USA.","DOI":"10.1109\/CDC.2011.6161521"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2015.2389215","article-title":"Efficient sampling-based motion planning for on-road autonomous driving","volume":"16","author":"Ma","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1038\/369742a0","article-title":"Where do we look when we steer","volume":"369","author":"Land","year":"1994","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1068\/p5343","article-title":"A two-point visual control model of steering","volume":"33","author":"Salvucci","year":"2004","journal-title":"Perception"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/8.11.10","article-title":"Driving around bends with manipulated eye-steeringcoordination","volume":"8","author":"Mars","year":"2008","journal-title":"J. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lappi, O. (2014). Future path and tangent point models in the visual control of locomotion in curve driving. J. Vis., 14.","DOI":"10.1167\/14.12.21"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sentouh, C., Chevrel, P., and Mars, F. (2009, January 11\u201314). A sensorimotor driver model for steering control. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5346350"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1360\/972012-1360","article-title":"Modeling the effect of driving experience on lane keeping performance using ACT-R cognitive architecture","volume":"58","author":"Cao","year":"2013","journal-title":"Chin. Sci. Bull."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/9.1.11","article-title":"Driving is smoother and more stable when using the tangent point","volume":"9","author":"Kandil","year":"2009","journal-title":"J. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.aap.2014.04.002","article-title":"Effect of driving experience on anticipatory look-ahead fixations in real curve driving","volume":"70","author":"Lehtonen","year":"2014","journal-title":"Accid. Anal. Prev."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Minh, V.T., and Pumwa, J. (2014). Feasible Path Planning for Autonomous Vehicles. Math. Probl. Eng., 2014.","DOI":"10.1155\/2014\/317494"},{"key":"ref_34","unstructured":"Rushton, S.K., Wen, J., and Allison, R.S. (2002). Biologically Motivated Computer Vision, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21931","DOI":"10.3390\/s150921931","article-title":"A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment","volume":"15","author":"Liu","year":"2015","journal-title":"Sensors"},{"key":"ref_36","first-page":"443","article-title":"RRT-based Motion Planning Algorithm for Intelligent Vehicle in Complex Environments","volume":"37","author":"Du","year":"2015","journal-title":"Robot"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0921-8890(00)00127-5","article-title":"Classification of the dubins set","volume":"34","author":"Shkel","year":"2001","journal-title":"Robot. Auton. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"497","DOI":"10.2307\/2372560","article-title":"On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions andtangents","volume":"79","author":"Dubins","year":"1957","journal-title":"Am. J. Math."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:17:48Z","timestamp":1760210268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,15]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["s16010102"],"URL":"https:\/\/doi.org\/10.3390\/s16010102","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,15]]}}}