{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:31:06Z","timestamp":1784046666929,"version":"3.55.0"},"reference-count":109,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Emirates Center for Mobility Research (ECMR) of the United Arab Emirates University","award":["31R151"],"award-info":[{"award-number":["31R151"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.<\/jats:p>","DOI":"10.3390\/s23010317","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:38:43Z","timestamp":1672205923000},"page":"317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["How Do Autonomous Vehicles Decide?"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2759-3144","authenticated-orcid":false,"given":"Sumbal","family":"Malik","sequence":"first","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research (ECMR), United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0319-8126","authenticated-orcid":false,"given":"Manzoor Ahmed","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research (ECMR), United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7488-0915","authenticated-orcid":false,"given":"Hesham","family":"El-Sayed","sequence":"additional","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research (ECMR), United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6230-1760","authenticated-orcid":false,"given":"Jalal","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1434-7948","authenticated-orcid":false,"given":"Obaid","family":"Ullah","sequence":"additional","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","unstructured":"Batkovic, I. (2022). Enabling Safe Autonomous Driving in Uncertain Environments. [Ph.D. Thesis, Chalmers Tekniska Hogskola]."},{"key":"ref_2","unstructured":"(2016). Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicles (Standard No. J3016_201806)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1109\/TITS.2019.2913998","article-title":"A review of motion planning for highway autonomous driving","volume":"21","author":"Claussmann","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pendleton, S.D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y.H., Rus, D., and Ang, M.H. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5.","DOI":"10.3390\/machines5010006"},{"key":"ref_5","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. Part C Emerg. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/0600000079","article-title":"Computer vision for autonomous vehicles: Problems, datasets and state of the art","volume":"12","author":"Janai","year":"2020","journal-title":"Found. Trends Comput. Graph. Vis."},{"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","unstructured":"Hoel, C.J.E. (2021). Decision-Making in Autonomous Driving Using Reinforcement Learning. [Ph.D. Thesis, Chalmers Tekniska Hogskola].","DOI":"10.1109\/IV47402.2020.9304614"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1146\/annurev-control-060117-105157","article-title":"Planning and decision-making for autonomous vehicles","volume":"1","author":"Schwarting","year":"2018","journal-title":"Annu. Rev. Control. Robot. Auton. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s10033-021-00639-3","article-title":"Planning and decision-making for connected autonomous vehicles at road intersections: A review","volume":"34","author":"Li","year":"2021","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Leon, F., and Gavrilescu, M. (2021). A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving. Mathematics, 9.","DOI":"10.3390\/math9060660"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485767","article-title":"Level-5 Autonomous Driving\u2014Are We There Yet? A Review of Research Literature","volume":"55","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Malik, S., Khan, M.A., and El-Sayed, H. (2021). Collaborative autonomous driving\u2014A survey of solution approaches and future challenges. Sensors, 21.","DOI":"10.3390\/s21113783"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deshpande, N., Vaufreydaz, D., and Spalanzani, A. (2020, January 13\u201315). Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network. Proceedings of the 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), Shenzhen, China.","DOI":"10.1109\/ICARCV50220.2020.9305435"},{"key":"ref_15","unstructured":"Bahram, M. (2017). Interactive Maneuver Prediction and Planning for Highly Automated Driving Functions. [Ph.D. Thesis, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Palatti, J., Aksjonov, A., Alcan, G., and Kyrki, V. (2021, January 19\u201322). Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564499"},{"key":"ref_17","unstructured":"(2022, February 25). DARPA Urban Challenge. Available online: https:\/\/www.darpa.mil\/about-us\/timeline\/darpa-urban-challenge."},{"key":"ref_18","unstructured":"Urmson, C., Bagnell, J.A., Baker, C., Hebert, M., Kelly, A., Rajkumar, R., Rybski, P.E., Scherer, S., Simmons, R., and Singh, S. (2007). Tartan Racing: A Multi-Modal Approach to the Darpa Urban Challenge, Carnegie Mellon University."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1002\/rob.20258","article-title":"Junior: The stanford entry in the urban challenge","volume":"25","author":"Montemerlo","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, P., Liu, D., Chen, J., Li, H., and Chan, C.Y. (June, January 30). Decision making for autonomous driving via augmented adversarial inverse reinforcement learning. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560907"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"566","DOI":"10.3182\/20100712-3-DE-2013.00006","article-title":"Strategic decision-making process in advanced driver assistance systems","volume":"43","author":"Ardelt","year":"2010","journal-title":"IFAC Proc. Vol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MITS.2014.2306552","article-title":"Making bertha drive\u2014An autonomous journey on a historic route","volume":"6","author":"Ziegler","year":"2014","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, L., Kong, D., and Yan, X. (2018). A driving behavior planning and trajectory generation method for autonomous electric bus. Future Internet, 10.","DOI":"10.3390\/fi10060051"},{"key":"ref_24","unstructured":"Olsson, M. (2022, September 02). Behavior Trees for Decision-Making in Autonomous Driving. Available online: https:\/\/www.semanticscholar.org\/paper\/Behavior-Trees-for-decision-making-in-Autonomous-Olsson\/2fe811fd9b8e466aa353d94ee7cf67ebae456e91."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gu, T., Dolan, J.M., and Lee, J.W. (2016, January 9\u201314). Automated tactical maneuver discovery, reasoning and trajectory planning for autonomous driving. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759805"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"180039","DOI":"10.1109\/ACCESS.2019.2959432","article-title":"Real-time motion planning approach for automated driving in urban environments","volume":"7","author":"Artunedo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Artu\u00f1edo, A., Godoy, J., and Villagra, J. (2019, January 9\u201312). A decision-making architecture for automated driving without detailed prior maps. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814070"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, C.W., Lv, C., Wang, H., Wang, H., Cao, D., Velenis, E., and Wang, F.Y. (2017, January 16\u201319). Multi-point turn decision making framework for human-like automated driving. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317831"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Orzechowski, P.F., Burger, C., and Lauer, M. (November, January 19). Decision-making for automated vehicles using a hierarchical behavior-based arbitration scheme. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304723"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MITS.2016.2565718","article-title":"If, when, and how to perform lane change maneuvers on highways","volume":"8","author":"Nilsson","year":"2016","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Aksjonov, A., and Kyrki, V. (2021, January 19\u201322). Rule-Based Decision-Making System for Autonomous Vehicles at Intersections with Mixed Traffic Environment. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9565085"},{"key":"ref_32","unstructured":"Thurachen, S. (2022). Decision Making in Autonomous Driving by Integrating Rules with Deep Reinforcement Learning. [Master\u2019s Thesis, Aalto University]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hoel, C.J. (2019). Tactical Decision Making for Autonomous Driving: A Reinforcement Learning Approach. [Ph.D. Thesis, Chalmers University of Technology].","DOI":"10.1109\/IV47402.2020.9304614"},{"key":"ref_34","unstructured":"Qiao, Z. (2021). Reinforcement Learning for Behavior Planning of Autonomous Vehicles in Urban Scenarios. [Ph.D. Thesis, Carnegie Mellon University]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIV.2019.2955905","article-title":"Combining planning and deep reinforcement learning in tactical decision making for autonomous driving","volume":"5","author":"Hoel","year":"2019","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hubmann, C., Aeberhard, M., and Stiller, C. (2016, January 1\u20134). A generic driving strategy for urban environments. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795679"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, W., Kim, S.W., Chong, Z.J., Shen, X., and Ang, M.H. (2013, January 12\u201315). Motion planning using cooperative perception on urban road. Proceedings of the 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), Manila, Philippines.","DOI":"10.1109\/RAM.2013.6758572"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arab, A., Yu, K., Yi, J., and Song, D. (2016, January 21\u201325). Motion planning for aggressive autonomous vehicle maneuvers. Proceedings of the 2016 IEEE International Conference on Automation Science and Engineering (CASE), Fort Worth, TX, USA.","DOI":"10.1109\/COASE.2016.7743384"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, H., Yu, G., Zhou, B., Li, D., and Wang, Z. (2020, January 14\u201316). Trajectory Planning of Autonomous Driving Vehicles Based on Road-Vehicle Fusion. Proceedings of the 20th COTA International Conference of Transportation Professionals, Xi\u2019an, China.","DOI":"10.1061\/9780784483053.069"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hegedus, T., N\u00e9meth, B., and G\u00e1sp\u00e1r, P. (2020). Design of a low-complexity graph-based motion-planning algorithm for autonomous vehicles. Appl. Sci., 10.","DOI":"10.3390\/app10217716"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Speidel, O., Ruof, J., and Dietmayer, K. (2021, January 11\u201313). Graph-Based Motion Planning For Automated Vehicles Using Multi-Model Branching And Admissible Heuristics. Proceedings of the 2021 IEEE International Conference on Autonomous Systems (ICAS), Montreal, QC, Canada.","DOI":"10.1109\/ICAS49788.2021.9551185"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, J., and Chen, W.H. (2021, January 2\u20134). Automatic Lane Merge based on Model Predictive Control. Proceedings of the 2021 26th International Conference on Automation and Computing (ICAC), Portsmouth, UK.","DOI":"10.23919\/ICAC50006.2021.9594261"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, Y.C., Lin, C.L., Huang, S.T., and Kuo, C.H. (2021). Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control. Electronics, 10.","DOI":"10.3390\/electronics10182293"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Viana, \u00cd.B., Kanchwala, H., and Aouf, N. (2019, January 4\u20138). Cooperative trajectory planning for autonomous driving using nonlinear model predictive control. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria.","DOI":"10.1109\/ICCVE45908.2019.8965227"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bey, H., Dierkes, F., Bayerl, S., Lange, A., Fa\u00dfender, D., and Thielecke, J. (2019, January 9\u201312). Optimization-based tactical behavior planning for autonomous freeway driving in favor of the traffic flow. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813787"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, D., Redmill, K., and \u00d6zg\u00fcner, \u00dc. (November, January 19). A multi-state social force based framework for vehicle-pedestrian interaction in uncontrolled pedestrian crossing scenarios. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304561"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sun, L., Zhan, W., Chan, C.Y., and Tomizuka, M. (2019, January 9\u201312). Behavior planning of autonomous cars with social perception. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814223"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Werling, M., Ziegler, J., Kammel, S., and Thrun, S. (2010, January 3\u20138). Optimal trajectory generation for dynamic street scenarios in a frenet frame. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, Alaska.","DOI":"10.1109\/ROBOT.2010.5509799"},{"key":"ref_49","unstructured":"Chandiramani, J. (2017). Decision Making under Uncertainty for Automated Vehicles in Urban Situations. [Master\u2019s Thesis, Delft University of Technology]."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Peng, B., Yu, D., Zhou, H., Xiao, X., and Xie, C. (2022). A Motion Planning Method for Automated Vehicles in Dynamic Traffic Scenarios. Symmetry, 14.","DOI":"10.3390\/sym14020208"},{"key":"ref_51","unstructured":"Lima, P.F. (2018). Optimization-Based Motion Planning and Model Predictive Control for Autonomous Driving: With Experimental Evaluation on a Heavy-Duty Construction Truck. [Ph.D. Thesis, KTH Royal Institute of Technology]."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, Q., Li, X., Yuan, S., and Li, Z. (2021, January 19\u201322). Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564580"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ma, J., Xie, H., Song, K., and Liu, H. (2022). Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning. Symmetry, 14.","DOI":"10.3390\/sym14010031"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.comcom.2020.04.021","article-title":"A driving intention prediction method based on hidden Markov model for autonomous driving","volume":"157","author":"Liu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hsu, T.M., Chen, Y.R., and Wang, C.H. (2020, January 4\u20137). Decision Making Process of Autonomous Vehicle with Intention-Aware Prediction at Unsignalized Intersections. Proceedings of the 2020 International Automatic Control Conference (CACS), Hsinchu, Taiwan.","DOI":"10.1109\/CACS50047.2020.9289815"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1025349","DOI":"10.1155\/2016\/1025349","article-title":"Intention-aware autonomous driving decision-making in an uncontrolled intersection","volume":"2016","author":"Song","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/TIV.2017.2788208","article-title":"Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction","volume":"3","author":"Hubmann","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1177\/09544070211039720","article-title":"An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change","volume":"236","author":"Huang","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Coskun, S., and Langari, R. (2018, January 21\u201324). Predictive fuzzy markov decision strategy for autonomous driving in highways. Proceedings of the 2018 IEEE Conference on Control Technology and Applications (CCTA), Copenhagen, Denmark.","DOI":"10.1109\/CCTA.2018.8511369"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Song, W., Su, B., Xiong, G., and Li, S. (2018, January 12\u201314). Intention-aware Decision Making in Urban Lane Change Scenario for Autonomous Driving. Proceedings of the 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain.","DOI":"10.1109\/ICVES.2018.8519506"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, C., Steinhauser, F., Hinz, G., and Knoll, A. (2022, January 13). Traffic Mirror-Aware POMDP Behavior Planning for Autonomous Urban Driving. Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany.","DOI":"10.1109\/IV51971.2022.9827139"},{"key":"ref_62","unstructured":"Sierra Gonzalez, D. (2019). Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios. [Ph.D. Thesis, Universit\u00e9 Grenoble Alpes (ComUE)]."},{"key":"ref_63","unstructured":"Saad, W. (2010). Coalitional Game Theory for Distributed Cooperation in Next Generation Wireless Networks. [Ph.D. Thesis, University of Oslo]."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1007\/s40815-021-01196-6","article-title":"A Leader\u2013Follower Sequential Game Approach to Optimizing Parameters for Intelligent Vehicle Formation Control","volume":"24","author":"Zhao","year":"2022","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3829","DOI":"10.1109\/TITS.2021.3069463","article-title":"Cooperative decision making of connected automated vehicles at multi-lane merging zone: A coalitional game approach","volume":"23","author":"Hang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.trb.2021.05.007","article-title":"Competitive and cooperative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach","volume":"149","author":"Wang","year":"2021","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1177\/10775463211009383","article-title":"Cooperative-game-theoretic optimal robust path tracking control for autonomous vehicles","volume":"28","author":"Hu","year":"2022","journal-title":"J. Vib. Control"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Liu, M., Wan, Y., Lewis, F.L., Nageshrao, S., and Filev, D. (2022). A Three-Level Game-Theoretic Decision-Making Framework for Autonomous Vehicles. IEEE Trans. Intell. Transp. Syst.","DOI":"10.1109\/TITS.2022.3172926"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"102213","DOI":"10.1016\/j.simpat.2020.102213","article-title":"A cooperative game based mechanism for autonomous organization and ubiquitous connectivity in VANETs","volume":"107","author":"Mabrouk","year":"2021","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hang, P., Lv, C., Huang, C., and Hu, Z. (2021, January 26\u201328). Cooperative Decision Making of Lane-change for Automated Vehicles Considering Human-like Driving Characteristics. Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China.","DOI":"10.23919\/CCC52363.2021.9550305"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hang, P., Huang, C., Hu, Z., and Lv, C. (2022). Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits. arXiv.","DOI":"10.1109\/TITS.2022.3209607"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.compeleceng.2017.10.016","article-title":"Cooperative game-theoretic approach to traffic flow optimization for multiple intersections","volume":"71","author":"Bui","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wei, H., Mashayekhy, L., and Papineau, J. (2018, January 4\u20137). Intersection management for connected autonomous vehicles: A game theoretic framework. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569307"},{"key":"ref_74","unstructured":"Calvo, J.A.L., and Mathar, R. (2018, January 18\u201321). Connected Vehicles Coordination: A Coalitional Game-Theory Approach. Proceedings of the 2018 European Conference on Networks and Communications (EuCNC), Ljubljana, Slovenia."},{"key":"ref_75","unstructured":"Khan, M.A., and Boloni, L. (2005, January 7\u201310). Convoy driving through ad-hoc coalition formation. Proceedings of the 11th IEEE Real Time and Embedded Technology and Applications Symposium, San Francisco, CA, USA."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zong, C., Han, X., Zhang, D., Zheng, H., and Shi, C. (2020). Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach. Appl. Sci., 10.","DOI":"10.3390\/app10165589"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e6246","DOI":"10.1002\/cpe.6246","article-title":"A game theory-based route planning approach for automated vehicle collection","volume":"33","author":"Hadded","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3232848","article-title":"A survey on game-theoretic approaches for intrusion detection and response optimization","volume":"51","author":"Kiennert","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.trpro.2015.06.022","article-title":"Modeling lane-changing behavior in a connected environment: A game theory approach","volume":"7","author":"Talebpour","year":"2015","journal-title":"Transp. Res. Procedia"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1109\/TITS.2020.3036984","article-title":"Human-like decision making for autonomous driving: A noncooperative game theoretic approach","volume":"22","author":"Hang","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_81","first-page":"100164","article-title":"Autonomous driving architectures: Insights of machine learning and deep learning algorithms","volume":"6","author":"Bachute","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e4427","DOI":"10.1002\/ett.4427","article-title":"Machine learning for next-generation intelligent transportation systems: A survey","volume":"33","author":"Yuan","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Shetty, S.H., Shetty, S., Singh, C., and Rao, A. (2022). Supervised Machine Learning: Algorithms and Applications. Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, John Wiley & Sons, Inc.","DOI":"10.1002\/9781119821908.ch1"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Lv, K., Pei, X., Chen, C., and Xu, J. (2022). A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning. Mathematics, 10.","DOI":"10.3390\/math10091551"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"103452","DOI":"10.1016\/j.trc.2021.103452","article-title":"Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness","volume":"134","author":"Li","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Garc\u00eda Cuenca, L., Puertas, E., Fernandez Andr\u00e9s, J., and Aliane, N. (2019). Autonomous driving in roundabout maneuvers using reinforcement learning with Q-learning. Electronics, 8.","DOI":"10.3390\/electronics8121536"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"5526","DOI":"10.1109\/TNNLS.2020.3042981","article-title":"Adaptive decision-making for automated vehicles under roundabout scenarios using optimization embedded reinforcement learning","volume":"32","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"13340","DOI":"10.1109\/TVT.2021.3122257","article-title":"A hybrid deep reinforcement learning for autonomous vehicles smart-platooning","volume":"70","author":"Prathiba","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5068","DOI":"10.1109\/TITS.2020.3046646","article-title":"Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning","volume":"23","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8707","DOI":"10.1109\/TVT.2021.3098321","article-title":"Interpretable decision-making for autonomous vehicles at highway on-ramps with latent space reinforcement learning","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Chen, J., Xu, Z., and Tomizuka, M. (2020, January 25\u201329). End-to-end autonomous driving perception with sequential latent representation learning. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341020"},{"key":"ref_92","unstructured":"Mei, X., Sun, Y., Chen, Y., Liu, C., and Liu, M. (2021). Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TITS.2020.3008612","article-title":"Deep reinforcement learning for intelligent transportation systems: A survey","volume":"23","author":"Haydari","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/TITS.2021.3054625","article-title":"Deep reinforcement learning for autonomous driving: A survey","volume":"23","author":"Kiran","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Chen, J., Yuan, B., and Tomizuka, M. (2019, January 3\u20138). Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968225"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Wang, W., Jiang, L., Lin, S., Fang, H., and Meng, Q. (2022). Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts. Multimed. Tools Appl., 1\u201317.","DOI":"10.1007\/s11042-022-12300-9"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Yun, W.J., Shin, M., Jung, S., Kwon, S., and Kim, J. (2022). Parallelized and randomized adversarial imitation learning for safety-critical self-driving vehicles. J. Commun. Netw.","DOI":"10.23919\/JCN.2022.000012"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, R.P., Phillips, D.J., Liu, C., Gupta, J.K., Driggs-Campbell, K., and Kochenderfer, M.J. (2019, January 20\u201324). Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793750"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3054912","article-title":"Imitation learning: A survey of learning methods","volume":"50","author":"Hussein","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"49738","DOI":"10.1109\/ACCESS.2022.3172712","article-title":"Active Inference Integrated With Imitation Learning for Autonomous Driving","volume":"10","author":"Nozari","year":"2022","journal-title":"IEEE Access"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"14128","DOI":"10.1109\/TITS.2022.3144867","article-title":"A survey on imitation learning techniques for end-to-end autonomous vehicles","volume":"23","author":"Yi","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_102","unstructured":"Zheng, B., Verma, S., Zhou, J., Tsang, I.W., and Chen, F. (2021). Imitation Learning: Progress, Taxonomies and Opportunities. arXiv."},{"key":"ref_103","first-page":"11","article-title":"Accelerating the next revolution in roadway safety","volume":"86","author":"Policy","year":"2016","journal-title":"ITE J."},{"key":"ref_104","unstructured":"Sado, F., Loo, C.K., Liew, W.S., Kerzel, M., and Wermter, S. (2020). Explainable Goal-Driven Agents and Robots\u2014A Comprehensive Review. arXiv."},{"key":"ref_105","unstructured":"Korpan, R., and Epstein, S.L. (2018, January 5\u20138). Toward natural explanations for a robot\u2019s navigation plans. Proceedings of the HRI WS on Explainable Robotic Systems, Chicago, IL, USA."},{"key":"ref_106","unstructured":"Borgo, R., Cashmore, M., and Magazzeni, D. (2018). Towards providing explanations for AI planner decisions. arXiv."},{"key":"ref_107","unstructured":"Bidot, J., Biundo, S., Heinroth, T., Minker, W., Nothdurft, F., and Schattenberg, B. (2010, January 23\u201325). Verbal Plan Explanations for Hybrid Planning. Proceedings of the MKWI, Goettingen, Germany."},{"key":"ref_108","unstructured":"(2022, August 28). Global Status Report on Road Safety 2018. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789241565684."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/00423114.2018.1492142","article-title":"Towards connected autonomous driving: Review of use-cases","volume":"57","author":"Montanaro","year":"2019","journal-title":"Veh. Syst. Dyn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:49Z","timestamp":1760147629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,28]]},"references-count":109,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010317"],"URL":"https:\/\/doi.org\/10.3390\/s23010317","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,28]]}}}