{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T03:01:11Z","timestamp":1783047671248,"version":"3.54.6"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"VeDeCoM Institute","award":["0033 1 30 97 01 80"],"award-info":[{"award-number":["0033 1 30 97 01 80"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite the great technological advances in ADAS, autonomous driving still faces many challenges. Among them is improving decision-making algorithms so that vehicles can make the right decision inspired by human driving. Not only must these decisions ensure the safety of the car occupants and the other road users, but they have to be understandable by them. This article focuses on decision-making algorithms for autonomous vehicles, specifically for lane changing on highways and sub-urban roads. The challenge to overcome is to develop a decision-making algorithm that combines fidelity to human behavior and that is based on machine learning, with a global structure that allows understanding the behavior of the algorithm and that is not opaque such as black box algorithms. To this end, a three-step decision-making method was developed: trajectory prediction of the surrounding vehicles, risk and gain computation associated with the maneuver and based on the predicted trajectories, and finally decision making. For the decision making, three algorithms: decision tree, random forest, and artificial neural network are proposed and compared based on a naturalistic driving database and a driving simulator.<\/jats:p>","DOI":"10.3390\/s22218148","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T21:09:17Z","timestamp":1666645757000},"page":"8148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Detection and Risk Analysis with Lane-Changing Decision Algorithms for Autonomous Vehicles"],"prefix":"10.3390","volume":"22","author":[{"given":"Amin","family":"Mechernene","sequence":"first","affiliation":[{"name":"ESTACA Engineering School, 12 Rue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7094-1370","authenticated-orcid":false,"given":"Vincent","family":"Judalet","sequence":"additional","affiliation":[{"name":"ESTACA Engineering School, 12 Rue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Chaibet","sequence":"additional","affiliation":[{"name":"DRIVE, Universit\u00e9 de Bourgogne, 49 rue Mademoiselle Bourgeois, BP 31, CEDEX, 58027 Nevers, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moussa","family":"Boukhnifer","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, LCOMS, F-57000 Metz, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"ref_1","unstructured":"(2021). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (Standard No. J3016_201806)."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abdeen, M.A.R., Yasar, A., Benaida, M., Sheltami, T., Zavantis, D., and El-Hansali, Y. (2022). Evaluating the Impacts of Autonomous Vehicles\u2019 Market Penetration on a Complex Urban Freeway during Autonomous Vehicles\u2019 Transition Period. Sustainability, 14.","DOI":"10.3390\/su141610094"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.aap.2018.12.019","article-title":"Evaluating the safety impact of connected and autonomous vehicles on motorways","volume":"124","author":"Papadoulis","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Salonen, A.O., and Haavisto, N. (2019). Towards Autonomous Transportation. Passengers\u2019 Experiences, Perceptions and Feelings in a Driverless Shuttle Bus in Finland. Sustainability, 11.","DOI":"10.3390\/su11030588"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/TITS.2019.2901817","article-title":"Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice","volume":"21","author":"Rasouli","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/0191-2615(81)90037-0","article-title":"A behavioral car-following model for computer simulation","volume":"15","author":"Gipps","year":"1981","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Cai, S., Zhang, Y., and Zhang, M. (2010, January 16\u201319). Comparison of lane changing algorithms between NGSIM and CORSIM. Proceedings of the 2010 IEEE 71st Vehicular Technology Conference (VTC), Taipei, Taiwan.","DOI":"10.1109\/VETECS.2010.5493945"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Deng, Q., and S\u00f6ffker, D. (2018, January 26\u201330). Improved Driving Behaviors Prediction Based on Fuzzy Logic-Hidden Markov Model (FL-HMM). Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500533"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mechernene, A., Judalet, V., Chaibet, A., and Boukhnifer, M. (2021). Lane Change Decision Algorithm based on Risk Prediction and Fuzzy Logic Method. Proceedings of the 2021 25th International Conference on System Theory, Control and Computing (ICSTCC), IEEE.","DOI":"10.1109\/ICSTCC52150.2021.9607228"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1109\/TITS.2019.2926042","article-title":"Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving","volume":"21","author":"Guo","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Iberraken, D., Adouane, L., and Denis, D. (2019, January 9\u201312). Reliable risk management for autonomous vehicles based on sequential bayesian decision networks and dynamic inter-vehicular assessment. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813800"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/TITS.2010.2048314","article-title":"Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions","volume":"11","author":"Brannstrom","year":"2010","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tamke, A., Dang, T., and Breuel, G. (2011, January 5\u20139). A flexible method for criticality assessment in driver assistance systems. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940482"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TITS.2006.883115","article-title":"A Multilevel Collision Mitigation Approach-Its Situation Assessment, Decision Making, and Performance Tradeoffs","volume":"7","author":"Hillenbrand","year":"2006","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MITS.2011.942779","article-title":"Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety","volume":"3","author":"Laugier","year":"2011","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/TITS.2013.2272074","article-title":"Review of microscopic lane-changing models and future research opportunities","volume":"14","author":"Rahman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.trc.2004.12.003","article-title":"Modelling vehicle interactions in microscopic simulation of merging and weaving","volume":"13","author":"Hidas","year":"2005","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, S., Zeng, J., Zhang, B., and Sreenath, K. (2021, January 25\u201328). Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change. Proceedings of the 2021 American Control Conference (ACC), New Orleans, LA, USA.","DOI":"10.23919\/ACC50511.2021.9482848"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Q., Zhao, D., and Chen, Y. (2019, January 14\u201319). Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852110"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vechione, M., and Cheu, R.L. (2021). Comparative evaluation of adaptive fuzzy inference system and adaptive neuro-fuzzy inference system for mandatory lane changing decisions on freeways. J. Intell. Transp. Syst., 1\u201315.","DOI":"10.1080\/15472450.2021.1967153"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.3141\/1999-10","article-title":"General lane-changing model MOBIL for car-following models","volume":"1999","author":"Kesting","year":"2007","journal-title":"Transportation Research Record: J. Transp. Res. Board"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3141\/2316-06","article-title":"Integrated lane change model with relaxation and synchronization","volume":"2316","author":"Schakel","year":"2012","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Latrech, C., Chaibet, A., Boukhnifer, M., and Glaser, S. (2018). Integrated longitudinal and lateral networked control system design for vehicle platooning. Sensors, 18.","DOI":"10.3390\/s18093085"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77285","DOI":"10.1109\/ACCESS.2020.2989082","article-title":"Advanced Driver Assistance Strategies for a Single-Vehicle Overtaking a Platoon on the Two-Lane Two-Way Road","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Bonic, L., Galizia, A.D., and Bracquemond, A. (2017). Identification of Real-World Driving Scenarios for the Functional Safety of Autonomous Vehicles, EVS30 Symposium."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mechernene, A., Judalet, V., Chaibet, A., and Boukhnifer, M. (2020, January 7\u20139). Risk analysis method for a lane change maneuvers on highways. Proceedings of the 2020 International Conference on Control, Automation and Diagnosis (ICCAD), Paris, France.","DOI":"10.1109\/ICCAD49821.2020.9260515"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56992","DOI":"10.1109\/ACCESS.2020.2982170","article-title":"Artificial intelligence for vehicle behavior anticipation: Hybrid approach based on maneuver classification and trajectory prediction","volume":"8","author":"Benterki","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Benterki, A., Judalet, V., Choubeila, M., and Boukhnifer, M. (2019, January 14\u201317). Long-Term Prediction of Vehicle Trajectory Using Recurrent Neural Networks. Proceedings of the IECON 2019\u201445th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal.","DOI":"10.1109\/IECON.2019.8927604"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Krajewski, R., Bock, J., Kloeker, L., and Eckstein, L. (2018, January 4\u20137). The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569552"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:00Z","timestamp":1760144520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":30,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218148"],"URL":"https:\/\/doi.org\/10.3390\/s22218148","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,24]]}}}