{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T14:27:09Z","timestamp":1782224829030,"version":"3.54.5"},"reference-count":154,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Science and Innovation","award":["KOSMOS PID2024-155363OB-C41"],"award-info":[{"award-number":["KOSMOS PID2024-155363OB-C41"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["ISI2A2 DGP_PIDI_2024_01174"],"award-info":[{"award-number":["ISI2A2 DGP_PIDI_2024_01174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Efficient vehicle driving generally intends to reduce fuel consumption, emissions of harmful substances, and accident rates based on energy-efficient driving patterns as a set of parameters defining optimal vehicle and route characteristics, together with specific ways of driving a vehicle that the particular driver applies. To gain environmental friendliness in driving, two main approaches can be outlined: optimal route planning and driver training based on the principles of ecological driving. The latter can be supported by using software for real-time, efficient vehicle driving recommendations. In order to develop the principles of ecological driving as well as generate relevant real-time recommendations, it is necessary to identify the specific parameters required to analyze driver behavior and vehicle performance, determine the corresponding energy consumption, and understand the influence of route and environmental conditions on overall efficient vehicle driving. These tasks require a large amount of data, often obtained from heterogeneous sources, which, when publicly available, are complex for consolidation, transmission, and processing, not to mention the complexity of the data model itself. This study provides a thorough review of the current data sources and techniques for efficient vehicle driving analysis, focusing on the availability and relevance of dataset sources and repositories. The categorization of parameters and data processing techniques enabling efficient vehicle driving analysis is carried out according to efficiency types such as driver\u2019s efficiency, resource consumption efficiency, and route planning efficiency. For each type of efficiency, we provide a list of contextual groups and features, identifying the dataset containing the necessary feature, making it possible not only to determine the parameters defining, for example, driver efficiency, but also locate the corresponding dataset serving as a stepping stone for researchers and practitioners to join the community investigating efficient vehicle driving analysis. We also discuss future trends and perspectives, identifying alternative data sources for efficient vehicle driving analysis, and focus on data collection issues revealed by the practical use case of collecting data from mobile phone sensors.<\/jats:p>","DOI":"10.3390\/jsan14030052","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T07:34:03Z","timestamp":1747640043000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Data-Driven Approaches for Efficient Vehicle Driving Analysis: A Survey"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8762-3918","authenticated-orcid":false,"given":"Iryna I.","family":"Husyeva","sequence":"first","affiliation":[{"name":"Department of Software Engineering in Energy, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7819-5416","authenticated-orcid":false,"given":"Ismael","family":"Navas-Delgado","sequence":"additional","affiliation":[{"name":"Khaos Research, ITIS Software, Departamento de Lenguajes y Ciencias de la Computaci\u00f3n, University of M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2985-3480","authenticated-orcid":false,"given":"Jos\u00e9","family":"Garc\u00eda-Nieto","sequence":"additional","affiliation":[{"name":"Khaos Research, ITIS Software, Departamento de Lenguajes y Ciencias de la Computaci\u00f3n, University of M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.trb.2018.03.011","article-title":"Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications","volume":"111","author":"Wang","year":"2018","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1243\/095440704322829155","article-title":"Driving style and traffic measures\u2014Influence on vehicle emissions and fuel consumption","volume":"218","author":"Mierlo","year":"2004","journal-title":"J. Automob. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3141\/2645-07","article-title":"Development and Application of an Ecodriving Support Platform Based on Internet+:Case Study in Beijing Taxicabs","volume":"2645","author":"Wu","year":"2017","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1177\/014107680309600304","article-title":"Five steps to conducting a systematic review","volume":"96","author":"Khan","year":"2003","journal-title":"J. R. Soc. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2309376","DOI":"10.1155\/2022\/2309376","article-title":"Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence","volume":"2022","author":"Ling","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Al-refai, G., and Elmoaqet, H.R.M. (2022). In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning. Appl. Sci., 12.","DOI":"10.3390\/app12188928"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3390\/modelling3030025","article-title":"Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information","volume":"3","author":"Breub","year":"2022","journal-title":"Modelling"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3470648","article-title":"Driving Maneuver Anomaly Detection based on Deep Auto-Encoder and Geographical Partitioning","volume":"19","author":"Liu","year":"2022","journal-title":"ACM Trans. Sen. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, P., Fu, Y., Zhang, J., Wang, P., Zheng, Y., and Aggarwal, C. (2018, January 19\u201323). You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery, Data Mining (KDD \u201918), London, UK.","DOI":"10.1145\/3219819.3219985"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Mao, Z., Zheng, Y., Chen, T., and Chen, Z. (2022). Electric Vehicle Range Estimation Using Regression Techniques. World Electr. Veh. J., 13.","DOI":"10.3390\/wevj13060105"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5879568","DOI":"10.1155\/2022\/5879568","article-title":"Differentiated Speed Planning for Connected and Automated Electric Vehicles at Signalized Intersections considering Dynamic Wireless Power Transfer","volume":"2022","author":"Yang","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Song, Q., Tan, R., and Wang, J. (2023, January 9\u201312). Towards Efficient Personalized Driver Behavior Modeling with Machine Unlearning. Proceedings of the Cyber-Physical Systems and Internet of Things Week, San Antonio, TX, USA.","DOI":"10.1145\/3576914.3587489"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, S., Cheng, K., Yang, J., Zang, X., Luo, Q., and Li, J. (2023). Driving Behavior Risk Measurement and Cluster Analysis Driven by Vehicle Trajectory Data. Appl. Sci., 13.","DOI":"10.3390\/app13095675"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3570503","article-title":"EASYR: Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors","volume":"22","author":"Yi","year":"2022","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108793","DOI":"10.1016\/j.asoc.2022.108793","article-title":"Exploring thermal images for object detection in underexposure regions for autonomous driving","volume":"121","author":"Munir","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3433992","article-title":"Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles","volume":"22","author":"Ayman","year":"2021","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Elmi, S., and Tan, K.L. (2021, January 19\u201323). DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities. Proceedings of the Web Conference, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3449983"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2020.11.002","article-title":"Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy","volume":"68","author":"Fernes","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106682","DOI":"10.1016\/j.asoc.2020.106682","article-title":"AGLNet: Towards real-time semantic segmentation of self-driving images via attention-guided lightweight network","volume":"96","author":"Zhou","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161408","article-title":"MORP: Data-Driven Multi-Objective Route Planning and Optimization for Electric Vehicles","volume":"1","author":"Sarker","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TIV.2017.2771233","article-title":"A Time and Energy Efficient Routing Algorithm for Electric Vehicles Based on Historical Driving Data","volume":"2","author":"Bozorgi","year":"2017","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_22","first-page":"12","article-title":"A machine learning approach for driver identification","volume":"30","author":"Khan","year":"2023","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3579842","article-title":"Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems","volume":"33","author":"Chan","year":"2023","journal-title":"ACM Trans. Model. Comput. Simul."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Skar, A., Vestergaard, A., Pour, S.M., and Pettinari, M. (2023). Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring. Sensors, 23.","DOI":"10.3390\/s23052756"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S1568-4946(01)00022-9","article-title":"Analysis and modeling of human driving behaviors using adaptive cruise control","volume":"1","author":"Ohno","year":"2001","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, R., Igreja, \u00c2, De Castro, R., and Araujo, R.E. (2012, January 3\u20137). Driving Coach: A Smartphone Application to Evaluate Driving Efficient Patterns. Proceedings of the IEEE Intelligent Vehicles Symposium, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232304"},{"key":"ref_27","first-page":"807805","article-title":"A Neurofuzzy Approach to Modeling Longitudinal Driving Behavior and Driving Task Complexity","volume":"2012","author":"Hoogendoorn","year":"2012","journal-title":"Int. J. Veh. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Meseguer, J.E., Calafate, C.T., Cano, J.C., and Manzoni, P. (2013, January 7\u201310). DrivingStyles: A smartphone application to assess driver behavior. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Split, Croatia.","DOI":"10.1109\/ISCC.2013.6755001"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1016\/j.eswa.2012.11.006","article-title":"Discovering driving strategies with a multiobjective optimization algorithm","volume":"40","author":"Dovgan","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.trpro.2017.12.142","article-title":"Influence of driver characteristics on emissions and fuel consumption","volume":"27","author":"Zheng","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103058","DOI":"10.1016\/j.trb.2024.103058","article-title":"What drives drivers to start cruising for parking? Modeling the start of the search process","volume":"188","author":"Saki","year":"2024","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9530470","DOI":"10.1155\/2018\/9530470","article-title":"Driver\u2019s Eco-Driving Behavior Evaluation Modeling Based on Driving Events","volume":"2018","author":"Chen","year":"2018","journal-title":"J. Adv. Transp."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TITS.2018.2839102","article-title":"Energy Aware Driving: Optimal Electric Vehicle Speed Profiles for Sustainability in Transportation","volume":"20","author":"Yi","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","first-page":"34","article-title":"Driving Behavior Profiling and Prediction in KSA using Smart Phone Sensors and MLAs","volume":"2019","author":"Rahman","year":"2019","journal-title":"IEEE Jordan Int. Jt. Conf. Electr. Eng. Inf. Technol. (JEEIT)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"83","DOI":"10.21307\/tp-2020-050","article-title":"Driving Style Analysis and Classification Using OBD Data of a Hybrid Electric Vehicle","volume":"15","author":"Puchalski","year":"2020","journal-title":"Transp. Probl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103312","DOI":"10.1016\/j.engappai.2019.103312","article-title":"The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review","volume":"87","author":"Mousannif","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kilimnik, S., Mu\u00b4slewski, \u0141, and Kilimnik, W. (2021, January 15\u201316). Assessment and analysis of road transport driver\u2019s behavior in terms of eco-driving. Proceedings of the 19th International Conference Diagnostics of Machines and Vehicle, Bydgoszcz, Poland.","DOI":"10.1051\/matecconf\/202133201009"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.trpro.2022.02.062","article-title":"Micro driving behaviour in different roundabout layouts: Pollutant emissions, vehicular jerk, and traffic conflicts analysis","volume":"62","author":"Bahmankhah","year":"2022","journal-title":"Transp. Res. Procedia"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.3390\/vehicles4040069","article-title":"Adaptive Driving Style Classification through Transfer Learning with Synthetic Oversampling","volume":"4","author":"Jardin","year":"2022","journal-title":"Vehicles"},{"key":"ref_40","first-page":"391650","article-title":"Fuel Efficiency by Coasting in the Vehicle","volume":"2013","author":"Shakouri","year":"2013","journal-title":"Int. J. Veh. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"836375","DOI":"10.1155\/2014\/836375","article-title":"Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems","volume":"2014","author":"Nasir","year":"2014","journal-title":"Sci. World J."},{"key":"ref_42","unstructured":"Karmakar, M., and Nandi, A.K. (2015, January 14\u201317). Driving assistance for energy management in electric vehicle. Proceedings of the IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Trivandrum, India."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1109\/TITS.2017.2672542","article-title":"Predictive Energy Management Strategy for Fully Electric Vehicles Based on Preceding Vehicle Movement","volume":"18","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TITS.2016.2634019","article-title":"Optimal Energy Management for HEVs in Eco-Driving Applications Using Bi-Level MPC","volume":"18","author":"Guo","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4693","DOI":"10.1109\/TVT.2018.2806400","article-title":"Real-Time Energy-Efficient Control for Fully Electric Vehicles Based on an Explicit Model Predictive Control Method","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1109\/TITS.2019.2918019","article-title":"A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions","volume":"21","author":"Verbeke","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/TIV.2020.3011055","article-title":"A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions","volume":"5","author":"Shen","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kundu, S., Singh, A., Kundu, S., Qiao, C., and Hou, Y. (2014, January 3\u20137). Vehicle speed control algorithms for data delivery and eco-driving. Proceedings of the International Conference on Connected Vehicles and Expo (ICCVE), Vienna, Austria.","DOI":"10.1109\/ICCVE.2014.7297554"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108866","DOI":"10.1109\/ACCESS.2019.2933531","article-title":"An Energy-Efficient Dynamic Route Optimization Algorithm for Connected and Automated Vehicles Using Velocity-Space-Time Networks","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Acces"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gupta, A., Hu, S., Zhong, W., Sadek, A., Su, L., and Qiao, C. (2020, January 21\u201324). Road Grade Estimation Using Crowd-Sourced Smartphone Data. Proceedings of the 19th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Sydney, NSW, Australia.","DOI":"10.1109\/IPSN48710.2020.00-25"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gu, Z., Liu, Z., Wang, Q., Mao, Q., Shuai, Z., and Ma, Z. (2023). Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions. Sensors, 23.","DOI":"10.3390\/s23084176"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhao, D., Li, H., Hou, J., Gong, P., Zhong, Y., He, W., and Fu, Z. (2023). A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption. Energies, 16.","DOI":"10.3390\/en16145258"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4372168","DOI":"10.1155\/2022\/4372168","article-title":"Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model","volume":"2022","author":"Zhao","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4109148","DOI":"10.1155\/2019\/4109148","article-title":"A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles","volume":"2019","author":"Sun","year":"2019","journal-title":"J. Adv. Transp."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18800","DOI":"10.1007\/s11227-023-05364-3","article-title":"Driving behavior analysis and classification by vehicle OBD data using machine learning","volume":"79","author":"Kumar","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Nguyen, T., Rauch, Y., Kriesten, R., and Chrenko, D. (2023). Approach for a Global Route-Based Energy Management System for Electric Vehicles with a Hybrid Energy Storage System. Energies, 16.","DOI":"10.3390\/en16020837"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"106258","DOI":"10.1016\/j.asoc.2020.106258","article-title":"Noise gradient strategy for an enhanced hybrid convolutional-recurrent deep network to control a self-driving vehicle","volume":"92","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5571271","DOI":"10.1155\/2021\/5571271","article-title":"An Attention-Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information","volume":"2021","author":"Li","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MIC.2014.21","article-title":"iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data","volume":"18","author":"Prendinger","year":"2014","journal-title":"IEEE Internet Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"36252","DOI":"10.1109\/ACCESS.2021.3062325","article-title":"Implementation and Analytics of the Distributed Eco-Driving Simulation iCO2","volume":"9","author":"Hollerit","year":"2021","journal-title":"IEEE Access"},{"key":"ref_61","unstructured":"Assies, D. (2021). Developing a Smart Telemetry Feedback System for Sim Racing. [Bachelor\u2019s Thesis, University of Twente]."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.trpro.2021.11.076","article-title":"Assessing sim racing software for low-cost driving simulator to road geometric research","volume":"58","year":"2021","journal-title":"Transp. Res. Procedia"},{"key":"ref_63","first-page":"365","article-title":"Influence of a Serious Video Game on the Behavior of Drivers in the Face of Automobile Incidents","volume":"15","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/MVT.2023.3263330","article-title":"Metamobility: Connecting Future Mobility with Metaverse","volume":"18","author":"Wang","year":"2023","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111577","DOI":"10.1016\/j.asoc.2024.111577","article-title":"A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles","volume":"159","author":"Anwar","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"102899","DOI":"10.1016\/j.simpat.2024.102899","article-title":"Latency-aware placement of vehicular metaverses using virtual network functions","volume":"133","author":"AlKhoori","year":"2024","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zou, Y., Zhou, T., Zhang, X., and Xu, Z. (2021). Energy Consumption Prediction of Electric Vehicles Based on Digital Twin Technology. World Electr. Veh. J., 12.","DOI":"10.3390\/wevj12040160"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"105479","DOI":"10.1016\/j.scs.2024.105479","article-title":"Digital twin enabled transition towards the smart electric vehicle charging infrastructure: A review","volume":"108","author":"Yu","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"ref_69","unstructured":"Hossain, S.M.M., Saha, S.K., Banik, S., and Banik, T. (2023, January 7\u201310). A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems. Proceedings of the 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"35928","DOI":"10.1109\/JIOT.2024.3391296","article-title":"When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration","volume":"11","author":"Kang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111690","DOI":"10.1016\/j.asoc.2024.111690","article-title":"Traffic trajectory generation via conditional Generative Adversarial Networks for transportation Metaverse","volume":"160","author":"Kong","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"34254","DOI":"10.1109\/JIOT.2024.3404559","article-title":"Blockchain-Based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse","volume":"11","author":"Kang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"43255","DOI":"10.1109\/ACCESS.2024.3378527","article-title":"Advancing State of Charge Management in Electric Vehicles with Machine Learning: A Technological Review","volume":"12","author":"Mousaei","year":"2024","journal-title":"IEEE Access"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"106998","DOI":"10.1016\/j.asoc.2020.106998","article-title":"Velocity prediction using Markov Chain combined with driving pattern recognition and applied to Dual-Motor Electric Vehicle energy consumption evaluation","volume":"101","author":"Lin","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.3390\/vehicles4040075","article-title":"From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles","volume":"4","author":"Caruso","year":"2022","journal-title":"Vehicles"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Yu, S., Yang, Q., Wang, J., and Wu, C. (2024). FedUSL: A Federated Annotation Method for Driving Fatigue Detection based on Multimodal Sensing Data. ACM Trans. Sens. Netw., Just Accepted.","DOI":"10.1145\/3657291"},{"key":"ref_77","unstructured":"Pashchenko, D. (2025, February 20). Instrumental Means of Transport Telematics. Electronic Archive of Igor Sikorsky Kyiv Polytechnic Institute. Available online: https:\/\/ela.kpi.ua\/handle\/123456789\/36438."},{"key":"ref_78","unstructured":"Bortnichuk, N. (2025, February 20). Instrumental Means of Forming Energy-Efficient Routes for Electric Vehicles. Electronic Archive of Igor Sikorsky Kyiv Polytechnic Institute. Available online: https:\/\/ela.kpi.ua\/jspui\/handle\/123456789\/51702."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"107684","DOI":"10.1016\/j.asoc.2021.107684","article-title":"Multi-objective optimized driving strategy of dual-motor EVs using NSGA-II as a case study and comparison of various intelligent algorithms","volume":"111","author":"Lin","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Wikner, E., Orbay, R., Fogelstr\u00f6m, S., and Thiringer, T. (2022). Gender Aspects in Driving Style and Its Impact on Battery Ageing. Energies, 15.","DOI":"10.3390\/en15186791"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Stabile, P., Ballo, F., Previati, G., Mastinu, G., and Gobbi, M. (2023). Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios. Energies, 16.","DOI":"10.3390\/en16031394"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.inffus.2010.06.001","article-title":"Data fusion in intelligent transportation systems: Progress and challenges\u2014A survey","volume":"12","author":"Leung","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Rakha, H.A., Kamalanathsharma, R.K., and Ahn, K. (2012). AERIS: Eco-Vehicle Speed Control at Signalized Intersections Using I2V Communication, Technical Report.","DOI":"10.1109\/ITSC.2011.6083084"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"261085","DOI":"10.1155\/2014\/261085","article-title":"A Traction Control Strategy with an Efficiency Model in a Distributed Driving Electric Vehicle","volume":"2014","author":"Lin","year":"2014","journal-title":"Sci. World J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3390\/software1030011","article-title":"Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing","volume":"1","author":"Allidina","year":"2022","journal-title":"Software"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Alyamani, H., Alharbi, N., Roboey, A., and Kavakli, M. (2023). The Impact of Gamifications and Serious Games on Driving under Unfamiliar Traffic Regulations. Appl. Sci., 13.","DOI":"10.3390\/app13053262"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Ye, M., Li, P., Yang, Z., and Liu, Y. (2022). Research on Lane Changing Game and Behavioral Decision Making Based on Driving Styles and Micro-Interaction Behaviors. Sensors, 22.","DOI":"10.3390\/s22186729"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Feng, T., Liu, K., and Liang, C. (2023). An Improved Cellular Automata Traffic Flow Model Considering Driving Styles. Sustainability, 15.","DOI":"10.3390\/su15020952"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"125462","DOI":"10.1016\/j.apenergy.2025.125462","article-title":"Energy-efficient driving for distributed electric vehicles considering wheel loss energy: A distributed strategy based on multi-agent architecture","volume":"384","author":"Liang","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S1566-2535(03)00042-3","article-title":"IDRES: A rule-based system for driving situation recognition with uncertainty management","volume":"4","author":"Nigro","year":"2003","journal-title":"Inf. Fusion"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.asoc.2015.04.024","article-title":"Optimal driving during electric vehicle acceleration using evolutionary algorithms","volume":"2015","author":"Chakraborty","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"He, H., Liu, D., Lu, X., and Xu, J. (2021). ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy. Electronics, 10.","DOI":"10.3390\/electronics10212613"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3572034","article-title":"Highly Efficient Traffic Planning for Autonomous Vehicles to Cross Intersections without a Stop","volume":"14","author":"Kang","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"106777","DOI":"10.1016\/j.aap.2022.106777","article-title":"Developing a two-stage auditory warning system for safe driving and eco-driving at signalized intersections: A driving simulation study","volume":"175","author":"Zhang","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Jurecki, R.S., Sta\u0144czyk, T.L., and Ziubi\u0144ski, M. (2022). Analysis of the Structure of Driver Maneuvers in Different Road Conditions. Energies, 15.","DOI":"10.3390\/en15197073"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ma, Z., J\u00f8rgensen, B.N., and Ma, Z. (2024). A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods. Energies, 17.","DOI":"10.3390\/en17020500"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Frank, R., Castignani, G., Schmitz, R., and Engel, T. (2013, January 2\u20136). A novel eco-driving application to reduce energy consumption of electric vehicles. Proceedings of the International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCVE.2013.6799807"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Anjum, M., Shahab, S., Dimitrakopoulos, G., and Guye, H.F. (2023). An In-Vehicle Behaviour-Based Response Model for Traffic Monitoring and Driving Assistance in the Context of Smart Cities. Electronics, 12.","DOI":"10.3390\/electronics12071644"},{"key":"ref_99","unstructured":"Kurani, K.S., Stillwater, T., Jones, M., and Caperello, N. (2013). Ecodrive I-80: A Large Sample Fuel Economy Feedback Field Test, Institute of Transportation Studies, University of California. Research Report UCD-ITS-RR-13-15."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"108782","DOI":"10.1016\/j.asoc.2022.108782","article-title":"Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds","volume":"122","author":"Huang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Fern ez, S., Ito, T., Cruz-Piris, L., and Marsa-Maestre, I. (2022). Fuzzy Ontology-Based System for Driver Behavior Classification. Sensors, 22.","DOI":"10.3390\/s22207954"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"108499","DOI":"10.1016\/j.asoc.2022.108499","article-title":"Human-like motion planning of autonomous vehicle based on probabilistic trajectory prediction","volume":"118","author":"Li","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Gao, J., Tian, J., Gong, L., and Zhang, Y. (2024). An Innovative Cooperative Driving Strategy for Signal-Free Intersection Navigation with CAV Platoons. Appl. Sci., 14.","DOI":"10.3390\/app14083498"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3632180","article-title":"Dynamic Planning of Optimally Safe Lane-change Trajectory for Autonomous Driving on Multi-lane Highways Using a Fuzzy Logic\u2013based Collision Estimator","volume":"1","author":"Sharma","year":"2024","journal-title":"ACM J. Auton. Transp. Syst."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3664605","article-title":"Focused Test Generation for Autonomous Driving Systems","volume":"33","author":"Zohdinasab","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2111532","DOI":"10.1155\/2023\/2111532","article-title":"Risk Assessment of Distracted Driving Behavior Based on Visual Stability Coefficient","volume":"2023","author":"Wang","year":"2023","journal-title":"J. Adv. Transp."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Koch, K., Maritsch, M., Weenen, E.V., Feuerriegel, S., Pf\u00e4ffli, M., Fleisch, E., Weinmann, W., and Wortmann, F. (2023, January 23\u201328). Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving. Proceedings of the Conference on Human Factors in Computing Systems, Hamburg, Germany.","DOI":"10.2139\/ssrn.4186589"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"62268","DOI":"10.1109\/ACCESS.2024.3393909","article-title":"Deep Learning Model for Driver Behavior Detection in Cyber-Physical System-Based Intelligent Transport Systems","volume":"12","author":"Gupta","year":"2024","journal-title":"IEEE Access"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.inffus.2015.08.001","article-title":"Situation awareness within the context of connected cars: A comprehensive review and recent trends","volume":"29","author":"Golestan","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_110","first-page":"1074817","article-title":"Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire","volume":"2018","author":"Hu","year":"2018","journal-title":"Hindawi J. Adv. Transp."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"8813137","DOI":"10.1155\/2020\/8813137","article-title":"Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users\u2019 Driving and Charging Behavior","volume":"2020","author":"Lu","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Wang, Q., Jiang, J., Gao, T., and Ren, S. (2022). State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer. Sensors, 22.","DOI":"10.3390\/s22197678"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Mediouni, H., Ezzouhri, A., Charouh, Z., El Harouri, K., El Hani, S., and Ghogho, M. (2022). Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach. Energies, 15.","DOI":"10.3390\/en15176490"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Lin, W., Zhao, H., Zhang, B., Wang, Y., Xiao, Y., Xu, K., and Zhao, R. (2022). Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start\u2013stop Optimization with Vehicle Velocity Forecast. Energies, 15.","DOI":"10.3390\/en15207774"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Yan, F., Wang, J., Du, C., and Hua, M. (2023). Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function. Energies, 16.","DOI":"10.3390\/en16010074"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Pielecha, I., Cieslik, W., and Szwajca, F. (2023). Energy Flow and Electric Drive Mode Efficiency Evaluation of Different Generations of Hybrid Vehicles under Diversified Urban Traffic Conditions. Energies, 16.","DOI":"10.3390\/en16020794"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Imed, B. (2022). Implementation of a Fuel Estimation Algorithm Using Approximated Computing. J. Low Power Electron. Appl., 12.","DOI":"10.3390\/jlpea12010017"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Lin, B., Wei, C., Feng, F., and Liu, T. (2024). A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon. Energies, 17.","DOI":"10.3390\/en17102288"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s40747-024-01698-4","article-title":"Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios","volume":"11","author":"Zhu","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Fafoutellis, P., Mantouka, E.G., and Vlahogianni, E.I. (2021). Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. Sustainability, 13.","DOI":"10.3390\/su13010226"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"135372","DOI":"10.1016\/j.chemosphere.2022.135372","article-title":"Developing and analyzing eco-driving strategies for on-road emission reduction in urban transport systems\u2014A VR-enabled digital-twin approach","volume":"305","author":"Xu","year":"2022","journal-title":"Chemosphere"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10009-022-00689-5","article-title":"On the road with RTLola","volume":"25","author":"Biewer","year":"2023","journal-title":"Int. J. Softw. Tools Technol. Transf."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ifacol.2021.10.152","article-title":"Real-time eco-driving for connected electric vehicles","volume":"54","author":"Ngo","year":"2021","journal-title":"IFAC-PapersOnLine"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Joa, E., Lee, H., Choi, E.Y., and Borrelli, F. (2023). Energy-Efficient Lane Changes Planning and Control for Connected Autonomous Vehicles on Urban Roads. arXiv.","DOI":"10.1109\/IV55152.2023.10186574"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"9263605","DOI":"10.1155\/2020\/9263605","article-title":"Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones","volume":"2020","author":"Yao","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Li, T., Cui, W., and Cui, N. (2022). Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle. World Electr. Veh. J., 13.","DOI":"10.3390\/wevj13100193"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, R., Wu, Z., Huang, W., and Xu, M. (2023). An Economic Velocity Planning Strategy Based on Driving Style and Improved Dynamic Programming for a Hybrid Electric Truck. World Electr. Veh. J., 14.","DOI":"10.3390\/wevj14070194"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"NaitMalek, Y., Najib, M., Lahlou, A., Bakhouya, M., Gaber, J., and Essaaidi, M. (2022). A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles. Sustainability, 14.","DOI":"10.3390\/su14169993"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIV.2018.2873922","article-title":"A Unified Approach for Electric Vehicles Range Maximization via Eco-Routing, Eco-Driving, and Energy Consumption Prediction","volume":"3","author":"Thibault","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Li, D., Li, C., Miwa, T., and Morikawa, T. (2019). An Exploration of Factors Affecting Drivers\u2019 Daily Fuel Consumption Efficiencies Considering Multi-Level Random Effects. Sustainability, 11.","DOI":"10.3390\/su11020393"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Wei, Z., Hao, P., and Barth, M.J. (2019, January 9\u201312). Developing an Adaptive Strategy for Connected Eco-Driving under Uncertain Traffic Condition. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813819"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"130985","DOI":"10.1016\/j.jclepro.2022.130985","article-title":"Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style","volume":"343","author":"Wang","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Younes, M. (2022). Towards Green Driving: A Review of Efficient Driving Techniques. World Electr. Veh. J., 13.","DOI":"10.3390\/wevj13060103"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"103876","DOI":"10.1016\/j.trc.2022.103876","article-title":"An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections","volume":"144","author":"Sun","year":"2022","journal-title":"Transp. Res. Part Emerg. Technol."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Chen, X., and Liu, Z. (2023). Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors. Sensors, 23.","DOI":"10.3390\/s23125496"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Tian, S. (2023). Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model. Sustainability, 15.","DOI":"10.3390\/su15129394"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Ding, H., Guo, K., and Zhang, N. (2023). Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning. Electronics, 12.","DOI":"10.3390\/electronics12061350"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"4518","DOI":"10.1109\/TIV.2024.3358797","article-title":"Energy-Saving Driving Assistance System Integrated with Predictive Cruise Control for Electric Vehicles","volume":"9","author":"Hong","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Pla, B., Bares, P., Pey, V., Rodr\u00edguez, L.S., and Armas, O. (2025). Speed Advisor for Fuel Consumption Minimisation Under Real Driving Conditions. Appl. Sci., 15.","DOI":"10.3390\/app15020654"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Han, J., Wang, X., Shi, H., Wang, B., Wang, G., Chen, L., and Wang, Q. (2022). Research on the Impacts of Vehicle Type on Car-Following Behavior, Fuel Consumption and Exhaust Emission in the V2X Environment. Sustainability, 14.","DOI":"10.3390\/su142215231"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Liu, C., and Liu, Y. (2022). Energy Management Strategy for Plug-In Hybrid Electric Vehicles Based on Driving Condition Recognition: A Review. Electronics, 11.","DOI":"10.3390\/electronics11030342"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"13904","DOI":"10.1109\/ACCESS.2025.3530087","article-title":"Deep Reinforcement Learning-Based Speed Predictor for Distributionally Robust Eco-Driving","volume":"13","author":"Chaudhary","year":"2025","journal-title":"IEEE Access"},{"key":"ref_143","first-page":"9450178","article-title":"Fuel Consumption Using OBD-II and Support Vector Machine Model","volume":"2020","author":"Abukhalil","year":"2020","journal-title":"J. Robot."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2631692","DOI":"10.1155\/2022\/2631692","article-title":"Can Autonomous Vehicles Save Fuel? Findings from Field Experiments","volume":"2022","author":"Zhang","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"9016510","DOI":"10.1155\/2023\/9016510","article-title":"Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions","volume":"2023","author":"Peng","year":"2023","journal-title":"J. Adv. Transp."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"106875","DOI":"10.1016\/j.asoc.2020.106875","article-title":"Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads","volume":"99","author":"Bakibillah","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"110958","DOI":"10.1016\/j.cie.2025.110958","article-title":"A deep learning method for assessment of ecological potential in traffic environments","volume":"202","author":"Yan","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"106361","DOI":"10.1016\/j.asoc.2020.106361","article-title":"Optimal driving based trip planning of electric vehicles using evolutionary algorithms: A driving assistance system","volume":"93","author":"Khanra","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"6329203","DOI":"10.1155\/2017\/6329203","article-title":"Vehicle Routing Problems with Fuel Consumption and Stochastic Travel Speeds","volume":"2017","author":"Feng","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Aboelsoud, K., Diab, H.Y., Abdelsalam, M., and Hegaze, M.M. (2024). An Efficient GPS Algorithm for Maximizing Electric Vehicle Range. Appl. Sci., 14.","DOI":"10.3390\/app14114858"},{"key":"ref_151","first-page":"3713918","article-title":"An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem","volume":"2016","author":"Rao","year":"2016","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Yang, W., Li, C., and Zhou, Y. (2022). Path Planning Method for Autonomous Vehicles Based on Risk Assessment. World Electr. Veh. J., 13.","DOI":"10.2139\/ssrn.4238322"},{"key":"ref_153","first-page":"1","article-title":"Physics-guided Energy-efficient Path Selection Using On-board Diagnostics Data","volume":"1","author":"Li","year":"2020","journal-title":"ACM\/IMS Trans. Data Sci."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Muslim, N.H., Keyvanfar, A., Shafaghat, A., Abdullahi, M.A.M., and Khorami, M. (2018). Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission. Sustainability, 10.","DOI":"10.3390\/su10020325"}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/3\/52\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:35:03Z","timestamp":1760031303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/3\/52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":154,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jsan14030052"],"URL":"https:\/\/doi.org\/10.3390\/jsan14030052","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,19]]}}}