{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T05:33:52Z","timestamp":1736228032308,"version":"3.32.0"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T00:00:00Z","timestamp":1569542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61503048"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific research project of Double First-Class International Cooperation and Development Project of Changsha University of Science and Technology","award":["2019IC28"]},{"name":"MOE(Ministry of Education in China) Youth Foundation Project of Humanities and Social Sciences","award":["19YJCZH214"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles\u2019 abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles\u2019 lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle\u2019s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.<\/jats:p>","DOI":"10.3390\/s19194199","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T15:14:35Z","timestamp":1569597275000},"page":"4199","source":"Crossref","is-referenced-by-count":36,"title":["Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario"],"prefix":"10.3390","volume":"19","author":[{"given":"Kai","family":"Gao","sequence":"first","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha 410114, China"}]},{"given":"Di","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Security Engineering, Zhongnan University of Economics and law, Wuhan 430073, China"}]},{"given":"Jin","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China"}]},{"given":"Ronghua","family":"Du","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China"},{"name":"Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha 410114, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4635","authenticated-orcid":false,"given":"Naixue","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.arcontrol.2018.04.011","article-title":"Control of connected and automated vehicles: State of the art and future challenges","volume":"45","author":"Guanetti","year":"2018","journal-title":"Annu. Rev. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.trc.2018.03.014","article-title":"Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous\/human driving environment","volume":"90","author":"Noruzoliaee","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gao, K., Han, F., Dong, P., Xiong, N., and Du, R. (2019). Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections. Sensors, 19.","DOI":"10.3390\/s19092059"},{"key":"ref_4","unstructured":"Luo, Y., and Cai, P. (2019). GAMMA: A General Agent Motion Prediction Model for Autonomous Driving. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1109\/TVT.2019.2903299","article-title":"Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges","volume":"68","author":"Xing","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1109\/TITS.2018.2865575","article-title":"Consensus-based cooperative control for multi-platoon under the connected vehicles environment","volume":"20","author":"Li","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TIV.2018.2843178","article-title":"Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving","volume":"3","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kumar, P., Perrollaz, M., Lefevre, S., Laugier, C., and Kumar, P. (2013, January 23\u201326). Learning-based approach for online lane change intention prediction. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia.","DOI":"10.1109\/IVS.2013.6629564"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tomar, R.S., Verma, S., and Tomar, G.S. (2011, January 7\u20139). Neural network based lane change trajectory predictions for collision prevention. Proceedings of the 2011 International Conference on Computational Intelligence and Communication Networks, Gwalior, India.","DOI":"10.1109\/CICN.2011.120"},{"key":"ref_11","first-page":"316","article-title":"Identification of Lane change Maneuver of Side Lane Vehicles Based on Fuzzy Support Vector Machines","volume":"36","author":"Ma","year":"2014","journal-title":"Automot. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, L., and Diao, X. (2019). Deep-Learning-Based Human Intention Prediction Using RGB Images and Optical Flow. J. Intell. Robot. Syst., 1\u201313.","DOI":"10.1007\/s10846-019-01049-3"},{"key":"ref_13","unstructured":"Lemmer, K., and Harris, R.M. (2013, January 4\u20136). Windows of Driver Gaze Data: How Early and How Much for Robust Predictions of Driver Intent?. Proceedings of the 11th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2013), Lausanne, Switzerland."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jang, Y.-M., Mallipeddi, R., and Lee, M. (2014, January 10\u201313). Driver\u2019s lane-change intent identification based on pupillary variation. Proceedings of the 2014 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2014.6775970"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TITS.2009.2026675","article-title":"On the Roles of Eye Gaze and Head Dynamics in Predicting Driver\u2019s Intent to Change Lanes","volume":"10","author":"Doshi","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TITS.2011.2174786","article-title":"Processing of eye\/head-tracking data in large-scale naturalistic driving data sets","volume":"13","author":"Ahlstrom","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"An, H., and Jung, J.-I. (2018). Design of a Cooperative Lane Change Protocol for a Connected and Automated Vehicle Based on an Estimation of the Communication Delay. Sensors, 18.","DOI":"10.3390\/s18103499"},{"key":"ref_18","unstructured":"Wang, G., Hu, J., Li, Z., and Li, L. (2019). Cooperative Lane Changing via Deep Reinforcement Learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9413","DOI":"10.1109\/ACCESS.2017.2649567","article-title":"Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles","volume":"4","author":"Nie","year":"2016","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.trc.2019.06.006","article-title":"Pay to change lanes: A cooperative lane-changing strategy for connected\/automated driving","volume":"105","author":"Lin","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1504\/IJSNET.2012.047720","article-title":"Distributed k-connected fault-tolerant topology control algorithms with PSO in future autonomic sensor systems","volume":"12","author":"Guo","year":"2012","journal-title":"Int. J. Sens. Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ins.2015.09.039","article-title":"Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model","volume":"329","author":"Cheng","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, I.-H., Bong, J.-H., Park, J., and Park, S. (2017). Prediction of Driver\u2019s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques. Sensors, 17.","DOI":"10.3390\/s17061350"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cerdeira, M., Falc\u00f3n, P., Delgado, E., and Barreiro, A. (2018). Reset Controller Design Based on Error Minimization for a Lane Change Maneuver. Sensors, 18.","DOI":"10.3390\/s18072204"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C., Delport, J., and Wang, Y. (2019). Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach. Sensors, 19.","DOI":"10.3390\/s19092111"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIV.2018.2804159","article-title":"How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction","volume":"3","author":"Deo","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jnca.2018.02.005","article-title":"Reducing transport latency for short flows with multipath TCP","volume":"108","author":"Dong","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, P., Gao, K., Xie, J., Tang, W., Xiong, N., and Vasilakos, A.V. (2019). Receiver-Side TCP Countermeasure in Cellular Networks. Sensors, 19.","DOI":"10.3390\/s19122791"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"59695","DOI":"10.1109\/ACCESS.2018.2871843","article-title":"Optimal Route Algorithm Considering Traffic Light and Energy Consumption","volume":"6","author":"Hu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.trpro.2017.05.207","article-title":"Real-time Traffic Signal Control for Isolated Intersection, using Car-following Logic under Connected Vehicle Environment","volume":"25","author":"Chandan","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_31","unstructured":"Zhao, C., Xing, Y., Li, Z., Li, L., Wang, X., Wang, F.Y., and Wu, X. (2019). A Right-of-Way Assignment Strategy to Ensure Traffic Safety and Efficiency in Lane Change. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/TITS.2017.2768318","article-title":"Collision Risk Assessment Algorithm via Lane-Based Probabilistic Motion Prediction of Surrounding Vehicles","volume":"19","author":"Kim","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6391","DOI":"10.1109\/TVT.2019.2917025","article-title":"Virtual-to-Real Knowledge Transfer for Driving Behavior Recognition: Framework and a Case Study","volume":"68","author":"Lu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/TITS.2007.902640","article-title":"Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning","volume":"8","author":"McCall","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Remmen, F., Cara, I., de Gelder, E., and Willemsen, D. (2018, January 12\u201314). Lane change scenario prediction for automated vehicles. Proceedings of the 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain.","DOI":"10.1109\/ICVES.2018.8519594"},{"key":"ref_36","unstructured":"Ma, Y., Zhu, X., Zhang, S., Yang, R., Wang, W., and Manocha, D. (February, January 27). Traffic predict: Trajectory prediction for heterogeneous traffic-agents. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/TITS.2008.2011691","article-title":"IMM-Based Lane-Change Prediction in Highways with Low-Cost GPS\/INS","volume":"10","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.trc.2010.01.001","article-title":"Collision avoidance support in roads with lateral and longitudinal maneuver prediction by fusing GPS\/IMU and digital maps","volume":"18","year":"2010","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Althoff, M., and L\u00f6sch, R. (2016, January 1\u20134). Can automated road vehicles harmonize with traffic flow while guaranteeing a safe distance?. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795599"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4199\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T01:19:15Z","timestamp":1736212755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,27]]},"references-count":39,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194199"],"URL":"https:\/\/doi.org\/10.3390\/s19194199","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,27]]}}}