Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method

References

• Ahn, Soyoung, and Michael J. Cassidy. 2007. “Freeway Traffic Oscillations and Vehicle Lane-Change Maneuvers.” Paper presented in Transportation and Traffic Theory 2007, London, July 691-710.
   Google Scholar
• Berndt, Holger, Jorg Emmert, and Klaus Dietmayer. 2008 October. “Continuous Driver Intention Recognition with Hidden Markov Models.” Paper presented in 2008 11th International IEEE Conference on Intelligent Transportation systems. Beijing, 1189–1194.
   Google Scholar
• Daily, Robert, and David M. Bevly. 2008. “Harmonic Potential Field Path Planning for High Speed Vehicles.” Paper presented in 2008 American Control Conference, Seattle, June 4609-4614.
   Google Scholar
• Deng, Qi, Jiao Wang, Kevin Hillebrand, Christoper Ragenold Benjamin, and Dirk Söffker. 2019. “Prediction Performance of Lane Changing Behaviors: a Study of Combining Environmental and eye-Tracking Data in a Driving Simulator.” IEEE Transactions on Intelligent Transportation Systems 21 (8): 3561–3570. doi:10.1109/TITS.2019.2937287.
   Google Scholar
• Ding, Chenxi, Wuhong Wang, Xiao Wang, and Martin Baumann. 2013. “A Neural Network Model for Driver’s Lane-Change Trajectory Prediction in Urban Traffic Flow.” Mathematical Problems in Engineering 2013: 1–8. doi:10.1155/2013/967358.
   Google Scholar
• Dong, Changyin, Hao Wang, Ye Li, Xiaomeng Shi, Daiheng Ni, and Wei Wang. 2020. “Application of Machine Learning Algorithms in Lane-Changing Model for Intelligent Vehicles Exiting to off-Ramp.” Transportmetrica A: Transport Science 17 (1): 124–150. doi:10.1080/23249935.2020.1746861.
   Google Scholar
• Dou, Yangliu, Fengjun Yan, and Daiwei Feng. 2016 July. “Lane Changing Prediction at Highway Lane Drops Using Support Vector Machine and Artificial Neural Network Classifiers.” Paper presented in 2016 IEEE International Conference on advanced Intelligent mechatronics (AIM). Banff, 901–906
   Google Scholar
• Errampalli, Madhu, Masashi Okushima, and Takamasa Akiyama. 2008. “Fuzzy Logic Based Lane Change Model for Microscopic Traffic Flow Simulation.” JACIII 12 (2): 172–181.
   Google Scholar
• e Silva, Edson Prestes, Paulo M. Engel, Marcelo Trevisan, and Marco AP Idiart. 2002. “Exploration Method Using Harmonic Functions.” Robotics and Autonomous Systems 40 (1): 25–42. doi:10.1016/s0921-8890(02)00209-9.
   Google Scholar
• Fahimi, Farbod. 2009. Autonomous Robots: Modeling, Path Planning and Control. Boston, MA: Springer.
   Google Scholar
• Gao, Jun, Yi Lu Murphey, and Honghui Zhu. 2018. “Multivariate Time Series Prediction of Lane Changing Behavior Using Deep Neural Network.” Applied Intelligence 48 (10): 3523–3537. doi:10.1007/s10489-018-1163-9.
   Google Scholar
• Gao, Jun, Honghui Zhu, and Yi Lu Murphey. 2019. “A Personalized Model for Driver Lane-Change Behavior Prediction Using Deep Neural Network.” Paper presented in 2019 2nd International Conference on artificial Intelligence and Big data (ICAIBD), Chengdu, May 90-96.
   Google Scholar
• Geurts, Pierre, Damien Ernst, and Louis Wehenkel. 2006. “Extremely Randomized Trees.” Machine Learning 63 (1): 3–42. doi:10.1007/s10994-006-6226-1.
   Google Scholar
• Gindele, Tobias, Sebastian Brechtel, and Rudiger Dillmann. 2015. “Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning.” IEEE Intelligent Transportation Systems Magazine 7 (1): 69–79. doi:10.1109/mits.2014.2357038.
   Google Scholar
• Gipps, Peter G. 1986. “A Model for the Structure of Lane-Change Decisions.” Transportation Research Part B: Methodological 20 (5): 403–414. doi:10.1016/0191-2615(86)90012-3.
   Google Scholar
• Gu, Xinping, Junfu Yu, Yunpeng Han, Mengxin Han, and Lianxing Wei. 2019. “Vehicle Lane Change Decision Model Based on Random Forest.” Paper presented in 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, July 115-120.
   Google Scholar
• Hidas, Peter. 2002. “Modelling Lane Changing and Merging in Microscopic Traffic Simulation.” Transportation Research Part C: Emerging Technologies 10 (5-6): 351–371. doi:10.1016/s0968-090x(02)00026-8.
   Google Scholar
• Hidas, Peter. 2005. “Modelling Vehicle Interactions in Microscopic Simulation of Merging and Weaving.” Transportation Research Part C: Emerging Technologies 13 (1): 37–62. doi:10.1016/j.trc.2004.12.003.
   Google Scholar
• Hou, Yi, Praveen Edara, and Carlos Sun. 2012 September. “A Genetic Fuzzy System for Modeling Mandatory Lane Changing.” Paper presented in 2012 15th International IEEE Conference on Intelligent Transportation systems, Anchorage, 1044–1048.
   Google Scholar
• Hou, Yi, Praveen Edara, and Carlos Sun. 2014. “Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees.” IEEE Transactions on Intelligent Transportation Systems 15 (2): 647–655. doi:10.1109/tits.2013.2285337.
   Google Scholar
• Hou, Yi, Praveen Edara, and Carlos Sun. 2015. “Situation Assessment and Decision Making for Lane Change Assistance Using Ensemble Learning Methods.” Expert Systems with Applications 42 (8): 3875–3882. doi:10.1016/j.eswa.2015.01.029.
   Google Scholar
• Hurwitz, David S., Haizhong Wang, Michael A. Knodler, Daiheng Ni, and Derek Moore. 2012. “Fuzzy Sets to Describe Driver Behavior in the Dilemma Zone of High-Speed Signalized Intersections.” Transportation Research Part F: Traffic Psychology and Behaviour 15 (2): 132–143. doi:10.1016/j.trf.2011.11.003.
   Google Scholar
• Hwang, Yong Koo, and Narendra Ahuja. 1992. “A Potential Field Approach to Path Planning.” IEEE Transactions on Robotics and Automation 8 (1): 23–32.
   Google Scholar
• Ikenishi, Toshihtio, and Takayoshi Kamada. 2014. “Estimation of Driver’s Steering Direction About Lane Change Maneuver at the Preceding Car Avoidance By Brain Source Current Estimation Method.” Paper presented in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, October 2808-2814.
   Google Scholar
• Ji, Ang, and David Levinson. 2020. “A Review of Game Theory Models of Lane Changing.” Transportmetrica A: Transport Science 16 (3): 1628–1647. doi:10.1080/23249935.2020.1770368.
   Google Scholar
• Jin, Cheng-Jie, Victor L. Knoop, Dawei Li, Ling-Yu Meng, and Hao Wang. 2019. “Discretionary Lane-Changing Behavior: Empirical Validation for one Realistic Rule-Based Model.” Transportmetrica A: Transport Science 15 (2): 244–262. doi:10.1080/23249935.2018.1464526.
   Google Scholar
• Kalavsky, M., and Z. Ferkova. 2012. “Harmonic Potential Field Method for Path Planning of Mobile Robot.” International Virtual Conference (ICTIC 2012) 1: 41–46.
   Google Scholar
• Kesting, Arne, Martin Treiber, and Dirk Helbing. 2007. “General Lane-Changing Model MOBIL for car-Following Models.” Transportation Research Record: Journal of the Transportation Research Board 1999 (1): 86–94. doi:10.3141/1999-10.
   Google Scholar
• Khatib, Oussama. 1990. “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots.” Autonomous Robot Vehicles 5 (1): 396–404. doi:10.1007/978-1-4613-8997-2_29.
   Google Scholar
• Kim, Jin-Oh, and Pradeep Khosla. 1992. “Real-time Obstacle Avoidance Using Harmonic Potential Functions.” IEEE Transactions on Robotics and Automation 8 (3): 338–349. doi:10.1109/70.143352.
   Google Scholar
• Knoop, Victor L., and Christine Buisson. 2014. “Calibration and Validation of Probabilistic Discretionary Lane-Change Models.” IEEE Transactions on Intelligent Transportation Systems 16 (2): 834–843.
   Google Scholar
• Kumar, Puneet, Mathias Perrollaz, Stéphanie Lefevre, and Christian Laugier. 2013. “Learning-Based Approach for Online Lane Change Intention Prediction.” Paper presented in 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, June 797-802.
   Google Scholar
• Li, Zirui, Cheng Gong, Chao Lu, Jianwei Gong, Junyan Lu, Youzhi Xu, and Fengqing Hu. 2019. “Transferable Driver Behavior Learning via Distribution Adaption in the Lane Change Scenario.” Paper presented in 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, June 193-200.
   Google Scholar
• Li, Keqiang, Xiao Wang, Youchun Xu, and Jianqiang Wang. 2016. “Lane Changing Intention Recognition Based on Speech Recognition Models.” Transportation Research Part C: Emerging Technologies 69: 497–514. doi:10.1016/j.trc.2015.11.007.
   Google Scholar
• Li, Li, Mingfang Zhang, and Rui Liu. 2015. “The Application of Bayesian Filter and Neural Networks in Lane Changing Prediction.” 5th International Conference on Civil Engineering and transportation., 2004-2007.
   Google Scholar
• Lin, Chin-Teng, Jung-Tai King, Avinash Kumar Singh, Akshansh Gupta, Zhenyuan Ma, Jheng-Wei Lin, Alexei Manso Correa Machado, Abhishek Appaji, and Mukesh Prasad. 2018. “Voice Navigation Effects on Real-World Lane Change Driving Analysis Using an Electroencephalogram.” IEEE 6: 26483–26492. doi:10.1109/access.2018.2820161.
   Google Scholar
• Liu, Andrew, and Dario Salvucci. 2001. “Modeling and Prediction of Human Driver Behavior.” In Intl. Conference on HCI, 1479-1483.
   Google Scholar
• Louppe, Gilles. 2014. “Understanding Random Forests: From Theory to Practice.” (PhD diss). University of Liège.
   Google Scholar
• Mahajan, Vishal, Christos Katrakazas, and Constantinos Antoniou. 2020. “Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning.” Transportation Research Record: Journal of the Transportation Research Board 2674 (7): 336–347.
   Google Scholar
• Meyer-Delius, Daniel, Christian Plagemann, and Wolfram Burgard. 2009. “Probabilistic Situation Recognition for Vehicular Traffic Scenarios.” Paper presented in 2009 IEEE International Conference on Robotics and automation. Kobe, May 459-464.
   Google Scholar
• Morris, Brendan, Anup Doshi, and Mohan Trivedi. 2011. “Lane Change Intent Prediction for Driver Assistance: On-Road Design And Evaluation.” Paper presented in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, June 895-901.
   Google Scholar
• Motamedidehkordi, Nassim, Sasan Amini, Silja Hoffmann, Fritz Busch, and Mustika Riziki Fitriyanti. 2017. “Modeling Tactical Lane-Change Behavior For Automated Vehicles: A Supervised Machine Learning Approach.” Paper presented in 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, June 268-273.
   Google Scholar
• Mousa, Saleh R., Peter R. Bakhit, Osama A. Osman, and Sherif Ishak. 2018. “A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments.” Transportation Research Record: Journal of the Transportation Research Board 2672 (42): 268–279.
   Google Scholar
• NGSIM: Next Generation Simulation. (FHWA Department of Transportation, America; accessed December. 2006. http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.
   Google Scholar
• Pande, Anurag, and Mohamed Abdel-Aty. 2006. “Assessment of Freeway Traffic Parameters Leading to Lane-Change Related Collisions.” Accident Analysis & Prevention 38 (5): 936–948. doi:10.1016/j.aap.2006.03.004.
   Google Scholar
• Park, Minju, Kitae Jang, Jinwoo Lee, and Hwasoo Yeo. 2015. “Logistic Regression Model for Discretionary Lane Changing Under Congested Traffic.” Transportmetrica A: Transport Science 11 (4): 333–344. doi:10.1080/23249935.2014.994686.
   Google Scholar
• Pavlov, V. V., and A. N. Voronin. 1984. “The Method of Potential Functions for Coding Constraints of the External Space in an Intelligent Mobile Robot.” Soviet Automatic Control 17 (6): 45–51.
   Google Scholar
• Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. 2011. “Scikit-learn: Machine Learning in Python.” the Journal of Machine Learning Research 12: 2825–2830.
   Google Scholar
• Pêtrès, Clément, M.-A. Romero-Ramirez, and Frédéric Plumet. 2012. “A Potential Field Approach for Reactive Navigation of Autonomous Sailboats.” Robotics and Autonomous Systems 60 (12): 1520–1527. doi:10.1016/j.robot.2012.08.004.
   Google Scholar
• Petzoldt, Tibor, Stephanie Brüggemann, and Josef F. Krems. 2014. “Learning Effects in the Lane Change Task (LCT) – Realistic Secondary Tasks and Transfer of Learning.” Applied Ergonomics 45 (3): 639–646. doi:10.1016/j.apergo.2013.09.003.
   Google Scholar
• Shang, Jiaxing, Xiaofan Yan, Linhui Feng, Zheng Dong, Haojie Wang, and Shangbo Zhou. 2018. “ExtTra: Short-Term Traffic Flow Prediction Based on Extremely Randomized Trees.”.” In International Conference on Neural Information Processing 11304: 532–544.
   Google Scholar
• Shi, Wen, and Ya Ping Zhang. 2013. “Decision Analysis of Lane Change Based on Fuzzy Logic.” Applied Mechanics and Materials 419: 790–794. doi:10.4028/www.scientific.net/amm.419.790.
   Google Scholar
• Shi, Chaojian, Mingming Zhang, and Jing Peng. 2007. “Harmonic Potential Field Method for Autonomous Ship Navigation.” Paper presented in 2007 7th International Conference on ITS Telecommunications, Sophia Antipolis, June 1-6.
   Google Scholar
• Sivak, Michael, Brandon Schoettle, Matthew P. Reed, and Michael J. Flannagan. 2007. “Body-pillar Vision Obstructions and Lane-Change Crashes.” Journal of Safety Research 38 (5): 557–561. doi:10.1016/j.jsr.2007.06.003.
   Google Scholar
• Sun, Daniel, and Lily Elefteriadou. 2012. “Lane-change Behavior on Urban Streets: An “In-Vehicle” Field Experiment-Based Study.” Computer-Aided Civil and Infrastructure Engineering 27 (7): 525–542. doi:10.1111/j.1467-8667.2011.00747.x.
   Google Scholar
• Tang, Jinjun, Fang Liu, Wenhui Zhang, Ruimin Ke, and Yajie Zou. 2018. “Lane-changes Prediction Based on Adaptive Fuzzy Neural Network.” Expert Systems with Applications 91: 452–463. doi:10.1016/j.eswa.2017.09.025.
   Google Scholar
• Tang, Jinjun, Shaowei Yu, Fang Liu, Xinqiang Chen, and Helai Huang. 2019. “A Hierarchical Prediction Model for Lane-Changes Based on Combination of Fuzzy C-Means and Adaptive Neural Network.” Expert Systems with Applications 130: 265–275. doi:10.1016/j.eswa.2019.04.032.
   Google Scholar
• Tay, Christopher. 2009. “Analysis of Dynamic Scenes: Application to Driving Assistance.” (PhD diss.). Institut National Polytechnique de Grenoble-INPG.
   Google Scholar
• Thiemann, Christian, Martin Treiber, and Arne Kesting. 2008. “Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data.” Transportation Research Record 2088 (1): 90–101.
   Google Scholar
• Tilove, Robert B. 1990, May 1. “Local Obstacle Avoidance for Mobile Robots Based on the Method of Artificial Potentials.” Paper Presented in Proceedings., IEEE International Conference on Robotics and Automation, Cincinnati, 566–571.
   Google Scholar
• Toledo, Tomer, and David Zohar. 2007. “Modeling Duration of Lane Changes.” Transportation Research Record: Journal of the Transportation Research Board 1999 (1): 71–78.
   Google Scholar
• Tomar, Ranjeet Singh, Shekhar Verma, and Geetam Singh Tomar. 2010. “Prediction of Lane Change Trajectories through Neural Network.” Paper presented in 2010 International Conference on Computational Intelligence and Communication Networks, Bhopal, November 249-253.
   Google Scholar
• van Lint, Hans, Wouter Schakel, Guus Tamminga, Peter Knoppers, and Alexander Verbraeck. 2016. “Getting the Human Factor Into Traffic Flow Models: new Open-Source Design to Simulate Next Generation of Traffic Operations.” Transportation Research Record 2561 (1): 25–33.
   Google Scholar
• Woo, Hanwool, Yonghoon Ji, Hitoshi Kono, Yusuke Tamura, Yasuhide Kuroda, Takashi Sugano, Yasunori Yamamoto, Atsushi Yamashita, and Hajime Asama. 2017. “Lane-Change Detection Based on Vehicle-Trajectory Prediction.” IEEE Robotics and Automation Letters 2 (2): 1109–1116. doi:10.1109/lra.2017.266054.
   Google Scholar
• Xie, Dong-Fan, Zhe-Zhe Fang, Bin Jia, and Zhengbing He. 2019. “A Data-Driven Lane-Change Model Based on Deep Learning.” Transportation Research Part C: Emerging Technologies 106: 41–60. doi:10.1016/j.trc.2019.07.002.
   Google Scholar
• Zhang, Fan, Jing Bai, Xiaoyu Li, Changxing Pei, and Vincent Havyarimana. 2019. “An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction.” TIIS 13 (4): 1975–1988.
   Google Scholar
• Zhang, Yingda, Chunfu Shao, Huixuan Li, and Xuejing Ma. 2015. “Microscopic Characteristics of Lane-Change Maneuvers Based on NGSIM.” Journal of Transport Information and Safety 6 (33): 19–32.
   Google Scholar
• Zhang, Xiang, S. Travis Waller, and Peng Jiang. 2020. “An Ensemble Machine Learning-Based Modeling Framework for Analysis of Traffic Crash Frequency.” Computer-Aided Civil and Infrastructure Engineering 35 (3): 258–276.
   Google Scholar