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Decision Making and Trajectory Planning of Intelligent Vehicle’ s Lane-Changing Behavior on Highways under Multi-Objective Constrains
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed. The inputs of the Bi-LSTM were data fragments of several influencing factors including the relative velocity and the distance between the relative vehicles, the type of the preceding vehicles, and the average velocity of the adjacent traffic flows, that over a certain period of time, which was determined via examining subjects’ visual behaviors of the left view mirror or the right view mirror. By combining the LCIG model with a feasibility judgement model which was based on minimum safety spacing (MSS), a lane-changing decision-making model satisfying driving safety and drivers’ expectation was proposed. The model was trained with a part of trajectory dataset obtained from the simulation driving experiments. The jerk was taken into full consideration as boundary condition on the basis of seventh-degree polynomial trajectory planning. The proposed decision-making model were verified against a test dataset from the other parts of experimental data and the results show that the model resembles the lane-changing decision-making process of human drivers in real-world.
CitationNie, L., Yin, Z., and Huang, H., "Decision Making and Trajectory Planning of Intelligent Vehicle’ s Lane-Changing Behavior on Highways under Multi-Objective Constrains," SAE Technical Paper 2020-01-0124, 2020, https://doi.org/10.4271/2020-01-0124.
Data Sets - Support Documents
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- PG, G. , “A Model for the Structure of Lane-Changing Decisions,” Transportation Research Part B: Methodological 20(5):403-414, 1986, doi:10.1016/0191-2615(86)90012-3.
- Hidas, P. , “Modelling Vehicle Interactions in Microscopic Simulation of Merging and Weaving,” Transportation Research Part C 13:37-62, 2005, doi:10.1016/j.trc.2004.12.003.
- Xing, Y., Lv, C., Wang, H., Wang, H. et al. , “Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges,” IEEE Transactions on Vehicular Technology 68(5):4377-4390, 2019, doi:10.1109/tvt.2019.2903299.
- Schmidt, K., Beggiato, M., KH, H., and JF, K. , “A Mathematical Model for Predicting Lane Changes Using the Steering Wheel Angle,” Journal of Safety Research 49:85-90, 2014, doi:10.1016/j.jsr.2014.02.014.
- Peng, J., Guo, Y., Fu, R., Yuan, W., and Wang, C. , “Multi-Parameter Prediction of Drivers’ Lane-Changing Behavior with Neural Network Model,” Applied Ergonomics 50:207-217, 2015, doi:10.1016/j.apergo.2015.03.017.
- Ding, J., Dang, R., Wang, J., and Li, K. , “Driver Lane Change Decision Analysis and Intention Recognition Algorithm,” Tsinghua Univ (Science and Technology) 55(7), 2015, doi:10.1109/ivs.2014. 6856483.
- Esmaeil, B., Ruey, L.C., and Thompson, S.G. , “A Binary Decision Model for Discretionary Lane Changing Move Based on Fuzzy Inference System,” Transportation Research Part C 67:47-61, 2016, doi:10.1016/j.trc.2016.02.009.
- Tang, J., Liu, F., Zhang, W., Ke, R., and Zou, Y. , “Lane-Changes Prediction Based on Adaptive Fuzzy Neural Network,” Expert Systems with Applications 91:452-463, 2018, doi:10.1016 /j.eswa.2017.09.025.
- Song, X., Zheng, Y., and Cao, H. , “Research on driver’s Lane Change Intention Recofnition Based on HMM and SVM,” Journal of Electronic Measurement and Instrumentation 30(1):58-65, 2016, doi:10.13382/j.jemi.2016.01.008.
- Berndt, H., Jorg, E., Klaus, Dietmayer. , “Continuous Driver Intention Recognition with Hidden Markov Models,” in Intelligent Transportation System, 11th International IEEE Conference, 2008, doi:10.1109/itsc.2008.4732630.
- Hou, H. et al. , “Driver Intention Recognition Method Using Continuous Hidden Markov Model,” Intertional Journal of Computational Intelligence Systems 4(3):386-393, 2011.
- Dou, Y., Yan, F., Feng, D. , “Lane Changing Prediction at Highway Lane Drops Using Support Vector Machine and Artificial Neural Network Classifiers,” in 2016 IEEE Intenational Conference on Advanced Intelligent Machatronics (AIM), 2016, 901-906, doi:10.1109/aim.2016.7576883.
- Shi, J., Wang, Z., Deng, W., and Zhang, S. , “Vehicle Automatic Lane Changing Based on Model Predictive Control,” SAE International Journal of Passenger Cars-Electronic and Systems 9(1):231-236, 2016, doi:10.4271/2016-01-0142.
- Li, A. , “Research on Motion Trajectory Planning Method for Intelligent Vehicles,” Nanjing University of Aeronautics and Astronautics, 2013.
- Yang, Z. , “Trajectory Planning of Lane Changing for Intelligent Vehicles,” Journal of Chongqing Transportation University (Natural Science) 32(3):520-524, 2013, doi:10.3969/j.issn. 1674-0696.2013.03.35.
- Wu, M. , “Traffic Modelling for Vehicle Intelligence,” Jilin University, 2015.
- Wang, Z. , “Research on Autonomous Lane Changing Method of Intelligent Vehicle,” Jilin University, 2016.
- Gu, G. , “Cooperative Lane-Changing Trajectory Planning of Intelligent Vehicle with Consideration of Driver Characteristics,” South China University of Technology, 2018.
- Zhang, R., You, F., Chu, X., Guo, L. et al. , “Lane Change Merging Control Method for Unmanned Vehicle under V2V Cooperative Enviroment,” China Journal of Highway Transportation 31(4):180-191, 2018, doi:10.19721/j.cnki.1001-7372.2018.04.022.
- Hogema, J.H. , Janssen, W.H. , “ Effects of Intelligent Cruise Control on Driving Behavior,” TNO Human Factors, 1996.
- Minderhoud, M.M. and Bovy, P.H. , “Extended Time-to-Collision Measures for Road Traffic Safety Assessment,” Accident Analysis and Prevention 33(1):89-97, 2001, doi:10.1016/s0001-4575(00)00019-1.
- Wang, C., Fu, R., Zhang, Q., Guo, Y., and Yuan, W. , “Research on Parameter TTC Characteristics of Lane Change Warning System,” China Journal of Highway and Transport 28(8):91-108, 2015, doi:10.3969/j.issn.1001-7372.2015.08.012.
- You, F. and Gu, G. , “Collaborative Lane Changing Trajectory Planning of Autonomous Vehicles,” Science Technology and Engineering 18(15):155-161, 2018, doi:10.3969/j.issn.1671-1815.2018.15. 023.
- You, F. , “Study on Autonomous Lane Changing and Autonomous Overtaking Control Method of Intelligent Vehicle,” Jilin University, 2005.
- Sun, Z. , “An Intelligent Control System for Autonomous Land Vehicle,” National University of Defense Technology, 2004.
- Caywood, W.C., Donnelly, H.L., and Rubinstein, N. , “Guideline for Ride-Quality Specifications Based on Transpo' 72 Test Data,” Intelligent Transportation Systems 10(46), 1977.
- Ammoun, S., Nashashibi, F., and Laurgeau, C. , “An Analysis of the Lane Changing Manoeuvre on Roads: The Contribution of Inter-Vehicle Cooperation Via Communication,” Intelligent Vehicles Symposium 18(2):1095-1100, 2007, doi:10.1109/ivs.2007. 4290263.