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A Lane-Changing Decision-Making Method for Intelligent Vehicle Based on Acceleration Field

Published April 3, 2018 by SAE International in United States
A Lane-Changing Decision-Making Method for Intelligent Vehicle Based on Acceleration Field
Citation: Zhu, B., Liu, S., and Zhao, J., "A Lane-Changing Decision-Making Method for Intelligent Vehicle Based on Acceleration Field," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 11(3):219-230, 2018,
Language: English


  1. Goodall, N. , “Ethical Decision Making During Automated Vehicle Crashes,” Transportation Research Record: Journal of the Transportation Research Board 2424(1):58-65, 2014, doi:10.3141/2424-07.
  2. Gipps, P.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.
  3. Ahmed, K.I. , “Modeling Drivers’ Acceleration and Lane Changing Behavior,” Massachusetts Institute of Technology, Cambridge, MA, 1999.
  4. Hidas, P. , “Modeling Lane Changing and Merging in Microscopic Traffic Simulation,” Transportation Research Part C: Emerging Technologies 10(1):351-337, 2002, doi:10.1016/S0968-090X(02)00026-8.
  5. Seo, J. and Yi, K. , “Robust Mode Predictive Control for Lane Change of Automated Driving Vehicles,” SAE Technical Paper 2015-01-0317, 2015, doi:10.4271/2015-01-0317.
  6. Chen, X., Miao, Y., Jin, M., and Zhang, Q. , “Driving Decision-Making Analysis of Lane-Changing for Autonomous Vehicle under Complex Urban Environment,” in Control and Decision Conference (CCDC), 2017, doi:10.1109/CCDC.2017.7978420.
  7. Ding, J., Dang, R., Wang, J., and Li, K. , “Driver Intention Recognition Method Based on Comprehensive Lane-Change Environment Assessment,” in Intelligent Vehicles Symposium Proceedings, IEEE, 2014, doi:10.1109/IVS.2014.6856483.
  8. Zhao, D., Lam, H., Peng, H. et al. , “Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques,” IEEE Trans Intell Transp Syst 18(3):595-607, 2016, doi:10.1109/TITS.2016.2582208.
  9. Zelek, J.S. and Levine, M.D. , “Local-Global Concurrent Path Planning and Execution,” IEEE Transaction on System, Man and Cybernetics 30(6):865-870, 2000, doi:10.1109/3468.895924.
  10. Lavalle, S.M. , “Rapidly-Exploring Random Trees: A New Tool for Path Planning,” Technical Report TR98-11, Department of Computer Science, 12(4):1-4, 1998.
  11. Urmson, C. and Simmons, R. , “Approaches for Heuristically Biasing RRT Growth,” in Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003 (IROS 2003), 2003, doi:10.1109/IROS.2003.1248805.
  12. Ma, L., Xue, J., K, K., Zhu, J. et al. , “Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving,” IEEE Transactions on Intelligent Transportation Systems 16(4):1961-1976, 2015, doi:10.1109/TITS.2015.2389215.
  13. Jiang, Q., Deng, W., and Zhu, B. , “Integrated Threat Assessment for Trajectory Planning of Intelligent Vehicles,” SAE Technical Paper 2016-01-0153, 2016, doi:10.4271/2016-01-0153.
  14. Du, M., Chen, J., Zhao, P., Liang, H. et al. , “An Improved RRT-Based Motion Planner for Autonomous Vehicle in Cluttered Environments,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, doi:10.1109/ICRA.2014.6907542.
  15. Song, J.Z., Dai, B., Shan, E.Z. et al. , “An Improved RRT Path Planning Algorithm,” Acta Electronica Sinica 38(B02):225-228, 2010.
  16. Zhang, S., Deng, W., Zhao, Q., Sun, H. et al. , “Dynamic Trajectory Planning for Vehicle Autonomous Driving,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, doi:10.1109/SMC.2013.709.

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