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access icon openaccess Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

The real time and adaptability of emergency lane-changing trajectory planning method is critical to the safe operation of the intelligent vehicle. This study presents the design and implementation of the trajectory planning and optimisation method of intelligent vehicle lane changing emergently based on hp-adaptive pseudospectral method. The method divides the emergency lane-changing process into the initial stage and tracking stage for trajectory planning based on road steering experiment and sigmoid functions. An hp-adaptive pseudospectral method is introduced to connect and optimise the lane-changing trajectory. PreScan and Matlab are used to simulate the planning of emergency lane-changing trajectory under six conditions, and the simulation results verify the effectiveness and real time of the planning and optimisation method. In comparison with trajectory planning methods based on polynomial functions, this method is characterised by shorter response time and safety distance and has better adaptability under different conditions.

References

    1. 1)
      • 29. Chen, Q.Y., Zhang, X.B., Sun, Z.P., et al: ‘Trajectory planning for autonomous driving in unstructured environments’, J. Central South Univ., 2011, 42, (11), pp. 33773383.
    2. 2)
      • 28. Atto, A.M., Pastor, D., Mercier, G.: ‘Smooth sigmoid wavelet shrinkage for non-parametric estimation’. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Las Vegas, NV, USA, 2008, pp. 32653268.
    3. 3)
      • 7. Kanellakopoulos, I., Nelson, P., Stafsudd, O.: ‘Intelligent sensors and control for commercial vehicle automation’, Annu. Rev. Control, 1999, 23, pp. 117124.
    4. 4)
      • 30. Gong, F., Yuan, K., Yanqiu, M.A.: ‘Trajectory optimization of continuous descent approach (CDA) by a gauss pseudospectral method’, J. Transp. Inf. Safety, 2016, 34, (4), pp. 1521.
    5. 5)
      • 2. Bishop, R.: ‘Intelligent vehicle applications worldwide’, IEEE Intell. Syst. Appl., 2000, 15, (1), pp. 7881.
    6. 6)
      • 13. Crane, C.D.III, Armstrong, D.G.II, Touchtou, R., et al: ‘Team CIMAR's NaviGator: an unmanned ground vehicle for the 2005 DARPA grand challenge’, J. Field Robot., 2006, 23, (8), pp. 599623.
    7. 7)
      • 32. Salvucci, D.D., Liu, A.: ‘The time course of a lane change: driver control and eye-movement behavior’, Transp. Res. F, Traffic Psychol. Behav., 2002, 5, (2), pp. 123132.
    8. 8)
      • 1. Sai, S., Altintas, O., Kenney, J., et al: ‘Current and future ITS’, IEICE Trans. Inf. Syst., 2013, 96, (2), pp. 176183.
    9. 9)
      • 21. Mohseni, N.A., Fakharian, A.: ‘Optimal trajectory planning for an omni-directional mobile robot with static obstacles: a polynomial based approach’. IEEE AI & Robotics, Qazvin, Iran, 2015, pp. 16.
    10. 10)
      • 10. Cao, H.: ‘Simulation research on emergency path planning of an active collision avoidance system combined with longitudinal control for an autonomous vehicle’, Proc. Inst. Mech. Eng. D, J. Automob. Eng., 2016, 230, (12), pp. 16241653.
    11. 11)
      • 9. Guo, L., Ge, P.-S., Yue, M., et al: ‘Lane changing trajectory planning and tracking controller design for intelligent vehicle running on curved road’, Math. Probl. Eng., 2014, 2014, (8), pp. 19.
    12. 12)
      • 27. Chan, K.Y., Engelkebulrich, U., Abhayasinghe, N.: ‘An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons’, Expert Syst. Appl., 2016, 67, pp. 272284.
    13. 13)
      • 16. Luo, Y.-G., Xiang, Y., Cao, K., et al: ‘A dynamic automated lane change maneuver based on vehicle-to-vehicle communication’, Transp. Res. C, 2016, 62, pp. 87102.
    14. 14)
      • 12. Vasile, M., Zuiani, F.: ‘Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design’, Proc. Inst. Mech. Eng. G, J. Aerosp. Eng., 2011, 225, (11), pp. 12111227.
    15. 15)
      • 15. Ghosh, S., Panigrahi, P.K., Parhi, D.R.: ‘Analysis of FPA and BA meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment’, IET Sci. Meas. Technol., 2017, 11, (7), pp. 817828.
    16. 16)
      • 3. You, F., Zhang, R., Guo, L., et al: ‘Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system’, Expert Syst. Appl., 2015, 42, (14), pp. 59325946.
    17. 17)
      • 18. Hassanzadeh, M., Lidberg, M., Keshavarz, M., et al: ‘Path and speed control of a heavy vehicle for collision avoidance manoeuvres’. IEEE Intelligent Vehicles Symp, Alcala de Henares, Spain, 2012, pp. 129134.
    18. 18)
      • 14. Lin, Y., Saripalli, S.: ‘Sampling-based path planning for UAV collision avoidance’, IEEE Trans. Intell. Transp. Syst., 2017, 14, (99), pp. 114.
    19. 19)
      • 5. Zhu, H., Yuen, K.V., Mihaylova, L., et al: ‘Overview of environment perception for intelligent vehicles’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (10), pp. 25842601.
    20. 20)
      • 25. Qiu, W.J., Meng, X.Y.: ‘Multi-phase trajectory optimization of vehicle based on hp-adaptive pseudospectral method’, Trans. Beijing Inst. Technol., 2017, 37, (4), pp. 412417.
    21. 21)
      • 26. Chen, W.-W., Liu, M., Lei, J.-H., et al: ‘A trajectory planning algorithm for quadrotor UAVs based on sigmoid function’, Control Eng., 2016, 23, (6), pp. 922927.
    22. 22)
      • 19. Shim, T., Adireddy, G., Yuan, H.: ‘Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control’, Proc. Inst. Mech. Eng. D, J. Automob. Eng., 2012, 226, (6), pp. 767778.
    23. 23)
      • 31. Hong, T., Kwon, J., Park, K., et al: ‘Development of a driver's intention determining algorithm for a steering system based collision avoidance system’, Alcohol Alcoholism, 2013, 38, (5), pp. 443445.
    24. 24)
      • 4. Chang, B.J., Yu, B.H., Liang, Y.H.: ‘Colouring vehicle threat and minimising threat avoidance trajectory cost for adaptive vehicle collision defence system in active safe driving’, IET Intell. Transp. Syst., 2017, 11, (6), pp. 309318.
    25. 25)
      • 20. Gosselin, C., Foucault, S.: ‘Dynamic point-to-point trajectory planning of a Two-DOF cable-suspended parallel robot’, IEEE Trans. Robot., 2017, 30, (3), pp. 728736.
    26. 26)
      • 17. Lim, W., Lee, S., Sunwoo, M., et al: ‘Hierarchical trajectory planning of an autonomous car based on the integration of a sampling and an optimization method’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (99), pp. 114.
    27. 27)
      • 6. Tang, J., Liu, F., Zhang, W., et al: ‘Lane-changes prediction based on adaptive fuzzy neural network’, Expert Syst. Appl., 2018, 91, pp. 452463.
    28. 28)
      • 11. Hilgert, J., Hirsch, K., Bertram, T., et al: ‘Emergency path planning for autonomous vehicles using elastic band theory’. IEEE Int. Conf. Advanced Intelligent Mechatronics, Duisburg, Germany, 2003, pp. 13901395.
    29. 29)
      • 23. Ma, G., Zhuang, Y., Li, C., et al: ‘Pseudospectral method for optimal motion planning of a rigid underactuated spacecraft’. IEEE Int. Conf. Control and Automation, Xiamen, China, 2010, pp. 684688.
    30. 30)
      • 22. Ross, I.M., Karpenko, M.: ‘A review of pseudospectral optimal control: from theory to flight’, Annu. Rev. Control, 2012, 36, (2), pp. 182197.
    31. 31)
      • 33. Zhu, X., Liu, Z., Lin, L.I.: ‘Open-loop model of drivers’ emergency lane-change behavior based on the naturalistic driving data’, J. Autom. Safety Energy, 2015, 06, (4), pp. 328332.
    32. 32)
      • 24. Wang, H.T., Jun-Ying, L.I., Liang, L.W., et al: ‘Track optimizing for reentry vehicle based on hp-adaptive Radau pseudospectral method’, Sci. Technol. Eng., 2015, 15, (2), pp. 165171.
    33. 33)
      • 8. Zhang, R.H., He, Z.C., Wang, H.W., et al: ‘Study on self-tuning tyre friction control for developing main-servo loop integrated chassis control system’, IEEE. Access., 2017, 5, pp. 66496660.
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