Moving horizon shared steering strategy for intelligent vehicle based on potential-hazard analysis

Moving horizon shared steering strategy for intelligent vehicle based on potential-hazard analysis

For access to this article, please select a purchase option:

Buy eFirst article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The collaboration between a human driver and an automation system will serve as an effective measure before the autonomous driving technology is fully implemented. To discuss the driving authority between a human driver and an automation system, a novel moving horizon shared steering framework is proposed, in which the controller assists the driver when the vehicle is in danger. First, a potential-hazard analysis is presented based on the potential steering operation error and lane departure distance to predict the degree of a hazard and to determine the driving authority between the human driver and the automation system. Then, the minimum collaborative steering operation is determined using a moving horizon optimisation approach with safety constraints. Thus, the automation system shares steering with the driver protect the vehicle from risks in a non-invasive manner. To demonstrate the effectiveness of the proposed strategy, simulations with different types of drivers and scenarios are conducted. The results of these simulations demonstrate that the proposed approach can enhance the driving ability of drivers with different skill levels in dangerous situations. The approach can also guarantee the safety of the intelligent vehicle to some extent when drivers lose focus for a period of time.


    1. 1)
      • 1. Lin, C., Juang, J., Li, K.: ‘Active collision avoidance system for steering control of autonomous vehicles’, IET Intell. Transp. Syst., 2017, 8, (6), pp. 550557.
    2. 2)
      • 2. Soualmi, B., Sentouh, C., Popieul, J.C., et al: ‘Automation–driver cooperative driving in presence of undetected obstacles’, Control Eng. Pract., 2014, 24, pp. 106119.
    3. 3)
      • 3. Huang, Y., Wang, H., Khajepour, A., et al: ‘Model predictive control power management strategies for HEVs: a review’, J. Power Sources, 2017, 341, pp. 91106.
    4. 4)
      • 4. Sachin, B.S.M., Golla, V.: ‘Detection of potholes in autonomous vehicle’, IET Intell. Transp. Syst., 2014, 8, (6), pp. 543549.
    5. 5)
      • 5. Kim, E., Kim, J., Sunwoo, M.: ‘Model predictive control strategy for smooth path tracking of autonomous vehicles with steering actuator dynamics’, Int. J. Automot. Technol., 2014, 15, (7), pp. 11551164.
    6. 6)
      • 6. SAE International: ‘Taxonomy and definitions for terms related to on road motor vehicle automated driving systems (standard no. j3016)’, 2014.
    7. 7)
      • 7. Hu, C., Wang, R., Yan, F.: ‘Integral sliding mode-based composite nonlinear feedback control for path following of four-wheel independently actuated autonomous vehicles’, IEEE Trans. Transp. Electrification, 2016, 2, (2), pp. 221230.
    8. 8)
      • 8. Hu, C., Jing, W., Wang, R, et al: ‘Robust H-infinity output-feedback control for path following of autonomous ground vehicles’, Mech. Syst. Signal Process., 2015, 70, pp. 414427.
    9. 9)
      • 9. Wang, R., Jing, H., Hu, C., et al: ‘Robust H-infinity path following control for autonomous ground vehicles with delay and data dropout’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (7), pp. 20422050.
    10. 10)
      • 10. Liebner, M., Klanner, F., Baumann, M., et al: ‘Velocity-based driver intent inference at urban intersections in the presence of preceding vehicles’, IEEE Intell. Transp. Syst., 2013, 5, (2), pp. 1021.
    11. 11)
      • 11. Hu, C., Wang, R., Yan, F., et al: ‘Differential steering based Yaw stabilization using ISMC for independently actuated electric vehicles’, IEEE Trans. Intell. Transp. Syst., 2018, 99, pp. 112.
    12. 12)
      • 12. Hsu, Y.H.J., Laws, S.M., Gerdes, J.C.: ‘Estimation of tire slip angle and friction limits using steering torque’, IEEE Trans. Control Syst. Technol., 2010, 18, (4), pp. 896907.
    13. 13)
      • 13. Anderson, S.J., Karumanchi, S.B., Iagnemma, K., et al: ‘The intelligent copilot: a constraint-based approach to shared-adaptive control of ground vehicles’, IEEE Intell. Transp. Syst., 2013, 5, (2), pp. 4554.
    14. 14)
      • 14. Koo, Y., Kim, J., Han, W.: ‘A method for driving control authority transition for cooperative autonomous vehicle’. IEEE Intelligent Vehicles Symp. (IV), Seoul, Korea, 2015, pp. 394399.
    15. 15)
      • 15. Wada, T., Sonoda, K., Okasaka, T., et al: ‘Authority transfer method from automated to manual driving via haptic shared control’. 2016 IEEE Int. Conf. Syst., Man, and Cybernetics (SMC), Budapest, Hungary, 2016, pp. 002659002664.
    16. 16)
      • 16. Mulder, M., Abbink, D.A., Boer, E.R.: ‘Sharing control with haptics seamless driver support from manual to automatic control’, Hum. Factors, 2012, 54, (5), pp. 786798.
    17. 17)
      • 17. Anderson, S.J., Peters, S.C., Pilutti, T.E., et al: ‘An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios’, Int. J. Veh. Auton. Syst., 2010, 8, (2–3), pp. 190216.
    18. 18)
      • 18. Na, X., Cole, D.J.: ‘Game-theoretic modeling of the steering interaction between a human driver and a vehicle collision avoidance controller’, IEEE Trans. Hum. Mach. Syst., 2015, 45, (1), pp. 2538.
    19. 19)
      • 19. Tan, D., Chen, W., Wang, H., et al: ‘Shared control for lane departure prevention based on the safe envelope of steering wheel angle’, Control Eng. Pract., 2017, 64, pp. 1526.
    20. 20)
      • 20. Li, Y., Ge, S.S.: ‘Human–robot collaboration based on motion intention estimation’, IEEE/ASME Trans. Mechatronics, 2014, 19, (3), pp. 10071014.
    21. 21)
      • 21. Gao, Y., Lin, T., Borrelli, F., et al: ‘Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads’. IEEE ASME 2010 Dynamic Systems Control Conf., Cambridge, MA, USA, 2010, pp. 265272.
    22. 22)
      • 22. Saleh, L., Chevrel, P., Claveau, F., et al: ‘Shared steering control between a driver and an automation: stability in the presence of driver behavior uncertainty’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 974983.
    23. 23)
      • 23. Erlien, S.M., Fujita, S., Gerdes, J.C., et al: ‘Shared steering control using safe envelopes for obstacle avoidance and vehicle stability’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (2), pp. 441451.
    24. 24)
      • 24. Keen, S., Cole, D.: ‘Application of time-variant predictive control to modelling driver steering skill’, Veh. Syst. Dyn., 2011, 49, (4), pp. 527559.
    25. 25)
      • 25. MacAdam, C.C.: ‘An optimal preview control for linear systems’, J. Dyn. Syst. Meas. Control, 1980, 102, (3), pp. 188190.
    26. 26)
      • 26. Choi, M., Jiwon, J.O., Choi, S.B.: ‘Predictive and linear quadratic methods for potential application to modelling driver steering control’, Veh. Syst. Dyn., 2006, 44, (3), pp. 259284.
    27. 27)
      • 27. Bradai, A., Fekher, K., Singh, K., et al: ‘Localization and energy efficient data routing for unmanned aerial vehicles: fuzzy logic based approach’, IEEE Commun. Mag., 2017, 56, (4), pp. 129133.
    28. 28)
      • 28. Wang, J., Alexander, L., Rajamani, R.: ‘Friction estimation on highway vehicles using longitudinal measurements’, J. Dyn. Syst. Meas. Control, 2004, 126, (2), pp. 265275.
    29. 29)
      • 29. Rajamani, R., Phanomchoeng, G., Piyabongkarn, D., et al: ‘Algorithms for real–time estimation of individual wheel tire-road friction coefficients’, IEEE/ASME Trans. Mechatronics, 2012, 17, (6), pp. 11831195.
    30. 30)
      • 30. Li, B.Y., Du, H.P., Li, W.H., et al: ‘Side-slip angle estimation based lateral dynamics control for omni-directional vehicles with optimal steering angle and traction/brake torque distribution’, Mechatronics, 2015, 30, pp. 348362.
    31. 31)
      • 31. Pan, F.Q., Zhang, L.X., Liu, T., et al: ‘Modeling of driving behaviors at countdown signalized intersections considering the value of car’, J. Transp. Syst. Eng. Inf. Technol., 2016, 16, (2), pp. 6469.

Related content

This is a required field
Please enter a valid email address