Advertisement

Lateral Collision Avoidance Robust Control of Electric Vehicles Combining a Lane-Changing Model Based on Vehicle Edge Turning Trajectory and a Vehicle Semi-Uncertainty Dynamic Model

Article
  • 144 Downloads

Abstract

This paper presents a new control scheme for lateral collision avoidance (CA) systems to improve the safety of four-in-wheel-motor-driven electric vehicles (FIWMD-EVs). There are two major contributions in the design of lateral CA systems. The first contribution is a new lane-changing model based on vehicle edge turning trajectory (VETT) to make vehicle adapt to different driving roads and conform to drivers’ characteristic, in addition to ensure vehicle steering safety. The second contribution is vehicle semi-uncertainty dynamic model (SUDM), which is SISO model. The problem of stability performance without the information on sideslip angle is solved by the proposed SUDM. Based on the proposed VETT and SUDM, the lateral CA system can be designed with H robust controller to restrain the effect of uncertainties resulting from parameter perturbation and lateral wind disturbance. Single and mixed driving cycles simulation experiments are carried out with CarSim to demonstrate the effectiveness in control scheme, simplicity in structure for lateral CA system based on the proposed VETT and SUDM.

Key Words

Lateral Collision Avoidance (CA) system Lane-changing model Semi-Uncertainty Dynamic Model (SUDM) Vehicle Edge Turning Trajectory (VETT) 

Nomenclature

ax

longitudinal acceleration at the center of gravity (CG)

ay

lateral acceleration at the CG

a

longitudinal minimum distance model constant

b

longitudinal minimum distance model constant

d0

minimum distance

d

track width (the front and rear track widths are assumed to be equal)

dz

updating threshold of data

g

acceleration due to gravity

k

driving intention parameter

lf

distance from CG to front axle

lr

distance from CG to rear axle (l = lf + lr)

m

vehicle mass

nominal value of m

pm

perturbation range of m

pI

perturbation range of Iz

p

number of pole pairs

r

wheel radius

vx

longitudinal velocity at the CG

vxf

longitudinal velocity of following vehicle

vxl

longitudinal velocity of leading vehicle

vrel

relative velocity

vy

lateral velocity at the CG

vwind

lateral wind velocity

ye

lane width

Cf

cornering stiffness of front tires

Cr

cornering stiffness of rear tires

Cy

lateral force coefficient

D

vehicle-to-vehicle distance

Faero

equivalent longitudinal aerodynamic drag force

Fflx

longitudinal force acting on the front-left tire

Ffrx

longitudinal force acting on the front-right tire

Frlx

longitudinal force acting on the rear-left tire

Frrx

longitudinal force acting on the rear-right tire

Ffly

lateral force acting on the front-left tire

Ffry

lateral force acting on the front-right tire

Frly

lateral force acting on the rear-left tire

Frry

: lateral force acting on the rear-right tire

Fzf

normal force of front tire

Fzr

normal force of rear tire

GPI(s)

transfer function of PI controller

Iz

yaw moment of inertia

Īz

nominal value of Iz

Mz

yaw moment

Pmax

max. power

Rxf

rolling resistance force at the front tires

Rxr

rolling resistance force at the rear tires

Sveh

vehicle frontal area

Tfl

longitudinal moment acting on the front-left tire

Tfr

longitudinal moment acting on the front-right tire

Trl

longitudinal moment acting on the rear-left tire

Trr

longitudinal moment acting on the rear-right tire

Tmax

max. torque

β

vehicle sideslip angle at CG

γ

yaw rate

ρ

air density

δf

front steering angle

θ(t)

estimated parameters in RLS

φ(t)

regression vector in RLS

y(t)

measured output in RLS

e(t)

identification error in RLS

λ

forgetting factor in RLS

φμ

adhesive coefficient between the tire and the road

ψr

interlinkage magnetic flux

ωmax

max. speed

δm

perturbation of m

δI

perturbation of Iz

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abe, M. and Manning, W. (2009). Vehicle Handling Dynamics Theory and Application. Elsevier. Butterworth-Heinemann, UK.Google Scholar
  2. Bian, M. Y. (2012). A vehicle safety distance model for collision avoidance system based on emergency lane change motion. J. Chongqing University of Technology (Natural Science), 4, 1–4.Google Scholar
  3. Chovan, J. D., Tijerina, L., Alexander, G. and Hendricks, D. L. (1994). Examination of Lane Change Crashes and Potential IVHS Countermeasures. NHTSA Technical Report. DOT-VNTSC-NHTSA-93-2.Google Scholar
  4. Doumiati, M., Victorino, A. C., Charara, A. and Lechner, D. (2011). Onboard real-time estimation of vehicle lateral tire-road forces and sideslip angle. IEEE/ASME Trans. Mechatronics 16, 4, 601–614.CrossRefGoogle Scholar
  5. Eidehall, A., Pohl, J., Gustafsson, F. and Ekmark, J. (2007). Toward Autonomous Collision Avoidance by Steering. IEEE Trans. Intelligent Transportation Systems 8, 1, 84–94.CrossRefGoogle Scholar
  6. EnKe, K. (1979). Possibilities for improving safety within the driver vehicle environment control loop. Proc. 7th Int. Technical Conf. Experimental Safety Vehicles, 789–802.Google Scholar
  7. Ge, R. H., Zhang, W. W. and Zhang, W. (2010). Research on the driver reaction time of safety distance model on highway based on fuzzy mathematics. Proc. IEEE Int. Conf. Optoelectronics and Image Processing, Haiko, Hainan, China.Google Scholar
  8. Girbés, V., Armesto, L., Dols, J. and Tornero, J. (2017). An active safety-system for low-speed bus braking assistance. IEEE Trans. Intelligent Transportation Systems 18, 2, 377–387.CrossRefGoogle Scholar
  9. Guo, J., Hu, P. and Wang, R. (2016). Nonlinear coordinated steering and braking control of vision-based autonomous vehicles in emergency obstacle avoidance. IEEE Trans. Intelligent Transportation Systems 17, 11, 3230–3240.CrossRefGoogle Scholar
  10. Han, S. and Huh, K. (2011). Monitoring system design for lateral vehicle motion. IEEE Trans. Vehicular Technology 60, 4, 1394–1403.CrossRefGoogle Scholar
  11. Jin, L. S., Fang, W. P., Zhang, Y. N., Yang, S. B. and Hou, H. J. (2009). Research on safety lane change model of driver assistant system on highway. Proc. IEEE Intelligent Vehicles Symp., Xi’an, China.Google Scholar
  12. Lian, Y. F., Zhao, Y., Hu, L. L. and Tian, Y. T. (2015a). Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information. Int. J. Automotive Technology 8, 4, 669–683.CrossRefGoogle Scholar
  13. Lian, Y. F., Zhao, Y., Hu, L. L. and Tian, Y. T. (2015b). Longitudinal collision avoidance control of electric vehicles based on a new safety distance model and constrained regenerative braking strength continuity braking force distribution strategy. IEEE Trans. Vehicular Technology 65, 6, 4079–4094.CrossRefGoogle Scholar
  14. Luo, Q., Xun, L. H., Cao, Z. H. and Huang, Y. G. (2011). Simulation analysis and study on car-following safety distance model based on braking process of leading vehicle. Proc. IEEE 9th World Cong. Intelligent Control and Automation, Taipei, Taiwan.Google Scholar
  15. Nakaoka, M., Raksincharoensak, P. and Nagai, M. (2008). Study on forward collision warning system adapted to driver characteristics and road environment. Proc. IEEE Int. Conf. Control, Automation and Systems, Seoul, Korea.Google Scholar
  16. Nam, K., Fujimoto, H. and Hori, Y. (2012). Lateral stability control of in-wheel-motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors. IEEE Trans. Vehicular Technology 61, 5, 1972–1985.CrossRefGoogle Scholar
  17. Nguyen, B. M., Nam, K., Fujimoto, H. and Hori, Y. (2011). Proposal of cornering stiffness estimation without vehicle sideslip angle using lateral force sensor. IEEJ Technical Meeting Record IIC-11-140, 37–42.Google Scholar
  18. Rahman, M., Chowdhury, M., Xie, Y. C. and He, Y. M. (2013). Review of microscopic lane-changing models and future research opportunities. IEEE Trans. Intelligent Transportation Systems 14, 4, 1942–1956.CrossRefGoogle Scholar
  19. Rajamani, R., Phanomchoeng, G., Piyabongkarn, D. and Lew, J. Y. (2012). Algorithms for real-time estimation of individual wheel tire-road friction coefficients. IEEE/ASME Trans. Mechatronics 17, 6, 1183–1195.CrossRefGoogle Scholar
  20. Ray, L. R. (1995). Nonlinear state and tire force estimation for advanced vehicle control. IEEE Trans. Control Systems Technology 3, 1, 117–124.CrossRefGoogle Scholar
  21. Sierra, C., Tseng, E., Jain, A. and Peng, H. (2006). Cornering stiffness estimation based on vehicle lateral dynamics. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 44, 1, 24–38.CrossRefGoogle Scholar
  22. Tjoennas, J. and Johansen, T. A. (2006). Adaptive optimizing dynamic control allocation algorithm for yaw stabilization of an automotive vehicle using brakes. Proc. IEEE 14th Mediterranean Conf. Control and Automation, Ancona, Italy.Google Scholar
  23. Tunonen, A. J. (2008). Optical position detection to measure tyre carcass deflection. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 46, 6, 471–481.CrossRefGoogle Scholar
  24. Xu, G. Q., Liu, L., Ou, Y. S. and Song, Z. J. (2012). Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction. IEEE Trans. Intelligent Transportation Systems 13, 3, 1138–1155.CrossRefGoogle Scholar
  25. Zou, G. C., Luo, Y. G. and Li, K. Q. (2009). 4WD vehicle DYC based on tire longitudinal forces optimization distribution. Trans. Chinese Society for Agricultural Machinery 40, 5, 1–6.Google Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yufeng Lian
    • 1
  • Xiaoyu Wang
    • 2
  • Yantao Tian
    • 2
    • 3
  • Keping Liu
    • 1
  1. 1.School of Electrical and Electronic EngineeringChangchun University of TechnologyChangchunChina
  2. 2.College of Communication EngineeringJilin UniversityChangchunChina
  3. 3.Key Laboratory of Bionics Engineering, Ministry of EducationJilin UniversityChangchunChina

Personalised recommendations