Lane Keeping Control Based on Model Predictive Control Under Region of Interest Prediction Considering Vehicle Motion States

Abstract

To address the failure to consider vehicle states in region of interest (ROI) prediction, we propose the use of a Kalman filter to estimate the position of vehicles relative to lanes by vehicle states on the basis of a vehicle–road micro traffic model in the world coordinate system. The central position of the ROI is determined through a combination of optimal preview time theory with the ROI prediction. The range of the ROI is determined by offsetting upward, downward, leftward, and rightward from the central position of the ROI. The left and right ROI are processed separately to detect lane lines. Simulation results show that the proposed prediction method reduces the ROI range, and the model predictive control controller can make the vehicle run smoothly from the initial position to the road centerline.

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Abbreviations

Ψ :

the angle between the longitudinal vehicle axis and the lane, deg

d :

the distance from the vehicle center of mass to lane line, m

y 1 :

the distance between the vehicle longitudinal axis and road centerline at the preview point, m

ε 1 :

the angle between the vehicle longitudinal axis and lane tangent at preview point, deg

δ f :

front wheel angle, rad

η :

camera optical axis yaw angle, deg

θ :

camera optical axis pitch angle, deg

Φ :

camera optical axis roll angle, deg

v x :

longitudinal velocity, km/h

v y :

lateral velocity, km/h

φ :

yaw rate, rad/s

m :

total mass of the vehicle, kg

I z :

total vehicle inertia around the center of mass, kg•m2

lf (lr):

the distances of the front (rear) axles from the vehicle center of mass, m

cf (cr):

cornering stiffness of the front (rear) tires, N/rad

αfr):

side slip angles of the front (rear) tires, rad

Ff(Fr):

the forces acting on the front (rear) tires, N

ß :

side slip angle, rad

L :

preview distance, m

p :

road curvature

dr(d1):

the distance from the vehicle center of mass to the right (left) lane line, m

k :

Kalman state

- k :

predicted state

P - k :

predicted covariance of the state

P k :

covariance of the state

Q :

covariance of the process noise

R :

covariance of the measurement

Z(k) :

the measurement

K k :

kalman gain

H(k) :

the measurement matri

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 51905045), Key Technology on Major program of Jilin Province (No. 20170201005GX), the Science and Technology Research Planning Project of the Education Department of Jilin Province (No. JJKH20181035KJ), the Development and Reform Commission of Jilin City (Nos. 2019C036-1 and 2019C038-3).

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Correspondence to Shaosong Li.

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Li, Z., Cui, G., Li, S. et al. Lane Keeping Control Based on Model Predictive Control Under Region of Interest Prediction Considering Vehicle Motion States. Int.J Automot. Technol. 21, 1001–1011 (2020). https://doi.org/10.1007/s12239-020-0095-7

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Key Words

  • Kalman filter
  • Lane keeping control
  • Model predictive control
  • ROI prediction
  • Vision-based vehicle