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Robust Gain-scheduling Control of Dynamic Lateral Obstacle Avoidance for Connected and Automated Vehicles

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Abstract

Dynamic trajectory planning (DTP) and Dynamic trajectory tracking (DTT) are the real-time mutual coupling in the process of the dynamic lateral obstacle avoidance (DLOA) of connected and automated vehicles (CAVs). Meanwhile, the varying velocity and acceleration of obstacle vehicles (OVs) increase the difficulties of DTP. Furthermore, the parameters perturbation in CAVs (such as mass and cornering stiffness), the varying velocities of CAVs and the signal disturbances, raise the difficulties of DTT. Therefore, the DLOA is challenging due to the interaction of the above multiple factors. To address the problem, this paper proposes a robust gain-scheduling control strategy of DLOA for CAVs. The strategy is divided into two modules namely DTP and DTT, and the two modules cooperate with each other in real time. In the module of DTP, the optimal trajectory considering the efficiency, passenger comfort and safety is real-time optimized in the dynamic safe limit which is real time predicted according to the information from CAVs and OVs. In the module of DTT, the real-time trajectory reference is tracked. Robust gain-scheduling control is realized to cope with variation of real-time trajectory reference, varying velocity, parameters perturbation and signal disturbances during the process of DLOA. The simulation results indicate that the strategy can effectively achieve DLOA maintaining the vehicle stability across various working conditions.

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Abbreviations

F 1, F 2, F 3, F 4 :

lateral and longitudinal force of front and rear axles

ΔF x1, ΔF x2, ΔF x3, ΔF x4 :

altered longitudinal force of each tire

k f, k r :

cornering stiffness of the front and rear tires

u m, u sx, u sy :

velocity, longitudinal velocity and lateral velocity in the vehicle coordinate system

δ f :

steering angle

\(\beta ,\dot \beta \) :

sideslip angle, velocity of sideslip angle

\(\dot \psi ,\ddot \psi \) :

heading angle and yaw velocity

a, b :

distance from the center of mass to the front axle and rear axle

l w :

width of the vehicle

m, I zz :

mass and yaw moment of inertia

ΔT x1, ΔT x2, ΔT x3, ΔT x4 :

altered driving torque of each tire

Y r :

lateral displacement of the curve

Re :

lane width

X r :

longitudinal displacement

D :

longitudinal length of the curve

u gx, u gy :

longitudinal and lateral velocity in the global coordinate system

u f0, a f :

initial velocity and acceleration of OVs

t p :

predicted time from the real-time position to the limit position

u fr :

velocity of OVs

a fr :

longitudinal acceleration of OVs

l zc :

half vehicle width

l zb :

longitudinal distance from the centroid to limit position of CAVs

l fb :

longitudinal distance from the centroid to limit position of OVs

D f0 :

initial longitudinal distance between the CAVs and OVs at start time

a cy :

maximum lateral acceleration of the trajectory

a ym :

maximum safe lateral acceleration

t w :

completed time of lateral obstacle avoidance

t max :

maximum allowed cost time of lateral obstacle avoidance

η :

factor adjusting efficiency and passenger comfort

t p :

used time of lane change

Y r0, Y r1, Y r2 :

lateral displacement of the reference trajectory at neighbored time instants

T :

sample time

w p :

related to external disturbance

W u :

used to constrain control output

S :

defined as the sensitivity function

d :

disturbance

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Acknowledgement

This work was supported by National Natural Science Foundation of China (Grant No. 52262053), by Basic Research Project of Yunnan Science and Technology Program (202101AT070108), and by the Xingdian Talent Support Planning Project.

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Authors and Affiliations

  1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, 650500, China

    Zhigen Nie & Wanqiong Wang

  2. LIS Lab (UMR CNRS 7020), Aix-Marseille University, Marseille, 15397, France

    Zhongliang Li & Rachid Outbib

  3. School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, 130012, China

    Yufeng Lian

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  1. Zhigen Nie
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  2. Zhongliang Li
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Correspondence to Zhigen Nie.

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Nie, Z., Li, Z., Wang, W. et al. Robust Gain-scheduling Control of Dynamic Lateral Obstacle Avoidance for Connected and Automated Vehicles. Int.J Automot. Technol. 24, 63–78 (2023). https://doi.org/10.1007/s12239-023-0007-8

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  • DOI: https://doi.org/10.1007/s12239-023-0007-8

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