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Optimal Path Planning for Autonomous Vehicles Using Artificial Potential Field Algorithm

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Abstract

This paper proposes an optimal path planning algorithm to make the autonomous vehicle follow the desired path profile while avoiding nearby obstacles safely. Also, it utilizes only readily available sensors equipped with typical autonomous vehicle system. For optimal path planning, an artificial potential field (APF) algorithm to derive both desired vehicle longitudinal velocity and desired vehicle yaw angle in real time is newly designed, which includes both a repulsive field for avoiding road boundaries and nearby obstacles ahead, and an attractive field for following the proper lane. Next, the path tracking control algorithm consists of longitudinal and lateral motion controllers. Especially, a model predictive control (MPC) for vehicle lateral motion causes the yaw angle error between the desired path profile and the vehicle to approach zero. Then, it can derive an optimal front steering angle considering vehicle state and input constraints. Using CarSim and MATLAB/Simulink simulations, the effectiveness of the proposed algorithm in this paper is verified in some driving scenarios. Accordingly, its high performance for the path planning and tracking of autonomous vehicles can be clearly confirmed.

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Abbreviations

ν :

vehicle longitudinal velocity, km/h

ψ :

vehicle yaw angle, deg

l f :

distance between the vehicle center and the front axle, m

l r :

distance between the vehicle center and the rear axle, m

t :

track width, mm

a x&y :

vehicle longitudinal and lateral accelerations, m/s2

μ :

tire-road friction coefficient, -

R e :

tire effective rolling radius, m

m :

total mass of vehicle, kg

T w :

total wheel torque, N·m

β :

vehicle sideslip angle, deg

C f :

tire cornering stiffness of front axle, N/deg

C r :

tire cornering stiffness of rear axle, N/deg

r :

vehicle yaw rate, deg/s

δ f :

front steering angle, deg

I z :

vehicle yaw moment of inertia, kg·m2

s :

travel distance of vehicle, m

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Acknowledgement

This research was supported by the 2023 Research Fund of University of Ulsan.

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Ulsan, Ulsan, 44610, Korea

    Giseo Park

  2. NMOTION Co., Ltd., 204 Convensia-daero, Yeonsu-gu, Incheon, 22004, Korea

    Mooryong Choi

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  1. Giseo Park
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  2. Mooryong Choi
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Correspondence to Mooryong Choi.

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Park, G., Choi, M. Optimal Path Planning for Autonomous Vehicles Using Artificial Potential Field Algorithm. Int.J Automot. Technol. 24, 1259–1267 (2023). https://doi.org/10.1007/s12239-023-0102-x

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  • DOI: https://doi.org/10.1007/s12239-023-0102-x

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