Open AccessVehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler
Author(s) -
Wahid Nurbaiti,
Zamzuri Hairi,
Amer Noor H.,
Dwijotomo Abdurahman,
Saruchi Sarah Atifah,
Mazlan Saiful Amri
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0048
Subject(s) - obstacle avoidance , collision avoidance , motion planning , control theory (sociology) , computer science , trajectory , collision , simulation , motion control , particle swarm optimization , engineering , artificial intelligence , control engineering , robot , mobile robot , control (management) , algorithm , physics , astronomy , computer security
This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi‐speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base‐type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware‐in‐loop test is conducted to verify the proposed strategy using a steering wheel system.