Open AccessDriving and steering collision avoidance system of autonomous vehicle with model predictive control based on non-convex optimization
Author(s) -
Yuho Song,
Kunsoo Huh
Publication year - 2021
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/16878140211027669
Subject(s) - collision avoidance , model predictive control , control theory (sociology) , collision avoidance system , collision , controller (irrigation) , supervisor , genetic algorithm , probabilistic logic , trajectory , computer science , convex optimization , software , simulation , engineering , control engineering , control (management) , regular polygon , artificial intelligence , mathematics , astronomy , physics , geometry , computer security , machine learning , law , political science , agronomy , biology , programming language
A planar motion control system is proposed for autonomous vehicles not only to follow the lanes, but also to avoid collisions by braking, accelerating, and steering. The supervisor is designed first to determine the desired speed and the risk of the maneuvering due to road boundaries and obstacles. In order to allow lane changes on multi-lane roads, the model predictive controller is formulated based on the probabilistic non-convex optimization. The micro-genetic algorithm is applied to calculate the target speed and target steering angle in real time. A software-in-the-loop unit is constructed with the Rapid Control Prototyping device in the vehicle communication environment. The performance of the proposed system is verified for various collision avoidance scenarios and the simulation results demonstrate the safe and effective driving performance of autonomous vehicles with no collision on multi-lane road.