TY - JOUR
T1 - Imitation Learning of Nonlinear Model Predictive Control for Emergency Collision Avoidance
AU - Kim, Seungtaek
AU - Han, Kyoungseok
AU - Choi, Seibum B.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - This study proposes a control structure based on imitation learning (IL) of nonlinear model predictive control (NMPC) for vehicle collision avoidance systems. An NMPC was employed to achieve maximum collision avoidance ability by integrated steering and braking, then later imitated by a deep neural network (DNN) to satisfy real-time capability. Previous studies that imitate NMPC have proven its control performance and computation efficiency. However, there were limitations in applying to vehicle collision avoidance systems. Despite its dangerous situation, data set for imitation should be obtained by experiments using the controlled plant, and weaknesses in handling model parameters were shown. Therefore, this article proposes a novel IL-based control structure suitable for collision avoidance systems that overcame such limitations by building a feedforward feedback structure so that the data set trained for imitation can be made offline and applying an input dimensionalization process to ensure robustness to parameter changes. CarSim-based human-vehicle interactive simulation experiments demonstrated that the proposed IL-based control structure had no issue applying the offline trained DNN in the simulation while showing robustness to parameter changes.
AB - This study proposes a control structure based on imitation learning (IL) of nonlinear model predictive control (NMPC) for vehicle collision avoidance systems. An NMPC was employed to achieve maximum collision avoidance ability by integrated steering and braking, then later imitated by a deep neural network (DNN) to satisfy real-time capability. Previous studies that imitate NMPC have proven its control performance and computation efficiency. However, there were limitations in applying to vehicle collision avoidance systems. Despite its dangerous situation, data set for imitation should be obtained by experiments using the controlled plant, and weaknesses in handling model parameters were shown. Therefore, this article proposes a novel IL-based control structure suitable for collision avoidance systems that overcame such limitations by building a feedforward feedback structure so that the data set trained for imitation can be made offline and applying an input dimensionalization process to ensure robustness to parameter changes. CarSim-based human-vehicle interactive simulation experiments demonstrated that the proposed IL-based control structure had no issue applying the offline trained DNN in the simulation while showing robustness to parameter changes.
KW - Advanced driving assistance systems
KW - Collision avoidance control
KW - Imitation learning
KW - Nonlinear model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85171524540&partnerID=8YFLogxK
U2 - 10.1109/TIV.2023.3309962
DO - 10.1109/TIV.2023.3309962
M3 - Article
AN - SCOPUS:85171524540
SN - 2379-8858
VL - 9
SP - 2908
EP - 2922
JO - IEEE Transactions on Intelligent Vehicles
JF - IEEE Transactions on Intelligent Vehicles
IS - 1
ER -