TY - GEN
T1 - Hierarchical Motion Planning and Offline Robust Model Predictive Control for Autonomous Vehicles
AU - Nguyen, Hung Duy
AU - Vu, Minh Nhat
AU - Nguyen, Ngoc Nam
AU - Han, Kyoungseok
PY - 2024
Y1 - 2024
N2 - Driving vehicles in complex scenarios under harshconditions is the biggest challenge for autonomous vehicles(AVs). To address this issue, we propose hierarchical motionplanning and robust control strategy using the front activesteering system in complex scenarios with various slipperyroad adhesion coefficients while considering vehicle uncertainparameters. Behaviors of human vehicles (HVs) are consideredand modeled in the form of a car-following model via theIntelligent Driver Model (IDM). Then, in the upper layer,the motion planner first generates an optimal trajectory byusing the artificial potential field (APF) algorithm to formulateany surrounding objects, e.g., road marks, boundaries, andstatic/dynamic obstacles. To track the generated optimal trajectory,in the lower layer, an offline-constrained output feedbackrobust model predictive control (RMPC) is employed for thelinear parameter varying (LPV) system by applying linearmatrix inequality (LMI) optimization method that ensures therobustness against the model parameter uncertainties. Furthermore,by augmenting the system model, our proposed approach,called offline RMPC, achieves outstanding efficiency comparedto three existing RMPC approaches, e.g., offset-offline RMPC,online RMPC, and offline RMPC without an augmented model(offline RMPC w/o AM), in both improving computing timeand reducing input vibrations.
AB - Driving vehicles in complex scenarios under harshconditions is the biggest challenge for autonomous vehicles(AVs). To address this issue, we propose hierarchical motionplanning and robust control strategy using the front activesteering system in complex scenarios with various slipperyroad adhesion coefficients while considering vehicle uncertainparameters. Behaviors of human vehicles (HVs) are consideredand modeled in the form of a car-following model via theIntelligent Driver Model (IDM). Then, in the upper layer,the motion planner first generates an optimal trajectory byusing the artificial potential field (APF) algorithm to formulateany surrounding objects, e.g., road marks, boundaries, andstatic/dynamic obstacles. To track the generated optimal trajectory,in the lower layer, an offline-constrained output feedbackrobust model predictive control (RMPC) is employed for thelinear parameter varying (LPV) system by applying linearmatrix inequality (LMI) optimization method that ensures therobustness against the model parameter uncertainties. Furthermore,by augmenting the system model, our proposed approach,called offline RMPC, achieves outstanding efficiency comparedto three existing RMPC approaches, e.g., offset-offline RMPC,online RMPC, and offline RMPC without an augmented model(offline RMPC w/o AM), in both improving computing timeand reducing input vibrations.
U2 - 10.23919/ACC60939.2024.10644537
DO - 10.23919/ACC60939.2024.10644537
M3 - Conference Proceedings with Oral Presentation
T3 - 2024 American Control Conference (ACC)
SP - 4936
EP - 4941
BT - 2024 American Control Conference (ACC)
T2 - 2024 American Control Conference (ACC)
Y2 - 10 July 2024 through 12 July 2024
ER -