Using Cross-Validation to Design Trend Function in Kriging Surrogate Modeling

AIAA Journal ◽  
2014 ◽  
Vol 52 (10) ◽  
pp. 2313-2327 ◽  
Author(s):  
Haoquan Liang ◽  
Ming Zhu ◽  
Zhe Wu
Keyword(s):  
Cross Validation ◽  
Surrogate Modeling ◽  
Trend Function ◽  
Kriging Surrogate Modeling

Collision-Avoidance Reliability Analysis of Autonomous Vehicle Based on Adaptive Kriging Surrogate Modeling

2019 ◽  
Author(s):  
Yixuan Liu ◽  
Ying Zhao ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
Dimitrios Papadimitriou
Keyword(s):  
Reliability Analysis ◽  
Collision Avoidance ◽  
Surrogate Modeling ◽  
Uncertainty Modeling ◽  
Gaussian Mixture ◽  
Cruise Control ◽  
Traffic Conditions ◽  
Pilot Model ◽  
Kriging Surrogate Modeling ◽  
Adaptive Kriging

Abstract This paper presents a reliability analysis method for automated vehicles equipped with adaptive cruise control (ACC) and autonomous emergency braking (AEB) systems to avoid collision with an obstacle in front of the vehicle. The proposed approach consists of two main elements, namely uncertainty modeling of traffic conditions and model-based reliability analysis. In the uncertainty modeling step, a recently developed Gaussian mixture copula method is employed to accurately represent the uncertainty in the road traffic conditions using the real-world data, and to capture the complicated correlations between different variables. Based on the uncertainty modeling of traffic conditions, an adaptive Kriging surrogate modeling method with an active learning function is then used to efficiently and accurately evaluate the collision-avoidance reliability of an automated vehicle. The application of the proposed method to the Department of Transportation Safety Pilot Model Deployment database and an in-house built Advanced Driver Assist Systems with ACC and AEB controllers demonstrate the effectiveness of the proposed method in evaluating the collision-avoidance reliability.

Efficient Kriging surrogate modeling approach for system reliability analysis

2017 ◽  
Vol 31 (2) ◽  
pp. 143-160 ◽  
Author(s):  
Zhen Hu ◽  
Saideep Nannapaneni ◽  
Sankaran Mahadevan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Surrogate Model ◽  
Limit State ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Stopping Criterion ◽  
Limit States ◽  
System Reliability Analysis ◽  
Kriging Surrogate Modeling

AbstractCurrent limit state surrogate modeling methods for system reliability analysis usually build surrogate models for failure modes individually or build composite limit states. In practical engineering applications, multiple system responses may be obtained from a single setting of inputs. In such cases, building surrogate models individually will ignore the correlation between different system responses and building composite limit states may be computationally expensive because the nonlinearity of composite limit state is usually higher than individual limit states. This paper proposes a new efficient Kriging surrogate modeling approach for system reliability analysis by constructing composite Kriging surrogates through selection of Kriging surrogates constructed individually and Kriging surrogates built based on singular value decomposition. The resulting composite surrogate model will combine the advantages of both types of Kriging surrogate models and thus reduce the number of required training points. A new stopping criterion and a new surrogate model refinement strategy are proposed to further improve the efficiency of this approach. The surrogate models are refined adaptively with high accuracy near the active failure boundary until the proposed new stopping criterion is satisfied. Three numerical examples including a series, a parallel, and a combined system are used to demonstrate the effectiveness of the proposed method.

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