Simulation-Based Mission Mobility Reliability Analysis of Off-Road Ground Vehicles

This paper develops a simulation-based mission mobility reliability (MMR) analysis framework to account for uncertainty in mobility prediction of off-road ground vehicles in mission planning. A concept of MMR is first proposed to quantify reliability of a mission path which passes through different types of soils. A single-loop Kriging surrogate modeling method is then employed to overcome the computational challenge in MMR assessment caused by expensive mobility simulations. Built upon the surrogate model-based MMR analysis, a dynamic updating scheme is proposed to update the MMR estimation using online mobility data, during the course of a specific mission and for a particular vehicle. The online dynamic updating of MMR allows us for effective and dynamic decision-making during the mission phase, thus proactively avoid rare events of immobility during the mission. A case study demonstrates the efficacy of the proposed MMR analysis and updating framework.

Mission Mobility Reliability Analysis of Off-Road Ground Vehicles

The NATO Reference Mobility Model (NRMM) has been developed to predict the mobility of off-road ground vehicles based on modeling and simulation (M&S). Due to various uncertainty sources in the M&S, uncertainty is inherent in the vehicle mobility. Aims to account for the uncertainty in the mobility prediction in mission planning, this paper develops a simulation-based mission mobility reliability analysis framework for off-road ground vehicles. A concept of mission mobility reliability (MMR) is first proposed to quantify the reliability of a mission path which passes through different types of soils. A single-loop Kriging surrogate modeling method is then employed to overcome the challenge in the mission mobility reliability assessment caused by the computationally expensive mobility simulation. Built upon the surrogate model-based mission mobility reliability analysis, a dynamic updating scheme is proposed to update the MMR estimation based on the on-line mobility data, during the course of a specific mission and for a particular vehicle. The online dynamic updating of MMR allows for effective and dynamic decision making during the mission phase. A case study is used to demonstrate the effectiveness of the proposed MMR analysis and updating framework.

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

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

Current 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.