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

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.

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

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4042974 ◽

2019 ◽

Vol 5 (2) ◽

Cited By ~ 3

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 ◽

Automated Vehicle ◽

Traffic Conditions ◽

Pilot Model ◽

Adaptive Kriging

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 (GMC) 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.

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Efficient Kriging surrogate modeling approach for system reliability analysis

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006041700004x ◽

2017 ◽

Vol 31 (2) ◽

pp. 143-160 ◽

Cited By ~ 6

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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Smart Driving System for Improving Traffic Flow

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0174 ◽

2017 ◽

Vol 7 (7) ◽

pp. 236

Author(s):

Rajesh Kumar Gupta ◽

L. N. Padhy ◽

Sanjay Kumar Padhi

Keyword(s):

Traffic Flow ◽

Traffic Congestion ◽

Urban Areas ◽

Adaptive Cruise Control ◽

Human Perception ◽

Cruise Control ◽

Driving System ◽

V2v Communication ◽

Traffic Conditions ◽

Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay. For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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Uncertainty modeling in reliability analysis of floating wind turbine support structures

Renewable Energy ◽

10.1016/j.renene.2020.10.068 ◽

2021 ◽

Vol 165 ◽

pp. 88-108

Author(s):

Salem Okpokparoro ◽

Srinivas Sriramula

Keyword(s):

Wind Turbine ◽

Reliability Analysis ◽

Uncertainty Modeling ◽

Support Structures ◽

Floating Wind Turbine

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Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121999382 ◽

2021 ◽

pp. 036119812199938

Author(s):

Li Zhao ◽

Laurence Rilett ◽

Mm Shakiul Haque

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Gaussian Mixture ◽

Vehicle Body ◽

Lane Change ◽

Lane Changing ◽

Car Following ◽

Pilot Model ◽

Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

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MPC based collaborative adaptive cruise control with rear end collision avoidance

2014 IEEE Intelligent Vehicles Symposium Proceedings ◽

10.1109/ivs.2014.6856559 ◽

2014 ◽

Cited By ~ 10

Author(s):

Feyyaz Emre Sancar ◽

Baris Fidan ◽

Jan P. Huissoon ◽

Steven L. Waslander

Keyword(s):

Collision Avoidance ◽

Adaptive Cruise Control ◽

Cruise Control

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Assessing the Impact of a Cooperative Merging System on Highway Traffic Using a Microscopic Flow Simulator

Volume 12: Transportation Systems ◽

10.1115/imece2014-39850 ◽

2014 ◽

Cited By ~ 8

Author(s):

Ioannis A. Ntousakis ◽

Kallirroi Porfyri ◽

Ioannis K. Nikolos ◽

Markos Papageorgiou

Keyword(s):

Road Traffic ◽

Highway Traffic ◽

Cruise Control ◽

V2v Communication ◽

Traffic Conditions ◽

Cooperative Adaptive Cruise Control ◽

Low Efficiency ◽

Critical Traffic ◽

Vehicle Technologies ◽

The Impact

Vehicle merging on highways has always been an important aspect, which directly affects the capacity of the highway. Under critical traffic conditions, the merging of main road traffic and on-ramp traffic is known to trigger speed breakdown and congestion. Additionally, merging is one of the most stressful tasks for the driver, since it requires a synchronized set of observations and actions. Consequently, drivers often perform merging maneuvers with low efficiency. Emerging vehicle technologies, such as cooperative adaptive cruise control and/or merging-assistance systems, are expected to enable the so-called “cooperative merging”. The purpose of this work is to propose a cooperative merging system and evaluate its performance and its impact on highway capacity. The modeling and simulation of the proposed methodology is performed within the framework of a microscopic traffic simulator. The proposed model allows for the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, which enables the effective handling of the available gaps between vehicles. Different cases are examined through simulations, in order to assess the impact of the system on traffic flow, under various traffic conditions. Useful conclusions are derived from the simulation results, which can form the basis for more complex merging algorithms and/or strategies that adapt to traffic conditions.

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