Vehicle accident reconstruction by a reduced order impact model

Vangi, Dario; Begani, Filippo; Spitzhüttl, Florian; Gulino, Michelangelo-Santo

doi:10.1016/j.forsciint.2019.02.042

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…18 For an exhaustive description regarding the RODM functioning, refer to previous work. 54 To comprehend which scenarios are included in the database, let us refer to Figure 2: hypothesizing the position of vehicle A always coincident with the origin of the axes, Table 1 reports the discretization of velocity for A and B, position and heading of B, intervention on steering and braking of A. These initial scenarios which constitute the database represent approximately 20% of the real fatal accident scenarios on European roads.…”

Section: Application Of the Adaptive Logicmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

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Section: Application Of the Adaptive Logicmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…41 Recently, a parametric and non-intensive reduced order model is developed for crash simulation that can introduce expert’s judgments directly into the construction of the reduced order model; however, this method is not possible with the closed-source crash analysis software, like LS-DYNA. 42 Due to the fact that these intrusive methods are hardly practical in existing design processes and the modifications of the solver are restricted by property and certification considerations, so far, the nonlinear MOR methods have not been successfully incorporated into highly nonlinear structural topology optimization under crash loads. Meanwhile, linear MOR techniques are mature in comparison to nonlinear MOR methods.…”

Section: The Basic Theory Of the Reslto Methodsmentioning

confidence: 99%

Efficient structure crash topology optimization strategy using a model order reduction method combined with equivalent static loads

Ren

Min

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this study, an efficient topology optimization method under crash loads is proposed by combining the equivalent static loads with a model order reduction method, which is referred as the reduced model–based equivalent static loads method for nonlinear dynamic response topology optimization method. Considering that some parts of the vehicle experience large nonlinear deformations, whereas others exhibit only small linear deformations in a vehicle crash scenario, the linear and nonlinear behavior parts are identified and the whole model of the complete structure is divided into nonlinear and linear sub-models. At each cycle, the model order reduction method is used in the linear sub-model during crash analysis to solve the low-density-elements-induced mesh distortion problem and accelerate this process. In the linear static topology optimization, the nonlinear sub-model that was initially used to describe the nonlinear behavior part is linearized by the equivalent static loads method and then reduced by the Guyan reduction method. Then, the reduced equivalent static load model is assembled into the linear sub-model that is defined as the design space to formulate a reduced topology optimization model of the complete structure and the reduced equivalent static loads that only act on master degrees of freedom are calculated. Finally, the linear static topology optimization is performed based on the reduced topology optimization model with the reduced equivalent static loads to enhance the efficiency and improve the numerical stability. The process is repeated until the convergence criterion is satisfied. The effectiveness of the proposed method is demonstrated by investing a numerical example. The results show that the proposed method provides a feasible strategy for the topology optimization under crash loads, which can effectively improve the numerical stability and convergence.

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“…EES parameter along with methods that describe dissipation of energy on after crash car movement is used to determine the vehicle velocity right before the impact. Determination of pre-crash velocity is a standard procedure in car accident reconstruction [3,6,34] and it is necessary to define it as precisely as possible.…”

Section: Introductionmentioning

confidence: 99%

Precrash vehicle velocity determination using inverse system and tensor product of Legendre polynomials - subcompact car class

Gosławski

Madziara

Droś

et al. 2022

The Archives of Automotive Engineering – Archiwum Motoryzacji

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Presented paper discusses new approach to EES parameter determination in frontal car crash based on the tensor product of Legendre polynomials. In this paper Subcompact Car Class was analyzed using that method. Data that was used to perform analyses introduced in this paper was taken from National Highway Traffic Safety Administration (NHTSA) database. Such database consists of considerate number of test cases along with various information including vehicle mass, crash velocity, chassis deformation etc. New approach to the problem of determining the EES parameter was necessary due to the low accuracy of the currently used methods. Linear models used up till now for accident reconstruction show significant error as the relationship between mass, velocity and deformation cannot be well approximated with a flat plane. Proposed model produces better results, because of the nonlinear dependence of said parameters. This paper also includes a calculation example presenting a comparison of linear and nonlinear method on an actual crash test.

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Vehicle accident reconstruction by a reduced order impact model

Cited by 17 publications

References 19 publications

Adaptive intervention logic for automated driving systems based on injury risk minimization

Adaptive intervention logic for automated driving systems based on injury risk minimization

Efficient structure crash topology optimization strategy using a model order reduction method combined with equivalent static loads

Precrash vehicle velocity determination using inverse system and tensor product of Legendre polynomials - subcompact car class

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