Optimizing coverage of simulated driving scenarios for the autonomous vehicle

International audienceSelf-driving cars and advanced driver-assistance systems are perceived as a game-changer in the future of road transportation. However, their validation is mandatory before industrialization; testing every component should be assessed intensively in order to mitigate potential failures and avoid unwanted problems on the road. In order to cover all possible scenarios, virtual simulations are used to complement real-test driving and aid in the validation process. This paper focuses on the validation of the command law during realistic virtual simulations. Its aim is to detect the maximum amount of failures while exploring the input search space of the scenarios. A key industrial restriction, however, is to launch simulations as little as possible in order to minimize computing power needed. Thus, a reduced model based on a random forest model helps in decreasing the number of simulations launched. It accompanies the algorithm in detecting the maximum amount of faulty scenarios everywhere in the search space. The methodology is tested on a tracking vehicle use case, which produces highly effective results

Learning data-driven reduced elastic and inelastic models of spot-welded patches

Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors

Optimizing coverage of simulated driving scenarios for the autonomous vehicle

International audienceSelf-driving cars and advanced driver-assistance systems are perceived as a game-changer in the future of road transportation. However, their validation is mandatory before industrialization; testing every component should be assessed intensively in order to mitigate potential failures and avoid unwanted problems on the road. In order to cover all possible scenarios, virtual simulations are used to complement real-test driving and aid in the validation process. This paper focuses on the validation of the command law during realistic virtual simulations. Its aim is to detect the maximum amount of failures while exploring the input search space of the scenarios. A key industrial restriction, however, is to launch simulations as little as possible in order to minimize computing power needed. Thus, a reduced model based on a random forest model helps in decreasing the number of simulations launched. It accompanies the algorithm in detecting the maximum amount of faulty scenarios everywhere in the search space. The methodology is tested on a tracking vehicle use case, which produces highly effective results

Encadrement de la fiabilité du véhicule autonome pour guider les tests de validation

International audienceThis paper deals with the problem of the reliability certification for an autonomous driving (AD) system and provides some insights for assessing the system reliability in a minimum testing period. Preliminary works propose a decomposition of the system reliability as a function of known and unknown driving scenarios. Here, the method focuses on the reliability assessment when the driving situations are assumed to be identified and partially known. A Bayesian framework is designed for mixing numerical simulation based and field data obtained during open road tests. A simplified real-world example has been designed to illustrate the efficiency of the methodology in terms of speed of convergence for the validation process.Ce document décrit une méthode aidant à la certification de la fiabilité du véhicule autonome. Il fournit de premières idées pour évaluer la fiabilité du système en minimisant la durée des essais de validation. Un premier travail propose une décomposition des impacts des scénarios de conduite connus et inconnus sur la fiabilité. L'étude présentée est centrée sur l'évaluation de la fiabilité dans les scénarios de conduite identifiés mais dont l'influence sur la fiabilité est partiellement connue. Une méthode bayésienne est construite pour intégrer différentes sources d'information. Un exemple académique simplifié est construit pour analyser l'efficacité d'une telle méthode en termes de vitesse de convergence pour le processus de validation

A parametric and non-intrusive reduced order model of car crash simulation

International audienceIndustrials have an intensive use of numerical simulations in order to avoid physical testing and to speed up the design stages of their products. The numerical testing is indeed quicker to set-up, less expensive, and supplies a lot of information about the system under study. Moreover, it can be much closer to the physical tests as the computation power increases. Despite the rise of this power, time consuming simulations remain challenging to be used in design process, especially in an optimization study. Crash simulations belong to this category. These rapid dynamic computations are used by RENAULT during the sizing of the vehicle structure in order to ensure that it meets specifications set up to reach safety criteria in case of accidents. They are completed using finite element software such as VPS (Virtual Performance Solver) developed by ESI group that will be used in this study. For car manufacturers, the goal of the optimization study is to minimize the mass of the vehicle (and thus its consumption) by modifying the thicknesses of some parts (from 20 to 100 variables). Industrials such as RENAULT currently perform optimization studies based on numerical design of experiments. The number of computations required by this technique is from 3 to 10 times the number of variables. This is too much in order to be intensively used in a design process.In order to decrease the time-to-market and to explore alternative technical solutions, we explore the potential of using a parametrized reduced order model in the optimization studies. The parametrized reduced order model gives an estimation of the high-fidelity result for a new set of parameters without using the solver, by analysing the existing results of previous computations with various sets of parameters. The developed reduced order model is called ReCUR. It is partly based on a CUR approach embedded in a regression analysis. The regression statistical model uses the data of a few calculations made with the solver. Other tools such as clustering and linear programming are used to get the regression analysis more efficient.It is hoped to drastically reduce the number of required simulations of a standard optimization study. In this paper, the construction of the reduced order model will be presented. Then, the relevancy of using the reduced order model into a design process will be exhibited through the treatment of two industrial test-cases. Some improvements of the method as well as several potential uses will then be outlined. The applications will highlights the promising power of the method to shorten design process using optimisation and long-run simulations

Functional Parametric Elasto-Dynamics for Efficient Multicomponent Design

In industrial settings, engineering products are often divided into separate components for detailed conception. They often require iterative corrections between different designers/teams to optimize the final product with all components assembled into a system. This article proposes a surrogate modeling approach with functional descriptions of parts in the model and aims to accelerate the design and optimization phase in real projects. The approach is applied to a vibration problem of a two-component plate structure, where the model estimates the dynamic behavior of the assembled system when only the properties of each individual part are available. A database is built using high-fidelity numerical simulations, and neural-network-based regressions provide reliable predictions on unseen data

A parametric and non-intrusive reduced order model of car crash simulation

International audienceIndustrials have an intensive use of numerical simulations in order to avoid physical testing and to speed up the design stages of their products. The numerical testing is indeed quicker to set-up, less expensive, and supplies a lot of information about the system under study. Moreover, it can be much closer to the physical tests as the computation power increases. Despite the rise of this power, time consuming simulations remain challenging to be used in design process, especially in an optimization study. Crash simulations belong to this category. These rapid dynamic computations are used by RENAULT during the sizing of the vehicle structure in order to ensure that it meets specifications set up to reach safety criteria in case of accidents. They are completed using finite element software such as VPS (Virtual Performance Solver) developed by ESI group that will be used in this study. For car manufacturers, the goal of the optimization study is to minimize the mass of the vehicle (and thus its consumption) by modifying the thicknesses of some parts (from 20 to 100 variables). Industrials such as RENAULT currently perform optimization studies based on numerical design of experiments. The number of computations required by this technique is from 3 to 10 times the number of variables. This is too much in order to be intensively used in a design process.In order to decrease the time-to-market and to explore alternative technical solutions, we explore the potential of using a parametrized reduced order model in the optimization studies. The parametrized reduced order model gives an estimation of the high-fidelity result for a new set of parameters without using the solver, by analysing the existing results of previous computations with various sets of parameters. The developed reduced order model is called ReCUR. It is partly based on a CUR approach embedded in a regression analysis. The regression statistical model uses the data of a few calculations made with the solver. Other tools such as clustering and linear programming are used to get the regression analysis more efficient.It is hoped to drastically reduce the number of required simulations of a standard optimization study. In this paper, the construction of the reduced order model will be presented. Then, the relevancy of using the reduced order model into a design process will be exhibited through the treatment of two industrial test-cases. Some improvements of the method as well as several potential uses will then be outlined. The applications will highlights the promising power of the method to shorten design process using optimisation and long-run simulations