Showing papers by "Herman Van der Auweraer published in 2022"
TL;DR: In this paper , the authors present key building blocks such as model order reduction, real-time models, state estimation and co-simulation are reviewed, and a number of characteristic use cases are presented.
Abstract: While the digital twin has become an intrinsic part of the product creation process, its true power lies in the connectivity of the digital representation with its physical counterpart. Data acquired on the physical asset can validate, update and enrich the digital twin. The knowledge contained in the digital representation brings value to the physical asset itself. When a dedicated encapsulation is extracted from the digital twin to model a specific set of behaviors in a specific context, delivering a stand-alone executable representation, such instantiated and self-contained model is referred to as an Executable Digital Twin. In this contribution, key building blocks such as model order reduction, real-time models, state estimation and co-simulation are reviewed, and a number of characteristic use cases are presented. These include virtual sensing, hybrid testing and hardware-in-the loop, model-based control and model-based diagnostics.
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TL;DR: This paper presents a virtual reality based ADAS testing framework that enhances human perception evaluation and introduces a large and high-quality 3D model of the Munich city in Germany that is integrated into an ADAS framework for testing and validating ADAS functionalities and perceived comfort performance.
Abstract: —As the development of autonomous driving (AD) and advanced driver assistance systems (ADAS) progresses, the relevance of the comfort of users is gaining increasing interest. It becomes significant to test and validate perceived comfort performance from the early phase of system development before driving on roads. Most of the present ADAS test procedures are not efficient in performing such comfort evaluation. One of the main challenges is to integrate high-quality, realistic and predictable virtual traffic scenarios into an ADAS testing framework that has physics-based sensors capable of sensing the virtual environment. In this paper, we present our development of a virtual reality based ADAS testing framework that enhances human perception evaluation. The main contribution relies on three aspects. First, we introduce our development of a large and high-quality (realism, structure, texture) 3D traffic model of the Munich city in Germany. Second, we optimize the 3D model for virtual reality purpose, and real-time capable for human-in-the-loop ADAS testing. Finally, the model is then integrated into an ADAS framework for testing and validating ADAS functionalities and perceived comfort performance. The developed framework components are presented with illustra- tive examples.
TL;DR: In this paper , a convex safety envelope containing the trajectory is constructed by using Legendre polynomials to span the solution over an orthogonal basis using the Bernstein approximation of a polynomial's extrema.
Abstract: Orthogonal collocation methods are direct approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it more favorable for embedded and real-time NMPC applications. However, collocation approaches lack a guarantee about the safety of the resulting trajectory as inequality constraints are only set on a finite number of collocation points. In this paper we propose a method to efficiently create a convex safety envelope containing the trajectory such that the solution fully satisfies the OCP constraints. We make use of Bernstein approximations of a polynomial's extrema and span the solution over an orthogonal basis using Legendre polynomials. The tightness of the safety envelope estimation, high accuracy in solving the underlying differential equations, fast rate of convergence and little conservatism are properties of the presented approach making it a suitable method for safe real-time NMPC deployment. We show that our method has comparable computational performance to pseudospectral approaches and can accurately approximate the original OCP up to 9 times more quickly than standard multiple-shooting method in autonomous driving applications, without adding complexity to the formulation.