BMW Group

In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the better the motion cueing is rated, the lower the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.

Motivated by the emergence of rough surfaces in various areas of design, we address the computational design of triangle meshes with controlled roughness. Our focus lies on small levels of roughness. There, roughness or smoothness mainly arises through the local positioning of the mesh edges and faces with respect to the curvature behavior of the reference surface. The analysis of this interaction between curvature and roughness is simplified by a 2D dual diagram and its generation within so-called isotropic geometry, which may be seen as a structure-preserving simplification of Euclidean geometry. Isotropic dihedral angles of the mesh are close to the Euclidean angles and appear as Euclidean edge lengths in the dual diagram, which also serves as a tool for visualization and interactive local design. We present a computational framework that includes appearance-aware remeshing, optimization-based automatic roughening, and control of dihedral angles.

Automotive supply chains, representative of other industries, demonstrate how production in value creation systems relies on the interaction of various companies and different stages of value creation. Decisions about production capacities, which significantly determine the possible output quantity, are made already in the formation phase of supply chains. Unexpectedly high or low, or strongly fluctuating customer demand, pose the challenge for actors in the supply chain to continually reconcile needs and capacities. Collaborative capacity management allows for operational cross-company coordination in addition to contractual agreements. Customer needs are grouped into part families in digital tools and matched with the production capacities of suppliers. Customer and supplier can identify deviations in time through the shared view in the demand-capacity reconciliation, initiate measures, and increase the effectiveness of the supply chain. Scenarios can be planned together and then precisely such can be depicted in the operational planning systems that can be managed by the customer and supplier in terms of capacity. The supply chain is calmed and can achieve self-regulation. The exemplary presented digital tools for collaborative capacity management show how different user groups, granularity requirements, and IT integration possibilities can be addressed. Beyond scenario planning, such digital tools can be expanded with functions for raw material and volatility monitoring.

Neural networks have recently been employed as material discretizations within adjoint optimization frameworks for inverse problems and topology optimization. While advantageous regularization effects and better optima have been found for some inverse problems, the benefit for topology optimization has been limited—where the focus of investigations has been the compliance problem. We demonstrate how neural network material discretizations can, under certain conditions, find better local optima in more challenging optimization problems, where we here specifically consider acoustic topology optimization. The chances of identifying a better optimum can significantly be improved by running multiple partial optimizations with different neural network initializations. Furthermore, we show that the neural network material discretization’s advantage comes from the interplay with the Adam optimizer and emphasize its current limitations when competing with constrained and higher-order optimization techniques. At the moment, this discretization has only been shown to be beneficial for unconstrained first-order optimization.

Driver Monitoring Systems (DMS) enable Intelligent Vehicles to capture the in-cabin scene and help determine the driver’s level of attention and ability to take over. The task of driver gaze classification is the most important proxy for determining driver attention for DMS. In recent years, different approaches for driver gaze classification have been proposed. However, results and comparisons are barely valid. Different metrics are presented and datasets are kept private and are often collected under constraints that do not reflect realistic driving behavior. This work aims to provide an in-depth discussion and comparison of existing methods for driver gaze classification based on a dataset that is elaborately collected and constitutes realistic driving from real customers under no supervision. In particular, we evaluate the approaches with means of a nested leave-one-driver-out cross-validation on 20 different drivers. Moreover, we analyze the impact of the number of drivers in the training dataset on the generalization ability for unseen drivers and introduce a new error-based metric that allows us to assess how well a model is trained. Observations are that for end-to-end approaches, misclassifications between regions far apart occur more often and for all drivers, whereas the feature-engineered approach appears better qualified to build gaze estimators with limited data.

International industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.

An essential problem in robotic systems that are to be deployed in unstructured environments is the accurate and autonomous perception of the shapes of previously unseen objects. Existing methods for shape estimation or reconstruction have leveraged either visual or tactile interactive exploration techniques or have relied on comprehensive visual or tactile information acquired in an offline manner. In this work, a novel visuo-tactile interactive perception framework- ViTract, is introduced for shape estimation of unseen objects. Our framework estimates the shape of diverse objects robustly using low-dimensional, efficient, and generalizable shape primitives, which are superquadrics. The probabilistic formulation within our framework takes advantage of the complementary information provided by vision and tactile observations while accounting for associated noise. As part of our framework, we propose a novel modality-specific information gain to select the most informative and reliable exploratory action (using vision/tactile) to obtain iterative visuo/tactile information. Our real-robot experiments demonstrate superior and robust performance compared to state-of-the-art visuo-tactile-based shape estimation techniques.

An efficient optimization method is proposed for linear- quadratic optimal control problems with state and control constraints. We describe an active set solver that uses Riccati recursions to solve a sequence of equality-constrained subproblems. The main contribution is a homotopy method based on relaxing inequality constraints. This overcomes known shortcomings of Riccati active set solvers relating to their initialisation and their application to problems with time-varying model data. It can be used exclusively or in combination with established Riccati active set solvers. The efficiency is demonstrated in numerical examples against state-of-the-art quadratic programming solvers.

In mobile robotics, perception in uncontrolled environments like autonomous driving is a central hurdle. Existing active learning frameworks can help enhance perception by efficiently selecting data samples for labeling, but they are often constrained by the necessity of full data availability in data centers, hindering real-time, on-field adaptations. To address this, our work unveils a novel active learning formulation optimized for multi-robot settings. It harnesses the collaborative power of several robotic agents, considerably enhancing the data acquisition and synchronization processes. Experimental evidence indicates that our approach markedly surpasses traditional active learning frameworks by up to 2.5 percent points and 90% less data uploads, delivering new possibilities for advancements in the realms of mobile robotics and autonomous systems.