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With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions., Comment: 8 pages, 5 figures

In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.

A sample of Lambda's produced in 2 A*GeV Ni + Cu collisions has been obtained with the EOS Time Projection Chamber at the Bevalac. Low background in the invariant mass distribution allows for the unambiguous demonstration of Lambda directed flow. The transverse mass spectrum at mid-rapidity has the characteristic shoulder-arm shape of particles undergoing radial transverse expansion. A linear dependence of Lambda multiplicity on impact parameter is observed, from which a total Lambda + Sigma^0 production cross section of $112 +/- 24 mb is deduced. Detailed comparisons with the ARC and RVUU models are made., Comment: Revised version accepted for publication in Phys. Lett. B

The mean transverse kinetic energies of the fragments formed in the interaction of 1 A GeV Au+C have been determined. An energy balance argument indicates the presence of a collective energy which increases in magnitude with increasing multiplicity and accounts for nearly half of the measured mean transverse kinetic energy. The radial flow velocity associated with the collective energy yields estimates for the time required to expand to the freeze-out volume. Isentropic trajectories in the temperature-density plane are shown for the expansion and indicate that the system goes through the critical region at the same multiplicities as deduced from a statistical analysis. Here, the expansion time is approximately 70 fm/c.

A systematic study of energy spectra for light particles emitted at midrapidity from Au+Au collisions at E=0.25-1.15 A GeV reveals a significant non-thermal component consistent with a collective radial flow. This component is evaluated as a function of bombarding energy and event centrality. Comparisons to Quantum Molecular Dynamics (QMD) and Boltzmann-Uehling-Uhlenbeck (BUU) models are made for different equations of state., Comment: 10 pages of text and 4 figures (all ps files in a uuencoded package).