Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment

A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly popular for testing in automated driving is scenario-based testing. It is founded on the assumption that most time on the road can be seen as uncritical and in mainly critical situations contribute to the safety case. Metrics describing the criticality are necessary to automatically identify the critical situations and scenarios from measurement data. However, established metrics lack universality or a concept for metric combination. In this work, we present a multidimensional evaluation model that, based on conventional metrics, can evaluate scenes independently of the scene type. Furthermore, we present two new, further enhanced evaluation approaches, which can additionally serve as universal metrics. The metrics we introduce are then evaluated and discussed using real data from a motion dataset

Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes

An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur comparatively rarely. Accordingly, large amounts of data must be examined. A further issue is that recorded real-world traffic often consists of scenes with a high number of vehicles, and it can be challenging to determine which are the most critical vehicles regarding the safety of an ego vehicle. Therefore, we present the inverse universal traffic quality, a criticality metric for urban traffic independent of predefined adversary vehicles and vehicle constellations such as intersection trajectories or car-following scenarios. Our metric is universally applicable for different urban traffic situations, e.g., intersections or roundabouts, and can be adjusted to certain situations if needed. Additionally, in this paper, we evaluate the proposed metric and compares its result to other well-known criticality metrics of this field, such as time-to-collision or post-encroachment time.Comment: accepted at IEEE IV 202

Generation of Relativistic Electron Bunches with Arbitrary Current Distribution via Transverse-to-Longitudinal Phase Space Exchange

We propose a general method for tailoring the current distribution of relativistic electron bunches. The technique relies on a recently proposed method to exchange the longitudinal phase space emittance with one of the transverse emittances. The method consists of transversely shaping the bunch and then converting its transverse profile into a current profile via a transverse-to-longitudinal phase-space-exchange beamline. We show that it is possible to tailor the current profile to follow, in principle, any desired distributions. We demonstrate, via computer simulations, the application of the method to generate trains of microbunches with tunable spacing and linearly-ramped current profiles. We also briefly explore potential applications of the technique.Comment: 13 pages, 17 figure

Electronic Structure of the C₆₀ Fragment in Alkali- and Alkaline-earth-doped Fullerides

The electronic structure of the C60 fragment in alkali- and alkaline-earth-doped fullerides is studied theoretically. With increasing metal-to-C60 charge transfer (CT) the n electronic properties of the soccerball are changed. In the undoped solid and for not too high a concentration of doping atoms the hexagon-hexagon (6-6) bonds show sizeable double bond character while the hexagon-pentagon (6-5) bonds are essentially of single bond type. In systems with a high concentration of doping atoms this relative ordering is changed. Now the 6-5 bonds have partial double bond character and the 6-6 bonds are essentially single bonds. The high ability of the C60 unit to accomodate excess electrons prevents any sizeable weakening of the overall n bonding in systems with up to 12 excess electrons on the soccerball. A crystal orbital (CO) formalism on the basis of an INDO (intermediate neglect of differential overlap) Hamiltonian has been employed to derive solid state results for potassium- and barium-doped C60 fullerides. For both types of doping atoms an incomplete metal-to-C60 CT is predicted. In the potassium-doped fullerides the magnitude of the CT depends on the interstitial site of the dopant. The solid state data have been supplemented by INDO and ab initio calculations on molecular C60, C6-60 and C12-60. The calculated bondlength alternation in the neutral molecule is changed in C12-60 where the length of the 6-6 bonds exceeds the length of the 6-5 bonds. The geometries of the three molecular species have been optimized with a 3-21 G* basis. The theoretically derived modification of the C60 (π) electronic structure as a function of the electron count is explained microscopically in the framework of two quantum statistics accessible for π electronic ensembles. In the π ensemble of the C60 fragment so-called hard core bosonic properties are maximized where the Pauli antisymmetry principle has the character of a hidden variable only. Here the electronic degrees of freedom are attenuated only by the Pauli exclusion principle. This behaviour leads to the changes in the π electronic structure mentioned above

Footpaths: pedogenic and geomorphological long-term effects of human trampling

Footpaths are of the oldest and most widely distributed forms of human imprint on the landscape. These elongated features are the result of continuous usage of a certain route for walking, at time scales ranging from days to centuries or millennia. In this qualitative investigation, we take a holistic approach combining micromorphology (including voids analysis), chemical soil parameters (such as selective iron oxide dissolution), and remote sensing (spatial distribution and orientation of footpaths in the landscape) to evaluate the long-term residues and environmental effects resulting from the formation of footpaths. Our diverse case studies incorporate footpaths used for recreational and transport purposes in temperate and sub-humid climates from both recent and historical perspectives. A reduction of the large pores was observed down to 3 cm below current and historical surfaces compared to control areas without footpaths. The lower porosity subsequently hinders of the supply of oxygen and/or water into the sub-surface and encourages water stagnation on the compacted footpath surface. These processes result in higher amounts of pedogenic Fe oxides and, at times, macro-organic residues under footpaths and hindering of soil formation. As an additional result of compaction, surface runoff is promoted. The latter may either trigger the initiation of gullies directly downslope from footpaths or lead to incision of the footpaths themselves. Incised footpaths are more likely to occur when the footpath is oriented parallel to the stream network. Once an incised footpath is formed, it may reduce gully erosion susceptibility downslope as the incised footpath acts as a channel that decreases a footpath’s ‘overbank’ flow. With a better understanding of footpaths as landscape units we can (1) pose archaeological questions related to human environmental interaction, (2) assess carbon storage potential under footpaths and (3) use incised footpaths as possible measures against gully erosion

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry