Structural constraints on Gondwana breakup along the East African margin

[no abstract

Interdisziplinäres Forschungskolloquium Protestbewegungen

Die Protestbewegung der 1960er und 1970er Jahre werden nicht mehr allein in Erinnerungsliteratur und Feuilletons aufgearbeitet, sondern sind im vergangenen Jahrzehnt auch zum Gegenstand der historischen Wissenschaften geworden. Dies zeigt sich an der gewachsenen Zahl von Neuerscheinungen und Tagungen zum Thema. Dabei ist bemerkenswert, dass die Geschichtsschreibung von ehemaligen Aktivisten und Betroffenen immer mehr auf Wissenschaftlerinnen und Wissenschaftler übergeht, die keine Zeitzeugen waren. Von diesem Generationenwechsel ist ein neuer Blick auf diese sozialen Bewegungen zu erwarten. So arbeiten gegenwärtig zahlreiche Nachwuchswissenschaftlerinnen und -wissenschaftler aus Linguistik, Geschichtswissenschaft, Politologie, Soziologie und Literaturwissenschaft an Forschungsprojekten, die bislang vernachlässigte Aspekte der 1960er Jahre in den Blick nehmen. Das "Interdisziplinäre Forschungskolloquium Protestbewegungen" bietet Nachwuchswissenschaftlerinnen und -wissenschaftlern ein Forum, in dem sie neue Zugänge zur Historisierung und wissenschaftlichen Behandlung der Studentenbewegung diskutieren könne

Utopia in Practise: The Discovery of Performativity in Sixties' Protest, Arts and Sciences

no abstrac

Utopia in Practise: The Discovery of Performativity in Sixties' Protest, Arts and Sciences

no abstrac

Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving

Reinforcement learning has received high research interest for developing planning approaches in automated driving. Most prior works consider the end-to-end planning task that yields direct control commands and rarely deploy their algorithm to real vehicles. In this work, we propose a method to employ a trained deep reinforcement learning policy for dedicated high-level behavior planning. By populating an abstract objective interface, established motion planning algorithms can be leveraged, which derive smooth and drivable trajectories. Given the current environment model, we propose to use a built-in simulator to predict the traffic scene for a given horizon into the future. The behavior of automated vehicles in mixed traffic is determined by querying the learned policy. To the best of our knowledge, this work is the first to apply deep reinforcement learning in this manner, and as such lacks a state-of-the-art benchmark. Thus, we validate the proposed approach by comparing an idealistic single-shot plan with cyclic replanning through the learned policy. Experiments with a real testing vehicle on proving grounds demonstrate the potential of our approach to shrink the simulation to real world gap of deep reinforcement learning based planning approaches. Additional simulative analyses reveal that more complex multi-agent maneuvers can be managed by employing the cycling replanning approach.Comment: 8 pages, 10 figures, to be published in 34th IEEE Intelligent Vehicles Symposium (IV

Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks

Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple vehicles. Most existing approaches to automatic intersection management, however, only consider fully automated traffic. In practice, mixed traffic, i.e., the simultaneous road usage by automated and human-driven vehicles, will be prevalent. The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning. We build upon our previous works that showed the applicability of such machine learning methods to fully automated traffic. The scene representation is extended for mixed traffic and considers uncertainty in the human drivers' intentions. In the simulation-based evaluation, we model measurement uncertainties through noise processes that are tuned using real-world data. The paper evaluates the proposed method against an enhanced first in - first out scheme, our baseline for mixed traffic management. With increasing share of automated vehicles, the learned planner significantly increases the vehicle throughput and reduces the delay due to interaction. Non-automated vehicles benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV), updated to accepted versio