Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

The role of driver models in testing highly-automated driving: a survey [Die Rolle von Fahrermodellen für das Testen hoch-automatisierter Fahrfunktionen: Eine Übersicht]

Eine besondere Herausforderung bei der Entwicklung hoch-automatisierter Fahrfunktionen ist die Validierung dieser Systeme. Ein möglicher Ansatz, den Validierungsaufwand zu meistern, ist der Einsatz von Simulationen. Hierbei können Simulatoren für verschiedene Aspekte des Validierungs-Prozesses verwendet werden. Um verwendbare Ergebnisse zu erhalten, müssen die einzelnen Aspekte der Realität dabei durch entsprechende Modelle abgebildet werden. Basierend auf einer Analyse verschiedener Anwendungsfälle für Simulationen, werden in diesem Beitrag verschiedene Klassen von Modellen für das menschliche Fahrverhalten hinsichtlich ihrer An-wendbarkeit im Rahmen der simulativen Absicherung evaluiert

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Autonomous mobility is emerging as a new mode of urban transportation for moving cargo and passengers. However, such fleet coordination schemes face significant challenges in scaling to accommodate fast-growing fleet sizes that vary in their operational range, capacity, and communication capabilities. We introduce the concept of partially observable advanced air mobility games to coordinate a fleet of aerial vehicle agents accounting for their heterogeneity and self-interest inherent to commercial mobility fleets. We propose a novel heterogeneous graph attention-based encoder-decoder (HetGAT Enc-Dec) neural network to construct a generalizable stochastic policy stemming from the inter- and intra-agent relations within the mobility system. We train our policy by leveraging deep multi-agent reinforcement learning, allowing decentralized decision-making for the agents using their local observations. Through extensive experimentation, we show that the fleets operating under the HetGAT Enc-Dec policy outperform other state-of-the-art graph neural network-based policies by achieving the highest fleet reward and fulfillment ratios in an on-demand mobility network.Comment: 12 pages, 12 figures, 3 table

Urban Public Transportation Planning with Endogenous Passenger Demand

An effective and efficient public transportation system is crucial to people\u27s mobility, economic production, and social activities. The Operations Research community has been studying transit system optimization for the past decades. With disruptions from the private sector, especially the parking operators, ride-sharing platforms, and micro-mobility services, new challenges and opportunities have emerged. This thesis contributes to investigating the interaction of the public transportation systems with significant private sector players considering endogenous passenger choice. To be more specific, this thesis aims to optimize public transportation systems considering the interaction with parking operators, competition and collaboration from ride-sharing platforms and micro-mobility platforms. Optimization models, algorithms and heuristic solution approaches are developed to design the transportation systems. Parking operator plays an important role in determining the passenger travel mode. The capacity and pricing decisions of parking and transit operators are investigated under a game-theoretic framework. A mixed-integer non-linear programming (MINLP) model is formulated to simulate the player\u27s strategy to maximize profits considering endogenous passenger mode choice. A three-step solution heuristic is developed to solve the large-scale MINLP problem. With emerging transportation modes like ride-sharing services and micro-mobility platforms, this thesis aims to co-optimize the integrated transportation system. To improve the mobility for residents in the transit desert regions, we co-optimize the public transit and ride-sharing services to provide a more environment-friendly and equitable system. Similarly, we design an integrated system of public transit and micro-mobility services to provide a more sustainable transportation system in the post-pandemic world