Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

The Flight Authorization of the Automatized VTOL UAS for Meteorological Sensor Measurement

Considering the exponential growth of drone technology and its expected economic effect [1], the Hungarian Government called on the industrial and academic sectors for an innovative cooperation in the field of intelligent autonomous mobility. The tender was launched by the Institute of Transport Sciences Non-profit Ltd. (ITS) last December, and among the winners was the joint tender of the Mould Tech Systems Ltd., the Budapest University of Technology and Economics and the University of Public Service (UPS). The common research is focusing on the implementation of a special Vertical Take-Off and Landing Unmanned Aircraft System (VTOL UAS)-based meteorological support system which is to measure the lower parts of the atmosphere within the planetary boundary layer (PBL). The researchers of UPS are examining the legal issues of operational conditions, flight authorization, flight safety aspects concerning the identification of potential dangers, and the airworthiness issues and will formulate their recommendations. In this paper the authors introduce the numbers of issues that have to be solved in favour of the safe and legal VTOL UAS operation.

Analysis and Control of Autonomous Mobility-on-Demand Systems

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles

The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.

»Nahtlose Autonomie«

In diesem Artikel befasst sich Sam Hind mit der "nahtlosen Autonomie" eines autonomen Fahrzeugprojekts von Nissan, bei dem die Entscheidungsfindung auf menschliche Mitarbeiter, sogenannte "Mobilitätsmanager", übertragen wird. Anstatt sich eine vollständig autonome Zukunft vorzustellen, behaupte ich, dass das Projekt "Seamless Autonomous Mobility" (SAM) darauf abzielt, ferngesteuerte Eingriffe zu normalisieren, die sich kategorisch von anderen Arten der KI-bezogenen "Mikroarbeit" unterscheiden.