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Autonomous Mobility-on-Demand Systems for Future Urban Mobility402 companies and traditional car manufacturers to aggressively pursue the “AMoD technol- ogy,” with activities ranging from the design of vehicles specifically tailored to AMoD operations [10, 11], to the expected launch by Google of a 100-vehicle AMoD pilot project within the next two years  (see Figure 19.1, right). Rapid advances in vehicle automation technologies coupled with the increased econom- ic and societal interest in MoD systems have fueled heated debates about the potential of AMoD systems and their economic and societal value. How many robotic vehicles would be needed to achieve a certain quality of service? What would be the cost of their operation? Would AMoD systems decrease congestion? In general, do AMoD systems represent an economically viable, sustainable, and societally-acceptable solution to the future of per- sonal urban mobility? 19.1.5 Chapter contributions To answer the above questions, one needs to first understand how to control AMoD sys- tems, which entails optimally routing in real-time potentially hundreds of thousands of robotic vehicles. Such routing process must take into account the spatiotemporal variabil- ity of mobility demand, together with a number of constraints such as congestion and battery recharging. This represents a networked, heterogeneous, stochastic decision pro- blem with uncertain information, hence complexity is at its heart. Within this context, the contribution of this chapter is threefold: 1. We present a spatial queueing-theoretical model for AMoD systems capturing salient dynamic and stochastic features of customer demand. A spatial queueing model entails an exogenous dynamical process that generates “transportation requests” at spatially- localized queues. Fig. 19.1 Left figure: A Car2Go vehicle used in a traditional (i.e., non-robotic) MoD system. Right figure: Self-driving vehicle that Google will use in a 100-vehicle AMoD pilot project within the next two years. Image credit: Car2Go and Google.
Autonomes Fahren Technische, rechtliche und gesellschaftliche Aspekte
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