  Within a decade, almost every major auto company, along with fleet operators
such as Uber, have announced plans to put autonomous vehicles on the road. At
the same time, electric vehicles are quickly emerging as a next-generation
technology that is cost effective, in addition to offering the benefits of
reducing the carbon footprint. The combination of a centrally managed fleet of
driverless vehicles, along with the operating characteristics of electric
vehicles, is creating a transformative new technology that offers significant
cost savings with high service levels. This problem involves a dispatch problem
for assigning riders to cars, a surge pricing problem for deciding on the price
per trip and a planning problem for deciding on the fleet size. We use
approximate dynamic programming to develop high-quality operational dispatch
strategies to determine which car is best for a particular trip, when a car
should be recharged, and when it should be re-positioned to a different zone
which offers a higher density of trips. We prove that the value functions are
monotone in the battery and time dimensions and use hierarchical aggregation to
get better estimates of the value functions with a small number of
observations. Then, surge pricing is discussed using an adaptive learning
approach to decide on the price for each trip. Finally, we discuss the fleet
size problem which depends on the previous two problems.
