  Autonomous vehicles (AVs) have the potential of reshaping the human mobility
in a wide variety of aspects. This paper focuses on a new possibility that the
AV owners have the option of "renting" their AVs to a company, which can use
these collected AVs to provide on-demand ride services without any drivers. We
call such a mobility market with AV renting options the "AV crowdsourcing
market". This paper establishes an aggregate equilibrium model with multiple
transport modes to analyze the AV crowdsourcing market. The modeling framework
can capture the customers' mode choices and AV owners' rental decisions with
the presence of traffic congestion. Then, we explore different scenarios that
either maximize the crowdsourcing platform's profit or maximize social welfare.
Gradient-based optimization algorithms are designed for solving the problems.
The results obtained by numerical examples reveal the welfare enhancement and
the strong profitability of the AV crowdsourcing service. However, when the
crowdsourcing scale is small, the crowdsourcing platform might not be
profitable. A second-best pricing scheme is able to avoid such undesirable
cases. The insights generated from the analyses provide guidance for
regulators, service providers and citizens to make future decisions regarding
the utilization of the AV crowdsourcing markets for serving the good of the
society.
