  A high-quality fresh high-definition (HD) map is vital in enhancing
transportation efficiency and safety in autonomous driving. Vehicle-based
crowdsourcing offers a promising approach for updating HD maps. However,
recruiting crowdsourcing vehicles involves making the challenging tradeoff
between the HD map freshness and recruitment costs. Existing studies on HD map
crowdsourcing often (1) prioritize maximizing spatial coverage and (2) overlook
the dual role of crowdsourcing vehicles in HD maps, as vehicles serve both as
contributors and customers of HD maps. This motivates us to propose the
Dual-Role Age of Information (AoI) based Incentive Mechanism (DRAIM) to address
these issues. % Specifically, we propose the trajectory age of information,
incorporating the expected AoI of the HD map and the trajectory, to quantify a
vehicle's HD map usage utility, which is freshness- and trajectory-dependent.
DRAIM aims to achieve the company's tradeoff between freshness and recruitment
costs.
