Dual-Role AoI-based Incentive Mechanism for HD map Crowdsourcing

TL;DR

The paper addresses the challenge of maintaining fresh HD maps via vehicle crowdsourcing while controlling recruitment costs. It proposes DRAIM, a dual-role incentive mechanism built as a two-stage Stackelberg game that models the company-vehicle interaction with $AoI$-driven vehicle utilities and potential negative rewards. The analysis reveals how optimal rewards and participation levels vary with the number of vehicles and their cost heterogeneity, highlighting conditions under which the approach reduces overall costs while preserving map freshness. The work offers a practical framework for cost-efficient, fresh HD map maintenance in dynamic mobility environments.

Abstract

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.