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

TL;DR

This paper introduces a novel incentive mechanism for HD map crowdsourcing that balances map freshness and recruitment costs by considering vehicles' dual roles as contributors and consumers, using a new AoI-based metric.

Contribution

It proposes the Dual-Role AoI-based Incentive Mechanism (DRAIM) that models vehicle utility considering both contribution and consumption, addressing a key gap in existing HD map crowdsourcing strategies.

Findings

DRAIM effectively balances map freshness and recruitment costs.

The AoI-based utility model captures dual roles of vehicles.

Simulation results demonstrate improved map update efficiency.

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.