PRS-HDMC: An online participant recruitment scheme for vehicular crowdsensing-enabled HD map collection

Abstract

The emergence of vehicular crowdsensing (VCS) brings new opportunities and possibilities for the collection of High-Definition (HD) maps. Unlike traditional mapping methods, VCS leverages ubiquitous vehicles and on-board cameras to collect data inexpensively and efficiently. Though promising, such a data collection mode still faces an unsolved problem of ensuring the availability and integrity of collected map data. To address this issue, this paper proposes PRS-HDMC, a participant recruitment scheme for VCS-enabled HD map collection. Specifically, we first analyze the unique requirements that distinguish HD map acquisition from other sensing tasks. On this basis, a metric participant contribution is proposed to describe the accuracy and content richness of the collected photo. Combining this metric with participant reliability and task coverage, we establish a novel quality of service (QoS) quantification system and develop the participant recruitment optimization model. Then, an improved greedy algorithm is proposed to recruit the appropriate vehicular set online according to their real-time locations, thereby maximizing the system QoS of each task round. Finally, we conduct extensive simulations based on a synthetic dataset under different settings. Experimental results demonstrate that the proposed scheme can guarantee the availability and integrity of collected map data, and outperforms all benchmarks.