HD Map Update for Autonomous Driving With Crowdsourced Data

Abstract

Current self-driving cars can perform precise localization and generate collision-free trajectories using high definition (HD) maps which provide accurate road information. Therefore, keeping HD maps up to date is important for safe autonomous driving. In general, automotive HD maps are built by the use of expensive mapping systems. In addition, a lot of manual modifications are required in many cases. The conventional HD mapping cannot be frequently carried out due to the high cost. In this letter, we used a large amount of road data collected by crowdsourcing devices. Crowdsourcing devices consist of low-cost sensors. The devices are mounted on repeatedly traveling vehicles such as buses. Although collected data shows high uncertainty and low accuracy, a large amount of data can be obtained in a short time with low expense. We present a solution that keeps HD maps up to date by using crowdsourced data. The developed solution concentrates on landmark information among crowdsourced data and HD maps. By using uncertainty information, we chose reliable observations for map updating. Observation learner algorithms were carefully designed under the consideration of differences between discrete and continuous landmarks. The triggering condition for the map update can be adjusted by the proposed update mode selection strategy. The proposed map updating scheme has been experimentally verified by the use of crowdsourced data collected from real road environments.