Title:	Crowdsourced mapping for autonomous driving
DNr:	Berzelius-2025-307
Project Type:	LiU Berzelius
Principal Investigator:	Gustaf Hendeby <gustaf.hendeby@liu.se>
Affiliation:	Linköpings universitet
Duration:	2025-09-11 – 2026-04-01
Classification:	10207
Keywords:

Abstract

This is a WASP industrial postdoc project with Automatic Control, Dept. Electrical Engineering, LiU and Zenseact. Within the research group, we have one postdoc Yiping Xie and 1-2 master students either from Zenseact or LiU in 2026 Spring and 2027 Spring . The project is to create high-definition (HD) maps for autonomous driving in a crowdsourced manner. Autonomous driving has the potential to revolutionize transportation by making it safer, more efficient, and environmentally friendly. In realizing this vision, one of the key challenges is the construction of HD maps, which are roadmaps with centimeter accuracy and high fidelity. HD maps enable self-driving vehicles to perform precise localization and make informed decisions about how to navigate through complex environments. However, the creation and maintenance of HD maps typically require the use of expensive mapping systems with heavy manual modifications. An economically efficient and highly scalable solution to address this problem is to use crowdsourced mapping by leveraging massively available crowdsourcing devices.