Difference between revisions of "Zenseact Scalable Mapping"
(Created page with "{{StudentProjectTemplate |Summary=Scalable mapping through crowd sourcing |TimeFrame=Fall 2022 |Supervisor=TBD |Level=Master |Status=Open }} This thesis aims to develop a scal...") |
(No difference)
|
Latest revision as of 20:30, 10 October 2022
Title | Zenseact Scalable Mapping |
---|---|
Summary | Scalable mapping through crowd sourcing |
Keywords | |
TimeFrame | Fall 2022 |
References | |
Prerequisites | |
Author | |
Supervisor | TBD |
Level | Master |
Status | Open |
This thesis aims to develop a scalable method to crowdsource certain elements of an HD map by collecting sensor data from a fleet of vehicles and fusing the information into a single coherent road model. The focus is to describe the geometry of lane markers, traffic signs, and natural driving profiles on a large scale, or at least from a handful of drives on the ring roads of Göteborg.
The topic is proposed by Zenseact. More details are available here:
https://career.zenseact.com/jobs/2113621-master-thesis-scalable-mapping-through-crowd-sourcing
About Zenseact: We develop the complete software stack for ADAS and AD, from sensing to actuation. Our focus is to build a single cutting-edge software platform in order to serve various levels of autonomy and offer unequaled scalability at the same time. We operate in Sweden and China.