  Maps are essential for diverse applications, such as vehicle navigation and
autonomous robotics. Both require spatial models for effective route planning
and localization. This paper addresses the challenge of road graph construction
for autonomous vehicles. Despite recent advances, creating a road graph remains
labor-intensive and has yet to achieve full automation. The goal of this paper
is to generate such graphs automatically and accurately. Modern cars are
equipped with onboard sensors used for today's advanced driver assistance
systems like lane keeping. We propose using global navigation satellite system
(GNSS) traces and basic image data acquired from these standard sensors in
consumer vehicles to estimate road-level maps with minimal effort. We exploit
the spatial information in the data by framing the problem as a road centerline
semantic segmentation task using a convolutional neural network. We also
utilize the data's time series nature to refine the neural network's output by
using map matching. We implemented and evaluated our method using a fleet of
real consumer vehicles, only using the deployed onboard sensors. Our evaluation
demonstrates that our approach not only matches existing methods on simpler
road configurations but also significantly outperforms them on more complex
road geometries and topologies. This work received the 2023 Woven by Toyota
Invention Award.
