Nexar
flow-image

Maps vs. the ground truth in Phoenix, AZ: ready for autonomous driving

Published by Nexar

The analysis by Nexar identifies substantial inaccuracies in existing speed limit data for Phoenix, Arizona, when compared to ground-truth detections from crowd-sourced vision data. This study underscores the necessity of real-time, crowd-sourced data updates for high-accuracy maps crucial to autonomous driving and advanced driver-assistance systems. Nexar’s findings demonstrate that traditional mapping methods fall short in precision and timeliness, suggesting that crowd-sourced vision technology offers a scalable solution for continuously refreshed, reliable map data to support emerging automotive applications.

Download Now

box-icon-download

Required fields*

Please agree to the conditions

By requesting this resource you agree to our terms of use. All data is protected by our Privacy Policy.

Related Categories Cybersecurity, Artificial Intelligence, Image Recognition, Object Detection, Facial Recognition, Image Segmentation, Video Analytics, Sales & Marketing, Supply Chain & Manufacturing, Smart Factories, Healthcare, Smart Grids, Autonomous Vehicles

More resources from Nexar