Tag: GPS
Science at Uber: Bringing Research to the Roads
Uber Principal Engineer Waleed Kadous discusses how we assess technologies our teams can leverage to improve the reliability and performance of our platform.
Improving Uber’s Mapping Accuracy with CatchME
CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.
Enhancing the Quality of Uber’s Maps with Metrics Computation
Maps make up the bedrock of Uber's transportation solutions. Find out how we ensure the quality of our map data through extensive metrics computation, maintaining fidelity to real world locations and pinpointing allowable pick up and drop off locations for riders.
How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eats
Using GPS and sensor data from Android phones, Uber engineers develop a state model for trips taken by Uber Eats delivery-partners, helping to optimize trip timing for delivery-partners and eaters alike.
Advanced Technologies for Detecting and Preventing Fraud at Uber
To detect and prevent fraud, Uber brings to bear data science and machine learning, analyzing GPS traces and usage patterns to identify suspicious behavior.
Rethinking GPS: Engineering Next-Gen Location at Uber
Uber’s Sensing, Inference, and Research team released a software upgrade for GPS on Android phones that significantly improves location accuracy in urban environments.
Engineering More Reliable Transportation with Machine Learning and AI at Uber
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
How Uber Engineering Increases Safe Driving with Telematics
The engineering behind how Uber's Driving Safety team is using telematics to raise awareness of driving patterns to our partners.
