How a Traffic AI Detection System Can Help Reduce Injuries & Save Lives (WasteExpo 2021)

An industry leader is prototyping a detection system for oncoming traffic for the purpose of maintaining rider safety.  Rear loader residential collection vehicles utilize rear passengers for waste collection.  Each year, riders risk serious injury from rear-end collisions with passenger cars.  To date, there is no detection system, besides physical awareness of riders and drivers.  Creating an automated detection system for oncoming vehicles could potentially reduce injuries and save lives.  Two technology companies are leveraging their deep expertise in computer vision to design and develop a machine learning detection system that is capable of alerting riders of potential oncoming vehicle collisions.  To successfully train the proof of concept model, one of the largest industry haulers will rely on Google Cloud Platform resources.  Learn how to innovate with partners utilizing machine learning in industrial applications.

Speakers:
Connor Pokorny, Cloud Customer Engineer, Google
Vu Nguyen, Corporate Development & Innovation, Waste Management
ZZ Si, Co-Founder and Computer Vision Practice Lead, KUNGFU.AI