No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

S7170 - Bicycle Green Waves Powered by Deep Learning

Session Speakers
Session Description

We'll explore using deep learning to improve urban traffic signaling. Bicycles (both self-powered and pedelecs) are the future of urban transport alongside (self-driving) electric cars, buses, and rail services. Green waves make cycling more efficient, attractive, and safer. Instead of fixed ""green wave"" timings or priorities, a work in progress system is presented that learns to increase the flow of bicycle traffic while minimizing the impact on other traffic actors -- and in many use cases also results in improvements in general traffic times. Using low power efficient SoCs -- Tegra X1 -- the ""smarts"" are integrated in traffic lights and provide V2I interfaces -- also to mobile phones of cyclists -- about signal changes and warn of pedestrians or cyclists. Dispensing with inductive loop, magnetometer, or radar-based sensors buried in the pavement makes the system inexpensive. We'll present initial results from pilot testing in a German city.


Additional Session Information
All
Talk
AI for In-Vehicle Applications Computer Vision and Machine Vision Deep Learning and AI
25 minutes
Session Schedule