Dec 22, 2016
Learning to Drive Under Unstructured Conditions
ABOUT THE PROJECT
At a glance
Different methods for training the SDMC’s will be used. First is the classic approach of mapping from camera output to steering angles and accelerator level, which will serve as a baseline for comparison. The second is a multi-stage approach, using one deep network to map from the camera output to a potential function, followed by a second (pre-trained) network for mapping from the potential function to steering angles and speed. The use of a potential function is a proven technique for finding goal-directed and obstacle avoiding trajectories. Within the potential function, goals are represented as dips, obstacles as hills, and the preferred behavior is found by following the gradient.
Oct 19, 2016
Project Update: Learning to Drive under Unstructured Conditions with Autonomous Model Cars
BAIR/CPAR/BDD Internal Weekly Seminar
The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and the Berkeley Deep Drive Consortium. The seminars are every Friday afternoon in room 250 Sutardja Dai Hall from 3:10-4:10 PM, and are open to BAIR/BDD faculty, students, and sponsors. Seminars will be webcast live and recorded talks will be available online following the seminar.
Annual Fall Meeting
8:00am - 9:00am: Coffee and breakfast available
9:00am - 11:30am: Presentations
11:30am - 1:00pm: Lunch and poster session
1:00pm - 5:00pm: Presentations
5:00pm - 7:00pm Reception - University Club, Memorial Stadium
Please RSVP by October 5, 2016