Dec 22, 2016
Pedestrian Models in Urban Environment for Autonomous Driving
Pedestrian Models in Urban Environment for Autonomous Driving and Database of Video Sequences for Model Training, Testing, and Implementation
At a glance
The overall project goal is to advance the state-of-the-art deep learning methodologies for the applications of pedestrian detection and perception. The two main objectives of this project are: 1) Implement a model for pedestrian detection and a model for pedestrian intent recognition that can be applied to AV urban driving; 2) Collect a real-world dataset that can be used to train and test pedestrian models.
|Ching-Yao Chan||Cedric Mauquoi||Autonomous Vehicles|
Oct 17, 2016
Project Update: Pedestrian Models Summary
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.