Dec 23, 2016
Augmenting Autonomous Vehicle Technology with I2V Information
About this Project
At a glance
The high level workflow of the semi-autonomous vehicle decision support system has three steps. Step 1 comprises video processing and estimating vehicle trajectories from video frames. Step 2 involves extrapolating the retrieved vehicle trajectories to the near‐term future and flagging projected violators. Control decisions based on the information from step 2 are made in step 3. This project focuses on step 2 of the described workflow: development of the deep learning algorithm and related software for prediction and classification of vehicle trajectories at intersections. The team will train this algorithm using high-resolution traffic measurements collected from an instrumented intersection in Danville, CA.
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.