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

Accurately and reliably sensing and interpreting the driving environment is a critical task for the development of autonomous driving systems (ADS). Realistic models for pedestrians, cyclists and other road users or elements are essential, but remain a challenge to make ADS respond and interact safely with traffic in a dynamic urban environment. This project focuses on the creation of pedestrian models and a library for their training, testing, and implementation.

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.
Principal investigatorsResearchersthemes
Ching-Yao ChanCedric MauquoiAutonomous Vehicles
Deep Learning

 

BAIR/CPAR/BDD Internal Weekly Seminar

Event Location: 
250 Sutardja Dai Hall

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. 

Schedule:

http://citris-uc.org/bair-seminar-series/