Dec 22, 2016
Motion Prediction for Urban Autonomous Driving Based on Stochastic Policy Learned via Deep Neural Network
ABOUT THE PROJECT
At a glance
Human drivers have the capability to execute a complex stochastic driving policy with fast reflex, comprehensively taking multiple factors into consideration such as time‐efficiency, comfort, traffic rule/courtesy, vehicle kinematics/dynamics, road structure, static obstacles, and behavior of other road participants. Some of these factors can hardly be modeled and quantified. DNN is a powerful tool to mimic and learn the complex policy of human drivers to predict the possible future motion without heavy online computational loads. In this project, we will design and train an action prediction DNN to evaluate how human-like the subject autonomous vehicle behaves, as well as a reaction prediction DNN to predict the impact of planned trajectories.
|Masayoshi Tomizuka||Wei Zhan||Deep Neural Networks|
Oct 19, 2016
Project Update: Toward Safe, Feasible and Human-Like Motion Generation for Urban Autonomous Driving
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.