About The Forum
Calendar 2009 Poster Sessions : Model Based Vehicle Tracking for Autonomous Driving in Urban Environments
Abstract:
Situational awareness is crucial for autonomous driving in urban environments. This paper describes moving vehicle tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge (UGC), an autonomous driving race organized by the U.S. Government in 2007. The tracking module provides reliable tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We also show how to build efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the UGC as well as other urban settings.
Bio:
Anna Petrovskaya is a PhD candidate at the Computer Science Department at Stanford University. Anna's research focuses on model based Bayesian perception for robotic applications. She has developed new efficient algorithms for tactile object localization, mobile manipulation and vehicle tracking. Anna's contributions to Robotics have been recognized by the Stanley Scholar fellowship and the Achievement Rewards for College Scholars fellowship.
Situational awareness is crucial for autonomous driving in urban environments. This paper describes moving vehicle tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge (UGC), an autonomous driving race organized by the U.S. Government in 2007. The tracking module provides reliable tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We also show how to build efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the UGC as well as other urban settings.
Bio:
Anna Petrovskaya is a PhD candidate at the Computer Science Department at Stanford University. Anna's research focuses on model based Bayesian perception for robotic applications. She has developed new efficient algorithms for tactile object localization, mobile manipulation and vehicle tracking. Anna's contributions to Robotics have been recognized by the Stanley Scholar fellowship and the Achievement Rewards for College Scholars fellowship.