Title: Model Based Vehicle Tracking for Autonomous Driving in Urban Environments
Authors: Anna Petrovskaya and Sebastian Thrun
Abstract: Situational awareness is crucial for autonomous driving in urban environments. This paper describes moving vehicle tracking module that we developed for our successful entry in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The 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. We also show how to build efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles.
In contrast to prior art, we propose a model based approach which encompasses both geometric and dynamic properties of the tracked vehicle in a single Bayes filter. The approach naturally handles data segmentation and association, so that these pre-processing steps are not required.
RSS Online Proceedings: here
Abstract: here
PDF: here
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