Michael Darms

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Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, M. N. Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matt McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Bryan(More)
This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex(More)
We present an approach for robust detection, prediction, and avoidance of dynamic obstacles in urban environments. After detecting a dynamic obstacle, our approach exploits structure in the environment where possible to generate a set of likely hypotheses for the future behavior of the obstacle and efficiently incorporates these hypotheses into the planning(More)
Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages(More)
In this article a modular system architecture for fusion of data from environment sensors for advanced driver-assistance systems (ADAS) is proposed. The architecture allows different applications to have access to the fused sensor data by processing the data with respect to specific demands of different application groups. In the article the growing(More)
For most of the existing commercial driver assistance systems the use of a single environmental sensor and a tracking model tied to the characteristics of this sensor is sufficient. When using a multi-sensor fusion approach with heterogeneous sensors the information available for tracking depends on the sensors detecting the object. This paper describes an(More)
(DARPA) announced the first Grand Challenge with the goal of developing vehicles capable of autonomously navigating desert trails and roads at high speeds. The competition was generated as a response to a mandate from the United States Congress that a third of U.S. military ground vehicles be unmanned by 2015. To achieve this goal DARPA drew from(More)
Knowledge about the road shape is a key element for driver assistance systems which support the driver in complex scenarios like construction sites. Systems only using information derived from lane markings reach a limit here. The paper presents an approach to estimate road boundaries based on static objects bounding the road. A map based environment(More)
The Urban Challenge 2007 was a race of autonomous vehicles through an urban environment organized by the U.S. government. During the competition the vehicles encountered various typical scenarios of urban driving. Here they had to interact with other traffic which was human or machine driven. This paper describes the perception approach taken by Team Tartan(More)
This paper presents the tracking system of Boss, Carnegie Mellon University’s winning entry in the DARPA Urban Challenge in 2007. We present the key challenges for implementing the tracking system, the design principles that guided its implementation, the software architecture of the tracking system and the sensor setup used by Boss. The system has been(More)