High-Speed Obstacle Detection for Automated Highway Applications. Thesis proposal. Carnegie.pdf

High-Speed Obstacle Detection for Automated Highway Applications. Thesis proposal. Carnegie.pdf

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High-Speed Obstacle Detection for Automated Highway Applications. Thesis proposal. Carnegie

The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213 May 1997 c 1997 Carnegie Mellon University High-Speed Obstacle Detection for Automated Highway Applications Thesis Proposal John A. Hancock CMU-RI-TR-97-17 This research was partly sponsored by: the USDOT under Cooperative Agreement Num- ber DTFH61-94-X-00001 as part of the National Automated Highway System Consor- tium, and a DARPA/TACOM grant, “Technology Enhancements for UGVs”, DAAE07- 96-C-X075. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government. Abstract Highway obstacle detection is a challenging problem. Highways present an unknown and dynamic environment with real-time constraints. In addition, the high speeds of travel force a system to detect objects at long ranges. Although there are a number of methods that can suc- cessfully detect moving vehicles, the more difficult problem of finding small, static road debris such as tires or crates remains unsolved. Systems such as the Automated Highway System (AHS) which demand high levels of safety are not feasible unless these critical problems are addressed. Although the problem of detecting static obstacles has been tackled in both the cross- country and indoor mobile robot navigation literature, these systems have operated at low speeds (5 - 10 mph or less) and short range. This thesis will improve on the current state-of-the-art, by demonstrating how small static road debris can be safely detected at long distances and high speeds. In particular, it will focus on using two sensor modalities: laser reflectance and stereo vision. Laser reflectance, to our knowledge, has not been used for obstacle detection before. The thesis will show that reliable detection can be achieved by using the right methods (sensitive enough) and the right models (no more complicated than necessary) for both ro

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