Perception for collision avoidance and autonomous driving
Abstract
The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads.
BibTeX
@article{Aufrere-2003-8824,author = {Romuald Aufrere and Jay Gowdy and Christoph Mertz and Chuck Thorpe and Chieh-Chih Wang and Teruko Yata},
title = {Perception for collision avoidance and autonomous driving},
journal = {Mechatronics},
year = {2003},
month = {December},
volume = {13},
number = {10},
pages = {1149 - 1161},
keywords = {Collision avoidance, Autonomous driving, Short-range surround sensing, Optical flow, Triangulation laser sensor, Curb detection, LIDAR object detection, Sensor fusion, Collision prediction},
}