/Perception for collision avoidance and autonomous driving

Perception for collision avoidance and autonomous driving

Romuald Aufrere, Jay Gowdy, Christoph Mertz, Chuck Thorpe, Chieh-Chih Wang and Teruko Yata
Journal Article, Carnegie Mellon University, Mechatronics, Vol. 13, No. 10, pp. 1149-1161, December, 2003

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


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 Reference
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},