15.3.3.5 CMU Road Followers, ALVINN YARF MANIAC

Chapter Contents (Back)
Road Following. Path Planning. YARF. ALVINN. System: ALVINN.

Autonomous Land Vehicle In a Neural Network (ALVINN),
1996.
HTML Version. The ALVINN project site.

Pomerleau, D.A.[Dean A.],
Efficient Training of Artificial Neural Networks for Autonomous Navigation,
NeurComp(3), No. 1, 1991, pp. 88-97. BibRef 9100

Pomerleau, D.A.[Dean A.],
Neural Network Vision for Robot Driving,
NAVLAB96(Chapter 4). BibRef 9600

Pomerleau, D.A.[Dean A.],
Neural Network Based Autonomous Navigation,
NAVLAB90(558-614). BibRef 9000

Pomerleau, D.A.,
Neural Network Perception for Mobile Robot Guidance,
Hingham: KluwerAcademic, 1993. ISBN 0-7923-9373-2.
WWW Link. BibRef 9300
And: Ph.D.Thesis (CS), February 1992, BibRef CMU-CS-TR-92-115. System: ALVINN. The Neural-Network road follower (ALVINN). Has run on real highways with light traffic at 55mph. BibRef

Pomerleau, D.A.,
Understanding Internal Representations through Hidden Unit Senstivity Analysis,
IAS93(xx-yy). BibRef 9300

Pomerleau, D.A.,
ALVINN: An Autonomous Land Vehicle in a Neural Network,
CMU-CS-TR-89-107, January 1989. BibRef 8901

Pomerleau, D.A.[Dean A.],
Visibility Estimation from a Moving Vehicle Using the RALPH Vision System,
ITSC97(906-911).
IEEE DOI BibRef 9700
And: DARPA97(339-344). BibRef

Crisman, J.D., Thorpe, C.E.,
SCARF: A Color Vision System That Tracks Roads and Intersections,
RA(9), 1993, pp. 49-58. BibRef 9300

Jochem, T.M., Pomerleau, D.A., Thorpe, C.E.,
Vision-Based Neural Network Road and Intersection Detection and Traversal,
ARPA96(1365-1371). BibRef 9600
And:
Vision-Based Neural Network Road and Intersection Detection,
IROS95(xx). BibRef
And: A1, A2 only: NAVLAB96(Chapter 5). Build on ALVINN to track through intersections. BibRef

Jochem, T.M.,
Vision Based Tactical Driving,
CMU-RI-TR-96-14, January 1996. BibRef 9601 Ph.D.Thesis. Add lane changing and intersection traversal to the existing reliable lane following programs. BibRef

Jochem, T.M.[Todd M.],
Initial Results in Vision Based Road and Intersection Detection and Traversal,
CMU-RI-TR-95-21, April 1995. BibRef 9504

Jochem, T.M.,
Using Virtual Active Vision Tools to Improve Autonomous Driving Tasks,
CMU-RI-TR-94-39, October 1994. Use the ALVINN system for what it does, but apply geometric transformation to the imput image and the output steering vector. BibRef 9410

Jochem, T.M., Pomerleau, D.A., and Thorpe, C.E.,
MANIAC: A Next Generation Neurally Based Autonomous Road Follower,
DARPA93(473-479). BibRef 9300
And: A1 only: IAS93(xx-yy). System: MANIAC. BibRef

Pomerleau, D.A.[Dean A.], Baluja, S.[Shumeet],
Non-Intrusive Gaze Tracking Using Artificial Neural Networks,
AAAI-MLCV93 BibRef 9300
And: A2, A1: CMU-CS-TR-94-102, January 1994.
PS File. BibRef

Baluja, S.[Shumeet],
Expectation-Based Selective Attention,
Ph.D.Thesis, October 1996. BibRef 9610 CMU-CS-TR-96-182. BibRef

Batavia, P.H.[Parag H.], Pomerleau, D.A.[Dean A.], and Thorpe, C.E.[Charles E.],
Applying Advanced Learning Algorithms to ALVINN,
CMU-RI-TR-96-31, October 1996.
PDF File. BibRef 9610

Pomerleau, D.A., Gowdy, J., and Thorpe, C.E.,
Combining Artificial Neural Networks and Symbolic Processing for Autonomous Robot Guidance,
DARPA92(961-967). Neural Networks. System: YARF. YARF plus neural net for road tracking. ALVINN. BibRef 9200

Sukthankar, R.[Rahul], Pomerleau, D.A., and Thorpe, C.E.,
Panacea: An Active Sensor Controller for the ALVINN Autonomous Driving System,
CMU-RI-TR-93-09, April 1993. System: ALVINN. Adds steering of the camera to ALVINN, this improves performance where sharp turns are required. BibRef 9304

Hancock, J.A., and Thorpe, C.E.,
ELVIS: Eigenvectors for Land Vehicle Image System,
CMU-RI-TR-94-43, December 1994.
PS File. Based on ALVINN, an attempt to understand why it works. BibRef 9412

Kluge, K., Thorpe, C.E.,
The YARF system for vision-based road following,
MathMod(22), 1995, pp. 213-233.
WWW Link. BibRef 9500

Kluge, K., Thorpe, C.E.,
Explicit Models For Robot Road Following,
NAVLAB90(). BibRef 9000

Kluge, K.,
YARF: An Open-Ended Framework for Robot Road Following,
CMU-CS-TR-93-104, February 1993. BibRef 9302 Ph.D.Thesis, CS. The theme of YARF is that using rich models improves performance. It uses model driven segmentation, model coherence, and data driven recognition of model changes. Models of road structure are generated off line. BibRef

Kluge, K., and Thorpe, C.E.,
Intersection Detection in the YARF Road Following System,
IAS93(xx-yy). BibRef 9300

Wallace, R.S., Stentz, A., Thorpe, C.E., Whittaker, W., Kanade, T.,
First Results in Robot Road-Following,
IJCAI85(1089-1093). BibRef 8500

Stentz, A., Goto, Y.,
The CMU Navigational Architecture,
DARPA87(440-446). BibRef 8700

Wallace, R.S., Matsuzaki, K., Goto, Y., Crisman, J.D., Webb, J.A., Kanade, T.,
Progress in Robot Road-Following,
CRA86(XX-YY). BibRef 8600

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Obstacle Dectection, Other Vehicles, Objects on the Road .


Last update:May 25, 2017 at 22:18:08