LISC
Contributors:
C. Liu, S. Ferrari, Y. Shi
Project Description
This project seeks to improve the efficiency and safety of commercial airports by developing autonomous taxiing capability for airplanes. By harnessing the recent development of perception, planning, and control of autonomous systems, we can help airplanes avoid accidents that usually arise from mis-coordination between airplanes, intrusion of unexpected objects, and adverse weather.
Research Goals
- Reliable environment perception under various lighting and weather conditions
- Detection and prediction of surrounding obstacles
- Decentralized planning and control of airplanes for autonomous taxing
- Robust collision avoidance mechanism in safety-critical situations
Peer-Reviewed Publications
- C. Liu and S. Ferrari, “Vision-guided Planning and Control for Autonomous Taxiing via Convolutional Neural Networks,” AIAA Guidance, Navigation, and Control (GNC)/ Intelligent System (IS) Conference, January 2019. [PDF]
Presentations
- “Vision-guided Planning and Control for Autonomous Taxiing via Convolutional Neural Networks,” AIAA Guidance, Navigation, and Control (GNC)/ Intelligent System (IS) Conference, San Diego, CA, January 2019. [PDF]