AVCS Research at Carnegie Mellon University

Abstract

For the last 10 years, Carnegie Mellon University has been building increasingly competent systems for autonomous driving. Our approach has been to develop smart vehicles, capable of driving in natural outdoor environments without intervehicle communication or infrastructure modifications. Our computer-controlled vehicles now drive themselves at speeds up to 55 mph and for distances of over 90 miles on public roads without human intervention. They are capable of driving both during the day and night, on a wide variety of road types. They can sense and avoid obstacles, and even automatically parallel park. These technologies have been develop as part of ARPA's Unmanned Ground Vehicle (UGV) program, with the goal of reducing the need for human presence in hazardous situations such as battlefield surveillance missions. These advances can also reduce the risk to civilian drivers as part of advanced vehicle control systems. The techniques we have developed are suitable both for AHS applications where the vehicle is controlled automatically, and in driver warning systems where the role of the AVCS system to monitor the environment and suggest actions for the human driver. This paper presents some of the capabilities of our systems, and the processing techniques that underlie them. These techniques include: artificial neural networks for road following, model-based image processing for convoy following, smart obstacle maps based on sonar, ladar and microwave sensor processing and integrated control systems.