Skip to main content

Tools for Assured Autonomy

Primary Investigators:
Gabor Karsai
Abhishek Dubey 

Brief Description of Project:
Autonomous vehicles (cars, drones, underwater vehicles, etc.) have started using software components that are built using machine learning techniques. This is due to the fact that these vehicles must operate in highly uncertain environments and the we cannot design a correct algorithm for all situations. Instead, we collect data from a real or simulated environment and train a general purpose system - typically a neural net - to perform a certain function using machine learning techniques. But the challenge is that the training data cannot cover all possible cases, yet we need to know that the system works safely and has acceptable performance. 

Our project is doing fundamental research and building tools for supporting the engineering of such system. The tools are for modeling the system (e.g. an underwater vehicle), executing the training and testing of the learning-based components, and building formal arguments (called assurance cases) to show that the system is safe.

Desired Qualifications:
CS/CmpE/EE background with familiarity with concepts and techniques of signals and systems, computer architecture, software design, and embedded systems. Knowledge of the Python/C/C++ languages is a plus, as well as experience with ROS (the Robot Operating System).
Nature of Supervision:
The intern will work in a team of researchers and staff engineers on the project, under the supervision of a researcher. The intern will be given weekly assignments and report on those in project meetings.
A Brief Research Plan (period is for 10 weeks):
Week 1: getting familiar with the project
Week 2-4: Learning about the specific tools used and being developed on the project
Week 5-9: Contributing to the tool development addressing a specific research problem
Week 10: Preparing a final report on the research results 

Number of Open Slots: 2
Contact Information:
Name: Gabor Karsai
Department: Electrical Engineering and Computer Science