CAREER: Safe and Agile Autonomous Cyber-Physical Systems
Lead PI:
Madhur Behl
Abstract

Autonomous Cyber-Physical Systems (CPS), such as self-driving cars, and drones, powered by deep learning and AI based perception, planning, and control algorithms, are forming the basis for significant pieces of our nation?s critical infrastructure, and present direct, and urgent safety-critical challenges. A major limitation with current approaches towards deploying autonomous CPS is in ensuring that the system operates safely, and reliably in situations that do not happen very often under normal operating conditions and are therefore difficult to gather data on. For instance, a self-driving car trained to follow the ?rules of the road? will perform well most of the time, but it is the unusual conditions, the edge cases, which pose the hardest safety challenges. This project brings forward an innovative idea ? can increasing the agility of an autonomous vehicle improve its safety? This notion is somewhat controversial since agility (like that of race cars) is more frequently associated with decreased safety margins.

Motivated by these challenges underlying real-world testing and safety for autonomous vehicles, the goal of this project is to develop the foundations for autonomous cyber-physical systems along two dimensions: agility, safety, and their interplay. The project is centered on (1) increasing agility for AVs by developing new methods for agile motion planning, so they can maneuver at the limits of their handling and control when it matters most to escape potentially unsafe conditions, (2) automated reasoning about uncertain dynamic situations that may occur during autonomous CPS operation, and (3) developing novel methods for automatically generating testing and edge-case scenarios at design time, to explore scenarios under which the autonomous CPS would fail. The proposed methods will be evaluated on scaled autonomous vehicles testbeds, on photorealistic and high-fidelity simulation platforms, and on full scale AV prototypes. The project will also consider not just safety of an unoccupied AV ? but one in which passengers may be present. This CAREER project includes designing exciting new courses, and initiatives centered around autonomous racing to engage with research and mentoring for K-12, undergraduate, and graduate students. The project aims to ensure that students cultivate a holistic view of cyber-physical systems and autonomous systems by drawing stronger connections between theory, applications, and hands-on platform development. The project will help enhance the capabilities of autonomous cyber-physical systems and facilitate with their safe deployment.

Madhur Behl

Dr. Madhur Behl is an Associate Professor in the departments of Computer Science, and Systems and Information Engineering, and a member of the Cyber-Physical Systems Link Lab at the University of Virginia.
He received his Ph.D. (2015) and M.S. (2012), in Electrical and Systems Engineering, both from the University of Pennsylvania; and his bachelor's degree (2009) in ECE from PEC University of Technology in India.
He is the team principal of the Cavalaier Autonomous Racing team. Behl is also the co-founder, organizer, and the race director for the F1/10 (F1tenth) International Autonomous Racing Competitions. He is an associate editor for the SAE Journal on Connected and Autonomous Vehciles, and a guest editor for the Journal of Field Robotics. He also serves on the on the Academic Advisory Council of the Partners for Automated Vehicle Education (PAVE) campaign, to help promote public understanding about autonomous vehicles and their potential benefits. Dr. Behl is an IEEE Senior Member and the recipient of the NSF CAREER Award (2021).

Performance Period: 04/15/2021 - 03/31/2026
Institution: University of Virginia
Sponsor: National Science Foundation
Award Number: 2046582