Lab

mLab - Real-Time Embedded Systems Lab

Featured projects (1)

Project
Develop formal and data-driven methods to verify high-risk medical devices

Featured research (3)

The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.

Lab head

Rahul Mangharam
Department
  • Department of Electrical and Systems Engineering

Members (8)

Johannes Betz
  • University of Pennsylvania
Houssam Abbas
  • University of Pennsylvania
Madhur Behl
  • University of Virginia
Truong Xuan Nghiem
  • Northern Arizona University
Kuk Jin Jang
  • University of Pennsylvania
Jayanth Bhargav
  • Purdue University
Zirui Zang
  • University of Pennsylvania
Renukanandan Tumu
  • University of Connecticut
Sanjay Dixit
Sanjay Dixit
  • Not confirmed yet
Matthew O'Kelly
Matthew O'Kelly
  • Not confirmed yet
Marco Beccani
Marco Beccani
  • Not confirmed yet
Yash Vardhan
Yash Vardhan
  • Not confirmed yet
Jiyue He
Jiyue He
  • Not confirmed yet