The DRIVE Lab performs research on behaviors, planning, control, and perception for autonomous vehicles. Its emphases include socially cooperative autonomous driving, safe control under uncertainty, high-performance driving for autonomous racing, and adaptation to dynamic environments.
Adversarial Multi-Agent Systems
We want to enable robots to operate safely and optimally in the presence of adverserial agents. Our toolbox includes game theory, reinforcement learning, and optimal control. We also want to address safety assurances in learning-based control systems.
As a demonstrative scenario, part of our research deploys systems on autonomous race cars that are capable of reaching 200 MPH, and battle against other opposing agents. We participate in the Indy Autonomous Challenge (IAC) and are the top U.S. team.
This research project is in collaboration with UC Berkeley EECS, UC San Diego, and University of Hawaii through AI Racing Tech and DARPA Assured Neuro Symbolic Learning and Reasoning (ANSR) program.
Learn more on the DRIVE Lab website.
Research Topics
Applications
current head
current staff
past members
past
- Chiyu Dong
- Tianyu Gu
- Corey Ippolito
- Haiyue Li
- Bryan Low
- Luis Navarro-Serment
- C. Spence Oliver
- Yaron Rachlin
- Mahesh Saptharishi
- Poj Tangamchit
- Ker-Jiun Wang
- Junqinq Wei
- Xiangrui Yin