Master of Science in Robotics Thesis Talk

  • Remote Access Enabled - Zoom
  • Virtual Presentation
  • Masters Student
  • Robotics Institute
  • Carnegie Mellon University
Master's Thesis Presentation

Cooperative Perception for Pairs of Self-Driving Cars

Fully autonomous vehicles are expected to share the road with less advanced vehicles for a significant period of time. Furthermore, an increasing number of vehicles on the road are equipped with a variety of low-fidelity sensors which provide some perception and localization data, but not at a high enough quality for full autonomy. In this work, we develop a fused perception system that allows a vehicle with low-fidelity sensors to incorporate high-fidelity observations from a vehicle in front of it, allowing both vehicles to operate with full autonomy. The resulting system generates perception information that is both low-noise in regions covered by high-fidelity sensors and avoids false negatives in areas only observed by low-fidelity sensors, while dealing with latency and dropout of the communication link between the two vehicles. At its core, the system uses a set of Extended Kalman filters which incorporate observations from both vehicles’ sensors and extrapolate them using information about the road geometry. Our perception algorithm is evaluated both in simulation and on real vehicles as part of a full cooperative driving system.

Thesis Committee:
Maxim Likhachev (Advisor)
Kris Kitani
Achal Dave

Zoom Participation Enabled. See announcement.

For More Information, Please Contact: