Uncertainty Quantification on Collaborative Perception

Multi-agent collaborative perception has been proposed to leverage the viewpoints of other agents to improve the detection accuracy compared with the individual viewpoint. Recent research has shown the effectiveness of early, late, and intermediate fusion of collaborative perception, which respectively transmits raw data, output bounding boxes, and intermediate features, and the improved collaborative perception results will benefit the self-driving decisions of connected and autonomous vehicles (CAVs).

Game Theoretic Security Framework for Quantum Cryptography

We analyze quantum key distribution (QKD) protocols through a game theoretic framework. We make several contributions in this work. We propose a general-purpose framework allowing for a game-theoretic analysis of QKD protocols.

Multi-Agent Reinforcement Learning for Connected Autonomous Vehicles

Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless connectivity is the next frontier in road transportation, which will greatly benefit the safety and reliability of autonomous cars. Information shared among autonomous vehicles provides opportunities to better coordination schemes and also raises novel challenges.