4 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving
Qi Wei, Junchao Fan, Zhao Yang +3
Reinforcement learning (RL) has shown considerable potential in autonomous driving (AD), yet its vulnerability to perturbations remains a critical barrier to real-world deployment.…
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
Junchao Fan, Qi Wei, Ruichen Zhang +4
Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies. However, its vulnerability to adversarial attacks remains a critica…
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Bocheng Ju, Junchao Fan, Jiaqi Liu +1
Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data fr…
Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies
Junchao Fan, Xuyang Lei, Xiaolin Chang
Deep reinforcement learning (DRL) has emerged as a promising paradigm for autonomous driving. However, despite their advanced capabilities, DRL-based policies remain highly vulnera…