Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction
for Connected Autonomous Vehicles

Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles

8 March 2023

Lili Su

Papers citing "Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles"

7 / 7 papers shown

Title
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability Ming Xiang Stratis Ioannidis Edmund Yeh Carlee Joe-Wong Lili Su FedML 37 5 0 26 Sep 2024
Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles Tongfei Guo Rui Liu Lili Su 43 1 0 25 Sep 2024
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments Muxing Wang Pengkun Yang Lili Su FedML 31 1 0 05 Sep 2024
Fast and Robust State Estimation and Tracking via Hierarchical Learning Connor Mclaughlin Matthew Ding Deniz Edogmus Lili Su 11 0 0 29 Jun 2023
Uncertainty Quantification of Collaborative Detection for Self-Driving Sanbao Su Yiming Li Sihong He Songyang Han Chen Feng Caiwen Ding Fei Miao 50 53 0 16 Sep 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015