5 papers
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation
Farid Mafi, Ladan Khoshnevisan, Mohammad Pirani +1
Vehicle state estimation presents a fundamental challenge for autonomous driving systems, requiring both physical interpretability and the ability to capture complex nonlinear beha…
Decision Making in Urban Traffic: A Game Theoretic Approach for Autonomous Vehicles Adhering to Traffic Rules
Keqi Shu, Minghao Ning, Ahmad Alghooneh +3
One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants charact…
Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework
Farid Mafi, Mohammad Pirani
Vehicle platooning has been a promising solution for improving traffic efficiency and throughput. However, a failure in a single vehicle, including communication loss with neighbor…
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection
Farid Mafi, Ladan Khoshnevisan, Mohammad Pirani +1
This paper presents a novel theoretical framework for reducing the computational complexity of multi-model adaptive control/estimation systems through systematic transformation to…
An Efficient Continual Learning Framework for Multivariate Time Series Prediction Tasks with Application to Vehicle State Estimation
Arvin Hosseinzadeh, Ladan Khoshnevisan, Mohammad Pirani +2
In continual time series analysis using neural networks, catastrophic forgetting (CF) of previously learned models when training on new data domains has always been a significant c…