63 citations · 96 across the 13 of their papers we have counts for
8 papers · 1 filter
Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles
Mushuang Liu, Yan Wan, Frank Lewis +3
This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for…
A Risk-Averse Preview-based -Learning Algorithm: Application to Highway Driving of Autonomous Vehicles
Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
A risk-averse preview-based -learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-…
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters
Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
In this paper, a unified batch-online learning approach is introduced to learn a linear representation of nonlinear system dynamics using the Koopman operator. The presented system…
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning
Yuzhen Han, Majid Mazouchi, Subramanya Nageshrao +1
This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-averse optimal control (RAOC) problem for discrete-time nonlinear systems. While successful…
An online evolving framework for advancing reinforcement-learning based automated vehicle control
Teawon Han, Subramanya Nageshrao, Dimitar P. Filev +1
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolv…
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
Songan Zhang, Huei Peng, Subramanya Nageshrao +1
Deep reinforcement learning methods have been widely used in recent years for autonomous vehicle's decision-making. A key issue is that deep neural networks can be fragile to adver…
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
Ali Baheri, Subramanya Nageshrao, H. Eric Tseng +3
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches…
Reinforcement learning for port-Hamiltonian systems
Olivier Sprangers, Gabriel A. D. Lopes, Robert Babuska
Passivity-based control (PBC) for port-Hamiltonian systems provides an intuitive way of achieving stabilization by rendering a system passive with respect to a desired storage func…