51 citations · 96 across the 20 of their papers we have counts for
23 papers
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Lu Wen, Songan Zhang, H. Eric Tseng +1
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-o…
Safe Control and Learning Using Generalized Action Governor
Peiyuan Fang, Weiqi Zhang, Lu Xiong +7
This paper introduces the Generalized Action Governor (AG), a supervisory scheme that augments a nominal closed-loop system with the capability to enforce state and input constrain…
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning
Yutong Li, Nan Li, H. Eric Tseng +3
The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as pointwise-in-time stat…
Robust AI Driving Strategy for Autonomous Vehicles
Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic +4
There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the lo…
Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach
Mathijs Schuurmans, Alexander Katriniok, Christopher Meissen +2
We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior…
Potential Game-Based Decision-Making for Autonomous Driving
Mushuang Liu, Ilya Kolmanovsky, H. Eric Tseng +3
Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (e.g., autonomous vehicles (AVs), human drivers, and pedes…
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
Kaiwen Liu, Nan Li, H. Eric Tseng +2
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with…
Stochastic MPC with Multi-modal Predictions for Traffic Intersections
Siddharth H. Nair, Vijay Govindarajan, Theresa Lin +3
We propose a Stochastic MPC (SMPC) formulation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles for collision avoi…
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
Lu Wen, Songan Zhang, H. Eric Tseng +3
Meta Reinforcement Learning (Meta-RL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniq…
Sensitivity Analysis of Passenger Behavioral Model for Dynamic Pricing of Shared Mobility on Demand
Vineet Jagadeesan Nair, Yue Guan, Anuradha M. Annaswamy +2
This paper provides a framework to quantify the sensitivity associated with behavioral models based on Cumulative Prospect Theory (CPT). These are used to design dynamic pricing st…
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
Songan Zhang, Lu Wen, Huei Peng +1
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endang…
Models and Predictive Control for Nonplanar Vehicle Navigation
Thomas Fork, H. Eric Tseng, Francesco Borrelli
We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature,…
Safe Reinforcement Learning Using Robust Action Governor
Yutong Li, Nan Li, H. Eric Tseng +3
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the ap…
Action Governor for Discrete-Time Linear Systems with Non-Convex Constraints
Nan Li, Kyoungseok Han, Anouck Girard +3
This paper introduces an add-on, supervisory scheme, referred to as Action Governor (AG), for discrete-time linear systems to enforce exclusion-zone avoidance requirements. It does…
A Game Theoretic Approach for Parking Spot Search with Limited Parking Lot Information
Yutong Li, Nan Li, H. Eric Tseng +4
We propose a game theoretic approach to address the problem of searching for available parking spots in a parking lot and picking the ``optimal'' one to park. The approach exploits…
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models
Mathijs Schuurmans, Alexander Katriniok, Hongtei Eric Tseng +1
We propose a learning-based, distributionally robust model predictive control approach towards the design of adaptive cruise control (ACC) systems. We model the preceding vehicle a…
Vision-Based Autonomous Driving: A Model Learning Approach
Ali Baheri, Ilya Kolmanovsky, Anouck Girard +2
We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first…
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…
Towards Dynamic Pricing for Shared Mobility on Demand using Markov Decision Processes and Dynamic Programming
Yue Guan, Anuradha M. Annaswamy, H. Eric Tseng
In a Shared Mobility on Demand Service (SMoDS), dynamic pricing plays an important role in the form of an incentive for the decision of the empowered passenger on the ride offer. S…
Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services
Yue Guan, Anuradha M. Annaswamy, H. Eric Tseng
Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk…
Autonomous Highway Driving using Deep Reinforcement Learning
Subramanya Nageshrao, Eric Tseng, Dimitar Filev
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, for…
A Dynamic Routing Framework for Shared Mobility Services
Yue Guan, Anuradha M. Annaswamy, H. Eric Tseng
Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the…
Adaptive MPC for Autonomous Lane Keeping
Monimoy Bujarbaruah, Xiaojing Zhang, H. Eric Tseng +1
This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering…