13 citations · 18 across the 10 of their papers we have counts for
12 papers
Interleaved Projected Gradient Descent for Safe Imitation Learning
Shengfan Cao, Francesco Borrelli
We propose an imitation-learning design for neural-network control policies under state and input constraints. Training alternates a standard imitation gradient step with a block o…
SHIELD: Scalable Optimal Control with Certification using Duality and Convexity
Hansung Kim, Siddharth H. Nair, Francesco Borrelli
We present SHIELD, a hierarchical algorithm that reduces both the decision-variable dimension and the constraint set in -regularized convex programs. From strong convexity…
Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation
Hansung Kim, Eric Yongkeun Choi, Eunhyek Joa +4
Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a…
A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing
Shengfan Cao, Eunhyek Joa, Francesco Borrelli
Guaranteeing constraint satisfaction is challenging in imitation learning (IL), particularly in tasks that require operating near a system's handling limits. Traditional IL methods…
Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
Hansung Kim, Edward L. Zhu, Chang Seok Lim +1
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic i…
Energy-efficient predictive control for connected, automated driving under localization uncertainty
Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli
This paper presents a data-driven Model Predictive Control (MPC) for energy-efficient urban road driving for connected, automated vehicles. The proposed MPC aims to minimize total…
Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
Xu Shen, Yongkeun Choi, Alex Wong +3
This paper introduces a comprehensive approach to optimize parking efficiency for connected and Automated vehicle (CAVs) fleets. We present a multi-vehicle parking simulator, equip…
Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
Hansung Kim, Siddharth H. Nair, Francesco Borrelli
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key…
Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification
Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli
This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and…
Approximate solution of stochastic infinite horizon optimal control problems for constrained linear uncertain systems
Eunhyek Joa, Francesco Borrelli
We propose a Model Predictive Control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of…
Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions
Siddharth H. Nair, Hotae Lee, Eunhyek Joa +3
We propose a Stochastic MPC (SMPC) formulation for path planning with autonomous vehicles in scenarios involving multiple agents with multi-modal predictions. The multi-modal predi…
Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing
Haoru Xue, Edward L. Zhu, John M. Dolan +1
This work presents a novel Learning Model Predictive Control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in…