Search Results for author:
Found 18 papers, 0 papers with code
Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control
In the scenario of Adaptive Cruise Control (ACC), we employ the Deep Ensemble to estimate distance headway to the lead vehicle from RGB images and enable the downstream controller to account for the estimation uncertainty.
System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models
The Neural Network (NN), as a black-box function approximator, has been considered in many control and robotics applications.
Game Projection and Robustness for Game-Theoretic Autonomous Driving
This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated in a game without changing the optimality of the game solution.
Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network
Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic.
Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors
Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging.
Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model
Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment.
Safe Control and Learning Using Generalized Action Governor
This paper introduces the Generalized Action Governor, which is a supervisory scheme for augmenting a nominal closed-loop system with the capability of strictly handling constraints.
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach
To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed.
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning
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 state and control constraints.
Potential Game-Based Decision-Making for Autonomous Driving
Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms.
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
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 other vehicles to identify or create a gap and safely merge into.
Safe Reinforcement Learning Using Robust Action Governor
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process.
Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed.
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles.
Robotics Systems and Control Systems and Control
Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making.
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment.
Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection.
Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests.
