research
∙
06/22/2023
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Autoencoders have been extensively used in the development of recent ano...
share
research
∙
05/24/2023
KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks
Autonomous driving has received a great deal of attention in the automot...
research
∙
04/09/2023
Experience-Based Evolutionary Algorithms for Expensive Optimization
Optimization algorithms are very different from human optimizers. A huma...
research
∙
11/22/2022
Safe Control and Learning Using Generalized Action Governor
This paper introduces the Generalized Action Governor, which is a superv...
research
∙
07/17/2022
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 m...
research
∙
07/16/2022
Robust AI Driving Strategy for Autonomous Vehicles
There has been significant progress in sensing, perception, and localiza...
research
∙
05/18/2022
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Autonomous driving has received a lot of attention in the automotive ind...
research
∙
08/19/2021
Prior Is All You Need to Improve the Robustness and Safety for the First Time Deployment of Meta RL
The field of Meta Reinforcement Learning (Meta-RL) has seen substantial ...
research
∙
05/05/2021
Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data
Understanding human driving behaviors quantitatively is critical even in...
research
∙
03/03/2021
Efficient data-driven encoding of scene motion using Eccentricity
This paper presents a novel approach of representing dynamic visual scen...
research
∙
02/21/2021
Safe Reinforcement Learning Using Robust Action Governor
Reinforcement Learning (RL) is essentially a trial-and-error learning pr...
research
∙
09/25/2020
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World
We propose a multi-agent based computational framework for modeling deci...
research
∙
09/20/2020
Interpretable-AI Policies using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
Black-box artificial intelligence (AI) induction methods such as deep re...
research
∙
05/11/2020
A Game Theoretic Approach for Parking Spot Search with Limited Parking Lot Information
We propose a game theoretic approach to address the problem of searching...
research
∙
10/28/2019
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
In this paper, we present a safe deep reinforcement learning system for ...
research
∙
08/28/2019
An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks
An online evolving framework is proposed to support modeling the safe Au...
research
∙
03/29/2019