activity
20182024
most citedDriving in Dense Traffic with Model-Free Reinforcement Learning

86 citations · 167 across the 14 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.RO2023★ 25 cited

Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control

Piyush Gupta, David Isele, Donggun Lee +1

Autonomous vehicles (AVs) must share the driving space with other drivers and often employ conservative motion planning strategies to ensure safety. These conservative strategies c…

cs.RO2023★ 13 cited

SLAS: Speed and Lane Advisory System for Highway Navigation

Faizan M. Tariq, David Isele, John S. Baras +1

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory gene…

cs.RO2023★ 1 cited

Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach

Haimin Hu, David Isele, Sangjae Bae +1

The ability to accurately predict others' behavior is central to the safety and efficiency of interactive robotics. Unfortunately, robots often lack access to key information on wh…

cs.RO2023

Predicting Parameters for Modeling Traffic Participants

Ahmadreza Moradipari, Sangjae Bae, Mahnoosh Alizadeh +2

Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based…

cs.RO2023★ 1 cited

Game Theoretic Decision Making by Actively Learning Human Intentions Applied on Autonomous Driving

Siyu Dai, Sangjae Bae, David Isele

The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives. In this paper, w…