most citedMinimally Disruptive Cooperative Lane-change Maneuvers

6 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2023

Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging

Jacob Knaup, Jovin D'sa, Behdad Chalaki +4

Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding hu…

cs.RO2023

Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach

Viet-Anh Le, Vaishnav Tadiparthi, Behdad Chalaki +4

In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predic…

cs.RO2023

Social Navigation in Crowded Environments with Model Predictive Control and Deep Learning-Based Human Trajectory Prediction

Viet-Anh Le, Behdad Chalaki, Vaishnav Tadiparthi +3

Crowd navigation has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing this problem to…

eess.SY20231 cited

MR-IDM -- Merge Reactive Intelligent Driver Model: Towards Enhancing Laterally Aware Car-following Models

Dustin Holley, Jovin D'sa, Hossein Nourkhiz Mahjoub +3

This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane v…

math.OC20236 cited

Minimally Disruptive Cooperative Lane-change Maneuvers

Behdad Chalaki, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub +5

A lane-change maneuver on a congested highway could be severely disruptive or even infeasible without the cooperation of neighboring cars. However, cooperation with other vehicles…