38 citations · 98 across the 21 of their papers we have counts for
21 papers
ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation
Lili Gao, Yanbo Xu, William Koch +8
We introduce ScenarioControl, the first vision-language control mechanism for learned driving scenario generation. Given a text prompt or an input image, Scenario-Control synthesiz…
SSR: A Generic Framework for Text-Aided Map Compression for Localization
Mohammad Omama, Po-han Li, Harsh Goel +6
Mapping is crucial in robotics for localization and downstream decision-making. As robots are deployed in ever-broader settings, the maps they rely on continue to increase in size.…
SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Toktam Mohammadnejad, Jovin D'sa, Behdad Chalaki +2
Merging onto a highway is a complex driving task that requires identifying a safe gap, adjusting speed, often interactions to create a merging gap, and completing the merge maneuve…
R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Harsh Goel, Mohammad Omama, Behdad Chalaki +3
Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological s…
Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
Muhan Lin, Shuyang Shi, Yue Guo +7
Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-…
Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion
Jacob Knaup, Jovin D'sa, Behdad Chalaki +3
Interactive decision-making is essential in applications such as autonomous driving, where the agent must infer the behavior of nearby human drivers while planning in real-time. Tr…
In Search of a Lost Metric: Human Empowerment as a Pillar of Socially Conscious Navigation
Vasanth Reddy Baddam, Behdad Chalaki, Vaishnav Tadiparthi +4
In social robot navigation, traditional metrics like proxemics and behavior naturalness emphasize human comfort and adherence to social norms but often fail to capture an agent's a…
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
Muhan Lin, Shuyang Shi, Yue Guo +6
The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and m…
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki +5
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks.…
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…
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…
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…
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…
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…
Cooperative Energy and Time-Optimal Lane Change Maneuvers with Minimal Highway Traffic Disruption
Andres S. Chavez Armijos, Anni Li, Christos G. Cassandras +6
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase w…
A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehicles
Behdad Chalaki, Andreas A. Malikopoulos
In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection by integrating a safety lay…
Combined Optimal Routing and Coordination of Connected and Automated Vehicles
Heeseung Bang, Behdad Chalaki, Andreas A. Malikopoulos
In this letter, we consider a transportation network with a 100\% penetration rate of connected and automated vehicles (CAVs) and present an optimal routing approach that takes int…
A Constrained Optimal Control Framework for Vehicle Platoons with Delayed Communication
A M Ishtiaque Mahbub, Behdad Chalaki, Andreas A. Malikopoulos
Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons…
A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment
Meera Ratnagiri, Clare O'Dwyer, Logan E. Beaver +3
In this paper, we investigate the problem of a last-mile delivery service that selects up to available vehicles to deliver packages from a centralized depot to delivery…
A Priority-Aware Replanning and Resequencing Framework for Coordination of Connected and Automated Vehicles
Behdad Chalaki, Andreas A. Malikopoulos
Deriving optimal control strategies for coordination of connected and automated vehicles (CAVs) often requires re-evaluating the strategies in order to respond to unexpected change…
A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments
Behdad Chalaki, Logan E. Beaver, A M Ishtiaque Mahbub +2
Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, and electric vehicles, provide the most intriguing opportunity for enabling users to bett…