8 citations · 15 across the 21 of their papers we have counts for
18 papers · 1 filter
What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models
Amirhosein Chahe, Tyler Naes, Jovin D'sa +4
Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view. Current approaches often treat all occlusions with uniform con…
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Youngjae Min, Jovin D'sa, Faizan M. Tariq +3
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios b…
Exact, Efficient, and Safe Occlusion-Aware Planning Using AH-Polyhedrons
Long Kiu Chung, David Isele, Toktam Mohammadnejad +4
Safely handling occlusions is a fundamental challenge for autonomous mobile robots operating in dynamic environments. This issue is especially prominent in autonomous valet parking…
VLM-Based Advanced Rider Assistance System for Motorcycle Safety
Mohamed Elnoor, Francesca Baldini, Ananya Trivedi +6
Motorcycles face disproportionately high crash risks compared to cars due to limited protection and heightened sensitivity to surface hazards, yet Advanced Rider Assistance Systems…
N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
Yifan Xue, Toktam Mohammadnejad, Faizan M Tariq +5
Autonomous parking requires efficient path planning that ensures kinematic feasibility and collision avoidance in constrained environments. Hybrid A* is widely used but computation…
Selecting Spots by Explicitly Predicting Intention from Motion History Improves Performance in Autonomous Parking
Long Kiu Chung, David Isele, Faizan M. Tariq +3
In many applications of social navigation, existing works have shown that predicting and reasoning about human intentions can help robotic agents make safer and more socially accep…
ONRAP: Occupancy-driven Noise-Resilient Autonomous Path Planning
Faizan M. Tariq, Avinash Singh, Vipul Ramtekkar +4
Dynamic path planning must remain reliable in the presence of sensing noise, uncertain localization, and incomplete semantic perception. We propose a practical, implementation-frie…
Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning
Ananya Trivedi, Anjian Li, Mohamed Elnoor +7
Autonomous driving requires reasoning about interactions with surrounding traffic. A prevailing approach is large-scale imitation learning on expert driving datasets, aimed at gene…
Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
Farhad Nawaz, Faizan M. Tariq, Sangjae Bae +5
Autonomous Valet Parking (AVP) requires planning under partial observability, where parking spot availability evolves as dynamic agents enter and exit spots. Existing approaches ei…
Active Probing with Multimodal Predictions for Motion Planning
Darshan Gadginmath, Farhad Nawaz, Minjun Sung +5
Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that c…
IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta +4
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems fro…
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…
Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking
Farhad Nawaz, Minjun Sung, Darshan Gadginmath +6
Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To ad…
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…
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Dustin Holley, Jovin Dsa, Hossein Nourkhiz Mahjoub +4
Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yiel…
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…