The Vehicle Autonomy and Intelligence Lab (VAIL) focuses on developing methodologies that enhance the autonomy and intelligence of robotic systems such as unmanned ground, aerial, and aquatic vehicles. Our research interests include planning, learning, and coordination techniques for single or multiple robots with potential applications including autonomous navigation, environmental monitoring, search and rescue, as well as smart transportation.

VAIL is affiliated with the Department of Intelligent Systems Engineering and the Department of Computer Science in the Luddy School of Informatics, Computing, and Engineering at Indiana University - Bloomington.

Latest News & Events

  • Autonomous Racing with 163 mph

    At our second autonomous race, held at the iconic Las Vegas Motor Speedway during CES 2025, our team hit an incredible milestone—reaching our record-breaking speed of 163 mph! This achievement places us among the world's elite in autonomous racing.

  • Grant from U.S. Army Research Lab (ARL)

    Forthcoming: we are excited to bring together various autonomous systems we built in the past years and team them for challenging multi-robot missions.

  • Debut of Autonomous Racing

    We successfully accomplished our mission of achieving high-speed racing, exceeding 120 mph in our first Indy Autonomous Challenge. All IU-Luddy team members performed exceptionally well throughout the summer, contributing to this remarkable achievement.

  • Two IJRR papers accepted

    We have two papers accepted to IJRR. One is "Boundary-Aware Value Function Generation for Safe Stochastic Motion Planning" and the other is "Kernel-based Diffusion Approximated Markov Decision Processes for Autonomous Navigation and Control on Unstructured Terrains". Papers are available on IJRR website.

  • Ranked 4th in Indy Autonomous Challenge Sim Competition

    We ranked the 4th among 18 global teams in Indy Autonomous Challenge simulation, after 3 European teams all of which are long term players. We ranked the 1st among US teams. See news and video.

  • We received Audience Choice Award in MassRobotics Competition

    We are 1 of 4 teams awarded (other awarded teams are Tufts, MIT, WPI). This competition calls for teams from around the globe to create an innovative robotics/automation project. The audience choice award is a unique award voted and selected by ALL attendees.

  • One RSS paper accepted

    We proposed a new framework that can simultaneously support navigation, mapping and exploration in outdoor unstructured environments. Paper will be available soon.

  • Grant from U.S. Army Research Lab (ARL)

    We will continue an Option Performance Period working on off-road autonomy with other 3 teams including MIT, CMU and U Washington.

  • Top 5 stories of 2022

    Our work on Collision-Free Navigation in Cluttered Environments has been selected as one of "Top 5 stories of 2022" by Clearpath Robotics.

  • Grant from USACE

    The project is about Automated and Robotic Inspection of Flood Control Systems. My group will contribute to a large research team involving researchers from Texas A&M, Rice, UT-Austin etc.

  • Best Student Paper Award at RSS 2022

    We got the Best Student Paper Award in this year's Robotics: Science and Systems (RSS) conference. The paper "AK: Attentive Kernel for Information Gathering" can be found in this link. Also see code and other results and a short video on the right panel.

  • Grant from NAVSEA

    The grant will support us to research on high fidelity radio frequency scene generation for real-time processing.

  • Our work is featured by Clearpath Robotics

    Our work of Log-MPPI for navigation has been selected and featured by Clearpath Robotics. See this link. Also see the related paper first-authored by lab member Ihab Mohamed.

  • Grant from NAVSEA

    The grant will support us to develop navigation methods that allow AUV/ASV to perform complex inspection tasks in unstructured ocean environments.

  • Two RSS papers accepted

    One paper presents a new kernel called attentive kernel for information gathering (arXiv), and the other one is a new unsupervised domain adaptation approach for off-road autonomy (arXiv).

  • Our work is featured at Amazon

    Our work has been selected and featured in the successful stories of the Amazon Machine Learning Research Award program. See this link for details.

  • Grant from U.S. Army Research Lab (ARL)

    We are 1 of 4 teams selected (others are MIT, CMU and U Washington). This grant will support our team for research on unmanned ground vehicle's off-road navigation and control.

  • NSF CAREER

    The funds will support autonomous environmental sensing and modeling.

  • One RSS paper accepted

    We proposed a new mechanism (kernel Taylor-based value function approximation) for robotic decision-making. Paper will be available soon.

  • Grant from NSF Robust Intelligence Program

    NSF will support our project of air/aquatic vehicle planning in challenging time-varying environments.

  • Grant from U.S. Army Research Lab (ARL)

    This grant will support our team for research on enhancing autonomous system capabilities to maneuver in complex and contested environments. We are 1 of 8 teams selected. Here is a media link.

  • Amazon AWS Machine Learning Research Award

    The award will allow VAIL researchers to access and leverage Amazon's state-of-the-art cloud computing tools and services for boosting our work on both machine learning and autonomous systems.

  • Two RSS papers accepted

    We use reachability analysis to characterize time-varying Markov Decision Processes and Pareto Monte Carlo Tree Search for multi-objective informative planning.

Click here to view all of the latest news and events »

Recent Videos

IU-Luddy Autonomous Racing's Top Speed Lap at Las Vegas during CES 2025

Debut of IU-Luddy Autonomous Racing at Indy Autonomous Challenge 2024

Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation

Navigating Cluttered Environments through U-MPPI Trajectory Generation

Decision-Making Among Bounded Rational Micro-Drones

Autonomous Mapless Navigation On Uneven Terrains

Attentive Kernel based Underwater Terrain (Bathymetry) Mapping using an Autonomous Surface Vehicle

Autonomous Navigation, Mapping and Exploration with Gaussian Processes

UAV and Tethered UGV for Narrow Space Exploration

Domain Adaptive Segmentation for Autonomous Navigation in Natural Environments

Navigating robot in uneven and uncertain off-road environments

Environmental sensing, modeling, and monitoring using Autonomous Surface Vehicles

Navigable space construction for robot visual navigation

Debut of our Autonomous Underwater Vehicle (AUV)

Demonstration of using drones for surveillance tasks

Aerial vehicle motion control