Inference and Decision Making for Autonomous Vehicles 

RSS 2023 Workshop

July 10th 2023

Daegu, South Korea 

EXCO, Room 324B

Link for Virtual Workshop 

Workshop Description and Call for Contributions

The impacts of autonomous vehicles are reaching more and more industries and domains, bringing about transformative changes to society. However, they do not come without their obvious challenges in making reliable and trustworthy decisions in the real world. At the core of safely integrating autonomous vehicles into society is equipping them with the ability to reason under uncertainty. Improved technology means higher demands on autonomous vehicles to properly infer the knowledge they need about the world and make decisions. 

Inference and Decision Making for Autonomous Vehicles (IDMAV) is a full-day workshop that will be a forum for researchers and practitioners from academia and industry to explore the latest advancements and challenges in the field of decision making for autonomous vehicles. Our goal is to highlight some of the latest research efforts in inference and decision making geared towards improving vehicle autonomy. 

Presenters and panelists invited to the workshop represent a broad spectrum of research domains including safe autonomy, multi-agent planning, formal methods, estimation/perception etc. The intended audience consists of researchers in these fields who are eager to interact with a diverse set of ideas related to autonomy and acquaint themselves with the state of the art in IDMAV. 

In addition to presentations and panel discussions, the workshop will also include poster sessions and oral presentations. The goal of these activities is to encourage active participation and collaboration among attendees and to promote the exchange of ideas.


We are interested in answering the following questions:


We welcome contributions in form of a paper (2 to 4 pages in double column format excluding references) addressing the topics of the workshop. Accepted submissions will be presented through either posters or oral presentations, depending on the number of received contributions.

The followings are representative topics, however, the scope of interest extends beyond these topics:

Important Dates & Submission Guidelines

The submissions can be either recent publications, works in the review process, or ongoing projects. Dual submission is also allowed. Please ensure that all submissions comply with the RSS paper format (link).

Submission Portal: https://cmt3.research.microsoft.com/IDMAV2023/Submission/Index

Submission Deadline:  June 8 2023 (AoE)

Acceptance Notification:  June 10 2023 (AoE)

Camera-Ready Submission Deadline:  July 9 2023 (AoE)

Workshop: July 10th 2023

Schedule

All Times are Korean Standard Time (KST)

Invited Speakers

Shreyas Kousik

Assistant Professor

Georgia Tech

Sangwoo Moon

Postdoctoral Fellow

Jet Propulsion Laboratory

Malika Meghjani

Assistant Professor

SUTD

Ayoung Kim

Associate Professor

Seoul National University

Huy Trong Tran

Assistant Professor

UIUC

Jaemyung Ahn

Associate Professor

KAIST

Presentation Sessions

All Times are Korean Standard Time (KST) 

Presentation Session 1

Vishnu D Sharma, Harnaik Dhami, and Pratap Tokekar, Pred-NBV: Prediction-guided Next-Best-View Planning for 3D Object Reconstruction (Virtual)

Zakariya Laouar, Zachary Sunberg, HIPPO: Human-Informed Planning with Probabilistic Observations

Changkyo Shin, Ofer Dagan, Nisar R Ahmed, and Han-Lim Choi, Fault-tolerant Bayesian Data Fusion Using Reliability Variables and Mixture Models

Su-Jeong Park, Zachary Sunberg, Han-Lim Choi, One Step Look-ahead Trajectory Games with Motion Primitives for Mixed Strategies

Mayuree Binjolkar, Sami Park, Se Min Kim, Explainability as a Path to Foster Trust in Autonomous Driving Decision-Making

Hongro Jang, Junhee Lee, Hyondong Oh, Efficient Information-Driven Strategy for Localizing and Searching Multiple Gas Sources in Turbulent Environments

Seung Keol Ryu,  Michael Moncton, Han-Lim Choi, Eric W Frew, Path Planning in 3D with Motion Primitives for Wind Energy-Harvesting Fixed-Wing Aircraft


Presentation Session 2

Ofer Dagan, Christopher Funk, Nisar R Ahmed, Benjamin Noack, Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation (Virtual)

Chaehyeon Song, Ayoung Kim, Sungho Yoon, Minhyeok Heo, Sujung Kim, Deep Ego-lane Inference for Lane-level Navigation via Vanishing Points and Lines

Alec Reed, Christoffer Heckman, Looking Around Corners: Generative Methods in Terrain Extension

Giovanni Cioffi, Leonard Bauersfeld, Davide Scaramuzza, HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO

Junwon Seo, Jungwi Mun, Taekyung Kim, Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception

Dain Yoon, Chang-Hun Lee, A Computationally Effective Multi-Output Recursive Gaussian Process-based Path-following Guidance for Unmanned Aerial Vehicle

Hyo-Sang Shin, Teng Li, Decentralised Sample Threshold Task Allocation for Multiple Robots

Minwoo Kim, Geunsik Bae, Heejung Shin, Pawel Ladosz, Hyondong Oh, Learning From the Best: Inverse Imitation Learning for Agile Drone Flight

Organizers

Zakariya Laouar

Ph.D. Student

CU Boulder

Himanshu Gupta

Ph.D. Student

CU Boulder

Qi Heng Ho

Ph.D. Student

CU Boulder

Juyeop Han

Researcher

KAIST

Ramya Kanlapuli

Postdoctoral Fellow

CU Boulder

Christoffer Heckman

Assistant Professor

CU Boulder

Zachary Sunberg

Assistant Professor

CU Boulder

Han-Lim Choi

Professor

KAIST