Autonomy in Transportation: Emerging Challenges in multi-agent planning and control

Overview

With the advent of self-driving cars and delivery robots, automation promises to revolutionize the transportation sector in our modern society. Unfortunately, autonomous vehicles and robots in real-world traffic often cause collisions and experience gridlock, raising doubts about their ability to safely and efficiently interact with surrounding vehicles and pedestrians. To address these issues, researchers across control, robotics, and machine learning have developed a wide range of methods to improve the reliability of autonomous vehicles and robots during their deployment. Such techniques include implicit scene representations for high-accuracy autonomous perception, to dynamic games and multi-agent reinforcement learning for interactive prediction and motion planning, to distributionally robust control or barrier function or model predictive control-based methods for designing safe control strategies. However, it is often unclear whether the inefficient or unsafe behaviors exhibited by autonomous agents should be attributed to failures of the perception, estimation, or multi-agent prediction and planning stack, and could thus be mitigated by designing more reliable methods for the corresponding portion of the autonomy pipeline. Moreover, since the design and deployment of these methods in real-world interactive robotics applications are nascent, the relative merits and limitations of each method remain unclear. Thus, to develop holistic perspectives of state-of-the-art approaches for assured multi-agent autonomy, this workshop will address the question: How can we design prediction and planning paradigms to guarantee safe and performant multi-agent interactions in emerging autonomous transportation platforms?

Our workshop aims to tackle the above question by examining various perspectives from the fields of controls and robotics on emerging and urgent challenges in autonomous transportation. Concretely, we will bring together speakers representing expertise in control theory, dynamic games, and machine learning. We will hear from Dr. Sarah Li (GT Aero), who will present her work on stochastic reach-avoid problems within the context of Markov Potential Games; Dr. David Fridovich-Keil and Dr. Jingqi Li (UT Austin Aero), who will present their work on the design of efficient algorithms to solve multi-agent, non-cooperative dynamic games; Dr. Samuel Coogan (GT ECE), who will present his work on control barrier function-based methods for human-autonomy teaming; and Dr. Chih-Yuan Chiu (GT ECE), who will present an organizer talk on robust game-theoretic motion planning methods for safe multi-agent navigation under intent and dynamics uncertainty. We currently also aim to invite one or two additional researchers in multi-agent autonomy to speak at our workshop, pending their confirmation. Moreover, we will solicit a group of students and postdocs to present posters on a broad range of topics, aiming to seed valuable, cross-disciplinary discussions. Alongside these poster presentations, the broad expertise of our invited speakers will highlight the main challenges in deploying autonomous vehicles in societal-scale transportation systems and the proposed solutions to address these challenges.

More information regarding our invited talks can be found at the Invited Presentations tab.

We have divided our workshop into three session types, each of which allows for a different mixture of voices to be heard:

• Invited Talks - These sessions will allow attendees to learn about recent results and current trends from experts in the research areas of multi-agent motion planning in the context of transportation and autonomous vehicles.

• Interactive Poster Session - This session will showcase the contributed work of young researchers and will provide opportunities for brief talks and informal discussions between participants and speakers. Contributed poster papers will undergo a limited review process, and the presenters will be showcased on the conference website. Presented works may appear elsewhere in the conference program, but need not.

• Panel Discussion - This moderated discussion will include our invited speakers. The moderator will prepare several questions for each panelist. Attendees may also ask questions and participate in the discussion, which the moderator will select for the panelists. Tentative discussion topics include:

• To what extent does perception or estimation error cripple the safety or performance of downstream motion prediction and planning algorithms?

• What are the most pressing current and future challenges in autonomous vehicle planning and prediction, and what are the most promising approaches we have to deal with them?

• How do the mathematical foundations of game theory impact the way we approach challenges and opportunities in controlling individual or multiple autonomous vehicles?

• What are the additional challenges that arise in autonomous transportation due to the multiplicity of vehicle manufacturers with heterogeneous autonomy stacks?

• How does our current understanding of strategic multi-agent decision-making inform the design of environments shared by human-driven and autonomous vehicles?

Areas of interest: 

Autonomous Vehicles, Safety, Multi-Agent Reinforcement Learning, Route Planning and Prediction, Human-Autonomy Teaming

Techniques include: 

Hybrid Systems and Control, Game Theory, Barrier Functions, Markov Games, Dynamic Games, Optimization, Reinforcement Learning