Fwd: RI Ph.D. Thesis Proposal: Adam Villaflor - starting in 8 minutes!

[Jeff Schneider]

Hi Everyone,

Please come hear Adam tell us about self-driving cars and RL in his 
thesis proposal!

Jeff.




-------- Forwarded Message --------
Subject: 	RI Ph.D. Thesis Proposal: Adam Villaflor
Date: 	Wed, 7 Sep 2022 13:58:33 -0400
From: 	Suzanne Muth <lyonsmuth at cmu.edu>
To: 	ri-people at lists.andrew.cmu.edu



Date: 16 September 2022
Time: 1:00 p.m. (ET)
Location: GHC 4405
Zoom Link: 
https://cmu.zoom.us/j/95581369179?pwd=THdjUnVkQnBaUUFlNDdOcDBMcVhNQT09
Type: Ph.D. Thesis Proposal
Who: Adam Villaflor
Title: Combining Offline Reinforcement Learning with Stochastic 
Multi-Agent Planning for Autonomous Driving

Abstract:
Fully autonomous vehicles have the potential to greatly reduce vehicular 
accidents and revolutionize how people travel and how we transport 
goods. Many of the major challenges for autonomous driving systems 
emerge from the numerous traffic situations that require complex 
interactions with other agents. For the foreseeable future, autonomous 
vehicles will have to share the road with human-drivers and pedestrians, 
and thus cannot rely on centralized communication to address these 
interactive scenarios. Therefore, autonomous driving systems need to be 
able to negotiate and respond to unknown agents that exhibit uncertain 
behavior. To tackle these problems, most commercial autonomous driving 
stacks use a modular approach that splits perception, agent forecasting, 
and planning into separately engineered modules. By decomposing 
autonomous driving into smaller modules, it allows for simplifying 
abstractions and greater parallelization of the engineering effort.

However, fully separating prediction and planning makes it difficult to 
reason about how other vehicles will respond to the planned trajectory 
for the controlled ego-vehicle. Thus to maintain safety, many modular 
approaches have to be overly conservative when interacting with other 
agents. Ideally, we want autonomous vehicles to drive in a natural and 
confident manner, while still maintaining safety. We believe that to 
achieve this behavior we need 3 major components. First, we need an 
approach that unifies prediction and planning in a single probabilistic 
closed-loop planning framework. Second, we need to use a multi-agent 
formulation in combination with deep learning models that can scale to 
the complexities of real-world driving and effectively model the 
interactive multi-modal distributions of real-world traffic. Finally, we 
need approaches that can effectively search the space of potential 
multi-agent interactions across time efficiently in order to produce a 
suitable planned behavior. In this proposal, we will show our current 
progress in applying deep offline reinforcement learning to autonomous 
driving, and present future work to continue scaling deep learning 
approaches to more complicated and interactive autonomous driving problems.

Thesis Committee Members:
Jeff Schneider, Chair
John Dolan, Co-Chair
David Held
Philipp Krähenbühl (UT Austin)

A draft of the thesis proposal document is available at
https://drive.google.com/drive/folders/14DoKABsNlCx9AKK9O1ZY77Roau4ugEmG?usp=sharing 
<https://drive.google.com/drive/folders/14DoKABsNlCx9AKK9O1ZY77Roau4ugEmG?usp=sharing>
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