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<p>Hi Everyone,</p>
<p>Please come hear Adam tell us about self-driving cars and RL in
his thesis proposal!</p>
<p>Jeff.</p>
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<td>RI Ph.D. Thesis Proposal: Adam Villaflor</td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Date: </th>
<td>Wed, 7 Sep 2022 13:58:33 -0400</td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">From: </th>
<td>Suzanne Muth <a class="moz-txt-link-rfc2396E" href="mailto:lyonsmuth@cmu.edu"><lyonsmuth@cmu.edu></a></td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">To: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:ri-people@lists.andrew.cmu.edu">ri-people@lists.andrew.cmu.edu</a></td>
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<div style=""><font style="" face="arial, sans-serif">Date: 16
September 2022<br>
Time: 1:00 p.m. (ET)<br>
Location: GHC 4405<br>
Zoom Link: <a
href="https://cmu.zoom.us/j/95581369179?pwd=THdjUnVkQnBaUUFlNDdOcDBMcVhNQT09"
target="_blank" style="" moz-do-not-send="true"
class="moz-txt-link-freetext">https://cmu.zoom.us/j/95581369179?pwd=THdjUnVkQnBaUUFlNDdOcDBMcVhNQT09</a><br>
Type: Ph.D. Thesis Proposal<br>
Who: Adam Villaflor<br>
Title: Combining Offline Reinforcement Learning with
Stochastic Multi-Agent Planning for Autonomous Driving</font></div>
<div style=""><font style="" face="arial, sans-serif"><br>
</font></div>
<div style=""><font face="arial, sans-serif">Abstract:</font></div>
<div style=""><font face="arial, sans-serif">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.<br>
<br>
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.</font></div>
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<div style=""><font face="arial, sans-serif">Thesis Committee
Members:</font></div>
<div style=""><font face="arial, sans-serif">Jeff Schneider,
Chair<br>
John Dolan, Co-Chair<br>
David Held<br>
Philipp Krähenbühl (UT Austin)</font></div>
<div style=""><font face="arial, sans-serif"><br>
A draft of the thesis proposal document is available at<br>
</font></div>
<div style=""><a
href="https://drive.google.com/drive/folders/14DoKABsNlCx9AKK9O1ZY77Roau4ugEmG?usp=sharing"
target="_blank" style="" moz-do-not-send="true"><font
style="" face="arial, sans-serif">https://drive.google.com/drive/folders/14DoKABsNlCx9AKK9O1ZY77Roau4ugEmG?usp=sharing</font></a></div>
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