Cathy Wu

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2018-132

September 11, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-132.pdf

Mixed autonomy characterizes problems surrounding the gradual and complex integration of automation and AI into existing systems. In the context of mobility, we consider: how will the gradual introduction of self-driving cars change urban mobility? In this dissertation, we develop machine learning and optimization techniques to address three key challenges: 1) quantifying the behavior of such complex systems, 2) addressing inherent sensing limitations, and 3) mitigating negative effects of introducing the automation.

We demonstrate that deep reinforcement learning (RL) can serve as a unifying framework for studying the behavior of disparate and complex scenarios common in mixed autonomy systems. In particular, using deep RL, we find that automating a small fraction of vehicles in various traffic scenarios can result in a significant system-level velocity increase and numerous emergent driving behaviors. We demonstrate through the development of variance reduction techniques for policy gradient methods, that deep RL has the potential to scale to high-dimensional control systems, such as traffic networks and other mixed autonomy systems. We additionally present Flow, an open source RL platform with the goal of easing the design and study of disparate traffic scenarios. To address sensing limitations inherent when only parts of a system are automated, sensor fusion is explored. In particular, we introduce a convex optimization method for cellular network measurements from AT&T at the scale of the Greater Los Angeles Area, to address a flow estimation problem previously believed to be intractable. Finally, when automation reduces the cost of the activity (of transport), anticipated negative effects include induced demand and increased energy consumption. We study how the design of the mobility system itself can mitigate these effects. In particular, joint work with Microsoft Research provides insight into how high-occupancy vehicle lanes can simultaneously satisfy comfort and time preferences of users, and provide system benefits. We introduce combinatorial optimization methods based on clustering and local search for the resulting ridesharing problem. Together, these learning and optimization methods demonstrate that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility, and shed light into the future study of mixed autonomy systems.

Advisors: Alexandre Bayen

---

BibTeX citation:

@phdthesis{Wu:EECS-2018-132,
    Author= {Wu, Cathy},
    Title= {Learning and Optimization for Mixed Autonomy Systems - A Mobility Context},
    School= {EECS Department, University of California, Berkeley},
    Year= {2018},
    Month= {Sep},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-132.html},
    Number= {UCB/EECS-2018-132},
    Abstract= {Mixed autonomy characterizes problems surrounding the gradual and complex integration of automation and AI into existing systems. In the context of mobility, we consider: how will the gradual introduction of self-driving cars change urban mobility? In this dissertation, we develop machine learning and optimization techniques to address three key challenges: 1) quantifying the behavior of such complex systems, 2) addressing inherent sensing limitations, and 3) mitigating negative effects of introducing the automation.

We demonstrate that deep reinforcement learning (RL) can serve as a unifying framework for studying the behavior of disparate and complex scenarios common in mixed autonomy systems. In particular, using deep RL, we find that automating a small fraction of vehicles in various traffic scenarios can result in a significant system-level velocity increase and numerous emergent driving behaviors. We demonstrate through the development of variance reduction techniques for policy gradient methods, that deep RL has the potential to scale to high-dimensional control systems, such as traffic networks and other mixed autonomy systems. We additionally present Flow, an open source RL platform with the goal of easing the design and study of disparate traffic scenarios. To address sensing limitations inherent when only parts of a system are automated, sensor fusion is explored. In particular, we introduce a convex optimization method for cellular network measurements from AT&T at the scale of the Greater Los Angeles Area, to address a flow estimation problem previously believed to be intractable. Finally, when automation reduces the cost of the activity (of transport), anticipated negative effects include induced demand and increased energy consumption. We study how the design of the mobility system itself can mitigate these effects. In particular, joint work with Microsoft Research provides insight into how high-occupancy vehicle lanes can simultaneously satisfy comfort and time preferences of users, and provide system benefits. We introduce combinatorial optimization methods based on clustering and local search for the resulting ridesharing problem. Together, these learning and optimization methods demonstrate that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility, and shed light into the future study of mixed autonomy systems.},
}

EndNote citation:

%0 Thesis
%A Wu, Cathy 
%T Learning and Optimization for Mixed Autonomy Systems - A Mobility Context
%I EECS Department, University of California, Berkeley
%D 2018
%8 September 11
%@ UCB/EECS-2018-132
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-132.html
%F Wu:EECS-2018-132