71 research outputs found
Privacy-Preserving Vehicle Assignment for Mobility-on-Demand Systems
Urban transportation is being transformed by mobility-on-demand (MoD)
systems. One of the goals of MoD systems is to provide personalized
transportation services to passengers. This process is facilitated by a
centralized operator that coordinates the assignment of vehicles to individual
passengers, based on location data. However, current approaches assume that
accurate positioning information for passengers and vehicles is readily
available. This assumption raises privacy concerns. In this work, we address
this issue by proposing a method that protects passengers' drop-off locations
(i.e., their travel destinations). Formally, we solve a batch assignment
problem that routes vehicles at obfuscated origin locations to passenger
locations (since origin locations correspond to previous drop-off locations),
such that the mean waiting time is minimized. Our main contributions are
two-fold. First, we formalize the notion of privacy for continuous
vehicle-to-passenger assignment in MoD systems, and integrate a privacy
mechanism that provides formal guarantees. Second, we present a scalable
algorithm that takes advantage of superfluous (idle) vehicles in the system,
combining multiple iterations of the Hungarian algorithm to allocate a
redundant number of vehicles to a single passenger. As a result, we are able to
reduce the performance deterioration induced by the privacy mechanism. We
evaluate our methods on a real, large-scale data set consisting of over 11
million taxi rides (specifying vehicle availability and passenger requests),
recorded over a month's duration, in the area of Manhattan, New York. Our work
demonstrates that privacy can be integrated into MoD systems without incurring
a significant loss of performance, and moreover, that this loss can be further
minimized at the cost of deploying additional (redundant) vehicles into the
fleet.Comment: 8 pages; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), 201
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Co-Evolving Built Environments and Mobile Autonomy for Future Transport and Mobility
What would an autonomous vehicle’s dream city look like? Autonomous navigation and automated mobility are hard problem that are compounded by the unpredictability and dynamics of urban environment. As we re-think and re-design our built environments, we have the unique opportunity to transform them so that automated mobility systems a can perform efficiently and robustly. The main idea behind this project is to tackle the challenges of autonomous navigation and automated mobility by explicitly considering the coupling of the autonomous vehicles with the environments that they operate in
Redundant Robot Assignment on Graphs with Uncertain Edge Costs
We provide a framework for the assignment of multiple robots to goal locations, when robot travel times are uncertain. Our premise is that time is the most valuable asset in the system. Hence, we make use of redundant robots to counter the effect of uncertainty and minimize the average waiting time at destinations. We apply our framework to transport networks represented as graphs, and consider uncertainty in the edge costs (i.e., travel time). Since solving the redundant assignment problem is strongly NP-hard, we exploit structural properties of our problem to propose a polynomial-time solution with provable sub-optimality bounds. Our method uses distributive aggregate functions, which allow us to efficiently (i.e., incrementally) compute the effective cost of assigning redundant robots. Experimental results on random graphs show that the deployment of redundant robots through our method reduces waiting times at goal locations, when edge traversals are uncertain
Environment Optimization for Multi-Agent Navigation
Traditional approaches to the design of multi-agent navigation algorithms
consider the environment as a fixed constraint, despite the obvious influence
of spatial constraints on agents' performance. Yet hand-designing improved
environment layouts and structures is inefficient and potentially expensive.
The goal of this paper is to consider the environment as a decision variable in
a system-level optimization problem, where both agent performance and
environment cost can be accounted for. We begin by proposing a novel
environment optimization problem. We show, through formal proofs, under which
conditions the environment can change while guaranteeing completeness (i.e.,
all agents reach their navigation goals). Our solution leverages a model-free
reinforcement learning approach. In order to accommodate a broad range of
implementation scenarios, we include both online and offline optimization, and
both discrete and continuous environment representations. Numerical results
corroborate our theoretical findings and validate our approach
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