SE PhD Final Defense of Yue Zhang

   
Summary

SE PhD Final Defense of Yue Zhang

Description

TITLE: METHODS IN INTELLIGENT TRANSPORTATION SYSTEMS EXPLOITING VEHICLE CONNECTIVITY, AUTONOMY AND ROADWAY DATA

ABSTRACT:Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle and vehicle-to-infrastructure communication, to information fusion from multiple traffic sensing modalities. The first set of problems addressed in this dissertation relates to optimally controlling connected automated vehicles (CAVs) crossing signal-free intersections. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensure the absence of collision. In the meantime, a measurement of passenger comfort is considered while the vehicles make turns. In addition to the first-in-first-out ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This dissertation also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic.

To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the eventdriven aspects of transportation systems, and the effects of communication delays.

In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be non-actionable bumps(e.g., speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs.

COMMITTEE ADVISOR Christos Cassandras, SE/ECE/CISE; Ioannis Paschalidis,SE/ECE/CISE; Calin Belta, SE/ME; Sean Andersson, SE/ME; CHAIR Pirooz Vakili, SE/ME/CISE

Starts

12:00pm on Wednesday, June 19th 2019

End Time

2:00pm

Location

8 Saint Mary's Street, Room 339

Topics

SE Home/CISE

Hosting Professor

Cassandras

 
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