PhD Defense by Anye Zhou

Event Details
  • Date/Time:
    • Tuesday November 29, 2022
      2:00 pm - 4:00 pm
  • Location: Mason 2228
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Summary Sentence: Vehicle Longitudinal Control Under Autonomy, Connectivity, and Mixed-Flow Traffic

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Ph.D. Thesis Defense Announcement 

Vehicle Longitudinal Control Under Autonomy, Connectivity, and Mixed-Flow Traffic

By 

Anye Zhou

Advisor: Dr. Srinivas Peeta

 

Committee Members: Dr. Jorge Laval (CEE), Dr. Patricia Mokhtarian (CEE), Dr. Guanghui Lan (ISyE), Dr. Xiaozheng He (CEE - RPI)

Date & Time: Tuesday, 11/29/2022, 2:00 PM (ET)

Location: Mason 2228 / Zoom Meeting: 99259233768, Passcode: 470938

Rapid advances in connectivity and vehicle automation technologies are enabling the development of connected and autonomous vehicles (CAVs), providing opportunities to improve the current transportation system. CAV longitudinal control can leverage the information obtained using connectivity and sensor measurements to optimize vehicle trajectories to improve travel comfort, safety, fuel economy, and traffic efficiency. However, critical challenges related to vehicle longitudinal control can reduce CAV benefits to the transportation system, including: (i) propagation of traffic congestion due to inappropriate autonomy design, (ii) connectivity disruptions and traffic oscillations induced by human driving behaviors in mixed-flow traffic of CAVs and human-driven vehicles (HDVs), (iii) uncertainties in vehicle dynamics that prevent vehicles from executing planned trajectories, and (iv) failures and falsified information injection in the communication process that compromise safety and mobility.
This dissertation seeks to develop effective and efficient solutions to address challenges in vehicle longitudinal control from the current to the future. First, as current commercially-available adaptive cruise control (ACC) systems exacerbate traffic congestion, a cost-effective control mechanism is developed that enables congestion mitigation without altering existing ACC control algorithms. Second, as the near-term future can entail mixed-flow traffic, a driving simulator study is first used to understand HDV car-following behavior when following CAVs to provide insights on developing intelligent longitudinal control. Then, a cooperative control strategy is proposed to mitigate the connectivity disruptions and traffic oscillations induced by HDVs. Third, for the pure CAV environment in the long-term future, an adaptive smooth switching control strategy and a robust control strategy are proposed to enable CAV operations under communication failures and falsified information injection. Finally, virtual-platooning control and traffic flow regulation are used to devise a cooperative signal-free intersection control strategy for a pure CAV environment to improve traffic efficiency and mobility compared to traditional signalized intersection control.
 

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Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 15, 2022 - 5:09pm
  • Last Updated: Nov 15, 2022 - 5:09pm