School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

NAVIGATING MIXED TRAFFIC: LATERAL AND LONGITUDINAL CONTROL FOR CONNECTED AND AUTONOMOUS VEHICLES WITH A HUMAN-CENTRIC APPROACH

By Yongyang Liu

Advisor:

Dr. Srinivas Peeta

Committee Members: Dr. Patricia L. Mokhtarian (CEE), Dr. Jorge A. Laval (CEE), Dr. Guanghui Lan (ISyE), Dr. Shubham Agrawal (Clemson University)

Date and Time: November 21, 2024. 1:00 PM EST

Location: Mason 4162

Zoom: https://gatech.zoom.us/j/99832256500

ABSTRACT
With advanced situational awareness and automation capabilities, connected and
autonomous vehicles (CAVs) promise to improve traffic safety, smoothness, and
efficiency. However, the impending coexistence of CAVs with human-driven vehicles
(HDVs) in mixed-traffic environments presents significant challenges, particularly
for lane changes. This dissertation develops human-centric control strategies for
CAVs to improve interactions with HDVs in lane changes. First, the dissertation
introduces a proactive longitudinal control strategy for CAVs to preclude potentially
disruptive HDV lane changes, to improve the stability of CAV platooning operations.
Second, it develops a human-emulation-based approach that uses legible CAV
motions to assist HDV lane changes, to enhance HDV drivers’ mental comfort and
CAV platoon smoothness. Next, the dissertation proposes a hierarchical humancentric
control strategy for CAVs to manage HDV lane changes to improve overall traffic performance while ensuring the mental comfort of both HDV drivers and CAV
users. Then, using a driving simulator environment, it explores the challenges
experienced by HDV drivers during lane changes in mixed traffic and analyzes their
behavior evolution under repeated interactions with CAVs. The dissertation then
proposes a comprehensive safety performance framework that combines multiple
surrogate safety metrics to analyze the safety of HDV lane changes in mixed traffic.
Finally, by integrating human intelligence and cognition with CAV situational
awareness and response time capabilities, it develops a human-like lane-change
control strategy for CAVs that leverages human-like lane-change behavior to
improve CAV-HDV interactions.