Back to all work HAII · Human Factors · Research

NHanCE Autonomous Vehicle Research

How does initial exposure to accurate or inaccurate AV confidence information shape driver behavior, trust, and physiological response over time, particularly among elderly users? This is the question I'm designing and researching around at NHanCE Lab.

RoleUndergraduate Research Assistant

TimelineFeb 2026 to Present

ToolsFigma, Python, HMI Design

TeamSolo, PhD-mentored

Dash AVS in action. Check out the source code and the demo. In this Dash AVS app, you can play an interactive clip containing a scene, which is a segment of a car trip with data collected from LIDAR sensors, cameras, GPS, the car itself, and from human annotators (e.g. the bounding box annotations).

Overview

Autonomous vehicles constantly communicate confidence to the humans inside them, whether or not that confidence is accurate. My research at NHanCE Lab looks at what happens when that first signal is right versus wrong, and how that shapes a rider's trust, behavior, and even physiological response over time, with a particular focus on elderly users.

The work spans three threads: grounding the study in existing literature on trust calibration, building the real-time confidence display participants interact with, and communicating findings across a cross-disciplinary research team.

Background

How does information accuracy shape trust in autonomous vehicles?

Read more Read less

Autonomous vehicles are classified by the Society of Automotive Engineers into six levels of automation. At SAE Level 3, a vehicle can handle most driving tasks on its own, steering, braking, monitoring the environment, but it is not fully autonomous. It still requires a human driver to be available and ready to take control when the system reaches its limits, like navigating unexpected road conditions or complex intersections. Think of it as a co-pilot that does most of the work but can hand the controls back to you at any moment.

To help drivers know when to step in, Level 3 AVs display real-time confidence information through in-vehicle human-machine interfaces, essentially showing how certain the system is about handling a given situation. The problem is that this confidence display is not always accurate. Sensors fail, systems misjudge conditions, and when drivers repeatedly encounter information that does not match reality, they form cognitive biases that shape whether they trust the system, and when they decide to take over.

Key literature finding

Trust calibration over time

How initial exposure to accurate vs. inaccurate AV confidence information creates diverging trust trajectories

Research consistently shows that a rider's first exposure to AV confidence information sets the trajectory for everything after. When that first signal is accurate, trust climbs quickly and settles into a stable, well-calibrated range. When it's wrong, even once, trust drops sharply and only partially recovers, even after a long run of accurate signals that follow.

Accurate first

Trust anchors high and persists

Inaccurate first

Trust degrades, only partial recovery

        
        Accurate information first, trust calibrates well and persists
      

        
        Inaccurate information first, trust degrades, limited recovery
      

Key sources: Lee & See (2004) · Mishler & Chen (2023) · Liu et al. (2024) · Tijerina et al. (2016) · Pennycook et al. (2018) · Endsley (1988)

Process

Conducted a comprehensive review of how users develop and calibrate trust in automated systems, and how elderly populations specifically perceive and respond to AV technology, covering cognitive load, trust repair after failures, and age-related differences in automation acceptance.

Plotly Dash AVS Explorer, an open-source LIDAR and sensor visualization tool used as a base UI reference

Designed and built the real-time HMI participants see during the study, visualizing the AV's self-assessed confidence navigating a roadway obstacle, clear enough for fast interpretation under real driving conditions.

Base UI referenced Plotly's Dash AVS Explorer

Presented research progress and HCI concepts to collaborators across engineering and human factors, translating technical findings into accessible insights for a cross-disciplinary audience.

AI & Technical Approach

Prototyped and refined the confidence display in Figma before building it as a real, working interface in Python. Used Claude Code to move quickly between iterations while keeping the interaction design decisions, what to show, when, and how urgently, my own.

Figma Python Claude Code

Where it stands

Human factorsHands-on experience in research methodology, from literature synthesis to protocol design

Inclusive designDirect experience designing for a vulnerable user population, not just a general audience

Technical fluencyReal-time data visualization with Python, applied to a live research context