Na Du

schoolUniversity of Pittsburghbar_chart#1459 Global Rank

Total Papers

HCI Rank

#1459

Research Areas

Years Active

Recent Publications

How Humans Naturally Refer to Targets: Understanding Multimodal Instruction Patterns in Human–Robot Interaction

Current multimodal instruction-recognition algorithms in human-robot interaction, developed largely from a purely technical perspective, remain rigid and incomplete in their use of human communicative cues. Therefore, a full understanding of how humans naturally refer to targets in interaction is central to enabling r…

CHI '26CorrespondingExplainable AI (XAI) +1

When to Explain: Modeling User Need for Explanations in Real-World Autonomous Driving

The integration of artificial intelligence into autonomous vehicles (AVs) raises transparency challenges that can hinder user acceptance and experience. To address when users need AV explanations, we created a large-scale dataset of 3327 diverse driving scenarios, each paired with a user-friendly explanation, and cond…

CHI '25CorrespondingExplainable AI (XAI) +1

Explanations Help: Leveraging Human Capabilities to Detect Cyberattacks on Automated Vehicles

Existing defense strategies against cyberattacks on automated vehicles (AVs) often overlook the great potential of humans in detecting such attacks. To address this, we identified three types of human-detectable attacks targeting transportation infrastructure, AV perception modules, and AV execution modules. We propos…

CHI '24CorrespondingAutomated Driving Interface & Takeover Design +1

One Size Does Not Fit All: Designing and Evaluating Criticality-Adaptive Displays in Highly Automated Vehicles

To promote drivers' overall experiences in highly automated vehicles, we designed three objective criticality-adaptive displays:IO display highlighting Influential Objects, CO display highlighting Critical Objects, and ICO display highlighting Influential and Critical Objects differently. We conducted an online video-…

CHI '24CorrespondingAutomated Driving Interface & Takeover Design +1

Understanding Human-machine Cooperation in Game-theoretical Driving Scenarios amid Mixed Traffic

Introducing automated vehicles (AVs) on roads may challenge established norms as drivers of human-driven vehicles (HVs) interact with AVs. Our study explored drivers' decisions in game-theoretical scenarios amid mixed traffic using an online survey study. We manipulated factors including interaction types (HV-HV vs. H…

HRI '24CorrespondingAutomated Driving Interface & Takeover Design +2

Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles

The rapid evolution of automated vehicles (AVs) has the potential to provide safer, more efficient, and comfortable travel options. However, these systems face challenges regarding reliability in complex driving scenarios. Recent explainable AV architectures, whether designed end-to-end or through pipelines, neglect c…

AUTO_UI '211st AuthorAutomated Driving Interface & Takeover Design +2

Designing Alert Systems in Takeover Transitions: The Effects of Display Information and Modality

In conditionally automated driving, in-vehicle alert systems can provide drivers with information to assist their takeovers from automated driving. This study investigated how display modality and information influenced drivers' acceptance of the in-vehicle alert systems under different event criticality situations. W…

Quick Actions

Share this page

ios_share

X in mail

https://hci.top/en/authors/12392?source=all