Demo Data from VLDB 2026 / reviewers in the wild / expert
Weiqi Mei
dblp:433/1308
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-0284-464XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% | |
| Artificial intelligence
1 paper |
Autonomous driving · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › augmented reality display
augmented reality head-up display |
1.0 | 1 | 2026 | Prosocial AI Apologies on the Road: Emotional Compensation for Other Drivers' Misbehavior · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
mixed-design experiment · 3.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Prosocial AI Apologies on the Road: Emotional Compensation for Other Drivers' MisbehaviorabstractAggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts. Jun Zhang 0072, Weiqi Mei, Weibo Ling, Qianwen Fu, Jie Zhang 0090, Fang You, Yan Luximon |
CHI | 2 |