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

要旨

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-based survey study with 295 participants to evaluate them in varying traffic conditions. Results showed that low-trust propensity participants found ICO display more useful while high-trust propensity participants found CO displays more useful. When interacting with vulnerable road users (VRUs), participants had higher situational awareness (SA) but worse non-driving related task (NDRT) performance. Aging and CO displays also led to slower NDRT reactions. Nonetheless, older participants found displays more useful. We recommend providing different criticality-adaptive displays based on drivers' trust propensity, age, and NDRT choice to enhance driving and NDRT performance and suggest carefully treating objects of different categories in traffic.

著者
Yaohan Ding
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Lesong Jia
University of Pittsburgh , Pittsburgh, Pennsylvania, United States
Na Du
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
論文URL

doi.org/10.1145/3613904.3642648

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Autonomous Vehicles

317
5 件の発表
2024-05-15 23:00:00
2024-05-16 00:20:00