Background: Recent advances in the growing domain of automated driving suggest the need for thoughtful design of human-computer interaction strategies. For example, human drivers can process scene variability on implicit levels, but automated systems require explicit rule-based judgments of similarity and difference. What level of abstraction an automation uses in its visual perception may mean the difference between effective human-automation communication, or “uncanny valley”-like conflicts leading to problems of automation disuse, misuse, or abuse. Purpose of study: In the present research, different quantifications (semantic coding vs. computer vision features) of driving scene-to-scene similarity and difference were compared against intuitive human judgments as a reference point for future human-automation interactions.
|Number of pages||1|
|Publication status||Published - 2016|
|Event||HFES 2016: Annual Meeting Human Factors and Ergonomics Society : Human Factors and User Needs in Transport, Control, and the Workplace - Prague, Czech Republic|
Duration: 26 Oct 2016 → 28 Oct 2016
|Conference||HFES 2016: Annual Meeting Human Factors and Ergonomics Society|
|Period||26/10/16 → 28/10/16|