Subjective and objective descriptions of driving scenes in support of driver-automation interactions

Delft University of Technology

Subjective and objective descriptions of driving scenes in support of driver-automation interactions

Cabrall, Christopher; Happee, Riender; de Winter, Joost

Publication date 2016

Document Version Final published version

Citation (APA)

Cabrall, C., Happee, R., & de Winter, J. (2016). Subjective and objective descriptions of driving scenes in support of driver-automation interactions. Poster session presented at HFES 2016: Annual Meeting Human Factors and Ergonomics Society , Prague, Czech Republic.

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Subjective and Objective Descriptions of Driving Scenes in

Support of Driver-Automation Interactions

Christopher D. D. Cabrall, Riender Happee, Joost C. F. de Winter

Delft University of Technology

www.hf-auto.eu

Human Factors and Ergonomics Society

Europe Chaper, Annual Conference

October 26 – 28, Prague, Czech Republic

HFAuto is funded by a Marie Curie Initial

Training Network (PITN-GA-2013-605817)

Introduction

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.

Methods

Participants

.

12 MSc students (11 male : 1 female) Mean age = 22.9 yrs old (SD = 1.4)

Mean driving license = 4.8 yrs (SD = 1.9)

Procedure

.

Each participant rated the same 100 randomly paired driving video clips (i.e., 3 seconds long)

on a scale from “0 – Very Different” to “9 – Very Similar”

Results/Conclusions

Scene similarity/difference ratings from semantic coding

quantification showed closer matches to human participant

judgments than those generated from computer vision.

Humans evidence apparent non-random individual

differences in judging various driving scenes. Both ‘meaning’ and particularly ‘feature’ level descriptions require

improvements to coordinate common ground with human intuition of driving scene similarity/difference.

“somewhat similar” … “somewhat different” Road curve? (0 or 1) Traffic? (0 or 1) Misc. details? (0 or 1) intersection, stopping, lane change, signs, paint

“1, 1, 0” “0, 0, 0”

Output = edge(myImage,'Canny'); White pixel count?

“9708 pxls” = 5.5% “somewhat similar” “16635 pxls” = 9.4% Level 3 111 Level 2 110, 101, 011 Level 1 100, 010, 001 Level 0 000