Driver Training for Automated Vehicle Technology – Knowledge,Behaviors,and Perceived Familiarity

Abstract

Advanced driver-assistance systems and partial driving automation are becoming increasingly common, yet despite their growing prevalence, drivers seem to know very little about them. Previous studies have found that owners of ADAS equipped vehicles have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. The purpose of this study was to determine the effectiveness of two training strategies on drivers’ knowledge and perceived familiarity of vehicle automation as well as their environment monitoring behaviors during system use. Forty volunteers participated in a multi-stage research study in which they were exposed to either a conventional training protocol, self-learning through the owner’s manual, or an experimental (multimedia) training protocol, using the in-vehicle display technologies as training tools. Results indicate training strategy elicits limited differences in knowledge and no difference in driver behaviors or attitudes. Behaviors and attitudes were heavily influenced by time and experience with the driving automation system while knowledge of the vehicle systems remained unchanged.

References

Abraham

Reimer

Seppelt

Fitzgerald

Coughlin

J. F.

(2017). Consumer Interest in Automation: Preliminary Observations Exploring a Year’s Change. (May 2016), 1–20.

Banks

V. A.

Eriksson

O’donoghue

Stanton

N. A.

(2017). Is partially automated driving a bad idea? Observations from an on-road study. Applied Ergonomics, 68, 138–145. https://doi.org/10.1016/j.apergo.2017.11.010

Cannon-Bowers

J. A.

Burns

J. J.

Salas

Pruitt

J. S.

(1998). Advanced Technology in Scenario-Based Training. In Cannon-Bowers

J. A.

Salas

(Eds.), Making decisions under stress: Implications for individual and team training (pp. 365–374). https://doi.org/10.1037/10278-014

Consumer Reports. (2019). Cars With Advanced Safety Systems - Consumer Reports. Retrieved February 19, 2019, from Consumer Reports website: https://www.consumerreports.org/car-safety/cars-with-advanced-safety-systems/

Coultas

C. W.

Grossman

Salas

(2012). Design, Delivery, Evaluation, and Transfer of Training Systems. In Salvendy

(Ed.), Handbook of Human Factors and Ergonomics (4th ed., pp. 490–533). https://doi.org/10.1002/9781118131350.ch17

Endsley

M. R.

(2000). Situation Models: An Avenue to the Modeling of Mental Models. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(1), 61–64. https://doi.org/10.1177/154193120004400117

Gee

J. P.

(2008). Game-like learning: An example of situated learning and implications for opportunity to learn. Assessment, Equity, and Opportunity to Learn, 200–221. https://doi.org/https://dx-doi-org.web.bisu.edu.cn/10.1017/CBO9780511802157.010

Kyriakidis

de Winter

J. C. F.

Stanton

Bellet

van Arem

Brookhuis

… Happee

(2017). A human factors perspective on automated driving. Theoretical Issues in Ergonomics Science, pp. 1–27. https://doi.org/10.1080/1463922X.2017.1293187

Langer

E. J.

Moldoveanu

(2000). The Construct of Mindfulness. Journal of Social Issues, 56(1), 1–4.

10.

Larsson

A. F. L.

(2012). Driver usage and understanding of adaptive cruise control. Applied Ergonomics, 43(3), 501–506. https://doi.org/10.1016/j.apergo.2011.08.005

11.

Lee

J. D.

See

K. A.

(2004). Trust in automation: designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50.30392

12.

Llaneras

R. E.

(2006). Exploratory Study of Early Adopters, Safety Related Driving With Advanced Technologies. Draft Final Task 2 Report: In-Vehicle Systems Inventory, Recruitment Methods & Approaches, and Owner Interview Results. Retrieved from https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/doths_809972.pdf

13.

McDonald

A. B.

Carney

McGehee

D. V.

(2018). Vehicle Owners’ Experiences with and Reactions to Advanced Driver Assistance Systems. (September), 1–2.

14.

McDonald

A. B.

Reyes

Roe

McGehee

D. V.

(2017). Driver Understanding of ADAS and Evolving Consumer Education. 25th International Technical Conference on the Enhanced Safety of Vehicles. Retrieved from http://indexsmart.mirasmart.com/25esv/PDFfiles/25ESV-000373.pdf

15.

Merat

Jamson

A. H.

Lai

F. C. H.

Carsten

(2012). Highly Automated Driving, Secondary Task Performance, and Driver State. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 762–771. https://doi.org/10.1177/0018720812442087

16.

Olsen

S. E.

Rasmussen

(1989). The Reflective Expert and the Prenovice: Notes on Skill-, Rule-, and Knowledge-based Performance in the Setting of Instruction and Training. In Bainbridge

Quintanilla

S. A. R.

(Eds.), Developing Skills with Information Technology (pp. 9–33). https://doi.org/10.1017/S000748530002229X

17.

SAE. (2018). J3016 - Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. https://doi.org/10.4271/2012-01-0107.

18.

Stanton

N. A.

Young

M. S.

McCaulder

(1997). Drive-by-wire: The case of driver workload and reclaiming control with adaptive cruise control. Safety Science, 27(2–3), 149–159. https://doi.org/10.1016/S0925-7535(97)00054-4

19.

Vasquez

H. M.

(2015). The Effects of a Driver Monitoring System on Drivers’ Trust , Satisfaction , and Performance with an Automated Driving System (Virginia Tech). Retrieved from https://vtechworks.lib.vt.edu/handle/10919/64506