Tools for transport: Driven to learn with connected vehicles

Nichole Morris, Curtis Craig, Jessica H Mirman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Vehicle automation and assistance technologies have been touted as a means by which to reduce traffic collisions by minimizing or eliminating “error-prone” and inefficient human operators. In concept, automation exists on a continuum that includes engaged driving by a human operator augmented by automated support features, vigilant driver monitoring of vehicle behavior with the possibility of driver take-over, to full automation with no active monitoring by a human operator. Moreover, the degree of automation varies by vehicle features (e.g., lane centering, emergency braking, adaptive cruise control, parking), by setting, meaning that automated features may or may not be available depending on specific attributes of the traffic environment (e.g., traffic volume, road geometry, etc), and by implementation (e.g., haptic vs auditory warnings). Thus, these automotive “transportation tools” are highly heterogeneous and pose unique challenges and opportunities for driver training. In this paper, we report the results of an experimental study (n=36) to determine if enhanced vehicle feedback influences driver trust, effort, frustration, and performance (indexed by reaction time) in a virtual driving environment. Results are contextualized in the extant literature on learning to operate motor vehicles and outline key research questions essential for understanding the processes by which skilled performance develops with respect to a real-world practical tool: the increasingly automated automobile.
Original languageEnglish
JournalTopics in Cognitive Science
Early online date10 Jul 2021
DOIs
Publication statusE-pub ahead of print - 10 Jul 2021

Keywords

  • tools
  • connected vehicles
  • automated vehicles
  • vehicle automation
  • learning to drive
  • mental models
  • ADAS
  • novice drivers
  • collision warnings

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