Modeling take-over performance in level 3 conditionally automated vehicles

Christian Gold, Riender Happee, Klaus J. Bengler

Research output: Contribution to journalArticleScientificpeer-review

42 Citations (Scopus)

Abstract

Taking over vehicle control from a Level 3 conditionally automated vehicle can be a demanding task for a driver. The take-over determines the controllability of automated vehicle functions and thereby also traffic safety. This paper presents models predicting the main take-over performance variables take-over time, minimum time-to-collision, brake application and crash probability. These variables are considered in relation to the situational and driver-related factors time-budget, traffic density, non-driving-related task, repetition, the current lane and driver’s age. Regression models were developed using 753 take-over situations recorded in a series of driving simulator experiments. The models were validated with data from five other driving simulator experiments of mostly unrelated authors with another 729 take-over situations. The models accurately captured take-over time, time-to-collision and crash probability, and moderately predicted the brake application. Especially the time-budget, traffic density and the repetition strongly influenced the take-over performance, while the non-driving-related tasks, the lane and drivers’ age explained a minor portion of the variance in the take-over performances
Original languageEnglish
Pages (from-to)3-13
JournalAccident Analysis & Prevention
Volume116 (2018)
DOIs
Publication statusPublished - 2017

Keywords

  • Automated driving
  • Take-Over
  • Modeling
  • Driver performance
  • Regression
  • Human factors

Fingerprint Dive into the research topics of 'Modeling take-over performance in level 3 conditionally automated vehicles'. Together they form a unique fingerprint.

Cite this