Take-over performance in evasive manoeuvres

Riender Happee · Jonas Radlmayr · Sebastian Hergeth · Klaus J. Bengler

89 citations0/0 files storedAccident Analysis & Prevention

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Human-Automation Interaction and Safety Traffic and Road Safety Traffic control and management

Abstract

We investigated after effects of automation in take-over scenarios in a high-end moving-base driving simulator. Drivers performed evasive manoeuvres encountering a blocked lane in highway driving. We compared the performance of drivers 1) during manual driving, 2) after automated driving with eyes on the road while performing the cognitively demanding n-back task, and 3) after automated driving with eyes off the road performing the visually demanding SuRT task. Both minimum time to collision (TTC) and minimum clearance towards the obstacle disclosed a substantial number of near miss events and are regarded as valuable surrogate safety metrics in evasive manoeuvres. TTC proved highly sensitive to the applied definition of colliding paths, and we prefer robust solutions using lane position while disregarding heading. The extended time to collision (ETTC) which takes into account acceleration was close to the more robust conventional TTC. In line with other publications, the initial steering or braking intervention was delayed after using automation compared to manual driving. This resulted in lower TTC values and stronger steering and braking actions. Using automation, effects of cognitive distraction were similar to visual distraction for the intervention time with effects on the surrogate safety metric TTC being larger with visual distraction. However the precision of the evasive manoeuvres was hardly affected with a similar clearance towards the obstacle, similar overshoots and similar excursions to the hard shoulder. Further research is needed to validate and complement the current simulator based results with human behaviour in real world driving conditions. Experiments with real vehicles can disclose possible systematic differences in behaviour, and naturalistic data can serve to validate surrogate safety measures like TTC and obstacle clearance in evasive manoeuvres.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 89 citations exist upstream.

References

2013“Take over!” How long does it take to get the driver back into the loop?2014Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence 2014Transition to manual: Driver behaviour when resuming control from a highly automated vehicle 2015What determines the take-over time? An integrated model approach of driver take-over after automated driving 2014How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving 2016The influence of age on the take-over of vehicle control in highly automated driving 2016Human factors of transitions in automated driving: A general framework and literature survey 2007Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems

Cited by

2019Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving 2021Takeover performance evaluation using driving simulation: a systematic review and meta-analysis 2019Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving 2019

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.