Exploring Human-Machine Interaction in Level 4 AVS: A Study on Take-Over Maneuver in a Roundabout Scenario

Abstract Currently, one of the most important challenges in the field of road vehicles concerns the reduction of road accidents and fatalities. The development of new advanced driver assistance or automated driving technologies may contribute to this goal in the future. This research delves into the analysis of Level 4 (L4) automated vehicles (AVs), specifically focusing on the take-over manoeuvre when the AV exits its Operational Design Domain (ODD). It aims to analyse how the take-over changes, considering factors such as the human driver’s state, the machine’s operation, and the output devices used to alert the driver. To carry out the preliminary tests, an urban road network, including a mini-roundabout, was replicated in the VI-WorldSim environment, and real human drivers experienced the simulation through a dynamic driving simulator. Drivers were assessed qualitatively, using questionnaires to analyse their perception of L4 AVs, and quantitatively, examining the take-over time needed and their heart rate to investigate the physiological response. Preliminary results show that participants perceived the simulation as realistic and immersive, and they reported an ease of use of AVs. Furthermore, physiological data highlighted an increase in heart rate upon the start of take-over, validating the cognitive load of the individual. The study is part of the HL4IT project aimed at supporting the development of Level 4 AVs in an Italian traffic environment.

Authors

Institutions

Publication Details

Published
2024-08-25
DOI
https://doi.org/10.1115/detc2024-143430
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.64
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Human-Machine Interaction in Level 4 AVS: A Study on Take-Over Maneuver in a Roundabout Scenario

Andrea Galbiati, Giorgio Previati, Andrea Fossati, Edoardo Sabbioni et al.
1 citations
Human-Automation Interaction and Safety
0.64
article

Exploring Human-Machine Interaction in Level 4 AVS: A Study on Take-Over Maneuver in a Roundabout Scenario

Andrea Galbiati, Giorgio Previati, Andrea Fossati, Edoardo Sabbioni, Gianpiero R M Mastinu, M.G. Signorini, Antonella Somma, Linda Boscaro, Massimiliano Gobbi, Lorenzo Uccello, Luca Subitoni, Veronica De Guglielmo
article en
1 citations

Abstract

Abstract Currently, one of the most important challenges in the field of road vehicles concerns the reduction of road accidents and fatalities. The development of new advanced driver assistance or automated driving technologies may contribute to this goal in the future. This research delves into the analysis of Level 4 (L4) automated vehicles (AVs), specifically focusing on the take-over manoeuvre when the AV exits its Operational Design Domain (ODD). It aims to analyse how the take-over changes, considering factors such as the human driver’s state, the machine’s operation, and the output devices used to alert the driver. To carry out the preliminary tests, an urban road network, including a mini-roundabout, was replicated in the VI-WorldSim environment, and real human drivers experienced the simulation through a dynamic driving simulator. Drivers were assessed qualitatively, using questionnaires to analyse their perception of L4 AVs, and quantitatively, examining the take-over time needed and their heart rate to investigate the physiological response. Preliminary results show that participants perceived the simulation as realistic and immersive, and they reported an ease of use of AVs. Furthermore, physiological data highlighted an increase in heart rate upon the start of take-over, validating the cognitive load of the individual. The study is part of the HL4IT project aimed at supporting the development of Level 4 AVs in an Italian traffic environment.

Vita-Salute San Raffaele University (IT), Politecnico di Milano (IT)
Sustainable cities and communities
Openalex Percentile: Top 25%
Human-Automation Interaction and Safety
0.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.