Drivers and automation: a systematic review on road suitability for assisted and partially/conditionally automated vehicles

• Rapid vehicle automation is outpacing road upgrades, making integration difficult. • This review examines the impact of automation on driver behaviour and road suitability. • Results show deterioration in driver behaviour: slower decisions, longer tasks and more variability. • Knowledge gaps remain on the impact of automation on merging, weaving and overtaking. • Analysis suggests detailed methods, diverse demographics and robust validation. Vehicle automation technology is advancing faster than our road system can adapt. It is vital to integrate automated vehicles effectively while ensuring drivers can safely and efficiently perform driving tasks when required. This systematic review examines vehicle automation’s impact on driver behaviour and performance with respect to the suitability of existing roads. Focusing on guidance tasks influenced by road geometry and traffic, the review screens 1569 studies; forty met eligibility criteria. The results reveal a significant deterioration in driver behaviour and performance during guidance tasks, characterized by protracted decision-making and reaction times, longer durations for guidance tasks, and increased variability in gaze movement, lateral displacement, and longitudinal and traversal kinematic parameters. While some technological solutions have been suggested to alleviate these declines, the need for more research into how road design affects driver behaviour in automated settings remains evident. A key finding is the limited evidence on both road geometric variables and traffic-related factors; the latter appears more often but remains limited and heterogeneously reported. Significant gaps in knowledge persist regarding automation’s impact on driving guidance in merging, diverging, weaving, and overtaking scenarios, especially on two-lane roads, requiring further study. Additionally, there are gaps related to environmental factors in the context of automation’s impact on guidance tasks and coverage of older drivers and interactions with pedestrians/cyclists and heavy vehicles (including professional drivers) were limited or absent. These points identify the most critical gaps and outline concrete directions for future work on road suitability under assisted and partially/conditionally automated driving.

Authors

Institutions

Publication Details

Journal
Transportation Research Interdisciplinary Perspectives
Published
2026-04-17
DOI
https://doi.org/10.1016/j.trip.2026.101986
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Drivers and automation: a systematic review on road suitability for assisted and partially/conditionally automated vehicles

Joey Lee, Arastoo Karimi, Marco Bassani, Suyi Mao et al.
Transportation Research Interdisciplinary Perspectives
Autonomous Vehicle Technology and Safety
article

Drivers and automation: a systematic review on road suitability for assisted and partially/conditionally automated vehicles

Joey Lee, Arastoo Karimi, Marco Bassani, Suyi Mao, Giorgio Lotto
article en

Abstract

• Rapid vehicle automation is outpacing road upgrades, making integration difficult. • This review examines the impact of automation on driver behaviour and road suitability. • Results show deterioration in driver behaviour: slower decisions, longer tasks and more variability. • Knowledge gaps remain on the impact of automation on merging, weaving and overtaking. • Analysis suggests detailed methods, diverse demographics and robust validation. Vehicle automation technology is advancing faster than our road system can adapt. It is vital to integrate automated vehicles effectively while ensuring drivers can safely and efficiently perform driving tasks when required. This systematic review examines vehicle automation’s impact on driver behaviour and performance with respect to the suitability of existing roads. Focusing on guidance tasks influenced by road geometry and traffic, the review screens 1569 studies; forty met eligibility criteria. The results reveal a significant deterioration in driver behaviour and performance during guidance tasks, characterized by protracted decision-making and reaction times, longer durations for guidance tasks, and increased variability in gaze movement, lateral displacement, and longitudinal and traversal kinematic parameters. While some technological solutions have been suggested to alleviate these declines, the need for more research into how road design affects driver behaviour in automated settings remains evident. A key finding is the limited evidence on both road geometric variables and traffic-related factors; the latter appears more often but remains limited and heterogeneously reported. Significant gaps in knowledge persist regarding automation’s impact on driving guidance in merging, diverging, weaving, and overtaking scenarios, especially on two-lane roads, requiring further study. Additionally, there are gaps related to environmental factors in the context of automation’s impact on guidance tasks and coverage of older drivers and interactions with pedestrians/cyclists and heavy vehicles (including professional drivers) were limited or absent. These points identify the most critical gaps and outline concrete directions for future work on road suitability under assisted and partially/conditionally automated driving.

Transportation Research Interdisciplinary PerspectivesVol. 37
University of Central Florida (US), Central South University (CN), Politecnico di Torino (IT)
Compagnia di San Paolo, European Commission, National Natural Science Foundation of China, China Scholarship Council, HORIZON EUROPE Marie Sklodowska-Curie Actions
Openalex Percentile: Top 12%
Autonomous Vehicle Technology and Safety
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.