HMI warning strategy based on radar-video fusion perception for freeway merging areas: A risk field approach considering beyond-visual-range event

Under beyond-visual-range conditions where the driver’s sight is obstructed for the freeway merging area, the merging vehicle may seriously affect the mainline driver’s safe passage. This study aims to evaluate the mitigation effect of radar-video fusion-based human–machine interface (rHMI) warnings on the interaction risk imposed on a mainline vehicle by a merging vehicle, addressing the risk warning under beyond-visual-range conditions. Three rHMI warning schemes were developed and tested through driving simulation. They are no radar-video fusion perception without rHMI warning (Baseline), partial coverage of radar-video fusion perception with one-level rHMI warning (Warning I), and full coverage of radar-video fusion perception with two-level rHMI warning (Warning II). The study constructed the kinetic energy field incorporating speed difference (KEF-SD). A convolution operation was used to extract the field strength’s time-varying characteristics. The KEF-SD was compared with surrogate safety measures (SSMs) to demonstrate its performance. Results indicate: (1) rHMI warnings help mainline drivers to effectively reduce speed. The progressive intervention of two-level warning induces earlier speed adjustment; (2) The KEF-SD is reliable and valid. It can more accurately and finely explain the vehicle interaction process under beyond-visual-range conditions; (3) Compared to Baseline, Warning I and Warning II achieve maximum field strength reductions of 56.33 % and 89.38 %. Under the progressive intervention of two-level warning, the interaction risk mitigation is significant; (4) The provision and earlier supplementation of rHMI warnings stabilize field strength and reduce instantaneous risk mutations to zero. rHMI warnings may alleviate drivers’ workload, and the two-level warning performs best; (5) It is recommended to prioritize full detection coverage of the merging area to maximize risk mitigation through staged rHMI warning for beyond-visual-range merging events. Results provide preliminary evidence for application of risk detection and warning in specific scenarios, enhancing safety in merging areas.

Authors

Institutions

Publication Details

Journal
Accident Analysis & Prevention
Published
2026-09-25
DOI
https://doi.org/10.1016/j.aap.2026.108787
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

HMI warning strategy based on radar-video fusion perception for freeway merging areas: A risk field approach considering beyond-visual-range event

Sen Luan, Jushang Ou, ying yao, Yibo Dai et al.
Accident Analysis & Prevention
Human-Automation Interaction and Safety
article

HMI warning strategy based on radar-video fusion perception for freeway merging areas: A risk field approach considering beyond-visual-range event

Sen Luan, Jushang Ou, ying yao, Yibo Dai, Xiaohua Zhao
article en

Abstract

Under beyond-visual-range conditions where the driver’s sight is obstructed for the freeway merging area, the merging vehicle may seriously affect the mainline driver’s safe passage. This study aims to evaluate the mitigation effect of radar-video fusion-based human–machine interface (rHMI) warnings on the interaction risk imposed on a mainline vehicle by a merging vehicle, addressing the risk warning under beyond-visual-range conditions. Three rHMI warning schemes were developed and tested through driving simulation. They are no radar-video fusion perception without rHMI warning (Baseline), partial coverage of radar-video fusion perception with one-level rHMI warning (Warning I), and full coverage of radar-video fusion perception with two-level rHMI warning (Warning II). The study constructed the kinetic energy field incorporating speed difference (KEF-SD). A convolution operation was used to extract the field strength’s time-varying characteristics. The KEF-SD was compared with surrogate safety measures (SSMs) to demonstrate its performance. Results indicate: (1) rHMI warnings help mainline drivers to effectively reduce speed. The progressive intervention of two-level warning induces earlier speed adjustment; (2) The KEF-SD is reliable and valid. It can more accurately and finely explain the vehicle interaction process under beyond-visual-range conditions; (3) Compared to Baseline, Warning I and Warning II achieve maximum field strength reductions of 56.33 % and 89.38 %. Under the progressive intervention of two-level warning, the interaction risk mitigation is significant; (4) The provision and earlier supplementation of rHMI warnings stabilize field strength and reduce instantaneous risk mutations to zero. rHMI warnings may alleviate drivers’ workload, and the two-level warning performs best; (5) It is recommended to prioritize full detection coverage of the merging area to maximize risk mitigation through staged rHMI warning for beyond-visual-range merging events. Results provide preliminary evidence for application of risk detection and warning in specific scenarios, enhancing safety in merging areas.

Accident Analysis & PreventionVol. 238
Beijing University of Technology (CN)
Openalex Percentile: Top 7%
Human-Automation Interaction 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.