Optimization of AEBS for Heavy Goods Vehicles Incorporating Driver’s Control and 3D Visibility of Vulnerable Road Users

While an advanced emergency braking system (AEBS) significantly improves the safety of a heavy goods vehicle (HGV), current implementations face limitations including inadequate scenario coverage for vulnerable road users (VRUs), overriding driver control and limited human–machine collaboration mechanisms, and an insufficient consideration of blind spot challenges in HGVs. To improve the adaptability of the AEBS for HGVs, this study proposes and validates a novel 2D AEBS control algorithm incorporating driver’s control and 3D visibility of VRUs. The proposed algorithm is designed to firstly identify the motion state scenarios based on the spatial relationship between the HGV and VRU. Then, based on the scenario classification result, the proposed algorithm determines whether the HGV needs to brake in the current scenario according to the 2D time to collision for both entities to reach the potential collision area while maintaining their current speeds. Finally, for situations requiring braking, it evaluates whether safety can be ensured under three conditions: the ego vehicle in free driving, the ego vehicle under driver-controlled braking (considering the 3D visibility of the VRU), and the ego vehicle under 2D AEBS-controlled braking. According to the test results, the proposed algorithm can deal with the VRU crossing scenario and leverage the driver’s control capabilities while utilizing AEBS as a safety net function.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2026-01-04
DOI
https://doi.org/10.3390/app16010516
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

Optimization of AEBS for Heavy Goods Vehicles Incorporating Driver’s Control and 3D Visibility of Vulnerable Road Users

Binglei Xie, Mingtao Song, Xi Zhang
Applied Sciences
Autonomous Vehicle Technology and Safety
article

Optimization of AEBS for Heavy Goods Vehicles Incorporating Driver’s Control and 3D Visibility of Vulnerable Road Users

Binglei Xie, Mingtao Song, Xi Zhang
article en

Abstract

While an advanced emergency braking system (AEBS) significantly improves the safety of a heavy goods vehicle (HGV), current implementations face limitations including inadequate scenario coverage for vulnerable road users (VRUs), overriding driver control and limited human–machine collaboration mechanisms, and an insufficient consideration of blind spot challenges in HGVs. To improve the adaptability of the AEBS for HGVs, this study proposes and validates a novel 2D AEBS control algorithm incorporating driver’s control and 3D visibility of VRUs. The proposed algorithm is designed to firstly identify the motion state scenarios based on the spatial relationship between the HGV and VRU. Then, based on the scenario classification result, the proposed algorithm determines whether the HGV needs to brake in the current scenario according to the 2D time to collision for both entities to reach the potential collision area while maintaining their current speeds. Finally, for situations requiring braking, it evaluates whether safety can be ensured under three conditions: the ego vehicle in free driving, the ego vehicle under driver-controlled braking (considering the 3D visibility of the VRU), and the ego vehicle under 2D AEBS-controlled braking. According to the test results, the proposed algorithm can deal with the VRU crossing scenario and leverage the driver’s control capabilities while utilizing AEBS as a safety net function.

Applied SciencesVol. 16(1)
Shenzhen Institute of Information Technology (CN), Harbin Institute of Technology (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 97%
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.