Cost-Effective Road Side Units Deployment via Hotspot Identification

Road Side Units (RSUs) play a pivotal role in enhancing the safety of Connected Vehicles (CVs), yet their safety benefits hinge significantly on effective deployment strategies. Traditional approaches often focus on high-risk areas, but such locations may not necessarily yield the most substantial safety improvements. This study redefines hot spots as road segments where RSU deployment results in the greatest reduction of collision risk and introduces a cost-efficient method for identifying such locations. The proposed method requires only small-scale real-world driving data, which is further augmented to support broader scenario evaluation. Additionally, an accelerated sampling strategy is incorporated to enhance the efficiency of the identification process. Simulation-based evaluations demonstrate that the method achieves superior performance in terms of safety impact, data efficiency, compared to conventional approaches.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/iv64158.2025.11097562
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Cost-Effective Road Side Units Deployment via Hotspot Identification

Chuangye Yu, Jintao Lai, Jia Hu, Hongchen Li et al.
Autonomous Vehicle Technology and Safety
article

Cost-Effective Road Side Units Deployment via Hotspot Identification

Chuangye Yu, Jintao Lai, Jia Hu, Hongchen Li, Z Zhang, Guan Wang, Jie Lai
article en

Abstract

Road Side Units (RSUs) play a pivotal role in enhancing the safety of Connected Vehicles (CVs), yet their safety benefits hinge significantly on effective deployment strategies. Traditional approaches often focus on high-risk areas, but such locations may not necessarily yield the most substantial safety improvements. This study redefines hot spots as road segments where RSU deployment results in the greatest reduction of collision risk and introduces a cost-efficient method for identifying such locations. The proposed method requires only small-scale real-world driving data, which is further augmented to support broader scenario evaluation. Additionally, an accelerated sampling strategy is incorporated to enhance the efficiency of the identification process. Simulation-based evaluations demonstrate that the method achieves superior performance in terms of safety impact, data efficiency, compared to conventional approaches.

Tongji University (CN), Hongzhiwei Technology (China) (CN)
Shanghai Automotive Industry Science and Technology Development Foundation, National Natural Science Foundation of China, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 79%
Autonomous Vehicle Technology and Safety
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Cost-Effective Road Side Units Deployment via Hotspot Identification — Chuangye Yu, Jintao Lai, et al. · (2025) | TGRS Research Map | TGRS