Systematic evaluation of lane-change risk for multi-vehicle types on freeways using high-resolution data

Huiying Wen; Xinyi Zhang; Junda Huang; Pengpeng Xu

doi:10.1117/12.3054772

20 December 2024 Systematic evaluation of lane-change risk for multi-vehicle types on freeways using high-resolution data

Huiying Wen, Xinyi Zhang, Junda Huang, Pengpeng Xu

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Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134212X (2024) https://doi.org/10.1117/12.3054772
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

Traffic collision technology is widely used to identify lane change risk, which provides support for improving the safety of automatic driving assistance system. In most existing studies, micro-conflict indicators such as Time-to-Collision (TTC) and Stopping Distance Index (SDI) are used to determine the risk level during the lane change process. Therefore, this study focuses on the safety of the whole lane changing process, regards the lane changing vehicle and its four surrounding vehicles as a system, and proposes a method to quantify the comprehensive risk of lane changing from both the spatial and temporal dimensions. First, lane change samples are extracted from the real trajectory data set. Then, based on TTC and SDI, a macro conflict index, Risk Exposure Level (REL) and Risk Severity Level (RSL), is formulated to quantify the risk between lane changing vehicles and surrounding vehicles in the whole process of lane change. Finally, two macro conflict indicators are integrated to quantify the risk of the whole lane change system via Fault Tree Analysis method. The effectiveness and authenticity of the proposed method are verified by the HighD dataset. Dividing vehicles into different types and analyzing the factors that influence the risk levels of each vehicle type. The results are expected to provide a reference for intelligent vehicle risk warning and trajectory planning, and is valuable in the formulation of highway safety operation management measures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huiying Wen, Xinyi Zhang, Junda Huang, and Pengpeng Xu "Systematic evaluation of lane-change risk for multi-vehicle types on freeways using high-resolution data", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134212X (20 December 2024); https://doi.org/10.1117/12.3054772

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KEYWORDS

Safety

Analytical research

Risk assessment

Roads

Transportation

Autonomous vehicles

Data modeling

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