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Updated: Jun 27, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

HICnet: A two-stage framework based on region partitioning for predicting HIC values from car point clouds.

Shuang Li¹, Jinran Wu², Aimin Shi³

¹China Automotive Engineering Research Institute Co., Ltd., Chongqing, 401122, China; School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing, 401122, China.

Accident; Analysis and Prevention

|June 25, 2026

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Predicting pedestrian Head Injury Criterion (HIC) is now more accurate with HICnet. This AI framework uses 3D vehicle data and specialized networks to improve safety assessments for diverse car designs.

Area of Science:

Automotive Safety Engineering
Computational Biomechanics
Artificial Intelligence in Transportation

Background:

Predicting Head Injury Criterion (HIC) values is crucial for pedestrian safety but is hindered by complex vehicle 3D geometry and imbalanced HIC data.
Existing methods struggle to accurately model the intricate spatial features of vehicle front-ends and handle the skewed distribution of impact data.

Purpose of the Study:

To develop an accurate and robust framework (HICnet) for predicting HIC values in pedestrian-vehicle impacts.
To address the challenges posed by complex 3D vehicle geometry and the long-tailed distribution of HIC data.

Main Methods:

HICnet utilizes a PointNet++ backbone for 3D point cloud feature extraction, integrating auxiliary physical attributes via multilayer perceptrons.

Keywords:

3D point clouds Head Injury Criterion prediction Imbalance regression Long-tailed distribution PointNet++

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A dual-pronged strategy addresses data imbalance: specialized regression networks for hood and non-hood regions and a probability density-based loss function.

Main Results:

HICnet demonstrated superior predictive performance compared to baseline architectures across 14 vehicle models.
The framework showed consistent accuracy and robustness across diverse vehicle geometries and impact scenarios.

Conclusions:

HICnet provides a reliable and accurate tool for automotive safety evaluation, significantly advancing pedestrian impact analysis.
The proposed methods effectively handle complex geometry and data imbalance, paving the way for improved vehicle safety designs.