State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
Accid Anal Prev. 2021 Jun;156:106149. doi: 10.1016/j.aap.2021.106149. Epub 2021 Apr 29.
Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deep-learning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 ± 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.
准确实时预测不可避免碰撞场景下的车内乘员伤害严重程度，是利用高度自动驾驶汽车提高道路交通安全的前提。具体来说，安全预测模型为预碰撞阶段的轨迹规划系统和碰撞阶段的自适应约束系统提供决策参考。本研究的主要目标是构建一个基于数据驱动的车辆运动学特征的模型，以实现对车内乘员伤害严重程度的准确、近实时预测。本研究建立了一个大型的数值数据库，重点研究了乘员动力学。采用卷积神经网络（CNN）建立了一个初步的深度学习模型，用于预测乘员动力学和伤害严重程度。为了降低实时应用的计算时间，第二步是通过特征提取方法从车辆碰撞脉冲中提取简化的运动学特征，该方法受到了一种可视化方法的启发，该方法应用于基于 CNN 的模型。这些特征与低复杂度的机器学习算法相结合，在预测任务中取得了令人满意的准确性（数值数据库上为 85.4%，192 个实际案例数据集上为 78.7%），并且计算时间减少（1.2±0.4ms）。本研究证明了在碰撞前使用基于数据驱动和基于特征的方法来实现准确的伤害风险估计是可行的。所提出的模型有望为下一代自动驾驶汽车的综合安全系统提供决策参考。
