In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for post-accident analysis by multi-modal ...
In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for post- accident analysis by multi-modal ...
The analysis results indicate that the proposed multi-modal deep learning model can identify hazardous events within a large volumes of data at an AUC of ...
In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for post-accident analysis by multi-modal ...
Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data ; English · IEEE 2019 · kinematics.
Ping Sun's 5 research works with 130 citations, including: Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile ...
Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data. Preprint. Full-text available. Jul 2018.
Statistics for Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data ...
Apr 12, 2024 · As such, AI models have been utilized to predict hazardous driving events from DMSs [18] or SCEs from naturalistic driving data [19]. DL models ...
This paper proposes a self-attention-based bidirectional long short-term memory (Att-Bi-LSTM) network model to predict driving risk based on multi-source data.
Missing: Hazardous Modal Motion