Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural net...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/11204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846124433779982336 |
|---|---|
| author | Kedong Wang Dayi Qu Dedong Shao Liangshuai Wei Zhi Zhang |
| author_facet | Kedong Wang Dayi Qu Dedong Shao Liangshuai Wei Zhi Zhang |
| author_sort | Kedong Wang |
| collection | DOAJ |
| description | Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons. |
| format | Article |
| id | doaj-art-607edfa7da1e40a98f77a4f0a79a6839 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-607edfa7da1e40a98f77a4f0a79a68392024-12-13T16:23:09ZengMDPI AGApplied Sciences2076-34172024-12-0114231120410.3390/app142311204Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTMKedong Wang0Dayi Qu1Dedong Shao2Liangshuai Wei3Zhi Zhang4School of Intelligent Manufacturing, Qingdao Huanghai University, Qingdao 266427, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaQuantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons.https://www.mdpi.com/2076-3417/14/23/11204traffic safetydeep learningrisk quantificationrisk prediction |
| spellingShingle | Kedong Wang Dayi Qu Dedong Shao Liangshuai Wei Zhi Zhang Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM Applied Sciences traffic safety deep learning risk quantification risk prediction |
| title | Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM |
| title_full | Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM |
| title_fullStr | Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM |
| title_full_unstemmed | Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM |
| title_short | Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM |
| title_sort | research on risk quantification methods for connected autonomous vehicles based on cnn lstm |
| topic | traffic safety deep learning risk quantification risk prediction |
| url | https://www.mdpi.com/2076-3417/14/23/11204 |
| work_keys_str_mv | AT kedongwang researchonriskquantificationmethodsforconnectedautonomousvehiclesbasedoncnnlstm AT dayiqu researchonriskquantificationmethodsforconnectedautonomousvehiclesbasedoncnnlstm AT dedongshao researchonriskquantificationmethodsforconnectedautonomousvehiclesbasedoncnnlstm AT liangshuaiwei researchonriskquantificationmethodsforconnectedautonomousvehiclesbasedoncnnlstm AT zhizhang researchonriskquantificationmethodsforconnectedautonomousvehiclesbasedoncnnlstm |