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Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.
Wenxiang Xu1, Junhua Wang1, Ting Fu1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.
Accident; Analysis and Prevention
|November 23, 2021
Summary
This study introduces a novel method for predicting aggressive driving behavior by incorporating driver intention. The combined Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) network achieved 80% accuracy, enhancing driving safety.
Area of Science:
- Artificial Intelligence
- Transportation Safety
- Behavioral Psychology
Background:
- Aggressive driving behavior prediction is crucial for road safety.
- Existing methods struggle to capture driver intention and complex temporal dynamics.
- Understanding driver intention is key to mitigating aggressive driving.
Purpose of the Study:
- To develop an advanced prediction method for aggressive driving behavior.
- To effectively characterize driver intention and model time-varying driving patterns.
- To improve the accuracy and interpretability of aggressive driving prediction models.
Main Methods:
- Utilized a Hidden Markov Model (HMM) to extract driver intentions.
- Employed an attention-based Long Short-Term Memory (LSTM) network for multivariate-temporal prediction.
- Trained the model using panel data from the Shanghai Naturalistic Driving Study.
Main Results:
- The proposed HMM-LSTM model achieved a mean accuracy of 80% in predicting aggressive driving.
- Performance was particularly strong with a 2-second time interval (82% training, 84% validation accuracy).
- The attention mechanism enhanced model interpretability, and driver intention improved accuracy.
Conclusions:
- The integrated approach effectively predicts aggressive driving behavior by combining driver intention and temporal analysis.
- This method offers a promising solution for real-world applications in Advanced Driver Assistance Systems (ADAS).
- The findings highlight the importance of considering driver intention for enhanced road safety.
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