Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data. (January 2022)

• Record Type: Journal Article
• Authors: Xu, Wenxiang; Wang, Junhua; Fu, Ting; Gong, Hongren; Sobhani, Anae
• Is Part Of: Accident analysis and prevention. Volume 164(2022)
• Publication Date: 2022-01
• Subjects: Aggressive driving prediction -- Panel data -- Hidden Markov model -- Long short-term memory network -- Attention mechanism; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106477
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Panel data is used for predicting aggressive driving. Driver intention is extracted and used for predicting aggressive driving. Developing a prediction model based on HMM and attention-based LSTM. The integrated model has a good performance to predict aggressive driving. Abstract: Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network. HMM is applied to extract the driver's intention which leads to aggressive driving behavior; attention-based LSTM networks are applied in the multivariate-temporal aggressive driving behavior prediction. The method input uses panel data which contains observations about different cross-sections across time. In the case study, the model was trained based on the Shanghai Naturalistic Driving Study data. After comparing with other deep learning methods and normal LSTM, results show the proposed method provides good performance for aggressive driving behavior prediction (Mean of Accuracy = 80%), especially with the 2-sec time interval applied (Training Accuracy = 82% and Validation Accuracy = 84%). Also, the result shows that the attention mechanism can improve the result's interpretability, and using the driver's intentions as input can enhance the model accuracy. This method for predicting aggressive driving behavior that combines driver's intention, variable contribution sorting, and time-series processing. This method can be used in real-world applications for improving driving safety with the applications in the Advanced Driver Assistance Systems.