A hybrid deep learning approach for driver anomalous lane changing identification

Pengcheng Fan; Jingqiu Guo; Yibing Wang; Jasper S Wijnands

doi:10.1016/j.aap.2022.106661

A hybrid deep learning approach for driver anomalous lane changing identification

Accid Anal Prev. 2022 Jun:171:106661. doi: 10.1016/j.aap.2022.106661. Epub 2022 Apr 21.

Authors

Pengcheng Fan¹, Jingqiu Guo², Yibing Wang³, Jasper S Wijnands⁴

Affiliations

¹ The Key Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
² The Key Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. Electronic address: guojingqiu@hotmail.com.
³ College of Civil Engineering & Architecture, Zhejiang University, Hangzhou 310058, China.
⁴ Transport, Health and Urban Design Research Lab, The University of Melbourne, Parkville, VIC 3010, Australia; Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, De Bilt, the Netherlands.

PMID: 35462211
DOI: 10.1016/j.aap.2022.106661

Abstract

Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly detection in driving behavior means recognizing anomalous driving states as a direct result of either environmental or psychological factors. This paper provides an efficient anomaly recognition approach to identify anomalous lane-changing events in a personalized manner. The proposed framework includes three unsupervised algorithms. First, a Recurrent-Convolutional Autoencoder extracts the spatio-temporal characteristics from a high-dimensional naturalistic driving dataset. Second, in order to recognize anomalous lane-changing events of individual drivers, the extracted latent feature space is analyzed using Pauta criterion-based reconstruction loss analysis, as well as one-class Support Vector Machine. Last, t-Distributed Stochastic Neighbor Embedding is employed to visualize the latent space for better understanding and interpretability. Temporal anomalies of lane-changing events were analyzed by a personalized grey relational coefficient analysis, to represent robust similarities for individual drivers. Validation and calibration were performed with a natural driving study dataset collected from 50 drivers with 59,372 lane change events. The results showed heterogeneity in the pattern of abnormal lane changing behavior across the sample. At the same time, each driver exhibited heterogeneous anomalous behaviors in both temporal and spatial sequences. Without prior labels, the proposed model effectively captures personalized driving patterns and abnormal lane-changing events from high-dimensional time-series data. This unsupervised hybrid approach is a novel attempt to complete personalized anomalous lane-changing behaviors identification based on naturalistic driving data involving various traffic environments. Our approach enables the extraction of natural individual lane-changing behavior patterns and provides insights for the improvement of personalized driving behavior monitoring systems.

Keywords: Anomaly detection; Autoencoder; Lane-changing; Naturalistic driving; t-SNE.

MeSH terms

Accidents, Traffic / prevention & control
Algorithms
Automobile Driving* / psychology
Deep Learning*
Humans