Anomalous State Recognition of Lane-changing Behavior using a Hybrid Autoencoder Architecture

This paper presents a hybrid unsupervised architecture for anomalous lane-changing behavior recognition. Anomaly detection aims to identify unusual driving behavior caused by either environmental or phycological stimuli, and is of great important in road safety. First, a Recurrent Convolutional Autoencoder (RC-AE) is built to explore the spatial-temporal features derived from the high-dimensional behavior data. Second, Reconstruct Error analysis of the autoencoder and one-class support vector machine method are both applied to identify anomalous lane-changing behavior in the learned feature space by autoencoder. Last, we employ T-Distributed Stochastic Neighbor Embedding (T-SNE) for data visualization in the anomaly detection. Based on the kernel density estimation analysis, anomalous and normal lane-changing sample groups display distinct difference over probability distributions. The findings contribute to a better understanding on drivers' natural lane-changing behavior, and can provide important insight into real-time personalized unusual lane-changing behavior monitoring system development.