Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning

Abstract

Highway safety has attracted significant research interest in recent years, especially as innovative technologies such as connected and autonomous vehicles (CAVs) are fast becoming a reality. Identification and prediction of driving intention are fundamental for avoiding collisions as it can provide useful information to drivers and vehicles in their vicinity. However, the state-of-the-art in maneuver prediction requires the utilization of large labeled datasets, which demand a significant amount of processing and might hinder real-time applications. In this paper, an end-to-end machine learning model for predicting lane-change maneuvers from unlabeled data using a limited number of features is developed and presented. The model is built on a novel comprehensive dataset (i.e., highD) obtained from German highways with camera-equipped drones. Density-based clustering is used to identify lane-changing and lane-keeping maneuvers and a support vector machine (SVM) model is then trained to learn the boundaries of the clustered labels and automatically label the new raw data. The labeled data are then input to a long short-term memory (LSTM) model which is used to predict maneuver class. The classification results show that lane changes can efficiently be predicted in real-time, with an average detection time of at least 3 s with a small percentage of false alarms. The utilization of unlabeled data and vehicle characteristics as features increases the prospects of transferability of the approach and its practical application for highway safety.