01742438

Component

https://doi.org/10.1177/0361198120922210

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.

© National Academy of Sciences: Transportation Research Board 2020.

English

pp 336-347

2020-7

Transportation Research Record: Journal of the Transportation Research Board

Web

References (45)

Highway safety; Labeling; Lane changing; Machine learning; Mathematical prediction

Highways; Operations and Traffic Management; Safety and Human Factors

TRIS, TRB, ATRI

Jun 14 2020 3:03PM