A hybrid deep learning framework for conflict prediction of diverse merge scenarios at roundabouts

Abstract

The unique traffic situation at roundabouts causes complex interactions between merging vehicles, thereby increasing the likelihood of conflicts. Reliable prediction of conflict risk contributes to active safety improvement, but few studies have investigated the merge risk of roundabouts at a microscopic level. In light of this, this study develops a hybrid deep learning framework for predicting potential conflict risks in complex merging scenarios at roundabouts. Specifically, a roundabout coordinate system is devised to define vehicle characteristics based on trajectory data. Then, an improved 2D-TTC (time-to-collision) indicator is employed to identify two-dimensional merge conflicts. Since the surrounding vehicles may change as vehicles merge into a roundabout, this study analyzes several merging scenarios involving different vehicle groups and conflict durations in order to provide a comprehensive understanding of the conflict mechanism. For these scenarios, a hybrid model consisting of a convolutional neural network (CNN) and a long short-term memory network (LSTM) integrated with the convolutional block attention module (CBAM) is utilized to identify key features. The superiority of the proposed prediction method is demonstrated in comparisons with benchmark models. Results showed that segmental predictions were more accurate than overall predictions in terms of conflict duration. Furthermore, it is possible that a specific vehicle group has a decisive effect on the merging conflict risk, as indicated by the fact that information from multiple vehicle groups does not significantly improve the prediction performance. Another finding is that the driving state of vehicles merging at the roundabout varies considerably, but rarely with consecutive or multiple changes. The study provides novel insights into roundabout conflict prediction, which could serve as a tool for enhancing safety management involving complex traffic scenarios.