A Deep Learning Method for Lane Changing Situation Assessment and Decision Making

Compared with the lane keeping maneuvers, lane changing maneuvers are much more complicated. Inappropriate ones may result in traffic accidents. Therefore, it is necessary to develop vehicular social networks (VSNs) and advanced driver assistance systems (ADAS), which can help to evaluate the traffic situation and provide additional warnings for drivers' unsafe lane changing operations. This paper proposes a deep learning model to simulate the situation assessment and decision making process during lane changing events. Compared with the existing assessment models, our model has two significant advantages. First, except for the instantaneous states of the subject and the surrounding vehicles, the proposed model also takes drivers' historical experience and the vehicle-to-vehicle (V2V) memory effect into consideration for the final lane changing maneuvers situation assessment. Second, our new assessment model is built based on Deep Neural Networks (DNNs), which is proved to outperform conventional machine learning classifiers. The empirical trajectory dataset NGSIM is used for evaluating the performance of the proposed model. Experiment results indicate that our model achieves high identification accuracy of both lane changing maneuvers and lane keeping maneuvers. It is verified to be an effective driverless technology in assisting vehicle warning systems.