Forwarding Collision Assessment with the Localization Information Using the Machine Learning Method

Abstract

Freeway crashes occupied 30% of total crashes. The advanced driver-assistance system (ADAS) often underestimates or omits the necessary collision warning. To investigate the forward collision in complex traffic conditions, the TTC (time-to-collision) is regarded as a surrogate for collision risk assessment. The study aims to design a forward-collision warning method for ADAS in the urban freeway scenario. The testing vehicle is equipped with sensors and a satellite navigation system. The TTC was collected from the Xi’an Rao Cheng expressway for the car-following scenario for three days. A comprehensive Gaussian model, which consists of three sub-GMM models is applied to describe the TTC distribution. The vehicle trajectories were extracted in the car-following state. To improve the efficiency of the forwarding collision system, four seconds are chosen as an analysis window in the car-following state. Two time-series machine models were used to predict TTC in advance. The TTC prediction models were constructed on the basis of the long short-term memory (LSTM). The prediction result was compared with the deep belief network (DBN) model. The comparison results show that the LSTM model is faster than the DBN model. The number of iterations is 100. The loss function of the LSTM is 0.06. The LSTM outperforms the DBN model and is suitable for car-following warnings.