Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

Abstract

The recognition of the preceding vehicle lane-changing manoeuvre (LCM) is essential for improving the rationality and safety of the decision-making of driverless vehicles. However, traditional recognition researches for preceding vehicle LCM are generally characterized by problems such as low recognition accuracy and poor research foundation. In response to these problems, this paper carried out naturalistic driving study (NDS) on the highway, from which a large amount of on-road data consisting of lane-keeping (LK) and lane-changing left and right (LCL, LCR) manoeuvres were collected. A stacking-based ensemble learning method for the recognition of the LCM of the preceding vehicle, which integrates the random forest, support vector machine, long and short-term memory network (LSTM), and bi-directional LSTM based on attention mechanism (AT-Bi-LSTM) algorithms, is proposed. Compared with traditional machine learning methods, the proposed method exhibits greater advantages in terms of its recognition accuracy. It is particularly of note that the recognition accuracy of the model at 0.4 s and 0.8 s after the LCM reached 90.77% and 95.54%, respectively. The study reported is of great significance for the construction of more intelligent vehicle-vehicle collaboration and the promotion of the industrial applications of intelligent vehicle technology.