Instantaneous Lane-Changing Type Aware Lane Change Prediction Based on LSTM in Mixed Traffic Scenario

With the rapid development and application of autonomous technology in vehicles, we are going to see more autonomous vehicles on the roads in a foreseeable future. While autonomous vehicles may have the advantage of reducing traffic accidents caused by human drivers' neglect and/or fatigue, one of the challenges is how to develop autonomous driving algorithms such that autonomous vehicles can be safely deployed in a mixed traffic environment with both autonomous vehicles and human-driven vehicles. Instantaneous lane-changing type may be significantly different for human drivers, which would lead to traffic accidents with other vehicles including autonomous vehicles. In this paper, we propose a resilient algorithm for the prediction of the human driver's lane-changing behaviors. The proposed algorithm uses a long-short term memory (LSTM) classifier to identify the conservative lane change and the aggressive lane changing and accordingly makes the accurate prediction on lane changes in the driving of vehicles by human drivers. The proposed method provides a useful addition in facilitating the design of more advanced driving algorithms for autonomous vehicles. Using the vehicle trajectory data in the NGSIM data set for a large number of simulations, the effectiveness of this method has been confirmed.