  Behavior prediction plays an essential role in both autonomous driving
systems and Advanced Driver Assistance Systems (ADAS), since it enhances
vehicle's awareness of the imminent hazards in the surrounding environment.
Many existing lane change prediction models take as input lateral or angle
information and make short-term (< 5 seconds) maneuver predictions. In this
study, we propose a longer-term (5~10 seconds) prediction model without any
lateral or angle information. Three prediction models are introduced, including
a logistic regression model, a multilayer perceptron (MLP) model, and a
recurrent neural network (RNN) model, and their performances are compared by
using the real-world NGSIM dataset. To properly label the trajectory data, this
study proposes a new time-window labeling scheme by adding a time gap between
positive and negative samples. Two approaches are also proposed to address the
unstable prediction issue, where the aggressive approach propagates each
positive prediction for certain seconds, while the conservative approach adopts
a roll-window average to smooth the prediction. Evaluation results show that
the developed prediction model is able to capture 75% of real lane change
maneuvers with an average advanced prediction time of 8.05 seconds.
