LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space - Xing Fu; Jun Liu; Zhitong Huang; Alex Hainen; Asad J. Khattak (Digital Transportation and Safety)
Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment. With cutting-edge artificial intelligence and sensing technologies, autonomous vehicles (AVs) are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes. By exploring the Waymo open motion dataset, this study proposes a framework to explore autonomous driving data and investigate lane change behaviors. In the framework, this study develops a Long Short-Term Memory (LSTM) model to predict lane changing behaviors. The concept of Vehicle Operating Space (VOS) is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes. To examine the robustness of the model, a series of sensitivity analysis are conducted by varying the feature selection, prediction horizon, and training data balancing ratios. The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors. This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.