Bidirectional Long Shot-Term Memory-Based Interactive Motion Prediction of Cut-In Vehicles in Urban Environments

Abstract

This paper presents an interactive motion predictor to infer the intention of cut-in vehicles using a bidirectional long short-term memory (Bi-LSTM) module. The proposed predictor consists of three modules: maneuver recognition, trajectory prediction, and interaction. The driving data for training and validating the Bi-LSTM module were collected by sensors mounted on an autonomous vehicle (AV). In total, 3,828 trajectories of human-driven vehicles around the AV are accumulated in a global coordinate system. After postprocessing the collected trajectories, 83,188 and 35,652 data samples were used to train and validate the Bi-LSTM module, respectively. In the Bi-LSTM module, a maneuver is defined as the desired driving lane of a vehicle, which extend the behavior coverage of the proposed approach. The trajectory prediction step is based on the path-following model with a motion parameter estimator to predict the trajectories for all possible maneuvers. The interaction module considers the likelihood of each maneuver and the collision risk to determine the future trajectories of the surrounding vehicles in terms of the driving scene. The proposed predictor was evaluated in terms of its prediction accuracy and its effects on the motion planner of the AV. It has been shown that the AV benefits from the improved motion prediction of target vehicles provided by the proposed predictor with respect to enhanced safety and reduced control effort in the case of cut-in situations.

Keywords

• Autonomous vehicle
• interactive motion prediction
• machine learning
• bidirectional long short-term memory
• motion planning