Data-based Vehicle Trajectory Prediction Model for Lane-change Maneuver

Several advanced driver assistance systems (ADASs) control a vehicle in the longitudinal direction. However, an ADAS that controls the vehicle in the lateral direction is uncommon since it requires the accurate lateral position prediction of the target vehicle because of the small safety margin in this direction. To reduce this problem, we suggest a data-based vehicle trajectory prediction model that mimics the human ability to predict the trajectory. The proposed model focuses on the lane-change maneuver because it is the most frequent and hard to predict from the road geometry, unlike other lateral maneuvers. The model is composed of four models to acquire interpretable outcomes. The first model predicts the longitudinal trajectory. The second and third models predict the lane-change maneuver and the time to lane change, and the last model predicts the lateral trajectory. These models are based on a recurrent neural network to consider the sequential characteristics of the input data. To train the proposed model, we generated a dataset that includes a vehicle's lateral dynamics information using the NGSIM I-80 dataset. To validate the proposed model, a test set in the dataset is used. The proposed model shows better accuracy than baseline methods based on vehicle kinematics.