For the path planning of autonomous vehicles, it is important to predict the future trajectory of the surrounding vehicles. However, predicting future trajectory is difficult because it needs to consider the invisible interaction between the vehicles in a dynamic driving environment. In this paper, a new approach, which considers the interaction between surrounding vehicles, is proposed for accurate prediction of the future trajectory. The proposed method provides continuous predicted trajectories over time in the longitudinal and lateral directions, respectively. The deep ensemble technique is also used to predict the uncertainty of the estimated trajectory. This paper performs the training and verification of the algorithm using NGSIM dataset, which is the vehicle driving data obtained through actual vehicle driving.
Interaction Aware Trajectory Prediction of Surrounding Vehicles with Interaction Network and Deep Ensemble
2020 IEEE Intelligent Vehicles Symposium (IV) ; 1714-1719
2020-10-19
1516126 byte
Conference paper
Electronic Resource
English
Interaction-Based Trajectory Prediction of Surrounding Vehicles with Driving Maneuvers Recognition
Springer Verlag | 2023
|Parametric trajectory prediction of surrounding vehicles
IEEE | 2017
|Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks
ArXiv | 2020
|