Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks

TL;DR

This paper introduces an interaction-aware CNN-LSTM model for predicting vehicle trajectories in congested traffic, effectively capturing interactions with surrounding vehicles to improve prediction accuracy.

Contribution

The paper proposes a novel CNN-LSTM architecture that models vehicle interactions with multiple neighbors, enhancing trajectory prediction in congested traffic scenarios.

Findings

Outperforms previous models in RMSE accuracy

Successfully predicts lane change trajectories before lateral movement occurs

Effective in congested traffic with multiple interacting vehicles

Abstract

Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the basic abilities of an autonomous vehicle. In congestion, a vehicle's future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method which predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared weights, and the interaction is extracted with a simple CNN. The proposed model is trained and tested on trajectories extracted from the publicly accessible NGSIM US-101 dataset. Quantitative experimental results show that the proposed model outperforms previous models in terms of root-mean-square error (RMSE). Results visualization shows that the model is able to predict future trajectory induced by lane change before the vehicle operate obvious lateral movement to initiate lane changing.