In intersection scenarios, agents exhibit diverse intent choices, making trajectory prediction problems fraught with significant uncertainty. This study proposes a cross-intersection trajectory prediction method that considers the probability of agent intent. It integrates a vehicle speed model, an intent predictor based on agent kinematics, and a trajectory prediction method based on graph neural networks to enhance the accuracy of vehicle agent trajectory predictions by precisely capturing the intent of vehicle agents at intersections. Through training and validation on a large dataset of real-world driving data, experiments have demonstrated the method's capability to predict the behavior of traffic agents in intersection scenarios accurately. Specific experimental results on the nuScenes dataset show MinADE_5, MinADE_10, MissRate_5,2, and MissRate_10,2 values of 1.70, 1.45, 0.63, and 0.48, respectively.
Predicting the motion and behavior of surrounding vehicles is an essential task for motion planning and decision-making of autonomous vehicles in complex traffic conditions. In this paper, we propose a short-term vehicle trajectory prediction framework using attention mechanism integrated GRU network. We use an encoder-decoder model as the main architecture. A gate recurrent unit (GRU) coupled with temporal attention and graph attention is used to extract and fuse more important information which could be used for trajectory prediction. The temporal attention could extract temporal information and graph attention could consider interactions between surrounding vehicles within sensing range. The extracted information is fed into fully connected layers to obtain predicted trajectory. The publicly next generation simulation (NGSIM) I-80 and US-101 datasets are used to evaluate proposed model. Compared to other prediction models, our model shows improvement on final displacement error (FDE) and average displacement error (ADE). The results show that model with attention mechanism improves prediction accuracy by 1% ~5% in 5 second prediction horizon.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.