Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light

Abstract

Trajectory prediction is essential for the safe driving of autonomous vehicles. With the advancement of advanced sensors and deep learning technologies, attempts have been made to reflect complex interactions. In this study, we propose a deep learning-based Multimodal Trajectory Prediction method that reflects traffic light conditions in complex urban intersection situations. Based on existing state-of-the-art research, the multi-path of multi-agents was predicted using a generative model, and the actor’s trajectory information, state, social interaction, and traffic light state, and scene context were reflected. Performance evaluation was conducted using metrics commonly used to evaluate the performance of stochastic trajectory prediction models. This study is meaningful in that trajectory prediction was performed by reflecting realistic elements of traffic lights in a complex urban environment. Future research will need to be conducted on efficient ways to reduce time and computational performance while reflecting different real-world environments.

Keywords

• trajectory prediction
• multimodal
• traffic light
• deep learning
• GAN
• LSTM