Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

TL;DR

This paper introduces a real-time vehicle trajectory prediction method using an LSTM encoder-decoder architecture with beam search, significantly outperforming traditional techniques in highway scenarios.

Contribution

It presents a novel deep learning approach combining LSTM encoder-decoder and beam search for accurate, real-time vehicle trajectory prediction.

Findings

Higher prediction accuracy than conventional methods

Generates multiple likely trajectories in real time

Effective in highway traffic scenarios

Abstract

In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the $K$ most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the $K$ locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques.