An LSTM Network for Highway Trajectory Prediction

TL;DR

This paper introduces an LSTM neural network for highway vehicle trajectory prediction, trained on extensive real-world data, enabling more accurate medium-term forecasts for autonomous driving systems.

Contribution

The paper presents a novel LSTM-based approach trained on large-scale, diverse trajectory data to improve highway vehicle trajectory prediction accuracy.

Findings

Achieved accurate longitudinal and lateral trajectory predictions.

Trained on 800 hours of real-world data from over 6000 drivers.

Demonstrated effectiveness in various traffic conditions.

Abstract

In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior accordingly. If experienced human drivers are generally good at inferring other vehicles' motion up to a few seconds in the future, most current Advanced Driving Assistance Systems (ADAS) are unable to perform such medium-term forecasts, and are usually limited to high-likelihood situations such as emergency braking. In this article, we present a first step towards consistent trajectory prediction by introducing a long short-term memory (LSTM) neural network, which is capable of accurately predicting future longitudinal and lateral trajectories for vehicles on highway. Unlike previous work focusing on a low number of trajectories collected from a few drivers, our network was trained and validated on the NGSIM US-101 dataset, which contains a total of 800 hours of recorded trajectories in various traffic densities, representing more than 6000 individual drivers.