An LSTM Network for Highway Trajectory Prediction
This work addresses the need for improved trajectory prediction in autonomous driving systems, though it is incremental as it applies an existing LSTM method to a new, larger dataset.
The paper tackled the problem of medium-term trajectory prediction for autonomous vehicles on highways by introducing an LSTM neural network, achieving accurate predictions of future longitudinal and lateral trajectories validated on the NGSIM US-101 dataset with over 800 hours of recorded data from more than 6000 drivers.
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.